var textForPages = ["PROCEEDINGS OF THE 15TH       INTERNATIONAL SCIENTIFIC CONFERENCE            EUROPEAN FINANCIAL SYSTEMS 2018                                                                          Brno, Czech Republic          June 25-26, 2018","Masaryk University                                  Faculty of Economics and Administration                                              Department of Finance                                                           and                                          Institute for Financial Market                                            European Financial Systems 2018                                                     Proceedings                                        of the 15  International                                                        th                                           Scientific Conference                                                                    June 25-26, 2018                                                Brno, Czech Republic","Suggested citation:                 AUTHOR, A. Title of the paper. In: European Financial Systems 2018. Proceedings of the                   th                15  International Scientific Conference, Brno: Masaryk University, 2018, pp. xx-xx. ISBN                978-80-210-8980-8 (for print version Proceeding), ISBN 978-80-210-8981-5 (for on-line                version Proceeding).                      Editors:                Josef Nešleha                Filip Hampl                Miroslav Svoboda                                    © 2018 Masarykova univerzita                ISBN 978-80-210-8980-8 (for print version Proceeding),                 ISBN 978-80-210-8981-5 (for on-line version Proceeding).                     All published papers have been reviewed before publishing. The proceedings have not been                amended or proofread. Editors are not responsible for the language used in the papers.                   All rights reserved. No part of this e-book may be reproduced or transmitted in any form or by any                means  without  prior  written  permission  of  copyright  administrator  which  can be  contacted  at                Masaryk University Press, Žerotínovo náměstí 9, 601 77 Brno.","","Program Committee of the Conference                Chair:                Ing. Petr Valouch, Ph.D. (Masaryk University)                 Members:                Prof. Dr. Ing. Dana Dluhošová (VŠB – Technical University of Ostrava, Czech Republic)                Prof. Ing. Danuše Nerudová, Ph.D. (Mendel University Brno, Czech Republic)                Prof. Ing. Eva Horvátová, PhD (University of Economics in Bratislava, Slovak Republic)                 Prof. Ing. Juraj Němec, CSc. (Matej Bel University, Slovak Republic)                Prof. Ing. Erika Pastoráková, PhD. (University of Economics in Bratislava)                Prof. Dr. Svend Reuse (FOM Hochschule für Oekonomie und Management, Federal Republic                of Germany)                Assoc. Prof. Ing. Petr Dvořák, Ph.D. (University of Economics in Prague, Czech Republic)                Univ.Doz.Mag.  Dr.  Peter  R.  Haiss,  PhD  (Lecturer,  Vienna  University  of  Economics  and                Business, Republic of Austria)                Assoc. Prof. Ing. Peter Krištofík, Ph.D. (Matej Bel University, Slovak Republic)                Assoc. Prof. Antonin Rusek, PhD. (Susquehanna University, USA)                 Assoc. Prof. Ing. Jaroslav Sedláček, CSc. (Masaryk University Brno, Czech Republic)                Assoc. Prof. Jitka Veselá, Ph.D. (University of Economics in Prague, Czech Republic)                Assoc. Prof. Ing. Eva Vávrová, Ph.D. (Masaryk University Brno, Czech Republic)                Assoc. Prof. Ing. Jana Vodáková, Ph.D. (University of Defense Brno, Czech Republic)                 Dr. Piotr Tworek (University of Economics in Katowice, Republic of Poland)                 Organizing Committee of the Conference                 Chair:                Ing. Jan Krajíček, Ph.D.                Members:                Ing. Filip Hampl                 Ing. Josef Nešleha                Ing. Tomáš Plíhal                Mgr. Zuzana Rakovská                 Mgr. Miroslav Svoboda                Mgr. Ing. Pavel Sedláček                Ing. Martina Sponerová                 Conference website:    www.efs.econ.muni.cz","","PREFACE                   Dear readers,                 It is my pleasure to introduce you a collection of papers from the 15th annual international                scientific  conference  The  European  Financial  Systems  2018  organized  annually  by                Department of Finance of the Faculty of Economics and Administration, Masaryk University                in  Brno,  Czech  Republic.  This  year's  conference  was  focused  especially  on  the  current                issues related to accounting, banking sector, insurance,  financial literacy, financial law,                new  regulations  of  financial  markets,  different  tax  systems,  corporate  finance,                cryptocurrencies, public finance and financing of non-profit organizations.                Since  the  collection  of  papers  presents  the  latest  scientific  knowledge  in  this  area,                I  believe  you  will  get  a  number  of  new  insights  usable  both  for  your  scientific,  and                educational or practical activities. I would also like to express my strong conviction that                we meet each other in occasion of the 16th year of this conference held in 2019.                  I wish you pleasant reading!                 Petr Valouch                Chairman of the Program Committee","","CONTENTS                 Mária Barteková, Ľudomír Šlahor                THE FINANCIAL ANALYSIS OF THE EDUCATION SUPPORT FUND: RECENT RESULTS                FROM SLOVAKIA ............................................................................................... 16                 Karina Benetti                LIFE INSURANCE IN THE CZECH REPUBLIC: NEW CHALLENGES? ...................... 24                 Karina Benetti                CORPORATE BANKRUPTCIES IN THE CZECH REPUBLIC: THE DEVELOPMENT OVER                THE LAST DECADE ............................................................................................ 32                Kamila Bielawska                RETIREMENT        DECISIONS       OF    SENIORS      IN    POLAND      IN    THE    LIGHT                OF THE QUALITATIVE RESEARCH ...................................................................... 39                 Martina Borovcová, Adéla Špačková                DETERMINATION  AND  VERIFICATION  OF  THE  KEY  ASSESSMENT  INDICATORS                FOR  THE  INSURANCE  MARKET  BY  APPLYING  THE  DECOMPOSITION  MULTI-                ATTRIBUTE METHODS AND REGRESSION ANALYSIS......................................... 44                 Rafał Buła                FRACTAL        DIMENSION         VS.      NON-FRACTAL         RISK       MEASURES         –                CORRELATION ANALYSIS ................................................................................. 53                 Małgorzata Cygańska, Magdalena Kludacz-Alessandri, Dimitrios Syrrakos                MEASURING  EFFICIENCY  IN  CLINICAL  DEPARTMENTS  USING  THE  DEA                APPROACH - A CASE OF THE POLISH HOSPITAL ............................................... 61                Darya Dancaková                THE  IMPACT  OF  EDUCATIONAL  EXPENDITURES  AND  HIGHER    EDUCATION                ATTAINMENT ON EMPLOYMENT IN V4 COUNTRIES ........................................... 70                 Karolina Daszyńska-Żygadło, Jakub Marszałek, Krzysztof Piontek                SUSTAINABLE  FINANCE  INSTRUMENTS'  RISK  -  GREEN  BOND  MARKET                ANALYSIS ......................................................................................................... 78                Bohuslava Doláková                 DEBT LITERACY AND ITS IMPORTANCE IN THE CZECH REPUBLIC .................... 86                Małgorzata Doman, Ryszard Doman                PATTERNS OF CURRENCY CO-MOVEMENT:  CHANGES IN THE IMPACT OF GLOBAL                CURRENCIES ..................................................................................................... 94                 Michal Fabuš, Viktória Čejková                MOTOR VEHICLE INSURANCE IN SMES ........................................................... 102                 Maria Forlicz, Tomasz Rólczyński                RATIO  BIAS  UNDER  CONDITIONS  OF  UNCERTAINTY  –  EXPERIMENTAL                RESULTS ......................................................................................................... 109","Ján Gogola, Lucie Kopecká                ACTUARIAL MODEL FOR PRICING DISABILITY INSURANCE POLICY ............... 116                 Ewa Gubernat                EFFECTIVENCES OF COMMERCIAL BANKS IN POLAND VERSUS BANK TAX ...... 123                 Peter Haiss, Jakob Schmid-Schmidsfelden                BITCOIN       COMPARED         ON      PRICE,      LIQUIDITY       AND      VOLATILITY:                CRYPTO “CURRENCIES” OR AN ASSET CLASS OF THEIR OWN? ....................... 128                 Filip Hampl                SPECIFICS  OF  THE  REPORTING  UNDER  INTERNATIONAL  ACCOUNTING                STANDARD IAS 41 AGRICULTURE AND ITS USAGE ON THE CENTRAL EUROPEAN                STOCK EXCHANGES......................................................................................... 139                 Haochen Guo                VOLATILITY MODEL BASED GARCH MINIMUM VARIANCE HEDGING ............... 147                 Taťána Hajdíková, Kateřina Trubačová                EFFICIENCY  HOSPITALS  IN  THE  CZECH  REPUBLIC:  THE  DIFFERENCE  BETWEEN                THE METHODS USED ....................................................................................... 154                 Martina Hedvičáková, Martin Král                LEVEL  OF  INDUSTRY  AUTOMATION  4.0  IN  THE  CZECH  REPUBLIC  AND  IMPACT                ON UNEMPLOYMENT ....................................................................................... 160                 Irena Honková                INDUSTRY 4.0 AIMED AT ACCOUNTING SYSTEM............................................. 168                 Jan Horvat, Eva Horvatova                CAPITAL CREATION FACTORS IN SELECTED BANKING SECTORS OF EUROPE   174                 Eva Horvatova                EFFICIENCY OF BANKS IN SLOVAK REPUBLIC ................................................ 180                 Agnieszka Huterska, Robert Huterski, Justyna Łapińska                IMPACT  OF  MACROECONOMIC  FACTORS  ON  THE  VALUE  OF  LOANS  GRANTED                TO  NON-FINANCIAL  ENTERPRISES  BY  MONETARY  FINANCIAL  INSTITUTIONS                IN THE EURO AREA COUNTRIES ...................................................................... 189                 Jana Hvozdenska                CONVERGENCE  OF  THE  GOVERNMENT  BOND  YIELDS  IN  ESTONIA,  LATVIA                AND LITHUANIA ............................................................................................. 196                 Lenka Janošová, Petr Mikuš                A  RELATIONSHIP  BETWEEN  GENDER  AND  AGE  DIVERSITY  AMONG  BOARD                MEMBERS AND FIRM PERFORMANCE .............................................................. 202                 Monika Kaczała                THE  INFLUENCE  OF  GOVERNMENT  SUPPORT  ON  THE  CROP  INSURANCE                UPTAKE .......................................................................................................... 210","Marcin Kalinowski, Grzegorz Krzykowski                STRUCTURAL  EVALUATION  OF  SECURITIES’  RATES  OF  RETURN  IN  SELECTED                BUSINESS SECTORS ON NEW YORK STOCK EXCHANGE ................................... 219                František Kalouda                 STABILITY  PREREQUISITES  OF  THE  CZECH  REPUBLIC  BANKING  SYSTEM                (CYBERNETIC POINT OF VIEW) ...................................................................... 227                 Özcan Karahan                HOW  THE  CHANGES  IN  EXCHANGE  RATE  AFFECT  THE  TURKISH  ECONOMIC                GROWTH UNDER INFLATION TARGETING REGIME? ........................................ 235                Özcan Karahan, Metehan Yılgör, Hakan Öndes                THE  EFFECT  OF  BANK  CREDIT  ON  THE  CURRENT  ACCOUNT  BALANCE                IN TURKEY ...................................................................................................... 243                 Marcin Kędzior, Dorota Kędzior                RELATIONS  BETWEEN  IFRS  ADOPTION  AND  FINANCIAL  AND  NON-FINANCIAL                MEASURES OF ECONOMIC ENTITIES, EMPIRICAL RESULTS FROM THE SELECTED                COUNTRIES OF WESTERN EUROPE ................................................................. 251                 Natália Klempaiová, Mária Bohdalová                COMPARISON  OF  GULKA’S  MODEL  AND  MODEL  IN05    ON  SAMPLE  OF  SLOVAK                INDUSTRIAL COMPANIES ............................................................................... 259                 Kristína Kočišová, Jakub Sopko                THE EFFICIENCY OF HEALTH SYSTEMS IN OECD COUNTRIES WITH NOVEL DEA                METHODOLOGY ............................................................................................... 267                Katarzyna Kochaniak                HOUSEHOLD         DEPOSITS’       AVAILABILITY        FOR     CREDIT      INSTITUTIONS                DURING  TENSIONS  ON  FUNDING  MARKETS    –  A  CASE  OF  THE  EURO  AREA                COUNTRIES .................................................................................................... 276                Anna Magdalena Korzeniowska                 MOTIVES OF HOUSEHOLDS FOR SAVING IN POLAND ..................................... 284                Anna Magdalena Korzeniowska, Wojciech Misterek                 THE RELATIONSHIP BETWEEN SAVING AND DEBT IN POLISH HOUSEHOLDS   292                Jaroslav Kovárník, Eva Hamplová                THE  SELECTED  ASPECTS  OF  FOREIGN  TRADE  IN  CENTRAL  EUROPEAN                COUNTRIES .................................................................................................... 299                 Anna Krajewska, Magdalena Kapela                LABOUR  TAXATION  IN  THE  EUROPEAN  UNION.    COMPARATIVE  ANALYSIS                AND CONCLUSIONS FOR POLAND ................................................................... 307                 Anna Krajewska, Katarzyna Osiecka                TAXATION  OF  CONSUMPTION  IN  POLAND.  REDISTRIBUTION  AND  FISCAL                EFFECTS .......................................................................................................... 315","Jan Krajíček                PROBLEMS IN THE BANKING SECTOR ............................................................. 323                 Peter Krištofík, Michaela Novotná                THE  IMPACT  OF  BEHAVIORAL  FINANCE  ON  THE  FINANCIAL  PERFORMANCE                OF AN ENTERPRISE ........................................................................................ 328                Zuzana Kubaščíková, Miloš Tumpach, Zuzana Juhászová                 USING OF NON-FINANCIAL DATA IN PREDICTIVE MODELS ............................ 334                 Michal Kuběnka, Renáta Myšková, Josef Novotný, Vít Jedlička                THE  ROLE  OF  APPARENT  SIGNS  OF  FINANCIAL  DISTRESS  IN  TEST  SAMPLES                AND VERIFICATION SAMPLES OF BANKRUPT MODELS ................................... 341                 Jana Kubicová                THE  ANTI-TAX-AVOIDANCE  MEASURES  IN  THE  EU  –  FROM  DENIALS  TO  WIDE                ACCEPTANCE .................................................................................................. 348                 Gabriela Kukalová, Lukáš Moravec, Jakub Ječmínek, Barbora Martásková                FINANCIAL CRISIS INFLUENCE ON TAX MIX CHANGES .................................. 355                 Malwina Lemkowska                ENVIRONMENTAL RISK INSURABILITY ISSUES – THE EU PERSPECTIVE ........ 364                 Karolina Lisztwanová, Iveta Ratmanová                ASSESSMENT  OF  FACTORS  INFLUENCING  OF  FINAL  CORPORATE  INCOME  TAX                LIABILITIES IN SELECTED SECTORS IN THE CZECH REPUBLIC ....................... 372                 Ondřej Machek, Aleš Kubíček                FINANCIAL PERFORMANCE OF CZECH PRIVATELY-HELD FIRMS WITH MULTIPLE                OWNERS ......................................................................................................... 380                 Marina Malkina                SPATIAL INEQUALITY IN OWNERSHIP AND ACTIVITY OF BANKING SECTOR: CASE                OF RUSSIA ...................................................................................................... 387                Tomáš Meluzín, Marcin Fałdziński, Michał Bernard Pietrzak, Adam P. Balcerzak,                Marek Zinecker                THE USE OF BACKTESTING IN ASSESSMENT OF THE VALUE-AT-RISK ON UNITES                STATES, GREAT BRITAIN AND GERMAN CAPITAL MARKETS ............................ 395                Michal Mešťan, Maroš Malíček, Matej Žofaj, Ivan Králik                 CHANGES IN CONSUMER CREDIT MARKET IN SLOVAKIA ................................ 402                 Grzegorz Michalski                THE  RELATIONSHIP  BETWEEN  DEBT  AND  EQUITY  IN  SOCIAL  ECONOMY                ORGANIZATIONS – POLISH CASE ................................................................... 410                 Katarzyna M. Miszczyńska                DETERMINANTS  OF  PUBLIC  HEALTHCARE  UNITS  INDEBTEDNESS  –  THE  CASE                OF PUBLIC HOSPITALS IN LODZ ..................................................................... 420","Piotr Miszczyński                PROFITABILITY  FORECAST  FOR  ASSESSMENT  OF  INVESTMENT  PROJECT                IN BANKING ................................................................................................... 427                Lukáš Moravec, Jana Hinke, Gabriela Kukalová, Zuzana Tichá                 EFFECTS OF FINANCIAL ACCOUNTING HARMONIZATION ON AGRIBUSINESS  436                László Nagy, Mihály Ormos                 FINANCIAL MARKET IMPLIED INDUSTRY CLASSIFICATION ........................... 445                 Danuse Nerudova, Veronika Solilova, Marian Dobranschi, Marek Litzman                THE ROLE OF CORPORATE INCOME TAX DIFFERENCES ON THE PROFIT SHIFTING                BEHAVIOR: EVIDENCE FROM THE CZECH REPUBLIC ....................................... 454                 Aleksandra Nocoń                MONETARY POLICY NORMALIZATION OF THE EUROPEAN CENTRAL BANK ..... 462                 Tomáš Ondruška, Zuzana Brokešová, Erika Pastoráková                DETERMINANTS OF NON-LIFE INSURANCE CONSUMPTION ............................ 470                 Denisa Oriskóová, Renáta Pakšiová                DECOMPOSITION OF ROE TO SELECTED FINANCIAL ANALYSIS INDICATORS . 479                 Gabriela Oškrdalová                INTERNET SHOPPING IN THE CZECH REPUBLIC WITH THE FOCUS ON THE TYPES                OF BOUGHT GOODS AND SERVICES BY CONSUMERS ...................................... 487                 Dalibor Pánek                THE TERMINATION OF FOREIGN EXCHANGE INTERVENTIONS AND THE BALANCE                SHEET OF THE CENTRAL BANK ........................................................................ 495                Sergey  Petrov,  Nadezhda  Yashina,  Oksana  Kashina,  Nataliya  Pronchatova-                Rubtsova                INVESTORS’  EXPECTATIONS,  PRICE-DIVIDEND  DEPENDENCE  AND  P/E  RATIO                FOR COMMON STOCK: SPECIFICS OF WELL-DEVELOPED MARKETS AND EMERGING                MARKETS ........................................................................................................ 500                 Aleksandra Pieloch-Babiarz                LONG-TERM  STOCK  RETURNS  OF  COMPANIES  IMPLEMENTING  DIVERSE                DIVIDEND POLICY: EVIDENCE FROM POLAND ................................................ 509                Sylwia Pieńkowska-Kamieniecka                THE  ISSUE  OF  RESPONSIBILITY  FOR  FINANCIAL  SECURITY  IN  OLD  AGE  AND                ADDITIONAL RETIREMENT SAVING ................................................................ 518                 Anatoliy Pilyavskyy, Lucie Kopecká                THE EFFICIENCY OF HEALTH CARE SYSTEMS IN OECD COUNTRIES. DOES IT MAKE                A DIFFERENCE? .............................................................................................. 525                 Sylvia Plottová                GOING  PUBLIC  IN  SELECTED  CEE  CAPITAL  MARKETS:  EMPIRICAL  EVIDENCE                ON IPO VOLUMES AND DRIVERS .................................................................... 531","Sylvia Plottová                CAPITAL MARKET DETERMINANTS OF GOING PUBLIC: THE EVIDENCE FROM CEE                CAPITAL MARKETS.......................................................................................... 536                Veronika Popelková                 NON-FINANCIAL  INFORMATION  ACCORDING  TO  THE  ACT  ON  ACCOUNTING                IN THE CZECH REPUBLIC ................................................................................ 543                 Lenka Přečková, Eva Vávrová                EVALUATION  OF  EXPENSE  RATIO  OF  INSURANCE  PORTFOLIOS  BELONGING                TO  SELECTED  INSURANCE  COMPANIES  OPERATING  ON  CZECH  INSURANCE                MARKET .......................................................................................................... 550                 Barbora Ptáčková, Dagmar Richtarová                COMPARISON  OF  ANALYSIS  DEVIATION  METHODS  FOR  THE  MULTIPLICATIVE                OPERATIONS .................................................................................................. 559                 Zuzana Rakovská                BELIEFS  IN  CRYPTOCURRENCY:  EXPLORING  THE  RELATIONSHIP  BETWEEN                SENTIMENT AND BITCOIN .............................................................................. 567                 Oldřich Rejnuš                THE     CURRENT       PROBLEMS       OF    THE     EUROZONE       AND      THE    FUTURE                TRANSFORMATION OF THE EUROPEAN UNION ............................................... 575                 Jerzy Różański                COMPARATIVE ANALYSIS OF FINANCING SOURCES IN DOMESTIC AND FOREIGN                ENTERPRISES IN POLAND .............................................................................. 583                Pavla Říhová, Milan Svoboda                 PROFITABILITY OF SELECTED TECHNICAL ANALYSIS INDICATORS ............... 591                Jaroslav Sedláček, Filip Hampl, Elena Láncošová                THE  INFLUENCE  OF  ACCOUNTING  FOR  GOODWILL  ON  THE  BOTTOM  LINE                OF BUSINESS CORPORATIONS ....................................................................... 599                 Pavel Sedláček, Miroslav Svoboda                GENERAL        DATA       PROTECTION         REGULATION         IN      THE     CONTEXT                OF MUNICIPALITIES ....................................................................................... 608                Ilja Schaab, Eric Frère, Alexander Zureck                 SYNDICATED LOAN MARKET GERMANY: DEEP DIVE INTO ROLE ALLOCATION 613                 Eugenia Schmitt                BACKTESTING  EXPECTED  SHORTFALL  –  IS  THERE  A  METHOD  SUITABLE  FOR                MIDDLE-SIZED BANKS? .................................................................................. 621                 Martin Sirucek, Jan Vystoupil, Petr Strejček                PROFITABILITY  OF  SECTOR  MUTUAL  FUNDS  AND  ETFS  DURING  MARKET                DEVELOPMENT ................................................................................................ 629","Roman Skalický                LEGAL BACKGROUND FOR A DIFFERENT EXPERT ASSESSMENTS OF A BUSINESS                ENTERPRISE  BELONGING  TO  A  COMMERCIAL  COMPANY  AND  OF  A  BUSINESS                SHARE IN A COMMERCIAL COMPANY .............................................................. 639                 Błażej Socha, Dariusz Urban                THE  FINANCIALIZATION  AND  OPERATING  PERFORMANCE  OF  NON-FINANCIAL                COMPANIES LISTED ON THE WARSAW STOCK EXCHANGE .............................. 646                 Tomasz Sosnowski, Anna Wawryszuk–Misztal                THE  EFFECT  OF  THE  ACCOUNTING  HOCUS–POCUS  ON  THE  MANAGEMENT                EARNINGS FORECAST PRECISION IN THE IPO ............................................... 653                 Ivan Soukal, Eva Hamplová                PRELIMINARY  RESULTS  OF  NON-BANK  CONSUMER  CREDIT  PROVIDERS                IN THE PROFESSIONAL QUALIFICATIONS TEST BY THE ACT NO. 257/2016 COLL.,                ON CONSUMER CREDIT ................................................................................... 662                 Martina Sponerová, Miroslav Svoboda, Miroslav Sponer                THE  ROLE  OF  NONFINANCIAL  INDICATORS  IN  THE  PROCESS  OF  CREDIT  RISK                ASSESSMENT .................................................................................................. 670                 Anna Sroczyńska-Baron                THE RECENT HHSTORY OF E-COMMERCE IN POLAND AND THE PERSPECTIVES FOR                ONLINE SALE GROWTH IN FUTURE. ................................................................ 681                 Jelena Z. Stanković, Evica Petrović, Ksenija Denčić-Mihajlov                THE  FINANCIAL  AND  INSTITUTIONAL  ENVIRONMENT  FOR  SUSTAINABLE                ECONOMIC  GROWTH  OF  DEVELOPING  COUNTRIES:  THE  CASE  OF  WESTERN                BALKAN .......................................................................................................... 689                Dirk Stein, Alexander Zureck, Tina Jäger                A  CALL  TO  THEORIZE  DIGITAL  INFORMATION  TECHNOLOGY  DUE  DILIGENCE:                ANALYSIS  OF  RESEARCH  GAPS  FOR  THE  SUBJECT  OF  INFORMATION                TECHNOLOGY DUE DILIGENCE IN MERGERS AND ACQUISITIONS .................. 697                Erik Suchý, Michal Šoltés                 INFLUENCE OF CAPITAL MARKET ON THE US UNEMPLOYMENT AND GDP ....... 708                Veronika Svatošová, Sylva Chalupová, Petra Dvořáková                 IDENTIFICATION OF FINANCIAL STRATEGY IN E-COMMERCE ........................ 715                 Martin Svítil                QUALITATIVE INDICATORS (SOFT-FACTS) IN INTERNAL RATING SYSTEMS .. 725                Martin Svoboda, Svend Reuse, Annika Rüder, Noel Boka                INTEREST  RATE  RISK  IN  THE  BANKING  BOOK  (IRRBB)  –  COMPARING                VARIANCE/COVARIANCE WITH HISTORICAL SIMULATION AND SEVERAL COPULA                FUNCTIONS WITH FOCUS ON THE ACTUAL EBA GUIDELINES ......................... 733                 Miroslav Svoboda, Tomáš Plíhal, Pavel Sedláček                RANDOM  STRATEGY  VERSUS  TECHNICAL  ANALYSIS  STRATEGY:  THE  CASE                OF GBP/USD INTRADAY TRADING .................................................................. 743","Libuše Svobodová, Jaroslava Dittrichová                BUSINESS ENTITIES AND LOANS IN THE CZECH REPUBLIC ............................ 749                 Łukasz Szewczyk                RECENT CHANGES IN THE POLISH DEPOSIT GUARANTEE SCHEME ................. 757                 Rafał Szmajser, Mariusz Andrzejewski, Marcin Kędzior                PROFITABILITY        DETERMINANTS          OF    POLISH      OUTSOURCING         SERVICE                ENTERPRISES        FROM     THE    POINT      OF    VIEW     OF    TOP    AND     MIDDLE                MANAGEMENT ................................................................................................. 764                 Elena Širá, Dana Kiseľáková, Beáta Šofranková                COMPETITIVENESS OF V4 COUNTRIES IN THE FIELD OF HIGHER EDUCATION 773                 Yuan Tian                COPULAS AND CREDIT RISK MODELS ............................................................. 780                 Petr Valouch, Josef Nesleha                IMPACT OF MERGERS ON THE COMPANY PERFORMANCE ................................ 788                 Eva Vávrová, Lenka Přečková                TECHNICAL  PROVISIONS  AS  ONE  OF  THE  RISK  MANAGEMENT  MODES                OF  COMMERCIAL  INSURANCE  COMPANIES  OPERATING  ON  THE  CZECH                INSURANCE MARKET ...................................................................................... 794                Jana Vodáková, Nela Sglundová                ACTIVITIES  CLASSIFICATION  FOR  CONTROLLING  PURPOSES  IN  THE  STATE                ADMINISTRATION UNIT ................................................................................. 803                 Lukáš Vodička                BANKING  SYSTEM  DEVELOPMENT  AND  ECONOMIC  GROWTH:  EVIDENCE  FROM                A  SAMPLE  OF  HIGH  INCOME  COUNTRIES  AND  LOW  \&  MIDDLE  INCOME                COUNTRIES .................................................................................................... 810                 Damian Walczak                EQUALITY IN OLD-AGE SECURITY IN POLAND ................................................ 818                 Radosław Witczak                THE  USE OF DIFFERENT  METHODS OF  THE  TAX  BASE  ESTIMATION  IN  INCOME                TAXES IN THE LIGHT OF SAC RULINGS IN 2017 IN POLAND .......................... 825                Elżbieta Wrońska-Bukalska                DEBT  CAPACITY  AND  CAPITAL  STRUCTURE    OF  THE  COMPANIES  MANAGED                BY OVERCONFIDENT CEO ............................................................................... 831                 Joanna Wyrobek                APPLICATION  OF  SELECTED  MACHINE  LEARNING  METHODS  TO  COMPANIES’                INSOLVENCY PREDICTION ............................................................................. 839","Sergey  Yashin,  Nadezhda  Yashina,  Nataliya  Pronchatova-Rubtsova,  Oksana                Kashina                METHODICAL  APPROACHES  TO  ASSESSING  THE  BUDGET  POTENTIAL  OF  THE                REGION TAKING INTO ACCOUNT THE INNOVATIVE DEVELOPMENT OF HIGH-TECH                INDUSTRIES ................................................................................................... 849                 Nadezhda  Yashina,  Nataliya  Pronchatova-Rubtsova,  Sergey  Petrov,  Oksana                Kashina                METHODICAL APPROACHES TO THE FORMATION OF MODEL BUDGETS IN ORDER                TO IMPROVE THE EFFECTIVENESS OF THE BUDGET PROCESS IN RUSSIA ....... 857","The Financial Analysis of the Education Support Fund: Recent                                               Results from Slovakia                                                                                 2                                                            1                                          Mária Barteková , Ľudomír Šlahor                                              1  Comenius University in Bratislava                                 Faculty of Management, Department of Economics and Finance                                          Odbojárov 10, 820 05 Bratislava, Slovakia                                            E-mail: maria.bartekova@fm.uniba.sk                                               2  Comenius University in Bratislava                                 Faculty of Management, Department of Economics and Finance                                          Odbojárov 10, 820 05 Bratislava, Slovakia                                             E-mail: ludomir.slahor@fm.uniba.sk                 Abstract:  The  analysis  of  institutions  providing  financial  services  has  always  been                particularly challenging because of the manner how the financial institutions operate and                generate profit. The purpose of this study is to analyse the financial performance of the                Education  Support  Fund  –  the  institution  providing  state  subsidised  student  loans  in                Slovakia. Tertiary students are eligible for the loan application from the Education Support                Fund. All European countries realize that tertiary education is one of the driving forces of                economic  growth.  The  empirical  part  of  the  paper  consists  of  the  financial  statement                analysis focused on the performing ratios. Ratio analysis is one of the most widely used                fundamental analysis technique. The most significant opportunity that emerged from our                analysis is to reach the positive net profit as the evidence of financial sustainability and                stability.  We  have  identified  the  net  interest  margin,  the  loan-to-assets  ratio,  and  the                return-on-assets  (ROA)  ratio  as  the  most  reliable  ratios  for  the  financial  performance                comparison. In recent years, the slightly increasing values of the performing ratios have                shown the financial stability of the Education Support Fund.                Keywords: student loans, financial ratios, Education Support Fund                JEL codes: G23, G29, G32                  1 Introduction                Education  has  long  be  seen  as  a  crucial  tool  for  national  development,  with  various                education initiatives designed to work towards eliminating poverty, increasing the health                of  a  population  or  enhancing  local  economies,  among  others.  Strengthening  the  social                dimension of higher education is still a key political goal on the European level. To expand                the knowledge base and foster progress, an increasing number of European citizens require                high level knowledge and competences. One of the key challenges in developing quality                mass higher education systems is to ensure that students have the necessary material                conditions to study and fulfil their potential. The question of how this is ensured at national                level is  a key  aspect  of  the  social  dimension  of  higher  education,  and  student  fee  and                support systems are thus important tools of national policies.                To assess the Education Support Fund’s financial performance we use the financial analyses                indicators.  Various  performance  aspects  cannot  be  observed  directly  whereas  they  are                economically important. While stockholders will view performance in terms of the profits                made  on  their  behalf,  whether  or  not  adjusted  for  risks  taken,  this  article  focuses  on                performance in a broader sense, that is, the contribution financial institutions make to the                common wealth, on behalf of consumers and businesses. They will be mainly interested in                whether financial products are not too expensive and whether the quality is sufficient. This                raises the issue of, on the one hand, the efficiency of financial institutions (i.e. whether                unnecessary costs are made in bringing a product to market) and, on the other, the level                of competition in the relevant markets (i.e. whether profit margins are not unnecessarily                high).                                                                16","Adequate performance of financial institutions is of crucial importance to their customers.                Prices and quality of their products are determined by efficiency and competition. Since                efficiency and competition cannot be observed directly, various indirect measures in the                form of simple indicators or complex models have been devised and used both in theory                and in practice (Bohdalová and Greguš, 2017).                As the main role of the Education Support Fund is the student loan providing, we compare                the Fund’s performance with the results reached by the banking sector in Slovakia. The                banking sector’s aggregate profit generated in Slovakia has come under severe pressure                from falling interest margins. Although the sector’s profit increased in 2016 by 19.8%, year                on year, that growth was driven by several one-off and exceptional factors. Excluding their                impact, the overall profit dropped by 11%. The sector’s total capital ratio increased slightly,                to 18% (The National Bank of Slovakia, 2016).                 Relation 1 describes the influence of competition to profitability of the financial institution                and market structure to competition at the market (see Figure 1). Competition also affects                financial innovations, Fund’s financial health, financial stability and the accessibility of the                Fund’s services to clients (Bikker, 2010). More efficient financial institutions will translate                lower costs into either increased profits or price reductions – the latter in order to improve                their competitiveness and increase their market share (indicated by a relation ‘2’ in Figure                1).  Efficiency  thus is not  an  effect  but  a  determinant  of  market  structure.  It  has  been                generally assumed that competitive pressure forces financial institutions to become more                efficient (indicated by a relation ‘3’). Excess profits enable financial institutions to lower                their  prices  and  become  more  competitive  in  order  to  increase  their  market  share                (indicated by a relation ‘4’).                      Figure 1 Relations Between Profitability, Efficiency and External Environment                                                                           Source: Bikker, J. A.                 Section  2  describes  the  Education  Support  Fund  as  the  unique  Slovak  student  loan                provider. Section 3 discusses methodology used in this paper and the main sources of data.                The financial analysis focused on the various performance ratios is a part of section 4.                Section 5 concludes.                 2 The Education Support Fund                The Education Support Fund was established on 1 January 2013 by Act No. 396/2012 Coll.                on the Education Support Fund as the legal successor of the Student Loan Fund and Loan                Fund for Starting Educators. The main activity of the Education Support Fund is to provide                loans  to  university  students,  teaching  staff,  professional  school  employee  and  doctoral                study programme students in the full-form of studies (Education Support Fund, 2017).                                                               17","The applicant must also meet the following requirements:                    •   Have a permanent residence in the territory of the Slovak Republic; or                    •   Have the status of a Slovak living abroad; or                    •   Be a citizen of the European Union with the right to permanent residence in the                       territory of the Slovak Republic or a family member of such person with the right                       to permanent residence.                Loans represent the primary earning asset at the Education Support Fund. Loans provided                by  the  Education  Support  Fund  are  dedicated  for  the  students  studying  at  the  higher                education institutions (HEIs) and are purposeless (Education Support Fund, 2018).                Figure 2 shows distribution of the approved student loans in the academic year 2017/2018.                As one can see, more than 55 % of the supported students are studying at HEIs located in                Western Slovakia. Just around 16 % of all supported students are studying abroad.                     Figure 2 Approved Student Loans Distribution in the Academic Year 2017/2018                                   Students studying at                                  abroad located HEIs                                       16%                          Students studying at                        HEsI located in Easter                             Slovakia                              19%                                               Students studying at                                                                                  HEIs located in                                                                                 Western Slovakia                                   Students studying at                                                                                      56%                                  HEIs located in Central                                        Slovakia                                          9%                                     Source: The Education Support Fund, own calculations                 Reducing disparities will require increasing the labor utilization ratio and improving the                structural and policy determinants of productivity in the eastern regions of Slovakia (Fadoš                and Bohdalová, 2017). Toll for the increasing labor utilization ratio is also the social support                for tertiary students. In the long term, the biggest group of the supported students are                coming from the following regions: Presov (eastern Slovakia), Kosice (eastern Slovakia)                and Zilina (northern Slovakia). The smallest group in the number of approved loans are                students coming from abroad (Figure 3).                 Figure 3 Amount of Supported Students in the Academic Year 2013/2014 – 2016/2017                                                 by the Place of Residence                              500                            437                             450                             400                             350                             300                             250                             200                             150                             100                              50                                                 7 6 8 5                               0                                                  2013/2014               2014/2015                                              2015/2016               2016/2017                                      Source: The Education Support Fund, own calculations                                                            18","Total costs per student loan include all expenses related to providing the loan and paid                from the budget of the Education Support Fund divided by the number of approved student                loans.  The  decreasing  number  of  provided loans  caused increase  of the  total  costs  per                student loan reported in 2016 (Figure 4).                              Figure 4 Total Costs per Provided Student Loan (2013 - 2016)                             400                                                                                 352,6861735                            350                                                                  299,3687713                                   279,3095507                            300                                                   251,4293698                            250                            200                            150                            100                              50                              0                                       2013           2014           2015           2016                                      Source: The Education Support Fund, own calculations                 The  operational  efficiency  is  the  capability  of  an  institution  to  deliver  services  to  its                customers in the most cost-effective manner possible while still ensuring the high quality                of its services. Figure 5 shows development of the operational expenditures from the Fund’s                establishment to 2017. Amount of the operational expenditures decreases every year and                the total value is under the legal limit. The operational expenses may not exceed 3.5% of                the outstanding principal of loans per year as of 31 December the previous year (Act No.                396 of 2012 Coll. on the Education Support Fund). The operational efficiency still increases.                          Figure 5 Development of the Operational Expenses from 2013 to 2017                                700000,0                                        584315,580                              600000,0             542042,010                                                               509734,490  496719,940  485408,430                              500000,0                              400000,0                               300000,0                              200000,0                              100000,0                                    ,0                                           2013       2014       2015        2016       2017                                        Source: Education Support Fund, own calculations                Efficiency based on the transformation of costs into benefits as measured. Efficiency is a                measure of how economically resources/inputs (funds, expertise, time, etc.) are converted                to results. These ratios enable the management to measure the effectiveness or usages of                the resources at the command of institution.                 3 Methodology and Data                Since the  purpose  of this  research is  to  gain  a  better insight into the  determining  and                presenting the relationship of items or group of items in the financial statement of the                Education Support Fund.                                                             19","We have identified the net interest margin (NIM), the return-on-assets (ROA) ratio, the                return-on-equity (ROE) ratio, Equity multiplier (EM), Burden/Total Assets ratio, Current                ratio and Quick ratio as the most reliable ratios for the financial performance analysis.                EM indicator presents the ratio between the financial assets of the banking sector and their                equity (Matuszaka and Rozanska, 2017). This can be used alongside other measurements                of  the  financial  leverage  of  this  sector  to  ascertain  its  overall  financial  stability  and  to                analyse its financial health.                Return  on  equity  (ROE)  analysis  provides  a  system  for  planning  as  well  as  analyzing                financial institution performance. The profit margin allows the analyst to develop a pro                forma income statement (Gavurova et al., 2017). That is, net income is equal to revenues                less expenses. Thus, the financial planner can determine the revenue level necessary to                achieve the net income target. The total asset turnover ratio allows the analyst to project                the total asset level necessary to generate the projected revenue level. The total asset                requirement  can  be  used  to  project  the  pro  forma  levels  of  all  of  the  asset  accounts                (Chorvatovičová and Saxunová, 2016). The fundamental equation of accounting is that                assets equal liabilities plus owners equity. Thus, the equity multiplier ratio can be used to                project the pro forma financial needs and the financial structure of the financial institution.                We draw on data from:                    •   Database of The National Bank of Slovakia,                    •   OECD database,                    •   The Education Support Fund’s database to track how the performance indicators                       evolved between 2016 and 2017.                The financial reports of the Education Support Fund are the main sources of data in this                paper.                 4 Results and Discussion                This session introduces fund financial statements and provides a traditional, ratio-based                procedure for analyzing fund financial performance using historical data. It demonstrates                the interrelationship between the income statement and balance sheet and describes the                risk and return trade-off underlying management decisions.                                   Table 1 Balance Sheet in EUR - Year 2016 and 2017                                                                              2016               2017                    A.  Invested Assets                                      33 957 079        32 857 561                    B.  Current Assets                                        4 175 857         5 489 992                    C.  Prepayments and Accrued Income                            1 029              1 318                Total Assets                                               38 133 965         38 348 871                    D.  Equity                                               38 071 437        38 287 084                    E.  Provisions                                                     0                 0                    F.  Liabilities                                              62 528             61 787                    G.  Accrued Expenses and Deferred Income                           0                 0                Total Liabilities                                          38 133 965         38 348 871                                             Source: The Education Support Fund                 Investment decisions affect the left-hand side of the balance sheet through asset purchases                (Del Giudice et al., 2016). Investment decisions determine the type of assets used by the                Education  Support  Fund,  the  industry  in  which  the  fund  operates,  and  the  degree  of                operating leverage of the fund. Financing decisions affect the right-hand side of the balance                sheet, which shows the financial structure of the fund through security issues and retained                earnings. Financing decisions determine the capital structure of the fund and the degree                of financial leverage.                 The Education Support Fund's income statement reflects the fact that most assets and                liabilities are financial (Table 2). Revenue consists primarily of interest income and interest                payments on liabilities represent the primary expense. The statement format starts with                                                            20","service revenues then subtracts operational expense. The next step is to subtract provision                for loan losses, which represents management's recognition that some revenues will be                lost due to bad loans. The format continues by adding interest income then subtracting                interest expense and taxes to produce net income.                                 Table 2 Income Statement in EUR - Year 2016 and 2017                                                                          2016          2017                         Service Revenues                                 136 373         134 219                         Operational Expenses                             560 557         550 801                         Other Revenues                                     58 299        113 295                         Other Expenses                                     20 817         19 745                         Trading Loss                                   - 386 702      - 323 032                         Interest Income                                  937 813         896 146                         Interest Expenses                                382 150         347 640                         Financial Profit                                555 663         548 506                         Profit on Ordinary Business Profit              168 961         225 474                         Profit/Loss on Extra Ordinary Events                    0              0                         Net Profit before Tax                           168 961         225 474                         Tax Liability                                       1 670          1 441                         After Tax Profit                                167 709         224 033                         Net Profit per Balance Sheet                    167 709         224 033                                             Source: The Education Support Fund                 The following analysis uses data from Table 1 and Table 2 to calculate and interpret the                various profitability ratios. The profitability ratio formulas are:                                                                                   =                                                                         (1)                                                                                                                                                   =                                                                         (2)                                                                                                                                        =                                                                           (3)                                                                                                                                                                   =                                                                         (4)                                                                          The liquidity ratio formulas used in this paper are following:                                                                                                                   =                                                      (5)                                                                                                                     ℎ                        +                                           +                                                                                     =                                                          (6)                                                                                                         Return on equity (1) equals net income divided by stockholders' equity and thus measures                the percentage return on stockholders' investment (Ray and Mitra, 2018). The higher the                return the better, as management can pay higher dividends and support greater future                growth. Return on equity reached in 2017 just 0.59 %.                ROE is tied to ROA (2) through a fund's equity multiplier (EM), which equals total assets                divided by stockholders' equity. EM (3) measures a fund's financial leverage, or its amount                of  liabilities  compared  with  equity.  The  greater  are  aggregate  liabilities,  the  greater  is                financial leverage and EM (OECD, 2018).                Net interest  margin  (4)  equals net interest income  divided  by  earning  assets and  thus                represents  the  net  interest  return  on  income  producing  assets.  The  results  of  the                profitability analysis are shown in Table 3. Almost every profitability ratio of the Education                Support Fund was miserable low and very far from set targets. In year-to-year comparison,                profitability ratios as ROE, ROA, NIM and Burden/Total Assets attained better values.                                                                 21","Table 3 Profitability Indicators in 2016 and 2017 and Profit Targets                                                               2016        2017       Target                            ROE                                0.51%       0.59%     >18.00%                            ROA                                0.51%       0.58%      >1.25%                            EM                                  1.00        1.00      >12.5X                            NIM                                1.70%       1.80%      >4.50%                            Burden/Total Assets               1.00 %       0.90%      < 2.00%                               Source: Own calculations, Bikker, J.A., The Education Support Fund                 As  one  can  see,  Burden/  Total  Assets  ratio  reached  in  both  years  is  better  than  the                recommended target (Table 3).                 The current ratio (5) measures the ability of a company to cover its short-term liabilities                with its current assets. Table 4 contains the financial results focused on the current liquidity                position  and  the  liquidity  targets.  A  quick  ratio  is  ineffectively  greater  than  industry                average. The current liquidity position of the Education Support Fund is really good and                sustainable. The reached values in both cases are influenced by the operating cycle of the                student loans provisioning.                                Table 4 Liquidity Indicators in 2017 and Liquidity Targets                                                                      2017       Target                                  Current ratio                     132,22       >1.00                                  Quick ratio                       130,51       >1.20                                     Source: The Education Support Fund, own calculations                 The  quick  ratio  (6)  is  more  conservative  than  the  current  ratio  because  it  excludes                inventory and other current assets, which generally are more difficult to turn into cash. A                higher  quick  ratio  means  more  liquid  current  position  (The  National  Bank  of  Slovakia,                2017).                 5 Conclusions                It has been established that with the help of a combination of appropriate indicators – we                could  make  a  good  deal  of  headway  towards  a  better  understanding  of  the  Education                Support Fund’s financial performance. Aggregate profitability among financial institutions                measured by ROE and ROA has varied very slightly from year to year.                The  main  purpose  of  the  Education  Support  Fund  is  to  provide  student  loans  with  the                favorable interest rate. However, profitability is the primary goal of all businesses. Without                profitability the business will not survive in the long run. So measuring current and past                profitability and projecting future profitability is very important. The most of profitability                ratios showed that the Education Support Fund is not profitable and confirmed the social                dimension of the student loans provisioning in Slovakia.                The current liquidity position of the Education Support Fund is very favorable in comparison                to set targets. Generally, companies would aim to maintain a current ratio of at least 1 to                ensure that the value of their current assets cover at least the amount of their short term                obligations. The value of the current and quick ratio reached in 2017 more than 130.                 References                Act no. 396/2012 Coll., on the Education Support Fund.                Bikker, J. A. (2010). Measuring Performance of Banks: An Assessment. Journal of Applied                Business and Economics, vol. 11(4), pp. 141-159.                Bohdalová,  M.,  Greguš,  M.  (2017).  Impact  of  uncertainty  on  European  market  indices                quantile regression approach. In: CBU International Conference on Innovations in Science                and Education. Praha: Central Bohemia University, pp. 57-61.                                                              22","Del  Giudice,  M.,  Campanella,  F.,  Dezi,  L.  (2016).  The  bank  of  things:  An  empirical                investigation on the profitability of the financial services of the future.  Business Process                Management Journal, vol. 22(2), pp. 324 - 340.                Education    Support     Fund    (2018).    Annual    report   2017.    Retrieved    from:                http://www.fnpv.sk/vyrocna-sprava-2017.                Education    Support     Fund    (2017).    Annual    report   2016.    Retrieved    from:                http://www.fnpv.sk/vyrocna-sprava-2016.                Fadoš,  M.,  Bohdalová,  M.  (2017).  Gender  inequality  in  unemployment  by  education                attainment in Spain, Switzerland and the European union. In: Education excellence and                innovation management through Vision 2020: From regional development sustainability                and competitive economic growth. Norristown, PA: IBIMA, pp. 2512-2526.                 Gavurova, B., Kocisova, K., Kotaskova, A. (2017). The Structure – Conduct – Performance                Paradigm in the European Union Banking. Economics \& Sociology, vol. 10(4), pp. 99 - 112.                Chorvatovičová, L., Saxunová, D. (2016). Usefulness of financial statements and annual                reports  in  the  process  of  accounting  fraud  detection.  In:  MIC  2016:  Managing  global                changes: Proceedings of the joint international conference. Koper: University of Primorska                Press, pp. 233-247.                Matuszaka,  L.,  Rozanska,  E.  (2017).  Corporate  social  responsibility  disclosures  and                                                                                                          th                financial  performance:  Evidence  from  Polish  banks.  In:  Proceedings  of  the  12                International Conference Accounting and Management Information Systems (AMIS 2017).                Bucharest: Bucharest University of Economic Studies, pp. 39 - 57.                OECD        (2018).       Banking        sector      leverage.       Retrieved       from:                https://data.oecd.org/corporate/banking-sector-leverage.htm.                Ray, K. K., Mitra, S. K. (2018). Firm's Financial Performance and Sustainability Efforts:                Application of Classifier Models. Global Business Review, vol. 19(3), pp. 722 - 736.                 The National Bank of Slovakia (2017). Balance sheet of assets and liabilities. Retrieved                from:     https://www.nbs.sk/en/statistics/financial-institutions/banks/statistical-data-of-                monetary-financial-institutions.                The  National  Bank  of  Slovakia  (2016).  Analysis  of  the  Slovak  Financial  Sector  2016.                Retrieved     from:    https://www.nbs.sk/_img/Documents/_Dohlad/ORM/Analyzy/ASFS                _2016.pdf.                                                                                            23","Life Insurance in the Czech Republic: New Challenges?                                                      Karina Benetti                                                Technical University of Liberec                                    Faculty of Economics, Department of Economic Statistics                                        Studentska 1402/2, 461 17 Liberec, Czech Republic                                                E-mail: karina.benetti@tul.cz                 Abstract: Do the life insurance products offered on the Czech insurance market provide                adequate insurance protection? Life insurance over the last decade has changed greatly,                not only in the offer of products. Originally this type of insurance resulted to cover the                consequences  of  the  risk  of  death  and  life  expectancy  risk.  Products  of  life  insurance                covering the risk of death should provide family of in case of death of the breadwinner,                products aimed at the risk of life expectancy were mainly-saving products which should                primarily secure the individual in old age. It was also possible to arrange a product called                endowment assurance. It is worth mentioning that there are various modern variations                and possibilities of arranging the different kinds of insurance within the life insurance of                people. The product development is escalated in investment life insurance product, which                has its strengths, but also weaknesses. It is a question if the situation on the Czech life                insurance market with regard to the development and importance of the products of the                investment life insurance for the insurance or especially financial market the opportunity                or the threat.The main objective of this paper is to capture the main trends of gross life                insurance premium, focusing on changes in gross premiums written by individual types of                life insurance products including the analysis of selected parameters (number of insurance                contracts,  insurance  penetration  and  other  selected  indicators).  The  software                STATGRAPHICS Centurion will be used for this analysis.                Keywords: life insurance, development, gross premium written                JEL codes: G18, G22                 1 Introduction                The main roles of life insurance in the financial services system are gradually changing.                According Ducháčková (2016) the life insurance is considered to be an instrument to cover                the needs of people, on the one hand, a tool of covering the consequences of the risk                (death and other risks - accident, invalidity, illness etc.), and on the other hand, a tool for                savings  to  cover  the  needs  of  people in  post-productive  age.  At  present, many  factors                affect the  development  of life insurance  and  especially its  efficiency.  In  the use  of life                insurance as a means of addressing the needs of people in old age is in the last period on                the Czech insurance market a number of problems. The problems, according Ducháčková                (2016) arise from the form of life insurance products, from regulatory approaches in life                insurance, from approaches to selling life insurance contracts.  However, life insurance is                a  standard  tool  of  the  insurance  (financial)  market  (Ducháčková,  2015).  Its  role,                significance  and  form  are  changing  throughout its  development in  relation to  changing                conditions of life insurance. It is characteristic for the recent period that life insurance has                been going through modifications, in particular the typical increase in the share of unit                linked life insurance which is representing in particular the product of the investment life                insurance. Life insurance and especially unit linked life insurance have been recently facing                a few challenges. Some of them resulted from changing financial markets and others were                connected with the conclusion of life insurance policies.                 The aim of the paper is to analyze the development of the gross premium written of life                insurance in the Czech Republic and the basic groups of life insurance products by analyzing                selected indicators for the period 1995 to 2017.                 In the following part research methods and data for analysis will be characterized.                                                               24","2 Methodology and Data                In the research were particular used scientific methods: induction, comparative analysis,                synthesis of partial knowledge, elementary statistical analysis and dependence analysis.                For elementary statistical analysis the following selected indicators were used (Hindls, et                al., 2000):                    •   the first difference                         (1)                         t  =  −   − t  1 .                                                          (1)                              t                    •   the second difference                         (2)  (1)  (1)                         t  =  t  −                                                                   (2)                                   − t  1                    •   the growth coefficient                             y                        k =   t                         t                            y t− 1                                                                      (3)                    •   the growth rate                         y t  = T − 100                                                                (4)                              y                              t                    •   the increase rate                        T  t y  = k  100                                                               (5)                              t                    •   the average absolute gain and                             n                             1 t                                                                    n                         =  t= 2  =  y −  y 1  ,                       1     n −  1  n −  1                                                             (6)                    •   the average growth coefficient                              y                        k =  n− 1  n                              y 1                                                                       (7)                where n is the number of values (in this paper n = 23).                For regression analysis was used software STATGRAPHICS Centurion XVI. For the analysis                secondary data from Czech National Bank (2018) were used.                According to the results of an elementary statistical analysis a suitable model for trend                analysis  will  be  chosen.  The  results  of  trend  analysis  will  be  evaluated  based  on  their                individual indices:                    •   RMSE (root mean squared error);                         2                    •       modified index of determination;                                               •   p-value  (of  parameters  and  model)  of  significance,  according  to  which  the                       robustness of a particular model is evaluated at the 5% significance level;                    •   t-test,                    •   F-ratio of model.                Other indicators that will assess the development of life insurance in the Czech Republic                will  include,  in  particular:  development  of  number  of  insurance  contracts,  insurance                penetration,  gross  premium  written  in  life  insurance  per  capita  and  the  ratio  of  the                individual life insurance groups (insurance relate to an investment fund, wedding insurance                or child-care insurance, accident insurance or sickness insurance, retirement insurance,                death and survival insurance) to the total prescribed life insurance in the Czech Republic.                 3 Results and Discussion                The results of elementary statistical analysis, by selected characteristics, of development                of  gross  premium  written  in  life  insurance  are given  below.  The  development  of  the                                                             25","percentage ratio of gross premium written in life insurance versus non-life insurance is                illustrated in Figure 1. This figure shows the importance of life insurance on the Czech                insurance market. It should be added that this ratio is reversed on average across the EU                (see Eurostat data). The basic development of gross premium written in life insurance of                its first difference illustrated Figure 2 and Table 1.                     Figure 1 Development of The Percentage Ratio of Gross Premium Written in Life                                           Insurance Versus Non-life Insurance                                                                               Source: own from Czech National Bank (2018)                    Figure 2 Development of Premium Written in Life Insurance with Development of its                                                      First Difference                                                           Source: own from Czech National Bank (2017)                 According to the development of the values specified in Figure 1 can be deduced that the                observed characteristics recorded growth till 2014. For this reason, does not make sense                to describe the examined values other statistical characteristics (such as e.g. coefficient                growth, growth rate and increase rate). For a basic overview of the development of the                                                             26","examined values sufficient to indicate the results of absolute average gain and average                growth coefficient.                 The  result  of  average  absolute  gain is for  gross  premium  written in life insurance  CZK                2 192 694 (for year 2016 was CZK 2 392 810 520, Benetti (2017)).                The  result  of  average  growth  coefficient  is  for  gross  premium  written  in  life  insurance                1.086181458  (for  year  2016  it  was  1.092248,  which  corresponds  to  9.23  %,  Benetti                (2017)).                    Table 1 Elementary Characteristic Development of Gross Premium Written in Life                                                         Insurance                                Gross                              premium                              written of                               non-life                            insurance (in                     Years    thousands                      (t)     CZK (yt))      ∆t (1)      ∆t (2)        kt            Tyt         δyt                      1995      9 341 715     ×           ×            ×             ×            ×                      1996     10 937 216   1 595 501     ×       1.170793157     0.170793157   17.08%                      1997     12 692 286   1 755 070    159 569    1.16046771     0.16046771   16.05%                       1998     15 089 372   2 397 086    642 016   1.188861644    0.188861644   18.89%                      1999     19 793 331   4 703 959   2 306 873   1.311739879   0.311739879   31.17%                      2000     22 770 132   2 976 801   -1 727 158   1.15039414    0.15039414   15.04%                      2001     28 281 966   5 511 834   2 535 033   1.242064209   0.242064209   24.21%                      2002     34 036 346   5 754 380    242 546   1.203464639    0.203464639   20.35%                      2003     41 128 802   7 092 456   1 338 076   1.20837889     0.20837889   20.84%                      2004     44 201 009   3 072 207   -4 020 249   1.074697216   0.074697216   7.47%                      2005     44 954 269    753 260   -2 318 947   1.017041692   0.017041692    1.70%                      2006     47 233 389   2 279 120   1 525 860   1.050698633   0.050698633    5.07%                       2007     54 128 225   6 894 836   4 615 716   1.145973773   0.145973773   14.60%                      2008     56 909 094   2 780 869   -4 113 967   1.051375581   0.051375581   5.14%                      2009     60 209 323   3 300 229    519 360   1.057991241    0.057991241    5.80%                      2010     71 764 862  11 555 539   8 255 310   1.191922753   0.191922753   19.19%                      2011     72 009 104    244 242   -11 311 297   1.003403365   0.003403365   0.34%                      2012     72 049 292     40 188     -204 054   1.000558096   0.000558096    0.06%                      2013     71 577 033   -472 259     -512 447   0.993445335   -0.006554665   -0.66%                       2014     71 186 464   -390 569      81 690   0.994543375   -0.005456625    -0.55%                      2015     62 415 277   -8 771 187   -8 380 618   0.876785747   -0.123214253   -12.32%                      2016     59 590 736   -2 824 541   5 946 646   0.954745999   -0.045254001   -4.53%                      2017     57 580 983   -2 009 753   814 788      0.966274       -0.03373   95.47%                                     Source: own elaboration Czech National Bank (2018)                 From Table 1 it is clear that the largest increase was recorded in the surveyed quantity in                2010, on the contrary, the largest decline in 2015. The question is which life insurance                group this increase / decrease was caused. This will be the focus below.                 The  results  of  the  ratio  of  the  individual  life  insurance  groups  (insurance  relate  to  an                investment fund, wedding insurance or child-care insurance, accident insurance or sickness                insurance, retirement insurance, death and survival insurance) to the total prescribed life                                                             27","insurance in the Czech Republic are shown in the Figure 3. The data for ends of years 2016                and 2017 was not published.                   Figure 3 The Development of the Ratio of the Individual Life Insurance Groups to the                              Total Premium Written in Life Insurance in the Czech Republic                                                                Source: own elaboration from Czech National Bank (2018)                 From Figure 3 it is clear that the increase in the total life insurance premium written in                2010  was  driven  by  an  increase  in  subscribed  life  insurance  premiums  linked  to  the                investment fund. On the other hand, the decline in total life insurance premiums written in                2015 was due to a decline in written life insurance premiums in the event of death and                survival,  as  well  as  a  partial  decline  in  gross  written  premium  written  linked  to  the                investment fund.                 The results of development of number of insurance contracts, insurance penetration and                gross premium written in life insurance per capita see in Table 2.                 The number of the insurance contract has a decreasing trend over the reference period,                except 2012. However, the gross premium written per insurance contract has a volatility                development period. This means that with the decreasing number of contracts, the gross                premiums written not decreases. Premium per capita increases in the period 2006 to 2011,                and in the following year, since 2012, it has declined. In the case of this indicator, it is                highly  desirable  for  its  value  to  have  a  growth  tendency.  The  decreasing  trend  of  this                indicator indicates the low use of the product by the population of that country. Insurance                penetration a growing tendency in 2006 to 2010, with a downward trend in the following                period since 2006, indicating the unfavorable development of the indicator.                Another research question is how to develop gross premiums written in life insurance in                the future  this will be used to analyze time series and software STATGRAPHICS Centurion                XVI. The trend in time series can be described by trend functions unless the development                of time series corresponds to a particular function of time (for example: linear, quadratic                and exponential).                                                                   28","Table 2 The Results of Selected Indicators                                             2006          2007         2008         2009         2010                  Number   of   insurance                  contracts                10 010 546   10 119 438   10 104 445     9 349 600    8 919 070                  Gross  premium  written                  per insurance contract        4 718        5 349        5 631        6 442        8 046                   Premium per capita            4 601        5 243        5 456        5 741        6 824                  Insurance penetration        1.35%        1.41%        1.42%        1.54%        1.82%                                            2011          2012         2013         2014         2015                  Number   of   insurance                  contracts                 8 675 566    9 357 769    8 060 735     7 740 318    7 392 770                  Gross  premium  written                  per insurance contract        8 300        7 699        8 880        9 197        8 443                  Premium per capita            6 860        6 856        6 810        6 764        5 920                  Insurance penetration        1.79%        1.77%        1.75%        1.65%        1.37%                                            2016          2017                  Number   of   insurance                  contracts                 5 996 463    5 865 132                  Gross  premium  written                  per insurance contract     9.937.65     9 817.51                  Premium per capita            5 640        5 427                  Insurance penetration        1.25%        1.14%                 Source: own elaboration from (Czech Statistical Office, 2017a and 2017b), (Czech National Bank,                                                           2018)                 Linear trend function (line) has the following form (Arlt et al, 2002):                          =    +                                                                        (8)                                                       0                                 1                Quadratic trend function (parabola) has the following form (Arlt et al, 2002):                          =    +       +                                                                (9)                                        2                                      2                             0                                                           1                Exponential trend function has the following form (Arlt et al, 2002):                          =                                                                           (10)                                                              0 1                                          To evaluate the suitability of the trend have been identified and assessed values of trend                function forecast, values of the root mean squared error (RMSE) and values of modified                index of determination (   ).                                          2                                                            RMSE (Arlt et al, 2002):                                  1                                              2                       RMSE = √ ∑       (   −   ̂ )                                                   (11)                                        =1                        where   ̂  is modeled values at time t.                                          Determination modified index (   ), (Arlt et al, 2002):                                                 2                                                                                       2                         2                              2                          =    −  (1−   )(  −1)                                                       (12)                                                                −                  Other indicators used for trend analysis include: p-value, t-test and F-ratio. Based on the                results of these indicators, using the software STATGRAPHICS Centurion XVI was selected                the best model – quadratic trend.                Trend function forecast is:    = − 6 259 260 000 + 6 718 730 000   − 155 071   .                                            ̂                                                                                         2                                                              Forecast for next two periods is – point for year 2018 CZK 65 669 400 000 (interval CZK:                50 955 300 000 – 80 038 360 000) and point for year 2019 CZK 64 789 700 000 (interval                CZK:  49 183 000 000  –  80 396 400 000).  Compared  with  results  of  previous published                                                            29","analysis  (Benetti,  2018)  the  point  estimation  was  for  year  2017  CZK  70 511 000 000                (interval  CZK:  56 552 400 000  –  84 471 300 000)  and  point  for  year  2018  CZK                70 729 300 000  (interval  CZK:  55 850 200 000  –  85 608 400 000).  The  estimation  for                year 2017 was 13 148 317 000 higher.                 Figure 4 Time Series Equalization by Quadratic Trend and Forecast of Development for                                                      Next Two Years                                                                                      Source: own elaboration                 4 Conclusions                The predicted development of the gross premiums written in life insurance according to                the selected time series model for the following two periods  – 2018 and 2019 – has a                decreasing tendency. If the model prediction would be fulfilled, it would indicate a positive                development in the  use  of life insurance  products.  However, it is  questionable  whether                declining  trends  in  the  use  of  traditional  life  insurance  products,  death  and  survival                insurance, rather than suggesting a change in clients' interest in other products – such as                the life insurance product linked to the investment fund. On the contrary, the increase in                the  use  of  life  insurance  products  that  are  linked  to  the  investment  fund  does  not                necessarily  imply  a  positive  development  in  the  use  of  life  insurance  products,  as  the                investment life insurance products are not the classic investment products but rather the                investment instrument (when the reasonable amount for the risk of death is not met). This                could be a basic research question for further research. From the above results, it can be                assumed that the development of written life insurance has unhealthy development on the                Czech insurance market. The situation with the investment life insurance product is very                unstable. From the results above, it can be assumed that the development of written life                insurance has unhealthy development on the Czech insurance market. The situation with                the investment life insurance product is very unstable. Already in the press, information                was  received  in  the  press  that  many  clients  of  insurance  companies  are  suing  for  the                invalidity of the investment product life insurance product. Not only from this point of view                can the product of investment life insurance be more a threat than a market opportunity.                 Acknowledgments                The paper was written under the support of the specific research of EF TUL.                                                             30","References                Arlt,  J.  et  al.  (2002).  Analýza  ekonomických  časových  řad  s  příklady.  Vysoká  škola                ekonomická v Praze, 2002.                 BENETTI, K. (2017). The Development of Gross Premiums Written in Life Insurance in the                Czech Republic. In: Proceedings of the 14th International Scientific Conference European                Financial Systems 2017. Brno: Masaryk University, pp. 15-22. ISBN 978-80-210-8609-8.                WOS: 000418110700002.                Czech National Bank (2018). ARAD. http://www.cnb.cz/docs/ARADY/HTML/index_en.htm                 Czech     Statistical   Office   (2017a).     Population    –    annual     time    series.                https://www.czso.cz/csu/czso/population_hd                Czech    Statistical   Office   (2017b).   Gross   domestic   product    –   time   series.                https://www.czso.cz/csu/czso/hdp_ts                Ducháčková, E. (2016). The Role of Life Insurance in the Context of Cover the Needs of                the  People  in  the  Czech  Republic.  In:  Proceedings  of  the  13th  International  Scientific                Conference  European  Financial  Systems  2016.  Brno:  Masaryk  University,  pp. 133–140.                WOS: 000385692200017                Ducháčková,  E.  (2015).  Life  Insurance  and  the  Role  of  Financial  Arbitrator  for  the                Resolution of Disputes within Life Insurance. In: 7th International Scientific Conference on                Finance and Performance of Firms in Science, Education and Practice. pp. 217–231. WOS:                000374107300016                 Hindls, R. et al. (2000). Statistika pro economy, 8th ed. Praha: Professional Publishing.                                                                                                        31","Corporate Bankruptcies in the Czech Republic: The Development                                               Over the Last Decade                                                      Karina Benetti                                                    Unicorn College s.r.o.                                       V Kapslovne 2767/2, 130 00  Praha 3, Czech Republic                                               E-mail: benetti.kari@gmail.com                 Abstract: This paper deals with the analysis of development of corporate bankruptcies in                the Czech Republic over the last decade. Corporate bankruptcies are an inherent element                of the market economy. In the Czech Republic in 2006 was published a law no. 182/2006                Coll.,  On  Bankruptcy  and  Its  Resolution  (Insolvency  Act),  which  came  into  effect  from                January 1st, 2008. This law came into effect just at a time when the new financial crisis                started getting stronger. This crisis has affected the development of corporate bankruptcies                not  only  in  the  Czech  Republic  but  also  in  other  countries.  Corporate  Bankruptcies                (Mączyńska, 2009) are the necessary selection mechanism in the world of business and                have an important rationalizing function – they rid the market of entities that are unable                to meet the appropriate efficiency requirements. The aim of this paper is to analyze the                development  of  corporate  bankruptcies  –  the  comparison  between  the  development  of                corporate insolvency proposals and corporate bankruptcies (using total and monthly data)                in the Czech Republic over the last decade – in the period 2008 to 2017 with a forecast for                2018.  The  software  STATGRAPHICS  Centurion  will  be  used  for  this  analysis.  The  data                needed  to  analyze  the  development  of  corporate  insolvency  proposals  and  corporate                bankruptcies has been taken over from the company Creditreform, s.r.o. Czech Republic.                Keywords: corporate bankruptcy, corporate insolvency proposals, development, financial                health.                JEL codes: G31, G33                 1 Introduction                From January 1st, 2008 the law no. 182/2006 Coll., On Bankruptcy and Its Resolution                (Insolvency Act), which was published in 2006 came into effect. This law regulates not only                bankruptcies of companies, but also personal bankruptcies. In the Czech Republic Bokšová                and Randáková (2015), Bokšová et al (2014), Hospodka et al (2015) and Maixner et al                (2014) published analysis results of personal bankruptcies in recent years. In this view                bankruptcy models (Čámská, 2012) and basic characteristics of enterprises, which are in                insolvency (Čámská, 2013), are very important. The analysis of the business property's                changing trends of the entity should be one of the most important tasks of the financial                analysis for the assessment of the financial situation of the enterprise (Pakšiová, 2017).                Detailed analysis of corporate insolvency during the crisis years (with data analysis from                2008 to 2013) were published by Kislingerová and Schoenfeld (2014), Benetti (2016 a,                2016 b and 2017) and forecasts of corporate insolvencies for the period 2013–2017 were                published in 2013 by Kislingerová (2013), Benetti (2017). However, no one has engaged                in research of corporate bankruptcies within individual regions in the Czech Republic and                their effects on their development (or economic performance).                The aim of this paper is to analyze the development of (using total and monthly data)                corporate bankruptcies – the comparison between the development of corporate insolvency                proposals (it means according to the law no. 182/2006 Coll., at the insolvency court filed                a  petition  to  initiate  insolvency  proceedings)  and  corporate  bankruptcies  (it  means                according to the law no. 182/2006 Coll., the debtor is in bankruptcy, if he has more than                one creditor, a pecuniary obligation for more than 30 days after maturity, and he is not                able to repay at the same time) in the Czech Republic over the last decade – in the period                2008 to 2017 with a forecast for 2018. The software STATGRAPHICS Centurion will be used                for  this  analysis. The  data  needed  to analyze  the  development  of  corporate insolvency                                                               32","proposals and corporate bankruptcies has been taken over from the company Creditreform,                s.r.o. Czech Republic.                 2 Methodology and Data                 In the research, scientific methods were particular used: induction, comparative analysis,                synthesis of partial knowledge, elementary statistical analysis and dependence analysis.                 For  elementary  statistical  analysis  were  used  the  following  selected  three  indicators                (Hindls, et al, 2000):                     •   the first difference (absolute gain, 1∆t IN – for corporate insolvencies and 1∆t BA – for                       corporate bakruptcies) (1)                         =  −     .                                                                   (1)                       1 t    t  t − 1                    •  the average absolute gain (2) and                              n                              1 t                                                                       n                         =  t = 2  =  y −  y 1 ,                                                             (2)                       1     n −  1    n −  1                    •   the average growth coefficient (3)                               y                        k =  n− 1  n                                                                     (3)                               y 1                 where n is the number of values (in this paper n = 120).                For the dependence analysis, the software STATGRAPHICS Centurion XVI was used. For                the analysis were used secondary data from Creditreform (2017).                 3 Results and Discussion                The  results  of  elementary  statistical  analysis,  by  selected  three  characteristics,  of                development of number of corporate insolvency proposals and corporate bankruptcies are                given  below.  The  basic  development  of  number  of  corporate  insolvency  proposals  and                corporate bankruptcies with development of its first difference illustrates Figure 1.                According to the development of the values specified in Figure 1 can be deduced that the                observed characteristics were examined over a period of very fluctuating development. For                this  reason,  does  not  make  sense  to  describe  the  examined  values  other  statistical                characteristics (such as e.g. coefficient growth, growth rate and increase rate). For a basic                overview of the development of the examined values sufficient to indicate the results of                absolute average gain and average growth coefficient.                The result of average absolute gain is for corporate insolvencies proposals –0.04202 and                for corporate bankruptcies 0.529412.                 The results of average growth coefficient are for corporate insolvencies proposals 0.999594                (which  corresponds  to  –0.0406%).  and  for  corporate  bankruptcies  1.020736  (which                corresponds to 2.0736%).                                                                     33","Figure 1 Development of Number of Corporate Insolvency Proposals and Corporate                                   Bankruptcies with Development of its First Difference                           1/2008  1/2009  1/2010  1/2011  1/2012  1/2013  1/2014  1/2015  1/2016  1/2017                      1000                                                                            250                       950                       900                       850                       800                                                                            200                       750                       700                       650                       600                       550                                                                            150                       500                       450                       400                                                                            100                     the number   300                       350                       250                       200                       150                       100                                                                            50                        50                        0                       -50                                                                            0                      -100                      -150                      -200                      -250                      -300                                                                            -50                      -350                      -400                      -450                      -500                                                                            -100                                                           Month/Year                             The number of corporate insolvency proposals  1Δt IN                            The number of corporate bankruptcies  1Δt BA                                            Source: author from Creditreform (2018)                 The automatic forecasting function was used for the forecasting of the corporate insolvency                proposals and corporate bankruptcies in Czech Republic. The ARIMA (0,1,2) model was                selected for the forecasting of the corporate insolvency proposals by the results of the p-                value, the standard error, the root mean squared error (RMSE), the mean absolute error                (MAE), the mean absolute percentage error (MAPE), the mean error (ME) and the mean                percentage error (MPE). The results of selected indicators for different ARIMA models see                in Table 1. Forecasted values of number of corporate insolvency proposals for the Czech                Republic for January 2018 was point value 113.219 and from February to June 2018 was                115.449. The number of corporate insolvency proposals which was reported (Creditreform,                2018) for January 2018 was 121. The forecast by the selected model was accurate only                95.04 %, which corresponds to the selected confidence interval. The number of corporate                insolvency  proposals  which  was  reported  for  February  was  112  and  for  March  131                (Creditreform,  2018).  The  forecast  by  the  selected  model  was  97.39  %  for  values  in                February,  which  corresponds  to  the  selected  confidence  interval.  The  forecast  by  the                selected model was accurate only 85.7 % for values in March, which does not corresponds                to the selected confidence interval. If we change in the program Statgraphics the analysis                options – by the ARIMA models we not optimize model order and not optimize parameters.                By the using of function of automatics forecasting options we have another result  – the                ARIMA  (2,2,2)  model  was  selected  with forecasting for  January  2018 in  value  111, for                February 2018 in value 100 and for March 2018 in value 97. The forecast by the selected                model was accurate only 90.99 % for values in January, 88.00 % for values in February                and 64.95 %, which does not corresponds to the selected confidence interval.                                                             34","Table 1 Results of Selected Indicators for Different ARIMA Models                             MODEL          RMSE       MAE       MAPE         ME          MPE                         ARIMA(0,1,2)  71.2976  51.2254  14.9138           -0.51787      -3.39436                         ARIMA(1,1,1)  71.7079        51.248  14.8673      -0.47211        -3.383                         ARIMA(2,1,0)  71.9718  49.4441          14.562    -0.20137      -2.99306                         ARIMA(2,1,1)  71.4841  50.8454  14.7782           -0.51198      -3.33274                                                       Source: author                 For the forecasting of the corporate bankruptcies was by the results of the p-value, the                standard error, the root mean squared error (RMSE), the mean absolute error (MAE), the                mean absolute percentage error (MAPE), the mean error (ME) and the mean percentage                error  (MPE)  selected  ARIMA  (0,2,2)  model.  Results  of  selected  indicators  for  different                ARIMA models see in Table 2.                            Table 2 Results of Selected Indicators for Different ARIMA Models                         MODEL            RMSE           MAE        MAPE         ME           MPE                        ARIMA(0,2,2)        22.4576    16.9632    12.5779     –3.29357      -3.93513                        ARIMA(0,1,1)        22.7724    17.2389    12.6029      1.45102     –0.556413                        ARIMA(0,1,2)        22.6982    17.1836    12.4361      1.55363     –0.407813                        ARIMA(1,1,1)        22.7337    17.1974    12.4608      1.57738     –0.393204                                                       Source: author                  Forecasted values of number of corporate bankruptcies for the Czech Republic in January                2018 was point value: 100, for February 2018 was 98, for March 2018 was 95. The number                of corporate bankruptcies which was reported for January 2018 was 83, for February was                84 and for March 120 (Creditreform, 2018). The forecast by the selected model was accurate                only 83.00 % for values in January, 85.7 % for values in February and 79.2 % for values                in March, which does not corresponds to the selected confidence interval. If we change in                the program Statgraphics the analysis options – by the ARIMA models we not optimize                model  order  and  not  optimize  parameters.  By  the  using  of  function  of  automatics                forecasting options we have another result – the ARIMA (2,2,2) model was selected with                forecasting for January 2018 in value 88, for February 2018 in value 83 and for March 2018                in value 76. The forecast by the selected model was accurate only 95.40 % for values in                January, 98.81 % for values in February and 79.2 %, which corresponds to the selected                confidence interval. For values in March was the forecast by the selected model accurate                only 63.33 %, which does not corresponds to the selected confidence interval.                For dependence analysis will be used monthly data. Firstly, was conducted multiple variable                analysis, summary statistics illustrated Table 3, results from correlations show Table 4 and                Figure 2.                The Table 1 shows summary statistics for each of the selected data variables.  It includes                measures  of  central  tendency,  measures  of  variability,  and  measures  of  shape.  Of                particular interest here are the standardized skewness and standardized kurtosis, which                can be used to determine whether the sample comes from a normal distribution.  Values                of  these  statistics  outside  the  range  of  -2  to  +2  indicate  significant  departures  from                normality,  which  would  tend  to  invalidate  many  of  the  statistical  procedures  normally                applied to this data.                                                                   35","Table 3 The Summary Statistics from Multiple Variable Analysis                                                  The Number of               The Number of                                                  Corporate Insolvencies  Corporate                                                  Proposals                   Bankruptcies                        Count                     120                         120                        Average                   385.1                       151.35                        Standard deviation        177.151                     39.4337                        Coefficient of            46.0014%                    26.0547%                        variation                        Minimum                   94.0                        6.0                        Maximum                   903.0                       228.0                        Range                     809.0                       222.0                        Standard skewness         2.02242                     -3.26109                        Standard kurtosis         -1.02139                    2.61712                                                  Source: own elaboration                                                     Table 4 Correlation                                                 The Number of              The Number of                                                 Corporate Insolvencies Corporate                                                 Proposals                  Bankruptcies                      The Number of                                         0.1337 (Correlation)                      Corporate Insolvencies                                (120) (Sample Size)                      Proposals                                             0.1454 (P-Value)                      The Number of              0.1337 (Correlation)                      Corporate                  (120)                      Bankruptcies               0.1454                                                  Source: own elaboration                The Table 4 shows Pearson product moment correlations between each pair of variables.                In  this  case  is  Pearson  product  moment  correlation  0.1337.  Pearson  product  moment                correlation coefficients range between -1 and +1 and measure the strength of the linear                relationship  between  the  variables.  Also  shown  in  parentheses  is  the  number  of  pairs                of data  values  used  to  compute  each  coefficient.  The  third  number  in  each  location                of the table  is  a  P-value  which  tests  the  statistical  significance  of  the  estimated                correlations. P-values below 0.05 indicate statistically significant non-zero correlations at                the 95.0% confidence level. None form the analyzed pairs of variables have P-values below                0.05. This is the reason, why is for this case the Pearson product moment correlations                indicator not correct, we must use for example Spearman correlation coefficient.                However, the results shown in Table 4 cannot be properly assessed without visualization -                see Figure 2.                                Figure 2 Scatterplot Matrix                                                                                    Note:                                                                           Col_1 = The Number of                                                                            Corporate Insolvencies Proposals                                 Source: own elaboration                   Col_2 = The Number of                                                            36             Corporate Bankruptcies","From  the  results in the Figure  2 it is evident  that  between  variables is  no  correlation.                Certainty as to whether between variables is or is not correlation will bring a result of the                Spearman rank correlation, the results are given in Table 5.                                           Table 5 Spearman Rank Correlations                                                  The Number of              The Number of                                                  Corporate Insolvencies Corporate                                                  Proposals                  Bankruptcies                     The Number of                                            0.0650 (Correlation)                     Corporate Insolvencies                     Proposals                                                                             (120) (Sample Size)                                                                             0.4781 (P-Value)                     The Number of                0.0650 (Correlation)                     Corporate Bankruptcies                                                  (120) (Sample Size)                                                  0.4781 (P-Value)                                                  Source: own elaboration                This  table  shows  Spearman  rank  correlations  between  each  pair  of  variables.  These                correlation  coefficients  range  between  –1  and  +1  and  measure  the  strength  of  the                association between the variables. In contrast to the more common Pearson correlations,                the Spearman coefficients are computed from the ranks of the data values rather than                from  the values  themselves.  Consequently, they  are less  sensitive  to  outliers  than the                Pearson coefficients. Also shown in parentheses is the number of pairs of data values used                to compute each coefficient. The third number in each location of the table is a P-value                which tests the statistical significance of the estimated correlations. P-values below 0.05                indicate statistically significant non-zero correlations at the 95.0% confidence level. None                from the analyzed pairs of variables have P-values below 0.05.                From the above analysis results, it is clear that it has not been proved dependence between                the number of corporate insolvencies proposals and the number of corporate bankruptcies.                 4 Conclusions                This paper had as objective: firstly, to describe the development of the number of corporate                insolvencies proposals and the number of corporate bankruptcies in the Czech Republic                over the last decade – in the period 2008 to 2017 with a forecast for 2018. Secondly,                analyze  the  dependence  between  these  examinees  variables.  From  the  results  of  the                analysis, it is clear that the development of the examined variables during the monitored                period was highly variable (fluctuating). Dependence between examined variables could                not be prove.                 For further research is recommended detailed analysis of corporate insolvencies proposals                and corporate bankruptcies by region and then also in terms of business sectors.                 Acknowledgments                 This paper was created with the support of specific research in Unicorn College s.r.o.                 References                 Benetti, K. (2016 a). Bankruptcies of Companies in the Czech Republic after New Financial                Crisis. In: Proceedings of the 13th International Scientific Conference European Financial                Systems 2016. Brno: Masaryk University, pp. 21–25.                Benetti, K. (2016 b) The Corporate Insolvency Proposals in The Czech Republic after The                Financial Crisis.  In:  4th  International  Virtual  Conference  on  Advanced  Scientific  Results                (SCIECONF-2016). pp. 98-100. DOI: 10.18638/scieconf.2016.4.1.374.                                                             37","Benetti, K. (2017). Company Bankruptcies: the Overall Development in the Czech Republic,                Including Comparison of its Development by the Regions. In: 11th International Days of                Statistics and Economics.   Prague: University of Economics, pp. 97-104. ISBN 978-80-                87990-12-4.                Bokšová,  J.,  Randaková,  M.  (2015).  Personal  bankruptcies  of  individuals  in  the  Czech                Republic. In: Proceedings ICABR 2015. Brno: Mendel University, pp. 460–469.                 Bokšová, J., Randaková, M., Hospodka, J., Maixner, J. (2014). Personal bankruptcies of                individuals in the Czech Republic in relation to different groups of creditors. In: Managing                and Modelling of Financial Risks. Ostrava: VSB – Technical University of Ostrava, pp. 80–                86.                Čámská, D. (2012). National View of Bankruptcy Models. In: 6th International Days of                Statistics and Economics.  Praha: University of Economics, pp. 268-278.                Čámská,  D.  (2013).  Basic  Characteristics  of  Enterprises  in  Insolvency.  In:  Hradecké                ekonomické dny 2013 – Ekonomický rozvoj a management regionů, díl I. Hradec Králové:                Gaudeamus, pp. 83-88.                Creditreform    (2018).   Vývoj    insolvencí   v   České   republice.   Retrieved   from:                www.creditreform.cz.                Hindls, R., Hronová, S., Novák, I. (2000). Metody statistické analýzy pro ekonomy. Praha:                Management Press.                Hospodka, J., Buben, O., Randáková, M., Bokšová, J. (n.d.). Personal Bankruptcy in the                Capital City Region and South Bohemian Region in the Czech Republic. In: 16th Annual                Conference on Finance and Accounting. Prague: University of Economics, pp. 41–52.                Kislingerová, E. (2013). Estimated development of the number of filings for insolvency and                declared  bankruptcies  in  the  Czech  Republic  between  2013  and  2017.  In:  Financial                Management of Firms and Financial Institutions: 9th International Scientific Conference                Proceedings. Ostrava: VSB – Technical University of Ostrava, pp. 356-366.                Kislingerová,  E.,  Schoenfeld,  J.  (2014).  The  development  of  insolvency  in  the                entrepreneurial sphere in the Czech Republic during the crisis years. In:  Managing and                Modelling of Financial Risks. Ostrava: VSB – Technical University of Ostrava, pp. 367-378.                Mączyńska,  E.  (2009).  Corporate  Bankrupcies  –  Disfunctions,  Their  Causes.  Research                Papers Collection, vol. 2(2), pp. 195–209.                 Maixner, J., Hospodka, J., Randáková, M., Bokšová, J. (2014). Personal Bankruptcy in the                South Bohemian Region in the Czech Republic. In: International Conference on Accounting,                Auditing, and Taxation. (ICAAT 2014). pp. 105–111.                 Pakšiová,  R.  (2017).  The  critical  analysis  of  profit  for  its  allocation  decision-making.                Scientific Annals of Economics and Business, vol. 64 (Specialissue), pp. 41–56.                                                                                    38","Retirement Decisions of Seniors in Poland in the Light of the                                                Qualitative Research                                                     Kamila Bielawska                                                    University of Gdansk                                  Faculty of Management, Department of Banking and Finance                                          Armii Krajowej 101, 81-824 Sopot, Poland                                             E-mail: kamila.bielawska@ug.edu.pl                 Abstract: Although the Polish economy, labour market and retirement system have been                going through serious changes over the last thirty years, the effective retirement age in                Poland is still relatively low. The aim of the paper was to evaluate if the main reasons to                retire among Polish pensioners have been changing through time. The analysis is based on                the  qualitative  research  carried  out  in November  2017  in  a  form  of individual  in-depth                interviews (IDI) with seniors in different age groups (65-74; 75-84; 85+) for whom the                old-age pension is a main source of income. Preliminary results have shown that seniors,                despite of their age, tended to retire as soon as possible. Respondents aged 65-74 often                indicated that during retirement they were continuing economic activity for a few years                and some of them, who quickly retired and did not continue their work, expressed the                opinion that from the perspective of time it was a wrong decision. Seniors in older ages                (75+) reported disability or health problems as an important reason of early retirement                and leaving the labour market (transition from disability pension to old-age pension was                treated as a natural path). This factor was less visible in opinions of younger retires. Few                of them pointed out that they retired as they wanted to take the advantage of a leisure                time. A retrospective look at pension decisions indicated as the results of the conducted                study may provide some interesting conclusions for public policies in the field of pension                systems.                 Keywords: retirement, decisions, Poland, effective retirement age                JEL codes: D1, H55, J22, J26                 1 Introduction                The process of inactivation of older workers is complex and influenced by different factors:                individual features (health status, competencies, values and motivations) and institutional                determinants of working lives, i.e. statutory retirement age (see i.e.: Barnes-Farrell, 2003;                Chłoń-Domińczak, 2017; Riedel and Hofer, 2013). The institutional factors, as recognized                in many studies (see i.e.: Blondal and Scarpetta, 1998; Duval, 2003; Bassanini and Duval,                2006), were important obstacles in prolonging working lives of workers.                Since the early nineties of 20  century, Polish pension system has faced several reforms,                                              th                of which the most important was the reform of 1999. This reform changed the structure                (from mono to multi-pillar system), pension formula and methods of financing pensions                (Bielawska  et  al.,  2015).  There  are  still  few  subsystems  for  military  service,  judges,                prosecutors  and  farmers  but  the  main  mandatory  retirement  scheme  covers  most                economically  active  people  in  Poland  (non-agricultural  workers,  contractors  and  self-                employed). The pension formula has been changed from DB to DC (NDC in the first public                pillar) but in fact it is a hybrid solution as it still provides the guarantee of the minimum                pension in its mandatory part for insured persons having minimum period of insurance (20                years women and 25 years men). The early retirement has been successively phased out                and limited to miners. The NDC formula was expected to keep people longer in employment                and  more  eager  to  postpone  retirement  as it increases  pension  wealth  (Palmer,  1999;                Holzmann,  2017).  For  full-career  workers  with  average  earnings  it  provides  adequate                pensions, but those with shorter employment records or with breaks in careers due to the                childbirth or family care which are not sufficiently compensated, increases the likelihood of                relative poverty (Chłoń-Domińczak et al., 2012).                                                               39","Most of the institutional factors of early retirement in Poland has been successfully phased-                out. Additionally for a limited period of time, from January 2013 to September 2017, the                retirement age started to increase by 3 months each calendar year from the level of 60                years for women and 65 years for men, to reach 61 years 3 months and 66 years 3 months                respectively for women and men. Since October 2017 the retirement age in Poland was                brought to the 2012 levels, what caused the increase in number of pensioners by 500                thousands).  The  consequences  of  decreasing  the  retirement  age  in  Poland  have  been                widely discussed in the literature also in terms of adequacy of future benefits (Szczepański,                2017).                In this paper, the author investigates the motives determining the retirement decisions of                seniors in different age groups in the light of institutional changes in Polish pension system.                The paper aims to evaluate ex-post retirement decisions and seeks the answer for the                questions if this retrospective view on timing the retirement has been changing through                the decades.                 2 Methodology and Data                 To understand the motives of people in older ages determining their retirement decisions                a qualitative research has been conducted in a form of individual in-depth interviews (IDI).                Respondents were asked about the date of the retirement and the factors which influenced                their retirement decisions. The interviewed persons were asked if they used any social                transfers prior to the retirement, if they were entitled to early retirement and had they                considered  postponing  the  date  of  the retirement.  The  IDI  scenario  also  contained  the                questions about the financial situation on the retirement, economic activity combined with                taking the old-age pension. The qualitative research allowed to investigate the different                reasoning leading to retirement decisions. The research also provided the opportunity to                get a retrospective overview of undertaken decisions, what according to the knowledge of                the author of the paper, is the first attempt of a such kind for the retirees in Poland.                IDI’s were conducted between October and November 2017 with 45 seniors, in three age                categories:                    •   65 - 74 (17 interviews)                    •   75 - 84 (16 interviews)                    •   85 and more (12 interviews)                 For all respondents, the public pension was the main source of income of their households.                Seniors  who  took  part  in  interviews  were  additionally  differentiated  by  sex,  area  of                residence (urban and rural areas) and number of people in the household. The structure                of interviewed seniors reflected a wide range of years spent on retirement that is from 2                to almost 30.                Outcomes  of  the  IDI  study  presented  in  this  paper  are  a  part  of  the  project  titled                “Retirement Risk in the Light of the Forecasted Changes of the Demand for Consumption                Goods  and  Services  of  Seniors  in  Poland”  financed  by  National  Science  Centre,  Poland                (UMO-2016/23/B/HS5/03768).                 3 Results and Discussion                The oldest interviewed seniors retired at the beginning of the nineties of the 20  century,                                                                                                 th                the youngest - two or three years ago. The time in which the respondents were approaching                or reaching retirement age had an influence on making decisions about retirement.                The main observations from the research indicate that most of the interviewed seniors                decided to retire as soon as possible. For many of them it was natural consequence of the                disability (especially for the oldest respondents, who were granted the disability pension                prior to the retirement). Others linked the moment of retirement (acceleration or delay of                the  transition  from  work  to  retirement)  with  the  level  of  income,  health  status,  family                relationships, as well as external factors, such as possibility to keep the employment in the                coming years, bridging programs for people in pre-retirement age, etc.).                                                             40","Those  who  had  retired  in  early  nineties,  indicated  that  when  they  met  the  retirement                qualifying  conditions,  they  used  them  immediately.  It  was  a  natural  response  to  the                unstable  situation  on  the  labour  market  and  the  growing  number  of  bankruptcies  of                enterprises (also these owned by the state). Within this group of the interviewed seniors                the invalidity also played a role in retirement decisions. Up to 1997 the criteria for the                invalidity pension were liberal and constituted a part of the social and economic policies in                the  beginning  of  transition  from  centrally-planned  to  market-orientated  economy.  The                disability pensions replaced the invalidity pensions in 1997 and required the evaluation not                only the general health status but its influence on the employment possibilities. Therefore,                the oldest respondents were pushed into retirement mainly by the situation on the labour                market and the health status. In general, they were reconciled with the fact of retirement,                then and now they have not seen any other possibility at that time. Some of them also                underlined the role of the pressure of the employers and other (younger) workers to leave                the workplace. This aspect occurred in the opinions of respondents (also younger ones)                despite their professional status, education level or material status. An illustration of this                phenomenon may be the following statement of  one of the interviewed women: “I had                been  working  in  a  company  in  a  managerial  position.  Because  of  my  age  (60),  an                atmosphere in the company was difficult to accept. I felt it seriously harmful”.                Between the younger respondents who retired 10-15 years ago the situation was more                differentiated. There was a numerous group of retirees who declared that they wanted to                use the early retirement or bridging programs to get the benefit as soon as possible, what                underlines the institutional factors influencing the retirement. It is worth to mention that                early retirement had put restrictions on the possibility to continue employment by the level                of income earned. Seniors with poor health or having the relatives with health problems                treated the early retirement path as natural. But those with relatively good health often                regretted that they left the labour market, because the reemployment was often impossible                due to the lack of the competencies in the new workplaces.                Interestingly, a few women participating in the interviews, who resigned from work to take                care of their grandchildren, in the ex-post evaluation of timing of retirement indicated that                they could continue employment and support their children financially (i.e. by funding the                babysitter). This view confirms the hypothesis that the ex-post evaluation of the retirement                timing may change among younger cohorts of retirees.                Seniors who retired solely by their will, just to have \"time to rest\" were a few. There are                some necessary conditions, which have to be fulfilled to fully enjoy the retirement, of which                the most important are: sufficient income and good health status.                The research indicates that youngest retires tend to continue employment on full or part-                time  formal  or  informal  basis  even  up  to  reaching  the  age  of  70  or  higher.  It  is  an                interesting observation as the official statistics of the Social Insurance Institution show that                reported  periods  of  employment  during  retirement  last  for  maximum  24  months  after                retirement (ZUS 2017). Seniors, who combine the retirement with employment usually are                more satisfied with their life, not only in terms of better financial situation. Those who                retired when reaching statutory retirement age, as they were pushed into retirement by                the younger workers, more often treated it as a loss of opportunities for both sides. They                reported that they could share experiences, give advice to younger workers.                 It  is  worth  to  mention,  that  the  health  problems  or  disability  as  a  main  reason  for                retirement between the youngest respondents was pointed out very rarely.                To provide a broader context for retirement decisions of seniors in Poland, Table 1 presents                the  changes  in  effective  retirement  age.  Since  the  1999  it  has  been  systematically                increasing, what was the result of phasing-out the early retirement and the episode of                increase in retirement age (January 2013 – September 2017).                                                                41","Table 1 Effective Retirement Age in Poland 2000-2016 (Social Insurance System)                 Effective retirement age              2000       2005       2010       2015       2016                 Men                                      58.9       58.4       60.1       62.8      63.3                 Women                                    55.9       56.0       59.5       60.1      61.0                                           Source: Social Security Institution (ZUS)                 Over the last seventeen years the effective retirement age increased by 4.4 years for men                and 5.1 years for women. This tendency may be stopped by the decrease in statutory                retirement  age  in  Poland  effective  from  October  2017.  In  the  end  of  2017  the                representatives of the government argued that decrease in retirement age was positive as                40%  of  people  who  applied  for  the  old-age  pension  (approximately  200  out  of  500                thousands people) were not economically active before claiming a pension. Therefore  the                earlier access to old-age pension allowed for protection against poverty (old-age pension                was higher than other social transfers especially for those, who met the criteria for the                minimum guaranteed pension).                 In  this  context  it  is  worth  to  mention,  that  the  recent  study  on  unemployment  of  the                workers age 50+ in Poland proves, that being close to the point at which they are eligible                to receive pension benefits leads individuals 'wait' to fulfil these eligibility criteria instead                of making an effort to maintain and facilitate their competencies on the labour market                (Gałecka-Burdziak and Góra 2017). Reversing the increase in retirement age may support                this attitude and cause the serious consequences for further generations. The latest Ageing                Report (European Commission 2018) confirms, that under “no policy change” scenario the                participation rates of men and women in the labour market in all age categories will be                lower that EU averages. In the NDC scheme it means that the   replacement rates and                benefit rates for retires in Poland may be one of the lowest in the EU.                 4 Conclusions                Although  the  effective  retirement  age  in  Poland  is  still  relatively  low,  the  outcomes  of                individual in-depth interviews with seniors in three age groups show the changing attitude                to the retirement. Despite the fact, that interviewed retires most often indicated that they                retired as soon as it was possible, the reasoning for retirement timing and further economic                activity is different in particular age groups. Between the oldest respondents, who spent                on the retirement 30 years or more, there is a belief that in that time there was no other                way  for  them then early  retirement.  These  decisions  were taken in  unstable  economic                environment and were also connected with the health problems (disability).                In the younger groups of respondents the issue of health status was not predominant.                They more often declared to continue economic activity on the retirement for 5-10 years.                Those of them, who quickly retired and did not continue their work, expressed the opinion                that from  the  perspective  of time it  was  a  wrong  decision,  and few  underlined it  even                though  they  resigned  from  work  to  take  care  over  their  grandchildren.  It  is  worth  to                mention,  that  the  respondents  pointed  out  that  still  some  employers  and  younger                employees are not prepared for the changes in age structure of the labour force and tend                to push older workers into retirement.                The results shown in the paper indicate that young retires are more able and willing to                work longer with the respect of their needs and capabilities. There is a need to strengthen                the public policies in the area of inclusion the older workers on the labour market and                informing about the consequences of early retirement with the NDC formula.                When they lose their earning capability, the low level of pension deriving form relatively                early retirement may be not sufficient to cover for basic expenses and thus put a pressure                on the increase in other public spending.                                                                  42","Acknowledgments                The research was financed by the National Science Centre, Poland under the agreement                No. UMO-2016/23/B/HS5/03768).                 References                Barnes-Farrell, J. (2003). Beyond Health and Wealth: Attitudinal and Other Influences on                Retirement Decision-Making. In: Adams, G., Beehr T., ed., Retirement: Reasons, Process                and Results. Springer Publishing Company, pp.159-187.                 Bassanini, A., Duval, R. (2006). Employment Patterns in OECD Countries: Reassessing the                Role of Policies and Institutions. Paris: OECD Social, Employment and Migration Working                Papers, no. 35.                Bielawska, K., Chłoń-Domińczak, A., Stańko, D. (2015). Retreat from Mandatory Pension                Funds in Countries of the Eastern and Central Europe in Result of Financial and Fiscal Crisis:                Causes,    Effects   and    Recommendations      for   Fiscal   Rules.   Retrieved   from:                http://uczelnia.sgh.waw.pl/pl/uczelnia/badania/grupy_badawcze/ppg/Documents/NCN%                20Sta%C5%84ko%202015/Report%20CEE%20reversals%20-%20final.pdf.                Bielawska  K.,  Pieńkowska-Kamieniecka,  S.  (2015).  Aktualna  sytuacja  na  rynku  pracy                wśród osób starszych a wiek emerytalny w Unii Europejskiej (The Current Situation of Older                Workers on the Labour Market in Poland and the Retirement Age in the European Union).                In:  Chybalski,  F.,  Marcinkiewicz,  E.,  ed.,  Current  Issues  of  Pension  Systems:  Selected                Issues. Lodz: Publishing House of Lodz University of Technology.                 Blöndal, S., Scarpetta, S. (1999). The Retirement Decision in OECD Countries. Paris: OECD                Economics Department Working Papers, no. 202.                Chłoń-Domińczak, A. (2017). Poles above Age 50: Work and Retirement. In: Myck, M.,                Oczkowska, M., eds., Generation 50+ in Poland in the View of Europe: Activity, Health and                Quality of Life. Outcomes of the SHARE. Warsaw: Center for Economic Analysis, pp.65-84.                 Chłoń-Domińczak, A., Franco, D., Palmer, E. (2012). The First Wave of NDC Reforms: The                Experiences of Italy, Latvia, Poland and Sweden. In: Holzmann, R., Palmer, E., Robalino,                D., ed., Nonfinancial Defined Contribution Pension Schemes in a Changing Pension World.                Washington, D.C.: The World Bank, p. 31.                 Duval,  R.  (2003).  The  Retirement  Effects  of  Old-Age  Pension  and  Early  Retirement                Schemes in OECD countries. Paris: OECD.                European  Commission  (2018).  The  2018  Ageing  Report.  Economic  and  Budgetary                Projections for the EU Member States (2016-2070). Institutional Paper, no. 079.                Gałecka-Burdziak  E.,  Góra,  M.  (2017).  How  Do  Unemployed  Workers  Behave  Prior  to                Retirement? A Multi-State Multiple-Spell Approach. IZA Discussion Paper, no. 10680.                Holzmann, R. (2017). The ABCs of Nonfinancial Defined Contribution (NDC) Schemes. IZA.                Institute of Labor Economics Policy Paper, no. 130.                 Holzmann,  R.,  Palmer,  E.,  Robalino,  D.,  ed.  Nonfinancial  Defined  Contribution  Pension                Schemes in a Changing Pension World. Washington, DC: The World Bank.                Palmer, E. (1999). Exit from the Labor Force for Older Workers in Sweden: Can the NDC                Pension System Help? The Geneva Papers on Risk and Insurance, vol. 24 (4), p. 462.                Riedel, M., Hofer, H. (2013). Determinants of the Transition from Work into Retirement.                Linz: NRN Working Paper, no. 1310.                Social Security Institution (2018). Data from the statistical portal: http://psz.zus.pl/                Szczepanski, M. (2017). The Impact of Pension Reforms in Poland on Public Finances. 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Prague: University of Economics, pp. 60-66.                                                             43","Determination and Verification of the Key Assessment Indicators                   for the Insurance Market by Applying the Decomposition Multi-                                 attribute Methods and Regression Analysis                                                                1                                                                                  2                                         Martina Borovcová , Adéla Špačková                                                     1  VŠB – TU Ostrava                                         Faculty of Economics, Department of Finance                                      Sokolská třída 33, 702 00 Ostrava, Czech Republic                                              E-mail: martina.borovcova@vsb.cz                                                      2  VŠB – TU Ostrava                                         Faculty of Economics, Department of Finance                                      Sokolská třída 33, 702 00 Ostrava, Czech Republic                                               E-mail: adela.spackova@vsb.cz                 Abstract: The insurance industry is one of the most important sectors of the economy.                The  insurance  market  is  very  much  intertwined  in  the  financial  markets,  therefore                assessment of its level is important. The assessment and analysis of the insurance market                is done by using selected indicators. The aim of the article is determination and verification                of the key assessment indicators for the insurance market by applying the decomposition                multi-attribute methods and regression analysis. This paper is focused on the description,                verification  and  application  of  the multi-attribute  decomposition methods  AHP  and  ANP                based on the Saaty pair comparison approach. The AHP and ANP methods are described,                including the computation procedure. The applicability of the methods is presented at the                preferences determination. The linear AHP and nonlinear ANP methods are applied. These                methods are applied for insurance market assessment, particularly, for determination of                preference indicators for the assessment of the insurance market. We consider importance                of setting the ratio for evaluation indicators of the development of the insurance market                by  applying  Saaty  methods  in  the  framework  of  decomposition  methods  AHP  and  ANP                (insurance  penetration  ratio,  claims  frequency  ratio,  concentration  ratio,  premium                indicator,  benefit  indicator,  number  of  insurance  company  indicator  and  more).                Subsequently, a custom regression model is created.                 Keywords:  multi-attribute  methods,  AHP,  ANP,  Key  Assessment  Indicators,  Saaty  Pair                Comparison Approach, regression analysis                JEL codes: C02, C4, G2, G11                  1 Introduction                Multicriteria decision-making is one of options, how to choose optimal variant of certain                sets of variants. Only very rarely it is possible to find the very optimal variant which meets                all specified criteria. The solution of decision-making problem is more often a compromise                variant,  which  meets  just  the  most  important  criteria,  while  it  does  not  meet  all  the                specified criteria the best.                It is preferable to take into account more than one decision-making criterion when making                the decision. Although may arise a situation, where the choice of options has been used                only a single evaluation criteria. Conditions for the quantitative nature of the criteria would                then be enough to organize a variant according to the values of the criteria and the variant                with the highest or the lowest value would be the best (optimal) option. However, there                are relatively a few decision-making problems with monocriterial character. More and more                frequently,  it  is  possible  to  meet  with  problems,  when  the  solution  variants  should  be                assessed using a larger number of evaluation criteria. Such decision-making problems then                have the character of multicriteria decision-making. It is necessary to determine the goals                of decision-making for the application of methods of function evaluation of variants and                criteria of decision-making.                                                               44","The aim of the article is to describe the multi-attribute methods AHP (analytic hierarchy                process) and ANP (analytic network process), and their applications to verify the simplified                example of determining weights partial indicators of the evaluation of the development of                the  insurance  market.  Qualitative,  quantitative  and  other  indicators  of  the  level  of  the                insurance market are considered in the study.                 2 Methodology Description of the multicriteria decision making                evaluation of alternatives                The aim of the application of the multi-criteria decision making evaluation of variants is                primary finding the best (optimal) variant and layout of these variants from the best to the                worst. The best option is usually a variant of the compromise. The compromise solution is                the least distant one from the ideal variant, or the furthest away from the variants of basal,                while the ideal option is the one that has all the criteria with the best possible value. On                the contrary, variant with the worst values of the criteria is the basal variant. Ideal and                basal variants are usually hypothetical. If the ideal variant really existed, it would be at                the same time, a variant of the optimal solution. However, this situation usually does not                occur and therefore any selected solution is the solution to the compromise. Compromise                variant must be undominated in all tasks, which means that there is no dominating variant                among decision-making variants (Ramík, 1999).                 Criteria and methods of determining the values of the criteria                Alternatives are specified by using variants and the measurement of satisfaction depends                on each variant. Determination of the criteria is difficult process, which requires certain                knowledge of the area. The criteria used to selection of the most appropriate variants can                be classified according to several aspect. Firstly it's possible to divide criteria as maximizing                (income,  profit)  or  minimizing  (cost,  loss)  according  to  the  level  of  desirable  values.                According  to  the  type  Secondly  it  is  possible  to  divide  criteria  into  qualitative  and                quantitative. These are expressed in the units of measurement.                For calculations and comparison it is usually desirable for specified criteria values yij to be                normalized  the unit interval, i.e.                        x ij     1 ; 0                                                               (1)                 Generally it is possible to obtain these values of the criteria from the sub-functions of the                utility (value) as                        x =  u ( )                                                                      (2)                               y                         ij     ij                Utility of the criteria, which acquire the worst values is equal to 0 or close to 0, and the                utility of the criteria with the best value is equal to 1.                Saaty method AHP and ANP will be used in the application part of the study, therefore the                following description will be focused on these methods.                Saaty's method of pairwise comparison                 The Saaty's method of weights determination of the criteria can be divided into two steps.                The first step consists of a pairwise comparison when finding the preferential relations of                criteria pairs. It is presented as so-called Saaty's matrix S. This matrix is symmetric with                elements sij. It is possible to determine also the size of this preference expressed by  a                certain number of points from the selected point scale in addition to the direction of the                preference of pair of criteria. Scale of relative importance (descriptors) was recommended                by Saaty and it is shown in Table 2. Other values can be used to express sub-preferences.                The strength of preferences is expressed in the interval      ,    ∈ [0; 9]. The result of this step                is to obtain the right upper triangular part of the matrix size preferences (Saaty's matrix).                The diagonal element have to be       ,    = 1 and for the inverse elements (in the lower left                triangular part of matrix) is true the following:                                                               45","1                        s  =     .                                                                      (3)                         i                         , j                             s                               ,i j                The elements     Saaty matrix are estimated shares of weights of criteria     and    , so:                                                                                                                                                                                                                                   ,                                v                        s i , j    i  .                                                                (4)                             v  j                The scales can be obtained in the following manner:                                  n                                    n                                                                                                             min F  =     s i , j  −  v i   2 ,                                          (5)                                                                                                                      i  j      v j                                      n                                      v                with the condition    i  =  . 1                                    i                Because of difficulty it is possible to obtain the weights using an algorithm based on the                geometric average.                        2                                    n                                 n                        min F  =    ln s i , j  − (lnv i  − lnv j  ) ,                              (6)                                  1 = i  j i                                    n                                      v                with the condition    i  =  . 1                                    i                The final solution is based on the geometric mean of rows (Saaty, 2010):                                               1                                        N      N                                         s i ,                                              j                              v         j                            w i  =  N  i  =          1  ,                               v i  N    N     N                                                                      i           s i ,                                               j                                      i    j                            (7)                           Table 1 Recommended Point of Scale with the Descriptors by Saaty                  The number of                                  Descriptor                       points                         1                         Element A and B are equally important                         3                Element A is moderately more important than element B                         5                  Element A is strongly more important than element B                         7               Element A is very strongly more important than element B                         9                 Element A is extremely more important than element B                                            Source: Saaty (2006), own processing                 The sign of relevant evaluation is the consistency of Saaty's matrix, in other words when                the elements satisfy the condition of transitivity the most. It should be emphasized that in                many  methods  this  aspect  is  not  accounted.  Consistency  can  be  measured  using  the                coefficient of consistency CR (Consistency Ratio). The coefficient for consistent evaluation                                                                                               CI                should  be  CR     1 , 0 .  Consistency  ratio  is  calculated  as  following  CR =  ,  where                                                                                               RI                          − N                CI  =  max     ,  (Saaty,  2010).  The  characteristic  number  of  the  matrix      can  be                        N  − 1                                                                 max                                                             46","1  N                determined  by  various  procedures.  One  option  is     =    S     ) w ,  while  w is  a                                                                                 (  w /                                                                       max             i   i                                                                             N  i                vector  and  (S  w )  is  the  i-th  element  of  the  vector.  Furtherly  RI   (Random  Index)  is                                   i                derived from an empirical examination and reaches the following values depending on the                number of criteria, see in Table 3.                                Table 2 The Value RI According to the Number of Criteria                    N       1       2       3       4       5       6       7       8      9       10                    RI    0,00     0,00   0,52    0,89    1,11    1,25    1,35    1,40    1,45    1,49                                            Source: Saaty (2009), own processing                 Multi-attribute methods AHP and ANP                 Weights  or  values  of  criteria  are  in  the  case  of  decomposition  tasks  set  by  gradual                decomposition from the goal, global groups of criteria, sub-groups, to the the initial sub-                criteria and variants. For AHP method these linkages may be linear and for ANP method in                the shape of a pyramid or nonlinear with feedbacks. Evaluation of preferences (weights)                of the criteria is carried out using the Saaty's method of pairwise comparison.                Local  weights  (preferences)  of  the  subgroups  or indicators  with  regard  to the  specified                purpose are determined by using Saaty's method of pairwise comparison. The next step is                calculation  of  the  global  weights  including  the  initial  sub-weights.  The  sum  of  all  sub-                weights is equal to one.                 In AHP method can be used analytical procedure and also method of supermatrix. In the                ANP, it is possible to calculate global weights by using only the method of supermatrix                (Saaty, 2010).                For  analytical  method  AHP  the  indicator  subgroup  weights  are  obtained  as  follows,                 w i,  j  =  v i  v   i,  j  where  w  is global weight of j-th indicator and i-th group,  v  is local weight                                        j                                      i,                                                                                           i                of i-th group and  v  is local weight of j-th indicator and i-th group. By this way we can                                    i,                                     j                gradually get all the global weights of primary indicators.                The procedure for the calculation of sought weights in case of AHP and ANP supermatrix                method can be divided into three steps:                    •   First step is determination of default supermatrix W  In Fig. 1 is shown supermatrix.                       The local  weights  v  are  typed  to the  columns inside  this  supermatrix  W . The                                             j                                            i,                       weights  of  criteria  are  highlighted  from  e   to  e  , 2 n 2    according  to  the  purpose                                                                     1 , 2                       (criteria) e  in Table 1.                                   2 , 1                    •   Subsequently the default supermatrix is transformed into the weighted supermatrix                        W  so, that sums of columns are equal 1.                                                                                                       •   The last step is the calculation of limit (final) supermatrix W . This supermatrix can                                                                                                                                 be calculated like acyclical weighted matrix as following W    = lim W  k    ,where  W                                                                                      k→                                                       k                       is limit (final) supermatrix,  W  is weighted supermatrix without existence cycle,                       and  this  supermatrix  is  k  times  amplified.  Global  weights  are  found  in  the  first                       column considering the goal.                 3 Determining the preferences of the indicators assessment of the level                insurance market according to the AHP and ANP                 We  consider  a  task  to  set  the  weights  (preferences)  of  evaluation  indicators  of  the                development  of  the  insurance  market  to  apply  Saaty's  method  in  the  framework  of                decomposition methods AHP and ANP.                                                              47","Decomposition for AHP and ANP                Decomposition is designed in two ways from the goal, categories of indicators and each                indicator of the assessment of the level of the insurance market is. The first way is linear                system of AHP, see Figure 1, and subsequently the second is nonlinear system ANP with                typical feedbacks. The intention is to determine the weights of individual indicators of the                assessment of the level of the insurance market by using AHP method and ANP method.                Analytical procedure and supermatrix method can be used in the case of AHP method.                Qualitative  and  quantitative  indicators  of  the  assessment  of  the  level  of  the  insurance                market  are  taken into  account for  the  purposes  of  the  article.  Following indicators  are                included  among  qualitative indicators  according to  the  subjective  opinion  of  an  expert:                insurance penetration (IP) and claims frequency (CF). Following indicators are included                among  quantitative indicators:  gross  premium  (GP), insurance  benefit  (IB), number  of                concluded insurance contracts (IC), average insurance premium on one insured contract                (ØIP), number of settled insurance claims (SIC),  the average insurance benefit on one                insured  contract  (ØIB),  number  of  employees  (NE),  number  of  commercial  insurance                companies (CC), concentration of the insurance market (CM).                Solution and the result for the method of supermatrix AHP and ANP                Local and global weights based on Saaty's method of paired comparison are calculated in                this  subchapter.  At  first  the local  weights  are  established  and  then  the  global  weights.                Global  weights  are  calculated  by  both  AHP  and  ANP  method.  Analytical  procedure  and                supermatrix method were used in case of AHP. In the second case ANP, was used the only                possible way and it was supermatrix method.                                           Table 3 The Result of AHP and ANP                                                                          Global -          Global -                                                                        analytical       supermatrix                              Goal                 Local   Groups        method            method                                                                           AHP          AHP        ANP                                                   75,00                           Qualitative               %                                                   25,00                          Quantitative               %          0                                                   33,33                     Insurance penetration           %                   25,00 %     25,00 %  19,44 %                                                   66,67                        Claim frequency              %      75,00 %      50,00 %     50,00 %  38,89 %                                                   28,36                         Gross premium               %                   7,09 %       7,09 %  11,81 %                                                   23,24                        Insurance benefit            %                   5,81 %       5,81 %     9,68 %                    The number of concluded        14,81                       insurance contracts           %                   3,70 %       3,70 %     6,17 %                     The average insurance         10,58                            premium                  %                   2,64 %       2,64 %     4,41 %                     The number of settled                        insurance claims          9,31 %                 2,33 %       2,33 %     3,88 %                 The average insurance benefit  5,17 %                   1,29 %       1,29 %     2,15 %                   The number of employees        1,83 %                 0,46 %       0,46 %     0,76 %                   The number of commercial                           companies              4,02 %                 1,00 %       1,00 %     1,67 %                 Concentration of the insurance                             market               2,69 %    25,00 %      0,67 %       0,67 %     1,12 %                                                              48","100,00                   100,00                 ∑                                             %        100,00 %         %       100,0 %                                                   Source: own processing                 Determination of regression model                Multi-criterial assessment is a subjective method, where the preferences are divided into                the  subjective  feelings  of  decision-maker.  Final  order  of  idicators  assessment  of  the                insurance market level using AHP and ANP method was verified by regression analysis.                Using  regression  analysis  it  is  possible  to  study  not  only  the  relationship  between                dependent and independent variable, but also to find out the strenght of this relationship.                In  this  part  the  indicators  were  divided  into  the  same  two  groups  qualitative  and                quantitavive,  where  subsequently  for  each  group  the  regression  model  is  created.  The                model was estimated without constant, because of more accurate results.                To the qroup of qualitative indicators have been included insurance penetration and claim                frequency. In this step it is necessary to choose dependet variable. As the most suitable                indicator appears claim frequency. Independent variable remains insurance penetration.                The results of model estimation it is advisable to test by F-test. The model was verified                using F-test, which is used for verification of statistical significance of the model as a whole.                According to the results of this test, see Table 5, the model is statistically significant.                                                Table 4 The Result of F-test                                                        ANOVA   a,b                         Model             Sum of         df        Mean            F           Sig.                                          Squares                  Square                                                                                                    c                     1     Regression       4,973         1         4,973        637,809        ,000                            Residual        ,125         16          ,008                                                 d                              Total        5,097         17                                                   Source: own processing                In the case of the quantitative indicators the selection process was more complicated. The                quantitative  indicators  is  nine.  As  dependent  variable  was  selected  indicator  of  gross                premium and other indicators are independent variables. Dependend variable was chosen                based on their statistical significance.                 In modelling process and the gradual inslusion of explanatory variables can be used three                ways. First option is called forward stepwise, it is the upward selection, when it starts to                estimate empty model and gradually comes to adding statistically significant variables. The                second  opposite  way  is  called  backward  elimination,  where  all  statistically  significant                variables are included and gradually exclude insignificant variables. The third and last wy                is,  that  first  occurs  to  estimate  coefficients  by  one-dimensional  analysis  and  then  the                suitable variables are included in model. For the purposes of this paper the second way is                choosen.                 In this part the regression model was estimated with dependent variable gross premium                and others explanatory variables. Statistical significance was verified by F-test, the same                like in previous model. According to the results of this test, it is possible to confirm the                statistical significance of the model as a whole. The results of F-test, see Table 6.                                               Table 5 The Result of F-test                                                         ANOVA   a,b                   Model                Sum of Squares        df     Mean Square           F       Sig.                   1      Regressio    277737851214752        8     347172314018        21740,     ,000                              n              832,000                  44104,000          991                                                                                                     c                          Residual      14371704054231,       9     159685600602                                               875                       5,764                                                             49","Total      277752222918807        1                                                     d                                            072,000           7                   a. Dependent Variable: GP                                                   Source: own processing                In the framework of the evaluation the materiality of indicators was taken into account the                value of significance, which is determined by the statistical significance of each variable.                The value of significance should be maximum around 0,05 for a significance level 95 %.                When the value is higher than 0,05, the indicator is considered statistically insignificant                and should be excluded from the model.                                              Table 6 Final Regression Model                                                      Coefficients a,b                       Model               Unstandardized           Standardized         t         Sig.                                             Coefficients            Coefficients                                           B          Std. Error         Beta                    1        IB          1,806           ,088            ,999         20,519       ,000                             IC          5,211           ,227            ,958         22,925       ,000                            ØIP       25102,442       1388,655          1,055         18,077       ,000                            SIC        -56,205          2,776           -1,074       -20,248       ,000                            ØIB       -4216,859        250,568          -,931        -16,829       ,000                                                   Source: own processing                The  final  order  of  variables  was  created  by  correlation  matrix  of  these  independent                variables and response variable. The final order of the indicators was determined on the                basis results of the correlation matrix. Correlation analysis is shown on Table 7.                                          Table 7 Correlation matrix of variables                                                        Correlations                                                GP         IB        IC        SIC        ØIP       ØIB                                                                        **                                                              **                 GP     Pearson                    1     ,882       ,976      ,784       ,841       ,871                                                                                                        **                                                                                              **                                                                                   **                        Correlation                        Sig. (2-tailed)                    ,000      ,000       ,000       ,000      ,000                        N                         17         17        17         17         17        17                                                                                                        **                                                                                   **                                                                        **                                                   **                                                                                              **                 IB     Pearson                ,882           1     ,906      ,901       ,623       ,950                        Correlation                        Sig. (2-tailed)         ,000                 ,000       ,000       ,008      ,000                        N                         17         17        17         17         17        17                                                              **                                                   **                                                                                   **                                                                                                        **                                                                                              **                 IC     Pearson                ,976      ,906            1    ,789       ,708       ,895                        Correlation                        Sig. (2-tailed)         ,000       ,000                 ,000       ,001      ,000                        N                         17         17        17         17         17        17                                                              **                                                                                                        **                                                                                               *                                                                        **                 SIC    Pearson                ,784      ,901       ,789           1      ,589      ,725                                                   **                        Correlation                        Sig. (2-tailed)         ,000       ,000      ,000                  ,013      ,001                        N                         17         17        17         17         17        17                                                   **                 ØIP    Pearson                ,841      ,623       ,708       ,589           1     ,624                                                                        **                                                                                    *                                                              **                                                                                                        **                        Correlation                        Sig. (2-tailed)         ,000       ,008      ,001       ,013                 ,007                        N                         17         17        17         17         17        17                 ØI     Pearson                ,871      ,950       ,895      ,725       ,624            1                                                                                              **                                                                                   **                                                              **                                                                        **                                                   **                 B      Correlation                        Sig. (2-tailed)         ,000       ,000      ,000       ,001       ,007                        N                         17         17        17         17         17        17                 **. Correlation is significant at the 0.01 level (2-tailed).                 *. Correlation is significant at the 0.05 level (2-tailed).                                                   Source: own processing                                                            50","Based on the results of the regression analysis, it is clear that the order of qualitative                indicators is the same as in the case of multicriterial decision making, but the order of                quantitative indicators is slightly different.                                  Table 8 Final Order of the Key Assessment Indicators                                               Indicators                                      Order                                          Claims frequency                                      1.                                        Insurance penetration                                   2.                                           Gross premium                                        3.                            The number of concluded insurance contracts                         4.                                          Insurance benefit                                     5.                                   The average insurance benefit                                6.                                  The average insurance premium                                 7.                               The number of settled insurance claims                           8.                               Concentration of the insurance market                            9.                                      The number of employees                                  10.                          The number of commercial insurance companies                         11.                                                   Source: own processing                 4 Conclusions                The aim of this paper was to determine the preferences of indicators of evaluation of the                level  of  insurance  market  using  multi-attribute  methods  AHP  and  ANP  on  the  basis  of                Saaty's  method  of  paired  comparison.  Methods  including  multi-attribute  decomposition                AHP  and  ANP  on  the  basis  of  Saaty's  method  of  paired  comparison  were  described.                Subsequently analytical method and supermatrix method AHP were applied on the example                of determination of preferences of indicators assessment of the level of insurance market.                It was found that both approaches AHP and ANP lead to the same results and at the same                time, that the results obtained on the basis of linear methods of AHP and non-linear method                ANP differ significantly.                Subsequently, the final order was verified by regression model. In the framework of the                regression analysis were estimated of total two models. At first was estimated the model                with qualitative variables, where the dependent variable was claim frequency. The second                regression model was estimated using quantitative indicators, where dependent variable                was  gross  premium.  The  results  showed  that  the  final  order  according  to  AHP  and                regression analysis for qualitative indicators was the same, but the order of quantitative                indicators were slightly different.                 Acknowledgments                 This paper was supported by the SGS Project VŠB – TU Ostrava SP2018/154 \"Finanční                rozhodování podniků a finančních institucí za rizika”.                 References                Fotr, J., Dědina, J., Hrůzová, H. (2010). Manažerské rozhodování. 2. aktualiz. vyd. Praha:                Ekopress.                Hasoňová,  D.  (2018)  Hodnocení  úrovně  pojistného  trhu  aplikací  vícekriteriálních                dekompozičních metod. Ostrava: VSB – Technická univerzita Ostrava. Diploma Thesis.                 Ramík,  J.  (1999).  Vícekriteriální  rozhodování  –  analytický  hierarchický  process  (AHP).                1. vyd. Karviná: Slezská univerzita v Opavě.                Saaty,  T.  L.  (2012).  Decision  making  form  leaders:  the  analytic  hierarchy  process  for                decisions in a complex world. 3rd rev. ed. Pittsburgh: RWS Publications.                                                              51","Saaty,  T.  L.  (2010).  Mathematical  principles  of  decision  making:  generalization  of  the                analytic  network  process  to  neural  firing  and  synthesis.  1st  ed.  Pittsburgh:  RWS                Publications.                Saaty,  T.  L.  (2009).  Theory  and  applications  of  the  analytic  network  process:  decision                making with benefits, opportunities, costs, and risks. 3rd ed. Pittsburgh: RWS Publications.                Zmeškal, Z., Dluhošová, D., Tichý T. (2013). Finanční modely: koncepty, metody, aplikace.                3., přeprac. a rozš. vyd. Praha: Ekopress, 2013. ISBN 978-80-86929-91-0.                                                                                                                                      52","Fractal Dimension vs. Non-fractal Risk Measures –                                                Correlation Analysis                                                         Rafał Buła                                             University of Economics in Katowice                          Faculty of Finance and Insurance, Department of Investment and Real Estate                                             1 Maja 50, 40-287 Katowice, Poland                                              E-mail: rafal.bula@ue.katowice.pl                 Abstract: In the presented paper the relationships between fractal dimension and chosen                non-fractal measures of risk are analyzed and discussed. The main objective of the study                is to reveal the nature of these dependencies. Moreover, the article is aimed at analyzing                whether the abovementioned relations are stable independently of the length of investment                horizon  considered  and  of  period  chosen.  In  the  article  prices  of  shares  listed  on  the                Warsaw  Stock  Exchange  are  studied  using  methods  of  correlation  analysis  (various                correlation coefficients). Calculated values and results of various statistical tests enable to                draw a few conclusions. First of all, it must be noticed that the main scientific hypothesis                in case of risk measures like standard deviation ought to be rejected, while e.g. for the                omega or Farinelli-Tibiletti ratio we are unable to do it. Moreover, it must be concluded                that  this  regularity  is  valid  independently  of  the  length  of  the  investment  horizon  and                analyzed period of time.                Keywords: fractal dimension, risk measurement, correlation analysis                 JEL codes: G17                 1 Introduction                This study is devoted to the quantification of investment risk with particular emphasis on                the relationships between risk measures. The inspiration for undertaking the presented                research was the observation that, until now, the problem of existing relationships between                the fractal dimension and other measures used in the quantification of investment risk has                not been properly addressed. What’s more, there are virtually no studies focusing on the                identified problem. The analysis of the literature justifies the conclusion that the outlined                problems are relatively poorly examined.                 Among various risk measures, the fractal dimension (box-counting dimension) should be                considered as particularly interesting. It ought to be noted that this measure has not been                proposed ad hoc, but its use is a logical consequence of attempts to replace the existing                paradigm in finance science based on the use of Brownian geometric motion to modelling                asset prices with the fractal market concept. As the basic idea behind this concept is to                describe the observed prices by self-similar stochastic processes, the natural consequence                of its adoption is the use of adequate instruments to allow for the analysis of the nature of                this self-affinity. Thus, the fractal dimension is not so much gaining the right to exist in the                world  of  self-similar  trajectories  of  prices  or  rates  of  return,  but  rather  becomes                indispensable for the correct measurement of investment risk. Its use is therefore justified                on theoretical grounds.                The main purpose of the presented study is to analyze the correlation relationship between                the possibly broad spectrum of measures used in the quantification of investment risk and                the fractal dimension in relation to shares listed on the Warsaw Stock Exchange.                In  addition,  the  goal  of the  paper is  to  examine  whether the length  of  the investment                horizon for which risk measures are estimated influences the relationships between them                and the fractal dimension.                 The  third  goal  of  the  study  is  to  analyze  the  time  consistency  of  previously  identified                relations.                                                                53","The main research hypothesis is that there are no significant relationships between the                fractal dimension and the non-fractal measures of investment risk, regardless of the length                of the investment horizon for which they are calculated.                As part of the second research hypothesis, it was supposed that the postulated relations                are relatively stable over time.                While conducting the research, the author adopted appropriate statistical methods. The                obtained  results  allowed  for  a  comparative  analysis  in  relation  to  the  strength  of  the                correlation relationship between individual risk measures and the fractal dimension as well                as its  stability  over  time.  The  studies were  based  on  share  prices  originating  from  the                Warsaw Stock Exchange. All quotations used come from the stooq.com website. The author                used  the  MS  Excel  spreadsheet  and  the  Visual  Basic  for  Applications  language  for  the                calculations.                The present state of knowledge                 Conducting the literature  research,  the  author  noticed  that  the  problems  raised in this                study are virtually absent in the literature. Although the concept of a fractal dimension and                a new paradigm based on the theory of the fractal market are present in the scientific                discourse (Mandelbrot and Hudson, 2004), the issues of relationships between investment                risk measures and the fractal dimension remain unexplored.                In one of the first studies devoted to fractal empirical analysis of quotations from the Polish                stock market, Zwolankowska (2001) used the quotations of 21 companies and the WIG                index  levels  in  the  years  1994-1998.  She  estimated  both  standard  deviations  of  the                logarithmic return rates and the fractal dimension of the price series. The results for the                same group of companies, however, referring to the logarithmic and cumulative logarithmic                rates of return, were presented in one of her papers (Zwolankowska, 1999). The summary                results for the entire period 1994-1998 are presented in the chart below (Figure 1).                As Zwolankowska concludes, in the light of the presented data, it is difficult to postulate                the  existence  of  significant  relationships  between  the  fractal  dimension  and  standard                deviation.  This  statement,  however,  was  not  supported  by  any  in-depth  analysis  or                statistical test, but only by a subjective assessment of ordering the indicated values.                Analogous considerations regarding stock exchange indices published by the Warsaw Stock                Exchange  were  carried  out  by  Orzeszko  (2010).  It  should  be  noted  that  although  he                calculated the standard deviations for the rates of return (though he did not specify them),                the fractal dimension (using the Zwolankowska method) – for index levels. The results are                collected in the chart below (Figure 1).                On the basis of the obtained results, Orzeszko formulates the conclusion that the fractal                dimension and standard deviation lead to the ordering of the examined indices in a similar                way, and then analyzes them in sub-periods. It should be noted that also in this case no                formalized statistical inference methods were used, and the sample is very small.                A  similar  analysis  was  presented  by  Zeug-Żebro  (2015,  2016,  2017)  and  Miśkiewicz-                Nawrocka  (2016)  with  reference  to  the  standard  deviation  and  taxonomic  measure  of                investment attractiveness based on the Hellwig method (1968). The results obtained in the                graphical form are summarized below (Figure 1).                In  further  analyzes,  the  Authors  do  not  analyze  the  relationship  between  the  fractal                dimension and other measures in a formalized way (because it is not the purpose of their                work),  but  adopt  the  subjective  assessment  of  rankings  prepared  for  the  presented                companies.  However,  they  point  out  to  the  small  grounds  for  identifying  possible                dependencies.                As indicated, there are no in-depth analyzes of the relationship between commonly used                investment risk measures and the fractal dimension. For this reason, it was considered                appropriate to carry them out, as discussed below.                                                               54","Figure 1 Fractal Dimension and Standard Deviation                                       Estimated for Various Companies and Indices                                       Selected companies and the WIG index                 Selected stock indices                             (Zwolankowska, 1999)                           (Orzeszko, 2010)                                         Selected companies and indices                           (Zeug-Żebro, 2015, 2016)                           Source: Own elaboration based on Zwolankowska (1999), Orzeszko (2010),                                                     Zeug-Żebro (2015, 2016)                 2 Methodology and Data                An important issue faced by the author was selection of quantities from among the existing                risk measures which can be adopted in further research. While making the choice, several                criteria were adopted. In the first place, all measures taking into account in their structure                apart  from  the  risk  also  the  level  of  expected  income,  were  excluded  from  further                considerations.  Due  to  the  fact  that  the  goal  of  the  study  is  to  analyze  dependencies                between the fractal dimension and measures reflecting the riskiness of investments only,                using the quantities comparing the income and risk does not seem justified (Bacon, 2013).                Moreover, when making a choice, special attention was paid to measures of a universal                nature, the use of which involves the adoption of relatively few additional assumptions. In                particular, attempts were made to avoid measures requiring  a priori assumption  of the                type of probability distribution or the stochastic process describing the observed rates of                return.                In addition, the function measures were removed from the examined quantities. For this                reason, the analyzed set did not include variance or semi-variance of the rate of return,                or, for example, the kappa coefficient (as functionally related to the omega coefficient)                (Frey, 2017, Michalska and Kopańska-Bródka, 2015).                Due to the fact that the paper analyses the quotations of stock prices, the measures specific                to  fixed-income  instruments  (such  as  duration)  or  derivative  instruments  (such  as  the                Greeks) have been eliminated.                In further considerations, the analyzed risk measures have been appropriately grouped                due to the similarities resulting either from their construction, or due to the use of certain                                                             55","probability distribution properties to measure them. The author finally identified 8 groups                of risk measures by the way of a subjective assessment and they are compiled in Table 1.                            Table 1 An Adopted Division of Selected Investment Risk Measures                          Group      Measure      Group      Measure       Group      Measure                                         R                     VaR 0,95                Loss frac .                                         s                     GaR 0,95                 BR ( ) 0                                          d                   DVaR 0,95;0,95    F        BR ( )                                         d            C         CVaR 0,95                  g                                          p                            A                                                              1                                        Q                     CGaR                        ( ) 0                                                                   0,95                                                             R 0,95;0,95    G         FT 2 2 ( ) 0                                                                D                      FT 2 2 ( )                                          g                       D            H        dim  ( )                                                                                            X                                          2                       lar                     B                                              )                                           R                                    LPM   ( ,0                  D                                         2                        max                                              )                                    LPM  ( , R                DaR                                        2                          0,95                                          ( ,0                                     LPM R    )      D           DD                                         1                                              )                                    LPM 1 ( , R                 UI                                              )                                    UPM  2 ( ,0                  PI                                           R                                              )                                    UPM 2 ( , R                 LR                                          ( ,0                                    UPM R    )                   PR                                         1                                              )                                    UPM  ( , R                                                         1                        Sharpe                                         ( ,0                            B        LSD R   )                  + Sharpe                                        2                                         ( ,0                                     LSD R   )                                                         1                        − Sharpe                                         ( ,0                                    GSD R    )                 BTR Sharpe                                        2                                         ( ,0                                     GSD R   )                   CAPM                                        1                                             )                                     LSD 2 ( ,R     E          + CAPM                                             )                                     LSD 1 ( , R               − CAPM                                             )                                    GSD  ( , R                BTR                                        2                         CAPM                                             )                                    GSD  ( , R                Syst .risk                                        1                                                              Spec .risk                                                  Source: own elaboration                The time series of closing prices for shares listed on the Warsaw Stock Exchange  were                analyzed based on the quotations provided by the stooq.com website. One of the initial                problems was to determine the set of companies for the analysis and the length of time                series used in further research. The only assumption that was adopted prior to the initial                data analysis was the premise that the time series should represent the same period, so                that  the  comparable  risk  measures  could  be  obtained.  In  addition,  it  was  considered                desirable to have series with a similar number of observations.                Based on the analysis of the data obtained, it was decided to qualify for further research                companies for which the average number of quotations in the year was not less than 227                (with an average of 252 quotations in a year), and the maximum interval in days between                quotations was no more than 21 days. The adoption of less strict criteria does not lead to                a significant increase in the number of surveyed companies, while the acceptance of more                                                             56","strict  ones  results in  a  drastic  reduction in  the  number  of  entities  analyzed. Finally, in                further research, the quotations of 58 companies (1 January 1998  – 31 October 2017,                investment horizon length varied from 1 to 252 trading days), 87 companies (1 January                2003 – 31 October 2007, 1 to 189 trading days) and 207 companies (1 January 2008 – 31                October 2017, 1 to 126 days) were used.                In  the  next  step,  the  logarithmic  and  cumulative  logarithmic  rates  of  return  for  each                investment horizon were calculated. If quotes of a particular company were not available                for a given day, then they were replaced with the last available quote. Next, previously                indicated risk measures were calculated for each investment horizon. As a result, in relation                to the period from 1 January 1998 to 31 October 2017, 14,364 sequences of risk measures                were available (each with 58 terms), for the period from 1 January 2003 to 31 October                2007  –  10,773  (each  with 87  terms), while  for  the  period  from  1  January  2008  to  31                October 2017 – 7182 (each with 207 terms).                When  estimating  the  measures  related  to  the  Sharpe  diagonal  model  or  CAPM,  it  was                necessary  to  select  a  market  factor  and  an  instrument  playing  the  role  of  a  risk-free                instrument. In the first case, the broad market index was used, which is the Warsaw Stock                Exchange Index, while in the latter, the WIBID TN rate (transformed into a logarithmic                rate) was used.                The  main  purpose  of the  paper is to  analyze  the  relationships  between  the  non-fractal                measures of risk and the fractal dimension. For this reason, the focus has been on methods                enabling the study of the strength and direction of potential dependencies. To this end,                standard  correlation  analysis methods  were  employed. Different  correlation  coefficients                were used, i.e. Pearson correlation coefficient, Spearman’s correlation coefficient, Kendall’s                tau  and  correlation  ratio  (Domański,  1990),  (Kendall,  1962).  Due  to  the  possibility  of                statistically insignificant correlations, in the next step, the hypothesis of the null correlation                coefficient  between  individual  risk  measures  was  verified  with  the  use  of  appropriate                statistical tests. The level of significance was assumed 0.05 in all the tests.                 3 Results and Discussion                First of all, all the presented risk measures were calculated. If these measures were defined                in relation to a certain threshold value, such as partial moments, the zero and the mean                value were taken as the limit value. Because in addition to the relationship between the                fractal dimension and the level of classical risk measures, the relationship between the                fractal dimension and their fluctuations in time is also significant, additionally available for                different time intervals, the measures were scaled by dividing them by the appropriate risk                measure calculated for a time interval equal to one session. Both primary and scaled data                were analyzed.                In  the  next  step,  correlation  coefficients  were  calculated  and  the  null  hypothesis  was                verified.  Below,  a  graphic illustration for  the  investment  horizon length  of  1 session is                presented (Figure 2).                Since  the  analysis  of  the  correlations  between  all  the  measures  used  and  the  fractal                dimension would not be possible due to the limited size of this paper, it was decided to                limit it to  presenting a few of the most important ones. Guided by the importance and                common use of a given quantity, it was decided to analyze the relationship between the                fractal  dimension  and:  standard  deviation,  standard  semi-deviation  (relative  to  zero),                value-at-risk, ulcer index, conditional value-at-risk, largest individual drawdown as well as                omega, beta coefficient for the classical Sharpe model, Farinelli-Tibiletti ratio to zero and                bias ratio (Figure 3).                                                                      57","Figure 2 Correlation Coefficients and Correlation Ratio Between Selected Investment                 Risk Measures for 1-Session Logarithmic Return Rates on Shares of Selected Companies                                  in the Period from 1 January 1998 to 31 October 2017                                                Pearson’s correlation coefficient    Spearman’s rank correlation coefficient                            -1                                                1     -1                                                1                                                    Kendall’s tau coefficient                  Correlation ratio                           -1                                                1      0                                                1                                                  Source: own elaboration                   Figure 3 Spearman’s Rank Correlation Coefficient Between the Fractal Dimension and                    the Selected Risk Measures in the Period from 1 January 1998 to 31 October 2017                                                  Primary data                               Rescaled data                                                   Source: own elaboration                                                               58","When  analyzing  the  calculated  values,  several  regularities  can  be  noticed.  Firstly,  the                quotient measures (bias ratio, omega, Farinelli-Tibiletti ratio) show little or no relation to                the  fractal  dimension,  both  before  and  after  the  re-scaling.  Secondly,  the  measures                referring to extreme events (largest drawdown, conditional  value at risk) are relatively                strongly correlated with the fractal dimension in terms of their absolute level, while the                scaled  ones  show  rather  negative  relationships.  Thirdly,  classical  volatility  and  stress                measures  (standard  deviation,  second  partial  moment,  value  at  risk)  are  negatively                correlated with the fractal dimension, both at their relative and absolute levels. A similar                trend is shown by the beta coefficient and ulcer index.                 4 Conclusions                The  considerations  carried  out  and  analysis  of  empirical  data  give  some  ground  to                formulate a number of conclusions.                Examining the relationship between the fractal dimension and other risk measures, it has                been shown that in relation to the shares listed on the Warsaw Stock Exchange, the nature                of these dependencies varies depending on the measure chosen. In the case of measures                that can be described as measures comparing the shapes of the probability distribution                with respect to a certain cut-off level (such as bias ratio, omega or Farinelli-Tibiletti ratio),                the analysis shows that the fractal dimension is not linked with these measures. Lack of                dependence is revealed in both static and dynamic aspects. Thus, irrespective of the length                of  the  considered  investment  horizon,  the  information  provided  by  these  measures  is                different, and therefore simultaneous use of the fractal dimension and indicated quantities                is expedient. The obtained result is also a natural consequence of the essence of the fractal                dimension,  which  refers  to  the  variability  rather  than  the  shape  of  the  distribution                describing the prices or rates of return. It should be noted that the main hypothesis was                confirmed with reference to the above-mentioned measures.                 With respect to volatility and stress measures, such as standard deviation, standard semi-                deviation,  but  also  value-at-risk  or  ulcer  index  between  the  fractal  dimension  and  the                mentioned values, dependencies were found both in relation to their absolute and relative                level – in both cases of negative nature. At the same time, the indicated relationships seem                to get stronger along with the extension of the investment horizon, which is an additional                confirmation of the hypothesis made by the author (Buła, 2013), (Buła and Pera, 2015),                (Buła, 2017) namely that for short periods the increase in the fractal dimension means an                increase in the risk level ceteris paribus, while for the lengthening investment horizons –                its decrease – ceteris paribus. This study confirms the said hypothesis not only for a static                but  also  a  dynamic  approach.  Thus,  for  the  indicated  measures,  the  main  research                hypothesis should be rejected.                For risk measures relating to extreme events, i.e. conditional value at risk or the largest                drawdown, the conclusions are identical to the conclusions  for the previously described                group. The only difference is due to the fact that in the case of the indicated measures,                their fall is considered to be equivalent to increasing the riskiness of investments, thus the                direction of dependence is reversed (i.e. positive). The essence of things remains the same,                which again confirms the hypothesis, but for a completely different set of risk measures.                Having presented the nature of the relationship between individual risk measures and the                fractal dimension, it should be noted that despite some variability resulting either from the                number of companies surveyed or the number of available quotations, the relations are                relatively stable over time, as demonstrated by comparative analysis carried out for the                analyzed risk measures calculated for three periods: twenty-year, fifteen-year and ten-                year. It is true that the analyzed periods were not separable, but in the author’s view, the                need to have an adequate set of quotations justifies the decision made. Thus, confirmation                of the second research hypothesis was obtained. At the same time, it can be stated that                the objectives set out by the author have been fully attained.                                                                 59","References                Bacon,  C.  (2013).  Practical  Risk-Adjusted  Performance  Measurement.  Chichester:  John                Wiley \& Sons.                Buła,  R.  (2013).  Ryzyko  inwestycji  a  wymiar  fraktalny.  Studia  Ekonomiczne,  vol.  155,                pp. 450-466.                Buła, R., Pera, K. (2015). Применение концепции фрактальной размерности для оценки                инвестиционного  риска  на  финансовых  рынках.  In:  Белозеров,  С.  А.,  ed.,                Международный экономический симпозиум. Санкт-Петербург: Скифия-принт, pp. 217-                235.                 Buła,  R.  (2017).  Analiza  wymiaru  fraktalnego  spółek  notowanych  na  Giełdzie  Papierów                Wartościowych w Warszawie – aspekty metodyczne. 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Szczecin: WN US.                                                                             60","Measuring Efficiency in Clinical Departments Using the DEA                                  Approach - A Case of the Polish Hospital                    Małgorzata Cygańska , Magdalena Kludacz-Alessandri , Dimitrios Syrrakos             3                                                                                2                                           1                                         1  University of Warmia and Mazury in Olsztyn                                 The Faculty of Economics, Department of Finance and Banking                                          Oczapowskiego 4, 10-719 Olsztyn, Poland                                               E-mail: m.cyganska@uwm.edu.pl                                               2  Warsaw University of Technology                                         The College of Economics and Social Sciences                                            Łukasiewicza 17, 09-400 Płock, Poland                                            E-mail: Magdalena.Kludacz@pw.edu.pl                                              3  Manchester Metropolitan University                                                  Business School Building                                                         All Saints                                                    Manchester M15 6BH                                                      United Kingdom                                               E-mail: D.Syrrakos@mmu.ac.uk                 Abstract: Evaluating hospitals' efficiency in Poland is vital considering in the light of limited                public  funds  dedicated  to  healthcare.  As  such,  employing  a  mechanism  capable  of                identifying inefficiencies by hospital administrators would be conducive to the quality of                the  services  delivered.  The  aim  of  this  article  is  to  ascertain  the  merits  of  efficiency                measures  as  instruments  of  monitoring  and  distributing  resources,  by  focusing  on  the                clinical departments of provincial specialist hospital in Olsztyn in Poland. The hospital under                perspective, provides diagnostics, therapy, care, specialist advice, education prevention                and  health  promotion.  Efficiency  of  hospital  departments  was  measured  using  Data                Envelopment  Analysis  (DEA).  The  distribution  of  efficiency  and  the  ranking  of  clinical                departments was compared across surgical and non-surgical departments’ profile. Input                and output data was collected from the hospital database for 19 578 patients admitted to                14 departments between January and June 2017. The research shows that only 5 clinical                departments  (35.71%)  run  efficiently.  These  were  the  Departments  of  Hematology,                Gynecology, Vascular Surgery, Rehabilitation and Ophthalmology. Most of them provide                surgical procedures. The least efficient was the Department of Transplantology and General                Surgery.  The  paper’s  findings  could  potentially  inform  manager’s  choices  in  relation  to                increasing efficiency. Since hospital managers have more control over their inputs, they                may  devote  more  attention  to  the  examination  of  total  inefficiencies  generated  by                excessive input usage.                 Keywords: clinical department efficiency, data envelopment analysis (DEA), input, output                JEL codes: M41, C67, I19                 1  Introduction                Hospital health care occupies a special place in all health care systems, and it is the biggest                consumer  of  health  care  financial  resources  in  Poland  and  in  most  other  developed                countries. For instance, a considerable share (16–48%) of total health expenditure in OECD                countries is devoted to the hospital sector. Hospitals are increasingly under pressure to                improve  their  efficiency  of  operations,  as  well  as  improving  the  quality  of  the  health                services provided. This is due to the growing expectations of patients and the huge costs                caused by demographic changes. On the other hand, the health care sector is characterized                by limited inflow of financial resources whose rules of spending are subject to detailed                regulations and supervision. Decision makers are increasingly presented with the challenge                of reconciling the growing demand for health care services on the one hand, and the limited                financial resources on the other (Nakagawa et al., 2011).                                                               61","Public institutions’ activities are not subject to normal competition rules and are not profit                oriented.  As  governments’  subsidies  to  public  hospitals  have  been  decreasing,  more                efficient  use  of  available  resources  becomes  essential  for  hospital  administrators.  As  a                result,  the  distribution  of  financial  resources  should  be  linked  to  the  effectiveness  of                hospitals and other entities providing medical services (Grzesiak and Wyrozębska, 2014).                Efficiency is amongst the most pressing issues in health care and hospital efficiency is one                of  the  key  indicators  of  hospital  performance.  Efficiency  is  concerned  with  the  relation                between  resource inputs  (e.g.  costs, in  the  form  of labour,  capital,  or  equipment)  and                either intermediate outputs (e.g. numbers of treated patients, waiting time) or final health                outcomes  (e.g.  life  years  gained,  quality  adjusted  life  years  -  QALYs)  (Palmer  and                Torgerson,  1999).  From  a  measurement  perspective,  efficiency  can  be  divided  into                technical and economic efficiency. In the theory of production and costs of organization,                the third aspect of efficiency is also often used - allocative efficiency. Quite often when                defining efficiency the authors make only the distinction between technical and allocative                efficiency measures, which together comprises the overall (economic) efficiency, and scale                efficiency (Varabyova and Schreyögg, 2013).                 Economic  efficiency  implies  an  economic  state  in  which  every  resource  is  optimally                allocated to serve each individual or entity in the best possible way while minimizing waste                and inefficiency. In health care, economic efficiency implies that society makes choices                which maximize the health outcomes gained from the resources allocated to healthcare.                Inefficiency means that resources could be re-allocated in a way that would increase the                health  outcomes  produced.  Technical  efficiency  refers  to  the  physical  relation  between                resources  (input)  and  health  outcomes  (output).  It  refers  to  the  ability  to  obtain  the                maximum  possible  improvement  in  outcome  (maximum  output)  from  a  given  set  of                resources (inputs) or to minimize input factors to achieve a given outcome. (Ludwig, 2008).                This paper analyzes only technical efficiency.                 In practice, various methods can be used to precisely determine which hospitals and their                departments  are  more  active  and  effective,  and  to  indicate  the  factors  affecting  this                efficiency. The methods of measuring effectiveness can be broadly divided into three basic                groups (Suchecka, 2009):                     •   ratio analysis - it is a quantitative analysis used to evaluate various aspects of an                        operating and financial performance,                    •   parametric  methods  -  e.g.  Stochastic  Frontier  Analysis  (SFA),  Thick  Frontier                        Approach (TFA), Distribution Free Approach (DFA),                    •   non-parametric methods - Free Disposal Hull analysis (FDH), Data Envelopment                        Analysis (DEA).                Nonparametric  methods  are  used  to  measure  the  technical  efficiency  of  hospitals  and                enable  the  analysis  of  inputs  and  results  (Vincova,  2005).  Data  envelopment  analysis                (DEA) is receiving increasing importance as a tool for evaluating efficiency and improving                the performance of manufacturing and service operations for a wide variety of industries                and institutions. In recent years, DEA has gained great popularity in healthcare branches                and it has been extensively applied in productivity measurement, performance evaluation                and benchmarking of organizational units, like hospitals, which use numerous resources to                produce  multiple  products  (Cook  et  al.,  2014).  There  are  many  contributions  in  the                literature  regarding  DEA  effectiveness  in  health  care  sector.  The  scale  of  conducted                research  differs,  e.g.  research  on  the  efficiency  of  hospitals  within  larger  units  (e.g.                administrative)  or  research  on  health  systems  of  individual  countries.  Areas  of  DEA                application  in  health  care  sector  also  include  physicians,  nursing  homes,  and  health                maintenance organizations.                An  application  of  DEA  in  health  care  offers  several  advantages  over  other  techniques.                Comparison of three performance methodologies (a ratio analysis, regression analysis, and                data  envelopment  analysis)  showed  that  DEA  is  superior  to  other  methods  because  it                incorporates  an  optimizing  principle  rather  than  an  averaging  principle  (Nyhan  et  al.,                2000). The DEA approach offers also other advantages over the other ratio methods, such                as:                                                            62","•   it combines multiple ratios into a single ratio of productive efficiency;                    •   it outperforms other methods in identifying sources and amounts of inefficiencies;                    •   it compares each DMU (e.g. hospitals) to its peers and identifies benchmark facilities                       for inefficient hospitals (Chilingerian, 1990).                Data envelopment analysis (DEA) is a linear-programming-based technique, established                for measuring the relative efficiencies of units delivering similar services, which are called                decision making units (DMUs). The input and output data of the units under analysis show                what is achieved with the technology presently available. The aim of the DEA is to establish                the  relative  efficiency  of  each  DMU  within  a  sample  and  identify  which  units  operate                efficiently  and  therefore  belong  to  the  efficient  frontier,  and  which  of  them  operate                inefficiently. Increasing efficiency of inefficient DMUs is possible by making appropriate                adjustments in their outputs or inputs (Ancarani et al., 2009). DEA has the advantage of                being able to deal with multidimensional nature of input/output variables and measures                efficiency for when multiple outputs and inputs are simultaneously present and provides a                single (relative to best) productivity index that is generally referred to as relative efficiency.                This paper employs the envelopment analysis (DEA) as a tool for evaluating the relative                technical efficiency of hospital wards at the Polish public hospital in Olsztyn. This method                provides a single measure of efficiency of each ward and allows hospital managers to make                comparisons of hospital departments using the same set of criteria (inputs and outputs).                 2  Methodology and Data                This section introduces the key principles of the DEA as a productivity analysis tool for a                hospital. Single efficiency scores have been calculated and compared using hospital wards                as decision-making units (DMU). To the authors’ knowledge the calculation of ward-specific                efficiency scores is not often the unit of non-parametric efficiency analysis, and thus the                studies of efficiency within the hospitals, are very limited. The majority of studies make                use  of  the  hospital  as  the  decision-making  unit,  rather  than  the  hospital  ward  (e.g.                Steinman and Zweifel, 2003). The rationale for not using hospital wards as the preferred                units of analysis is that intermediate outputs (e.g. discharges and patient days) and inputs                (e.g. medical equipment) are very heterogeneous across the departments representing                various medical fields. This implies that differences among wards as DMUs due to specialty                may  be confused with differences in technical efficiency (Ancarani et al., 2009).On the                other hand, the study of hospital wards’ efficiency could shed further light on managerial                and organizational aspects of the central management policy concerning capacity.  The                organizational structure of hospital may vary, but departments serve as the core units of                a  hospital.  The  decisions  made  in  the  clinical  departments  will  determine  the  use  of                resources. This justifies viewing each clinical department as a single decision making unit                (DMU).                This  model  provides  the  efficiency  score  in  the  presence  of  multiple  input  and  output                factors defined as:                                             ℎ                                                                             =                                                              (1)                                            ℎ                                               Assuming that there are n DMUs, each with m inputs and s outputs, the relative efficiency                score of a test DMU p is obtained by solving the following model proposed by Charnes et                al. (1978):                        max  ∑      =1                                                                   (2)                             ∑                                   =1                                      s.t.   ∑      =1                 ≤ 1 ∀                                           (3)                           ∑                                            =1                                            ≥ 0 ∀                                  ,                                               where                k = 1 to s,                j = 1 to m,                i = 1 to n,                                                            63","y ki = amount of output k produced by DMU i,                x ji = amount of input j utilized by DMU i,                v k = weight given to output k,                u j = weight given to input j.                The fractional program shown as (2, 3) can be converted to a linear program as shown in                (4,5). For more details on model development see Charnes et al. (1978).                       ax ∑      =1                                                                      (4)                                                              s.t. ∑            = 1                                                            (5)                               =1                                                               ∑      =1        - ∑           ≤ 0 ∀                                                                                       =1                                                                                                            ,    ≥ 0 ∀                                                                                          ,                  The above problem is run n times in identifying the relative efficiency scores of all the                DMUs. Each DMU selects input and output weights that maximize its efficiency score. In                general, a DMU is considered to be efficient if it obtains a score of 1, and a score of less                than 1 implies that it is inefficient. We used Data Envelopment Analysis Program (version                2.1) to examine the data.                The  DEA  analysis  was  conducted  in  a  Polish  specialist  hospital.  The  hospital  under                consideration provides diagnostics, therapy, care, specialist advice, education prevention                and  health  promotion.  The  unit  of  analysis  was  the  hospital  ward  (DMU).  Inputs  and                outputs  data  was  collected  from  the  hospital  database  and  concerns  19  578  patients                admitted  to  14  departments  between  January  and  June  2017.  DEA  analysis has  been                performed on DMUs which produce only inpatient care in order to control their potential                heterogeneity. The literature (Ancarani et. al., 2009) considers that the main source of                heterogeneity among the DMUs is the one between units producing inpatient care versus                those producing outpatient care.                Hospitals treat a variety of patients using several different inputs. Inputs and outputs for                the DEA analysis were derived based on previous healthcare studies (Kirigia, 2002). Most                often, operating costs, the number of hospital beds and Full Time Equivalent (FTE), the                number of personnel, total assets (assets), complexity of services measured by the number                of clinical services offered, number of doctors and the number of beds are used as inputs.                Whereas outputs include: the number of days of hospitalization, the number of surgical                procedures, weighted admissions, number of surgical interventions. The input measures                used in other studies regarding hospital wards were: doctors’ and nurses’ gross wages,                total  personnel  costs  and  materials  and  energy  consumption,  whereas  the  output                measures were: the number of in-patients and outpatients, bed productivity, and average                turnover interval, number of bed-days, DRG points and income from medical activity (Al-                Shayea, 2011; Grzesiak and Wyrozębska, 2014).                Finally,  four  input  measures  and  two  output  measures  were  selected  to  measure  the                efficiency for each department. In this paper, medical wages (1), drugs (2) diagnosis                tests (3) and the numbers of departments’ beds are used as inputs. They are presented                in table 1. The first three inputs were measured by costs. Costs in PLN were converted                to  Euros  on  the  basis  of  average  exchange  rate  of  Polish  National  Bank  (NBP)  from                07.05.2018 (1€ = 4,2513).                The  largest  department,  in  terms  of  beds,  is  Gynecology-Obstetrics  and  Oncological                Gynecology  Department.  In  relation  to  of  medical  wages  the  most  expensive  is  the                Cardiac  Surgery  Department  (1383,96€)  and  Anesthesiology  and  Intensive  Care                Department (1464,29€). The last one is also the most expensive in terms of drug costs                (2494,34 €).                                                                  64","Table 1 Descriptive Statistics for Data Envelopment Efficiency Model Variables Inputs                 Department of                                     Medical    Drug     Diagnosis   Number                                                                   wages      costs      tests     of beds                                                                  costs(€)     (€)     costs (€)                                                                                  Mean (SD)          n                                                                   273,70     48,48       5,75       5                 Rehabilitation                                   (138,33)    (76,24)   (18,63)                                                                   158,53     185,10     97,88       20                 Transplantology and General Surgery              (328,41)   (629,23)   (142,14)                                                                   1464,29    2494,34   227,26       15                 Anesthesiology and Intensive Care                (1428,63)   (4447,5)   (274,89)                 Cardiac Surgery                                   1383,96    637,91     54,17       20                                                                   (1206,08)   (1397,4)   (60,91)                 Endocrinology, Diabetology and Internal Diseases   75,13     132,29     36,84       18                                                                   (70,10)   (712,44)   (55,20)                                                                    84,01     50,39      32,67       35                 Traumatic, Orthopedic Surgery and Spine Surgery                                                                   (65,71)    (98,66)    (60,73)                                                                    86,30     127,78     70,79       30                 Neurosurgery                                                                   (83,41)   (309,87)    (150,62)                                                                   150,97     80,86      83,72       16                 Gastroenterology                                                                  (166,61)    (239,85)   (89,70)                                                                   124,07     141,56     98,92       24                 Neurology                                                                  (102,19)   (577,43)    (181,62)                                                                   107,76     123,03     175,67      20                 Vascular Surgery                                                                  (140,81)   (315,38)   (257,34)                                                                    42,70     43,93       2,75       18                 Ophthalmological                                                                   (41,31)    (84,76)   (16,56)                                                                   153,60     563,38     60,15       14                 Hematology                                                                  (365,29)   (1015,5)   (212,40)                                                                    62,66     61,55      16,91       45                 Cardiology                                                                   (96,60)   (214,69)    (39,64)                 Gynecology-Obstetrics and Oncological              70,25     81,98      12,98       76                 Gynecology                                       (117,45)   (434,63)   (53,89)                                                  Source: own elaboration                There is no consensus regarding the measurement of hospital outputs. Generally, the                chosen output vector depends on available data and problem to be analyzed (Magnussen,                1996). In this paper we used as outputs number of bed days (1) and number of inpatients                (2) that are presented in table 2.                 Table 2 Descriptive Statistics for Data Envelopment Efficiency Model Variables Outputs                  Department                                                Bed days        Patients                                                                           Mean (SD)       (n)                 Rehabilitation                                            28,58 (11,66)   98                 Transplantology and General Surgery                       4,98 (5,00)     415                 Anesthesiology and Intensive Care                         12,04 (11,17)   458                 Cardiac Surgery                                           9,32 (7,04)     472                 Endocrinology, Diabetology and Internal Diseases          4,33 (3,14)     892                 Traumatic, Orthopedic Surgery and Spine Surgery           4,16 (2,91)     934                 Neurosurgery                                              5,05 (4,48)     1120                 Gastroenterology                                          3,82 (4,29)     1122                 Neurology                                                 6,69 (5,11)     1412                 Vascular Surgery                                          5,88 (7,54)     1528                 Ophthalmological                                          2,41 (1,95)     1582                 Hematology                                                3,19 (4,23)     1608                 Cardiology                                                4,16 (3,38)     3094                 Gynecology-Obstetrics and Oncological Gynecology          6,75 (5,22)     4843                                              Source: authors’ own elaboration                 The  biggest  clinical  department  in  terms  of  number  of  patients  is  the  Department  of                Gynecology-Obstetrics  and  Oncological  Gynecology,  accounting  for  almost  25%  of  all                admitted  patients  to  the  hospital.  The  longest  hospitalization  took  place  at  the                                                             65","Rehabilitation  Department  (28,58  days)  and  at  Anesthesiology  and  Intensive  Care                Department  (12,04  days).  On  average,  the  shortest  stay  in  hospital  was  in  case  of                patients admitted to Ophthalmological Department (2,41 days).                 3 Results and Discussion                The efficiency measures are summarized in table 3. It is important to note that efficiency                scores  range  from  0  (totally  inefficient)  to  1  (100%  efficient).  Based  on  the  paper’s                results out of the 14 departments included in the analysis, only 5 clinical departments                (35,71%)  run  efficiently,  while  the  remaining  9  (64,29%)  were  inefficient.  Previous                studies  reported from  41,66 to  48,88% hospital departments run 100% efficient. For                example Al-Shayea (2011) reported 4 out of 9 departments (44,44%) perceived as 100%                efficient. In turn Hofmarcher et. al, (2002) reported that 20 of 48 operative departments                and 22 of 45 non-operative departments had an efficiency level of 100%. We presume                that these differences are related to the inputs and outputs used in DEA analysis, what                is also confirmed in Magnussen and Nyland (2008) findings. Our study shows that the                efficient  departments  with  the  highest  score  were  the  Departments  of  Hematology,                Gynecology, Vascular Surgery, Rehabilitation  and Ophthalmology. Among the efficient                departments, 3 (60%) provide surgical procedures, which is similar to Al-Shayea (2011)                findings  which  revealed  that  half  of  the  100%  efficient  departments  were  surgical                (Obstetrics  and  Gynecology  Department  and  Orthopedic  Department).  Among  the                inefficient departments, 1 department (11%) had an efficiency score between 91-99%,                6 (66%) between 70-90%, and 2 (22%) a score below 50%. The least efficient was the                Department  of  Transplantology  and  General  Surgery  (38,10%  E).  Al-Shayea  (2011)                reported 66,66% of hospital departments with efficiency score below 50%. The reason                for  such  a  low  efficiency  in  the  departments  of  the  last  group  (with  their  efficiencies                values maintained below 50%) is the highest levels for input expenditures  relative to                their outputs. Therefore, in order to increase their efficiencies, the input expenditures for                those departments must be decreased through cost control policy. The efficient wards on                average  admit  more  patients  per  year  than  inefficient  wards.  The  exception  is  the                rehabilitation department, which admits the least number of patients, but on the other                hand it features the highest number of bed-days. The frequent explanation is that large                amounts of fixed capital in efficient departments allows for the spreading of fixed cost                across a greater volume of output. Increasing of efficiency in other wards is possible e.g.                by better utilization  of existing equipment,  eliminating duplications  of  services  and/or                equipment, the sharing of overhead costs such as laboratory and diagnostic facilities.                                Table 3 Efficiency Measures in Terms of Type of Department                                      Department                         Efficiency   Surgical/n  Ranking                                                                            (E)       onsurgical                 Rehabilitation                                            1,000         NS          1                 Transplantology and General Surgery                       0,381          S          10                 Anesthesiology and Intensive Care                         0,714         NS          8                 Cardiac Surgery                                           0,467          S          9                 Endocrinology, Diabetology and Internal Diseases          0,745         NS          6                 Traumatic, Orthopedic Surgery and Spine Surgery           0,878          S          4                 Neurosurgery                                              0,706          S          7                 Gastroenterology                                          0,865         NS          5                 Neurology                                                 0,897          S          3                 Vascular Surgery                                          1,000          S          1                 Ophthalmological                                          1,000          S          1                 Hematology                                                1,000         NS          1                 Cardiology                                                0,924         NS          2                 Gynecology-Obstetrics and Oncological Gynecology          1,000          S          1                                                  Source: own elaboration                 The  DEA  approach  has  demonstrated  that  64,29%  of  clinical  departments  run                inefficiently, and they thus need to either reduce their inputs or increase their outputs in                order  to  be  rendered  efficient.  While  the  clinical  departments’  managers/doctors  may                have limited impact  on  outputs, they can seek  to  affect inputs. Due to the nature of                                                             66","health  case  involving  a  better  control  of  inputs  than  outputs,  the  DEA  input  oriented                model is used to identify potential improvements applicable in the inefficient departments                in  order  to  become  100%  efficient.  Total inputs  reduction  needed  to  make  inefficient                clinical departments efficient are presented in table 4.                     Table 4 Total Input Reduction to Make Inefficient Clinical Departments Efficient                                       Medical wages costs        Drug costs (€)      Diagnosis tests costs                                               (€)                                            (€)                 Department of                                       Original   Predicted    Original   Predicted   Original   Predicted                                        value       value       value       value      value       value                 Rehabilitation         26822,45   26822,45      4750,90    4750,90      563,42     563,42                 Transplantology        65790,86   25072,76     76816,21   28384,74    40620,83    3872,42                 General Surgery                 Anesthesiology and    670645,30   70002,60   1142406,00   47070,34   104084,80   59314,15                 Intensive Care                 Cardiac Surgery       653228,00   61111,61    301095,20   101370,70   25567,54   11946,31                 Endocrinology, and     67018,15   49913,74    118006,60   55515,91    32864,93    6846,83                 Internal Diseases                 Traumatic,             78464,29   54344,27     47064,70   41313,29    30510,37    2760,58                 Orthopedic Surgery                 Neurosurgery           96654,09   68239,64    143119,80   77396,63    79288,69   10663,74                 Gastroenterology      169386,30   71471,97     90726,20   78518,88    93933,37   57616,13                 Neurology             175193,60   139030,50   199891,20   179368,00   139671,70   125331,60                 Vascular Surgery      164657,50   164657,50   187986,40   187986,40   268430,50   268430,50                 Ophthalmological       67553,54   67553,54     69493,22   69493,22     4358,43    4358,43                 Hematology            246982,80   246982,80   905910,50   905910,50   96726,47   96726,47                 Cardiology            193870,40   171897,10   190433,70   176030,20   52337,05   19389,24                 Gynecology-           340242,90   340242,90   397060,30   397060,30   62878,10   62878,10                 Obstetrics                                                  Source: own elaboration                 In this table, the predicted value of an input variable represents the amount to which a                given DMU can decrease its consumption of that specific variable. It is noticed that the                Departments  of:  Transplantology  and  General  Surgery;  Anesthesiology  and  Intensive                Care;  Cardiac  Surgery;  Endocrinology;  Traumatic,  Orthopedic  Surgery,  in  addition  to                departments  in  Neurosurgery;  Gastroenterology;  Neurology  and  Cardiology  have  to                decrease all their input expenses by specific amounts in order to reach the targeted level                shown  in  order  to  be  efficient.  If  inefficient  wards  can  reduce  their  inputs  to  the                corresponding predicted target value, then, they would become efficient. In general, nine                departments need to reduce their wage costs, drug costs and diagnosis tests costs in order                to become efficient. The savings that can be achieved are considerable and in many cases                exceed 90%. In the case of Cardiac Surgery Department medical wages should be lower                by 91%, which would allow for savings in the amount of 592 thousand Euros. The largest                suggested  savings  for  drug  costs  relate  to  the  Anesthesiology  and  Intensive  Care                Department (96%), and for diagnosis tests costs for Traumatic, Orthopedic Surgery and                Spine Surgery Department (91%).                 4 Conclusions                 Clinical departments beside administrative and technical support and ancillary services                are core units of a hospital (Magnussen, Nyland, 2008). Because hospital departments                are the largest component of the hospital, to a large degree, efficiency of the clinical                departments determines the hospital efficiency. The DEA analysis provides information                about  the  surface  were  inputs  are  wasted  and  not  utilized  in  production  of  hospital                services.                 With this information, managers will be able to make informed choices in relation to an                increased efficiency path. Since hospital managers generally have more control over their                inputs,  they  may  devote  more  attention  to  the  examination  of  total  inefficiencies                generated by excessive input usage. However, examinations of output inefficiencies can                also  provide  strategic  direction  for  the  hospital  by  indicating  where  to  increase  its                efficiency. The DEA is more informative than the other efficiency measurement methods                                                             67","and can be successfully implemented in situations in which multiple inputs are used for                producing multiple outputs. Hospital managers can use DEA results as inputs in decision-                making processes involving resource planning, allocation, and utilization. By analyzing                output  inefficiencies  and  excess  inputs,  managers  can  attempt  to  make  hospital  and                health systems rational and efficient.                The DEA not only allows for the identification of areas of improvement, but also indicates                the realisation of the development. It also addresses questions regarding the strengths                and weaknesses of the units and their optimal size. Systematic evaluation of units in the                health  care  sector  can  bring  considerable  benefits  to  hospitals.  The  analysis  and  the                results derived enhance the understanding of the effectiveness of various departments                in  comparison  with  other  units.  The  results  also  indicate  achievable  results,  scale  of                possible savings and factors that have the greatest impact on the units’ efficiency.                 References                Al-Shayea, A. M. (2011). Measuring hospital’s units efficiency: A data envelopment analysis                approach. International Journal of Engineering \& Technology, vol. 11(6), pp. 7-19.                 Ancarani,  A.,  Di  Mauro,  C.,  Giammanco,  M.  D.  (2009).  The  impact  of  managerial  and                organizational aspects on hospital wards’ efficiency: Evidence from a case study. European                Journal of Operational Research, vol. 194(1), pp. 280-293.                Cook,  W.  D., Tone, K.,  Zhu, J.  (2014).  Data  envelopment  analysis:  Prior  to  choosing  a                model. 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International comparisons of the technical efficiency                of the hospital sector: panel data analysis of OECD countries using parametric and non-                parametric approaches. Health Policy, vol. 112(1), pp. 70-79.                Vincova,  K.  (2005).  Using  DEA  model  to  measure  efficiency.  In:  Analysis  of  aspect  of                competitiveness and readiness of selected Slovak Companies for EU membership. Kosice.                                                                                                                                   69","The Impact of Educational Expenditures and Higher                         Education Attainment on Employment in V4 Countries                                                     Darya Dancaková                                                Technical University of Kosice                                 Faculty of Economics, Department of Banking and Investment                                         Němcovej 32, 04001 Košice, Slovak Republic                                              E-mail: darya.dancakova@tuke.sk                 Abstract:  This  paper  provides  an  empirical  analysis  of  the  relationship  between                government  expenditures  on  education  and  employment  rates  within  V4  countries  by                comparison with European top performer: Germany. It is generally known that education                is  nowadays  the  most  important  weapon  of  nations,  recognized  as  the  key  driver  of                productivity and economic growth in the knowledge-based economies. A framework for                developing and well-skilled/educated workforce can be achieved through the motivation in                the public sector to create, share and transmit knowledge. We start with the assumption                that the higher level of education of the working age population may cause an employment                growth  and  thus  can  have  a  significant  impact  on  economic  growth  overall,  since  the                sustainable development of economy and society is closely related to education. The goal                of  this  study  is  to  point  out  the  relationship  between  government  expenditures  on                education and employment growth with aim to estimate whether government spending on                education  and  the  higher  level  of  educational  attainment  have  a  significant  impact                employment rates in selected countries.                 Keywords:  employment  rate,  education  level,  education,  government  expenditures  on                education                JEL codes: I22, I23, J21, J24, H52                 1 Introduction                 In contemporary society the meaning of education has changed dramatically. Now we are                witnessing the growing demand for intellectual activities related to knowledge production                in all spheres of social and economic life. The question of relationship between education                and  employment  has  been  a  subject  of  discussions  for  a  long  time.  The  essential  link                between education and economic development of the country has been revealed already                during  the  industrial  age  by  such  economists  like  W.  Petty,  A.  Smith,  or  J.S.  Mill                (Youngson,1959).  From  an  economic  perspective,  education  can  be  viewed  as  an                investment in knowledge and skills of population. Speaking about investing in education,                it is necessary to mention Schulz (1960), who proposed to treat education as an investment                in  individual  that  becomes  an  integral  part  of  a  person  receiving  it,  and  therefore  it                consequences shall be handled as a form of capital referring to the human capital. The                human capital theory was popularized by Becker (1992) and it became one of the most                important theories of modern economics. It puts emphasis on education of individuals,                since the well-skilled labour promotes economic growth, individual wellbeing and poverty                reduction.  Vawda  (2003)  suggested  that  investing  in  people  in  form  of  government                spending is critical for economic and social development of the country, since it may help                to achieve greater equity and efficiency in education among young people. According to                the  Dissou,  et  al.  (2016),  expenditures  on  education,  schooling  and  training  will  raise                labour productivity, earnings, improve general welfare and foster economic growth. Never-                ending learning equips people with new skills, enabling them to express themselves and                critically evaluate the world around. Some researchers have also demonstrated that higher                education makes employees more productive for carrying out their work (Ng \& Feldman,                2009; Kotur \& Anbazhagan, 2014). Moreover, the schooling promotes individuals to think                creative and makes them able to come up with new ideas and to think out of the box.                Additionally, better educated people can benefit from a higher income from their work and                they are less at risk of unemployment. This fact has been pointed out by Mincer (1991),                as  he  stressed  that  better  educated  workers  can  enjoy  several  advantages  over  less                                                             70","educated people in the labour market: higher wages, greater upward mobility in income                and carrier opportunities, and, finally, greater employment stability. Among other things,                Hanushek  (2009)  asserted that  education is  an  essential element in  a  global economic                competition  and  investments  in  human  capital  will  directly  affect  the  improvements  in                productivity and national income, because national income rises directly with earnings from                workers  with  more  qualification  and  better  skills.  Promoting  and  supporting  education                should be, therefore, one of the top priority areas of government´s development policy                around the world. Relationship between higher level of education and employment has                been demonstrated by Berger \& Parkin (2009), as they found out that graduates with post-                secondary and higher education are more likely to be employed in comparison to those                who did not continue their studies after attaining a high school.                The recent study “Education at a glance” prepared by OECD (2017), has shown an upward                trend  towards  a  higher  education  in  OECD  and  partner  countries.  The  Figure  1                demonstrates the educational attainment rates (%) among 25-34-year-olds across OECD                countries.                           Figure 1 Educational Attainment Rate Among Young Adults in OECD                                                          Source: Own elaboration from „Education at a glance 2017”, OECD                 Since  2000,  the  labour  force  has  become  more  educated  across  OECD  and  partner                countries. The share of young people and adults with only one level of education, below                secondary education, has tended to decrease over the monitored period and reached only                16 % in 2016 on average across OECD countries. By contrast, we are witnessing the steady                growth  trend  in  demand  for  higher  education.  The  share  of  young  adults  with  tertiary                education has increased over the period, from 26% in 2000 to the new high of 43 % in                2016.                The  educational  situation  in  V4  countries  has  been  following  the  similar  trend.   The                proportion of young adults with tertiary education increased from 11% to 33% in Czech                Republic.  At the same time, the number of young people with below secondary education                hasn’t changed dramatically over the mentioned period, from 8% in 2002 to 7% in 2016.                Educational  patterns in  Slovak Republic  followed  the  same  trend  as  observed in  Czech                Republic. The change in educational attainment rates was slightly bigger in Hungary than                in Czech Republic and Slovakia. The share of higher educated young adults has increased                rapidly, from 15% in 2000 to 30% in 2016. This trend was followed by a declining number                of young population with educational level below secondary education, from of 19 % to                15%.  The  pattern  of  Hungarian  educational  attainment  rates  can  be  compared  to  the                pattern in Germany, where the number of less educated people has fallen from 15% to                13%, and the proportion of those with higher education have risen from 22% to 31% over                                                             71","the whole period. The most striking change in higher education attainment rates among                25-34-year olds was observed in Poland, where numbers have significantly increased since                2000-16, from 14% to 43% and have reached an OECD´s average. The share of young                adults with lower education has also decreased dramatically, from 11% to 6%.                Educational attainment is often used as a measure of the skills and knowledge available at                the job market. It provides an initial information of employee´s potential capabilities, which                a prospective employer will use to decide whether or not the new employee will be hired.                The fact that the level of education has a significant impact on the employment rates has                been statistically demonstrated by OECD in Figure 2.                             Figure 2 Employment Rate by Educational Attainment in OECD                                                          Source: Own elaboration from „Education at a glance 2017”, OECD                 On average across OECD countries, employment rates for people with tertiary education                tend to be higher than for those with lower than secondary education. The latest research                has shown that 83% of the population among 25-34-year-olds with tertiary education is                employed. The OECD average falls to 76 % for young adults with upper secondary or post-                secondary non-tertiary education and to 58% for those without upper secondary education.                This implies that individuals with tertiary education have greater chances to be employed                in comparison with those who haven’t attained a tertiary education. In the current context                the  economies  and  labour  markets  are  highly  dependent  on  sufficient  supply  of  well-                educated  workforce.  In  Germany,  on  average  employment  rates  are  more  than  30                percentage points higher for those with tertiary education than for those who have not                completed an upper secondary education. In Czech Republic on average 46 % among 25-                34-year-olds with educational level below upper secondary is employed, those who have                completed secondary education and those with tertiary have almost the same chance to                be  employed  with  employment  rate  on  average  of  78%.  The  employment  rates  by                education  level  have  followed  the  similar  pattern  in  Germany,  Hungary,  Poland  and                Slovakia. The share of young adults with upper secondary and post-secondary non-tertiary                education who are employed is on average 75% in selected countries. The proportion of                those with tertiary education is on average about 84 %. Graduates with education level                below upper secondary have greater opportunities in the labour market in Germany, with                average rate of 56%. By contrast, the lowest employment rate of less educated people                was observed in Slovakia, on average about 36%. Employment rates development across                V4 and Germany confirms the trend that higher education leads to higher employability                among 25-34-year olds.                Gylfason (2001) insisted that faster economic development requires better education of                population, and consequently it should be accompanied with higher spending on schooling                                                             72","and training. At the same time, government spending on education and direct job creation                could  be  viewed  as  a  potential  engine  for  generating  employment  and  thus  reducing                poverty and inequity, as argued by Abdulah, Harun \& Jali (2017). Likewise, Pirim, Ownings                \& Kaplan (2017) examined the impact of educational expenditures on unemployment rates                and public health, and concluded that investment in human capital has positive economic                outcomes, since the higher spending on individual s education has been associated with                decrease in unemployment rates, and hence increase in employment rates.                 2 Methodology and Data                The methodology of this study is mostly based on the model proposed by Grimaccia and                Lima  (2013).  Authors  confirmed  a  hypothesis  of  the  positive  impact  of  government                educational  expenditures  on  employment  growth.  To  test  their  assumption,  they  have                designed a model, as shown below:                       EMPL = f (GDP, EDU, UNIV), where                                                 (1)                EMPL is employment rate of working age population (%); EDU is GDP per capita; EDU is                government expenditure on education (primary, secondary, tertiary total % of GDP); and                INIV is a share of working age population with tertiary education (%).                The model used in this study was based on the model proposed by Grimaccia and Lima                (2013), with adding an addition variable to the model: government expenditure on direct                job  creation.  To  estimate  the  effect  of  selected  variables  on  the  employment  rate  in                countries of Visegrad Group and Germany, the ordinary least squares (OLS) method on                the panel data in Gretl was used. The annual data was obtained from the OECD database                for the period of 2000 to 2014. The base model was specified in the following functional                form:                       EMP = α + β GDP + β GEXPEDU + β TEA + β GEXPJOB  +εi ,                           (2)                                                     it                                                         3                                                              it                                  1                                       it                                           2                                                                  4                                                                           it                where EMP is for dependent variable: employment rate of working age population (%);                GDPit is independent variable for GDP growth (%); GEXPEDUit is independent variable for                government  expenditure  on  education (primary,  secondary,  tertiary,  total,  %  of  GDP);                TEAit is independent variable for a share of working age population between 25-64-year-                olds  with  tertiary  education  (%);  GEXPJOBit  is  independent  variable  for  government                expenditure on direct job creation (% of GDP).                3 Results and Discussion                The  main  goal  of  this  study  was  to  test  the  hypothesis  of  the  positive  impact  of  the                government  educational  expenditures  on  employment  rates  in  selected  countries.  The                second aim was to estimate an impact of tertiary education attainment and government                expenditures  on  education  and  direct  job  creation  on  employment  rate. Therefore,  the                multivariate  ordinary  least  squares  regression  model  including  all  selected  explanatory                variables have been built.  The growth of GDP was selected as a control variable in the                model. However, not all explanatory variables appeared to be significantly related to the                employment  rate.  Hence,  the  insignificant  variables  with  p-value  >  0,05  have  been                removed from the base model. Table 1 shows the empirical results of the final model:                                   Table 1 The Final Model of Pooled OLS V4 Countries                 Variable                       Coefficients                  p-value                 Constant                       79,0949                       2,10e -31  ***                 GEXPEDU                        -5,36007                      6,16e -10  ***                 TEA                            0,274341                      0,0112 **                 Note: ***, **, * denote significance levels on 1%, 5% and 10% respectively.  R-Squared is 0,499551. According to White´s                 test,  there  is  a  heteroscedasticity  presented  in  the  model,  LM  =  17,3087.  Residuals  are  not  distributed  normally,  Chi-                 square=7,3732.                                                  Source: own elaboration                                                               73","Based on the OLS analysis, the total government expenditure on education and the share                of  population  with  tertiary  education  would  appear  to  be  significantly  related  to  the                employment rate in V4 countries. However, each unit increase in government expenditure                on education is being associated with a decrease of 5,36007 units in employment rates.                Consequently,  the  hypothesis  of  the  positive  impact  of  government  educational                expenditures on employment rates should be rejected. On the other hand, the tertiary                education attainment of working age population positively affects the employment rates in                V4 countries with each unit increase in tertiary education attainment being associated with                0,274341 units increase in employment rates.                According to the F-statistic of overall significance, the final model showed in the Table 1 is                                                             -9                statistical  significant,  with  p-value  =  3,82e   and  R-Squared  value  of  0,499551,  which                indicates  that  the  model  explains  approximately  50%  percent  of  the  variation  in  the                dependent variable - employment rate. To determine whether the residuals follow a normal                distribution, the p-value of a normality test was compared to the significance level of 0,05.                The p-value = 0,025057 is less than the significance level, so the decision is to reject the                null hypothesis (H0: error is normally distributed) and conclude that residuals do not follow                a  normal  distribution.  The  heteroskedasticity  test  showed  the  presence  of                heteroskedasticity    with   p-value=0,00395002,      so    the   null   hypothesis    (H0:                heteroskedasticity  not  present) is  to  be  rejected.  The  diagnostic  test  of  cross-sectional                dependence showed that the data is cross-sectional independent, with p-value=0,4503 the                null hypothesis (H0: no cross-sectional dependence) is not rejected.                Since not all assumptions of ordinary least squares approach have been fulfilled, to fix the                problem  with  heteroskedasticity  and  non-normality  of  residuals,  the  logarithmic                transformation of the model was made. Table 2 shows the empirical results of the final                model:                       Table 2 The Final OLS Model with Logarithmic Transformation V4 Countries                 Variable                       Coefficients                  p-value                 Constant                       4,46572                       2,15e -46  ***                 lnGEXPEDU                      -0,413114                     3,61e -10  ***                 lnTEA                          0,0848173                     0,00055 ***                 Note: ***, **, * denote significance levels on 1%, 5% and 10% respectively.  R-Squared is 0,508736. According to White´s                 test,  there  is  a  heteroscedasticity  presented  in  the  model,  LM  =  15,4833.  Residuals  are  not  distributed  normally,  Chi-                 square=7,68753.                                                  Source: own elaboration                The data, shown in the Table 2, have been transformed using the logarithm function. As                in the previous model, the total government expenditure on education and the share of                population  with  tertiary  education  would  appear  to  be  significantly  related  to  the                employment  rate  in  V4  countries.  And  again,  the  same  counts:  the  hypothesis  of  the                positive impact of government educational expenditures on employment rates is rejected,                since each unit increase in government expenditure on education is being associated with                a smaller decrease of 0,413114 units in employment rates. The positive effect of tertiary                education  on  employment  rates  is  being  accompanied  by  0,0848173  unit  increase  in                employment rates with each unit increase in tertiary education attainment of working age                population.                This  model  is  statistical  significant  with  p-value  of  overall  significance  =2,27e   and  it                                                                                                  -9                explains approximately 50% of the variation in the employment rates with R-Squared =                0,508736. The data are cross-sectional independent with p-value of the cross-sectional                dependence test =0,751942. However, the logarithmic transformation did not fix the issue                of heteroskedasticity presence (p-value=0,0084851) as well as non-normality of residuals                (p-value=0,0214128). To fix the problem with heteroskedasticity and non-normality was                decided to use weighted least squares method (WLS), which yields greater efficiency than                OLS.  To  identify  what  explanatory  variable  causes  heteroskedasticity,  the  dispersion                analysis was made. The variable tertiary education attainment has the highest dispersion                                                              74","among other variables, so it has been chosen as a weight in WLS model. The results of the                WLS analysis are shown in the Table 3:                                         Table 3 The Final WLS Model V4 Countries                 Variable                       Coefficients                  p-value                 Constant                       78,8942                       1,18e -30  ***                                                                                   -9                 GEXPEDU                        -5,31577                      1,28e  ***                 TEA                            0,274287                      0,0083 ***                 Note:  ***,  **,  *  denote  significance  levels  on  1%,  5%  and  10%  respectively.    R-Squared  is  0,488343.  There  is  no                 heteroskedasticity presented. Residuals are not distributed normally, Chi-square=7,53149.                                                  Source: own elaboration                 Results  from  the  WLS  analysis  are  very  similar  to  the  previous  two  approaches.  The                significant  negative  impact  on  employment  rates  across  V4  countries  has  government                expenditure  on  education  with  β  coefficient  =  -5,31577,  which  means  decrease  in                employment  rates  associated  with  each  unit  increase  in  government  expenditure  on                education.  Contrariwise,  employment  rates  will  increase  by  0,274287  units,  if  tertiary                education attainment among working age population increases by one unit.                The WLS approach removed heteroskedasticity from the model. According to the F-statistic                of overall significance, the WLS model is significant with p-value of 7,10e  and it explains                                                                                          -9                approximately  48,8%  of  the  variation  in  the  employment  rates  with  R-Squared  =                0,488343. There is no collinearity between explanatory variables presented. However, the                residuals  are  not  normally  distributed  at  significance  level  =  0,05  with  p-value                =0,0231504.                 The  same  methodology  was  applied  to  estimate  the  unknown  parameters  in  a  linear                regression model of employment rate in Germany. The results of logarithmic OLS model,                which fixed the issue with heteroskedasticity, are shown in the Table 4:                                    Table 4 The Final Logarithmic OLS Model Germany                 Variable                       Coefficients                  p-value                 Constant                       3,87392                       1,83e -16  ***                 lnGEXPJOB                      -0,0433545                    0,0008 ***                 lnGDP                          0,00606478                    0,5278                 Note: ***, **, * denote significance levels on 1%, 5% and 10% respectively.  R-Squared is 0,733965. According to Breusch                 Pagan´s test, there is no heteroscedasticity presented in the model, LM = 4,62606. Residuals are distributed normally, Chi-                 square=1,35977.                                                  Source: own elaboration                 After removing all insignificant explanatory variables, the only variable that appeared to                be significant was government expenditures on direct job creation. One would expect that                the expenditure on job creation would have a positive impact on the employment rate, but                in fact the reverse is true. Each unit increase in government expenditure on direct job                creation is being associated with 0,0433545 decreases in employment rate. For comparison                only:  this  variable  did  not  appear  to  be  significant  in  V4  countries  and  vice  versa  the                significant variables in V4 models did not appear to be significant in model for Germany.                According to the F-statistic, the model is significant as a whole, with p-value=0,002584                and it can explain approximately 73% of the variation in the employment rate. Diagnostic                tests showed that residuals are following the normal distribution (p-value = 0,506676);                there in no heteroskedasticity presented in the model (p-value =0,0989611).                Based on the results from the OLS model in Germany, it is not possible to confirm the                hypothesis that the model proposed by Grimaccia and Lima (2013) is suitable for explaining                the relationship between government educational expenditures and employment rates, as                well as to identify the impact of other variables (tertiary education attainment, government                expenditure on direct job creation) on the employment rates in selected countries.                                                               75","4 Conclusions                The  main  objective  of  this  study  was  to  identify  the  relationship  between  government                expenditures on education and employment rates within V4 countries and European top                performer Germany. The study was based on the model of the employment rate proposed                by Grimaccia and Lima (2013). Based on the results of this study, the hypothesis of the                positive impact of government educational expenditures on employment rates has been                rejected. The effect of government educational expenditures has proved to be negative for                employment rates in V4 countries, and even its impact has not been proven in Germany.                This  fact  may  be  caused  by  several  aspects:  the  model  thus  defined  is  not  specified                correctly, this means that such combination of selected variables does not make sense                from the statistical point of view, or there is no direct relationship between the government                expenditures on education and employment rates. Therefore, it seem to be inappropriate                to  presume  that  employment  rate  can  be  directly  affected  by  the  expenditures  on                education.  Moreover,  this  model  did  not  appear  to  be  well  suited  for  determining  the                relationship  as  mentioned  above,  for  several  reasons:  there  was  a  huge  problem  with                heteroskedasticity and non-normality in the residuals at significance level of 0,05, what                means that the amount of error in the model is not consistent across the full range of the                observed data. Consequently, the amount of predictive ability in explanatory variables is                not the same across the full range of the dependent variable. Thus, the model does not                fully explain the behavior of the system as a whole.                 Acknowledgments                This research was supported by VEGA project No. 1/0311/17 on Measuring and Reporting                Intangible Assets                References                Abdullah, M. B., Harun, M., Jali, M. R. M. (2017). Employment Generated by Government                Spending on Education. International Journal of Academic Research in Business and Social                Sciences, vol. 7(2), pp. 738-742.                 Becker, G. S. (1992). Human capital and the economy. In: Proceedings of the American                Philosophical Society. United States: American Philosophical Society, vol. 136(1), pp. 85-                92.                Berger, J., Parkin, A. (2009). The value of a degree: Education, employment and earnings                in Canada. Women, vol. 2(1.8), pp. 1-9.                 Dissou, Y., Didic, S., Yakautsava, T. (2016). Government spending on education, human                capital accumulation, and growth. Economic Modelling, vol. 58, pp. 9-21.                Grimaccia, L., Lima, R. (2013, September). Public expenditure on education, education                attainment  and  employment:  a  comparison  among  European  countries.  In:  XXVIII                Conference of the Italian Association of Labour Economists (AIEL) Rome.                Gylfason, T. (2001). Natural resources, education, and economic development. European                economic review, vol. 45(4-6), pp. 847-859.                 Kotur,  B.  R.,  Anbazhagan,  S.  (2014).  Education  and  work-experience-influence  on  the                performance. Journal of Business and Management, vol. 16(5), pp. 104-110.                Mincer, J. (1991). Education and unemployment (No. w3838). Cambridge (USA): National                bureau of economic research.                Ng,  T.  W.,  Feldman,  D.  C.  (2009).  How  broadly  does  education  contribute  to  job                performance?. Personnel psychology, vol. 62(1), pp. 89-134.                OECD (2017). Education at a Glance 2017: OECD Indicators. Paris: OECD Publishing. DOI:                http://dx.doi.org/10.1787/eag-2017-en.                                                                 76","Pirim, Z., Owings, W. A., Kaplan, L. S. (2017). The Long-Term Impact of Educational and                Health  Spending  on  Unemployment  Rates. Journal  of  Economics  and  Financial                Analysis, vol. 1(2), pp. 49-69.                Schultz, T. W. (1960). Capital formation by education. Journal of political economy, vol.                68(6), pp. 571-583.                Vawda, A. Y. (2003). Who benefits from public education expenditures?. Economic Affairs,                vol. 23(1), pp. 40-43.                Youngson, A. J. (1959). Possibilities of economic progress. CUP Archive.                                                                                                                                    77","Sustainable Finance Instruments' Risk - Green Bond Market                                                        Analysis                                                                             2                                                                                                   3                                                        1                        Karolina Daszyńska-Żygadło , Jakub Marszałek , Krzysztof Piontek                                              1  Wroclaw University of Economics                    Faculty of Management, Computer Science and Finance, Institute of Financial Management                                    118/120 Komandorska Street, 53-345 Wroclaw, Poland                                             E-mail: karolina.zygadlo@ue.wroc.pl                                                      2  University of Lodz                           Faculty of Management, Department of Finance and Strategic Management                                          22/26 Matejki Street, 90-237 Lodz, Poland                                               E-mail: jmarszalek@uni.lodz.pl                                               3  Wroclaw University of Economics                    Faculty of Management, Computer Science and Finance, Institute of Financial Management                                    118/120 Komandorska Street, 53-345 Wroclaw, Poland                                             E-mail: krzysztof.piontek@ue.wroc.pl                 Abstract: Sustainable finance opportunities for retail investors such as green bonds are                the  example  of  instruments  that  are  dedicated  to  transition  of  economy towards  more                sustainable model and are serving as a source of financing for large-scale investments.                The paper aims at verification of volatility behaviour of green bond market and relationship                of green bond market volatility and conventional bond market volatility. Our study sheds                additional light to previous findings and extends the perspective, going beyond analysis of                behaviour of S\&P green bond index. Our study was conducted on a wider range of available                green bond indices in order to draw more general conclusions. We studied daily rates of                return in the period of 2014 – 2018 of the S\&P Green Bond Index, Bloomberg Barclays                MSCI  Global  Green  Bond  Index,  ICE  BofAM  Green  Bond  Index,  Solactive  Green  Bonds                Index.  Using  multivariate  GARCH  framework  we  were  able  to  verify  and  test  volatility                patterns and assess how the volatility of in the green bond market transmits to broader                conventional  bond  market.  Results  of  our  study  confirm  that  all  green  bond  indices,                experience  large  volatility  clustering.  We  also  found  out  that  shocks  in  the  overall                conventional bond markets tend not to be echoed in the green bond market. Findings of                this paper are significant from the perspective of providing informative insights for the                investors  risk  and  return  characteristics.  They  also  contribute  to  the  development  of                sustainable finance and risk analysis of sustainable finance instruments.                Keywords: green bonds, eco-investments, global market, climate change, GARCH                JEL codes: F21, G15, O13, O16, Q50                 1 Introduction                 Transition of economy towards more sustainable model requires financing of large-scale                investments mitigating  climate  change therefore there is  a  growing  need  for dedicated                sustainable finance instruments. Those large-scale investment projects face difficulties in                assessing their profitability in standard way, social and environmental impact is hard to                assess in money terms. Although pro-ecological projects encounter growing interests from                investors it is still small in scale and in reference to all financial instruments at capital                markets.  On  the  other  hand,  the  growing  sustainability  awareness  of  societies  creates                pressure on participants of capital markets and increases interest in such investments.                 The most significant instruments of sustainable finance – green bonds – meet the needs                of modern world, its challenges and the expectations of investors. This is a specific type of                bond  whose  proceeds  are  invested  in  an  environmentally  friendly  goals  with  projects                producing sustainable benefits. A “green” bond differentiates from a “traditional” bond by                its label. That means the obligation to exclusively use funds raised from the issue to finance                or refinance new or existing eligible green projects or business activities. Due to the fact                that pro-ecological investments are burdened with significant financial risk related to the                                                            78","uncertainty of expected results and encounter a barrier to obtaining capital, many green                bonds  are  issued  by  public  entities  or  international  financial  and  development                organizations. Offered instruments are most often issued in a secured or guaranteed form                (Morel,  2012).  Green  bonds  are  perceived  as  most  important  financial  instruments                supporting  sustainability  transition.  They  are  deliberately  emphasized  as  financial                instruments that should be promoted among retail investors. Especially in Europe, where                the driving force is European Commission and different public institutions, such as EU High-                Level Group on Sustainable Finance (HLEG 2018).                The specificity of the instruments themselves and the projects for which they are issued                prompts research into the profile of investors acquiring green bonds. The purpose of this                article is to verify volatility behaviour of green bond market and relationship of green bond                market  volatility  and  conventional  bond  market  volatility.  Results  of  this  research  are                significant from the perspective of providing informative insights for the investors risk and                return characteristics. They also contribute to the development of sustainable finance and                risk analysis of sustainable finance instruments. We studied daily rates of return in the                period of 2014 – 2018 of the S\&P Green Bond Index, Bloomberg Barclays MSCI Global                Green  Bond  Index,  ICE  BofAM  Green Bond  Index,  Solactive  Green  Bonds  Index.  Using                multivariate  GARCH  framework  we  were  able  to  verify  and  test  volatility  patterns  and                assess how the volatility of in the green bond market transmits to broader conventional                bond  market.  The  paper  is  organized  as  follows.  Section  2  provides  analysis  of                characteristics of green bond market risk. Section 3 presents the data, methodology and                research results, while section 4 provides discussion of results and concludes.                2 Green bond market risk aspects                 Green bonds are a relatively recent innovation in the debt markets. Since their debut in                2007 through next 11 years over USD 348.9 billion were issued. Green bonds are expected                to play an important role in responding to the existential threats and enormous costs posed                by climate change and other sustainability challenges that have emerged in recent years.                This financial instrument could be tied to mitigating the effects of climate change (e.g.                climate bonds) or to a specific environmental issue or technology, such as wind and solar                energy efficiency projects, energy retrofits and clean transportation (e.g. renewable energy                bonds, energy efficiency bonds and green transportation bonds) (Green bonds: victory ....,                2013). However, the observations of the existing allocation of funds derived from the issue                of green bonds might be surprising. The analysis of the structure of the issue's purpose                indicates that pro-ecological nature of green bonds is quite controversial (Marszałek and                Daszyńska-Żygadło 2016). Only two thirds of issuers declare an environmental goal, but                it is not determined impact on environment the project has. The degree of generality of                the ecological nature of the purpose of the green bond issue absolutely does not prejudge                the  lack  of  connection  with  environmental  protection  activity.  However,  there  is  an                impression that many companies, especially corporations, use the status of green bonds                to improve the attractiveness of the issue offer.                Green bonds do not differ from standard bonds, except for the fact that funds obtained by                the issues  are  dedicated  to  certain  green  projects.  Rate  of  return  of  this instrument is                connected to the risk related to the uncertainty of the expected results. Risk reduction is                guaranteed by a public or international organization. If the issuer is a non-public company,                it is often guaranteed by a public entity. A common solution is the securitization involving                the separation of selected assets to the Special Purpose Vehicle (SPV). It is also possible                to award bonds with a climate certificate, which is usually equivalent to the instruments                being covered by a guarantee of a public or international unit. The green bond risk may                also  be  limited  by  the  means  of  using  a  hybrid  bond  (Lee,  Zhong,  2015).  It  involves                financing  a  number  of  pro-ecological  investments.  Thanks  to  the  diversification  of  the                project  portfolio,  the  overall  risk  is  lower,  and  thus  the  bonds  are  more  attractive  to                investors. Yet another solution is to use the revenue bond structure. An example of such                an instrument is a carbon revenue bond (Tang et al., 2012). In addition, this investment                may be supported by the legal protection of the state in the scope of exclusivity of benefits                                                             79","or exemption from public levies. In the case of combining a diversified investment portfolio                with  the  structure  of  the  revenue  bond,  an  attractive  debt  instrument  for  long-term                financing is obtained (Mathews, Kidney, 2012).                Current research on the risk of green bonds indicates that green bonds would be less easy                to sell in panic (Atkins, 2015). Investors are more likely to be long-term traders holding                them to maturity (Schroders, 2015). However, there are mixed research results on how                green bonds perform compared to the standard ones. Some economists argue that despite                the lower liquidity in the green bond market yields and pricing of both types are the same                (Petrova, 2016). Others show that green bonds deliver a lower yield (Schroders, 2015). A                comparison of the basic characteristics of green and non-green corporate bonds with the                same ratings indicates a minor difference between those two groups of securities (Kuna-                Marszałek and Marszałek, 2017). Green bonds have slightly lower YTM value. In the context                of growing green bond market, it can be explained that investors are willing to pay the                price for being more socially responsible. A similar relationship can be observed in the case                of the issuers. Green bonds have on average a higher coupon value as well. It is worth                noting that the issuers additionally bear extra costs on labeling the bond “green”. They                also issue  them  for  a  shorter  time,  which  allows  for  more  flexible management  of  the                investor’s portfolio. Moreover, the research results show a positive relationship between                corporate  social  responsibility  and  financial  performance  (in  terms  of  return on  assets,                return on equity and non/net interest income) of financial institutions being intermediaries                on green bond market (Wu and Shen, 2013). This may explain the acceptance of lower                bond  yields  and  higher  financing  costs.  Green  bonds  attract  investors  with  additional                positive  signaling effects  (Tiselius  and Kronqvist,  2015). Possible involvement  of  public                institutions further reduces transaction costs.                The geography of the green bond market is expanding and diversifying. However, it is still                concentrated to a limited number of geographies and sectors. The green bonds are still a                niche  product  within  the  broader  global  debt-security  market,  and  account  for  a  small                fraction of the overall bond market (European Commission, 2016). That is why the integrity                of the green bonds market so far remains robust. Over the last years increasing numbers                of specialized green bond funds as well as institutional investors which have intended to                increase green bond holdings could be observed. This should result in a relatively lower                volatility in the valuation of these instruments, as investors are aware of their purchase                will not sell them as often as ordinary bond buyers. However, relatively little is known                about the volatility of the valuation of green bonds in the context of changes in the broad                bond market. Volatility analysis of S\&P GB Index, GPB Index and U.S. AB Index in period                2010-2015 gives evidence for time-varying volatility spill-over between the green bond                market  and  the  conventional  bond  market  (Pham,  2016).  The  green  bond  market’s                volatility is positively correlated with the conventional bond market. While one might find                results of Pham (2016) inconclusive for various reasons, one being time period of analysis.                Results may not be credible due to the fact that S\&P Green Bond index was established in                2014, so the actual behaviour of the index is analysed only for one year. Another analysis                of  yearly  volatility  of  Solactive  Green Bond  Index  and  MVIS  EM  Aggregate  Bond  Index                shows that volatility of green bonds index grows stronger and faster that the aggregated                bonds index (Daszyńska-Żygadło and Marszałek, 2018). However, this volatility achieves                higher level than aggregated bonds index. It indicates that risk of investing in green bonds                index is slightly higher than of the aggregated one. The analysis of the sensitivity of green                bonds index for the changes of the broader conventional bonds index using estimated beta                coefficients shows that along with the development of green bonds market the Solactive                Green Bond Index reflects reactions on the changes on overall bonds market.                 In the context of the above information, the problem of the risk of green bond estimation                and analysis can be formulated. The specific purpose of the investment, often based on                new,  unproven  technologies,  will  support  the  increase  of  risk.  It  may  be  additionally                increased by the lack of legislative regulations concerning a given ecological activity. On                the other hand, the involvement of a state or international institution, guarantees granted                or public issuance will mitigate the risk. Investments implemented by green bonds are in                fact  socially  important  and  cannot  be  subject  to  the  same  assessment  as  commercial                                                            80","projects. This does not mean, however, that these investments should not be assessed or                compared to commercial ventures but it is hard not to notice their uniqueness. This activity                involves an emotional charge of the investor, as it concerns the future of humanity, is                modern and innovative. Both investment funds and individual investors will take this into                account when estimating the expected rate of return. It will also determine their market                behaviour and stabilize volatility. However, the results of few studies in this area are not                consistent. They point to greater and lesser risk of investing in green bonds compared to                conventional  bonds.  Similar  opposites  are  observed  in  the  case  of  profitability  of  both                groups of bonds. The research carried out in this article is to broaden the knowledge in                that field and to contribute to finding behaviour patterns rates of returns and volatility in                green bond market.                 3 Data, methodology and research results                 Our study is based on the framework to model the volatility of a financial asset based on                the  multivariate  Generalized  Autoregressive  Conditional  Heteroskedasticity  (GARCH)                framework, a family of statistical models originally proposed by Bollerslev (1986) and Engle                (2002). That have been widely used in the literature studying the relationship between                different financial time series’ volatilities (Engle 2002; Glosten et al., 1993).                We use the sample composed of rates of returns of the following pairs of bond indices in                order to investigate the patterns of green bond market in relation to overall conventional                bond market:                       •   Bloomberg Barclays MSCI Global Green Bond Index Total Return Index Value                          Unhedged  USD  (GBGLTRUU)  –  Bloomberg  Barclays  US  AGG  TOTAL  RETURN                          VALUE UNHEDGED USD                       •   ICE BofAM GREEN BOND INDEX (GREN) – Bloomberg Barclays US AGG TOTAL                          RETURN VALUE UNHEDGED USD                       •   S\&P GREEN BOND INDEX TOTAL RETURN (SPUSGRN) – S\&P AGGREGATE BOND                          INDEX TOTAL RETURN                       •  Solactive  Green  Bonds  Index  (SOLGREEN)  –  S\&P  AGGREGATE  BOND  INDEX                          TOTAL RETURN                 Due to the fact that not every aggregated conventional bond index has a corresponding                green bond index a selection of matching pairs was made based on the assumption of                broad representation of bonds in a conventional bond index according to sector, issuer,                class of risk and geography. This was the reason why, for example Solactive index was                matched with S\&P aggregate bond index.                We used daily logarithm returns for the periods matching official launch of green bond                index with corresponding returns of aggregate bond index. A widely used technique to in                the  literature  studying  the  volatility  of  financial  time  series  is  GARCH,  which  uses  an                autoregressive  structure  to  model  the  conditional  variance  of  a  time  series,  thereby                allowing volatility shocks to persist over time (Bauwens,et al., 2006; Teräsvirta; 2009) .                                                                                                        1                One feature of the multivariate GARCH model is that it allows time-varying conditional                variances of asset returns’ as well as covariances between the returns of different assets.                This  allows  the  analysis  of  the  volatility  structure  of  individual  assets  as  well  as  the                interaction  between  various  assets.  In  this  paper,  the  specification  of  this  multivariate                model  consists  of  two  components.  First,  returns  are  modeled  using  a  vector                autoregression framework. Then, a multivariate GARCH model is used to model the time-                varying variances and covariances by analogy as in the study of Pahm (2006). In order to                find a common pattern of behaviour same analysis was conducted on a wider sample of                green bond indices, based on the list of The Green Bond Principles report (2017) we cover                four out of six existing indices of that type, missing only the ChinaBond China Green Bond                Index and ChinaBond China Green Bond Select Index that was launched in April 2016 and                 1  Due to limited required length of the paper equations were not included in the paper, but                are available upon request from interested readers.                                                            81","data is not available nor in Thomson Reuters Datastream neither in Bloomberg. Table 1                summarizes the descriptive statistics of the indices’ returns.                                    Table 1 Descriptive Statistics of All Analyzed Indices                                                                Bloomberg                            Solactive    S\&P                                                    S\&P                            green                    MSCI       Barclays                        green                                                     green      aggreg        ICE BofAM green                              1.9844e-    7.8521e-    5.2415e-                                   -7.2707e-                    Mean                                         6.1037e-005     2.8270e-005                                005         005         005                                         007                                                      6.7405e-                                   5.2781e-                  Median    0.00013806  0.00015128               0.00016264      6.7442e-005                                                        005                                         005                 Minimum     -0.016462  -0.0086641  -0.016023  -0.0098160         -0.016620      -0.013850                 Maximum     0.016675    0.0057227    0.016000    0.0059110       0.016466       0.014043                  Std. Dev.  0.0036330   0.0017214  0.0036450     0.0019741       0.0037696     0.0032891                    C.V.       183.07      21.923      69.541      32.342          133.34         4523.8                 Skewness     -0.19146    -0.22316    -0.21083    -0.32579        -0.18948       -0.19201                    Ex.                  kurtosis     1.7673     0.80985      1.3658      0.89008         1.5518         1.3786                  5% Perc.  -0.0059690  -0.0027682  -0.005874  -0.0033046        -0.0063739      -0.005246                   95%       0.0058089   0.0028734  0.0059301     0.0031373       0.0060113     0.0053230                   Perc.                 IQ range    0.0041133   0.0021299  0.0043557     0.0024371       0.0041945     0.0038501                                                    Source: own elaboration                 Among the four green indices Bloomberg Barclays MSCI Global Green Bond Index has the                highest average returns while the ICE BofAM Green Bond Index has the highest standard                deviation.                 In majority of our time series of indices returns the effect of autocorrelation wasn’t found,                but in order to verify given assumptions and findings  of previous studies we conducted                analysis according to indicated GARCH framework. First, returns were modelled using a                vector autoregression framework. Then, a multivariate GARCH model was used to model                the time-varying variances and covariances.                                  Table 2 Results of DCC Models Parameters Estimation                                     Index             parameter  Index               parameter                            w1                        7,81E-08**                       2,36E-08*                          alpha1      Bloomberg        0,0155**                       0,0166***                           beta1   Barclays aggreg     0,9649***          S\&P         0,9755***                            w2                          4,03E-07                        6,59E-08                          alpha2                        0,0284*                        0,0334**                           beta2      MSCI green       0,9412***    Solactive green  0,9626***                            corr                       0,4643***                      0,4037***                           alpha                        0,0991*                        0,0773**                           beta           pair          2,31E-06          pair        0,7825***                            w1                       9,17E-08*                       4,47E-08*                          alpha1      Bloomberg      0,018206***                     0,018***                           beta1   Barclays aggreg  0,959738***           S\&P        0,9677***                            w2                       2,11E-07                        1,83E-07**                          alpha2                     0,034484**                      0,0283***                           beta2   ICE BofAM green  0,950545***        S\&P green     0,9551***                            corr                     0,390307***                     0,4271***                           alpha                     0,060336*                       0,0574*                           beta           pair       0,796452***          pair       0,7943***                                                  Source: own elaboration                                                            82","Table 2 shows results of DCC model estimation. The conditional standard deviations for                each individual series and the conditional correlations among the series were estimated                using the DCC model as proposed by Engle (2002). Compared to other models, this model’s                flexibility  in  modeling  time-varying  conditional  correlations  has  clear  computational                advantages as it allows for the estimation of very large correlation matrices (Pahm 2016).                It indicates that majority of parameters for the indices are statistically significant. It also                shows that conditional correlation average that is statistically significant for each pair is                the highest for Bloomberg Barclays aggregate index and Bloomberg Barclays MSCI green                bond  index  and  the  lowest  for  the  pair  of  ICE  BofAM  green  and  Bloomberg  Barclays                aggregate index. Figure 1 plots daily formulation of conditional correlation.                 Results show that conditional correlation of all green bond indices tends to circulate around                0,4 with minor periodical changes. That indicates that in case of every green bond index                risk of investing is similar and rates of returns are positively correlated to conventional                bonds indices. What is more we can initially state that shocks from conventional bonds                markets are not being transmitted to green bond market. In times, for few days’ periods,                green bonds indices show negative correlation with conventional bonds, especially visible                for each of the analysed indices during spring of 2016, presumably due to the Brexit vote,                which was a big shock for the capital markets.                                   Figure 1 Conditional Correlation of Analyzed Indices                                           0.8             conditional correlation                                          0.6                                          0.4                                          0.2                                          0                                          -0.2                                          -0.4  0   100   200   300   400   500   600   700   800   900                                                       Time: from 2014.10.31 to 2018.03.08                                                            Model (a)*                                            0.8            conditional correlation                                            0.6                                            0.4                                            0.2                                             0                                            -0.2                                            -0.4                                            -0.6   0   200   400   600   800   1000   1200                                                       Time: from 2014.03.11 to 2018.03.09                                                           Model (b)*                                                                               83","conditional correlation                                            0.8                                             0.6                                             0.4                                             0.2                                              0                                            -0.2                                             -0.4                                              0   100   200   300   400   500   600   700   800   900                                                      Time: from 2014.11.03 to 2018.03.09                                                           Model (c)*                                             0.8            conditional correlation                                              0.6                                             0.4                                             0.2                                              0                                             -0.2                                             -0.4                                              0   100   200   300   400   500   600   700   800   900   1000                                                          Time: from 2014.08.05 to 2018.03.09                                                           Model (d)*                   *Model (a) MSCI green, model (b) SOLGREEN, model (c) ICE BofAM Green, model (d) S\&P Green                                                    Source: own elaboration                 While regression analysis for each green bond index shown that daily rates of return are                explained with statistical significance by rates of return from previous day (-1 day) of the                index and of the conventional bond index, only in the case of Solactive green bond index                results were also depended on the rates of return from three days before, but not for two.                 4 Discussion of results and conclusions                 Our  findings  shed  additional  light  at  the  previous  research  results  in  the  scope  of  risk                analysis of green bonds. Even though we chose similar method of analysis as other authors                (Pahm 2016), the GARCH effect wasn’t found in the majority of pairs of indices. It’s worth                noticing that green bonds indices are positively correlated with conventional bond indices                and conditional correlation tends to maintain the average lever of 0,4 in the whole sample.                However, they do not transfer violent changes from the conventional bond market. This                combination of a positive general correlation with less sensitivity makes green bonds a                very attractive instrument. Especially for investment funds that will be able to participate                more in financing pro-ecological investments. From our research it stands out that changes                in green bond indices rates of return do not impact changes in overall bond market indices.                But this effect works in the opposite direction. This seems to be due to the much smaller                size  of  the  green  bond  market  compared  to  traditional  bonds.  It  also  increases  the                attractiveness of these instruments, that apart from financial advantages can also play an                important role in sustainable development of economies. We plan to further investigate                the relationships due to the fact that GARCH analysis results weren’t satisfactory.                 References                Atkins,  R.  (2015).  Are  green  bonds  a  fair  weather  phenomenon?.  Retrieved  from:                https://www.ft.com/content/af74028a-a708-11e4-8a71-00144feab7de                                                             84","Bauwens, L., Laurent, S. and Rombouts, J. V. K. (2006). Multivariate GARCH Models: A                Survey. Journal of Applied Econometrics, vol. 21(1), pp. 79–109.                 Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal                of Econometrics, vol. 31, pp. 307–327.                Daszyńska-Żygadło,  K.,  Marszałek,  J.  (2018).  Green  Bonds  –  Sustainable  Finance                Instruments.  Presentation  on  21st  International  Scientific  Conference  Enterprise  and                Competitive Environment. Brno: Mendel University.                Engle,  R.  (2002).  Dynamic  Conditional  Correlation.  A  Simple  Class  of  Multivariate                Generalized Autoregressive Conditional Heteroskedasticity Models.  Journal of Business \&                Economic Statistics, vol. 20(3), pp. 339-350.                European Commission (2016). Study on the potential of green bond finance for resource-                efficient  investments.  Retrieved  from:  http://ec.europa.eu/environment/enveco/pdf/                potential-green-bond.pdf.                Financing a Sustainable European Economy. Final Report. (2018). High-Level Expert Group                on Sustainable Finance. Retrieved from: https://ec.europa.eu/info/sites/info/files/180131-                sustainable-finance-final-report_en.pdf                 Glosten,  L.  R.,  Jagannathan,  R.,  Runkle,  D.  E.  (1993).  On  the  Relation  between  the                Expected Value and the Volatility of the Nominal Excess Return on Stock. The Journal of                Finance, vol. XLVIII(5), pp.1779-1801.                Kuna-Marszałek, A., Marszałek, J. (2017). Some considerations on the green bonds market                development.  In:  Proceedings  of the 14th  International  Scientific  Conference European                Financial Systems 2017. Brno: Masaryk University, pp. 458-465.                Marszałek, J., Daszyńska-Żygadło, K. (2016). Charakterystyka globalnego rynku obligacji                klimatycznych. Finanse, Rynki Finansowe, Ubezpieczenia, vol. 4/2016(82), pp. 945–955.                Lee, C. W., Zhong, J. (2015). Financing and risk management of renewable energy projects                with a hybrid bond. Renewable Energy, vol. 75, pp. 779-787.                Mathews, J. A., Kidney, S. (2012). Financing climate-friendly energy development through                bonds. Development Southern Africa, vol. 29(2), s. 337-349.                 Morel, R. (2012). Financing the transition to a green economy: their word is their (green)                bond?. Climate Brief, vol. 14. Retrieved from: www.cdcclimat.com.                 Petrova,  A.  (2016).  Green  Bonds:  Lower  Returns  or  Higher  Responsibility?.  Nijmegen                School of Management, Radboud University. Master Thesis                Pham, L. (2016). Is it risky to go green? A volatility analysis of the green bond market.                Journal of Sustainable Finance \& Investment, vol. 6(4), pp. 263-291.                Schroders  (2015).  Green  Bonds  –  A  Primer.  Talking  Point.  Retrieved  from:                http://www.schroders.com/hu/sysglobalassets/digital/insights/pdfs/green-bonds-a-                primer.pdf.                Tang,  A.,  Chiara,  N.,  Taylor,  J.  E.  (2012).  Financing  renewable  energy  infrastructure:                Formulation, pricing and impact. Energy Policy, vol. 45, pp. 691–703.                TD Economics (2013). Green bonds: victory bonds for the environment. Special report.                Retrieved    from:     https://www.td.com/document/PDF/economics/special/GreenBonds                _Canada.pdf                Teräsvirta,  T.  (2009).  An  Introduction  to  Univariate  GARCH  Models.  In:  Handbook  of                Financial Time Series. Berlin: Springer, pp. 17–42.                Tiselius, J., Kronqvist, M. (2015). Drivers of Growth in the Corporate Green Bond Market.                Copenhagen: Copenhagen Business School. Master Thesis.                Wu,  M.  W.,  Shen,  C.H.  (2013).  Corporate  social  responsibility in  the  banking industry-                Motives and financial performance. Journal of Banking \& Finance, vol. 37.                                                             85","Debt Literacy and Its Importance in the Czech Republic                                                   Bohuslava Doláková                                                     Masaryk University                                 Faculty of Economics and Administration, Department of Finance                                         Lipová 41 a, 602 00 Brno, The Czech Republic                                                E-mail: 175975@mail.muni.cz                 Abstract: This paper aims to show the importance of one part of financial literacy that                causes most problems in the Czech Republic. According to the latest data, there is quite a                lot  of  Czech  overindebted  –  they  borrowed  too  much  money,  lost  jobs,  found  a  very                inconvenient loan, etc. Those people were not able to analyze their financial situation in                the broad context of having a loan. As we know from the start of last big financial crisis                risky loans (e.g., Mortgages) can cause a worldwide problem. In general people with lower                levels of debt, literacy tends to transact in high-cost manners, incurring higher fees and                using high-cost borrowing. Due to the New Consumer Credit Act from  2016, there  was                expected a change in the loan amounts and risk. It is too early to judge the impact of that                act, but we can see a basic tendency that shows us people are still willing to go in debts.                This makes debt literacy still very important as there are many overindebted people in the                Czech Republic.                 Keywords: financial literacy, debt literacy, indebtedness, mortgages                JEL codes: D11, D12, D14, D18, D91, G21                 1 Introduction                As there is a lot of attention to financial literacy in media during the last few years, people                started to be more aware of the importance of this term. It has also been included into                compulsory education in elementary schools and high schools too. We have to wait for the                real results of this change in education,  but we can see what is happening now in the                financial market in the Czech Republic. We can use the data from the Ministry of Finance                judging these abilities of the Czech Republic inhabitants. In 2010 and 2015, the Ministry                of Finance  measured the level of financial literacy of the  adult population of the Czech                Republic. This survey has become part of the global measurement together with the other                dozens  of  countries  of  the  Organization  for  Economic  Co-operation  and  Development                (OECD). The result was that Czech has in general “good awareness of financial products.”                That seems to be very optimistic. But on the other hand, Almost a tenth of Czechs over                the age of 15 have some property seizures. In total, there are 863,000 people and about                31,000 in the last year. What is a key problem in this area is called “debt literacy,” an                important component of overall financial literacy? Debt literacy refers to the ability to make                simple  decisions  regarding  debt  contracts,  applying  basic  knowledge  about  interest                compounding to everyday financial choices (Lusardi and Tufano, 2009). People can have                quite good knowledge of financial products, where is the best basic private bank account,                etc. But people can be completely unaware of what can happen when they finance their                money deficiency by a loan. People with lower levels of debt literacy tend to transact in                high-cost manners, incurring higher fees and using high-cost borrowingLatest data about                Czech indebtedness shows that some of the people are suffering to overindebtedness.                 2 Methodology and Data                In this  paper, there is used latest biggest financial literacy survey  from the Ministry of                Finance judging these abilities of the Czech Republic inhabitants. In this survey, there were                a few questions regarding also debt literacy. Most important one was taken in this research.                The data about the debts, indebtedness, and overindebtedness were taken from quite new                and  very  useful  Map  of  Property  Seizures  which  clearly  show  the  distribution  and                seriousness of indebtedness in the Czech Republic.                                                               86","Almost a tenth of Czechs and Czechs over 15 have some property seizures. In total, there                are 863,000 people and about 31,000 in the last year. The number of people in property                seizures grew 3.4 percent a year, despite record low unemployment and rising wages in                the Czech Republic. Even higher salaries and the fact that people have jobs do not help                them get out of the debt trap. Only interest and penalties are paid, not the principal of the                loan. Half of the people owe less than 10,000 crowns. Most of the claimed sums are mainly                costs. The average principal amounts to CZK 65,000. Three or more property seizures have                almost half a million people. A total of 151,000 people over 15 have over ten property                seizures. It's about 10,000 people between 18 and 29 years old and about 6,000 seniors                over 65 years old. 90 percent of property seizures are unenforceable.                 The amount of property seizures changes across the Czech Republic as we can see in Figure                1.  There  is  an  obvious  connection  to  others  social  problems  like  unemployment  but                especially in such interconnected issues is the debt literacy crucial.                           Figure 1 The Map of Property Seizure in the Czech Republic (2017)                                                       Source: Map of Property Seizures http://mapaexekuci.cz/index.php/mapa-2/                 Some property seizures encompassed by so-called unsuccessful property seizures for the                entire duration of court bailiffs.  This includes, for example, property seizures for which                insolvency  proceedings  are  conducted,  so  that  the  property  seizure  of  the  proceedings                cannot be executed for that reason alone.                The number is so high, among other things, because there is no effective tool to stop these                unsuccessful property seizures. It is necessary to simplify the termination of such property                seizures even without the creditor's consent so that the whole system is cleared of these                proceedings.  After  the  start  and  implementation  of  this  process,  the  number  of  both                mandatory and some proceedings will be several times smaller.                 The  demotivating  factor  is  that  social  benefits  are  calculated  by  net  wages,  without                deduction of property seizure. People in property seizure often do not reach different types                of support, though they are entitled to it because of the actual amount of income. In the                Czech  Republic,  there  is  also  no  court  fee  for  executing  the  property  seizure,  which,                according to experts, leads to abuse of the system. The creditors, in their view, use the                property seizure as a free repository of unpaid receivables.                The negative development of the over-indebtedness situation mainly affects the debt cycle.                It is proven that people in debt traps do not realize their full economic potential. There is                also the negative impacts on their physical and mental health and the enormous burden                                                             87","on the social system. An estimated two million property seizures of paralyzed  Czechs -                including family members - are not what the Czech Republic needs.                 3 Results and Discussion                 When we use the latest data from the Ministry of Finance most of the population (79%)                would not take credit to pay for Christmas gifts as we can see in Figure 1.                    Figure 2 Willingness to Take Credit Under Certain Conditions (n = 1000, data in%)                                                Without an answer                                                   Do not know                                   The credit would definitely not take                                   The loan would not have been taken                               The loan would only be taken if he had a                                        repayment                            The loan would have been taken, even if the                           amount of the repayment was on the edge…                                                             0     10   20    30   40    50    60                                               Source: Ministry of Finance (2016)                This seems to be very positive, but according to the latest data a lot of Czechs are indebted                and suffer from overindebtedness.                Very important is the approach of people to the troubles with repaying. We can see in                Figure 3 that most of the people (59,4%) would inform the creditor about late payment.                  Figure 3 Informing of Creditor when Borrower Cannot Repay on Time (n = 1000, data                                                           in%)                                Yes                                                               59,4                                 No                12,9                        Do not know                        20,8                     Without answer          6,9                                    0        10        20         30        40        50        60        70                                               Source: Ministry of Finance (2016)                                                              88","The reasons why people would inform the creditor about late payment are in Figure 4 and                it is just politeness, the effort to  agree on what to  do or just trying  to avoid some big                issues. People mostly want to protect themselves, and that is responsible.                 Figure 4 Why Inform the Creditor that Borrower Cannot Repay on Time (n = 1000, data                                                           in%)                30                          25                25                                     21           21                20                  15                                            14                  10                                                                           7                                                                                       5           5                 5                   0                      Politeness  Agreement  Avoiding some To agree to pay  To find the  Repayment  Other                                                issues       later      solution    timetable                                               Source: Ministry of Finance (2016)                The sample was also tested if they have had any repayment issues in the past – most of                the people from the sample did not have any repayment issues as shows Figure 5.                                         Figure 5 Repayment Issues (data in%)                               Without answer                                 Do not know                                         No                                       Yes                                           0    10    20    30    40    50    60     70    80    90                                               Source: Ministry of Finance (2016)                 85% of people have no problems with timely payment of their obligations. In 2010 it was                80%. We can see progress here. People who have had problems with repayment in the                past most often report that they have tried to pay the debt as quickly as possible (54%).                14% of people solved the problem with another loan. If these people got into the same                situation now, 19% of them would be trying to debt what makes the fastest payment. 13%                would have taken another loan. It is a visible change in the behavior – most of the people                                                            89","tried to repay their debts, but in the future, they would do something else (22% of the                people said “Other solution”). We can see people were responsible in the past, trying to be                without debts but in the future, they would find some other, unknown solution.                In the past, most of the people (54%) wanted to pay the debt as soon as possible as it is                clear in Figure 6. That is very rational and responsible solution.                              Figure 6 What Have Respondents Done in the Past (data in%)                  60                         54                  50                   40                   30                   20                                    14                                               9                                         9                 10                                      6                                                                    4         3                                                                                                   2                  0                     Willingness to  Loan   Payment   Agreement  Nothing, no Informing of  Other  Do not know                       pay ASAP           postonement what to do  payment  the creditor                                               Source: Ministry of Finance (2016)                 What seems quite problematic is the reaction showed in Figure 7 of the people in theoretical                repeating of that situation. Just 21% would pay as soon as possible again. Alarming is the                fact that 23% of respondents did not know what to do in that situation. That means that                they could probably just leave it and would not care about that issue – that is the worst                possible solution because they can get very easily to property seizure.                          Figure 7 Theoretical Situation: Problems with Repayment (data in%)                        25                                                                           23                             21                                    19                       20                        15                                                    9                       10                                                                 7                                            5                        5                                               4      4              4                                                          1                            1                        0                                                       Source: Ministry of Finance (2016)                                                              90","For  taking  a  loan,  in  general,  is  important  to  know  some  financial  terms,  especially  annual  cost                percentage rate. From the sample, just 37% of people knew what it means as it is shown in Figure                8.                             Figure 8 Knowledge of Annual Cost Percentage Rate (data in%)                                                    Without answer                                                         3%                                                                               Yes                                                                            37%                                                   No                                            60%                                                        Yes   No   Without answer                                               Source: Ministry of Finance (2016)                 Last Figure 9 shows that 58% of people from the sample knew what annual cost percentage                rate means, so it is clear that a lot of people just do not know what can be hidden in a                loan.                              Figure 9 Definition of Annual Cost Percentage Rate (data in%)                                         Incorrect answer                                      42%                                                                               Correct answer                                                                                 58%                                                         Correct answer  Incorrect answer                                               Source: Ministry of Finance (2016)                 The European Union has approached the regulation of the relationships of financial market                participants with the consumer, in particular as regards the provision of loans earlier. These                regulatory measures have been reflected in the Czech Republic, among other things, in the                adoption in 2010 of Act No. 145/2010 Coll., On consumer credit. However, since December                2016, completely new legislation is in force, which is Act No. 257/2016 Coll., On Consumer                Credit. This law covers all types of consumer credit, ranging from classic cash loans, credit                cards,  mortgage  purchases  to  mortgages  and  other  loans  that  are  not  covered  by  the                                                             91","Consumer Credit Act (i.e., housing loans and so-called micro-loans). The Consumer Credit                Act  will  thus  in  the  future  represent  a  comprehensive  adjustment  of  the  retail  credit                products  distribution.  Besides  the  product  area,  the  law  also  unifies  the  institutional                elements  of  regulation,  among  others,  establishes  uniform  conditions  for  obtaining  a                business  license  in  the  credit  sector,  regulates  the  process  of  registration  of  regulated                entities and supervising them, or defines uniform rules for the verification of expertise for                persons acting with consumers.                There  was  an  expected  change  in  the  increasing  trend  of  property  seizure  because  of                overindebtedness 2016 due to the change in credit legislation effective from 1 December                2016. New Consumer Credit Act should make it more difficult to trick the consumers with                inconvenient loans. We can compare the trend in (usually) the biggest loan that is taken,                a mortgage.                                 Table 1 Number of New Mortgages in the Czech Republic                                                         2014        2015        2016        2017                      Number of Mortgages                40 019     47 489       60 013      47 952                                                Source: Czech National Bank                 The  introduction  of  the  Consumer  Credit  Act  was  fully  reflected  in  mortgage  lending                procedures in 2017. The rules and procedures for granting mortgage loans are now much                stricter and more transparent for clients. The two-fold increase of the CNB's two-week repo                rate,  together  with  the  tightening  of  maximum  LTV  requirements,  contributed  to  the                reversal of the mortgage rate trend. However, the rise in interest rates last year was not                dramatic.                                Table 2 Total Household Debt (in CZK million), 30.4.2018                                                             Value        A year ago      Change                        Consumption                        287 074.00     272 470.70      5.36%                        Accomodation                     1 179 255.40  1 085 616.30       8.63%                        Other                             161 287.50      156 276.90      3.21%                                                Source: Czech National Bank                From Table 2 we can assume people still want to  take on more loans. Now, there is a                positive economic situation in the Czech Republic – as we have very low unemployment,                minimal wages are rising and also other wages. In this situation is the debt literacy the                most important. It should protect people who have taken a loan also against situations                that can come in not such a positive economic situation (like an economic crisis).                 4 Conclusions                This paper showed the importance of debt literacy as a special part of financial literacy that                is crucial for the everyday life of the people. Financial literacy is important as a sum of                knowledge, but it is different knowing how to safe few Czech crowns by choosing a cheaper                bank account then knowing how to protect yourself against the inability to pay your debts                and what to do in that situation. People should have the skill of reason to debt. The debt                should not be stigmatized but rationally drawn. Financial literacy is focused on the price of                the loan (knowledge of financial mathematics), debt literacy is focused more on the risk of                the  loan  (knowledge  of  the  law  consequences).  Debt  literate  person  should  know  the                strategies of individual lenders, the different mechanisms that generate profit. The debt                literate person should also be able to make sense of this knowledge in a responsible and                responsible manner, and he/she should be able to recognize unfair practices. To be able to                defend himself. Be able to find help in such a situation According to available and latest                data; financial literacy is improving (when we compare big surveys from 2010 and 2015                made  by  Ministry  of  Finance).  Debt literacy  questions  were  answered  very  often in  an                                                                 92","uncertain way – it is clear that people are struggling with this field of financial literacy.                Data about rising property seizure levels suggest the same conclusions.                 Acknowledgments                 Support  of  Masaryk  University  within  the  project  MUNI/A/0753/2017  “Behavioural,                knowledge and economic aspects affecting the investment decision-making of entities” is                gratefully acknowledged                 References                Czech National Bank (2018). Data retrieved from: http://www.cnb.cz.                 Lusardi, A., Tufano, P. (2009). Debt Literacy, Financial Experiences, and Overindebtedness.                NBER Working Paper, n. 14808.                Map       of      Property      Seizures      (2018).       Data      retrieved      from:                http://mapaexekuci.cz/index.php/mapa-2/                Mika,  A.,  Zumer,  T.  (2017).  Indebtedness  in  the  EU:  a  drag  or  a  catalyst  for  growth?                Working  paper  series.  Retrieved  from:  https://www.ecb.europa.eu/pub/pdf/scpwps/                ecb.wp2118.en.pdf?42f482cdc484b293a3b7eb9997dfe1db                 Ministry  of  Finance  (2016).  Měření  úrovně  finanční  gramotnosti  v  roce  2015. Retrieved                from:    http://www.psfv.cz/cs/pro-odborniky/mereni-urovne-financni-gramotnosti/2015/                mereni-urovne-financni-gramotnosti-2601.                 Turunen, E., Hiilamo, H. (2014). Health effects of indebtedness: a systematic review. BMC                Public Health, vol. 14, p. 489. http://doi.org/10.1186/1471-2458-14-489.                                                                                                            93","Patterns of Currency Co-movement:                                Changes in the Impact of Global Currencies                                                                                   2                                                              1                                         Małgorzata Doman , Ryszard Doman                                        1  Poznań University of Economics and Business                        Faculty of Informatics and Electronic Economy, Department of Applied Mathematics                                         Al. Niepodległości 10, 61-875 Poznań, Poland                                           E-mail: malgorzata.doman@ue.poznan.pl                                             2  Adam Mickiewicz University in Poznań                       Faculty of Mathematics and Computer Science, Laboratory of Financial Econometrics                                           Umultowska 87, 61-614 Poznań, Poland                                                E-mail: rydoman@amu.edu.pl                 Abstract:  In the paper, we analyze the pattern of currency co-movement and document                the  impact  of  global  currencies  (USD,  EUR,  GBP,    JPY)  on  selected  minor  ones.  The                investigation of linkages in the currency market is more complicated than it is in the case                of capital markets. The reason for this is that the analysis of co-movement between two                currencies requires the consideration of exchange rates which are always calculated against                a  third  currency.  The  choice  of  this  third  currency  certainly  influences  the  results  of                comparison. There prevails an opinion that the pattern of the co-movement in the currency                market is significantly driven by the relative influence of the global currencies, mostly the                US dollar and the euro, but to some degree also by the British pound and the Japanese                yen. In the paper, we examine the dynamics of strength and areas of this influence in the                global  currency  market  during  the  period  2011-2018.  We  pay  special  attention  to  the                changes in the pattern caused by the European debt crisis and the UK decision about Brexit.                The dynamics of the linkages is modeled by means of Markov regime switching copula                models,  and  the  strength  of  the  linkages  is  described  using  dynamic  Spearman’s  rho                coefficients and the dynamic coefficients of tail dependence.                Keywords: exchange rates, global currencies, minor currencies, linkages, copula, Markov                regime switching, tail dependence                JEL codes: G15, F31, C58, C32                 1 Introduction                In the paper, we show to what degree the selected minor currencies : AUD, CAD, CZK,                CNY, INR, KRW, PLN, TRY, ZAR, representing different parts of the world, co-move with                some major currencies (USD, EUR, GBP, JPY). To describe the dynamics of linkages in                currency market, we model the dependence between exchange rate returns using dynamic                copula models. This allows us to apply dependence measures which are better suited to                quantify dependence in the case of nonelliptical joint conditional distributions and take into                account dependence between extremes.                 Currencies are an important class of assets. The mechanism of currency comovement is                essential for diversifiability of currency exchange risk, which occurs during cross-border                investments and trade. However, the analysis of co-movement in the case of currency                markets is more complicated than it is, for example, in the case of capital market. The                specificity of this problem is connected with the fact that examining the linkages between                currencies, one has to use exchange rates which are always against some third currency                (usually the American dollar or the euro). Naturally, the results of the analysis essentially                depend on the choice of this third currency. Despite this difficulty and the fact that  the                exchange rate fluctuations are subject to different shocks, it seems to be  possible for some                currencies to find patterns of dependence indicating that they can co-move in a predictable                way.                In the paper, we analyze the phenomenon of the co-movement of a minor currency with a                major one, against other major. Our choice of the considered major currencies is done on                                                             94","the basis of opinions by practitioners and researchers. It is clear that the USD and the euro                are the most important, global currencies but the British pound and the Japanese yen play                a significant role in the currency market as well (Angeloni et al., 2011).                The concept of modeling currency co-movement we use in this paper goes back to Eun and                Lai (2004) and Eun at al. (2013). The core of it is the following observation: if a minor                currency XYZ is driven by a major one, say USD, then for an alternative major currency,                say EUR, the exchange rates XYZ/EUR and USD/EUR, co-move very closely. To support                this, the above mentioned authors use some linear models. Doman and Doman (2010,                2014,  2017)  pointed  out  that  the  linear  approach  may  be  insufficient  to  describe  the                complex dynamics of dependence in the contemporary financial market, and used copulas                to get a deeper insight into the structure of linkages.                In the first part of our analysis we determine the impact of the euro and the USD on each                of  the 11 considered currencies. In the second part, we show to what degree each of the                minor  currencies  co-moves  against  the  US  dollar  with  the  euro,  British  pound,  and                Japanese yen. The aim of the investigation is to describe the dynamics of dependence and                possible changes in the pattern of linkages in the currency market taking into account the                European debt crisis and the UK decision about Brexit.                 2 Methodology and Data                The  linear  correlation  still  plays  a  role  in  financial  theory.  This  tool  for  measuring                dependence  between  financial  returns  is,  however,  appropriate  only  in  the  case  of                multivariate normal or, more general, elliptical distributions (e.g. multivariate Student’s t                distribution).  Yet,  observed  asymmetries  in  one-dimensional  marginal  conditional                distributions as well as in the conditional dependence structure imply that join conditional                distributions of financial returns mostly are not elliptical (see e.g. McNeil  et al., 2005).                Because  of  that,  modeling  the  returns  using  standard  multivariate  GARCH  models                (Bauwens  et  al.,  2006)  may  be  unfounded.  Instead,  employing  dynamic  copula-based                dependence models, which allow to model dependence structure separately from univariate                marginal distributions, can be a better solution.                                                C                A bivariate copula is a function  :   ] 1 , 0 [  [   01 ] →  ] 1 , 0 [   from the unit square to the unit interval                that is a distribution function whose marginals are standard uniform (McNeil et al., 2005).                If  X   and  X   are  random  variables  with  joint  distribution  function  F   and  marginal                     1         2                distributions  F  and  F  then, by a theorem by Sklar (1959), the following decomposition                               1                                       2                holds:                        F  x , ( y ) C=  (F 1 (x ),F 2 (y )).                                                                                        (1)                Formula (1) shows that the joint distribution function splits into the marginals and a copula.                Thus a copula can be seen as the dependence structure between  X  and  X .                                                                                    1        2                If  F  and  F  are continuous, the function  C  is given by the formula                    1                            2                        C (u 1 ,u =  (F 1  (u 1 ),F 2  (u 2 )),                                                                             (2)                              ) F                             2                for  , vu  ] 1   , 0 [  , where G  (u ) = inf{x     : G (x )   } u . In that case, C  is called the copula of  X  and                                                                                                      1                 X  or of  F .                   2                The simplest copula, which corresponds to independence, is defined by                         C   u, (  v =)  uv .                                                                                                  (3)                In this paper we also apply the Gaussian (normal), Student, Joe-Clayton, and rotated Joe-                Clayton copulas. They are defined by as follows:                         Gauss                        C   (u 1 ,u 2 ) =   ( − 1 (u 1 ), − 1 (u 2  )) ,                                                               (4)                                                              95","Student                        C    ,  (u 1 ,u 2 ) t =   , (t  1 −  (u 1 ),t  1 −  (u 2 )) ,                                                                   (5)                         J                                                     ] +                                                                    ] −                        C  −C (u 1 ,u 2 ) = 1− ( 1− ([ 1−  1 ( −u 1 )  −   1 [ −  1 ( −u 2 )  −   ) 1  −   / 1  )  / 1   ,                               (6)                           ,                                       =                        C  J − C _ r 90 (u 1 ,u 2 ) u − C  J − C  1 ( − u 1 ,u 2 ) ,                                                              (7)                                               ,                          ,                                          2                where in  (4)   denotes  the  distribution function  of  a  bivariate  standardized  Gaussian                                                 vector with the correlation coefficient   , and   stands for the distribution function of the                standard  normal  distribution.  Similarly,  t   ,    in  (5)  denotes  the  bivariate  Student  t                distribution function with  degrees of freedom and the correlation coefficient   , and  t                 stands for the univariate Student t distribution function with  degrees of freedom. The                                                                                     1                                                                                             0                                                                                                  parameters in the Joe-Clayton copula (6) satisfy the conditions:  ,   . This copula,                also called the BB7 (Joe, 1997), is capable of modeling asymmetric dependence structures                and,  in  particular,  asymmetric  dependence  between  extreme  events.  The  rotated  Joe-                Clayton copula is a version of the Joe-Clayton copula that models negative dependence.                Spearman’s rho for variables  X  and  X  with marginal distribution functions  F  and  F  2                                                                                                  1                                                 1                                                         2                can be defined as                                 =                         S (X 1 , X  2 )  (F 1 (X 1 ), F 2 (X 2 )),                                                                            (8)                where   denotes  the  usual  Pearson  correlation.  This  is  a  dependence  measure,  which                depends solely on a copula linking  X  and  X . When C is the copula of  X  and  X , then                                                                                           1                                                                                                    2                                                              2                                                     1                         S (X 1 , X  2 )  =  C  =12    C (u 1 ,u 2 )du 1 du                                                               (9)                                                           2                                            ] 1 , 0 [  2                (Nelsen, 2006). From the definition of a copula it follows that if C and C are copulas then                                                                                  1      2                for any 0   1   the function  C  C +  1 ( −  )C  is also a copula. Thus, formula (9) implies                                                =                                                              2                                                    1                that in such a situation the following holds for the corresponding Spearman’s rhos:                         =  +   1 ( −  ) .                                                                                  (10)                                1 C                                           2 C                         C                We model the joint conditional distribution of bivariate returns assuming that there can be                three regimes in each of which a fixed copula describes the dependence structure, and the                regime switching is driven by some Markov chain (cf. Garcia and Tsafack, 2011). Thus, in                the applied Markov switching copula (MSC) model the conditional distribution of the vector                r  =  (r  ,r  ) , t =  , 1   T ,  , has the following form                 t       , 1 t    , 2 t                        r t  |   1 − t  ~ C  S t  (F  , 1 t  ), ( F  , 2 t    | ) (   1 − t  ) ,                                                                      (11)                where     denotes  the  up  to  time  t information  set,  r |  t 1 −  ~    F ,  =  2 , 1 ,  and  S  is  a                                                                                        i                                                                          t i,                          t                                                                                     t i,                                                                                                     t                homogeneous Markov chain with state space   ,1{      } 3   , 2  . The parameters of the MSC model                (i.e. those of the univariate ARMA-GARCH models for the marginal distributions, of the                copulas C , C  and C , and the transition probabilities  p = P (S =  j | S t  1 −  ) i =  ) are estimated                                                                                t                                                                          ij                          1                                       3                               2                by the maximum likelihood method. The main by-products of the estimation, which are                used  to  construct  time-varying  conditional  dependence  measures,  are  the  conditional                                              P                              P                probabilities  (S t  = j  |   − t  1 ) ,  (S =  | j  t ) and  (S =  | j  T )  (Hamilton, 1994).                                                               P                                                                  t                                                 t                We analyze the dynamics and strength of the impact of a major currency on a minor one                using the idea proposed by Eun and Lai (2004). This idea consists in an observation that                when a minor currency XYZ is driven, for instance, by the US dollar then the exchange                XYZ/EUR and USD/EUR co-move very closely. In the opposite case, i.e. when the XYZ is                influenced  by  the  euro,  the  exchange  rates  XYZ/USD  and  EUR/USD  show  strong                                                            96","interdependence.  The  aim  of  our  analysis  is  to  determine  to  what  degree  each  of  the                considered minor currencies co-moves with a major one against another major. The first                part of the analysis concerns the competitive influence of the US dollar and the euro on                the currency market. In the second part, we show to what degree each of the considered                minor currencies co-moves with the euro, British pound, and Japanese yen, examining the                corresponding exchange rates against the US dollar.                 The analysis presented in the paper is based on the percentage daily logarithmic returns                of exchange rates. The period under scrutiny is from January 3, 2011 to March 30, 2018                (1878  observations).  The  steps  of  the  analysis  were  as  follows.  First,  to  each  of  the                investigated return series a univariate ARMA-GARCH model was carefully fitted. It is worth                mentioning that in the case of the exchange rates of minor currencies against major ones                the  standardized  innovations  of  fitted  models  mainly  followed  skewed  Student’s  t                distributions (Laurent, 2013) with diverse degrees of freedom. This precluded from using                standard multivariate GARCH models, and justified applying dynamic copulas.                 The MSC models were fitted to the transformed ARMA-GARCH standardized residuals by                the maximum likelihood method. We tried to apply at most 3 regimes, and copulas from                various families. Our  final choice is the effect of taking into account the obtained values                of the information criteria and, where applicable, the results of the performed likelihood                ratio tests. All the calculations were done using  G@RCH 7.0 package (Laurent 2009) and                the  MATAB  software.  Due  to  space  limitations,  we  do  not  present  here  the  obtained                parameter estimates. Instead, we show estimates for the dynamic smoothed Spearman’s                rho coefficients. They were calculated by the formula                              3                         t  =   i ) ( P (S t  = | i   ) ,                                                                              (12)                                            T                            i =1                                       where (i is Spearman’s rho for the copula prevailing in regime i.                          )                3 Results and Discussion                Eun  and  Lai  (2004)  consider  trade  pattern,  financial  integration,  colonial  heritage  and                geographical  location  to  be  the  main  factors  determining  the  pattern  of  currency  co-                movement. This allowed us to formulate some conjectures concerning the future results.                For example, we expected that the PLN, CZK, TRY and GBP were linked with the euro; the                CAD, KRW and JPY with the USD; and the INR and ZAR with the GBP.                 Although  in  the  second  part  of  our  analysis  we  consider  the  GBP  and  JPY  as  major                currencies, the knowledge about their co-movement with the USD and euro is necessary                to understand their impact on minor currencies. Figures 1 and 2 show the estimates of                dynamics Spearman’s rhos for the GBP and JPY, respectively.                Figure 1 Dynamic Spearman’s Rhos for (GBP/USD, EUR/USD) and (GBP/EUR, USD/EUR)                         0,8                                                                  GBP-EUR       GBP-USD                        0,7                         0,6                        0,5                        0,4                        0,3                        0,2                         0,1                         0                                                               97","Source: authors                 The plots presented in Figure 1 show that the British pound alternately co-moves with the                euro and US dollar. This supports a common opinion that the GBP should be considered to                be one of major currencies. Another pattern is visible for the Japanese yen (Figure 2) –                high values of Spearman’s rho indicate strong linkages with the USD.                  Figure 2 Dynamic Spearman’s Rhos for (GBP/USD, EUR/USD) and (GBP/EUR, USD/EUR)                    1                                              JPY-EUR      JPY-USD                   0,8                   0,6                    0,4                   0,2                     0                   -0,2                                                        Source: authors                     Table 3 Co-movement of Minor Currencies with the USD against the EUR and with                  the EUR against the USD (copulas: N = Gauss, t = Student, PI = , JC = Joe-Clayton)                  Minor                                 with the USD against the EUR            with the EUR against the USD                 currency                           Spearman’s rho from 0.3302 to 0.6576;  Spearman’s  rho  from  0.0011  to  0.7187;                           strong  dependence  (rhos  about  0.65)  strong dynamics of dependence driven by                           until the end of 2015 with short periods  market events; occasionally no linkages.                  GBP                           of  weaker  (0.33)  linkages,  from  2016   Model: N-N-PI                           stable level of Spearman’s rho about 0.3.                           Model: N-t                           Spearman’s rho from 0 to 0.8666; during  Spearman’s  rho  from  0.1477  to  0.5362;                           almost all of the analysed period strong  negative dependence during the European                           linkages  (rho  greater  than  0.6),  very  debt   crisis   and   about   the   British                  JPY      short periods of independence.          referendum concerning Brexit ; from 2014                           Model: N-N-PI                           mostly  stable  level  of  Spearman’s  rho                                                                   (about 0.5).                                                                   Model: N-t                           Spearman’s rho from 0.1268 to 0.4914;  Spearman’s  rho  from  0.0006  to  0.6734;                           weak  linkages  at  the  beginning  of  the  the highest values at the beginning of the                           analyzed period (until August 2012) and  analyzed  period  (until  September  2012)                  AUD      during the British referendum concerning  and   during   the   British   referendum                           Brexit.                                 concerning Brexit.                           Model: N-t                              Model: N-N-PI                           Spearman’s rho from 0.3427 to 0.6584;  Spearman’s  rho  from  0.1642  to  0.6248;                           an increase in the strength of linkages in  generally weak linkages (rhos about 0.2)                           April  2012,  in  2011  weak  linkages  with  short  periods  of  increase,  among                  CAD      (probably the remnants of the subprime  others,  after  the  British  referendum                           mortgage crisis).                       concerning Brexit.                           Model: N-t                              Model: N-N                           Spearman’s rho from 0.0018 to 0.4077;  Spearman’s rho from 0 to 0.5191; stable                           strong   dynamics   of   linkages   with  level of Spearman’s rho  until June 2012,                  CNY                           intensive fluctuations.                 then  strong  dynamics  of  linkages  with                                                                   intensive fluctuations.                                                             98","Model: PI-JC                            Model: N-N-PI                           Spearman’s rho from -0.4462 to -0.220,  Spearman’s  rho  from  0.8119  to  0.9778,                           i.e.  negative  dependence  during  all  the  the highest values during the period 2015-                  CZK                           considered period.                      2017.                           Model: N-JC_r90                         Model: N-t                           Spearman’s rho from 0.0036 to 0.4295;  Spearman’s rho from -0.2023 to 0.1182;                           regular  oscilations  of  Spearman’s  rho  very weak positive depedence or negative                  INR      during  all  the  considered  period  (March  dependence (April 2014-April 2015, June                           2014-March 2016).                       2015, February 2016).                           Model: N-PI                             Model: N-C                           Spearman’s rho from 0.0002 to 0.2285;  Spearman’s rho 0.0588;                           stronger  linkages  until  May  2012,  and  almost independence.                  KRW      then fro May to August 2015.            Model: N                           Model: N-PI                           Spearman’s rho from -0.4764 to 0.1429;  Spearman’s  rho  from  0.5479  to  0.8523;                  PLN      regular oscillation.                    Stable connection with regular oscilation.                           Model: N-N-JC                           Model: N-N                           Spearman’s rho from 0.2903 to 0.6908;  Spearman’s  rho  from  0.1089  to  0.5993;                           short  periods  with  stronger  linkages  (in  strong  fluctuations  –  sensitivity  on                  TRY                           2012-2013; 2015, 2016, 2018).           markets events.                           Model: N-N                              Model: N-N-JC                           Spearman’s rho from 0 to 0.3055; Weak  Spearman’s  rho  from  0.1942  to  0.6961                           dependence  or  independence  (2011-    The strongest linkages from May 2011 to                           2012, 2017)                             April   2012;   regular   oscilations   of                  ZAR                                              Spearman’s rho during the remaining part                           Model: C-JC                                                                   of the period.                                                                   Model: N-N-JC                                                      Source: authors                 In Figure 3 we present the results for the Czech koruna. There is no surprise, the koruna                is linked with the euro and exhibits no depedence with the US dollar. In the case of linkages                with  the  euro,  the  highest  values  of  Spearman’s  rho  occur  during  the  period  of  the                exchange rate commitment, i.e. 7.11.2013-6.04.2017 (Noerr 2017). An interesting thing                is that in the fitted 2-regime MSC model the second regime is driven by the JC90 copula.                This means that there appeared negative dependence in extreme events between  the USD                and Czech koruna against the euro.                Figure 3 Dynamic Spearman’s Rhos for (CZK/USD, EUR/USD) and (CZK/EUR, USD/EUR)                       1                      0,8                      0,6                      0,4                                      CZK-EUR        CZK-USD                      0,2                      0                     -0,2                     -0,4                     -0,6                                                        Source: authors                                                                 99","The results of the first part of our analysis indicate that the CZK, PLN, TRY and ZAR belong                to the euro area of influence. The impact of the USD on the CAD, CNY, INR, KRW and JPY                is clear.                The aim of the second part of the analysis is to determine with which of the three major                currencies (EUR, GBP, JPY) each of the considered minor currencies co-moves against the                US dollar. In Figure 4 we present the results for the Indian rupee. One can see that the                linkages with the GBP are the strongest, though the observed values of Spearman’s rho                are not very high (0.06-0.35). The plots presented in Figure 5 combined with the results                in Table 1 clearly indicate that the Polish zloty strongly co-moves with the euro.                Our  results  show  that,  considering  co-movement  against  the  USD,  we  can  divide  the                investigated minor currencies into four groups. The directions of movements of the CZK,                PLN, TRY  against the USD are concordant with the behavior of the euro. The AUD and CAD                co-move  with  the  euro  and  GBP  with  similar  intensity.  The  INR  and  ZAR  seem  to  be                connected with the GBP. Surprisingly, we do not observe any significant linkages of the                analyzed  currencies  with  the  JPY. The KRW  and  CNY  are  not linked  with  any  of  major                currencies.                              Figure 4 Dynamic Spearman’s Rhos for (INR/USD, EUR/USD),                                      (INR/USD, GBP/USD) and (INR/USD, JPY/USD)                     0,4                                                 INR-EUR     INR-GBP     INR-JPY                    0,3                     0,2                    0,1                      0                    -0,1                    -0,2                     -0,3                                                        Source: authors                                 Figure 5 Dynamic Spearman’s Rhos for (PLN/USD, EUR/USD),                                     (PLN /USD, GBP/USD) and (PLN /USD, JPY/USD)                        1                       0,8                        0,6                        0,4                       0,2                                                                    PLN-EUR      PLN-GBP      PLN-JPY                       0                       -0,2                                                        Source: authors                                                              100","4 Conclusions                The investigation of linkages in the currency market is more complicated than it is in the                capital market case. The reason is that the analysis of the co-movement of two currencies                requires the consideration of exchange rates which are always calculated against a third                currency. The choice of this third currency certainly influences the results of comparison.                In  the  paper,  we  showed  to  what  degree  some  selected minor  currencies  representing                different parts of the world (AUD, CAD, CZK, CNY, INR, KRW, PLN, TRY, ZAR) co-move                with some major currencies (USD, EUR, GBP, JPY). We describe the dynamics of linkages                in the currency market by means of Markov switching  copula models. In the first part of                our analysis we determine the impact of the euro and USD on each of the 11 considered                currencies. For the CZK, PLN, TRY and ZAR, the dominating currency is the euro, and the                remaining currencies are in the USD area of influence. The second group of our results                deals with the co-movement against the US dollar. We find that the CZK, PLN and TRY co-                move with the euro against the USD, and  the AUD and CAD co-move with the euro and                GBP with similar intensity. The INR and ZAR seem to be connected with the GBP. We did                not observe any significant linkages of the analyzed currencies with  the JPY. The dynamics                of linkages is sensitive on market events. The impact of the British decicion concerning                Brexit is visible but short-lived.                 References                Angeloni, I., Bénassy-Quéré, A., Carton, B., Darvas, Z., Destais, C., Pisani-Ferry, J., Sapir,                A., Vallée, S. (2011). Global currencies for tomorrow: A European perspective. Brussels:                Breugel.                Doman, M., Doman, R. (2010). Patterns of currency co-movement: the USD and the EUR                areas of influence. In: Prachalias, C., ed., Proceedings of 7th International Conference on                Applied Financial Economics. Athens: National and Kapodistrian University of Athens, pp.                384-393.                Doman, M., Doman, R. (2014). Dynamic Linkages in the Pairs (GBP/EUR, USD/EUR) and                (GBP/USD, EUR/USD): How Do They Change During a Day? Central European Journal of                Economic Modelling and Econometrics, vol. 6(1), pp. 33-56.                Doman, M., Doman, R. (2017). The impact of the US dollar and the euro on currencies in                Europe  and  Asia. In:  Hic  Gencer,  A.,  Sözen,  I.,  Sari,  S.,  ed., Eurasian  Economies  in                Transition. Newcastle upon Tyne: Cambridge Scholars Publishing, pp. 166-182.                Eun,  C.  S.,  Lai  S.  S.  (2004).  Currency  Comovement.  Georgia  Institute  of  Technology                Working Paper.                Eun  C.,  Lai  S.,  Lee  K.  (2013).  Exchange  Rates  Comovement.  KAIST  Business  School                Working Paper Series, KCB-WP-2013-020.                Garcia,  R.,  Tsafack,  G.  (2011).  Dependence  structure  and  extreme  comovements  in                international  equity  and  bond  markets.  Journal  of  Banking  and  Finance,  vol.  35(8),                pp. 1954–1970.                Hamilton, J. D. (1994). Time Series Analysis. Princeton: Princeton University Press.                Joe, H. (1997). Multivariate Models and Dependence Concepts. London: Chapman and Hall.                Laurent  S.  (2013).  Estimating  and  Forecasting  ARCH  Models  Using G@RCH  7.  London:                Timberlake Consultants.                McNeil, A. J., Frey, A., Embrechts, P. (2005). Quantitative Risk Management. Princeton:                Princeton University Press.                Nelsen R. B. (2006). An Introduction to Copulas, 2nd ed. New York: Springer.                Noerr (2017). Czech Republic: Czech exit from the exchange rate commitment. Retrieved                from:    https://www.noerr.com/en/newsroom/News/czech-republic-czech-exit-from-the-                exchange-rate-commitment.aspx                Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de                l’Institut Statistique de l’Université de Paris, vol. 8, pp. 229-231.                                                            101","Motor Vehicle Insurance in SMEs                                                           1                                           Michal Fabuš , Viktória Čejková     2                           1  School of economics and management in public administration in Bratislava                                            Department of economics and finance                                        Furdekova 16, SK – 851 04 Bratislava, Slovakia                                               E-mail: michal.fabus@vsemvs.sk                            2  School of economics and management in public administration in Bratislava                                            Department of economics and finance                                        Furdekova 16, SK – 851 04 Bratislava, Slovakia                                             E-mail: viktoria.cejkova@vsemvs.sk                 Abstract: In the present paper we deal with the insurance of motor vehicles in small and                medium-sized enterprises. We deal with legal aspects of insurance of motor vehicles, we                characterize this type of insurance in the Slovak Republic. We analyze in more detail the                compulsory contractual liability insurance for motor vehicle damage, as well as emergency                insurance. We analyze the insurance of motor vehicles in the selected small business. In                the last part, we deal with the development of motor vehicle insurance, problems in motor                vehicle insurance and trends in motor vehicle insurance in the Slovak Republic.                Keywords: insurance, motor insurance, motor vehicle operation, compulsory contractual                liability insurance, white card, green card                 JEL codes: G 22                 1 Introduction                Every entrepreneur works with different risks. The risk arising from business activities can                be  eliminated  or  reduced  to  an  economically  acceptable  rate  by  applying  appropriate                measures.  The  negative  consequences of  risks  can  be  reduced  by the  entrepreneur  by                transferring them to another entity operating on the financial market  - to a commercial                insurance company. One of the most frequently encountered risks that threatens small and                medium-sized businesses is the risk of damage to, destruction or theft of a motor vehicle                and damage caused by the operation of a motor vehicle. Therefore, this paper deal with                the insurance of motor vehicles in small and medium-sized enterprises.                The market economy system (market economy) is a system in which the solution of the                basic questions of the organization of the economy is in the hands of individuals. These                decisions are made based on a price mechanism and the interaction of demand and supply.                The market economy is based on a system of free enterprise and free competition (Lisý,                2016).                At present, we understand insurance as a normal part of modern life. Without insurance,                many areas of today's society and economy could not function properly. The insurance                industry provides coverage of economic, climatic, technological, political and demographic                hazards, making it easier for individuals and businesses to live in business, innovation and                development. A dynamically developing insurance market positively affects the economic                growth (Grmanová, 2015).                Importance of insurance can be assessed both in terms of insured business and citizens,                as  well  as  in  terms  of  the  whole  society  (Grmanová,  Hošták,  2016).  For  businesses,                insurance stabilizes their economic level and affects the economic outcome. It allows the                distribution  of  costs  for  business  entities  and  the  reduction  of  large  costs  in  case  of                unforeseen events. In the event of adverse events that are the subject of insurance, the                losses  of  business  entities  will  moderate  and  thus  stabilize  their  economic  situation                (Čejková, 2013).                                                                102","2 Methodology and Data                In our papers, we characterize the insurance, its significance and its breakdown, as well                as the insurance products of the obligatory contractual liability insurance for the damage                caused by the operation of the motor vehicle, and the accident insurance of the vehicle.                We  will  highlight  the  development  of  motor  insurance  in  a  small  enterprise,  and  their                application in  this insurance in the  selected  SME.  Also,  problems  and  trends related to                insurance of motor vehicles and the anticipated development of insurance of motor vehicles                in the Slovak Republic. At the same time, we emphasize the importance of motor vehicle                insurance for small and medium-sized businesses, such as mandatory contractual liability                insurance and the possibility of reducing economic loss in the event of motor vehicle theft                or the impact of a motor vehicle caused by the businessman himself.                The methods of description, analysis, synthesis, deduction and comparison will be applied.                 Based on data from selected private company on the development of premiums and claims                paid and received in the period of 2008-2017 we analyze economic outcomes of motor                vehicle insurance. According the data we can see the difference between paid premiums                and received claims, which are positive form economics point of view.                 3 Results and Discussion                 Commercial  insurance  companies  in  the  Slovak  Republic  provide  insurance  for  motor                vehicles that cover several risks - the risks associated with the operation of the motor                vehicle and with the ownership of the motor vehicle. Damage caused by the operation of                a motor vehicle covers compulsory contractual liability insurance for damage caused by                the  operation  of  a  motor  vehicle.  Damage  that  arises  on  a  motor  vehicle,  his  theft  is                covered by accident insurance, also called KASKO insurance.                A motor vehicle may cause damage while operating, and at the same time damage to the                motor  vehicle  may  occur.  In  the  case  of  insurance  against  risks  related  to  the  motor                vehicle, the following insurances were incurred: compulsory contractual liability insurance                for damage caused by the operation of a motor vehicle, and accident insurance - KASKO.                In  terms  of  insurance  classification,  we  may  be  required  to  make  compulsory  liability                insurance  for  damage  caused  by  the  operation  of  a  motor  vehicle,  also  referred  to  as                \"compulsory  contractual  insurance\"  or  \"CCI\".  Under  Section  822  of  the  Civil  Code  of                Liability Insurance, the insured person has the right to insure the insurer, in the event of                a claim, to compensate him under the insurance conditions for damage to which the insured                person is liable. The insurer pays the refund to the injured party; however, the injured                party  has  the  right  to  be  paid  by the insurer,  unless  otherwise  provided in  the  special                regulations.                Obligatory contractual insurance is legislatively regulated in the Act of the National Council                of the Slovak Republic no. 381/2001 Coll. on compulsory contractual insurance for liability                for  damage  caused  by  the  operation  of  a  motor  vehicle  and  on  the  amendment  and                supplementation of certain laws.                The motor vehicle is a self-propelled vehicle as well as a non-self-propelled vehicle for                which a vehicle registration certificate, vehicle technical certificate or a similar license is                issued.                The obligation to conclude an insurance policy in a domestic motor vehicle is the one who                is the holder of a motor vehicle registered in the vehicle or the person who is registered in                the vehicle documents as the person to whom the motor vehicle has been transferred in                other cases the owner of the motor vehicle or its operators. If a rental contract is concluded                on  a  motor  vehicle  with  the  right  to  purchase  a  leased  property,  the  lessee  has  the                obligation to conclude the insurance contract. Liability insurance applies to anyone who is                responsible for the damage caused by the operation of the motor vehicle covered by the                insurance contract.                                                               103","The insured has the right to insure liability for the insurer to compensate for the damaged                and proven claims, namely: damage to health and the cost of death, damage caused by                damage, destruction, theft or loss of the expense incurred by the legal representation in                the exercise of claim, lost profits and others.                Compulsory contractual insurance covers the liability of the insured for the territory of the                Slovak  Republic  and  the  territory  of  the  states  with  which  the  Slovak  Republic  has                concluded an agreement on the mutual settlement of claims for damages. These countries                are listed in the green card. An insurer for compulsory contractual insurance issues the                insured insurance certificate (white card) and a green card. The white card proves the                liability insurance in the territory of the Slovak Republic, the green card is an international                document on the validity of the insurance abroad. In countries not listed in the green card,                insurance is required when entering the country border insurance.                 After the occurrence of the damage event, the insured is obliged to notify the insurer in                writing of the occurrence of the damage event within 15 days after its occurrence if the                damage occurred in the territory of the Slovak Republic and within 30 days after its origin,                as it originated outside the territory of the Slovak Republic.                Accident insurance is governed in the Slovak Republic by the Civil Code and insurance                conditions. The Civil Code regulates insurance contracts, the process of insurance contract                conclusion,  termination  of  insurance,  change  of  insurance,  property  insurance  and                limitation.                According to the provisions of Section 806 of the Civil Code of Property Insurance, the                insured person has the right to receive a payment in the amount determined according to                the insurance conditions if the insured event relates to a matter covered by the insurance.                 The conditions of accident insurance of individual insurers are quite different. Most insurers                have insurance options that offer different coverage options.                The subject of emergency insurance is the vehicle. The vehicle is most often defined as a                self-propelled  vehicle  as  well  as  other  non-self-propelled  vehicles  for  which  a  vehicle                registration document is issued and which is subject to vehicle registration in the Slovak                Republic. The trailer is a road vehicle designed to be connected to a motor vehicle. The                vehicle at the time of insurance closure must be undamaged, in proper technical condition                and fit for operation. Insurance is most often negotiated at a new value, i.e. the amount                corresponding to the price of a new vehicle of the same type, quality, equipment without                discounts at the time of purchase of a vehicle as new in the territory of the Slovak Republic.                The territorial validity of insurance is the most common geographical area of Europe.                Insurance risks in accident insurance may be broken down by individual insurers' products.                Insurers cover the most frequent risks of accident insurance: damaging or destroying the                vehicle because of an accident, damage or destruction of the vehicle as a result of a natural                occurrence, damage or destruction of the vehicle by vandalism, theft of part or all of the                vehicle.                Assistance to accident insurance is multiple, collision avoidance with animals, windscreen                attachment  and  others.  Motor  insurance  assurance  services  are  services  provided  by                insurers as part of insurance free of charge. For a fee, the assistance services are usually                with a larger range of assistance services, respectively higher coverage limits. Assistance                services include a range of services that can be used by insurers within the limits and under                certain conditions of the insurer: assistance services are covered by the vehicle during the                period  of  compulsory  insurance  or  accident  insurance,  the  driver  and  the  persons                transported by the insured vehicle are entitled to the assistance services.                Motor insurance will be monitored between 2008 and 2017 in a real company operating in                the Slovak Republic. State what motor vehicles the company used and what insurance it                had  for  motor  vehicles.  The  company,  a  small  business,  is  listed  in  the  financial  and                insurance activities sector. For our paper, we will call the company XY.                                                               104","In 2008, the company acquired seven motor vehicles. All vehicles were Škoda, four Octavia                and three Škoda Octavia Combi. One of the vehicles (Škoda Octavia) was stolen in 2009.                In 2015, the company sold four motor vehicles (two Škoda Octavia and two Škoda Octavia                Combi) and bought one Škoda Octavia. In 2016, the company bought two more Škoda                Octavia vehicles. At present, the company owns four vehicles Škoda Octavia and one Škoda                Octavia  Combi.  Compulsory insurance for  liability  for  damage  caused  by  motor  vehicle                company  in  2008,  negotiated  in  the  Česká  poisťovňa  -  Slovensko,  Inc.  such  as  fleet                insurance.  The  scope  of  compulsory  contracted  insurance  arises  from  the  law  on                compulsory  contractual  insurance.  The  obligatory  contract  insurance  was  provided  by                Česká poisťovňa - Slovensko, a.s. assistance services. Assistance services were provided                in the event of an unforeseen event because of a traffic accident or mechanical disruption                in the countries listed in the Green Card.                From  the  information  on  the  insurance  of  third  party  liability  insurance  company  and                information  on  claims  paid  by  insurers  show  that  in  the  reporting  period  was  paid  by                insurance in the amount of € 7,134.11 and paid indemnity insurers in that period was                € 6,156.78.                 Figure 1 shows a graphical representation of the paid insurance by the company and claims                paid by insurers for damaged from compulsory contractual insurance.                     Figure 1 Comparison of Paid Premiums and Insurance Payments of Compulsory                                                   Contracted Insurance                              Paid premiums and paid claims [EUR]  8000,0                            7000,0                            6000,0                            5000,0                            4000,0                             3000,0                             2000,0                             1000,0                                ,0                                 2008                                       2009                                             2010                                                    2011                                                          2012                                                                 2013                                                                        2014                                                                               2015    2016                                                                Year                          2017                                  Source: Own processing based on internal company documents                 In  the  monitored  years,  the  company  also  paid  premiums  for  accident  insurance  of                € 20,869.13. In the case of insurers, 12 insurance events were claimed from the accident                insurance, the amount of the indemnity amounting to € 27,600.65                Figure 2 shows a graphical representation of the paid insurance by the company and the                insurance benefits paid by the insurers.                                                                    105","Figure 2 Comparison of Paid Premiums and Insurance Payments of Accident Insurance                               Paid premiums and incurance claims[EUR]  30000,0                              25000,0                            20000,0                            15000,0                            10000,0                             5000,0                                 ,0                                  2008                                        2009                                              2010                                                    2011                                                          2012                                                                 2013                                                                        2014                                                                              2015                                                               Year                   2016   2017                                  Source: Own processing based on internal company documents                 The comparison shows that the company received € 27,600.65 on insurance claims from                insurers. In total, the company paid premiums of € 20,869.13. Therefore, the company                received 32% more than insurance premiums. The fact that the company had an accident                insurance with economic benefits was due to the performance of a specific insurance event                - theft of a motor vehicle in 2009.                       Every  year,  the  National  Bank  of  Slovakia  processes  the  analysis  of  the  Slovak                Financial  Sector.  In  the  section  on  insurance,  it  provides  information  on  motor  vehicle                insurance. One of the main indicators of motor vehicle insurance is a combined indicator                that expresses the loss and cost of insurance on the earned premium. Figure 3 shows a                composite indicator for compulsory contracted insurance and accident insurance for the                period 2010 to 2016.                                Figure 3 Combined Indicator in CCI and KASKO Insurance                             120,0                                                 112                                                               109                                                                         102               104                                             98,2     99,2                                    95,3                                  95,5               97                             90,0                           Combined indicator [%]  60,0  84,6  83,2  80,3  85,4     85                                   30,0                                 ,0                                    2010     2011      2012     2013     2014      2015     2016                                                                Year                                                            PZP     HP                                     Source: Own processing based on internal company documents                                                              106","In 2016, the combined indicator of motor vehicles continued to grow for a long time, and                by  September  2016,  this  sector  was  loss-making.  The  net  combined  motor  insurance                indicator was 101% (97% of the PZP, 104% of the accident insurance) as of September                2016. The premiums received did not cover the costs of claims. In the case of an estimate                of gross combined indicator, this would be 100% in the case of accident insurance and                98%  in  the  case  of  compulsory  contracted  insurance.  As  a  result,  motor  insurance                premiums do not cover insurance claims for long periods. The reasons why motor insurance                is long-term loss are long-term problems in motor vehicle insurance.                Motor  vehicle  insurance  in  practice  faces  several  problems  with  which  insurers  and                insurances must deal with. There are some problems, insurers and insurers are bored for                years such as insurance claims, total vehicle damage or non-material damage. With some                of them, most of the insurers have already dealt with, for example, counting for wear and                tear in real damage. Some of the problems are just overdue and insurers will have to deal                with them soon, such as applying a more appropriate bonus / malus system.                 4 Conclusions                 The  answer  to  the  question  of  what  development  trends  can  be  expected  in  motor                insurance in the coming years is not easy at all, as it is not entirely clear how the insurance                will change after legislative adjustments in the coming years. Some future things, such as,                for example, tax deduction from motor vehicles, systematic driver education, and changes                in age of driver adepts. The center of interest of the insurers, i.e. commercial insurers must                be  a  client  and  an  understanding  of  his  ever-changing  needs  in  these  motor  vehicle                insurances. The introduction of new technologies will then enable personalized offers to be                provided  by  commercial  insurance  companies  that  take  even  more  into  account  the                individual needs of clients and motorists.                Based  on  our  data  from  selected  private  company,  we  can  positively  identify  that  the                company received higher paid claims then the paid premiums in the analyzed period.                Expected trends in motor vehicle insurance will include the development of digitization and                innovation,  the  expansion  of  insurance  in  compulsory  contractual  insurance  and  the                convergence of compulsory contractual insurance and accident insurance. In the longer                term, insurers will be faced with solving the issue of insurance vehicles.                 Acknowledgments                 The  paper  is  the  output  of  a  scientific  projects  IGA  no.  3/2017  „Development  of                international business and international management in the conditions of globalization”                 References                 Act no. 39/2015 Coll., on Insurance and on Amendments to Certain Acts.                Act no. 747/2004 Coll., on Oversight of the Financial Market.                Act no. 40/1964 Coll., Civil Code.                 Čejková, V., Adamko, J., Kopanický, L. (2016). Insurance, Theory and Practice. Bratislava:                VŠEMvs.                Čejková,  V.  et  al.  (2013).  Insurance  market:  Practicum.  1st  ed.  Trenčín:  Institute  of                Applied Management.                Čejková, V., Martinovičová, D., Nečas, S. (2011). Insurance Market - Theory and Practice.                Bratislava: Iura Edition.                Čejková, V., Nečas, S. (2008). Insurance market. 1st ed. Bratislava: Merkury.                Grmanová, E., Čejková, V. (2016). Insurance market in the Slovak Republic and the Czech                Republic. Trenčín: TnUAD.                                                              107","Grmanová,  E.,  Hošták,  P.  (2016).  Influence  of  selected  environmental  factors  on  the                efficiency  of  commercial  insurers.  In:  Proceedings  of  the  13th  International  Scientific                Conference European Financial Systems 2016. Brno: Masaryk University, pp. 199-206.                Grmanová,  E.  (2015).  Efficiency  of  National  Life  Insurance  Markets  in  Europe.  In:                Proceedings of the 12th International Scientific Conference European Financial Systems                2015. Brno: Masaryk University, pp. 151-157.                 Lisý, J. et al. (2016). Economics. 1st ed. Bratislava: Wolters Kluwer.                Paulík,  D.,  Čejková,  V.  et  al.  (2012).  Fundamentals  of  Finance  and  Currency.  1st  ed.                Trenčín: Institute of Applied Management.                 Slovenská asociácia poisťovní. (2017). Official web site. Retrieved from: www.slaspo.sk.                XY's internal documents for 2008-2017.                                                                                                                              108","Ratio Bias under Conditions of Uncertainty – Experimental                                                         Results                                                                                 2                                                        1                                          Maria Forlicz , Tomasz Rólczyński                                              1  Wroclaw University of Economics                            Faculty of Engineering and Economics, Department of Economic Sciences.                                    118-120 Komandorska Street, 53-345 Wrocław, Poland                                               E-mail: maria.forlicz@ue.wroc.pl                                                  2  WSB University in Wroclaw                                  Faculty of Finance and Accounting, Department of Economics                                       29-31 Fabryczna Street, 53-609 Wrocław, Poland                                          E-mail: tomasz.rolczynski@wsb.wroclaw.pl                 Abstract: It has been found that when making decisions under risk regarding positive                results  (gains),  people  prefer  lotteries  with  probabilities  expressed  as  a  ratio  of  large                numbers (eg. the chance of winning is 30 to 1000) to lotteries with equal probabilities                expressed as a ratio of small numbers (eg. 3 to 100)- it is a phenomenon called ratio bias                (eg.  Denes-Raj,  Epstein  [1994],  Reyna,  Brainerd  [2008]).  However,  since  it  is  usually                impossible to know or to calculate probabilities of occurrence of various events, it seems                plausible  to  claim  that  most  decisions  made  in  everyday  life  are  decisions  under                uncertainty.  That  is  why  the  aim  of  this  article is  to  investigate  people’s  perception  of                chances to win in a lottery under conditions of uncertainty when for the same price they                obtain  fewer  or  more  tickets  (with  equal  prices  and  number  of  tickets  obtained  for  all                participants). It is important to know if buying 1 ticket for 1 euro is as attractive as buying                10 tickets for the same price while the (unknown) probability of winning stays the same.                The aim will be achieved by conducting an experiment with real but non-monetary pay-                offs. Students will be given an opportunity to participate in a lottery in which one can win                additional points to their final score in a subject. In Scenario I a student will be able to                buy one lottery ticket for a small price, while in Scenario II 10 lottery tickets. In both                scenarios the number of winning tickets will be the same. Along with ratio bias theory it is                hypothesized that (with unchanged probability of winning) an increased number of tickets                sold for a specified price will effect an increased demand for the tickets.                Keywords: decision under uncertainty, lottery, expected value                JEL codes: D120, D800                 1 Introduction                 Every person makes hundreds of decisions every day. What to eat, when to get up from                bed, what to wear, whether or not to buy a new car, to lend money to a friend. Scientists                have  shown  that  many  of  those  decisions  depend  on  the  way  the  decision  problem  is                described, which is a violation of the assumption of human rationality. Framing effect, well                known in economic psychology, has been shown to influence people’s decisions made under                risk. For example Tversky and Kahnemann (1981) described a research in which the same                decision-making situation was framed positively and negatively (concentrating on possible                gains or losses), with the participants’ decisions being significantly different. One of the                manifestations of framing effect might be ratio bias effect which occurs when people judge                the  possibility  of  occurrence  of  some  event  as  more  probable  when  its  probability  is                expressed in the form of an equivalent ratio of larger numbers than when it is expressed                in smaller numbers. The aim of the paper is to check whether ratio bias appears also in                decisions made under uncertainty. Definitions and difference between the notions of risk                and  uncertainty  are  a  point  of  interest  of  many  researchers  and  were  described,  for                example,  by Knight  (1964),  von  Thünen  (1910),  Keynes  (1921).  Most  commonly, it is                assumed that the difference lies in knowing (in the former case) or not knowing (in the                latter)  probabilities  of  occurrence  of  some  events  or  probability  distribution.  These                differences can be shown by using the example of a lottery. Deciding whether to play in a                                                             109","lottery in which the number of tickets and the number of winning tickets is known is to                make decision under risk. If the number of tickets is not known or it is not known how                many tickets are winning, we say we are in a situation of uncertainty. The hypothesis to                be  verified  in  the  paper  is  that  (with  unchanged  probability  of  winning)  an  increased                number of tickets sold for a specified price will effect in increased demand for the tickets                because the subjects of the experiment believe that obtaining more tickets will give them                more chances to win. The hypothesis is to be verified on the basis of an experimental                result.                2 Literature review                 Along with Framing Theory, Support Theory proposed by Tversky and Koehler (1994) says                that different descriptions of the same situation can lead to different values of subjective                probability. People offered home insurance against all hazards are less likely to buy this                policy than those who are offered insurance against fire, flooding, theft and other events.                If an event is unpacked into smaller parts, it seems more probable. This situation may be                caused by neglecting denominator and concentrating on the numerator, as in the case of                a jointly packed description, the nominator is 1, and with an unpacked description the                nominator increases to a number bigger than 1. In turn, denominator neglect might be a                reason for ratio bias effect. One of the first studies that revealed the existence of this                phenomenon was conducted by Miller et al. (1989). The authors asked participants what                was more suspicious a) that a child would draw a chocolate cookie from a jar in which there                were 19 oat cookies and one chocolate cookie, with the chocolate cookie being the child’s                favorite or b) that the child would draw a chocolate cookie from a jar in which there were                190 oat cookies and ten chocolate cookies. Participants on average judged a) as more                improbable. Kirkpatrick and Epstein (1992) showed (based on experimental results with                real but small payoffs) that people preferred drawing a ticket from a large pool when the                probability of winning was small and from a smaller pool when the probability of winning                was  high,  and  reversely,  they  preferred  drawing  a  ticket  from  a  large  pool  when  the                probability of losing was high and from a smaller pool when the probability of losing was                low. Denes-Raj and Epstein (1994) even showed that  82% of subjects made at least one                non-optimal choice deciding to draw a ticket (actually a jelly bean) from a greater pool                with a smaller percentage of winning tickets (actually red jelly beans) instead of drawing                from a smaller pool but with a greater percentage of winning tickets. Slovic et al. (2000)                demonstrated that 40% of clinical psychologists refused to discharge a mental patient from                an acute civil mental health facility when risk of violence was stated as follows: “20 out of                every 100 patients similar to Mr. Jones are estimated to commit an act of violence” and                only  20%  refused to  discharge the  patient  when  risk  was  given  as  “2  out  of every 10                patients  similar  to  Mr.  Jones  are  estimated  to  commit  an  act  of  violence.”  Alonso  and                Fernandez-Berrocal  asked  participants  a  hypothetical  question  which  job  position  they                would choose to apply for themselves, and which job position they thought other people                would choose to apply for if applying for one job gave 3 to 10 probability of obtaining the                job, and in the latter case the probability was expressed as a 10 to 100 ratio. 68.6% would                choose to apply for the first position but only 48.6% of participants believed that the others                would make the same decision. 25.7% believed that from a logical perspective it was better                to choose applying for the second job. Rudski and Volksdorf (2002) surprisingly found that                the ratio bias was more prevalent for choices presented graphically than for those described                textually. Amsel et al. asked participants which choice was rational: choosing a jar with 1                to 10 or 10 to 100 probability of winning, or being indifferent between drawing from the                two jars. Only around 31% of subjects was moderately or strongly convinced that the only                rational answer was being indifferent. However, Passerini et al. (2012) argue that ratio                bias is only an experimental artefact and it decreases sharply when participants are given                the option that allows them to express the correct answer (i.e. being indifferent between                two equally probable options). In the present paper the authors describe an experiment in                which subjects made a choice under condition of uncertainty and they were not given a                choice between lotteries, with each participant choosing only how many lottery tickets to                buy. Next, the demand for the tickets in two groups (which differed in the number of tickets                obtained for a specific price) was compared. Separately, it was checked in a survey (with                                                            110","different subjects) which lottery seemed more attractive and why. However, in the survey                it was possible to say that the two lotteries were equally attractive.                 2 Methodology and Data                 In order to test the hypothesis stating that an increased number of lottery tickets sold for                a specific price will increase the demand for lottery tickets, and to achieve the objective                outlined in the introduction, an experiment was carried out. Further on in the paper, this                study  will  be  referred  to  as  an  experimental  study.  Subjects  of  the  experiment  were                students  of  two  universities,  whose  total  number  was  139  persons.  The  experiment                involved  offering  students  to  participate in  a lottery in  which they  could  win additional                points needed to obtain credit in a subject. The experiment was conducted according to                two different scenarios:                 Scenario I:                As part of a scientific experiment, I’d like to offer you participation in a lottery where the                winning  ticket  is  21  points.  Everyone  of  you  can  buy  a  chance  of  drawing  10  lottery                tickets in that you write down your first name and surname by hand on a sheet of paper                a  100  times;  if  anyone  feels  like  buying  lottery  tickets  for  a  multiple  of  100  complete                signatures, in other words, for example, a chance to draw 20 lottery tickets from the box                for 200 complete signatures, he or she is very welcome do so. In the box from which you                will be drawing lottery tickets there will be 3 tickets which give you 21 points. The total                number of tickets in the box depends on how many tickets the entire group will buy. Now                please approach and tell me how many tickets you would like to buy. Please, make sure                your  declaration  is  well  thought-out,  because  your  withdrawing  will  have  negative                consequences in terms of points.                Scenario II:                 As part of a scientific experiment, I’d like to offer you participation in a lottery where the                winning ticket is 21 points. Everyone of you can buy a chance of drawing 1 lottery ticket                in that you write down your first name and surname by hand on a sheet of paper a 100                times; if anyone feels like buying lottery tickets for a multiple of a 100 complete signatures,                in  other  words,  for  example,  a  chance  to  draw  2  lottery  tickets  from  the  box  for  200                complete signatures, he or she is very welcome to do so. In the box from which you will                be drawing lottery tickets there will be 3 tickets which give you 21 points. The total number                of tickets in the box depends on how many tickets the entire group will buy. Now please                approach and tell me how many tickets you would like to buy. Please, make sure your                declaration is well thought-out, because your withdrawing will have negative consequences                in terms of points.                Students  were informed  that in  order to  participate in  the lottery  one  was  required  to                refrain from any communication with other participants before making the decision about                the number of lottery tickets one wanted to buy. The reason for this requirement was to                make the conditions for decision-making uncertain, that is, when deciding to buy lottery                tickets, participants would not know how many tickets had already been bought by other                participants, and thus would not be able to calculate the probability of winning. Including                in the scenario the clause about negative consequences, if one should fail to commit to                “payment” for the ordered tickets, was meant to prompt students to make well-thought                out decisions. Moreover, one should note that unlike other studies concerned with ratio                bias, one did not manipulate here with the proportions of winning tickets in relation to a                larger or smaller denominator (thus, the first group was not told that there were 3 winning                lottery tickets to 100, and in the second group 30 to 1000); what was changed was the                number  of  chances  a  given  person  could  win,  while  the  probability  of winning  was the                same.                Furthermore, with a view to verifying the assessment as to which lottery seemed more                favorable,  an  quasi-experiment  was  conducted  in  that  participants  were  offered  a                hypothetical possibility of purchasing lottery tickets in one of the above mentioned lotteries                (Scenario I or Scenario II), that is, purchasing 10 lottery tickets for a 100 signatures, or                                                            111","purchasing  1  lottery  ticket  for  100  signatures  (or  multiples  of  100).  Students  were                supposed to indicate which lottery they would like to participate in and in addition, they                were to provide a rationale for their choice. The objective was to collect information on the                evaluation of attractiveness of those lotteries, and, first and foremost, the information on                the  motives  which  guided  people  in  their  evaluation  of  attractiveness.  This  study  was                conducted  on  142  people,  who  were  not  the  same  as  those  who  were  subject  to  the                experiment  described  earlier.  This  afforded  the  possibility  to  define  which  lottery                participants would choose and which they would find more favorable.                3 Results and Discussion                 139  people  participated  in  the  experiment.  Table  1  presents  the  results  of  descriptive                statistics for the experimental study conducted. With a view of consolidating and comparing                the results as to the number of lottery tickets purchased, the number of the lottery tickets                bought in the experiment, which was carried out according to Scenario I, was divided by                10. The results showed that the mean number of lottery tickets bought in the Scenario II                experiment  (1  ticket  for  100  signatures)  was  bigger  than  the  mean  number  of  lottery                tickets bought in the Scenario I experiment (10 tickets for 100 signatures). Those means                were statistically significantly different at a significance level α=0.05. Also, the median for                Scenario II (3 lottery tickets) was bigger  than for the Scenario I experiment (2 lottery                tickets). The distributions were characterized by the same modes (1 lottery ticket) and                right-hand side asymmetries.                  Table 1 Descriptive Statistics on the Number of Lottery Tickets Bought in the Case of                                                 Choice under Uncertainty                                                                              Descriptive statistics                                       Variable                                                                          Mean    Median  Mode       Sd                  purchased tickets in the case of 10 tickets for                   100 signatures (number of tickets divided by          2.5429     2.0     1.0    2.7698                                   10) – Scenario I                    purchased tickets in the case of 1 ticket for        3.6522     3.0     1.0    3.4803                            100 signatures – Scenario II                                                 Source: Authors’ own study                 The distribution of the number of lottery tickets bought is presented in Figure 1. Extreme                observations were disregarded in the distribution. Subjects were characterized by a smaller                variation in terms of the number of tickets bought in the lottery conducted according to                Scenario II, as compared to the Scenario I lottery. Surprisingly, there were fewer people                who were not willing to buy any tickets at all in Scenario II (4% vs. 10% in Scenario I);                and in Scenario II there was a substantial number of people who were willing to buy 10                tickets. Moreover, in Scenario I, buying two tickets was as nearly popular as buying one                ticket.                                                                         112","Figure 1 Distribution of the Number of Purchased Tickets in the Case of 1 for 100                                           Signatures and 10 for 100 Signatures                                45%                              40%                              35%                              30%                              25%                              20%                              15%                              10%                              5%                              0%                                     0    1    2    3     4    5    6     7    8    9    10                                    number of purchased tickets 10 for 100 signatures divided by 10 (Scenario I)                                   number of purchased tickets 1 for 100 signatures (Scenario II)                                                  Source: Authors’ own study                 The second study intended to show which lottery respondents would choose if they had                such choice. 52% of respondents would play the lottery according to Scenario I, while 41%                would play the lottery following Scenario II. Moreover, considering the rationale for those                choices it was determined which lottery respondents found more attractive on the whole.                Attractiveness  evaluation  sometimes  differs  from  the  decision  as  to  which  lottery  a                particular respondent would participate in. The comment that was frequently stated was                that “my friends will surely choose lottery X, so I choose…”. This implies that in making                their decision on lottery choice some respondents were prompted by predictions as to how                other participants would behave and what would be their preferences, trying to increase in                this way their own chances of winning. The results collected in the form of commentaries                and rationale for respondents’ choices allowed for assessing which lottery was perceived                as more attractive. Even if the participant chose lottery designed according to Scenario II                justifying his choice by saying that others would choose the Scenario I lottery, “because it                is more attractive,” that would indicate that in his view the lottery according to Scenario I                was more attractive. The results of attractiveness evaluation are presented in Figure 2.                    Figure 2 Percentage of respondents stating specified lottery was more favourable                                                             13%                                                       28%                                                                    59%                                                   Scenario I  Scenario II  there is no difference                                                  Source: Authors’ own study                 Respondents decided that the more attractive lottery was the one designed according to                Scenario I. 59% found the Scenario I lottery more attractive, while 28% found the lottery                conducted according to Scenario II more attractive. 13% of respondents indicated that                                                             113","they saw no difference in terms of the lottery attractiveness, stressing, in the justification                of their choice, that the chances of winning were the same for both lotteries, since it did                not matter whether one would buy one lottery ticket (or a multiple) for 100 signatures or                10 tickets (or a multiple) for 100 signatures. Still, out of the 13% there were some who                chose to participate in either lottery, hence when it came to choosing the game, only 7%                was reported to be indifferent as to the choice of the lottery. The analysis of the arguments                justifying respondents’ decisions allowed for explaining their behaviour. As a rationale for                choosing  a  particular  lottery,  the  majority  of  respondents  wrote  that  they  discerned  a                greater chance of winning when buying 10 lottery tickets for 100 signatures. Moreover, in                the second form of arguments for a particular choice it was claimed that one needed to                buy more lottery tickets in the lottery according to Scenario II so as to even up the winning                chances, as compared to Scenario I. This kind of rationale explained why in the experiment                there was a higher average demand for lottery tickets in the lottery conducted according                to Scenario II. Respondents believed that buying tickets for lottery designed according to                Scenario I provided a bigger chance of winning than in the Scenario II lottery. In order to                increase their chance, they bought more lottery tickets in the experimental study in order                to  have  a  greater  chance  of  winning.  One  can  view  this  situation  from  the  reverse                perspective.  If  participants  bought  10  lottery  tickets  for  100  signatures  in  the  study                according to Scenario I, then they saw their chance of winning as high enough so as not                to feel induced to buy a bigger number of lottery tickets. Persons who found the Scenario                II lottery more favourable argued that in this case the lottery pot would be smaller and                thereby their chances of winning would increase. Persons who decided to participate in the                Scenario II lottery noted that many people would not be willing to write 100 signatures                just for one lottery ticket, and consequently they would buy fewer tickets, which in turn                would increase the winning chances of persons filling up the questionnaire because they                were less lazy than others.                 4 Conclusions                Two  studies  were  conducted  in  order  to  test  the  hypothesis  stating  that  an  increased                number of tickets sold for a specific price would increase the demand for lottery tickets.                The first study was experimental, while the second was quasi-experimental, since subjects                were placed in the situation of a hypothetical choice. Additionally, in the quasi-experimental                study, information was gathered on the motives behind the subjects’ choices. On the basis                of  the  collected  information,  one  can  assert  that  the  lottery  which  people  found  more                attractive was the one in which they could receive more lottery tickets for a specific price.                In particular, in comparing the two lotteries, respondents explained their choices saying                that they saw more chances of winning in the lottery designed according to Scenario I,                compared to the Scenario II lottery. The hypothesis advanced was not confirmed. A greater                number of lottery tickets to be bought for a specific price did not increase the demand for                lottery tickets. On the contrary, it was the smaller number of lottery tickets for the same                price that motivated  participants  to  greater  effort  and  to the  purchase  of more  lottery                tickets. The rationale for this situation came with the analysis of the answers provided by                the participants of the quasi-experimental study who argued that given that the lottery                conducted according to Scenario I gave a bigger chance of winning, there was no need to                buy a multiple of 10 lottery tickets, which resulted in a decreased demand for lottery tickets                in this lottery.                 References                 Alonso,  D.,  Fernández-Berrocal,  P.  (2003).  Irrational  decisions:  Attending  to  numbers                rather than ratios. Personality and Individual Differences, vol. 35(7), pp. 1537-1547.                Amsel,  E.,  Close,  J.,  Sadler,  E.,  Klaczynski,  P.  (2009).  College  Students'  Awareness  of                Irrational  Judgments  on  Gambling  Tasks:  A  Dual-Process  Account.  The  Journal  of                psychology, vol. 143, pp. 293-317.                                                                 114","Denes-Raj,  V.,  Epstein,  S.  (1994).  Conflict  between  intuitive  and  rational  processing:                When  people  behave  against  their  better  judgment.  Journal  of  Personality  and  Social                Psychology, vol. 66, pp. 819–829.                Keynes J. M. (1921). A Treatise on Probability. London: Macmillan and Co.                Kirkpatrick,  L.A.,  Epstein,  S.  (1992).  Cognitive-Experiential  Self-Theory  and  Subjective                Probability:  Further  Evidence  for  Two  Conceptual  Systems.  Journal  of  Personality  and                Social Psychology, vol. 63, pp. 534-544.                Knight, F. H. (1964). Risk, Uncertainty and Profit, Reprints of Economic Classics. New York:                Augustus M. Kelley.                 Miller, D. T., Turnbull, W., McFarland, C. (1989). When a coincidence is suspicious: The                role of mental simulation. Journal of Personality and Social Psychology, vol. 57(4), pp. 581-                589.                Passerini,  G.,  Macchi,  L.,  Bagassi,  M.  (2012).  A methodological  approach  to  ratio  bias.                Judgment and Decision Making, vol. 7(5), pp. 602–617.                 Reyna, V. F., Brainerd, C. J. (2008).  Numeracy, ratio bias, and denominator neglect in                judgments of risk and probability. Learning and Individual Differences, vol. 18, pp. 89–                107.                Rudski,  J.,  Volksdorf,  J.  (2002).  Pictorial  versus  textual  information  and  the  ratio-bias                effect. Perceptual and motor skills, vol. 95, pp. 547-54.                Slovic,  P.,  Monahan,  J.,  MacGregor,  D.  G.  (2000).  Violence  risk  assessment  and  risk                communication: the effects of using actual cases, providing instructions and employing                probability versus frequency formats. Law and Human Behavior, vol. 24, pp. 271–296.                Thünen,  J.H.  von  (1910).  Der  isolierte  Staat  in  Beziehung  auf  Landwirtschaft  und                Nationalökonomie. Jena: Verlag von Gustav Fischer.                Tversky,  A.,  Kahneman,  D.  (1981).  The  Framing  of  decisions  and  the  psychology  of                choice. Science, vol. 211 (4481), pp. 453–58.                 Tversky,  A.,  Koehler,  D.  (1994).  Support  Theory:  A  non-extensional  representation  of                subjective probability. Psychological Review, vol. 101, pp. 547–567.                                                                                             115","Actuarial Model for Pricing Disability Insurance Policy                                                Ján Gogola, Lucie Kopecká                                                       University of Pardubice                                           Faculty of Economics and Administration,                                      Institute of Mathematics and Quantitative Methods                                               Address: University of Pardubice                                           Faculty of Economics and Administration                                              Studentská 84, 532 10 Pardubice                                      E-mail: jan.gogola@upce.cz, lucie.kopecka@upce.cz                 Abstract:  The  main  objective  of  our  contribution  is  to  apply  stochastic  processes  for                disability policy that gives annuity benefit in case of temporary or permanent disability. We                apply durational effect to the disable state, by splitting it into several states. Using the                data supplied by the Continuous Mortality Investigation (CMI) we calculate the single and                annual premiums for that policy.                 Key words: disability insurance, Markov process, semi-Markov process, splitting of states,                JEL Classifications: C51, C52, G22, J11                 1 Introduction                The main goal of this paper is to apply Markov process for disability income insurance                benefits. The data we used in our contribution were supplied by the Continuous Mortality                Investigation  (CMI).  The  CMI  is  a  research  organisation  established  by  UK  actuarial                profession.                Disability  insurance,  long-term  care  insurance  and  critical  illness  cover  are  becoming                increasingly important in developed countries as it is mentioned in Pacáková, V., Jindrová,                P. (2014). The private sector insurance industry is providing solutions to problems resulting                from  these  pressures  and  other  demands  of  better  educated  and  more  prosperous                populations.                 Most of the disability policies in UK are accelerated policies (88%) and they are attached                to life insurance, term insurance or endowments. Typically, regular premiums are payable                throughout the term while the policy is in force.                We  describe  the  actuarial  structure  of  disability  insurance.  Actuarial  problems  such  as                pricing  and  reserving  are  considered  within  the  context  of  multiple  state  modelling,                providing a strong and sound framework for analysing personal insurances.                Our contribution is based on Markov process that can be used to develop a general, unified                and  rigorous  approach  for  describing  and  analysing  disability  and  related  insurance                benefits.  The  use  of  Markov  process  or  Markov  chain  in  life  contingencies  and  their                extensions has been proposed by several authors; for example Dickson, D. C., Hardy, M.                R., \& Waters, H. R. (2013), Haberman, S., \& Pitacco, E. (1998).                  2 Methodology and Data                 Multiple  state  models  are  one  of  the  most  exciting  developments  in  actuarial  science                nowadays.  They  are  a  natural  tool  for  many  important  areas  of  practical  interest  to                actuaries.  They  provide  solid  foundation  for  pricing  and  valuing  complex  insurance                contracts.  Many  actuarial  applications  are  modelled  as  time  inhomogeneous  Markov                processes. Markov process assumes that probabilities of transitions at any time t depend                only on the current state and not on the past. A Markov model for disability insurance has                state  space  S    =  {H,  S,  D},  where  ‘H’  means  healthy,  ‘S’  sick  (or  ill)  and  ‘D’  dead.                Transitions rates at each age x are illustrated in the Figure 1. An individual is, at any time                t, in one of three states, “Healthy”, “Sick” or “Dead”. We can use this simple three state                                                              116",")                                                   Y                model to define a random variable  (t  which takes one of the three values ‘H’, ‘S’ and ‘D’.                Suppose we have an individual aged x years at time t=0. The event Y(    t =)  H  means that                an individual is healthy at age x+t, and Y ) ( t =  D  means that an individual died before age                x+t. The set of random variables  (tY   )   t  0  is an example of a continuous time stochastic                                                 Y                process. We will assume that  (t   )   t  0  is a Markov process. A policyholder is supposed to                be healthy at the time of the commencement of the policy and he/she stays in this state                until at some time he/she transits to one of the 2 possible states, that means a death or                an illness occurred.                                  Figure 1 The Disability Income Model – Markov Model                                                                                                             Healthy                                     Sick                                                                                 S                                       H                                                                                                                                                                                                                                                                                                                                                       Dead                                                              D                                                    Source: Own processing                 It  is  perhaps  more  realistic  to  modify  the  previous  model  so  as  rates  from  “Sick”  to                “Healthy” and from “Sick” to “Dead” depend on the length of time z already spent in the                “Sick” state, as well as on the age x of the individual. But then the Markovian property of                the process is lost. This approach gives more complicated insurance policies which require                more sophisticated model – semi-Markov model.                                Figure 2 The Disability Income Model – Semi-Markov Model                                                                                                                                                                     Healthy                                     Sick                                                                                 S                                       H                                                                                                                               ,                                                                                    ,                                                                                                                                                   Dead                                                              D                                                    Source: Own processing                 The  disability  income  insurance  pays  a  benefit  during  periods  of  sickness,  the  benefit                ceases on recovery. Figure 2 shows a model suitable for policy which provides an annuity                benefit while person is sick, with premiums payable while the person is healthy. The model                represented  by  Figure  2  differs  from  that  in  Figure  1  in  one  important  aspect:  the                                                             117","dependence of transition rates (and probabilities) on the time spent in the state “Sick”                since the latest transition to that state.                                              Figure 3 Splitting of “Sick” State                                                                                                                                      1     Sick 1              1                                                                                                         S1                                        Healthy               1                                     Dead                                                                  1    2               D                                                                                                                                         2                                H                                                                                                     Sick 2                                                 2                                                             S                                                           2                                                                     2    3                                                                               . .                                                                                                          Sick m                                                          S                                                           m                     +1                                                                                                                                +1                         +1                                                                                                                        Sick m+1                                                          S m+1                                                    Source: Own processing                We restrict the influence of the durational effect to some specified states. Let us replace                state “Sick” by m+1 states S1, S2,…, Sm, Sm+1, where state                S1 means that an insured is disabled with duration of disability between 0 and 1 units of                time,                S2 means that an insured is disabled with duration of disability between 1 and 2 units of                time, …                 Sm means that an insured is disabled with duration of disability between m-1 and m units                of time,                 Sm+1 means that an insured is disabled with duration of disability longer than m units of                time.                 Thus, we have made a splitting of state “Sick” of the state space S  = {H, S, D}. New state                space is as follows: S  * = {H, S1, S2,…, Sm, Sm+1, D}. Hence, we formally revert to the                                      *                Markov process {  (  );    ≥ 0} based on the state space S  *.                                                                       *                Thus, the introduction of more states representing the durational effect is a notational tool                for treating semi-Markov model within the simpler Markov framework.                The splitting allows us to consider select intensities (and probabilities) without formally                introducing a semi-Markov model (leading to major difficulties).                                                                                  ij                In general case, with states H, S1, S2,…, Sm, Sm+1, D we refer to   as the force of transition                                                                                 x                or  transition  intensity  between  states  i  and  j  at  age  x.  The  transition  intensities  are                fundamental quantities which determine everything we need to know about a multiple state                model.                                                              118","The model requires the following transition intensities:                                   ,         1 ,       1    ,       1    2 ,       1    ,       2    ,          2    3  ,          2    , … ,                ,               +1 ,             ,         +1    ,            +1    .                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      As far as recovery is concerned, morbidity experience suggests:                                                    1     >           2     > ⋯ >                 >         +1    .                                                                                                                                                                               +1                   In particular it is possible put        = 0, in the case of no recovery is possible after m units                of time.                Let  us  assume  that  a  time-continuous  Markov  model  has  been  assigned.  Thus,  the                transition intensities have been specified and the transition probabilities have been derived.                It is self-evident that the implied time discrete probabilistic structure can be immediately                derived. To do this, we simply have to restrict our attention to transition probabilities                                                                                                                                                                                                                                               only, where x, t now denote integer values, so we get time-discrete Markov model.                Our splitting states model requires the following transition probabilities                                     ,         1 ,          1    ,          1    2 ,          1    ,          2    ,          2    3 ,          2    , … ,                ,                  +1 ,                ,            +1    ,            +1    .                                                 3 Results                Consider the model (Figure 3) for a disability income insurance. The disability state is split                into six states (due to available data). For states S1, S2,…, S5 recovery is possible, whilst                S6 is assumed to represent permanent disability. These assumptions lead to the one-year                transition matrix (or the transition matrix of one-year probabilities) of Table 1.                                       Table 1 One-year Transition Probabilities                                                                                                                                 H     S1    S2    S3     S4    S5     S6    D                                     H                        1    0   0   0   0   0                                                                                                    1                                           1                                      S1          0          1    2    0   0   0   0                                                                                                                                        2                                           2                                      S2          0     0          2    3    0   0   0                                                                                                                                        3                        3    4             3                                      S3          0     0      0            0     0                                                                                                                                S4        4       0   0   0    0           4    5    0         4                                                                                                                             S5        5       0   0   0    0      0           5    6         5                                                                                                                             S6    0     0     0      0     0      0             6    6            6                                       D    0     0     0      0     0      0     0     1                                                    Source: Own processing                 For calculation we use transition intensities from the CMI Working paper 12. From these                data we apply our model for the probabilities of disablement, i.e.            1 ,                                  1  =    −6,11+0,0458∙   ,                                             (1)                and for the probabilities of recovery as a function of attained age x. For each state S1, S2,…,                S6, the following functions are used                                                              =    −    ∙   ,                                                           (2)                                                                                      for j =1, 2,…, 6.                The parameters    ,     are given in Table 2.                                                                                                                                          119","Table 2 Parameters for Probabilities of Recovery                                                     j                                                                                                                                                                                                       1  1,4161  0,0222                                                    2  0,7400  0,0115                                                    3  0,3739  0,0058                                                     4  0,2855  0,0046                                                    5  0,1918  0,0031                                                    Source: CMI Report 12                We assume that the health mortality equals to general population mortality                                            1                                   =    = 1 − exp {− ∫      +    d  }  ,                                (3)                                                                            0                where       +     is  given  by  Gompertz-Makeham  law  of  mortality       +    =          =    +    ∙      +   ,                                                                                             +                  with    = 0.0005,    = 7,5858 ∙ 10  and     = 1.09144.                                              −5                                                                                     Probabilities of death for disabled insured,        (for j =1, 2,…, 6), are assumed to be equal                to (1 +   ) ∙            , where  is the extra level of mortality of disabled. The technical rate of                interest i = 0.015 p.a. (or 1.5 % p.a.) has been assumed.                Let   ̈   :    denote the actuarial value of an annuity                                                                    ̈   :    = ∑   −1              ∙     ,                                         (4)                                                                       =0                                                    1                where v is the discount factor (   =   ).                                                   1+                  The disability benefit (one monetary unit) is paid whatever disability state S1, S2,…, S6 is                occupied by insured.                Hence, the actuarial value of the disability benefit is defined, for a healthy individual, as                follows                                                                                = ∑     ∑ 6                 ∙      .                                    (5)                                 =1                          :                                       =1                                                          The disability annuity is assumed to be payable up to the end of the policy term n.                The actuarial value of the disability benefit is defined, for a disabled individual aged x+t                who occupies the state     (j = 1, 2, …, 6) as follows                                                                                                  ℎ                                                                                      = ∑   −    ∑ 6      ∙     ;  (t = 1, 2, …, n),                          (6)                          +  :  −  +1    =0  ℎ=1        +                                     ℎ                where       +    = 1 if ℎ =    and 0 otherwise.                        0                The annual premium,    , payable for n years while the insured is healthy, is given by:                                         :                                                                :    =       :   .                                                          (7)                               ̈   :                  The calculation of the actuarial values defined by equations (4), (5) and (6) and then the                calculation  of  premium  according  to  formulae  (7)  imply  the  use  of  the  underlying                probabilities,  which  can  be  derived  from  one-year  transition  matrix       (Table  1).  The                                                                                                            product of one-year transition matrices at successive ages is a two-year transition matrix                   ∙      +1  =    .  Continuing  the  matrix  multiplication  through  subsequent  ages,  we  can                            2                                                                    determine the k-year transition matrix      for any integer k.                                                                                                                                      Table 3 presents numerical results for policy terms n = 10, 15, 20 years and  =0.20 and                illustrate single and annual premiums calculation in a time-discrete Markov context.                                                               120","Table 3 Single and Annual Premium,    = 0.20.                                         Term\\Age       x =30     x=40       x=50                                            n = 10      0.18053  0.39654  0.81965                                            n = 10      0.01977  0.04469  0.09831                                            n = 15      0.36988  0.84171  1.72679                                           n = 15      0.02840  0.06785  0.15502                                            n = 20      0.66287  1.49959  2.88050                                            n = 20      0.04030  0.09819  0.22109                                                    Source: Own processing                 4 Conclusions                We have presented an application of multiple state models to problems in actuarial science.                There are various extensions of multiple state models. One way is to allow the transition                intensities out of a state to depend not only on individual’s current age but also on how                long they have been in current state. This breaks the Markov property assumption and                leads to the new process known as a semi-Markov process. This could be appropriate for                the disability income insurance process where the intensities of recovery and death from                the sick state could be assumed to depend on how long the individual had been sick, as                well as on current age.                We have to emphasize that no attempt has been made to discover a precise relationship                between          and                 (for j = 1, 2, …, 6), i.e. probabilities of death for healthy and disabled                                            insured. It does however suggest that an office which calculate disabled lives’ reserves for                claims in force, by assuming mortality according to a  standard life table, may have an                implicit margin of strength in its reserving basis. The Figure 4 illustrates the behaviour of                single premium as a function of the extra level of mortality .                 The transition intensities are fundamental quantities which determine everything we need                to know about a multiple state models. Therefore it would be useful to have data from                domestic insurance industry. Our further research will focus on estimation of transition                intensities for the Czech Republic (or other central European countries) in similar manner                as in Pacáková, V., Jindrová, P., Seinerová, K. (2013).                There is a need for awareness of model risk when assessing a disability income insurance                benefits and/or critical illness benefits, especially with long term. The fact that transition                intensities can be estimated does not imply that they can sensibly describe future medical                development.                                                                          121","Single premium as a function of eta                  Figure 4 Single Premium as a Function of the Extra Level of Mortality ; x=40, n=20.                      1.51                         1.50                   Single_premium  1.49                           1.48                          1.47                             0.0               0.2               0.4              0.6               0.8                                                   Source: Own processing                                                             eta                                                    Age x=40, Term n=20                Acknowledgments                This research could be performed due to the support of the University of Pardubice student                project  grant  no.  SGS_2018_012,  Využití  rozsáhlých  souborů  dat  pro  management                vybraných  rizik  ve  veřejném  a  finančním  sektoru.  (Faculty  of  Economics  and                Administration).                 References                Continuous Mortality Investigation Report  – CMIR12 (1991). The Analysis of Permanent                Health  Insurance  Data.  Continuous  Mortality  Investigation  Bureau,  The  Institute  of                Actuaries and the Faculty of Actuaries.                 Dickson,  D.  C.,  Hardy,  M.  R.,  Waters,  H.  R.  (2013).  Actuarial  mathematics  for  life                contingent risks. Cambridge University Press.                Gogola,  J.,  Kopecká,  L.  (2017)  Multiple  state  models for  Critical  illness  policy,  In:                Proceedings of the 14th International Scientific Conference European Financial Systems                2017. Brno: Masaryk University, pp. 159-164. ISBN: 978-80-210-8609-8.                 Haberman, S., Pitacco, E. (1998). Actuarial models for disability insurance. London: CRC                Press, Chapman \& Hall.                Pacáková,  V.,  Jindrová,  P.  (2014).  Quantification  of  Selected  Factors  of  Longevity.  In:                Proceedings  of  the  2014  International  Conference  on  Mathematical  Methods  in  Applied                Sciences  (MMAS’14).  Saint  Petersburg:  Saint  Petersburg  State  Polytechnic  University,                pp. 170-174.                Pacáková, V., Jindrová, P., Seinerová, K. (2013). Mortality Models of Insured Population in                the Slovak Republic. In: Proceedings of the 7th Professor Aleksander Zelias International                Conference on Modelling and Forecasting of Socio-Economic Phenomena. Zakopané, pp.                99-106.                                                              122","Effectivences of Commercial Banks in Poland versus Bank Tax                                                       Ewa Gubernat                                               Wroclaw University of Economics                                                    Finance Department,                                         ul. Komandorska 118-120, 53-345 Wrocław,                                               email: ewa.gubernat@ue.wroc.pl                 Abstract:  Effectiveness  in the  era  of a  market  economy is  one  of  the  most  important                financial categories regarding the degree of achievement of the assumed priority. The same                applies  to  banking  operations,  where  efficiency  management  has  already  become  a                separate area of unit management. As part of the efficiency quantification process, there                are  used  four  groups  of  indicators:  profitability,  margins,  workloads  and  employment                efficiency.  They  are  an  important  analytical  instrument  allowing  to  assess  whether  the                bank's activity is conducted economically. The aim of the article is to assess the impact of                a bank tax on the operational efficiency of commercial banks in Poland and the effects of                its introduction on banking services for costumer. The author draws attention to insufficient                research on the consequences of tax and the stability of the banking system, in particular                in the context of low interest rates and increased burdens borne by banks. The author                accepted the hypothesis that the introduction of a new tax burden resulted in lowering                basic ratios of banks' profitability and an increase in costs incurred by clients in connection                with the use of banking products. The analysis of aggregated financial data was used to                verify hypotheses.                Keywords: bank tax, commercial banks, ROA, ROE                JEL codes: G21, H24, H71                 1 Introduction                 Efficiency in the era of a market economy is one of the most important financial categories.                It mainly concerns the degree of achievement of the assumed goal. The same applies to                banking operations, where efficiency management has already become a separate area of                unit management. The banks' activities are additionally subject to taxes. The introduction                of  a  bank  tax  prevents  the instability of  financial  markets  and limits  the  risk  taken  by                banks.                 In 2009-2012, the bank tax was introduced in 14 EU countries. This action was a reaction                to the moral hazard of banks. The most important argument for the additional burden was                the fact that private banks on the one hand led to the financial crisis and often benefited                from  the  help  of  public  funds.  Therefore,  they  should  bear  financial  responsibility                responsibility (financial crisis liability fee). It was recognized then that it would limit the                unstable sources of bank financing and would allow the governments of the EU countries                to recover funds previously spent on rescuing the banking sector.                The Polish banking sector, which is the main source of financing the economy, is developing                steadily. However, the adoption of a bank tax in Poland requires deeper reflection. Lack of                evaluation of the potential effects of establishing this tribute can cause long-term negative                consequences for the development of both the banking sector and the entire economy. It                is extremely important that this type of tax does not generate a risk for financial stability                and  the  dynamics  of  bank  lending,  which  may  ultimately  harm  the  growth  of  the  real                economy. As a result, the conditions of loans or other services provided by banks could                deteriorate and uncertainty could be created for these institutions.                The aim of the article is to assess the impact of a bank tax on the operational efficiency of                commercial  banks in  Poland  and the  effects  of its introduction  on  banking  services  for                consumers. The author accepted the hypothesis that the introduction of a new tax burden                resulted in lowering basic ratios of banks' profitability and an increase in costs incurred by                clients in connection with the use of banking products. The analysis of aggregated financial                data was used to verify hypotheses.                                                            123","The introduction of a bank tax in Poland is unjustified due to the fact that these banks did                not  use  state  financial  assistance,  nor  were  they  the  cause  of  the  financial  crisis.                Meanwhile, his goal was to increase the state budget revenues. In Poland, it was decided                to accept additional banking tax in the form of tax. It was adopted by the law on tax from                some financial institutions in early 2016. In contrast to regulations adopted in most EU                countries, the Polish banking tax is purely fiscal. Bank tax in Poland differs significantly                from regulations adopted in most EU countries. In EU countries, the basis for taxation of                banks is usually their liabilities, these taxes are not strictly related to the scale of operations                of banks, do not have a fiscal objective, and their amount is mainly due to the risk of                operations. The project promoters justify its introduction with the necessity to increase the                share of the financial sector in incurring tax burdens and its high profitability (Cichy and                Puszer, 2016). The act stipulates that the bank tax is income of the state budget, whereas                the subject of tax is the assets of entities that are its taxpayers. The Polish banking tax is                not related to specific risks related to the financial system. Nor does it apply to public aid                previously granted to the financial sector. Therefore, it may happen that its taxpayers will                undertake actions (transactions) that do not affect the amount of their balance sheet total                due  to  their  desire  to  avoid  paying  this  tribute.  The  least  justified  reason  for  the                introduction of a bank tax is the fiscal objective, because the inevitable transfer of its costs                to bank customers will mean an increase in the cost of loans, possibly a drop in interest                rates on deposits or an increase in the prices of banking operations. The fiscal goal of a                redistributive nature, although justified from the point of view of the state budget, is not                justified  in  the  case  of  caring  for  the  safety  and  stability  of  the  banking  sector.  The                introduction of asset tax encourages financial institutions to optimize their balance sheets.                Asset taxation may reduce lending in the long term, including a move away from long-                term low-margin loans to more lucrative and short-term consumer loans and credits. Banks                can theoretically increase the share of risky off-balance sheet transactions. The method of                reducing the value of the tax base is, for example, the sale of portfolios of working and                non-working loans. In addition, banks may provide them with guarantees or sureties to                secure their bonds issued by these companies instead of financing their subsidiaries with                loans.                Banks  can  theoretically  increase  the  share  of  risky  off-balance  sheet  transactions.  The                method  of  reducing  the  value  of  the tax  base is,  for  example,  the  sale  of  portfolios  of                working and non-working loans. In addition, banks, in order to reduce the tax instead of                financing their subsidiaries with loans, may give them guarantees or sureties constituting                collateral for bonds. A bank contract is a construction classified in the so-called property                taxes. Many indications have been formulated for the introduction of a bank tax and its                introduction (Kil and Ślusarczyk, 2014):                 The most important arguments are:.                    •   Greater financial stability                    •   Less financial speculation                    •   Lower costs of bank billing in the future due to limited use of public funds                    •   Possibility  of  allocating the  collected funds  from  the tax for  the  restructuring  of                       banks in a difficult financial situation                    •   In turn, the following arguments include, among others:                     •   Increase in prices of products and banking services                    •   Lack of equal treatment of all financial institutions                    •   Lower ability of banks to accumulate capital                    •   Reducing the efficiency of banks and, consequently, weakening their competitive                       position in the global financial market                    •   Difficult separation of speculative and hedging transactions                The banking sector shows profits similar to other sectors of the economy, which is why                the legitimacy of charging banks due to their allegedly extraordinary profit in relation to                the non-financial sector is questioned.                                                               124","2 Methodology and Data                The theoretical part of the work was based on a study of the subject literature, while the                empirical part based on data obtained from the financial supervision committee. In order                to determine the economic and financial standing of the banking sector, profitability ratios                of total assets and return on equity ROE were used. As part of the efficiency quantification                process, 4 groups of profitability ratios are applied (Capiga, 2003): margins, workloads                and employment effectiveness. They are an important analytical instrument allowing to                assess whether the bank's activity is economically effective.                 Empirical studies covered the years 2015 - 2017 and they concerned the entire banking                sector in Poland. The test results are presented in tabular and descriptive form. For the                purpose of the article, three selected measures of profitability will be assessed: ROA, ROE                and interest margin (NIM).                 3 Results and Discussion                 The decision to introduce a banking tax raised serious doubts. Among other things, they                were concerned about unfavorable effects on lending, a reduction in the profitability level                of assets and equity as well as a direct transfer of tax-related costs to banking services                customers.  The  main  source  of  financing  for  losses  was  to  be  increased  fees  and                commissions  from  banking  operations.  In  order  to  verify  the  veracity  of  the  thesis,  a                comparison was made between the results from the basic banking activity before and after                the introduction  of the  tax  on  some  financial institutions.  It  can  be noticed  that in  the                analyzed period the margin increased. The total net interest income increased by PLN 4.6                billion, ie 12.0%. Reasons for this state of affairs should be sought on the side of stable                lending  and  changes  in  the  deposit  and  credit  policy.  Impulses  stimulating  lending                development  were  low  interest  rates,  improvement  in  the  labor  market  and  price                stabilization in the real estate market. Recovery can also be observed for the portfolio of                consumer  loans  and  corporate  loans.  Lowering  the  interest  rate  on  deposits,  while                increasing the price of some loans, allowed to raise the generated margins (Capiga 2003).                 The persistent environment of record-low interest rates along with the improvement of                investment and consumption moods allowed in the period from January 2016 to the end                of 2017 to increase the balance sheet total by PLN 140 billion (5.8% year-on-year). On the                assets side, the portfolio of debt assets and loans had the decisive share in the change,                while on the liabilities side deposits of households and the public sector. At the same time,                further strengthening of the capital base was noted. Own funds from the level of PLN 159.1                billion at the end of 2015 increased by PLN 39 billion at the end of 2017 (24.5% in the                perspective of 2 years). The average total capital ratio at the end of the year reached                18.2%, which also meant an improvement compared to previous years  - by 5.3 pp. in                relation to 2015. A visible phenomenon on the Polish banking market was the improvement                of operational efficiency through optimization  of employment and sales network (2013-                15,3 thousand of people to 2017-13.4 thousand people) (KNF, 2016).                 Even before the introduction of the bank tax, mortgage margins increased. In 2015, several                banks, despite strong competition in the market, decided to raise fees and commissions                and reduce interest rates on deposits for retail clients and enterprises. Financial institutions                also introduced fees for services that were not previously paid. However, the concerns                about decreasing lending growth have not been confirmed. The increase in the sales of                credit products was mainly driven by high internal demand and low interest rates.                 In the short term (see table 1 and table 2), a drop in the interest rate on new deposits                could  be  observed  with  an increasing interest  rate  on new loans.  The increase in loan                margins  was  temporary,  which  may  indicate  that  the  attempts  made  by  banks  to                compensate for the tax burden on some financial institutions were limited by competitive                pressure on the market. Thus, the assumption of the project was not confirmed that due                to strong market competition, the tax will not affect the prices of banking services. Most                of the costs of the bank tax were transferred to depositors, causing a quasi-reduction in                interest rates on deposits.                                                             125","Table 1 Selected Elements of the Banking Sector Income Statement                                                 Value (PLN mio)                                                                                   Change                                            2016           2017         Mln PLN              %                       Result of                       banking            59 315         61 763         2 448             4,1%                       operations                       a)  result  of     38 024         42 629         4 604             12,%                       banking rest                       b) result on                       fees and           12 592         13 743         1 151             9,1%                       commissions                       c) other fees       8 699         5 391         -3 307             -38%                                                     Source: Own study                                    Table 2 Selected Measures of Operational Efficiency                                        Sector             Commercial banks       Cooperative banks                                2015         2016   2017   2015   2016     2017    2015     2016     2017                       NIM     2,24     2,29    2,44     2,22     2,28    2,45     2,96     2,84     2,89                       ROA     0,71     0,84    0,78     0,84     0,86    0,79     1,09     0,51     0,59                       ROE     6,60     7,76    7,07     7,57     7,73    6,98    11,21     5,33     6,04                                                     Source: Own study                 For banks in Poland, the ROA calculated as the arithmetic mean increased from 0.71% to                0.78%. However, taking into account the results achieved in recent years, it should be                noted  that  the  value  of  the  ratio  is  still  low.  Negative  factors  include  the  increase  in                operating expenses resulting mainly from new tax liabilities and an increase in the negative                balance of impairment losses and provisions. However, it can be added that the average                rate of return on assets of the Polish banking sector is still higher than the average rate in                other EU countries.                The second analyzed profitability ratio is the ROE (rate of return). For the banking sector                in Poland, ROE increased from 6.60% to 7.07%. Similarly, however, it is necessary to refer                to previous years. Observation of data suggests that, in retrospect, current values are at                a low level. High return on equity in previous years was the result of relatively low equity.                At present, the rapid increase in the balance sheet total and the increase in equity as a                result of the introduced prudential regulations do not allow maintaining the ROA at a higher                level.                 4 Conclusions                     •   The concept of introducing a bank tax prevents the instability of financial markets                       and limits the risk taken by banks.                     •   In the analyzed period 2016-2017, was observed an increase in lending dynamics.                       The  increase  in  the  sales  of  credit  products  was  mainly  driven  by  high  internal                       demand and low interest rates. The increase was recorded for net interest income,                       which indicates an increase in the average interest margin. Using the environment                       of low interest rates, banks have reduced interest rates on deposits while at the                       same time increasing the interest rate on low loans.                    •   The Polish banking sector, which is the main source of financing the economy, has                       been developing steadily. The persistent environment of record-low interest rates,                       together with the improvement of investment and consumption moods, allowed,                       from 2016, an increase in the balance sheet total by approx. PLN 140 billion (5.8%).                                                              126","•   At the same time, further strengthening of the capital base was also registered.                       Own funds from the level of PLN 159.1 billion at the end of 2015 increased by PLN                       198,1 billion at the end of 2017- 24,5%. The average total capital ratio at the end                       of the year reached 18.2%, which also meant an improvement compared to the                       previous year.                    •   Due to the short period of the banking tax in Poland, the risk associated with it has                       not yet been fully materialized. Therefore, it can not be ruled out that in the longer                       term  the  situation  in  the  assessed  areas  will  change,  and  banks  will  try  to                       compensate for the costs of the bank tax through aggressive policy on margins and                       fees and commissions.                 References                 Capiga  M.  (2003).  Ocena  działalności  placówki  operacyjnej  banku:  dylematy                metodologiczne i praktyczne. Prace Naukowe Akademii Ekonomicznej w Katowicach, p. 62.                Cichy  A.,  Puszer  B.,  (2016)  Sektory  bankowe  w  Unii  Europejskiej.  Wydawnictwo                Uniwersytetu Ekonomicznego w Katowicach, p. 434.                Informacja  o  sytuacji  banków  w  2016  (2016).  Urząd  Komisji  Nadzoru  Finansowego,                Warszawa 2016, pp. 17.                Kil  K.,  Ślusarczyk  R.  (2014).  Podatek  bankowy  w  krajach  Unii  Europejskiej  –  ocena                implementacji. Prace Naukowe Uniwersytetu Ekonomicznego we Wrocławiu, vol. 348, p.                127.                Raport o stabilności systemu finansowego – grudzień 2016 (2016). Narodowy Bank Polski,                Departament Stabilności Finansowej, Warszawa 2016, pp. 10.                                                                                                           127","Bitcoin Compared on Price, Liquidity and Volatility:                          Crypto “Currencies” or an Asset Class of Their Own?                                       Peter Haiss, Jakob Schmid-Schmidsfelden                                           WU University of Economics and Business                            Department of Global Business and Trade, Institute of Export Management                                    Welthandelsplatz 1, Building D1, A-1020 Vienna, Austria                                  E-mail: peter.haiss@wu.ac.at; jakob.schmidsfelden@gmx.at                 Abstract: In comparing Bitcoin to other cryptocurrencies, we analyze whether these can                become a viable alternative to fiat currencies. In the theoretical part we assess if these                embody the main functions of a currency (Krugman, 1984). The empirical part analyzes                the price development, liquidity and velocity of Bitcoin in comparison to the major other                cryptocurrencies in 2017. We conclude that Bitcoin Cash, a cryptocurrency that evolved                during  a  hard  fork  of  Bitcoin,  may  develop  into  sharing  features  of  a  global  currency.                Currently cryptocurrencies are rather tokens and a highly volatile asset class of their own.                Keywords: blockchain; Bitcoin Cash; cryptocurrencies; liquidity; monetary velocity                 JEL Codes: E390, E420, E510, G15, E51                 1 Introduction                In 2017, the cryptocurrency market experienced a development the economy had not yet                seen before. Bitcoin and its competitors gained much attention in the financial sector of                both positive and negative nature. The system of this cryptocurrency is based on a peer-                to-peer  network  that  allows  transactions  which  are  verified  de-centralized  by  all  the                Bitcoin-users and then stored in the blockchain (Nakamoto, 2008; Haiss and Moser, 2017).                It represents the idea of de-centralizing a currency. Therefore, it is formally independent                from governmental authorities and financial institutions, though influenced by its activities                (e.g. reacted to announcements by the Chinese government to forbid mining and trading                in early 2018) (Allerstorfer, 2016). Cryptocurrencies not only could turn out to be a highly                lucrative  (though  risky)  investment  opportunity,  but  also  could  represent  a  potential                alternative (and thus a threat) to fiat currencies. Or is it just a token? A currency or rather                an asset class of its own?                This  paper is  a  comparative  study  based  on  descriptive  data.  The first  part provides  a                theoretical background, discussing whether Bitcoin shares the functions of a currency as                defined  by Krugman  (1984). The  second  part  compares  the  development  of the major                cryptocurrencies Bitcoin, Bitcoin Cash, Ethereum, Litecoin and Ripple in 2017. We analyze                their price development, liquidity and velocity. We conclude by discussing if these may                represent a viable alternative to fiat currencies as a kind of a new asset class.                 2 Cryptocurrencies and the Functions of a Currency                Features of Cryptocurrencies                Virtual currencies are digital on the one hand, and based on cryptography to secure the                system on the other hand (Lee, 2016). Cryptocurrencies are tokens that use “cryptographic                hashing  and  digital  signatures  to  verify  transactions  and  avoid  double  spending”                (Halaburda  \&  Sarvary,  2016).  Cryptography is  used to  prevent unauthorized  access  of                particular  content  (confidentiality)  and  unauthorized  changes  (integrity).  It  enables                involved parties to identify each other as well as the information sent (Allerstorfer, 2016).                When Bitcoin was introduced in 2008, it was the first of all cryptocurrencies. At the time                of writing (Q2/2018), there are about 1.500 different cryptocurrencies existing, which have                all  emerged  after  the  introduction  of  Bitcoin  (see  Coinmarketkap.com  for  the  current                status).  In  essence,  Bitcoin  features  as  a  decentralized  peer-to-peer  digital  “currency”                based on cryptography, with a more or less finite monetary supply  (Nakamoto, 2008).                Every direct transaction made is verified de-centrally by a network of Bitcoin users. This                                                             128","means that it is possible to carry out transactions “without the assistance and verification                of a trusted third party” (Lee L. , 2016). As most other cryptocurrencies are based on some                type of blockchain all of their functions including storing, transferring, buying and selling                are executed completely online (Osterrieder, Chan, Chu, \& Nadarjah, 2017).                As cryptocurrencies are a relatively new phenomenon, a precise legal definition of virtual                currencies  does  not  yet  exist,  though  several  countries  enacted  special  cryptocurrency                laws,  e.g.  the  4th  Anti-Money  Laundering  Directive  by  the  EU,  which  only  includes                exchanges between cryptocurrencies and fiat currencies, or the US virtual currency law                (EDCAB, n.d.; Waters, 2017). According to Tymoigne \& Wray (2005), it is crucial for any                monetary system to be based on a structure that is capable of recording transactions. In                the case of cryptocurrencies this necessity is fulfilled through the blockchain.                The blockchain, which is often seen as the key innovation of Bitcoin, is a distributed ledger                system that stores and keeps record of all Bitcoin transactions (Haiss \& Moser, 2017). This                ledger is increased as miners add new blocks to the chain in a chronological order, whereas                hundreds of transactions are stored and verified in every individual block. The blockchain                is called a public ledger, because every transaction can be associated with a particular                Bitcoin  address  (information  publicly  available  by  using  a  block  explorer,  e.g.:                https://blockchain.info/). In order for a block to be added to the ledger, a miner first has                to discover it, before all miners can verify it. This is achieved by solving a cryptographic                mathematical problem through computational effort. That process is called proof-of-work                (Dwyer, 2014). The first miner that verifies the blockchain receives a reward in the form                of newly created Bitcoins and a transaction fee. Besides mining, another option to acquire                Bitcoins is to exchange it for fiat money, by buying it either from people through Bitcoin                wallets or from an exchange platform (Swan, 2015). A major disadvantage of the proof-                of-work system is that this process makes mining and payment transactions extremely                energy extensive and thus very costly (Harm, Obregon, \& Stubbendick, 2016).                                         Figure 1 Bitcoin (BTC) Supply over Time                                                                         Source: Antonopoulos, 2017                 The supply of Bitcoin, which is governed by an algorithm, is limited to 21 million Bitcoins.                The algorithm of the issuance schedule adjusts itself by halving the amount of the possible                creation of new Bitcoins every four years (Antonopoulos, 2015). This adaptation is achieved                by increasing the difficulty of the algorithm that needs to be solved by the miners (Dwyer,                2014). It is important to note, that the number of blocks discovered per year does not                change (52.500), and only the amount of Bitcoins per block is halved every four years                (every 210.000 blocks). The idea behind this decreasing-supply algorithm is to best imitate                a rare commodity like gold. Cermak (2017) estimates that “99% of all Bitcoins will be                “mined” by 2040 and the remaining 1% will be mined over the next 80-100 years”. The                decreasing issuance model also contributes to an increased amount of energy (i.e. rising                cost), leading to a decreasing rate at which Bitcoins are mined (Antonopoulos, 2017; see                                                             129","figure 1). As the rate of newly created Bitcoins is decreasing, the value of each Bitcoin is                likely to rise. This is also one of the main reasons why investors are likely to hold Bitcoin                as a speculative investment. Furthermore, the decreasing issuance schedule suggests an                increase of the transaction fees, since the reward the miners receive, denominated in newly                created Bitcoins, will get smaller.                According to Krugman (1984), in the classical economist definition, money serves three                functions: it is a medium of exchange, a unit of account, and a store of value for both the                private and official sector. As  virtual currencies so far mainly operate in the private sector                (Bech \& Garratt, 2017), we will focus on that.                2.2 Features of Traditional (fiat) Currencies                According to Cermak (2017), “For a currency to function as a medium of exchange, it must                represent a standard of value accepted by all parties. The most essential function of a                medium of exchange is to measure value. A medium of exchange should have a constant                intrinsic value and a stable purchasing power on average.”                                                    Table 1 Functions of a Currency                                                Private                        Official                 Medium of exchange            Vehicle                        intervention                 Unit of account               Invoice                        peg                 Store of value                Banking                        reserve                                                    Source: Krugman, 1984                 The second dimension of money mentioned by Krugman (1984) is the function as a unit of                account. The main aspect of a unit of account is its ability to serve as a standard numerical                unit and measure the value and costs of goods, services, assets and liabilities (European                Central Bank, 2012). No monetary system could perform accordingly without an asset that                functions as a unit of account, as this function is also needed to ensure an accurate method                of recording transactions. Traditionally, the unit of account function was always associated                to some kind of a central authority that set standards (Tymoigne \& Wray, 2005). Yet, this                perception will most probably have to change in future times, because of the introduction                and emergence of the blockchain.                The third major feature of a currency is its function as store of value, ie to preserve value                over  time  (Lehdonvirta,  2014).  Clearly,  this  implies  the  need  for  a  stable  respectively                somewhat predictable price development. Enough liquidity is another basic function for the                store of value dimension, as the value of something that cannot be exchanged for other                goods and services at any point in time is useless to function as money (Cermak, 2017).                A further key characteristic concerning the store of value is the predictability of supply.                Knowing by what degree the supply will increase or decrease can give a suggestion of how                much inflation or deflation should be expected in the future. By being aware of the future                supply, one can be confident not to experience a sudden drop in purchasing power of used                the currency (Ammous, 2016). One very important aspect why gold is one of the most                reliable  mediums  of  exchange, is its  ability to  store  value  over  time.  This is due to its                property for being extremely durable and hard to counterfeit. Furthermore, its supply is                limited and difficult to harvest, which makes it a rare resource (Ammous, 2016).                 Throughout  the modern history of money, there has always been some kind of central                authority in charge of the monetary system (Cermak, 2017). Accepting a third party to be                in charge of the monetary base has many advantages, but also demands some degree of                trust by the general public in central banks. On the positive side, institutions in charge of                the monetary system will not only aim to act as a guardian of price stability, but also stand                ready  to  lend  money  to  financial  institutions  in  times  of  financial  crisis  or  deflationary                collapse and engage in various open market operations (Ammous, 2016). On the contrary,                a third authority has the power to influence and manipulate the workings and ground rules                of currencies. Within the boundaries of their bylaws, central banks can basically create as                                                            130","much money as they want (Malliaris, 2005). In some cases, this led to extreme cases of                inflation. Illustrative examples in this context would be the hyperinflation in Venezuela or                also Russia at the demise of the Soviet Union.                Krugman’s typology (1984) connects “trust” with the currency itself instead of connecting                it  with  the  authorities  in  charge.  As  part  of  the  store  of  value  function  of  a  currency,                Krugman simply argues that trust comes with stability. Thus, if a currency is not stable                enough due to high inflation or political uncertainties it will probably also be used less,                because the general public would not trust the currency. The ECB (2012) specifies the                importance of trust in the current monetary system:                “Fiat money is any legal tender designated and issued by a central authority. People are                willing to accept it in exchange for goods and services simply because they trust this central                authority. Trust is therefore a crucial element of any fiat money system.”                With  the  emergence  of  the  blockchain  and  cryptocurrencies,  a  new  way  of  regulating                monetary systems evolved. The blockchain system allows parties to transfer funds directly                to  one  another,  using  a  peer-to-peer  system  (Lee,  2016).  This  weakens  respectively                removes the need for a trusted third party. In such a decentralized monetary system, it is                possible to fully rely on a system to record transactions without the need of an intermediary                in control (Nakamoto, 2008). This game-changing fact redefines the definition of trust in a                currency. In contrast to fiat currencies, trust in the world of cryptocurrencies is based on                cryptographic proof and technology instead of intrinsic value or a third authority (Fink \&                Johann, 2014). By introducing the blockchain, Nakamoto not only removed the need for a                trusted third party, but also figured out a way to avoid double spending. This is possible                because  the  blockchain  records  every  transaction  on  a  “public  time-stamped  ledger”                (Cermak, 2017). Cryptocurrencies are also not obliged to a certain location or jurisdiction.                 3 Price development, Liquidity and Velocity of Major Crypto-“Currencies”                 Cryptocurrencies Chosen                In the following, we compare the five strongest cryptocurrencies, namely Bitcoin, Bitcoin                Cash, Ethereum, Litecoin and Ripple. The reason for choosing these cryptocurrencies was                mainly  their  market  capitalization,  but  also  because  they  show  distinct  approaches  in                offering digital currencies. Table 2 provides an overview of their major features.                 These are, however, not without alterations. For example, the community could not agree                about increasing the blocksize of Bitcoin (from initialy 1 MB), although trying for years. To                raise blocksize to 8 MB, Bitcoin Cash was launched  in a “hard fork” from Bitcoin as a new                cryptocurrency in August 2017. Every Bitcoin holder as of block number 478.558 received                equally as many Bitcoin Cash as they had Bitcoin at this exact time. Thus there are two                separate Bitcoin blockchains that coexist without interfering with one another. Bitcoin Cash                is exactly the same as Bitcoin, just with the advantage of lower transaction costs and faster                verification  times  (due  to  its  scalability;  BitcoinCash.org,  2017),  making  it  a  better                substitute for fiat currencies.                Ethereum is a platform that allows different kinds of distributed applications in the form of                peer-to peer “smart” contracts to function without fraud or trusted authority interference                (Ethereum.org,  2017).  The  fundamental  technical  differences  to  Bitcoin  lie  in  the                programming  language  and  the  required  time  to  verify  transactions  (Bajpai,  2017).                Ethereum could process an infinite amount of transactions, since it is not limited in its                blocksize as is the case for Bitcoin. There is no maximum amount of Ether, but the amount                issued per year is capped to 18 million Ether (Harm, Obregon, \& Stubbendick, 2016).                Litecoin was designed by modifying the Bitcoin software. Just like Bitcoin, Litecoin is based                on a peer-to-peer system and uses open-source software (Jakes, 2016). The reason for                launching this virtual currency was to decrease the block generation time to 2.5 minutes                to make it better suited for smaller transactions and to provide better cybersecurity.                Ripple  has  a  very  different  approach  compared  to  the  other  virtual  currencies,  as  it is                designed to only facilitate trade relationships to send money on a global basis. Therefore,                                                            131","it does not embody a substitute to the current financial system per se. The idea behind                Ripple is that every currency should only be exchangeable by going through Ripple itself,                instead  of  being  able  to  exchange  every  currency  with  any  other  currency.  However,                similarly to Bitcoin, Ripple also has a limited supply. It is a peer-to-peer system and intends                to allow financial transactions without the need of a third-party authority (Jakes, 2016).                                         Table 2 Comparison of Cryptocurrencies                     Name         Re-    Sym-      Market       Price       Hash      PoW/     Comment                  (Founder)     lease     bol    Capitali-     (USD)       algo-      PoS                                                  zation                   rithm                                                 USD bn                                                                                                st                 Bitcoin BTC  2009       BTC,       145,7      8.650,51  SHA-         PoW      1  based                 (Satoshi                XBT                              256d                on block-                 Nakamoto)                                                                      chain                 Bitcoin       2017      BCH         19,7    1.160,96    SHA-         PoW     Hard fork                 Cash                                                     256d                from BTC                                                                                              (increased                                                                                              blocksize)                 Ethereum      2015      ETH         87,8      901,28    Ethash      PoW/      Platform                 (Vitalik                                                             PoS     for smart                 Beterin)                                                                     contracts                 Litecoin      2011      LTC          7,1      128,19    Scrypt       PoW     similar to                 (C. Lee)                                                                        BTC                 Ripple        2013      XRP         33,2     0,85078     ECDSA      Con-     For peer-                 (Larsen \&                                                          sensus     to-peer                 McCaleb)                                                                       trans-                                                                                                actions                       Source: Adapted by the authors from Wikipedia (2018) and Coinmarket.com (2018)                 Measures Chosen                 The first selected measure is price development. A stable price is the strongest indicator                for  the  stability  of  a  currency,  high  volatility  shows  incompatibility  for  the  medium  of                exchange function. A currency that is very volatile in its price is very unlikely to be used                as  store  of  value  as  it  embodies  great  uncertainty  about  its  future  value.  An  unstable                currency is unsuited to function as a unit of account. For our analysis, we checked price                development in USD, as this is the most used currency to trade cryptocurrencies.                The second selected measure is the liquidity of each currency. Liquidity is a precondition                for any currency to function as medium of exchange. If there is no counterparty willing to                accept an asset, it cannot be exchanged immediately at a certain market (Damodaran,                2013), which would make it unsuitable to function as a medium of exchange, as it cannot                guarantee trade at any time at a fair price anymore. In order to measure illiquidity, either                the transaction costs or the time that is needed to execute a transaction can be taken as                a measure (Damodaran, 2013). Additionally, liquidity is also essential to fulfil the function                as a store of value. If an asset has a very low trade volume (which is a typical measure for                liquidity), it cannot be sold easily on the market (Chordia, Roll, \& Subrahmanyam, 1999).                This makes it less favourable to be used as a store of value. Furthermore, the trade volume                is  a  convenient measure  for the  breadth  of  a  market,  which  gives insights in  both  the                amount and size of transactions as well as the influence of transaction costs (Lybek \& Sarr,                2002). For our analysis, we use the daily 24h trade volume as a measure, compiled from                Coinmarketcap.com. We also looked at transaction costs.                The third measure we apply is the velocity (of circulation).  According to Amadeo (2017),                “The velocity of money is the rate at which people spend cash. It is how often each unit of                currency, such as the U.S. dollar or euro, is used to buy goods or services during a period.”                This definition implies that the lower the velocity the higher the tendency that the currency                is used as an investment, rather than as a medium of exchange. People will not want to                                                              132","pay with a “currency” which could exponentially rise in a very short period and rather hold                on to it. High velocity, on the other hand, implies that individual units of a currency change                hands on a very frequent basis (Amadeo, 2017). Therefore, the function as a medium of                exchange is the one function most affected by this measure. For our analysis of the velocity                of circulation, only data for Bitcoin was available, compiled from blockchain.info.                Descriptive Comparison                 As observation period, we chose November 2015 to November 2017. This covers the period                in which the breadth and depth of cryptocurrencies developed strongly, but excludes the                “fat tails” in its beginning and the outliers from the extreme hype around the change of                the year 2017/2018. We also tie in findings from related research.                Concerning the price development of the selected cryptocurrencies, each of them shows                both a significant increase in value, but also a very high degree of volatility, especially in                2017. If you had invested $5 on November 12, 2015, in Bitcoin, you would have got roughly                $100 in late 2017, which is more than twenty times as high. After a slow but constant                increase in price in 2016, in April 2017 Bitcoin sharply surged, due to the fact that Japan                announced Bitcoin as legal tender. This indicates that Bitcoin is not entirely independent                from governmental actions, which is not in line with the claims made by its supporters.                Bitcoin continued its race to almost $3.000, until the price started to fall again in June                2017, when it decreased about 40% in only one month. This freefall was mostly triggered                by  uncertainties  among  Bitcoin holders  about  the  upcoming  programme  code  upgrade,                which was set for 1st of August 2017 (Rizzo, 2017). Nevertheless, from mid-July 2017                onwards, Bitcoin set for another extreme rise in price, hitting a record high of $4.800 in                early  September.  This  development  was  strongly  supported  by  the hard  fork on  1st  of                August and the activation of Segregated Witness in late August, which drew a lot of media                attention towards Bitcoin (BitcoinCash.org, 2017). In early September, China announced                that it will ban ICO’s (Initial Coin Offerings), which might have been the cause for the next                downswing  (Merkel,  2017).  When  J.P.  Morgan  publicly  called  Bitcoin  a  “fraud  that  will                ultimately blow up”, Bitcoin continued its downward race (Monaghan, 2017). After hitting                a price slightly below $3.000 in mid-September, Bitcoin set off for another very fast price                hike. Once the CME Group, one of the biggest options and futures exchanges worldwide,                announced that it would offer Bitcoin Futures, the price of Bitcoin soared to a new record                high of over $6.500 in late October (Baker \& Leising, 2017). Bitcoin maintained its upswing                as the 16th of November approached, which was set to be the day for the introduction of                Segwit2x (Russo, 2017). However, shortly before this day arrived, the split was cancelled,                which caused the Bitcoin price to plunge from its all-time high of $7.400 down to $6.625                on 12th of November 2017.                 Similar to Bitcoin, the time between 2015 and 2017 was by far the strongest period for                Ethereum. In only two years its price increased by more than 3.000%, from less than $1                to over $300. On November 12, 2017 Ethereum had a price of roughly $308. Until March                2017, the price of Ethereum almost increased twentyfold to reach a price of almost $20.                From there on, it started a strong upward phase, before reaching its all-time high of $390                in mid-June 2017. This remarkable increase in price most probably occurred, because of a                rise in adoption of the whole cryptocurrency industry and an overall ICO hype in 2017 (Aru,                2017). Nevertheless, Ethereum saw itself on a downward race for a whole month until mid-                July when it hit a price of $150, which represented a price reduction of 60%. This extreme                fall represented a market correction and was also triggered by wrongly distributed news                about the death of Vitalik Buterin, the founder of Ethereum (Reiff, 2017). From there on,                Ethereum benefited from the increased media coverage of Bitcoin, which resulted in a rise                of Ethereum’s price. Ethereum regained almost all of its value of mid-June to reach around                $380 on 1st of September 2017. Ethereum was then heavily affected by the announcement                of China to ban ICO’s, as ICO’s most often run through Bitcoin and Ether (Merkel, 2017).                From  there  on  Ethereum  continued  a  very  volatile  development  in  October  and  early                                                                     th                November 2017, before it hit $308 on November 12 .                Compared to Ethereum and Bitcoin, Litecoin only increased marginally between November                12, 2015 and April 2017, trading at a stable price between $3 and $4. It was in mid-April                                                            133","2017, when Litecoin also profited from the general rise in acceptance of cryptocurrencies                by the general public. This upward trend was further supported by the successful activation                of Segregated Witness in early May (Hertig, 2017). From there on, Litecoin continued its                surge until it reached an all-time high of over $82 on 1st of September at the time. Litecoin                was also heavily affected by Chinas announcement to ban ICO’s in early to September,                which led to a drop to $42 within just one week (Merkel, 2017). I recovered to trade around                a price of $60 as of November 12, 2017.                As of November 12, 2017, Ripple’s is $0.196. It experienced its all-time high in mid-May                with a price of $0.41. This price peak could only be maintained for a very short period of                time.  In  general,  Ripple  followed  the  overall  trend  of  the  cryptocurrency  market  by                significantly increasing its value from May 2017 onwards. Up to the time of writing, there                is very limited literature on reasons for price changes of Ripple. However, in general it can                be assumed that the price of Ripple is strongly influenced by news about the acceptance                of  cryptocurrency  by  retail  banks  and central  banks.  This  originates from  the  fact  that                Ripple is also designed to function within the interbank SWIFT-network (Tsihitas, 2017).                The  high  volatility  of  the  selected  cryptocurrencies  could  have  its  origin  in  the  still                significantly  high  level  of  uncertainty  amongst  investors,  concerning  the  whole                cryptocurrency  market.  Even  minor  negative  news  and  information  gathered  through                media often strongly affect the decisions of investors and can quickly lead to mass selling                or mass buying. This often causes a sharp decrease or increase in price, respectively. The                fact  that  Bitcoin  is  still  often  only  used  as  a  speculative  investment,  rather  than  as  a                medium of exchange, invites a great number of uninformed investors to enter and to leave                the Bitcoin market, causing high volatility (Fink \& Johann, 2014).                                    Figure 2: Google Trends of the Selected Cryptocurrencies                                                                           Source: Google Trends, 2017                Enhanced media presence could have had a significant effect on investment activities as                well, regarding to Bovaird (2017). The heavy influence of an increased media profile, which                could even lead to temporary price bubbles, is also supported by Wang \& Vergne (2017).                Stenquvist \& Lonno (2017), who analyzed if there is a correlation between Bitcoin related                Twitter-posts and its price, observed „a partial correlation between binary sentiment and                price change for small subsets of data“. Conducting a word count analysis provides another                avenue of research. By looking at the Google search interest a high correlation between                the  price  development  of  each  cryptocurrency  and  the  search  interest  of  each                cryptocurrency, can be observed. As representative search key words, the names of the                different cryptocurrencies were selected. Figure 2 represents the “search interest relative                                                               134","to the highest point on the chart for the given time“ (Google Trends, 2017). Again, the                selected time span is November 12, 2015 to November 12, 2017.                 By comparing the Google trends to an overlapping chart of the price development of all                four selected cryptocurrencies within the same period of time, similar developments can                be examined. This shows a high correlation of the growing price and the growing interest                (measured by Google searches):                                         Figure 3: Overlapping Price Developments                                                                        Source: CryptoCurrencyChart.com                 With regards to liquidity, we find that all cryptocurrencies are still very volatile in their 24h                trade volume up to now. This is strongly influenced by the fact that cryptocurrencies are                not yet used for the same purpose as the USD or Euro. Most cryptocurrency investors only                use them as a speculative investment (Allerstorfer, 2016). Once one cryptocurrency will                be accepted broadly and commonly be used as a medium of exchange, this situation may                change. When examining the corresponding movements between liquidity and velocity, at                least in case of Bitcoin, a tendency of correlating liquidity and velocity can be observed. In                2017, almost every time when liquidity was high, velocity was high too, whereas when                liquidity was low, velocity only fell partly. This leads to the conclusion that Bitcoin is on its                way to turn from a speculative market towards a mass market.                 4 Conclusion                 This  paper  aims  to  provide  an  answer  to  the  research  question  if  one  of  the  selected                cryptocurrencies fulfils Krugman’s three functions of a currency (medium of exchange, unit                of account and store of value) and thus could substitute fiat currencies (Krugman, 1984).                Therefore,  three  measures  were  taken  into  consideration,  namely  price  development,                liquidity and velocity. After analyzing the four cryptocurrencies Bitcoin, Ethereum, Litecoin                and Ripple, as well as the newly emerged Bitcoin Cash, we draw the following conclusion.                The only cryptocurrency that could in theory fulfil the functions of a currency is Bitcoin                Cash, because of two main reasons. First, Bitcoin Cash is truly decentral, meaning that                there  is  no  single  group  or  third  authority,  nor  anything  that  is  comparable  to  a  third                authority that controls the Bitcoin network. Second, the supply of Bitcoin Cash is fixed with                a determined issuance schedule. Both the total supply and the issuance schedule are very                unlikely to be altered (Ammous, 2016).                Though Bitcoin (BTC) has these same attributes, there is one big disadvantage of Bitcoin,                namely  its  scalability.  Bitcoin  has  already  reached  a  point  where  it  cannot  handle  the                amount  of  transactions  anymore.  This  results  in  high  transaction  costs  and  long                confirmation times. Fortunately, there is already a solution for this problem. The solution                                                             135","is called Bitcoin Cash. The blocksize of Bitcoin Cash is adjustable, which means that it is                not facing the problem of scalability. Both transaction costs and the duration to verify a                transaction will be kept to a minimum (BitcoinCash.org, 2017). This is also in the sense of                the original invention of Bitcoin, since it was designed to be a decentral cryptocurrency,                that  is  scalable,  if  the  demand  rises  (Nakamoto,  2008).  As  all  the  other  technical                specifications of Bitcoin Cash stayed the same as the ones of Bitcoin, Bitcoin Cash could,                from a technical point of view, be preferred over Bitcoin.                Ethereum is not in the position to function as a viable substitute to fiat currencies, because                there is a small group of miners that could, together with the Ethereum Foundation, control                the network of Ethereum and thus function like a third authority in charge. It thus would                be less favourable compared to Bitcoin and Bitcoin Cash. Furthermore, Ethereum’s purpose                is not to be a substitute to fiat currencies, but to provide a platform to ease peer-to-peer                activities like smart contracts. Its currency, Ether, was only established to facilitate these                activities and not to function as a medium of exchange (Bajpai, 2017).                 Although Litecoin is most similar to Bitcoin, it still has a major disadvantage with regard to                security. A cryptocurrency that in theory is 100 times easier to be influenced by malicious                activity, which could for instance have an impact on the issuance schedule, should never                be chosen. Litecoin is also less accepted than Bitcoin globally (Ammous, 2016).                Finally, Ripple is also not in the position to substitute fiat currencies, as it is simply not                designed to function as such. The purpose of Ripple is rather to function as an exchange                medium between existing currencies. By doing so, it aims to facilitate the current financial                system. Ripple also has a company behind it which controls the Ripple network, which is                comparable to a central bank (Ripple.com, 2017).                 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Retrieved      from:                https://en.wikipedia.org/wiki/List_of_cryptocurrencies.                                                                                                                      138","Specifics of the Reporting under International Accounting                Standard IAS 41 Agriculture and its Usage on the Central European                                                  Stock Exchanges                                                         Filip Hampl                                                     Masaryk University                                Faculty of Economics and Administration, Department of Finance                                       Lipová 41a, 602 00 Brno-Pisárky, Czech Republic                                              E-mail: filip.hampl@mail.muni.cz                 Abstract:  Companies  listed  on  stock  exchanges  in  the  European  Union  prepare  and                present their financial reports mostly in accordance with International Financial Reporting                Standards (IAS/IFRS). International Accounting Standard IAS 41 Agriculture, prescribing                the accounting treatment and disclosures related to biological assets used for agricultural                activity,  is  an integral  part  of  these  standards.  The  need  of the  particular  standard  for                agriculture stands from the specific character of agricultural activity based on the biological                transformation of living organisms – plants and animal beings. This paper deals with the                specific  areas  of the accounting  treatment  incorporated  in  the  International  Accounting                Standard  IAS  41.  Furthermore,  it  examines  whether  there  are  agriculture  companies                reporting  in  accordance  with  IAS  41  among  corporations  publicly  listed  on  the  stock                exchanges in the Czech Republic, in Slovakia, Poland, Hungary and Austria. Results show                that only small number of agriculture companies is listed on the Central European stock                exchanges. Therefore,  there  is  the  low  usage  of  IAS  41.  Although  IAS  41  might  seem                redundant, it has its importance consisting in the specific accounting treatment that cannot                by substitute by other standards.                 Keywords:  agriculture  company,  biological  assets,  financial  reporting,  IAS/IFRS,  stock                exchange                JEL codes: M41, Q14                 1 Introduction                 Agriculture represents a specific sector of the  national  economy. Agriculture companies                differ within the meaning of nature of their activities from other business entities. Bohušová                et  al.  (2012) mention  that  agricultural  activity is in  comparison  with  other  activities  of                business  subjects  dependent  on  the  natural  and  environmental  conditions  (geographic                condition, weather, etc.). According to Dvořáková (2014), agricultural activity takes place                independently of human activities due to the biological characteristic of assets, such as                vegetation period, pollination by specific species, multi-annual length of the  production                cycle.  People  can  only  facilitate  or  influence  biological  transformation  by  enhancing  or                stabilizing conditions necessary for the process to take place (e.g. moisture, temperature,                fertility  and  light).  Sedláček  (2010)  claims  that  in  comparison  with  other  economic                branches, the agricultural activity is characterized by specific activities that require the                appropriate accounting attitudes. This is also confirmed by Bohušová et al. (2012) who                mention that the specifics of the biological character of transformation should be reflected                by  the  methodology  for  reporting  for  activities  of  agriculture  companies.  According  to                Dvořáková  (2012),  nature  of  agricultural  activity  based  on  biological  transformation                (comprising  growth,  degeneration,  production,  etc.)  that  changes  the  substance  of                biological asset, is difficult to capture in traditionally used accounting models established                on historical costs and realization principle.                On the international level of accounting and financial reporting particularities of agricultural                activity are treated in the International Accounting Standard 41 Agriculture (IAS 41). The                objective of IAS 41 Agriculture is to prescribe the accounting treatment and disclosures                related to agricultural activity. IAS 41 should be considered “lex specialis” to other “lex                generalis” International Financial Reporting Standards (IAS/IFRS). Although it is dedicated                to agriculture, it does not treat accounting of agricultural sector as a whole.  It only deals                                                             139","with the accounting treatment of specific assets related to agriculture, mainly with their                particular measurement. Other accounting matters of agriculture companies are prescribed                by other IAS/IFRS.                 2 Methodology and Data                This  paper  aims  to  present  specific  accounting  treatment  of  agricultural  activity                incorporated in the International Accounting Standard 41 Agriculture and find out whether                there are agriculture companies reporting in accordance with IAS 41 among publicly listed                corporations on the stock exchanges in the Czech Republic, in Slovakia, Poland, Hungary                and Austria.                 This paper is based on an analysis of the International Accounting Standard 41 Agriculture,                other  relevant  International  Financial  Reporting  Standards  and  appropriate  literature                sources, articles and monographs. To discover publicly listed companies applying IAS 41,                the data from the stock exchanges and corporations’ annual or quarterly reports from the                year 2017 is used. All stock market segments of the following stock exchanges are selected                for the research:                     •   Prague Stock Exchange (Burza cenných papírů Praha),                    •   Bratislava Stock Exchange (Burza cenných papierov v Bratislavě),                    •   Warsaw Stock Exchange (Giełda Papierów Wartościowych w Warszawie),                    •   Budapest Stock Exchange (Budapesti Értéktőzsde) and                    •   Vienna Stock Exchange (Wiener Börse).                The methods of description, analysis, synthesis and deduction will be applied in this paper.                The  conclusion  will  assess  the  necessity  of  IAS  41  regarding  specific  assets  related  to                agricultural activity.                 3 Results and Discussion                IAS  41  is  applied  to  account  for  biological  assets,  agricultural  produce  at  the  point  of                harvest and government grants related to agricultural activity. IAS 41 does not deal with                agricultural  produce  after  harvest  and  its  processing,  for  example,  the  processing  of                sugarcane into sugar, harvested cotton into thread or picked tea leaves into tea, although                such  processing  may  be  a  logical  and natural  extension  of  agriculture  activity  or  there                should be  a certain perceptible similarity to biological transformation. IAS 2 or another                suitable standard applies to harvested produce in dependence on the course of its use.                On the other hand, IAS 41 cannot be applied to assets which are in the scope of other                standards,  such  as  land  related  to  agricultural  activity  (IAS  16  Property,  Plant  and                Equipment), intangible assets related to agricultural activity (IAS 38 Intangible Assets),                right-of-use assets arising from a lease of land related to agricultural activity (IFRS 16                Leases).                Standard  defines  used  terms;  for  purposes  of  this  paper,  only  the  most  important                definitions  will  be  mentioned.  Under  the  agricultural  activity,  IAS  41  understands                management by an entity of the biological transformation and harvest of biological assets                for sale or conversion into agricultural produce or additional biological assets with biological                asset  defined  as  a  living  animal  or  plant.  Based  on  this  definition,  agricultural  activity                covers a diverse range of activities, e.g. forestry, annual or perennial cropping, cultivating                orchards  and  plantation,  fish  farming  or  cattle  farming.  Agricultural  produce  is  the                harvested product of the entity’s biological assets. Three common features can be identified                within  this  diversity  –  biological  asset  capability  of  biological  transformation  (growth,                degeneration,  production  and  procreation),  conditions  management  of  biological                transformation and measurement of qualitative and quantitative changes brought about                by biological transformation or harvest.                                                                140","Recognition and measurement                Recognition and measurement  are the pivotal part of IAS 41, such as other standards.                In compliance with the common definition of assets included in the Conceptual Framework,                an entity shall recognize biological assets or agricultural produce only when:                    a)  an entity controls the asset as a result of past events,                    b)  it is probable that future economic benefits associated with the asset will flow to                       the entity, and                    c)  the fair value or cost of the asset can be measured reliably.                IAS 41 requires all biological assets related to agricultural activity to be measured on initial                recognition and at the end of each reporting period at fair value less costs to sell where                costs to sell are defined as incremental costs directly attributable to the disposal of an                asset, excluding finance costs and income taxes. Agricultural produce harvested from an                entity’s biological asset is measured at its fair value less costs to sell at the point of harvest.                Such measurement is the cost at that date when applying IAS 2 Inventories or another                applicable standard.                 There is a presumption that fair value can be measured reliably for a biological asset. That                presumption  can  be  rebutted  only  on initial  recognition for  a  biological  asset for  which                quoted market prices are not available and for which alternative fair value measurements                are  determined  based  on  professional  judgement to  be  clearly unreliable.  Only in  such                case, entity measures the biological assets at its acquisition cost less any accumulated                depreciation  and  any  accumulated  impairment.  Once  the  fair  value  of  biological  asset                becomes reliable, an entity measures it at its fair view less costs to sell. IAS 41 allows that                acquisition/production  costs  may  approximate  fair  value,  particularly  when  only  little                biological transformation has taken place since initial cost incurrence, or the impact of the                biological transformation is immaterial. An entity that has previously measured a biological                asset  at  its  fair  value  less  costs  to  sell  is  obligated  to  continue  in  this  type  of  its                measurement.                 Measurement  prescribed  by  IAS  41  is  based  on  the  principle  that  the  biological                transformation that these assets undergo during their lifespan is best reflected by fair value                measurement (IFRS Foundation, 2014). Evaluating the agricultural activity at the point of                the  harvest  results  from  the  fact  that  the  transformation  process  is  immediately                represented in the financial statements and due to the fact that the stakeholders are able                to estimate the future economic benefits (Lefter, Roman, 2007). The fair value of biological                assets has the direct relation to changes in anticipating the future benefits. For example,                anticipated  benefits  are  directly  proportional  to  the  growth  of  trees  in  the  forest                (Dvořáková, 2012). On the other hand, the evaluation at the acquisition/production cost                does not reflect sufficiently the increase in value during development phase over the time                of growth of biological asset due to the small relation between expended costs and growth                of  biological  assets,  e.g.  trees  in  the  forest  (Lefter,  Roman,  2007).  Entities  using  the                evaluation at historical cost cannot recognize the revenue until the harvest or sale (it could                take  decades  in  case  of  trees).  By  using  fair  value  evaluation  model,  gains  can  be                recognized continuously during each accounting period until the harvest.                Fair value determination                 At the time of its creation, IAS 41 contained a definition of fair value and the options of its                determination.  Since  a  new  International  Financial  Reporting  Standard  13  Fair  Value                Measurement was approved (effective from 1  January 2013), its definition of fair value is                                                              st                currently prescribed for all standards. IFRS 13 defines fair value as a price that would be                received to sell an asset or paid to transfer a liability in an orderly transaction between                market participants at the measurement date. That definition of fair value emphasizes that                fair value is a market-based measurement (i.e. market approach), not an entity-specific                measurement (Stárová et al., 2016). According to Dvořáková (2012), active market with                biological assets, especially with long-term production cycle, may not function. For that                reason, other approaches to fair value (i.e. cost or income approach) offered by IFRS 13                                                              141","have to be employed. In that case, Svoboda, Bohušová (2017) and Cavalheiro et al. (2017)                suggest using discounted cash flow (income approach) to measure biological asset’s fair                value. But it should be noted that any cash flow for financing the assets, taxation, or re-                establishing biological assets after harvest, e.g. the cost of replanting trees in a plantation                forest after harvest, cannot be included in the calculation (IAS 41).                An entity should use maximally relevant and observable information in order to measure                fair value. IFRS 13 specifies the hierarchy of inputs in the determination of fair value using                valuation approaches mentioned above. Level 1 inputs are considered the most reliable                evidence  of  fair  value  and  have  to  be  used  without  adjustment  to  measure  fair  value                whenever available; it has the highest priority. Level 1 inputs are quoted prices in active                market for identical assets on the principal or the most advantageous market for the asset.                Related to biological assets Dvořáková (2014) points out that market prices  often have                unstable seasonal character – they can differ significantly at the end of reporting period                from future sale prices, which could cause recognition of unrealized gains or losses. These                unrealized  gains  could  be  a  subject  of  taxation  (Bartůňková,  Semerád,  2013).  Level  2                inputs are directly or indirectly observable quoted prices in active markets for similar assets                or, in case of missing active markets, quoted prices for identical or similar assets in markets                that  are  not  active.  Level  3  inputs  are  unobservable  on  the  markets.  They  reflect                management’s own assumptions about the assumptions market participants would use in                pricing the asset (including risk assumptions). At level 3 the asset valuation requires a                certain  degree  of  judgment  by  the  appraiser,  and  this  could  influence  reliability  and                relevance of the generated information (Yang et al., 2005).                Reporting                Biological assets are reported as non-current or current assets in the balance sheet. A gain                or a loss arising on initial recognition of a biological asset or agricultural produce at fair                value less costs to sell and from a change in fair value less costs to sell is included in the                profit  or  loss  for  the  period  in  which  it  arises  (IAS  41).  A  loss  might  arise  on  initial                recognition of a biological asset because costs to sell are deducted, and they can exceed                the fair value of the asset. Gains and losses are compensated and reported separately from                other costs/losses and revenues/gains in the statement of comprehensive income.                 Governments Grants                Accounting approach related to government grants incorporated in IAS 41 differs from an                approach  in  IAS 20  Accounting  for  Government  Grants  and  Disclosure  of  Government                Assistance. The common approach to grants based on deducting a grant from the carrying                amount  of  the  related  asset  would  be  inconsistent  with  fair  value  model.  (Dvořáková,                2014). For that reason, IAS 41 incorporates its own treatment                 Unconditional grants related to biological assets measured at fair value less costs to sell                are  recognized  in  income  when  the  government  grant  becomes  available.  Conditional                grants are recognized in income when the conditions related to the government grant are                met. If the terms of the grant allow part of it to be retained according to time that has                elapsed, the entity recognized that part in profit or loss as time passes (IAS 41).                Bearer Plants                Beside  biological  assets  mentioned  above,  IAS  41  distinguishes  a  specific  group  of                biological assets called bearer plants that differ from other biological assets. Bearer plants                are used solely to grow produce over several periods and at the end of their productive                lives  accounting  unit  usually  gets  rid  of  them.  Once  bearer  plant  attains  maturity  its                biological transformation is no longer significant in generating future economic benefits.                The only significant future economic benefits of bearer plant come from the agricultural                produce that it generates (IFRS Foundation, 2014). In other words, once they are mature                and productive, they are similar to “machinery used for manufacturing of goods”. IAS 41                defines the bearer plant as a living plant that:                    a)  is used in the production or supply of agricultural produce;                                                             142","b)  is expected to bear produce for more than one period; and                    c)  has a remote likelihood of being sold as agricultural produce.                 Originally the rules of IAS 41 were applied on bearer plants as well. But based on feedback                from stakeholders who argued that fair value measurement was not appropriate for these                bearer biological assets (Deloitte, 2014), the International Accounting Standards Board                (IASB) has decided to change their accounting treatment. On 30  June 2014, IASB issued                                                                                 th                Agriculture: Bearer Plants (Amendments to IAS 16 and IAS 41) effective for entities since                                                      st                annual periods beginning on or after 1  January 2016. IASB decided that bearer plants will                be  within  the  scope  of  IAS  16  Property,  Plant  and  Equipment  and  be  a  subject  to  all                requirements  therein.  This  includes  the  option  to  choose  between  the  cost  model  and                revaluation model for subsequent measurement (EY, 2014).                Plants like tea bushes, grape vines, oil palms and rubber trees usually meet the definition                of a bearer plant and are within the scope of IAS 16. However, it is necessary to mention                that the produce growing on bearer plants – for example, tea leaves, grapes, oil palm fruit                or latex – remains within the scope of IAS 41. Annual crops and other plants that held                solely to be harvested as agricultural produce, such as soya, wheat and soya or trees grown                for lumber, are not expected to meet the definition of bearer plants. In addition, biological                assets with dual use, both bearer (e.g. fruit) and consumable (e.g. lumber) attributes do                not hold the definition and remain under the rules of IAS 41 (EY, 2014). Bearer animals                are explicitly excluded from the definition and are accounted for under IAS 41.                 Bearer plants that are subject to IAS 36 have to meet all measurement requirements in                IAS 16. Before maturity, bearer plants will be measured at their accumulated cost, in the                same way as self-constructed items of property, plant and equipment before they are in                the location and condition necessary to be capable of operating in the manner intended by                management. After they become mature, entities choose to measure either the cost model                or the revaluation model. Following either model, entities have to determine the useful life                of the bearer plant in order to depreciate it. The useful life needs to be re-evaluated each                year. Unlike biological assets under IAS 41, property, plant and equipment under IAS 16                are not scoped out of IAS 36 Impairment of Assets – this means the need for assessing                whether  there  are  some  indicators  that  bearer  plants  are  impaired  at  the  end  of  the                accounting period.                Usage of IAS 41 on the Central European Stock Exchanges                 Regulation (EC) No. 1606/2002 requires that all publicly listed companies on a regulated                market in the European Union have to prepare their consolidated financial statements in                accordance with IFRS. Member states can still decide on the obligation to apply IFRS for                the  preparation  of  individual  financial  statements  of  publicly  listed  companies  on  a                regulated  market.  In  case  of  corporations  traded  on  a  non-regulated  market  (i.e.                multilateral  trading  facility)  responsible  market  organizer  (stock  exchange)  decides  on                using  IFRS.  For  example,  in  the  Czech  Republic,  all  publicly  listed  companies  on  the                regulated market have to prepare their financial statements (individual or consolidated) in                accordance with IFRS (Act No. 563/1991 Coll.). On the contrary, in Slovakia only some                publicly listed companies on the regulated market have to applied IFRS for their individual                financial  statements  (e.g.  banks,  insurance  companies  or  reinsurance  companies),  the                others may (but do not have to) use IFRS (Act No. 431/2002 Coll.). Both countries do not                regulate using IFRS on non-regulated markets.                Application  of  IAS  41  supposes  that  entity  is  involved  in  the  agricultural  activity  and                prepares its financial statements in accordance with IFRS. The examination of using IAS                41  was  conducted  among  corporations  on  regulated  and  non-regulated  stock  market                segments of the Central European Stock Exchanges. The results presented in Table 1 show                that only the low number of publicly listed companies uses IAS 41. In total, 1 653 listed                companies were examined, and only 19 of them use IAS 41.                                                               143","Table 1 Usage of IAS 41 on the Central European Stock Exchanges                                                Total number of        Number of                 Stock Exchange                  publicly listed    companies using     Companies using                                                                                           IAS 41 (%)                                                   companies             IAS 41                 Prague Stock Exchange                 53                   1                 1.887                 Bratislava Stock Exchange             60                   1                 1.667                 Warsaw Stock Exchange                880                  13                 1.477                 Budapest Stock Exchange               40                   0                 0.000                 Vienna Stock Exchange                620                   4                 0.645                 Total                               1 653                 19                 1.149                Source: Own arrangement based on the data retrieved from the Central European stock exchanges                                         and financial statements of listed corporations                 The low usage of IAS 41 can be explained by the small number of publicly listed agriculture                companies on the stock exchanges. Most agriculture companies are small, independent,                cash and tax-focused, family-operated business units, without the need to obtain capital                on stock exchanges (Dvořáková, 2012). According to Eurostat (2013), there were 10.8                million farms across the EU-28. There is a large number (4.9 million) of very small, family                farm (less than 2 hectares in size), and a small number (0.3 million) of very large farms                (over  100  hectares).  Apart  from  the  size,  Messah  (2011)  found  out  that  agriculture                companies in relation to listing on stock exchanges face major challenges such as high                listing fees, price instability and fluctuations for agricultural produce based on inconsistent                weather  conditions  or  fluctuations  in  profitability  in  the  agricultural  sector,  hence                considered a risky sector to invest in. These reasons can explain the low listing of these                companies as well.                Another reason for the results can  be found in the state law and stock exchange rules                which do not require IFRS application for all listed companies. Agriculture companies are                traded particularly on non-regulated market segments where the stock exchanges often                do not prescribe mandatory using IFRS. Given the additional costs related to IFRS adoption                (e.g.  Navarro-García  and  Bastida,  2010,  or  Morris  et  al.,  2014)  companies  are  not                motivated  to  apply  them.  Therefore,  not  every  entity  from  the  small  amount  of  listed                agriculture companies uses IAS 41. On the other hand, with continuing globalization and                increasing  pressure  on  international  comparability  of  financial  statements  by  owners,                potential  investors,  financial  institutions  or  regulators  it  can  be  assumed  that  the                importance of IFRS will rise and agriculture  companies will be more motivated to start                preparing their financial statements in accordance with IFRS.                Although at first sight, it might seem that IAS 41 is redundant due to its low usage, it is                not  the  case.  It  provides  accountants  with  coherent,  logically  structured  and                understandable set of specific rules that cannot be substituted by other existing IAS/IFRS.                Thanks to these rules agriculture companies are able to prepare reliable, transparent and                internationally  comparable  financial  statements  with  respect  to  their  activity  based  on                biological transformation.                 4 Conclusion                Agricultural  companies  differ  regarding  their  activities  from  the  other  business                corporations.  Their  activity  is  based  on  the  biological transformation that is  dependent                on the natural and environmental conditions and takes independently of human activities                – people can only facilitate or influence it. The nature of the agricultural activity is difficult                to capture in traditionally used accounting models based on historical cost and realization                principle (Dvořáková, 2012).                International Accounting Standard 41 offers the special accounting treatment of biological                assets (except bearer plants), agricultural produce at the point of harvest and government                grants related to agricultural activity. IAS 41 is based  on the fair value principle which                represents the best way how to capture the biological transformation in the accounting                                                             144","books and financial statements because the fair value has the direct relation to changes                in anticipating the future benefits. For that reason, IAS 41 requires all biological asset and                agricultural produce to be measured at fair value less costs to sell if fair value is reliably                measurable. Determination of fair value is incorporated in IFRS 13 which prescribes three                fair  value  determination  approaches  (market,  cost  and  income)  and  three  levels  of                applicable input information.                 Based on the conducted research among 1 653 publicly listed corporations on the stock                exchanges in the Czech Republic, in Slovakia, Poland, Hungary and Austria, there is the                only small number of agriculture companies using IAS 41. Although its low usage might                suggest its uselessness, it provides a specific set of accounting rules that cannot be found                in any other standard, and that is important for agriculture companies (non-listed as well).                IAS 41 allows them to prepare financial statements with respect to particularities of the                biological transformation based on comparable, internationally known, uniform and reliable                accounting  rules  and  principles.  It  can  be  expected  that  continuance  of  globalization                increases  the  importance  of  International  Financial  Reporting  Standard  even  more  for                owners, potential investors, banks or government agencies and more (not only) agriculture                companies will begin to apply IFRS.                 Acknowledgements                This contribution was created with the support of Masaryk University within the project of                the  specific  research  MUNI/A/0904/2017  “Performance  Evaluation  of  Agricultural                Companies”.                 References                 Act No. 431/2002 Coll., on Accounting, as amended. The Slovak Republic.                Act No. 563/1991 Coll., on Accounting, as amended. The Czech Republic.                Bartůňková, L., Semerád, P. (2013). Use of fair value in agriculture. In: PEFnet 2013. Brno:                Mendelova univerzita.                Bohušová,  H.,  Svoboda,  P.,  Nerudová,  D.  (2012).  Biological  assets  reporting:  Is  the                increase in value caused by the biological transformation revenue?. Agricultural Economics,                vol. 58(11), pp. 520-532.                 Cavalheiro, R. T., Kremer, A. M., Gimenes, R. M. T. (2017). Fair Value for Biological Assets:                An Empirical Approach. Mediterranean Journal of Social Sciences, vol. 8(3), pp. 55-68.                Conceptual Framework. International Accounting Standards Board, 2010.                 IAS 41, Agriculture, as amended. International Accounting Standards Board, 2000.                Deloitte (2014). IFRS in Focus: IASB amends IAS 16 and IAS 41 to include bearer plants                within    the   scope    of   IAS    16    instead    of   IAS    41.   Retrieved    from:                https://www.iasplus.com/en/publications/global/ifrs-in-focus/2014/ias-41-ias-16                Dvořáková,  D.  (2012).  Specifika  účetnictví  a  oceňování  v zemědělství,  1st  ed.  Praha:                Wolters Kluwer ČR.                Dvořáková, D. (2014).  Finanční účetnictví a výkaznictví podle mezinárodních standardů                IFRS, 4th ed. Brno: Albatros Media.                 Eurostat  (2013).  Farm  structure  survey  2013  –  main  results.  Retrieved  from:                http://ec.europa.eu/eurostat/statistics-explained/index.php/Farm_structure_survey_                2013_-_main_results                EY    (2014).    Bearer    plants   –    the    new    requirements.    Retrieved    from:                http://www.ey.com/Publication/vwLUAssets/IFRS_Developments_Issue_84:_Bearer_plan                ts_-_the_new_requirements/$FILE/Devel84-Agriculture-July2014.pdf.                                                                145","IFRS Foundation (2014). IASB issues amendments to IAS 16 and IAS 41 for bearer plants.                Retrieved        from:        http://archive.ifrs.org/Alerts/Publication/Pages/IASB-issues-                amendments-to-IAS-16-and-IAS-41-for-bearer-plants-June-2014.aspx.                Messah, O. B. (2011). Reasons for Low Listing by Agricultural Companies in the Bourse:                A Case Study of Del Monte Limited Kenya. Research Journal of Finance and Accounting,                vol. 2(3), pp.56-70.                 Morris, R. D., Gray, S. J., Pickering, J., Aisbitt, S. (2014). Preparers’ perceptions of the                costs  and  benefits  of  IFRS:  Evidence  from  Australia’s  Implementation  Experience.                Accounting Horizons, vol. 28(1), pp. 143-173.                 Navarro-García, J. C., Bastida, F. (2010). An empirical insight on Spanish listed companies’                perceptions  of  international  financial  reporting  standards.  Journal  of  International                Accounting, Auditing and Taxation, vol. 19(2), pp. 110-120.                Lefter, V., Roman, A. G. (2007). IAS 41 Agriculture: Fair Value Accounting. Theoretical and                Applied Economics, vol. 5, pp. 15-22.                Sedláček,  J.  (2010).  The  methods  of  valuation  in  agricultural  accounting.  Agricultural                Economics, vol. 56(2), pp. 59-66.                 Stárová, M., Čermáková, H., Hlavsa, T., Vostrovská, H., Levá M. (2016). Evaluation of                applicability of IAS 41 – Agriculture to the valuation of growing forest stands and their                accounting treatment in the Czech Republic. Journal of Forest Science, vol. 62(9), pp. 429-                440.                Svoboda, P., Bohušová, H. (2017). Amendments to IAS 16 and IAS 41: Are there any                differences  between  plant  and  animal  from  a  financial  reporting  point  of  view?.  Acta                Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, vol. 65, pp. 327-337.                Yang, Z., Rohrbach, K., Chen, S. (2005). The Impact of Standard Setting on Relevance                and Reliability of Accounting Information: Lower of Cos tor Market Accounting Reforms in                China. Journal of International Financial Management and Accounting, vol. 16(3), pp. 194-                228.                                                                                                    146","Volatility Model Based GARCH Minimum Variance Hedging                                                       Haochen Guo                                               VŠB-Technical University of Ostrava                                         Faculty of Economics, Department of Finance                                      Sokolská třída 33, 702 00 Ostrava, Czech Republic                                                E-mail: haochen.guo@vsb.cz                 Abstract: In the risk management, volatility as the important parameter for estimation in                the issue of hedging. Volatility model is the regression based forecasting model. GARCH                (Generalized  Autoregressive  Conditional  Heteroscedasticity)  model  is  one  of  volatility                model, it presents the variance rate at the current time step is a weighted average of a                constant long run average variance rate, the variance rate at the previous time steps and                the most recent information about the variance rate. Hence, there are many literatures                supposed to use the GARCH minimum variance hedging the financial derivatives. Thus, the                bivariate  GARCH  model  provides  a  superior  performance  to  other  dynamic  or  constant                hedge for financial derivatives. In the paper, it estimates the minimum variance hedge                based on an advanced econometric model (GARCH model) with time varying minimum                variance hedge.                Keywords: Volatility Model, GARCH Model, minimum variance, hedge ratio                JEL codes: C01, G17, G32                 1 Introduction                 The forecasting of volatility can be regarded as a significant problem of financial modelling.                Because the volatility is an important parameter for financial risk management and it is                applied in many issues such as option pricing, portfolio optimization, Value at Risk, hedging                and  so  on.  There  are many literatures  supposed  to  use  the  GARCH minimum  variance                hedging the financial derivatives. Hence, in this paper analyzes the volatility model applies                to hedging – GARCH minimum variance hedging.                  2 Methodology Volatility Model based GARCH Minimum Variance Hedge                This section describes the minimum variance hedge (section 2.1), the volatility forecasting                - GARCH model (section 2.2) and GARCH minimum variance hedge ratio (section 2.3).                Minimum Variance Hedge                 Suppose there is a maturity mismatch, so that the hedge position is closed at some time                   <   , where    is the expiry date of the futures. The value of the hedged portfolio at time                   is                         (  ) =    ×    ×   (  ,   ) −    ×   (  ).                                     (1)                                                                                                     The variance of this portfolio value is                                        (  (  )) =                       (  (  ,   )) +                    (  (  )) − 2              (  (  ,   )  (  )). (2)                                                                2                                         2                                            2                                                                                                                                                                                                                                                                                                               Note that if      =     as in the one-for-one hedge then                                                                                                                           (  (  )) =    × √                (  (  ,   )) +                 (  (  )) − 2      (  (  ,   ),   (  )).     (3)                                                              The hedging criterion is to choose    at time 0 to minimize equation (2). Differentiating with                respect to    and checking the second order condition gives the optimal number of contracts                in the hedge as                   = ( ) ×    ,                                                                                                                                             (4)                            ∗                                            ∗                                           where                                                            147","∗                          =        (  (  ,  ),  (  )) .                                                   (5)                                            (  (  ,  ))                The ratio equation is called the minimum variance hedge ratio.                Verification  of the  financial  risk  estimation  approaches following  the  minimum  variance                hedging under partial risk, which expressed variable parameter    ∈ (0; 1]. The parameter                   means, what part of the risk should be hedged. If the    equal 1, it means the whole risk is                hedged, if it is less than 1, it is only a partial hedged. Hence, the minimum variance partial                hedge ratio expressed by following equation                          ∗                          =    ∗      (  (  ,  ),  (  )) .                                              (6)                                                                      (  (  ,  ))                Estimating Volatility - The GARCH Model                Define  as the volatility of a market variable on day t, as estimated at the end of day t-                         t                                                          2                1. The square of the volatility on day t  is the variance rate.                                                         t                To estimate the volatility of a stock price empirically, the stock price is usually observed at                fixed intervals of time.                                  P                        R  = ln(   ,t i  ) for i=1,2,…,t.                                               (7)                          ,t i                                P ,t i  − 1                Where, t+1: Number of observations,                  P : Stock price at end of ith  interval (i=0,1,…,t).                   t i,                Because P =  P i 1 −  e ,  R is the continuously compounded return in the ith interval. The usual                                  i R                          i                                      i                estimate, P, of the standard deviation of the ’s is given by                                                               i                                1   n                                                   2                        =    N − 1  1 [R −  E (R i )] =   i 2  ,                                     (8)                                         ,t i                         i                                    t=                        E                            )                Where  (R is the mean of the R ’s.                            i                                                   t i,                          P             2                        ln  T  ~  [(R −  )T ,  T  ] ,                                                 (9)                          P 0       ,t i  2                                                              *                Following that   itself can be estimated as .                              P                         *                        =      ,                                                                                           (10)                                              The standard error of this estimate can be shown to be approximately    *  /  t 2 .                 Suppose that the value of the market variable at the end of day  i is P . The variable  R is                                                                                       i                                                                                                         i                defined as the continuously compounded return during dayi .                                P                        R  = ln  i    ,                                                               (11)                         i                              P i  − 1                                                                    2                An unbiased estimate of the variance rate per day,  using the most recent n observations                                                                    t                on the  R is                         i                                                               148","1   N                        2  =        R [  − E( R ],                                                  (12)                                                 2                                                )                         t                             N  −1  = i 1  − t 1  i                        E                            )                Where  (R is the mean of the R ’s:                                                  i                            i                                 1  N                        E( R )  =    R  i t,  ,                                                      (13)                            i                                N   = i 1                For  the  purposes  of  calculating  Value  at  Risk,  the  formula  in  equation  (13)  is  usually                                                  E                                                      )                changed in a number of ways R , (R , N-1.                                                i                                                      i                Where  R is defined as the proportional change in the market variable between the end of                         i                day i-1 and the end of day i so that                            P  − P                        R i  =  i  1 − i  ,                                                           (14)                              P  1 − i                 E (R i ) is assumed to be zero and N-1 is replaced by t.                 The formula for variance rate becomes                             1  N                        2  =    R 2  ,                                                              (15)                         t                             N  = i 1  t −i                The  Generalized  Autoregressive Heteroskedastic (GARCH)  model is  developed  by  Engle                (1982) and Bollerslev (1986).                                     2                In GARCH model,  is calculated from a long-run average variance rate, as well as from                                     t                  − t  1 and R n − 1 . The equation for GARCH is                                                            2  =  2  + R 2  +  2  ,                                                  (16)                         t      t      1 − t   1 − t                Where,  is the weight assigned to R  2  1 − t  , and   is the weight assigned to 2 1 − t  . Because the                weights must sum to one:                         + +  = 1,                                                                 (17)                 Setting =   V   , the GARCH model in following equation,                                                         t 2  =  + R 2 − t  1  +  2 − t  1 ,                                                (18)                This  is  the  form  of  the  model  that  is  usually  used  for  the  purposes  of  estimating  the                parameters. Once , ,   have been estimated, it can calculate  as  −    −  . The long-                                                                                       1                term  variance  can  then  be  calculated  as /  .  For  a  stable  GARCH  process  it  requires                  +   1. Otherwise the weight applied to the long-term variance is negative.                 The GARCH Minimum Variance Hedge                 This is the number of futures contracts in the hedge when minimize the variance of the                hedge portfolio. Note that when beliefs are expressed in terms of returns normally express                the minimum variance hedge ratio as                         ̃                          =           .                                                                (19)                                                  It estimated by performing a simply linear regression by OLS. The regression model is                                ̃                          =    +      +    ,                                                            (20)                                                                                                                                                                       149","where  the  dependent  variance  is  the  return  on  the  portfolio  to  be  hedged  and  the                independent variable is the return on the hedging instrument.                 Time  varying  minimum  variance  hedge  ratios  may  also  be  estimated  using  a  bivariate                GARCH model. These models extension to time varying minimum variance hedge ratios is                not entirely straightforward, since it is typical that the portfolio will be cointegrated with                its hedging instrument.                 The disequilibrium term in the error correction mechanism will be closely approximated by                the fair value of the basis. To see why, note that                              (  ,   ) −       (  ) = (   −   )(   −   ) =   (  ,   ).                                  (21)                          ∗                Although   (  ,   ) will be stationary when the spot and futures are cointegrated, which they                are when the basis is mean-reverting, it need not be the most stationary linear combination                of the log of the market price of the futures and the log of the spot time. nevertheless                since  the  mispricing  of  the  futures  contract  relative  to  its  fair  value  is  so  small  it  is                reasonable to assume the error correction term in the error correction model is equal to                  (  ,   ) when log returns rather than returns are used in the GARCH model.                 Introducing the notation                                         1        1     Γ   Γ                1,                            = (         ),    = (    2 ),    = (    2 ), Γ = ( Γ 11  Γ 12 ) and    = (    2,   ),                    (22)                                                                                                                                                               22                                                         21                where the vector    contains log returns, it can write the conditional mean equations as                          =    + Γ     −1  +        −1  +    .                                                                      (23)                                                                                          In  the  conditional  bivariate  GARCH  framework,  it  assumes          −1 ~  (0,       )  where       −1                                                                                  |                                                                        2                                                                                                      denotes the information set as time    − 1 and    = (                     ),                                                                                                                                             2                where     and     are the conditional standard deviations of the portfolio and the hedging                                                                                instrument  and              is  their  conditional  covariance  at  time    .  This  matrix  is  called  the                conditional covariance matrix. The GARCH minimum variance hedge ratio at time    is then                given by                        ̃                          =            .                                                                                      (24)                                                         2                     Note that the GARCH model hedge ratio is time varying as well as its estimate.                Verification  of  the  financial  risk  estimation  approaches  following  the  GARCH  minimum                variance partial hedging under partial risk, which expressed variable parameter    ∈ (0; 1].                The parameter    means, what part of the risk should be hedged. If the    equal 1, it means                the whole risk is hedged, if it is less than 1, it is only a partial hedged. Hence, the GARCH                minimum variance partial hedge ratio expressed by following equation                        ̃                          =              .                                                                                    (25)                                                       2                                                                               3  Empirical  Study  of  Volatility  Model  based  GARCH  minimum  Variance                Hedge                This  section  illustrates  the  minimum  variance  hedge  ratio  (section  3.1)  and  GARCH                minimum variance hedge as the empirical research with two Chinese indices (section 3.2).                Minimum Variance Hedge Ratios                 Illustrate  that  based  on  the  equation  (5),  (6)  and  (20)  to  calculate  the  OLS  minimum                variance futures hedge ratios for the two Chinese indices (Figure 1) of SSE composite index                (000001.SS), HANG SENG index (^HSI) that according to the hedging period of 1 day                (daily data). The following Table 1 presents the results of minimum variance hedge ratios                from 23  July 2012 – 13  October 2017.                        th                                         th                                                            150","Figure 1 Index Data from 2012-2017                                                                        Source: Yahoo Finance                                          Table 1 Minimum Variance Hedge Ratio                  Returns                                                    SSE            HANG SENG                 Spot volatility                                           23.51%            16.48%                 Futures Volatility                                       26.49%             17.77%                 Correlation                                               0.082              0.020                 Minimum Variance Hedge Ratio                              0.073              0.018                                                   Source: Own calculation                The minimum variance hedge ratio is the product of the correlation coefficient between the                changes  in  the  spot  and  futures  prices  and  the  ratio  of  the  standard  deviation  of  the                changes in the spot price to the standard deviation of the futures prices. The size of the                minimum variance hedge ratio increases with the spot-futures correlation. When the spot                and futures are very highly correlated the minimum variance hedge ratios are very close                to 1. Otherwise might opposite.                                      Table 2 Minimum Variance Partial Hedge Ratio                                         100% (full hedge)       80%        60%        40%         20%                 SSE                            0.073           0.058     0.044       0.029       0.015                 HANG SENG                      0.018           0.015     0.011       0.007       0.004                                                   Source: own calculation                                     Figure 2 Minimum Variance Partial Hedge Ratio                  0,08                 0,07                  0,06                 0,05                 0,04                 0,03                 0,02                 0,01                     0                      20%      30%       40%       50%      60%       70%       80%       90%      100%                                                      SSE       HANG SENG                                                              151","Source: own calculation                 The result under the risk of 20%, the partial hedging with SSE is 0.015 and HSI is 0.004;                under the risk of 40%, the partial hedging with SSE is 0.029 and HSI is 0.07; under the                risk of 60%, the partial hedging with SSE is 0.044 and HSI is 0.011; under the risk of 80%,                the partial hedging with SSE is 0.058 and HSI is 0.015; under the risk of 100% (full hedge),                the hedging with SSE is 0.073 and HSI is 0.018. Though the increase of risk the partial                hedging optimization is going up that is proof in the Figure 2.                 The GARCH Minimum Variance Hedge                 Illustrate  that  the  case  of  SSE  composite  index  and  HANG  SENG  index  apply  GARCH                minimum variance hedge which based on the equation (24) and (25). The time varying                GARCH hedge are extremely variable. Following Figure 3 and Figure 4 present the GARCH                minimum variance hedge ratio with full hedge risk and partial risk in SSE composite index                (000001.SS) and HANG SENG index (^HSI).                    Figure 3 (a) The GARCH Minimum                  Figure 4 (a) The GARCH Minimum                           Variance Hedge (SSE)                           Variance Hedge (HSI)                                                                   0,1                 0,7                                            0,09                 0,6                                            0,08                                                                0,07                 0,5                                            0,06                 0,4                                            0,05                                                                0,04                 0,3                                                                0,03                 0,2                                            0,02                 0,1                                            0,01                                                                   0                   0                      24-Jul-12  24-Nov-12  24-Mar-13  24-Jul-13  24-Nov-13  24-Mar-14  24-Jul-14  24-Nov-14  24-Mar-15  24-Jul-15  24-Nov-15  24-Mar-16  24-Jul-16  24-Nov-16  24-Mar-17  24-Jul-17  24-Jul-12  24-Dec-12  24-May-13  24-Oct-13  24-Mar-14  24-Aug-14  24-Jan-15  24-Jun-15  24-Nov-15  24-Apr-16  24-Sep-16  24-Feb-17  24-Jul-17                                Source: own calculation                        Source: own calculation                     Figure 3 (b) The GARCH Minimum                  Figure 4 (b) The GARCH Minimum                       Variance Partial Hedge (SSE)                    Variance Partial Hedge (HSI)                  0,7                                             0,1                                                                0,09                 0,6                                                                0,08                 0,5                                            0,07                 0,4                                            0,06                                                                0,05                 0,3                                                                0,04                 0,2                                            0,03                 0,1                                            0,02                                                                0,01                  0                                                0                      24-Jul-12  24-Nov-12  24-Mar-13  24-Jul-13  24-Nov-13  24-Mar-14  24-Jul-14  24-Nov-14  24-Mar-15  24-Jul-15  24-Nov-15  24-Mar-16  24-Jul-16  24-Nov-16  24-Mar-17  24-Jul-17  24-Jul-12  24-Dec-12  24-May-13  24-Oct-13  24-Mar-14  24-Aug-14  24-Jan-15  24-Jun-15  24-Nov-15  24-Apr-16  24-Sep-16  24-Feb-17  24-Jul-17                                 Source: own calculation                        Source: own calculation                                                                 152","Time varying minimum variance hedge ratios estimated GARCH incorporate too much noise                to be effective for hedging purposes and one is actually better off using OLS to estimate                the minimum variance hedge ratio. The benefits of an active hedging strategy should be                economically justifiable yet these models do not account for transactions costs, such as                margins and commissions. When the costs of hedging are considered the case against time                varying hedge ratios based on conditional covariances is strengthened even further. Result                presents the GARCH hedge ratios sensitivity does not influence performance.                 4 Conclusions                This paper analyzes the volatility model (GARCH model) to estimate minimum variance                hedge  ratio  when  the hedge is  placed over  a  very  short horizon  (daily  data)  with  SSE                composite index (000001.SS) and HANG SENG index (^HSI). The forecast based on longer                time series should be better. The characteristic property of the GARCH model is the slower                reversion to the observed variance after greater shocks. In principle, the GARCH model is                better compared with the EWMA model for short term forecasting. Hence, the following                step of empirical study will the volatility model based EWMA minimum variance hedging.                  References                Alexander, C. (2008).  Market Risk Analysis Volume I:  Quantitative Methods in Finance.                John Wiley \& Sons.                Alexander,  C.  (2008).  Market  Risk  Analysis  Volume  III:  pricing,  Hedging  and  Trading                Financial Instruments. John Wiley \& Sons.                 Billio, M., Casarin, R., Osuntuyi, A. (2018). Markov switching GARCH models for Bayesian                hedging on energy futures markets. Energy Economics, vol. 70, pp. 545-562.                Chang, C., Gonzalez-Serrano, L., Jimenez-Martin, J. (2013). Currency hedging strategies                using  dynamic  multivariate  GARCH.  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Ostrava: VSB  –                Technical University of Ostrava.                                                                 153","Efficiency Hospitals in the Czech Republic: the Difference Between                                                 the Methods Used                                        Taťána Hajdíková , Kateřina Trubačová       2                                                           1                                               1 University of Economics, Prague                                      Faculty of Management, Department of Management                                 Jarosovska 1117/II, 37701, Jindrichuv Hradec, Czech Republic                                                E-mail: hajdikova@fm.vse.cz                                                2 University of Economics, Prague                                      Faculty of Management, Department of Management                                 Jarosovska 1117/II, 37701, Jindrichuv Hradec, Czech Republic                                            E-mail: katerina.trubacova@seznam.cz                 Abstract. In the Czech Republic and other countries, the effective use of funds from public                sources in the health care sector is a long-term research topic. There is pressure to increase                the efficiency of healthcare providers. The paper deals with the analysis of the methods                used  to  determine  the  effectiveness  of  hospitals  in  the  Czech  Republic.  Many  tools  of                financial analysis are used to assess financial health. In particular, various ratios, but only                those situations that relate to them. However, there are a number of methods dealing with                a more comprehensive view of the overall effectiveness of the subject. The aim of the                paper is to discuss and assess whether the detection of financial health by various methods                will  contribute  to  the  improvement  of  the  financial  situation  of  Czech  hospitals.  For                assessing  the  benefits,  account  is  taken  of  the  financial  position  of  the  investigated                hospitals found in research and research by other authors. Contributions will be used by                hospitals from the calculation of the Ohlson O-Score Index, the Financial Strength Index,                the  Polish  Collective  Index  by  A.  Bem  and  H'index  in  2013-2016.  The  output  of  the                contribution is the H'index financial health information compared to other indexes.                Keywords: hospitals, performance, financial health                JEL Classification: I11, C6                  1 Introduction                The health system is the most comprehensive component of the entire nonprofit sector                amongst which the health services are generally the most commercial. More and more                gainful activity of these services depends more on management than on volunteers (Worth,                2012).                 The economic outturn of a healthcare facility may be positive or negative. Ideally, any                economic subject should achieve long-term profitability. In healthcare, there may also be                situations where it is not possible to say whether profit is a positive or negative aspect.                General economic theories may be contrary to the criteria of success in health care (Pekova                at al., 2012)                Since the end of the last century, a number of authors have been researching the economic                effectiveness of hospitals (Sherman,1984) (Zuckerman et al., 1994). In this millennium,                researching the measurement of hospital efficiency and its determinants is a major concern                of  the  healthcare  economy  (Jacobs,  2001)  (Hofmarcher,  2002).  To  evaluate efficiency,                quantitative economic  analysis  methods, including  envelope  analysis,  are  a useful  tool.                Their disadvantage is the difficulty of aggregating diverse data (Dlouhý, 2007). A summary                of  articles  on  efficiency  assessments  in  health  care  is  published  by  Hollingsworth                (Hollingsworth, 2003).                 2 Methodology and Data                For comparison, models that were created for and are applicable to hospitals were selected.                The  models  differ  from  each  other  by the number  of indicators  and  by  their selection.                Various limitations have been made in analyzing and evaluating results. Foreign models                                                             154","appeal to the use of financial indicators that do not have the exact equivalents in the Czech                Republic,  such  as  the  Cleverley  Financial  Strength  Index  and  its  \"Days  cash  on  hand\"                indicator. Czech hospitals often deal with loss and are perceived negatively in this respect.                While calculating foreign indexes, Czech hospitals with negative economic outcomes are                often ranked amongst the most stable and financially healthy.                Ohlson's O-Score is a multi-factor formula for the approaching bankruptcy's prediction.                Ohlson, a big critic of Altman's Z-score, built his model on the data of 2000 bankrupt US                companies. His model is considered to be more accurate than Altman's Z-score even due                to the influence of internal and external factors (Ohlson, 1980).                The O-score formula (Ohlson, 1980):                            = −1,32 − 0,407       (          ) +  6,03            − 1,43            +  0,0757            − 1,72   − 2,37            −                                                                                                                                1,83              +  0,285   − 0,521           −        −1                   (1)                                                  |        |+ |       −1 |                 •   TA total assets,                 •   GNP Gross National Product price index level,                 •   TL total liabilities,                 •   WC working capital,                 •   CL current liabilities,                 •   CA current assets,                 •   X=1 if TL > TA, 0 otherwise,                 •   NI net income,                 •   FFO funds from operations,                 •   Y=1 if a net loss for the last two years, 0 otherwise                The  Financial  Strength  Index  (FSI)  was  designed  by  William  Cleverley  specifically  for                American  hospitals.  This  prevents  problems  arising  from  the  use  of  bankruptcy  and                creditworthiness  models  and  indexes  designed  for  manufacturing  companies.  In  his                research, Cleverley dealt generally with health care systems. FSI is a composition of four                dimensions:  profitability,  liquidity,  financial  leverage  and  age  of  physical  equipment                (Cleverley, 2002).                Basic FSI formula (Cleverley, 2002):                                                       − 4,0                 ℎ      ℎ       − 50  50 −                             %                              = (              ) + (                    ) + (                    )                                     4,0                    50                       50,0                              9,0 −                                                                      + (                   )                                                                                               (2)                                         9,0                Polish indicators by Agnieszka Bem et al have been designed and tested to assess the                financial health of Central European hospitals. The synthetic pointer is constructed using a                gradient  method.  In  Table  1 the indicators  are  divided into  4  categories  -  profitability,                liquidity, debt and efficiency (Bem et al, 2015).                                                                                155","Table 1 Selected Financial Indicators                             Label       Formula                               Group                             OPM         EBIT/Sales                            profitability                             CR          Current Assets/Current liabilities    liquidity                             D%          Total debt/Total Assets               debt                                         (Net  profit  +  Depreciation)  /  Total                             CF/Debt                                           debt                                         debt                             TAT         Sales/Total Assets                    efficiency                             CES         Employee benefit expense/Sales        efficiency                                         (Net  profit  +  Depreciation)  /  Total                             ROCF                                              profitability                                         Assets                                             Source: own edit (Bem et al, 2015)                  Czech H'index is still the youngest model compiled through logistic regression on the data                of Czech hospitals. It was built on the basis of extensive analysis of existing indexes and                indicators (Hajdíková, 2016).                 Basic H'index formula (Hajdíková, 2016):                          ′           =   ,          +     ,              +   ,           2 +   ,           3−     ,           4      (3)                •  U1 = EBIT/operating income                •  U2 = Net profit + depreciation/total debt                •  U3 = Operating income/total assets                •  U4 = Cost of employee benefits/operating income                 There are weighing coefficients in front of individual indicators (Ui). The constant in the                formula is 0.8277, which increases the sum of the weighted values of the indicators. The                basis for the goal of this paper is to select a correct research sample. Between 2013 and                2016 there were from 188 to 189 hospitals registered in the Czech Republic (ÚZIS, 2018).                Secondary data from publicly available sources and the websites of individual hospitals                were used in the research. The data from the website has coincided and can be considered                as correct. Despite the legal obligation, some business entities do not publish their financial                statements, which is also the case of some hospitals. The sample of hospitals that published                and were included in the research sample is shown in Table 2.                                  Table 2 Number of Hospitals for the Research sample                                           Year          Number of hospitals                                          2013                    99                                          2014                    98                                          2015                    85                                          2016                    84                                                 Source: ÚZIS (ÚZIS, 2018)                 This research sample was selected using the cluster analysis. The clusters created contain                units that are similar. Individual clusters vary considerably between each other. The final                sample of examined hospitals is 84. The published evaluation applies the benchmarking                method to the top 10 hospitals.                 3 Results and Discussion                When calculating the O-score, a problem arises when an indicator within the equation is                equal to zero. Ohlson pronounced the conclusion where the boundary value is 0,5. Values                higher than 0,5 indicate the financial distress and, on the contrary, lower than 0,5 the                financial health. Table 3 presents hospitals that can be considered financially sound and                stable. Hospitals with financial loss have never been among the top 10 best-rated hospitals.                                                             156","Table 3 Comparison of the Best Hospitals in the Czech Republic by O-score in 2013-2016                                                                                 Year                                Hospital                                                               2013         2014         2015       2016               Centrum léčby pohyb. aparátu, s. r. o.          -9,43011            0    -5,51343  -6,54636               RHG spol. s r. o.                               -7,82853    -6,38471     -8,64625           0               Poličská nemocnice, s. r. o.                    -6,47412    -6,28977     -6,23757  -6,22558               Sdružené zdravot. zařízení Krnov, p. o.         -4,17787    -5,25399     -4,74197  -4,08734               Fakultní nemocnice Hradec Králové               -4,17593            0    -4,18853  -4,09987               Nemocnice Vyškov, p. o.                                 0   -5,11317     -4,56641  -5,23112               Nemocnice Ivančice, p. o.                               0   -4,91298     -4,67252  -4,78224                                                       Source: own research                 Cleverley's Financial Strength Index considers indicators that are barely used in the Czech                Republic  for  the  assessment  of  financial  health.  The  calculation  of  the  index  itself  was                seamless. Cleverley divides hospitals into three categories. The FSI ranging from -2,0 to                2,0 indicates  that  the hospital is  financially unhealthy;  values  from 2,0  to  3,0  refer  to                hospitals  with  average  financial  health.  The  FSI  with  values  greater  than  3,0  refers  to                hospitals  with  excellent financial  health.  The  results  of  the  calculations were extremely                high, which may be either due to a partial modification of the index indicators or due to                the FSI not being suitable for assessing the financial health of Czech hospitals. It's also the                only  index  considering  different  indicators  and  variables,  so  it  tends  to  be  more                inappropriate than to misconduct. Table 4 shows the top 10 hospitals that have occupied                the position for at least three of the four monitored years.                   Table 4 Comparison of the Best Hospitals in the Czech Republic by FSI in 2013-2016                                                                                   Year                                 Hospital                                                                  2013       2014        2015        2016              Nemocnice Žatec, o. p. s.                          5229,64  48061,07  6018,765        5956,26              Středomoravská nemocniční, a. s.                  4571,937     5604,65  5055,669  5323,665              Dopravní zdravotnictví, a. s.                     4281,706  5274,472  4095,724               0              Oblastní nemocnice Kolín, a. s.                   3928,919            0  5687,004  4987,923              Poliklinika Prosek, a. s.                         3629,001            0  3772,078  3645,239              Nem. s poliklinikou Karviná – Ráj, p. o.          2722,068  804613,2  1798924                0              Sdružené zdravot. zařízení Krnov, p. o.           2584,261  395149,5       139047      138897              Nemocnice Ivančice, p. o.                                 0  632003,7  707935,1  686234,3              Masaryk. městská nem. v Jilemnici, p. o.                  0  724249,4  867014,2              0              Nemocnice Vyškov, p. o.                                   0  227650,2  1174428  346787,76                                                    Source: own research                 The calculation of Polish financial health indicators in Table 5 differs slightly from previous                calculations. There is no formula or equation that would be definitive and authoritative for                application in assessing the financial health of hospitals. This is the calculation of seven                indicators, which are further independently evaluated. After calculating the indicators, the                OPM (EBIT / Sales) variable was chosen, according to which the hospitals were ranked                downwards from best to worst. Subsequent analysis has shown that, according to the OPM,                the financial condition of the hospitals is assessed as \"bad\". In addition, it is appropriate                to state that all hospitals of the research sample were evaluated negatively. The use of                these indicators is not appropriate for a person who did not participate in the research. It                serves as a different view on the subject and the impetus for a deeper analysis and an                subsequent research.                                                             157","Table 5 Comparison of Hospitals Using the Polish Index (OPM) in 2013-2016                                 Hospital                      2013         2014       2015        2016                 Centrum léčby pohyb. aparátu, s. r. o.        0,264843            0   0,088895 0,345566                 RHG spol. S r. o.                             0,216902  0,234035      0,245868          0                 Mediterra Sedlčany, s. r. o.                  0,149217  0,061046              0 0,098457                 Nemocnice Podlesí, a. s.                       0,14225  0,147729      0,084195 0,108834                 Nemocnice sv. Zdislavy, a. s.                 0,141749  0,051459      0,053516          0                 Poličská nemocnice, s. r. o.                  0,099874     0,14088    0,123099 0,134766                 Hornická nem. s polik., spol. s r. o.         0,091311  0,083072              0 0,089866                 SANATORIUM Helios, s. r. o.                            0  0,146503    0,190207 0,173322                 Nemocnice Valtice, s. r. o.                            0  0,108945    0,080747 0,090944                 Nemocnice Český Těšín, a. s.                           0  0,069887    0,078703 0,061233                                                    Source: own research                 H'index, as the only one outlined in Table 6, was designed to assess the financial health of                Czech hospitals. It responds to the trend when hospitals have a need to compare their                achievements with competition, while the competitive fight is quite large. H'index operates                with Czech data and considers indicators that are commonly used in the Czech Republic to                assess financial health.                    Table 6 Comparison of the Best hHspitals in the Czech Republic Using the H'index                                                                                   Year                                  Hospital                                                                2013        2014         2015       2016                Centrum léčby pohyb. aparátu, s. r. o.        44,02907        0       17,34271  22,43736                RHG spol. s r. o.                             31,7627     28,53735  38,99768          0                Nemocnice Podlesí, a. s.                      19,28659    18,31247     10,8022  17,8765                Hornická nem. s poliklinikou, spol. s r. o. 16,94159      15,15379         0     16,87655                Poličská nemocnice, s. r. o.                  14,0115     18,13052     16,7603  16,7822                Nemocnice Valtice, s. r. o.                   12,35539    11,01899  9,058287  11,00878                SANATORIUM Helios, s. r. o.                       0       15,03902  22,19845  22,26766                                                     Source: own research                 To make it more accurate to determine whether the researched indexes are suitable for                assessing the financial health of Czech hospitals, it is necessary to compare them with real                results or with a similar research. Because there is no similar research, where these indexes                would be tested on Czech hospital data, a benchmarking method was used. It is based on                HCI's list of success rate of hospitals (Health Care Institut, 2018). The list is made on the                data of Czech hospitals. The results of H'index in this paper are the same as those in the                list,  or  only  slightly  different  depending  on  the  origin  and  method  of  collection  of  the                researched  data.  Another  comparison  is  devoted  to  the  FSI  index.  FSI  values  are                completely different in size, often in hundreds of thousands. The FSI results can hardly be                grasped and evaluated. The listed index contained the most unknown indicators and its                modification could distort the results. The formula of the Ohlson's index has overturning                results.  Negative  values  mean  financially  sound  hospitals  and  positive  values  mean                hospitals  with  financial  problems.  It  was  not  possible  to  compare  numerical values,  so                hospitals were compared only in terms of whether they were placed in the top 10 list.                Polish indicators are deliberately omitted because of the lack of interpretation. The base                for the evaluation was probably not chosen appropriately, therefore it is not possible to                evaluate the suitability of the indicators.                                                             158","4 Conclusions                Healthcare is currently a widely discussed topic. Even in this sector, the trend of the present                time - the comparison with the competition  - is interfering. Indexes help the economic                subjects with fast comparison between each other. It is related to the need for leadership,                being the best in the group. A key compromise in the management of hospitals and other                healthcare facilities is to ensure prosperity and fulfillment of the mission in the care of the                health and well-being of the population. The main benefit of aggregate financial health                assessment indexes is their easy and quick application. The result is one value that has a                clear  message.  The  resulting  value  is  a  determinant  for  the  management  and  may  be                followed by a more thorough financial analysis. It is advisable to combine both methods,                not relying on just one. In the Czech Republic there are widely used other methods of                financial analysis and also indexes that are not suitable for Czech hospitals. Indexes built                primarily for hospitals were introduced in this paper. Also, the capability of the new H'index                was  introduced.  From  the  selected  foreign  indexes,  the  O-score  index  for  the  Czech                Republic has the greatest information. Of all the indexes examined, the Cleverley FSI index                is the least appropriate.                 Acknowledgments                Information on acknowledgments and financial support are posted here.                 References                 Bem, A., Siedlecki, R., Predkiewicz, P., Ucieklak-Jez, P., Hajdikova, T. (2015). Hospital’s                financial health assessment. Gradient method’s application. Proceedings Enterprise and the                Competitive Environment, pp. 76-86.                 Cleverley, W. O. (2002). Who is responsible for business failures?. Healthcare Financial                Management, vol. 56(10), pp. 46-50.                Dlouhy, M., Jablonsky, J., Novosadova, I. (2007). Use of Data Envelopment Analysis for                Efficiency Evaluation of Czech Hospitals. Politická ekonomie, vol. 54, pp. 60-71.                Hajdikova, T. (2016). Návrh indexu predikce finanční výkonnosti pro podporu rozhodování                managementu nemocnic.                Healthcare Institute (2018). Retrieved from: http://www.hc-institute.org/cz/.                Hofmarcher, M. M., Peterson, I., Riedel, M. (2002). Measuring Hospital Efficiency in Austria                - A DEA Approach. Health Care Management Science, vol. 5, pp. 7-14.                Hollingsworth, B. (2003). Non-Parametric and Parametric Applications Measuring Efficiency                in Health Care. Health Care Management Science, vol. 6, pp. 203-218.                Jacobs, R. (2001). Alternative Methods to Examine Hospital Efficiency: Data Envelopment                Analysis  and  Stochastic  Frontier  Analysis.  Health  Care  Management  Science,  vol.  4,                pp. 103-116.                Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal                of Accounting Research, vol. 18(1), pp. 109-131.                Pekova,  J.,  Pilny,  J.,  Jetmar,  M.  (2012).  Veřejný  sektor  –  řízení  a  financování.  Praha:                Wolters Kluwer Česká republika.                Sherman, D. H. (1984). Hospital Efficiency Measurement and Evaluation. Empirical Test of                a New Technique. Medical Care, vol. 22, pp. 922-938.                ÚZIS (2018). Institute of Health Information and Statistics of the Czech Republic. Retrieved                from: http://www.uzis.cz/.                Worth, M. J. (2012). Nonprofit management: principles and practice, 2nd ed. Los Angeles:                Sage.                 Zuckerman, S., Hadley, J., Iezzoni, L. (1994). Measuring Hospital Efficiency with Frontier                Cost Functions. Journal of Health Economics, vol. 13, pp. 255-280.                                                             159","Level of Industry Automation 4.0 in the Czech Republic and Impact                                                 on Unemployment                                            Martina Hedvičáková, Martin Král                                                1  University of Hradec Králové                               Faculty of Informatics and Management, Department of Economics                                    Rokitanského 62, 500 03 Hradec Králové, Czech Republic                                             E-mail: martina.hedvicakova@uhk.cz                                                 2  University of Hradec Králové                               Faculty of Informatics and Management, Department of Economics                                    Rokitanského 62, 500 03 Hradec Králové, Czech Republic                                                E-mail: martin.kral.2@uhk.cz                 Abstract: The Czech Republic has the lowest unemployment rate throughout the European                Union.  According  to  Eurostat  and  the  Czech  Statistical  Office,  in  March  2018,  the                unemployment rate in the Czech Republic reached 2.2%. Such a low unemployment rate                causes an excess of demand over supply. Companies are looking for ways of meeting the                demand  of  their  customers.  One  of  the  possibilities  is  technological  progress.  For  this                reason, both the Czech Republic as well as the European Union are promoting the Industry                4.0 initiative and at the present time also the Work 4.0. The aim of the paper is to analyse                the impact  of Industry 4.0 and Work 4.0 on the labour market. The paper contains an                analysis of the share of unemployed people in individual regions and the development of                wages. The manufacturing industry is one of the key sectors of the Czech economy. The                following hypotheses are tested: The impact of Industry 4.0 is markedly different in the                Czech  Republic  regions  and  second  As  a  result  of  the  enforcement  of  Industry  4.0                (production automation), unemployment varies across regions.                 Keywords: Region, Industry 4.0, wages, cluster analysis, unemployment                JEL codes: J21, E2, L16, O33                 1 Introduction                Originally  initiated  in  Germany,  Industry  4.0,  “the  fourth  industrial  revolution”,  has                attracted  much  attention in  recent literatures.  It is  closely  related  with the  Internet  of                Things (IoT), Cyber Physical System (CPS), information and communications technology                (ICT), Enterprise Architecture (EA), and Enterprise Integration (EI). (Lu, 2017). Industry                4.0  is  defined  as  “the  integration  of  complex  physical  machinery  and  devices  with                networked sensors and software, used to predict, control and plan for better business and                societal outcomes” (Industrial Internet Consortium, 2014) or “a new level of value chain                organization and management across the lifecycle of products” (Kagermann H, Helbig J.,                2013). or “a collective term for technologies and concepts of value chain organization”                (Hermann, Pentek, 2015). Thus, the concept of Industry 4.0 can be perceived as a strategy                for being competitive in the future. It is focused on the optimization of value chains due to                autonomously  controlled  and  dynamic  production  (Acatech,  2013,  2017,  Mrugalska,                Wyrwicka, 2017, Hedvicakova, 2018, Soukal, et al. 2012)                The  impact  of  Industry  4.0  on  the  labour  market  will  be  very  complex  and  also                contradictory.  So  far  their  research  in  the  Czech  Republic  hasn’t  been  given  the  right                attention.  One  of  the  exceptions  is  Chmelař  (2015)  from  the  Strategy  and  Trends                Department of the Czech Republic Government Office, who prepared a study: The Impact                of Digitisation on the Labour Market in the Czech Republic and the EU. The paper deals                with the effects of narrow digitalisation and attempts to estimate employment threats in                professional groups based on coefficients that are taken from a US labour market study.                Calculations made so far on the disappearance and creation of jobs are different in relation                to the methodology used. Specifically for the Czech Republic, it’s estimated that 10% of                jobs are seriously threatened by automation over the next 20 years, and 35% of jobs will                experience significant changes in the activities carried out. If this estimate, published in                                                             160","the OECD study (Employment Outlook 2016), counts on the number of people employed                in  2015,  about  408,000  jobs  will  be  significantly  threatened,  and  1.4  million  jobs  will                experience major changes. (Work Initiative 4.0). (Hedvicakova, 2018a)                 2 Methodology and Data                Mainly  secondary  sources  were  used  when  processing  this  paper.  Secondary  sources                include  specialised  literature,  gathering  information  from  professional  press,  websites,                databases,  discussions  or  previous  attendance  on  specialised  seminars  or  conferences                within  the  chosen  subject  area.  (Hedvičáková,  Svobodová,  2017)  Key  information  was                obtained from the official websites of the Czech Statistical Office, the Ministry of Industry                and  Trade,  OECD,  Eurostat,  as  well  as  from  specialised  servers  and  portals  such  as                iHned.cz,  etc.  Not  enough  attention  has  been  devoted  to  Labour  market  research  in                connection with Industry 4.0 yet. This topic was dealt with by Chmelař, 2015 The Impacts                of Globalisation on the Labour Market in the Czech Republic and the EU and the Ministry of                Labour  and  Social  Affairs  2015  by  anticipating  the  qualification  needs.  The  Ministry  of                Labour and Social Affairs has elaborated the Work 4.0 study that this paper is based on.                (Hedvičáková, Svobodová, 2016, 2017, Svobodová, Hedvičáková, 2017,)                The  paper  uses  the  general  unemployment  rate  according  to  the  CSO's  calculation                methodology, where the unemployment rate is calculated within Selected Survey of Labour                Force  (SSLF,  based  on  the  recommendations  of  Eurostat,  ILO  International  Labour                Organisation) as the so-called general unemployment rate. Furthermore, the values of the                share of unemployed people. (Hedvičáková, 2018)                 The aim of this paper is to analyse the possible impacts of the Industry 4.0 Initiative, which                can be expected on the labour market in the Czech Republic with a focus on individual                regions.                The following hypotheses are tested:                H1: The impact of Industry 4.0 is markedly different in the Czech Republic regions.                H2: As a result of the enforcement of Industry 4.0 (production automation), unemployment                varies across regions.                The  secondary  data  of  the  Czech  Statistical  Office  were  used  to  test  the  hypotheses                mentioned above. The data were both aggregate for the whole of the Czech Republic, as                well as individual data only for individual regions including the capital city of Prague. Some                of the most significant characteristics were the number of industrial companies with 100                or more employees, the sales of industrial goods and services, the average gross monthly                wage in industry, GDP, GDP per capita, emissions data (solids, sulfur dioxide, etc.) and                gross fixed capital increase (GFC).                 Based on the data for the period 2000 to 2016, incremental indices of individual indicators                were  calculated.  Further,  based  on  data  from  2016,  cluster  analysis  was  performed.  A                hierarchical  method  of  clustering  with  the  Euclidean  distances  of  the  centroid  of  each                cluster was used. Its aim was to merge regions with similar characteristics to make it easier                to determine whether Industry 4.0, by replacing labor with capital, has the potential to                increase  unemployment  in  individual  regions.  In  the  last  phase,  Pearson's  correlation                coefficients were calculated for the number of unemployed registered by the Labor Offices                and the selected above-mentioned characteristics.                 3 Results and Discussion                 Share of unemployed people in individual regions                According to the Czech Statistical Office, the share of unemployed people was 5.19% in                2016. In the following year, the share of unemployed people in the Czech Republic fell to                3.77%, which is lower than the natural rate of unemployment. For this reason, in 2017 the                labour market experienced excess of demand over the supply. Companies complained that                                                              161","they  couldn’t  find  a  sufficiently  skilled workforce  at  a  given  wage  rate.  In  all  analysed                regions, the proportion of unemployed people decreased in 2017.                 The highest share of unemployed people was in the Ústí nad Labem Region, where it was                7.79% in 2016, it then fell to 5.39% in 2017. The second highest share of unemployed                people in the Moravian-Silesian Region was 7.45% in 2016 and in the following year it                dropped to only 5.77%. In 2017 this region has the highest share of unemployed people                within the Czech Republic.                The lowest share of unemployed people was in the Capital City of Prague where the share                of unemployed people reached 3.35% in 2016 and fell to 2.34% in the following year.                Three percent of the share of unemployed people in 2017 also wasn’t exceeded by the                Pilsen, Hradec Králové and Pardubice regions. 3.09% was the share of unemployed people                in the South Bohemian Region. The share of unemployed in the Central Bohemia Region                was 3.17%.                The Figure 1 below shows that the share of unemployed people was below the natural rate                of unemployment in most regions in 2017 and that the firms didn’t have sufficient qualified                work. At a time of economic growth, capital substitution is appropriate and the share of                investment is growing in the Czech Republic. (Hedvicakova, 2018a)                     Figure 1 Share of Unemployed People in the Czech Republic by Region (as on 31                                                        December)                 9                                                       7,79                 8                                                                                  7,45                 7                                                                                   6,11  5,94                 6                               5,45   5,39                                          5,77                    5,19                                    5,17             5,17             4,92                 5              4,31  4,28                                           4,6  4,35                      3,77                                    3,76  3,76  4,04  3,8                 4        3,35   3,17  3,09  3,56  3,47                                         3,43                 3          2,34             2,55                   2,72  2,83                 2                 1                 0                                                                  2016  2017                                  Source: Czech Statistical Office (2018), (Hedvicakova, 2018a)                 Wage developments in the manufacturing industry                The manufacturing industry is significantly involved in GDP growth in the Czech Republic.                A significant percentage of workers are also employed in the manufacturing industry. For                this reason, manufacturing industry wages were analysed. (Hedvicakova, 2018a)                The development of personnel costs in the manufacturing industry over the period 2008 to                2016 corresponds to the development of sales with year-on-year relative changes being                mostly lower. (See figure 2.) The proportion of individual divisions in personal costs varies                between the share of sales and the share of value added. (Ministry of Industry and Trade,                2016a).                                                               162","Figure 2 Wages in Industry in CZK in 2017                   40000                                                   37835                  35000                     31452                   31880       32078                           29535      29162       29662  28042 28610                 29502  28632                  30000          27831                  25000                  20000                  15000                  10000                  5000                     0                                                 Average gross wage CZK  Wages in the industry CZK                                     Source: Czech Statistical Office (2017), own processing                 Main Region characteristics                The number of enterprises active in the industry differs significantly in the Czech Republic.                The highest concentration of these companies (with 100+ employees) is according to the                CZSO in Prague and the Central Bohemian Region, their total number in the last years was                around  450,  which  represents  approximately  20%  of  all  such  enterprises  in  the  Czech                Republic. On the contrary, the smallest number of industrial enterprises is in West Bohemia                - the Karlovarský Region, but this may be due to some extent, in addition to its location,                by its size. The number of companies is also related to total sales in the industry, which in                the Central Bohemia reached almost CZK 1 billion in 2016, ie almost 30% of the industry's                sales in the whole Czech Republic. This fact was also reflected in the average gross monthly                wage in industry, as in 2016 it exceeded CZK 30 thousand only in Prague (CZK 32,295)                and Central Bohemia (CZK 31,507), whereas the average wage in the industry for the                whole of the republic was only 27,676 CZK.                Other significant differences in connection with Industry 4.0 have also occurred in gross                fixed capital formation (acquisitions and disposals of tangible and intangible fixed assets                that have the character of an investment and the cost of improvements in capital goods)                in the past years where was the trend of accumulation to the already industrially developed                areas. From 2010 to 2015, gross fixed capital growth increased by 20% in only five of the                14  regions  -  Prague,  Central  Bohemia,  Zlínský  region,  Moravian-Silesian  region  and                Královéhradecký region, where it was even 36.7%, i.e. two and a half times the average                in the Czech Republic, and due to the labor shortage in the labor market, it can be expected                to increase further.                 It follows from the above that the impact of Industry 4.0 is not the same in the whole                Czech Republic. While in the industrially \"developed\" regions the growth rate of gross fixed                capital is rising in the last years, in others it is slowing down (e.g. the Ústecký region or                Karlovarský region).                 In order to obtain more specific and aggregate characteristics common to each region, a                cluster analysis was performed. Its aim was to include all regions and the Capital City of                Prague  in  certain  clusters  with  similar  characteristics,  which  would  allow  for  a  more                comprehensive estimation of the impacts of the development of the Industry of 4.0 regions.                At the same time, it will be possible to determine which regions and for what reason they                will  be  positively  affected  by  Industry  4.0  and  which  regions  are  among  the  most                vulnerable.                                                              163","For the purpose of this cluster analysis, variables that affect industry and innovation are                affected, i.e. the number of enterprises, industrial sales, average gross monthly wages in                the industry, GDP per capita, emissions data (solids, sulfur dioxide, nitrogen oxide, carbon                monoxide) and gross fixed capital formation per capita. A hierarchical method of clustering                was used with the Euclidean distances of each centroid (see Fig. 3).                                         Figure 3 Dendrogram – Cluster Analysis                                                                              Source: own processing                 Three clusters were created. The first contains only Prague, the second cluster consists of                the  Central  Bohemian  region  and  the  Moravian-Silesian  Region,  and  the  third  cluster                consists of the other regions. The strongest predictor for the inclusion of the region in the                respective cluster was GDP per capita and gross fixed capital formation per capita, while                sales, wages and emissions data were rather complementary. Table 1 gives the following                characteristics:                                               Table 1 Final Cluster Centers                                                                   Cluster 2 –                                             Cluster 1          Central Bohemia           Cluster 3                                              - Prague          region, Moravian-       – Other regions                                                                Silesian region                 Number of                 industry                      228.00                 229.00                150.00                 companies (100+                 employees)                 Industry -                  304,183.85             606,456.84            173,917.04                 turnover                 Industry - wages            32,295.00              29,487.00              24,104.00                 Unemployed                  30,179.00              51,501.00              22,563.00                 GDP per capita              937,542.23             399,812.66            384,340.99                 emissions - solids             1.79                   0.76                   0.52                 emissions - solids             0.50                   2.55                   1.54                 emissions - sulfur             11.83                  3.20                   1.89                 dioxide                 emissions -                    22.10                 17.80                   4.38                 nitrogen oxide                 emissions -                 271,213.55             103,834.60             88,420.02                 carbon monoxide                 GFCF per capita               228.00                 229.00                150.00                                    Source: Czech statistical office (2018a), own processing                                                            164","A correlation analysis was carried out on selected data for the period 2000 to 2016. Its aim                was  to  find  out  a  relationship  with unemployment  to  other  characteristics in  individual                regions - clusters. The results are shown in the following table 2.                     Table 2 Pearson Correlations Between Unemployment and Other Characteristics                                            Number of        Industry     Industry    GDP per  GFCF                                           industry         -            - wages     capita      per                                           companies        turnover                             capita                                           (100+                                           employees)                      Other regions         0.584            0.514        0.116       -0.094      -0.026                    Central Bohemia         0.579            0.511        0.107       -0.102      -0.032                  region + Moravian-                     Silesian region                         Prague             0.575            0.526        0.117       -0.092      -0.036                                    Source: Czech statistical office (2018a), own processing                 Table 2 shows that for all three clusters there are equally directed relationships with respect                to each characteristic. The strongest positive relationship with unemployment in the 2000                -  2016  period  was  demonstrated in the  number  of industrial  enterprises  and industrial                sales. With average wages in the industry, correlations are very low, with negative ratios                of  GDP  per  capita  negative  (the  higher  the  GDP,  the  lower  the  unemployment  rate).                Somewhat strange, however, is the almost zero relationship of unemployment with the                increase in gross fixed capital per capita. However, this is due to the labor shortage in the                labor market - despite the significant replacement of labor by capital, unemployment is not                increasing in any cluster.                 4 Conclusions                It  follows  from  the  above  that  the  industrial  revolution  will  have  negative  impacts  on                unemployment. But it will be a longer time horizon and if there is an education reform, the                labour market will be prepared for these changes. The Czech Republic has one of the lowest                unemployment rates across the EU and companies currently have the problem of finding a                skilled workforce. The second aspect is average wages, which are among the lowest in the                EU. This prevents the rapid take-up of modern technologies. The economic cycle, when                economists are already discussing the \"overheated economy\" (e.g. the Governor of the                Czech National Bank, Jiří Rusnok, 2018) and the political determination, to bring wages in                the Czech Republic closer to the European Union’s average, will be affected by the onset                of  modern  technologies  and  massive  purchasing  of  technologies.  Because  the  Czech                Republic is a small open economy, a big impact will be on the rapid rise of Industry 4.0 as                well as the inflow of foreign investment. (Hedvicakova, 2018a)                The paper was devoted to two parts and an analysis was carried out in order to determine                whether the industry is significantly different in individual regions of the Czech Republic                and whether the unemployment in the regions differs due to the enforcement of Industry                4.0  (production  automation).  From  the  point  of  view  of  industrial  production  data,  the                regions of the Czech Republic differ greatly. While the center of the Czech Republic and                the Moravian-Silesian region are among the areas where industry plays a very important                role, Western and Northwest Bohemia in the current period are experiencing a decline in                their industrial significance. This is reflected not only in rising wages but, in particular, in                the differential increase in gross fixed capital, which is concentrated in more developed                areas. By automating industrial production there is and is likely to be a further increase in                disparities  between  richer  regions  (especially  Prague  and  Central  Bohemia)  and  poorer                regions  (northwestern  Bohemia).  These  differences,  however,  do  not  show  up  in  the                current economic growth in the growth of unemployment, and from the economic point of                view it can be said that the Czech Republic is in a unique period in which it can exploit the                potential of Industry 4.0.                                                             165","Acknowledgments                The  paper  was  elaborated  in  the  framework  of  the  specific  research  \"Evaluation  of                investments within the Industry 4.0 concept\" and the internal grant project \"Support of                pedagogical work, technical equipment and communication with experts\" at the University                of Hradec Králové, Faculty of Informatics and Management                 References                Acatech  (2013).  Umsetzungsempfehlungen  für  das  zukunftsprojekt  industrie  4.0  –                abschlussbericht      des     arbeitskreises     industrie    4.0.     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Lecture  notes  in  business  information  processing.  Springer  International                Publishing, vol. 299, pp. 670-678. ISBN 978-3-319-65929-9.                                                                   167","Industry 4.0 Aimed at Accounting System                                                      Irena Honková                                                    University of Pardubice                     Faculty of Economics and Administration, Institute of Business Economics and Management                                        Studentská 95, 532 10 Pardubice, Czech Republic                                                 E-mail: irena.honkova@upce.cz                 Abstract: Industry 4.0 means a current trend of digitalisation and related automation of                production and changes at the labour market brought together with the Industry 4.0. More                and more internal as well as external integration occurs, more and more data is collected                for analysis and decision making, top management requires access to data in real time, we                transfer to unified protocols, the quantity of wireless communication devices grows and                thus the level of isolation of company systems is considerably reduced. This text discusses                the  digitisation  within  the  accounting  sphere.  With  its  case  study  it  shows  how  the                requirements on presentation and information sharing of accounting and taxation sphere                change: options to send tax documents in electronic form, links to Internet banking, XML                communication, Internet shops or accounting in mobile phones. This text aims to provide                the view not only on the accounting software but also on work of book-keepers. Thus the                text presents the view on the whole accounting system within the environment of Industry                4.0. The final part of this text discusses the readiness of companies to the Industry 4.0.                Keywords: Industry 4.0                JEL codes: M41                 1 Introduction                 The  scientific  and  technical  development  can  be  divided  in  several  revolutions                (Tab. 1). First three of them resulted from the technical revolution and the revolution in                electronics and mechanics. (Halenár, 2016).                The first industry revolution was based on the production mechanisation with the use of                water  and  vapour  energy.  The  second  one  included  the  electricity  in  the  industrial                production,  the  third  one  than  has  brought  the  automation  using  the  electronics  and                information and communication technologies. (Vacek,2016)                The  current  stage  of  the  industry  development  can  be  described  as  the  revolution  of                informatics and communication. This causes the high level of globalisation and foundation                of companies of which the existence is based on the communication (Halenár, 2016). This                trend is called as the Industry 4.0.                 The Industry 4.0 related to the previous wave but there is a convergence of technologies                that    tears   down     wall   among     physical,   digital   and    biological   worlds.                The  convergence  technologies  usually  include  information  and  communication                technologies, biotechnologies and nanotechnologies and cognitive technologies.                                       Table 1 Four Waves of the Industrial Revolution                    Wave       Year       Characteristic features                    1          1784       Vapour, water, mechanic production                    2          1870       Division of labour, electricity, mass production                    3          1969       Electronics, ICT, automated production                    4          ?          Cyber physical systems                                                     Source: Schwab, 2016                 Schwab (2016) mentions three reasons why the current transformation is not a simple                prolongation of the third industrial revolution: these are its speed, scope and system effect.                When  compared  to  previous  industrial  revolutions  the  fourth  one  develops  rather                exponentially than linearly, with all resulting consequences.                                                              168","Kopp and Basl (2017) state that it namely means the combination of new IT technologies,                especially of Internet things and new production, transportation and industry, handling                technologies plus new materials and related processes.                The German Institute of normalisation characterises the Industry 4.0 as a fusion of real                and virtual world. There will be a world in which the information technologies are fully                integrated  in  production  processes.  Systems  in  production,  logistics  or  services  will                communicate with each other in a new, intelligent way. Thanks to the Industry 4.0 the                production  cycles  get  shortened,  need  of  clients  are  registered  in  real  time  or  the                maintenance is mostly automatized (DIN, 2017).                The  term  Industry  4.0  is  characterised  by  the  European  Union  (namely  the  European                Parliament) as a notion for a group of quick transformation in design, production, operation                and use of systems (European Union, 2015).                The  Industry  4.0  trend  is  also  important  for  the  Czech  Republic.  That  is  why  the                government of the Czech Republic have approved its “Industry Initiative 4.0“. The goal of                the government is to strengthen the long-term cooperation of the competitiveness of the                Czech  Republic.  In  addition,  this  initiative  tries  to  show  possible  directions  that  could                support  the  Czech  economy  and  industry  and  at  the  same  time  help  to  prepare  the                company for absorbing of this trend (Mařík, 2015).                In general, we can define the concept Industry 4.0 as a transformation of production as a                separate  automatized  plant  into  a  fully  automatized  and  optimised  production                environments.                The interest in the issue of the Industry 4.0 nearly exponentially grows in last days, as                shows the figure 1.                          Figure 1 Interest in Searching of the Notion “Industry 4.0“ at Google as                             per 30.4.2018 (100 % = maximum within the monitored period.                                                                       Source: Google (2018)                 Although the Industry 4.0 namely relates to new approaches in implementation of new                technologies in the production, this process is closely linked to financial records of inputs,                intermediate outputs and outputs – to the accounting system. Many authors Chena and                Thais (2016), Veza, Mladineo and Gjeldum (2015), Schuh, Popente, Varandani and Schmitz                (2014)  discuss  the  production  optimisation.  The  other  discuss  the  standardisation  of                communication tools and software interfaces but the way how the accounting systems are                changing and will change during the fourth industrial revolution have not been described                yet.                This text aims to analyse, using a case study, the readiness for the Industry 4.0 and to                describe the accounting system within the context of incoming industrial revolution.                                                                 169","2 Methodology and Data                The company that was chosen for the case study is a small engineering company that deals                with modular production, it has 25 employees and the annual turnover about 70 million                CZK.                 It is evaluated based on five levels of digital company maturity (MPO, 2018).                1. The company has its established information system for production management, its                Internet presence is passive (web page). The company starts to consider the digitalisation                of  processes,  production,  maintenance,  product  design  etc.  It  has  no  defined  digital                strategy.  There  is  even  a  partial  capability  to  be  linked  in  information  flows  within                customer-client  relations.  The  basic  economic  software  enables it  to  communicate  with                some public administration authorities.                 2. There is its interactive web presence, the company is software-controlled, it starts to                understand  the  importance  of  data.  There  are  fist  integration  projects,  partial                automatization,  they  consider  the  setting  of  digital  strategy.  There  is  a  connection  to                information flows of customer-client chains (interconnected digital component codebooks,                interactive digital catalogues, semi-automated orders, etc.).                3. Multi-channel presence (web, mobiles and tablets, social networks, etc.), the company                has  its  defined  digital  strategy.  The  presence  of  data  culture  foundations  projects,                integration of data architecture, integrated automatization controlled in real time (MES),                personalised products with virtual component.                4. Integrated multi-channel presence in digital world. In the company, there is a distributed                and  personalised  digital  strategy.  The  data  architecture  is  integrated  within  whole                production chain, from the communication and data sharing with the client up to the sub-                suppliers. The use of digital diagnostics for prediction of failures and non-conformances in                systems (production systems, measurement systems, etc.).                5. The company is a digitalisation platform connecting the online and the offline worlds in                one fully integrated and economically effective ensemble. It offers a unique personalised                experience  to its  clients through virtual  products/assistants  communicating  with  clients                during the whole life cycle of the partnership relation. Through the advanced and most                effective approaches (full automatization, 3D print, etc.) it performs the cyber-physical                system  able  of  individualised  performance  of  eventual  physical  part  of  the  product.  It                provides digitalised services to its partners and sub-suppliers and thus it globally controls                the domain area.                 3 Results and Discussion                It was found that the monitored company does not achieve at all nor the first level of                readiness for the Industry 4.0. It has no implemented information system for production                control. The production is scheduled based on physical book of orders. The order sheets                are made in simple office programs but for the production itself they are passed in printed                form. Nor the supplier-customer relations are controlled with information systems. The                administered web pages are only passive.                Within the production the start of third industrial revolution rather comes in the analysed                company as only two year ago the first automatized machine was bought, using a grant.                Kopp and Basl (2017) has found in their empiric research that only 11,17 % of companies                is today preparing or performing pilot projects regarding this topic of the Industry 4.0.                In addition they found that micro companies (up to 10 employees) was not taking care of                the industrial trend 4.0. What is more important, even the small and middle companies                (from 50 to 250 employees) do only little for it. To date only 14,81 % of companies within                the research of small companies are getting acquainted with this trend and only 11,6 % of                middle companies within this category are about solving this trend.                                                               170","The situation is different as for the accounting software, probably thanks to the fact that                there is a pressure from public authorities and legislation so that the company is forced to                present accounting outputs in electronic form.                When creating the documents for the public administration, the file undergoes the check                of data and the erroneous file is automatically refused. This control removes the sending                of error messages.                 The  company has  adopted  well the  electronic  form  of  accounting  documents so  that it                receives and sends its accounting documents in electronic form.                This was supported with the amendment of VAT Act that allowed, since 2013, the electronic                tax  document  without  electronic  signature.  Provided  both  parties  agree  on  it  and  the                document has  all  appurtenances it  can  be  considered  as  valid. (Act  on  accounting  No.                563/1991 Sb.)                The  tax  document  does not need  to  contain  nor  a  stamp  nor  a  signature  of  document                issuer. (Strouhal, Židlická, Knapová, Cardová, 2012).                It shall be noted that about seventy percent of tax documents are still in printed form in                the company. The tax documents are send in electronic form only due to costs and time                savings. They are in pdf format and after being received they are printed and processed                as the documents received in paper form.                 It  is  highly  probable  that  the  electronically  sent  invoices  in  current  pdf  format  will  be                replaced  by  invoices  in  xml  format,  QR  codes,  etc.  in  the  future  that  will  enable  their                reading directly into the accounting system of the receiver. In addition to data copying,                the error rate shall also disappear.                 Now  they  have  a  format  enabling  the import  of  bank  statements  through  the  Internet                banking. Before it was necessary to account every item of the statement separately. Today                it  works  so  that  the  bank  statement  is  imported,  but  also  its  items  are  automatically                accounted, with conforming value and variable payment symbol.                The XML formats enable the transfer of large volume of data in relatively small file. Such                format are used not only for the communication with the public administration, but they                could be also used within the company.                 The analysed company owns a common accounting program that can currently import also                document and list agendas: invoices, orders, offers, demands, receipt vouchers, release                notes, transfer notes, production internal documents, cash documents, sale notes, jobs,                addresses, storages, division of stocks, storages, stocktaking list, account coding, user´s                lists, code series, groups of stocks, etc. All such agenda is also able to export, including                balances and account day books. These exports/imports are  not used by the company,                besides the bank statements.                Till today no business partner has asked for sending of an invoice in XML format.                Besides the analysis of accounting system it is necessary to discuss also effects of the                Industry 4.0 on the work of book-keepers.                Vacek (2016) thinks over competencies, knowledge and skills that will be necessary for                working with smart technologies. He notes that today the computers still do not manage                to perform a simple work with people. The division of work, based on substitutability of                people by machines, includes four groups:                    •   Work that can be done by people but robots can do it better (weaving machines,                       autopilot, mortgage pricing, tax declarations, evaluation of x-ray images),                    •   Work that the people cannot do but the robots can (serial processing, computer                       chips, web browsers)                    •   New works that can be done by robots (robotic surgery, remote control, computer                       games)                    •   Work that can be done only by people (personal services – taking care, education,                       doctors, art, athletics)                                                            171","Brynjolfsson  and  McAfee  (2014)  state  that  the  technological  progress  will  bring,  with                advantages, also the loss of jobs based on performance of routine tasks, not only manual                but also cognitive ones.                The question what jobs will be most endangered by the automatization is answered by                Carl. B. Frey and Michal A. Osborne in their study “The Future of Employment“ (2013) and                they have concluded that during next 20 years about 47 % of jobs will be endangered,                whereas the most secure, i.e. not endangered, are those that are hardly automatized and                require developed cognitive knowledge, creativity, social and emotional intelligence.                There is a study that presents the methodology for estimation of job loss endangered by                the automatization (Batten Institute, 2015).                Chmelař  (2015)  has  performed  the  similar  study  in  the  Czech  Republic.  The  general                dministrative  workers,  index  of  endangering  by  digitalisation  0,08,  other  officers  0,96,                cashiers 0,93. On the contrary, the managing workers are endangered much less.                 It result from the above stated that the routine work of book-keepers is much endangered.                Vacek (2016) note that the best way how to keep the job is to equip people with correct                knowledge and skills.                 Hennies and Raudjärv (2015) write that the employees shall accept other way of thinking                and learn the integration in modern processes.                 The role of book-keepers will change more and more quickly than in other professions.                In the future, the book-keepers shall have such knowledge and competencies to be able                to  manage  and  program  new events. Their  job  will include not  data insertion  but  data                administration.                 4 Conclusions                It was found that the monitored company does not reach nor the first level of readiness to                the Industry 4.0 and that it has automatized its operation recently and thus launched its                third industrial revolution.                 It results from the above stated analysis of accounting system that the monitored company                is conservative and passive to changes and that the largest move forward is made there                where it is required by legislative conditions.                Within  the  performed  analysis  and  outputs  of  other  empiric  studies  this  trend  can  be                generalised for most of micro and small companies                 As soon as the legislative requirements will change or under the pressure of large business                partners it is probable that the work of book-keepers would change. The documents will                be automatically loaded into the accounting system and thus the routine work of book-                keepers will change in a highly professional and qualified work.                 References                 Act no. 563/1991 Coll., on Accounting, as amended.                Batten  Institute  (2015).  Innovation  in  the  Age  of  Smart  Machines.  Retrieved  from:                http://issuu.com/batteninstitute/docs/smartmachines-120414-issuu.                 Brynjolfsson,  E.,  McAffee,  A.  (2014).  The  second  machine  age:  Work,  progress,  and                prosperity in a time of brilliant technologies. New York: W. W. Norton.                DIN (2017). What is Industry 4.0? DIN – German Institute for Standardization. Retrieved                from: http://www.din.de/en/innovation-and-research/industry-4-0/what-is-industry-4-0.                European Union (2015). Industry 4.0: Digitalisation for productivity and growth. Retrieved                from:                http://www.europarl.europa.eu/RegData/etudes/BRIE/2015/568337/                EPRS_BRI(2015)568337_EN.pdf.                                                              172","Frey, C. B., Osborne, M. (2013). The Future of Employment: How susceptible are jobs to                computerisation?     Retrieved    from:     http://www.orfordmartin.ox.ac.uk/publications                /view/1314.                Google     (2018).    Trends.     Retrieved    from:    https://trends.google.com/trends/                explore?date=all\&q=Industry%204.0, 30.4.2018                Halenár,  I.,  Juhásová,  B.,  Juhás,  M.  (2016).  Design  of  a  communication  schema  in  a                modern factory in accordance with the standard of industry 4.0.  Research Papers: Faculty                of  Materials  Science  and  Technology,  Slovak  University  of  Technology  in  Trnava,  vol.                24(39), pp. 101-109.                 Hennies,  M.,  Raudjarv,  M.  (2015).  Industry  4.0  Introductory  thoughts  on  the  current                situation. Eesti Majanduspoliitilised Vaitlused, vol. 23(2).                 Chena, T., Tsai, H. (2016). Ubiquitous manufacturing: Current practices, challenges, and                opportunities.  Robotics  and  Computer  Integrated  Manufacturing.  Retrieved  from:                http://dx.doi.org/10.1016/j.rcim.2016.01.001.                Chmelař  A.  (2015).  Dopady  digitalizace  na  trh  práce  v ČR  a  EU.  Retrieved  from:                https://www.vlada.cz/assets/evropske-zalezitosti/analyzy-EU/Dopady-digitalzace-na-trh-                prace-CR-a-EU.pdf.                Kopp, J., Basl, J.(2017). Study of the Readiness of Czech Companies to the Industry 4.0.                Journal of Systems Intergration, vol. 8(3), pp. 39-45.                Mařík, V. (2016). Průmysl 4.0 – Výzva pro Českou republiku. Praha: Management Press.                Mařík, V. (2015). Průmysl 4.0. Retrieved from http://www.mpo.cz/dokument162351.html.                 MPO      (2018).     Informace     o    situaci    a    opatřeních.     Retrieved    from:                http://ipodpora.odbory.info.../Informace_o_situaci_a_opatrenich.doc.                Roblek, V., Meško, M., Krapež, A. (2016). A Complex View of Industry 4.0. SAGE Open,                vol. 6(2).                Schwab,  K.  (2016).  The  Fourth  Industrial  Revolution:  what  is means, how  to  respond.                Retrieved     from:      https://www.weforum.org/agenda/2016/01/the-fourth-industrial-                revolution-what-it-means-and-how-to-respond/                Schuh, G., Potente T., Varandani , R., Schmitz, T. (2014). Global Footprint Design based                on  genetic  algorithms  –  An  „Industry  4.0“  perspective.  CIRP  Annals  –  Manufacturing                Technology, vol. 63, pp. 433-436.                Strouhal, J., Židlická, R., Knapová B. (2012). Účetnictví 2012. Velká kniha příkladů. Brno:                BizBooks.                 Vacek, J. (2016). Průmysl 4.0 a společenskovědní výzkum. Trendy v podnikání, vol. 6(2),                pp. 29-38.                Veza, I., Mladineo, M., Gjeldum, N. (2015). Managing Innovative Production Network of                Smart Factories. International Federation of Automatic Control, pp. 555-560.                                                                              173","Capital Creation Factors in Selected Banking Sectors of Europe                                                           1                                                                            2                                              Jan Horvat , Eva Horvatova                                  1  Jan Horvat graduated University of Economics in Bratislava                                                 E-mail: jhorvat@gmail.com                                             2  University of Economics in Bratislava                          Faculty of National Economy, Department of Banking and International Finance                                       Dolnozemská cesta 1, 85235 Bratislava, Slovakia                                               E-mail: eva.horvatova@euba.sk                                                             \&                                                  Masaryk University Brno                                              Faculty of Business Administration                                           Lipová 41a, 800 00 Brno, Czech Republic                                             E-mail: eva.horvatova@econ.muni.cz                 Abstract: This article has the objective to examine the impact of the financial crisis on the                banking sector, especially in terms of whether the change in approach to the regulation of                banks  contributed  to  the  reduction  of  cyclical  developments  of  the  European  banking                sector. We analyzed the banking sectors of Germany, France and the Central and Eastern                European banks for the period 2000-2013 (72 banks from Germany and France and 39                banks of Central and Eastern European countries). We have examined capital formation                and pretax profit as a prerequisite for capital formation through panel regression. We used                data from BankScope. We have come to the conclusion that there were different trends in                capital formation in the countries of Western Europe on the one hand and in CEE countries                on the other hand during the financial crisis.                Keywords: financial crisis, bank capital                 JEL codes: G01, G18                 1 Introduction                Globalization and the strong interconnectedness of economies and financial systems have                caused that local crises and inequalities have the potential to trigger a global crisis. These                are signals that point to the need to use regulatory mechanisms that can respond quickly                to stimuli of the real economy and financial markets, anticipate further developments in                time and prevent the negative consequences of such developments.                Regulatory issues are at the forefront of financial theory and practice in the context of the                financial crisis. In this context, the issue of banking regulation represented by Basel III, its                interaction  with  the  banking  sector,  the  financial  market  and  the  real  economy  is  also                particularly relevant. Use citation continuously in the text.                The role of regulation and supervision of the financial system is mainly to limit risks in                financial intermediation and in financial services.                The primary task of regulation and supervision of banks is to ensure stability in terms of                the stability of the banking system or the internal stability of individual banks. Ensuring                the stability of the banking system requires the fulfillment of a number of conditions in                macroeconomic and microeconomic approaches, in setting up the business environment of                financial institutions in a way that promotes sustainable economic growth and the stability                of financial institutions.                The relationship of the individual authors of the regulation is varied. In some opinions, the                view is that the problem of banks is caused by insufficient regulation; in others, on the                other hand, it is stated that over-regulation can be a problem.                There  are  unequal  attitudes  to  valuation.  There  are  supporters  of  valuation  at  market                prices, seeking to realistic representation of risk; on the other hand, there are opinions                                                               174","that  suggest  that  market  prices  are  significantly  influenced  by  cycles  and  further                exacerbate pro-cyclicality.                 Many views take into account partial issues, but a comprehensive view of the problem is                to take account of several aspects of the solution, and seek a healthy degree of regulation                that should support the stability and sustainable profitability in the banking sector.                For example, Stiglitz, J. (Stiglitz, 2010) states that \"regulation can greatly contribute to                the prevention of negative phenomena, but it may also be that an inadequate form, scope                or method of regulation is a source of new crises.\"                The  interdependence  of  the  development  between  the  real  economy  and  the  financial                sector needs to be analyzed from a number of perspectives. Individual regulatory acts have                different  specific  objectives,  but  most  regulatory  measures  are  motivated  by  creating                conditions for greater financial and banking stability and the elimination of moral hazard.                Especially important is the look  at  the  pro-cyclicality  of  regulation  and  the  relationship                between the economic cycle and the financial crisis, especially the influence of regulation                on the cyclicality of developments in the banking sector and the real economy. There are                many models that focus on predicting financial crises or leading indicators. We are told                that  financial  crises  can  be  predicted,  but  they  cannot  be  avoided;  just  mitigate  their                impacts or timely regulatory measures. Significant leading indicators include, for example,                composite  leading  indicators  that  can  predict  in  the  short  term  the  potential  negative                development of the economy (Jakubíková, Tkáčová, Bánciová, 2014).                According to Van Hoose (Van Hoose, 2007), the primary effect of any system of capital                requirements is to change the leverage effect of the bank's portfolio, the ratio between                capital and assets. It is clear that the result will be a change in the asset portfolio of the                financial institution.                Van  Hoose  states  that  the  authors  Koehn  and  Santomero  (Koehn,  Santomero,  1980)                pointed out that banks that were not risk averse had higher capital requirements but also                a  higher  probability  of  bankruptcy.  It  is  not  possible  to  effectively  reduce  the  risk  by                regulation as subject to regulation is not the composition of assets.                 Van Hoose finally raises the question of whether risk-based capital regulation really makes                individual banks and the banking system as a whole \"safer\". Relative growth of \"capital                cushion\" can quickly dissipate if the banks do not respond by not holding the portfolios of                risky  asset  classes  or  will  not  generate  sufficient  measures  to  evaluate  the  adverse                selection or moral hazard. Author defends the idea that banks are portfolio managers, and                that this approach gives qualified support for capital regulation.                We have also been inspired by research that addressed the relationship between capital                and risk decision, behavior of banks, because this is a key link in the regulation of capital                (Tanda, 2015). The author points out the differences in behavior of banks according to the                selected characters. Its benefit is mainly in comparison to several studies and methods                used in them.                The positive impact on capital may be found when the asymmetry of information (Gropp,                Heider, 2010) suggests that large banks hold higher capital buffers to offset their increased                complexity. The author reports recent studies confirming that banks could hold liquidity as                a  shock insurance  and  use it  as  a  bumper,  which limits  the need  for  additional  capital                (Jokipii and Milne, 2011), while other banks (such as small banks) capital to compensate                for the lack of liquidity (Distinguin et al., 2013).                  2 Methodology and Data                We compiled models that would allow determining the relationship between capital, loans                and  bank  profits.  Panel  regression  combines  cross-sectional  data  over  time.  The                informative  value  of  it  is  higher  compared  to  linear  regression.  The  data  structure  is                compiled  to  capture  each  bank  (i)  at  all  times  (t).  The  cross-sectional  dimension  is                expressed as i, which takes values from 1 to N, in our case the number of banks (72 banks                                                             175","from Germany and France and 39 banks of Central and Eastern European countries). We                have had observations for the period 2000-2013. The advantages of panel regression are                that we can get a more accurate estimate of the parameters of the proposed model.                Model Specification (Other heading – not numbered, Verdana 10, bold)                Our data was \"balanced panel\", meaning we had data for the same time period for each of                the same banks without missing values.                The basic model for panel regression is a linear model in shape                                    T                       yit = αit + βit *xit + uit                                                                           (1)                where:                i = 1,..., N     = cross-sectional index,                t = 1,...,T    = time index,                uit           = random error.                Component αit contains omitted observations, such as some bank characteristics that are                not part of explanatory variables.                 In practice, two models are used: Fixed Effects Model and Random Effects Model. For the                purpose of deciding which model is significant, we will use the Hausman test.                 3 Results and Discussion                 Research is focused on the analysis of the largest banking sectors, in Germany and France,                because we consider these banking sectors to be crucial to the stability of the Eurozone                and the economies of Germany and France are the most powerful economies of Western                European countries.                 The panel regression was done in several alternatives. The first was the analysis of the                German and French banks within one set for the period 2000-2013. We then divided the                banks by country, on the German banks and on the French banks into separate files.                 We then analyzed the time-sharing data, German banks from 2000 to 2013, and German                banks for the period from 2008-2013, in order to find out the differences in the period of                financial crisis.                 Table 1 The Results of the Panel Regression on a Set of all Banks (Germany, France and                                      CEE countries) for the Time Period 2000-2013                   Type of Panel               Pooling              Fixed             Random                   Regression Model                   Model 1: Common_Equity ~ Gross_Loans + Pretax_Profit + Total_Assets+                   Fixed_Assets + Personnel_Expenses                   (Intercept)                 686.029  ***                            687.887 ***                   Gross_Loans                 0,0758    ***        0,042       *      0.0490   *                   Pretax_Profit               -0,063     *         -0.052      .      -0.052    .                   Total_Assets                0.069      **        0.044       .      0.044     .                   Fixed_Assets                -0.0386              -0.018             -0.022                   Personnel_Expenses          -0.067     *         -0.015             -0.029                   Model 2: Common_Equity ~ Gross_Loans + Pretax_Profit + Total_Assets +                   Fixed_Assets                   (Intercept)                 645.565   ***                           670.230  ***                   Gross_Loans                 0,073       ***      0,041      *       0,048      *                   Pretax_Profit               -0.064      *        -0.050     .       -0.054     .                   Total_Assets                0.066       **       0.038              0.044      .                   Fixed_Assets                -0.035               -0.019             -0.022                   Model 3: Common_Equity ~  Pretax_Profit + Total_Assets                   (Intercept)                  692.084      ***                        701.12     ***                                                             176","Pretax_Profit               -0.054         .      -0.045            -0.047       .                   Total_Assets                 0.049          *      0.029              0.033      .                   Model 4: Pretax_Profit ~  Total_Assets + Bank_Deposits + Customer_                   Deposits + Net_Loans                   (Intercept)                  553.654   ***                           511.035    ***                   Total_Assets                -0.017                0.042      *       0.029        .                   Bank_Deposits               -0.041       *       -0.018             -0.023                   Customer_Deposits            0.051       **       0.035       *      0.038        *                   Net_Loans                    0.069       **       0.070       **     0.070        ***                                         Source: Data from BanScope, own processing                 As first we analyzed the complex of all banks (Western European and Eastern European                banks).                Common  equity  creation  supported  gross  loans  and  total  assets.  Pretax  profit  has  a                negative relationship with the creation of common equity. It points to the fact that the pro-                cyclicality of banks in dataset for the period 2000-2013 was not overcome. Net profit and                customer deposits supported the creation of pretax profit. In all 1-4 models, Hausman's                test recommended a fixed-effect model.                  Table 2 The Results of the Panel Regression on a Set of Banks of Germany and France                                                        2000-2013                   Type of Panel               Pooling              Fixed             Random                   Regression Model                   Model 1: Common_Equity ~ Gross_Loans + Pretax_Profit + Total_Assets+                   Fixed_Assets + Personnel_Expenses                   (Intercept)                 462.4057  ***                           465.869 ***                   Gross_Loans                 -0.022185            -0.0547   *        -0.0490  *                   Pretax_Profit               -0.016511            -0.0138            -0.0148                   Total_Assets                0.098646  ***        0.05191            0.0597    *                   Fixed_Assets                -0.019975            -0.0124            -0.0117                   Personnel_Expenses          -0.069995   *        0.02827            -0.0012                   Model 2: Common_Equity ~ Gross_Loans + Pretax_Profit + Total_Assets +                   Fixed_Assets                   (Intercept)                 430.565   ***                           465.41  ***                   Gross_Loans                 -0.02516             -0.0540  ***       -0.0491   *                   Pretax_Profit               -0.01283             -0.0136            -0.0148                   Total_Assets                0.098055  ***        0.0506             0.0596    *                   Fixed_Assets                -0.014588            -0.0110            -0.0117                   Model 3: Common_Equity ~  Pretax_Profit + Total_Assets                   (Intercept)                 414.812                                 439.064 ***                   Pretax_Profit               -0.015862            -0.0224            -0.0226                   Total_Assets                0.093326             0.0450             0.0544    *                   Model 4: Pretax_Profit ~  Total_Assets + Bank_Deposits + Customer_                   Deposits + Net_Loans                   (Intercept)                 303.394   ***                           274.294 ***                   Total_Assets                -0.0337              0.0331             0.0162                   Bank_Deposits               -0.0331              -0.0193            -0.0236                   Customer_Depo               0.0281               0.0233             0.0242                   Net_Loans                   0.0954   ***         0.0894   ***       0.0910   ***                                         Source: Data from BanScope, own processing                 In the observed banks of Germany and France, the gross loans growth did  not lead to                capital growth, which may mean that loans were low-risk or that other items of balance                (for example securities) were growing more. Capital growth is accompanied by a fall in                profit. It points to the fact that the pro-cyclicality of the banks in this dataset for the period                                                             177","2000-2013 was not overcome. Growth of assets contributed to capital growth from 0.02                to 0.059 per unit of capital growth. The fourth model shows that assets contributed to                profit making and that customer deposits supported the bank’s profit creation. In all 1-4                models, Hausman's test recommended a fixed-effect model.                  Table 3 The Results of the Panel Regression on a Set of Banks of Germany 2000-2013                   Type of Panel               Pooling              Fixed             Random                   Regression Model                   Model 1: Common_Equity ~ Gross_Loans + Pretax_Profit + Total_Assets+                   Fixed_Assets + Personnel_Expenses                   (Intercept)                 1.5743e+05   *                          1.7708e+05  *                   Gross_Loans                 -8.3173e-03   *      -0.052443 ***      -1.8117e-02 ***                   Pretax_Profit               1.0528e+00  ***    0.9160364 ***        1.0199e+00  ***                   Total_Assets                7.8964e-03  ***      0.01161073***  8.8705e-03  ***                   Fixed_Assets                2.7751e+00  ***    -0.10846301          2.6265e+00  ***                   Personnel_Expenses          2.7902e+00  ***      2.21013379***   3.1522e+00  ***                   Model 2: Common_Equity ~ Gross_Loans + Pretax_Profit + Total_Assets +                   Fixed_Assets                   (Intercept)                 1.4478e+05                              1.7478e+05*                   Gross_Loans                 1.4646e-02***        -0.044119 ***      6.6787e-03***                   Pretax_Profit               1.1232e+00 ***       0.9410463***       1.1076e+00***                   Total_Assets                1.1793e-02 ***       0.0116712***       1.3154e-02***                   Fixed_Assets                5.3427e+00***        0.15231367         5.6720e+00***                   Modle 3: Common_Equity ~  Pretax_Profit + Total_Assets                   (Intercept)                 3.6526e+05***                           7.9228e+05**                   Pretax_Profit               1.0516e+00***        0.7292069***       7.9112e-01***                   Total_Assets                1.9271e-02***        0.0073521***       1.0397e-02***                   Model 4: Pretax_Profit ~  Total_Assets + Bank_Deposits + Customer_                   Deposits + Net_Loans                   (Intercept)                 8.2294e+03           -0.001342***       2.6190e-03  ***                   Total_Assets                -1.0539e-03  **      0.0424408***       1.1271e-02  ***                   Bank_Deposits               1.5060e-02  ***      0.00248213         -3.4823e-02 ***                   Customer_Deposi             -1.0685e-02 ***      -0.01663 ***       1.5966e-02  ***                   Net_Loans                   3.2428e-03           -0.001342***       2.6190e-03  ***                                         Source: Data from BanScope, own processing                 In the observed German banks, capital growth and profit growth are positively correlated.                Growth of assets most prominently contributed to capital growth (from 1.03% to 8.8% per                unit of capital growth). On this basis, it is possible to assume that other groups of assets                contributed to the risk of the banking sector, which was subsequently taken into account                in capital formation.                In the observed German banks, capital growth was not accompanied by credit growth,                which shows, as in the previous set (Germany and France), in the better case, that the                loan was similarly low risk, or worse, pro-cyclicality in these banking sectors was present.                 Loans  have  contributed  to  profit  growth,  which  can  be  considered  as  positive,  because                loans are the most yielding asset. Customer´s deposits are in negative terms with the                profit. We could also see that banks could not make money on deposits during the period                of general decline in interest rates.                 4 Conclusions                 As first we analyzed the complex of all banks (Western European and Eastern European                banks).                Common  equity  creation  supported  gross  loans  and  total  assets.  Pretax  profit  has  a                negative relationship with the creation of common equity. It points to the fact that the pro-                                                             178","cyclicality of banks in dataset for the period 2000-2013 was not overcome. Net profit and                customer deposits supported the creation of pretax profit.                 In the set of banks of Germany and France, the gross loans growth did not lead to capital                growth and capital growth is accompanied by a fall in profit. Growth of assets contributed                to capital growth. The used model shows that assets and customer deposits contributed to                profit creation.                 In the observed German banks, capital growth and profit growth are positively correlated.                Growth of assets most contributed to capital growth, capital growth was not accompanied                by credit growth. Loans have contributed to profit growth, which can be considered as                positive, because loans are the most yielding asset. Customer´s deposits are in negative                terms with the profit. We could also see that banks could not make money on deposits                during the period of general decline in interest rates.                It points to the fact that the pro-cyclicality of the banks in this dataset for the period 2000-                2013 was not overcome and that support for anti-cyclicality in regulation is the right way.                 Acknowledgments                The research was created in the framework of the OP R \& D Project entitled “Creating an                Excellent Center for Economic Research for the Challenge of Civilization Challenges in the                21st  Century”  (ITMS  26240120032)  “We  support  research  activities  in  Slovakia”/  the                project is co-financed from EU sources, by the Slovak Science Foundation VEGA, project                VEGA 1/0693/17, project MUNI/A/1092/2017 and project KEGA 030EU-4/2017. All support                is greatly acknowledged.                 References                Distinguin, I., Roulet, C., Tarazi, A. (2013). Bank regulatory capital and liquidity: Evidence                from US and European publicly traded banks. Journal of Banking and Finance, vol. 37(9),                pp. 3295–3317.                 Gropp,  R.,  Heider,  F.  (2010).  The  determinants  of  bank  capital  structure.  Review  of                Finance, vol. 14(4), pp. 587–622.                Jakubíková,  E.,  Tkáčová,  A.,  Bánociová,  A.  (2014).  Kompozitné  predstihové  indikátory                hospodárskych cyklov krajín V4 a ich komparácia s CLI Eurostatu. Politická ekonomie, vol.                2014(2).                Jokipi, T., Milne, A. (2011). Bank capital buffer and risk adjustment decisions. Journal of                Financial Stability, vol. 7(3), pp. 165–178.                 Koehn, M., Santomero, A. (1980). Regulation of bank capital and portfolio risk. Journal of                Finance, vol. 35, pp. 1235–1244.                Stiglitz, J. (2010). Freefall. London: Pengium Group. ISBN 978-0-141-04512-2.                 Tanda, A. (2015). The Effects of Bank Regulation on the Relationship Between Capital and                Risk. Comparative Economic Studies, pp. 31–54.                Van Hoose, D. (2007). Theories of bank behavior under the capital regulation. Journal of                Banking \& Finance, vol. 31, pp. 3680–3697.                                                                          179","Efficiency of Banks in Slovak Republic                                                      Eva Horvatova                                               University of Economics in Bratislava                                                 Faculty of National Economy                                       Department of Banking and International Finance                                       Dolnozemská cesta 1, 85235 Bratislava, Slovakia                                               E-mail: eva.horvatova@euba.sk                                                             \&                                                  Masaryk University Brno                                              Faculty of Business Administration                                                   Department of Finance                                           Lipová 41a, 800 00 Brno, Czech Republic                                             E-mail: eva.horvatova@econ.muni.cz                 Abstract: The objective of this article is to examine the efficiency of Slovak banks in the                time period 2000-2013. Slovak banking system faced serious problems connected with the                process of transformation of the Slovak economy. They were manifested by difficulties in                obtaining of long-term resources, with keeping of capital adequacy requirements and with                the rapid growth of the non-performing loans. Transformation and privatization of large                banks led to improvement of financial stability and efficiency of banking sector. As first we                used a combination of fixed assets and staff costs as input and net interest margin as                output in terms of CCR and BCC-I models. In the next analysis we used the combination                of three factors - fixed assets and loan-loss provisions as inputs and volume of credits as                output in terms of CCR and BCC-I models. Both cases show that the banks in Slovakia                have too many fixed assets and high personnel costs with respect to the measured outputs;                to the loans and to the net interest margins.                Keywords: technical efficiency of banks, data envelope analysis                JEL codes: G01, G21                 1 Introduction                 Slovak  banking  system  has  undergone  a  major  change  and  faced  serious  problems                connected with the process of transformation of the Slovak economy. The three largest                banks were VUB, SLSP and IRB. The deposits were concentrated in SLSP; the loans were                concentrated in the VUB. IRB was specialized in the credits for cooperative housing.                 The  problems  were  manifested  by  difficulties in  obtaining  of long-term  resources,  with                keeping of capital adequacy requirements and with the rapid growth of the non-performing                loans. Money market instability was connected with the problem of high interest rates.                With regard to the extent of funds required to cover losses in the Slovak banking sector as                an appropriate solution was the restructuring and subsequent sale of state-owned banks.                The role of pre-privatization restructuring of banks was to get banks into such a condition                as  to  reach  the  required  capital  adequacy  ratio  and  decreasing  of  the  proportion  of                classified loans.                The  bank  as  a  business  unit  realizes  economies  of  scale.  These  are  resulting  from                economies of fixed costs. Fixed costs motivate debtors and creditors to form the coalitions                in  order  to  buy  or  sell  together  and  so  to  reduce  fixed  costs  per  unit  of  transactions.                Economies of scale are changing with the size of the banks.                DEA  analysis  belongs  to  the  non-parametric  methods  of  measuring  efficiency.  The                disadvantage is the fact that it is impossible to separate the effect of random errors and                errors  in  the  measurement  of  inefficiency.  DEA  measures  the  relative  efficiency  of                production units in the examined group of units.                 Technical efficiency refers to the ability of banks to obtain the maximum amount of output                from a given volume of inputs and indicates the minimum necessary input to produce a                                                             180","given volume of output. In contrast, allocative efficiency takes into account the size of the                bank as a production unit.                 The  efficient  production  units  are  located  on  the  efficient  frontier.  Efficiency  frontier                indicates the maximum possible output for a given input using a given technology.                It is also necessary to take into account whether the production units operate in conditions                of constant (CRS) or variable (VRS) returns to scale.                To the development of the theory and practice of using DEA models in the Czech Republic                contributed authors such as Jablonský, J. and Dlouhý, M., Novosádová, I. (2004),                With the application of DEA Analysis deal for example Řepková, I. (2012).                In Slovakia, we can mention the works of Halická, M. (2014) and Vincová, K. (2006).                 DEA  model  is  used  to  assess  the  efficiency  of  the  various  production  units  -  banks,                universities, hospitals and others where we compare the inputs and outputs. For example,                the  work  of  authors  Asanduluia,  L.,  Roman,  M.,  Fatulescua,  P.  (2014)  deal  with  the                efficiency of EU health system. The efficiency of investments to information technologies                evaluates the paper of authors Yao Chena, Liang Liangb, Feng Yangb, Joe Zhuc (2006).                In  the  article,  the  analysis  of  technical  efficiency  of  Slovak  banks  uses  the  data  of                BankScope  and  National  Bank  of  Slovakia.  The  analysis  uses  the  standard  tools  of                descriptive and analytical statistics and statistical program IBM SPSS. Statistical program                MaxDEA is used for the DEA (Data Envelopment Analysis) applied to the example of the                Slovak banks. Our aim was to examine whether the banks, which underwent a process                restructuring and privatization have become more efficient after privatization.                  2 Methodology and Data                For evaluation of Slovak banks efficiency was chosen the data envelope analysis (DEA)                models. The DEA models are divided into two main groups: model with constant returns                to scale  - CCR model, and model based on variable returns to scale  - BCC model. The                models are further subdivided into input and output oriented models.                 DEA analysis shows the relative efficiency. The theoretical nature of the DEA requires that                subjects  observed in the  group  of  decision  making units (DMU) have to  be  banks  with                similar focus.                Therefore, we omitted specialized banks (housing saving banks and state development and                guarantee banks). The number of production unit has an impact on the number of analyzed                factors in efficiency measurement.                Respecting the rules of DEA, the number of analyzed inputs and outputs was no more than                one third of analyzed production units. It was analyzed 9 banks and the number of inputs                and outputs was no more than 3.                 Therefore, the analysis was conducted in combinations of several inputs and outputs.                Model Specification (Other heading – not numbered, Verdana 10, bold)                CCR-I (Charnes-Cooper-Rhodes-Input) model can be written in the (Jablonský, Dlouhý,                2004):                                                          max    = ∑    ∗                                                                                                                     (1)                                                                                                                       =1                Under the conditions:                                                               ∑    ∗    ≤ ∑    ∗          ;             = 1,2, … . .                                                                                                                                          =1            =1                                                            181","                                       ∑    ∗    − ∑    ∗            ≤ 0      = 1,2, … . .                                                                                                                                          =1            =1                                                 ∑    ∗           = 1                                                       =1                          ≥ 0,    = 1,2, … . .                                                        ≥ 0,    = 1,2, … . .                                             BCC-I (Banker-Charnes-Cooper-Input) model can be written (Jablonský, Dlouhý, 2004):                                                          max    = ∑    ∗    +                                                                                                          (2)                                                                                                                       =1                Under the conditions:                                                                    ∑    ∗    +     ≤ ∑    ∗         ;             = 1,2, … . .                                                                                                                                               =1                 =1                                                 ∑    ∗           = 1                                                       =1                          ≥ 0,    = 1,2, … . .                                                        ≥ 0,    = 1,2, … . .                                             Parameter μ reflects the conditions of convexity of the BCC-I model.                 Figure 1 Efficiency Frontier at Constant Returns to Scale and Variable Returns to Scale                                        CRS (Constant Returns to Scale)                                                   CCR Model   U4     VRS (Variable Returns to Scale)                             Output                                 BCC Model                                                        U8                                                        6      U9                                                                       DRS                                          U3                                                      U7               CRS                                       U2                                                                        IRS                                    U1                        U10                                           U5     U12    U6                                          U11                                                                       Input                    Source: Jablonský, J., Dlouhý, M.: Modely hodnocení efektivnosti produkčních jednotek. Praha:                                                Professional Publishing, 2004                Where:                DRS   = Decreasing Return to Scale                CRS   = Constant Returns to Scale                IRS    = Increasing Return to Scale                                                            182","The efficiency frontier expresses the maximum possible output for a given input using a                given technology. The efficient units are located on the CRS or VRS frontier. On the frontier                CRS are efficient units U1, U2, U3 and U4, which operate under constant returns to scale.                It means, if increase the input, the output will be increased in the same range. Units U11,                U5,  U2,  U3,  U8  and  U9  are  efficient  at  the  efficiency  frontier  VRS.  Increasing  input  is                reflected by increasing or decreasing output growth (DRS = Decreasing Return to Scale,                IRS = Increasing Return to Scale). Units U12, U6, U7 and U10 are inefficient. In order to                become efficient, they should increase the output to reach to the frontier of efficiency. For                example, unit U7 can get to the border VRS on the position of U8, or to efficiency frontier                CRS on the position U4.                  Figure 2 The Relationship Between Technical and Allocative Efficiency of Input Oriented                                                           Model                                     X 2 /y                                          C                                                     P                                     A                                                Q                                               R       Q I                                                                C  I                                                                A  I  X 1 /y                  Source: Jablonský, J., Dlouhý, M.: Modely hodnocení efektivnosti produkčních jednotek. Praha:                                                Professional Publishing, 2004                 On  the  chart,  the  efficient  frontier  is  on  Isoquant  CC .  Production  unit  P  is  technically                                                                        I                                                         I                inefficient because it does not lie on CC . The rate of technical inefficiency is reflected by                the distance to the CC . Production unit at Q produces the same amount of output as a unit                                      I                P, but Q uses only part of inputs equal to 0Q/0P.                Technical efficiency is therefore expressed as the ratio 0Q/0P. The line AAI is the isocost,                its slope is determined by the ratio of input prices. Allocative efficiency will be expressed                as  the  ratio  0R/0Q.  The  total  efficiency  is  expressed  as  the  product  of  technical  and                allocative efficiency, on the basis of which we obtain that the total efficiency is equal to the                ratio 0R/0P.                  3 Results and Discussion                The  restructuring  of  the  banking  sector  in  Slovakia  was  an  important  factor  for  of                establishing macroeconomic equilibrium, and further privatization of banks. Foreign capital                entered into the privatized banks. Into VUB entered Italian banking group Intesa BCI, into                SLSP  entered  Austrian  Erste  Bank  and  into  IRB  -  Hungarian  OTP  Bank.  The  aim  of                privatization  was  to  create  and  maintain  a  stable  banking  system,  banking  system                comparable to developed countries, as well as its involvement in the European financial                structures (Chudják, F., Hallon, Ľ:, Leková, A, 2016).                The  privatization  of  the  largest  Slovak  banks  has  brought  pluses  in  form  of  standard                professional  banks  managements.  These  changes  have  contributed  to  improving  the                functioning of banks and to increase their efficiency and stability. The analysis assesses                the efficiency of banks studying the relationship of inputs to the selected outputs indicators.                                                              183","DEA analysis of selected Slovak banks                In the next section we will discuss the issue of efficiency banks that were included in the                process of state restructuring and subsequent privatization.                To  set  the  analyzed  banks  were  included  commercial  banks;  specialized  banks,  state                guarantee bank and housing-saving banks we have excluded. It remained in the file 9-12                banks, so we had to use a combination of inputs and outputs in the total number of three                indicators.                As first was used a combination of fixed assets (F), staff costs (C) as input and net interest                margin (M) as output, which further will be referred to as a combination of inputs and                outputs “FCM”. The results in terms of CCR and BCC-I Models are in following Table 1.                Several cases of inefficiency banks indicate that Slovak banks persist at a high level of                fixed assets and personnel costs. The privatized banks had to reduce staff costs, which                contributed to their effectiveness. Net interest margin as output assumes that banks fulfil                a mediating function in standard conditions. Reducing interest rates on deposits is not a                sufficient condition for growth of the interest margin; a very significant is the volume of                credits.                The values in the table can be represented graphically clearer and points out that the banks                that  were  included  in  the  restructuring  and  privatization  were  more  efficiently  above                average in the coming periods after 2001.Our analysis also showed that Bank 6 and Bank                9 during the reporting period were according to the criteria highly efficient. In case of Bank                9, has confirmed the high efficiency even at other combination of inputs and outputs.                In the next analysis was used the combination of three factors: fixed Assets (A), loan-loss                provisions (P) as inputs and volume of credits (L) as output. In the next text will bet his                combination signed as „FPL“. The results in terms of CCR and BCC-I Models are in the Table                2.                Taking into account fixed assets and loan-loss provisions as the inputs and credits as an                output; the highest efficiency reaches the Bank 9, which was efficient by the BCC–I Model                for all the observed period.                From the privatized banks the best level of efficiency has had Bank 1, and Bank 2; both                banks were privatized by foreign banks. Weaker results reached Bank 3.                Graphical values can be expressed in the Figure 2 and Figure 2.                 In this case, the input (FPM) fixed assets (F) and personnel costs (P) and the output, the                net interest margin (M).                                                                                     184","Table 1 The Coefficients of Efficiency in Terms of CCR and BCC-I Model for the Combination of Inputs and Outputs “FCM”                               2001   2002    2003     2004     2005     2006     2007      2008     2009     2010   2011     2012     2013     2014     Aver  age                Year                Bank1       1      0,99     0,73     0,79       1      0,99      0,32     0,95     0,94    0,9     0,77     0,91      1       0,86                CR                                                                                                                                      0,867                Bank1       1       1       0,77      1         1        1       0,32      1        1       1       1        1        1         1                BCC                                                                                                                                     0,935                Bank2       0,9                CCR         1      0,93      1       0,74     0,89     0,94       1        1        1       1       1       0,99      1       0,96      0,954                Bank2       0,9                BCC         1       1        1       0,94     0,89       1        1        1        1       1       1        1        1         1       0,981                Bank3       0,7     1       0,55     0,51     0,77     0,75      0,28     0,67     0,67    0,65    0,55     0,63     0,81     0,85                CCR                                                                                                                                     0,670                Bank3       0,8     1        1       0,56      0,8     0,76      0,31     0,71     0,77    0,72    0,59      1        1         1                BCC         8                                                                                                                           0,792                Bank4       0       0        0        0        0,8     0,72      0,26     0,67     0,79    0,72    0,59     0,58     0,77     0,67                CCR                                                                                                                                     0,469                Bank4       0       0        0        0        0,8     0,76      0,27     0,68     0,82    0,74    0,59     0,58     0,82       0,74                BCC                                                                                                                                     0,485                Bank5       0       1        1        0,5      0,5     0,85      0,37     0,93     0,64    0,57    0,55     0,55     0,86       0,71                CCR                                                                                                                                     0,645                Bank5       0       1        1        0,5     0,56     0,85      0,38     0,94     0,65    0,59    0,56     0,69     0,87       0,76                BCC                                                                                                                                     0,667                Bank6       0,6    0,84     0,1       1       0,79     0,87       1        1        1       1       1        1        1         1                CCR         9                                                                                                                           0,877                Bank6       1       1        1        1       0,83     0,89       1        1        1       1       1        1        1         1                BCC                                                                                                                                      0,98                Bank7       0       0        0       0,78     0,89       1       0,33     0,77      0,5    0,53    0,36     0,52     0,35       1                CCR                                                                                                                                     0,502                Bank7       0       0        0       0,78     0,95       1       0,33     0,79     0,55    0,58    0,39     0,92      1         1                BCC                                                                                                                                     0,592                Bank8       0       0        0       0,81     0,86     0,83      0,26     0,69     0,52    0,62    0,48     0,5      0,61       0,55                CCR                                                                                                                                     0,480                Bank8       0       0        0       0,81     0,96     0,85      0,31     0,73      0,6    0,69    0,52      1        1         0,83                BCC                                                                                                                                     0,592                Bank9       0,5     1       0,96      1        0,9     0,88      0,53     0,8       0,7    0,64    0,55     0,58     0,72     0,76                CCR         1                                                                                                                           0,752                Bank9       1       1        1        1         1        1       0,54      1        1       1      0,98     0,84     0,72     0,86                BCC                                                                                                                                     0,921                Average     0,4                              8    0,653     0,562    0,707    0,849    0,886    0,484     0,851    0,786   0,77   0,693     0,793    0,862    0,863                                                                   Source: Data from BankScope, own processing                                                                                    185","Table 1 The Coefficients of Efficiency in Terms of CCR and BCC-I Model for the Combination of Inputs and Outputs “FCM”       2001   2002   2003   2004   2005   2006   2007   2008   2009   2010   2011   2012   2013   2014   Aver  age  Year  Bank1   1   0,99   0,73   0,79   1   0,99   0,32   0,95   0,94   0,9   0,77   0,91   1   0,86  CR                                                               0,867  Bank1   1   1   0,77   1   1   1   0,32   1   1   1   1   1   1   1  BCC                                                              0,935  Bank2    0,9  CCR   1   0,93   1   0,74   0,89   0,94   1   1   1   1   1   0,99   1   0,96   0,954  Bank2    0,9  BCC   1   1   1   0,94   0,89   1   1   1   1   1   1   1   1   1   0,981  Bank3    0,7   1   0,55   0,51   0,77   0,75   0,28   0,67   0,67   0,65   0,55   0,63   0,81   0,85  CCR                                                              0,670  Bank3   0,8  1   1   0,56   0,8   0,76   0,31   0,71   0,77   0,72   0,59   1   1   1  BCC   8                                                          0,792  Bank4    0   0   0   0   0,8   0,72   0,26   0,67   0,79   0,72   0,59   0,58   0,77   0,67  CCR                                                              0,469  Bank4    0   0   0   0   0,8   0,76   0,27   0,68   0,82   0,74   0,59   0,58   0,82   0,74  BCC                                                              0,485  Bank5   0   1   1   0,5   0,5   0,85   0,37   0,93   0,64   0,57   0,55   0,55   0,86   0,71  CCR                                                              0,645  Bank5   0   1   1   0,5   0,56   0,85   0,38   0,94   0,65   0,59   0,56   0,69   0,87   0,76  BCC                                                              0,667  Bank6   0,6  0,84   0,1   1   0,79   0,87   1   1   1   1   1   1   1   1  CCR   9                                                          0,877  Bank6   1   1   1   1   0,83   0,89   1   1   1   1   1   1   1   1  BCC                                                               0,98  Bank7   0   0   0   0,78   0,89   1   0,33   0,77   0,5   0,53   0,36   0,52   0,35   1  CCR                                                              0,502  Bank7   0   0   0   0,78   0,95   1   0,33   0,79   0,55   0,58   0,39   0,92   1   1  BCC                                                              0,592  Bank8   0   0   0   0,81   0,86   0,83   0,26   0,69   0,52   0,62   0,48   0,5   0,61   0,55  CCR                                                              0,480  Bank8   0   0   0   0,81   0,96   0,85   0,31   0,73   0,6   0,69   0,52   1   1   0,83  BCC                                                              0,592  Bank9   0,5  1   0,96   1   0,9   0,88   0,53   0,8   0,7   0,64   0,55   0,58   0,72   0,76  CCR   1                                                          0,752  Bank9   1   1   1   1   1   1   0,54   1   1   1   0,98   0,84   0,72   0,86  BCC                                                              0,921  Average    0,4     8   0,653   0,562   0,707   0,849   0,886   0,484   0,851   0,786   0,77   0,693   0,793   0,862   0,863  Source: Data from BankScope, own processing  185","Table 1 The Coefficients of Efficiency in Terms of CCR and BCC-I Model for the Combination of Inputs and Outputs “FCM”                               2001   2002    2003     2004     2005     2006     2007      2008     2009     2010   2011     2012     2013     2014     Aver  age                Year                Bank1      1     0,43     1        0,31      0,32     0,24     0,19     0,5      0,58      0,57   0,12    0,66     0,83      1                CR                                                                                                                                      0,553                Bank1      1     0,43     1        1         1        0,93     0,58     1        1         1     1        1        0,83      1                BCC                                                                                                                                     0,912                Bank2                CCR        1     0,02     0,3      1         0,38     0,3      0,14     0,34     0,33      0,26   0,11    0,34     1         0,75       0,447                Bank2                BCC        1     0,23     0,39     1         0,88     1        0,46     0,45     0,33      0,34   0,34    0,78     1         1          0,657                Bank3      0,5   0,21     1        0,3       0,6      0,47     0,29     0,62     0,58      0,52   0,09    0,56     0,74      0,59                CCR        7                                                                                                                             0,51                Bank3      0,9   0,41     1        0,44      0,83     1        0,6      0,66     0,61      0,57   0,41    1        1         1                BCC                                                                                                                                     0,745                Bank4      0     0        0        0         1        1        0,25     0,37     0,52      0,48   0,34    0,63     0,81      1                CCR                                                                                                                                     0,457                Bank4      0     0        0        0         1        1        0,54     0,44     0,52      0,59   0,69    1        0,89      1                BCC                                                                                                                                     0,547                Bank5      0     1        0,34     0,33      0,46     0,28     0,45     1        1         0,84   0,28    1        1         0,91                CCR                                                                                                                                     0,635                Bank5      0     1        1        0,51      0,54     0,71     1        1        1         0,9   1        1        1         1                BCC                                                                                                                                     0,832                Bank6      0,5   0,19     0,23     0,17      0,14     0,02     0        0,35     0,55      0,55   0,07    0,66     0,48      1                CCR        4                                                                                                                            0,353                Bank6      1     1        1        0,21      0,16     0,07     0        0,4      0,56      0,62   0,5     0,8      1         1                BCC                                                                                                                                     0,594                Bank7      0     0        0        1         1        0,38     0,3      0,29     0,94      1     0,1      0,65     0,37      0,79                CCR                                                                                                                                     0,487                Bank7      0     0        0        1         1        0,85     0,97     0,7      0,95      1     0,6      0,99     1         1                BCC                                                                                                                                     0,718                Bank8      0     0        0        1         0,48     0,27     0,23     0,56     0,66      0,71   0,06    0,96     0,38      0,79                CCR                                                                                                                                     0,435                Bank8      0     0        0        1         0,48     0,52     0,48     0,56     0,67      0,77   0,73    1        0,96      0,92                BCC                                                                                                                                     0,577                Bank9      1     1        0,89     0,66      0,67     0,49     0,41     0,82     1         0,92   0,49    0,77     1         1                CCR                                                                                                                                     0,794                Bank9      1     1        1        1         1        1        1        1        1         1     1        1        1         1                BCC                                                                                                                                        1                Average     0,5                              0    0,384     0,508    0,607    0,663    0,585    0,438     0,614    0,711   0,70   0,440     0,822    0,849    0,930                                                                   Source: Data from BankScope, own processing                                                                                    186","Table 1 The Coefficients of Efficiency in Terms of CCR and BCC-I Model for the Combination of Inputs and Outputs “FCM”       2001   2002   2003   2004   2005   2006   2007   2008   2009   2010   2011   2012   2013   2014   Aver  age  Year  Bank1   1   0,43   1   0,31   0,32   0,24   0,19   0,5   0,58   0,57   0,12   0,66   0,83   1  CR                                                               0,553  Bank1   1   0,43   1   1   1   0,93   0,58   1   1   1   1   1   0,83   1  BCC                                                              0,912  Bank2  CCR   1   0,02   0,3   1   0,38   0,3   0,14   0,34   0,33   0,26   0,11   0,34   1   0,75   0,447  Bank2  BCC   1   0,23   0,39   1   0,88   1   0,46   0,45   0,33   0,34   0,34   0,78   1   1   0,657  Bank3    0,5  0,21   1   0,3   0,6   0,47   0,29   0,62   0,58   0,52   0,09   0,56   0,74   0,59  CCR   7                                                           0,51  Bank3   0,9   0,41   1   0,44   0,83   1   0,6   0,66   0,61   0,57   0,41   1   1   1  BCC                                                              0,745  Bank4    0   0   0   0   1   1   0,25   0,37   0,52   0,48   0,34   0,63   0,81   1  CCR                                                              0,457  Bank4    0   0   0   0   1   1   0,54   0,44   0,52   0,59   0,69   1   0,89   1  BCC                                                              0,547  Bank5   0   1   0,34   0,33   0,46   0,28   0,45   1   1   0,84   0,28   1   1   0,91  CCR                                                              0,635  Bank5   0   1   1   0,51   0,54   0,71   1   1   1   0,9   1   1   1   1  BCC                                                              0,832  Bank6   0,5  0,19   0,23   0,17   0,14   0,02   0   0,35   0,55   0,55   0,07   0,66   0,48   1  CCR   4                                                          0,353  Bank6   1   1   1   0,21   0,16   0,07   0   0,4   0,56   0,62   0,5   0,8   1   1  BCC                                                              0,594  Bank7   0   0   0   1   1   0,38   0,3   0,29   0,94   1   0,1   0,65   0,37   0,79  CCR                                                              0,487  Bank7   0   0   0   1   1   0,85   0,97   0,7   0,95   1   0,6   0,99   1   1  BCC                                                              0,718  Bank8   0   0   0   1   0,48   0,27   0,23   0,56   0,66   0,71   0,06   0,96   0,38   0,79  CCR                                                              0,435  Bank8   0   0   0   1   0,48   0,52   0,48   0,56   0,67   0,77   0,73   1   0,96   0,92  BCC                                                              0,577  Bank9   1   1   0,89   0,66   0,67   0,49   0,41   0,82   1   0,92   0,49   0,77   1   1  CCR                                                              0,794  Bank9   1   1   1   1   1   1   1   1   1   1   1   1   1   1  BCC                                                                  1  Average    0,5     0   0,384   0,508   0,607   0,663   0,585   0,438   0,614   0,711   0,70   0,440   0,822   0,849   0,930  Source: Data from BankScope, own processing  186","4 Conclusions                As first we used a combination of fixed assets (F), staff costs (C) as input and net interest                margin (M) as output, which further will be referred to as a combination of inputs and                outputs  signed  as  “FCM”.  We  than  combined  three  factors:  fixed  assets  (A),  loan-loss                provisions (P) as the inputs and volume of credits (L) as an output. This combination was                signed as „FPL“.                In both cases it appears that the banks in Slovakia identified as inefficient have too many                fixed assets and high personnel costs with respect to the measured outputs, i.e., to the                loans and to the net interest margins.                 DEA analysis clearly shows by a several criteria examined by the banks in Slovakia that                were privatized banks from Western Europe. These banks are long-term stable and more                efficient than other banks in Slovakia. A comparable result gave Bank 9, which was from                its start owned by the Reiffesen Zentralbank.                Figure 3 and figure 4 show that the restructured and privatized banks 1-3 have overcome                the  crisis  with  greater  efficiency.  DEA  analysis  provides  interesting  results  because  it                highlights the efficiency as the ratio between inputs and outputs, as a view on performance.                This is not identical with profitability.                From the privatized banks the best level of efficiency has had Bank 1 and Bank 2.                 Restructuring and privatization of banks in Slovakia so fulfills its historic role, as banks                privatized by Western European banks are efficient and investors are strategic because                they hold their investments to present.                 Acknowledgments                The research was created in the framework of the OP R \& D Project entitled “Creating an                Excellent Center for Economic Research for the Challenge of Civilization Challenges in the                21st  Century”  (ITMS  26240120032)  “We  support  research  activities  in  Slovakia”/  the                project is co-financed from EU sources, by the Slovak Science Foundation VEGA, project                VEGA 1/0693/17, project MUNI/A/1092/2017 and project KEGA 030EU-4/2017. All support                is greatly acknowledged.                 References                Asanduluia, L., Roman, M., Fatulescua, P. (2014). The efficiency of healthcare systems in                Europe: a Data Envelopment Analysis Approach. Procedia Economics and Finance, vol. 10,                pp. 1-366.                Halická, M. (2014). DEA modely. Bratislava: Univerzita Komenského.                 Chudják,  F.,  Hallon,  Ľ:,  Leková,  A.  (2015).  Centrálne  bankovníctvo  v stredoeurópskom                priestore. Bratislava:  National Bank of Slovakia in cooperation with the Institute of the                History of the Slovak Academy of Science. ISBN 978-80-8043-202-7.                Jablonský,  J.,  Dlouhý,  M.  (2004).  Modely  hodnocení  efektivnosti  produkčních  jednotek.                Praha: Professional Publishing. ISBN 80-86419-49-5.                Jablonský, J., Dlouhý, M., Novosádová, I. (2007). Využití analýzy obalu dat pro hodnocení                efektivnosti českých nemocnic. Politická ekonomie, vol. 2007(1), pp. 60–71.                 Řepková,  I.  (2012).  Measuring  the  efficiency  in  the  Czech  banking  industry:  Data                Envelopment  Analysis  and  Malmquist  index.  In:  Ramík,  J.  and  Stavárek,  D.,  eds.,                Proceedings  of  30th  International  Conference  Mathematical  Methods  in  Economics.                Karviná: Silesian University, School of Business Administration, pp. 781–786. ISBN 978-                80-7248-779-0.                Vincová, K. (2006). Neefektívnosť z rozsahu v bankovom sektore: Komparácia slovenského                a  českého  bankového  sektora.  In:  International  Conference  Proceedings:  National  and                                                             187","Regional  Economics  VI.  Košice:  Ekonomická  Fakulta,  Technická  Univerzita  v  Košiciach,                pp. 440–445.                 Zimková,  E.  (2014).  Technical  Efficiency  and  Super-efficiency  of  the  Banking  Sector  in                Slovakia. Procedia Economics and Finance, vol. 12, pp. 780–787.                Yao C., Liang B., Feng Y., Joe Z. (2006). Evaluation of information technology investment:                a data envelopment analysis approach.  Computers \& Operations Research, vol. 33, pp.                1368-1379.              Retrieved             from:            http://www.deafrontier.net                /papers/CORIT.pdf.                                                                                                                                   188","Impact of Macroeconomic Factors on the Value of Loans Granted                 to Non-Financial Enterprises by Monetary Financial Institutions in                                             the Euro Area Countries                                                                                               3                                                                       2                             Agnieszka Huterska , Robert Huterski , Justyna Łapińska                                                   1                                           1  Nicolaus Copernicus University in Toruń                                        Faculty of Economic Sciences and Management                                            ul. Gagarina 11, 87-100 Toruń, Poland                                                  E-mail: huterska@umk.pl                                            2  Nicolaus Copernicus University in Toruń                                        Faculty of Economic Sciences and Management                                            ul. Gagarina 11, 87-100 Toruń, Poland                                                  E-mail: robhuski@umk.pl                                            3 Nicolaus Copernicus University in Toruń                                        Faculty of Economic Sciences and Management                                            ul. Gagarina 11, 87-100 Toruń, Poland                                                   E-mail: justlap@umk.pl                 Abstract: The use of low interest rates by the European Central Bank as an instrument to                counteract the effects of the financial crisis in 2008 seemed to create favorable conditions                for a significant increase in demand from non-financial corporations for loans granted by                monetary  financial  institutions.  Indeed,  the  demand  for  loans  in  individual  euro  area                countries reported by non-financial corporations is shaped by both conditions common for                the entire euro area and numerous factors reflecting the specificity of a given national                economy. The aim of the article is to assess the impact of key macroeconomic factors on                the size of lending to non-financial enterprises by monetary financial institutions in the                euro  area  countries.  The  macroeconomic  factors  examined  include  GDP  growth  rate,                unemployment rate, wage growth, inflation and the long-term interest rate according to                the Maastricht criterion. The survey covers the euro area countries in 2008-2015, without                Estonia and Latvia due to incomplete data for these countries for the above period. The                data comes from the databases of the European Central Bank and Eurostat. For the purpose                of assessing the above-mentioned factors, an econometric model based on panel data will                be created.                Keywords: loans, credit, monetary financial institutions, non-financial corporations, panel                data model                JEL codes: C23, E51, F45, G21                 1 Introduction                 Geographically, the conducted research involved 17 out of 19 Eurozone countries. Only                Estonia and Latvia were excluded due to lack of complete data from the studied period.                When analyzing the potential factors which shaped the nominal value of loans granted by                monetary  financial  institutions  (MFI)  to  non-financial  corporations  (NFC)  in  Eurozone                countries  one  should  bear  in  mind  the  specificity  of  the  2008-2015  period  under                investigation. It was the beginning of a serious economic crisis leading to a breakdown in                the  total  economic  situation,  which  after  a  temporary  improvement  in  2011  suffered                another, yet not so serious decline in years 2012-2013 (Dajcman 2016, p. 129). Years                2014-2015 brought a halt to this negative trend, but the improvement was still rather                insignificant and meant only partial rebound after the losses suffered during the crisis. It                influenced both the shape of macroeconomic independent variables, as well as the ones                related to companies’ financial results (Deutsche Bank 2017, p. 54).                                                                 189","Figure 1 Schematic Presentation of Supply and Demand-side Factors that Influence Loan                                                      Developments                            Supply-side (bank- related)                 Demand-side (borrower related)                         Banks’     Banks’        Loan       Banks’ risk   Borrowers’    Alternative    Macro-                   funding     capital      market      perception/    balance      sources of    economic                     and       position   competition    valuation      sheets       external     determi-                   liquidity                                                          funds        nants                   position                                              Quantities               Prices (spreads)                  Source: ECB (2011). Recent developments in loans to the private sector, ECB Monthly Bulletin, p.                                                            65                 The  following  sections  will  outline  the  potential  mechanisms  of  impact  exerted  by                independent variables on the value of loans granted by MFI to NFC (hereinafter referred to                as MFI/NFC loans), which according to the used model proved to be statistically significant.                As  regards  the  macroeconomic  indicators,  they  are  real  GDP  growth  rate,  total                unemployment  rate  and  inflation  rate.  From  the  group  of  data  related  to  companies’                economic performance, the ones selected include production value, value added at factor                cost (both output related) and gross investment in tangible goods (capital input related).                 2 Methodology and Data                In order to identify the factors influencing the value of loans granted to NFCs by MFIs in                the Eurozone, an econometric model for panel data has been created.                The dependent variables in the study were selected macroeconomic variables in particular                Eurozone countries in the years 2008-2015. In the set of dependent variables there were:                real GDP growth rate, total unemployment rate and inflation rate. The dependent variables,                on the other hand, were also data concerning the results of company operation, such as                production value, value added at factor cost (both output related) and gross investment in                tangible goods (capital input related).                Model Specification                The model has the following form:                                 =    +          _     +               +           +             +                                                                        1                                                       2                                                                                        0                                                                                                                                                                                                                                             4                                                                     3                                                                                                              _       +              _             +                                  (1)                                        6                                                                                                                                                                                           5                          =    +    +                                                                   (2)                                                                                                                                                             Table 1 presents the description of individual variables. The source of data was the Eurostat                database.                                                                 190","Table 1 Variables Used in Empirical Investigation                       Variables                               Variables description                                        Loans vis-a-vis euro area NFC reported by MFI excluding ESCB in                         Loanjt                                        the euro area (stock EUR mln)                                                   Explanatory variables                        GDP_grjt        Real GDP growth rate, percentage change on previous year,                        Unempjt         Total unemployment rate (%)                       LCI_wagjt        Wages and salaries (total), percentage change on previous period                                        Labour costs other than wages and salaries, percentage change on                      LCI_otherjt                                        previous period                          Infljt        HICP - inflation rate, Annual average rate of change                                        EMU convergence criterion bond yields (%); EMU convergence                        Bondsjt                                        criterion series - annual data                         Prodjt         Production value (mln EUR)                          Surjt         Gross operating surplus – SBS (mln EUR)                     Total_purchjt      Total purchases of goods and services (mln EUR)                       Val_addjt        Value added at factor costs (%)                     Gross_Investjt     Gross investment in tangible goods (mln EUR)                                        The random error in the object j, in the time period t, which                                        consists of the following components:                                        et – impulses affecting all observations in the time period t,                           vjt                                        uj – impulses affecting all the observations in the object j,                                        εjt – impulses affecting only observations in the object j, in the time                                        period t.                                              Source: elaborated by the authors                3 Results and Discussion                 In  this  study, for  the  purpose  of  describing  the  dependencies  between  macroeconomic                variables and the value of loans granted by MFI to NFC in the Eurozone countries, a panel                model defined by Formula (1) was used. The data was collected for 17 Eurozone counties.                They concern an 8-year period (annual data for 2008-2015).                 The panel data model (1) was estimated using GRETL (GNU Regression Econometrics Time-                Series Library) software. The choice of estimation method was made based on the decision-                making procedure proposed by subject literature in econometrics (Baltagi 2001). First, the                estimation  of  a  simple  panel  model  was  made  using  a  classical  least  squares  method                (without individual effects), and the model’s diagnostic tests were conducted. The following                values of test statistics were obtained: Wald test (F=50,7397; p-value≈0,0000), Breusch-                Pagan  test  (LM=249,124;  p-value≈0,0000)  and  Hausman  test    (H=27,3913;  p-                value=0,00225757).                 When  analyzing  the  Wald  test  results  it  can  be  concluded  that  the  appropriate  model                describing  the  dependencies  between  macroeconomic  variables  and  the  value  of  loans                granted by MFI to NFC in the Eurozone is a fixed effects one (FEM). The results of Breusch-                Pagan point to a random effects model (REM) as a more reliable one. Finally, the results                of Hausman test allow to conclude at the risk of error at the level of 0,05 (α=0,05) that                the best model for describing the studied relationship the best tool will be fixed effects                model  (FEM).  Further  analysis  of  the  model’s  properties,  however,  has  confirmed  the                present of heteroscedasticity of the random component. In order to eliminate this flaw, for                the purpose of estimating the model’s parameters weighted least squares (WLS) method                was used.                 Table 2 presents the results of estimation for the above model.                                                                191","Table 2 The Results of Estimation of the Model Describing the Determinants of Loans                                     Granted by MFI to NFC in the Eurozone Countries                                                  Dependent variable LOANjt                      Independen          Coefficie         Std.                        p-        Signific                     t variables            nt            Error          t-ratio    value        ance  a)                                          −217819         38094,9        −5,71        <0,00          ***                        Constant                                                                          8           01                                          −1713,32        693,088        −2,47        0,0147         **                        GDP_grjt                                                                          2                                          4157,04         995,310        4,177        <0,00          ***                        Unempjt                                                                                      01                           Infjt          3676,36         1707,40        2,153        0,0332         **                                          0,210133       0,024300        8,647        <0,00          ***                          Prodjt                                                            9                         01                                          5011,94         863,832        5,802        <0,00          ***                        Val_addjt                                                                                      01                      Gross_Inves         2,08509        0,328226        6,353        <0,00          ***                          tjt                                                         01                              Observations                                      136                       Standard error of residuals                           0,771125                                    R                                        0,979654                                     2                                          2                               Adjusted R                                    0,978708                         F (6, 129) = 1035,221                      p-value for test F< 0,00001                a)*** The statistically significant variable at the level of 1%; ** The statistically significant variable                                                     at the level of 5%.                                            Source: the author’s own calculations                 The estimated model is correct in statistical terms. Among eleven potential independent                variables  six  turned  out  to  be  statistically  significant.  All  the  obtained  signs  of  the                evaluations of structural parameters of independent variables are in accordance with the                theoretical predictions.                The  obtained  results  allow  to  conclude  that  five  independent  variables  have  a  positive                influence on the dependent one (i.e. the value of loans granted by MFI to NFC). Among                those  variables  there  are:  total  unemployment  rate  (Unempjt)  inflation  rate  (Infjt),                production value (Prodjt), value added at factor cost (both output related) (Val_addjt) and                gross investment in tangible goods (capital input related) (Gross_Investjt). Interpretation                of  parameter  estimation  in  the  cases  of  particular  variables  is  relatively  simple.  For                example, a parameter estimation for the Unempjt variable (4157,01) should be interpreted                as follows: if the unemployment rate grows  by one percentage point, then the value of                loans granted by MFI to NFC in the surveyed group of countries will as a result grow by                approximately EUR 4157.01 mln, assuming the stability of other variables.                The research findings confirm that the factor which has a strong, and at the same time                negative,  influence  on  the  shape  of  dependent  variable  is  the  real  GDP  growth  rate                (GDP_grjt).                In the estimated model, the variables which turned out to be statistically insignificant were:                labour cost index  - wages and salaries (LCI_wagjt), labour costs other than wages and                salaries (LCI_otherjt), EMU convergence criterion bond yields (Bondsjt), gross operating                surplus (Surjt) and total purchases of goods and services. (Total_purchjt).                The econometric model for panel data used in the study does not take into account time                shifts as a reaction of dependent variable to the shape of independent variable. It means                that the model’s results do not determine the possibility of observing the results of the                impact of dependent variables on the stock of MFI/NFC loans in the subsequent period or                periods (Louri and Migiakis 2016, p. 40).                                                               192","Regardless of the above, the clearly negative impact of real GDP growth rate on stock of                MFI/NFC  loans  revealed  by  the  model  within  a  given  time  unit  (year)  has  a  logical                explanation,  confirmed  by  other  observations.  In  the  periods  when  the  total  economic                situation  improves  companies  have  better  possibilities  of  funding  their  activities  from                current  revenues.  However,  if  the  improvement  is  not  strong  enough  to  encourage                companies to radically expand the range of their activities, including funding their turnover-                related needs or investments by means of loans from MFI, then the demand for such funds                may decrease in relation to periods of worse economic situation (ECB 2009, p. 19). It is a                phenomenon also known from the studies of the channels of transmission of the impulses                of the central bank’s monetary policy. It proves that in countries that are inflation-stable                the factor of greater influence on banks’ loan-related activities than loan availability to                enterprises (including the costs of loans reflected in interest rates), is companies’ optimism                concerning  adequately  large  increase  in  the  market’s  power  of  absorption  of  their                production at the prices which are satisfactory for them (Mishkin 1996, p. 3 and 10). This                results in a broader assumption which is crucial for this model and any elaborations based                on it, according to which the stock of MFI/NFC loans is shaped to a much greater extent                by the described significant variables than the other way round.                 As noted above, due to the outcomes of the financial crisis, at the beginning of the studied                period the real GDP growth rate in the Eurozone experienced a breakdown, and its later                increase was related to the reconstruction of the size of economic activity to the level from                before the crisis, which was uneven in the course of time. One should also consider other                factors, such as the increase in the attractiveness of financing large companies’ needs by                emission  of  securities  on  the  capital  market  (both  shares  and  bonds),  which  typically                accompanies improvements in the total economic situation (Covas and den Haan 2006, p.                30).                The impact of the rising unemployment rate shown by the model on the growth of the                stock of MFI/NFC loans should also be associated with the specific economic situation in                connection with the financial crisis. Companies make more people redundant in the periods                of economic downturn, when they become more dependent on sustaining their financial                condition with loans. At the same time, a radically growing unemployment rate is evidence                of increased size of a low-income social group, which reduces the society’s consumption                potential,  influencing  companies’  financial  results  (PwC  2010,  p.  22).  State-funded                unemployment benefits may to a certain extent reduce the decline, but not eliminate it.                The process of companies’ adapting to the new circumstances is neither immediate nor                complete  (Verick  and  Islam  2010,  p.  36).  In  terms  of  increased  unemployment,  the                financial  crisis  from  2008  created  an  especially  difficult  situation  for  countries  from                southern Europe, such as Greece, Portugal, Spain, Italy, as well as Ireland.                The impact of inflation on the stock of MFI/NFC loans (expressed in millions of EUR) seems                obvious due to the very essence of inflation as a common increase in prices (and salaries)                and its impact on the values expressed nominally in money. The results of the calculations,                however, have confirmed that the impact was at a lower level of significance than in the                case of unemployment. The article deliberately makes use of the HICP measure of inflation                and not the industrial producer price index, which has a very direct impact on the stock of                loans  granted  to  enterprises.  A  separate  analysis  would  be  required  for  the  potential                influence on the results exerted by the inflation (and interest rates) level which is close to                zero, or even by deflation (and negative real interest rates) in the Eurozone countries in                the studied period (Plasil et al. 2013, p. 129).                Production value (millions of EUR) measured as the amount actually produced by the unit,                based  on  sales,  including  changes  in  stocks  and  the  resale  of  goods  and  services                demonstrates a significant influence on the stock of MFI/NFC loans. It can be treated as                evidence that an increase in the production value contributed to increased optimism by                NFCs in the Eurozone regarding the profitability of further expansion with the use of loans                from MFI. The loans taken may, of course, have a reciprocal influence on the increase in                the production value (Martinez and Landesberger 2010, p. 12).                                                               193","Also, the value added at factor costs (in %), which is the gross income from operating                activities after adjusting for operating subsidies and indirect taxes, according to the used                model,  has  significant  influence  on  the  stock  of  MFI/NFC  loans.  Due  to  its  nature,  the                mechanism of influence described at the production value should be even more visible in                relation to gross income from operating activities (Cesaroni et al. 2016, p. 4). An increase                in the production value does not always translate into an increase in the gross income, for                instance  due  to falling  profit  margins  or  growing  costs  of  production  and  prices,  which                means that an increase in the production value may be accomplished even with the same                profit margin and smaller amount of product sold.                According to the definition by Eurostat, gross investment in tangible goods (expressed in                millions of euro) is defined as investment during the reference period in all tangible goods.                Included are new and existing tangible capital goods, whether bought from third parties or                produced for own use, having a useful life of more than one year. However, the measure                also includes non-produced tangible goods such as land, but investments in intangible and                financial assets are excluded from it (EIB, p. 31). When analyzing the significant influence                of investments on the stock of MFI/NFC loans, one should thus bear in mind that what is                meant here is a special type of investments in relations to loans in general, which include                both investment loans, as well as working capital loans and others. Essentially, an increase                in  the  investment  in  tangible  goods,  apart  from  causing  an  increase  in  demand  for                investment loans, should at least partially increase the demand for working capital loans,                and even other types of loans.                4 Conclusions                 Interest in factors which influence the value of loans granted by MFI to NFC gained a new                dimension  when  from  September  2016  the  Bank  for  International  Settlements  (BIS)                started  publishing  time  series  on  the  credit-to-GDP  gap  for  more  than  40  economies.                According to BIS, the credit-to-GDP gap is defined as the difference between the credit-to-                GDP ratio and its long-run trend, and captures the build-up of excessive credit in a reduced-                form  fashion.  It  has  been  found  to  be  a  reliable  early  warning  indicator  of  impending                financial crises for a broad array of countries and a long time span that includes the most                recent crisis. According to ECB data, the value of loans granted by MFI to NFC reacted to                the 2008 crisis with greater downturns than the value of household loans (ECB 2011, p.                63).                 Analyses conducted in the course of research indicate that the shaping of the value of loans                granted by MFI to NFC in the Eurozone is influenced not only by selected macroeconomic                factors, but also values related to the results of companies’ economic activity. As regards                the  macroeconomic  indicators,  they  are  real  GDP  growth  rate  (GDP_grjt),  total                unemployment rate (Unempjt) and inflation rate (Inflationjt). From the group of data related                to companies’ economic performance, the ones selected include production value (Prodjt),                value added at factor cost (Val_addjt) – both output related  – and gross investment in                tangible goods (Gross_Investjt – capital input related). An important one, whose influence                is negative, is real GDP growth rate. A positive impact of macroeconomic indicators on the                value  of  MFI/NFC  loans  in  the  Eurozone  was  discovered  in  the  case  of  the  total                unemployment rate and inflation rate. All indicators of the results of the economic activity                of enterprises that turned out to be statistically significant, that is production value, value                added at factor cost and gross investment in tangible goods, have a clearly positive impact                on the value of loans granted. The tendencies shown by all the factors which determine                the value of MFI/NFC loans in the Eurozone countries have turned out to be in line with the                theoretical assumptions.                 Acknowledgments                 The paper was developed as part of the project \"Positive social change in an organization                as a factor of a company engagement in sustainable development\". The project was funded                by   the   National   Science    Centre,   Poland,   on   the   decision   number     DEC-                2017/25/B/HS4/01113.                                                             194","References                Baltagi, B. H. (2001). Econometric Analysis of Panel Data. Chichester: John Wiley \& Sons                Ltd.                Cesaroni,  T.,  De  Bonis,  R.,  Infante,  L.  (2016).  On  the  determinants  of  firms’  financial                surpluses and deficits. In: 8th IFC Conference Statistical implications of the new financial                landscape. Basel: Irving Fisher Committee on Central Bank Statistics, BIS, pp. 1-24.                Covas, F., den Haan, W. J. (2006). The Role of Debt and Equity Finance over the Business                Cycle. Bank of Canada Working Paper, no. 2006-45.                Deutsche  Bundesbank (2017).  Recent developments in  the indebtedness  of  the  private                non-financial sector in selected euro-area countries. Deutsche Bundesbank Monthly Report,                pp. 41-58.                 ECB (2009). Loans to the non-financial private sector over the business cycle in the euro                area. ECB Monthly Bulletin, vol. October 2009, pp. 18-21.                ECB (2011). Recent developments in loans to the private sector. ECB Monthly Bulletin,                pp. 57-72.                EIB (2015). Investment and Investment Finance in Europe. Investing in competitiveness.                Economics Department, European Investment Bank.                Louri H., Migiakis, P. M. (2016). Bank Lending Margins in the Euro Area: The Effects of                Financial  Fragmentation  and  ECB  Policies.  LSE  ‘Europe  in  Question’  Discussion  Paper                Series, Paper no. 105/2016.                Martinez-Carrascal, C., von Landesberger, J. (2010). Explaining the money demand of non-                financial corporations in the euro area – a macro and a micro view. ECB Working Paper,                no. 1257, pp. 4-32.                 Mishkin,  F.  S.  (1996).  The  Channels  of  Monetary  Transmission:  Lessons  for  Monetary                Policy. NBER Working Paper, no. 5464, pp. 1-27                Plašil, M., Radkovský, Š., Řežábek, P. (2012/2013). Modelling bank loans to non-financial                corporations. Czech National Bank, Financial Stability Report 2012/2013, pp. 128-136.                PwC (2010). Bank lending and the recovery. UK Economic Outlook, pp. 18-22.                Trabelsi, M. A. (2012). The impact of the sovereign debt crisis on the eurozone countries.                Procedia - Social and Behavioral Sciences, vol. 62, pp. 424 – 430.                Verick S., Islam, I. (2010). The Great Recession of 2008-2009: Causes, Consequences and                Policy  Responses.  Forschungsinstitut  zur  Zukunft  der  Arbeit,  IZA  Discussion  Paper,                no. 4934.                                                                                     195","Convergence of the Government Bond Yields in Estonia, Latvia and                                                       Lithuania                                                     Jana Hvozdenska                                                     Masaryk University                                Faculty of Economics and Administration, Department of Finance                                           Lipova 41a, 602 00 Brno, Czech Republic                                                E-mail: 174974@mail.muni.cz                 Abstract: The aim of this paper is to analyse the influence of the European Union accession                and financial crisis to convergence and integration of the bond yields and bond markets.                The results show the deepening of bond market convergence after the European Union                accession  and  the integration  has  continued  until  the end  of  the  observed  period.  The                chosen indicators are monthly mid-term bond yields (10-year bond yields). The period of                1/2000 to 12/2017 was chosen in order to show the impacts of the changes. The time                period was divided into periods 1/2000 - 4/2004 (before the European Union accession),                5/2004 - 7/2007 (after the accession and before financial crisis), 8/2007 - 3/2009 (period                of the deepest financial crisis), 4/2009 - 12/2016 (period after the financial crisis). Used                methods are 1) spread between the 10-year bond yields of countries of Estonia, Latvia,                Lithuania and German 10-year bond yield, 2) analysis of alignment, 3) β-convergence.                These findings can be beneficial for the financial market observers.                Keywords: β-convergence, bond yields, integration of bond markets                 JEL codes: E43, E44, E47, G01                 1 Introduction                The financial turmoil during 2007-2009 affected the euro area financial sector in ways that                differ considerably across market segments and countries. A consequence was a temporary                reduction of market activity within national borders. The impact was felt most strongly in                the  money  markets,  and  relatively  less  in  bond  activities.  However,  economic  growth                stopped and still many countries are not able to follow Maastricht Convergence Criteria.                On one hand, the integrated financial markets and the common currency may help protect                the countries from the negative impacts of a financial crisis, because the countries are part                of a large, stable economic unit. On the other hand – financial instability may spread easily                from country to country, since barriers to the capital movements have been reduced.                Across  the  economic  literature,  there  is  a  range  of  acceptable  definition  of  financial                integration. In a broader sense, it is possible to achieve financial integration when all the                conditions  necessary  for  the  continuous  implementation  of  financial  transactions  and                market functioning are met.                Commonly-used definition of financial integration is expressed by (Baele et al., 2004). The                market for a given set of financial instruments and services is fully integrated if all potential                market participants with the same relevant characteristics (1) face a single set of rules                when they decide to deal with those financial instruments and services; (2) have equal                access  to  the  above-mentioned  set  of  financial  instruments  and  services;  and  (3)  are                treated equally when they are active in the market.                Financial markets are integrated when the law of one price holds (Adam et al., 2002). This                states that assets generating identical cash flows command the same return, regardless of                the domicile of the issuer and of the asset holder. Given this definition, financial market                integration can be measured by comparing the returns of assets that are issued in different                countries and generate identical cash flows.                (Czech National Bank, 2012) states that it is possible to speak about the achievement of                full integration of financial markets only if financial assets with comparable risk factors and                yields are evaluated by the markets in the same way, regardless of the country where the                                                              196","assets  are  traded.  Fully  integrated  markets  without  any  barriers  permit  to  use  an                arbitration opportunities which lover the importance of local factors characteristic for given                countries  and  enable  direct  comparison  of  the  prices  of  financial  assets  in  individual                markets.                The aim of this paper is to analyse the influence of the EU accession and financial crisis to                convergence and integration of the bond yields and bond markets.                 2 Theoretical Concepts                 In general, the convergence of government bond yields to a stable level with reduced risk                aids  the  overall  economy,  by  allowing  cheaper  access  to  debt  financing  with  less                uncertainty  regarding  the  value  of  such  funds  over  time.  This,  in  turn,  stimulates                investment and output within converging countries.                 The methods described below are used for measuring of bond market integration.                Spread between Yield on a Local Asset and a Well-Chosen Benchmark Asset                Germany is the biggest and most solid economy within the EU. For that reason it can be                chosen as the benchmark asset for the countries of European Union. The smaller the spread                is the bigger the integration is.                Formally we can write the convergence in time t and t+1 as:                        |y 1,t − y 2,t | > |y 1,t+1 − y 2,t+1 |                                          (1)                where y1,t and y2,t are relevant economic variables of two countries in time t.                The case with opposite sign is called divergence. This is a situation when the countries in                terms of economic maturity are moving away.                Analysis of Alignment                Analysis of alignment is the first step of the concept of financial integration. It is based on                the correlation analysis in standard or rollover form. This analysis indicates the strength of                a  linear  relationship  between  two  variables.  Its  value  may  not  be  sufficient  for  the                evaluation  of  this  relationship,  particularly  in  those  cases  where  the  assumption  of                normality  is  incorrect.  The  correlation  coefficients,  being  aggregated  statistics,  cannot                substitute for individual evaluation of the data (Babecký et al., 2007).                Concept of β-convergence                β-convergence (2) is used to determine the approximation rate of asset returns in financial                markets. It was first used by (Adam et al., 2002). In order to quantify β-convergence, it                is possible to apply regression according to the following formula:                       ∆R i,t  = α + βR i,t−1  + ∑ L l=1 y ∆R i,t−1  + ε                                (2)                                               i                               i                                                         i,t                where Ri,t represents the distribution rate of specific assets between a country i in time t                with respect to the reference territory, Δ is the reference operator, αi is a specific constant                for the given country, έi,t is a white noise disturbance. Lag length L is based on Schwarz                Criterion,  maximal  length  is  set  to  1,  because  monthly  data  are  applied  and  financial                market memory is relatively short. β-coefficient is a direct criterion of the rate of global                market convergence.                The concept of beta-convergence enables identification of the speed with which eliminated                differences in yields on individual financial markets. If the beta coefficient is negative, then                signals the  existence  of  convergence  and  the  amount  of  beta  coefficient expresses  the                convergence rate, i.e. the rate of elimination of shocks to the yield differential pricing of                individual   assets   to   the   euro   area.   The    closer   beta   coefficient   is   to                -1, the greater the speed of convergence is.                                                                197","3 Methodology and Data                The selected countries are Estonia, Latvia and Lithuania.                 As  a  measure  of  the  yield  was  used  10-year  government  bond  yield  in  all  countries                mentioned above and Germany (monthly basis). Germany is the biggest and most solid                economy within the EU. For that reason it can be chosen as the benchmark asset for the                chosen countries (and whole European Union). The data of 10-year sovereign bonds of the                selected countries were taken from Bloomberg database (2018, [4]). The sample period                starts from 1/2000 and ends on 12/2017. This time range covers the period before the                accession  of  the  countries  to  European  Union  (1/2000  –  4/2004),  after  the  accession                (5/2004 – 7/2007), period of financial crisis (8/2007 – 3/2009) and period after financial                crisis  (4/2009  –  12/2017).  The  beginning  of  the  pre-crisis  period  (or  the  period  of                spectacular growth) coincides with an accession of the countries studied to the European                Union. We decided to start the crisis period with a different date than that of the Lehman                Brothers bankruptcy and major panic in the markets. We wanted to capture an earlier                market, which was when the 2007 banking crisis changed from high expectations to the                fear of a looming sovereign debt crisis. The pre-crisis period depicts a long run-up in prices                followed by a significant price drop during the crisis period.                The used methods are: spread between the yield on a local asset and a benchmark asset,                analysis of alignment and concept of β-convergence.                 4 Results and Discussion                We used the methods described above to measure the bond markets integration.                Spread between Yield on a Local Asset and a Well-Chosen Benchmark Asset                                      Figure 1 Spread of Estonia, Latvia and Lithuania                                     Spread of Estonia, Latvia and Lithuania                               0,045                              0,04                             0,035                              0,03                             0,025                           Spread  0,015                                                   EST-GER                              0,02                                                                                           LAT-GER                              0,01                                                                                           LIT-GER                             0,005                                 0                                    1/1/2000  4/1/2001  7/1/2002  1/1/2005  4/1/2006  7/1/2007  1/1/2010  4/1/2011  7/1/2012  1/1/2015  4/1/2016  7/1/2017                             -0,005                              -0,01           10/1/2003    10/1/2008    10/1/2013                                                  Source: author’s calculations                 As we mentioned above, the benchmark asset is German 10-year government bond yield.                The spreads between Estonia and Germany, Latvia and Germany, Lithuania and Germany                are possible to see in Figure 1.                The closer the spread is to zero the bigger the convergence is. It is clearly visible that                regarding to the Estonia and Latvia the spreads are the closest to zero between the years                2002 and 2007. The biggest spreads has Lithuania – in 2008 and 2013 the spreads are                bigger than 2.5 %. The financial crisis and later on debt crisis had the biggest impact on                Lithuania (because of the highest spreads), however we can see that the spreads were                quite similar in all countries.                                                             198","It is possible to say that according to this criterion, the most integrated country is the                Estonia (because of the lowest spread).                 Analysis of Alignment                Simple period average correlations of 10-year bond yields, shown in Table 1, show that                Estonian,  Latvian  and  Lithuanian  markets  are  strongly  linked  (in  whole  period)  among                themselves (correlations between 0.68 and 0.89), on the other hand with the Germany as                well (correlations between 0.65 and 0.88). Bold numbers indicate strong and significant                correlation coefficients.                                      Table 1 Correlations during 1/2000 –12/2017                                 1/00-12/17          EST         LAT        LIT        GER                                     EST              1                                     LAT         0.897542        1                                     LIT         0.689547  0.75958          1                                     GER         0.884586  0.84783  0.654875           1                                                Source: author’s calculations                                      Table 2 Correlations during 8/2007 – 3/2009                                   8/07-3/09          EST       LAT        LIT      GER                                       EST            1                                       LAT        0.32033       1                                        LIT       0.36646  0.123614        1                                       GER        0.16006  0.220882  -0.2907         1                                                Source: author’s calculations                 Table 2 shows the time period with the lowest correlation coefficient. It is the period of                financial crisis. In this case it is possible to see that countries are not correlated at all                among themselves. No country is correlated to Germany. Table 3 shows the best results –                time period from April 2009 to December 2017. It is the period after financial crisis and it                was expected that the government bond yields should show the highest correlations.                                      Table 3 Correlations during 4/2009 – 12/2016                                4/09-12/17        EST         LAT          LIT       GER                                     EST            1                                     LAT        0.95859         1                                     LIT       0.815875  0.92458            1                                    GER         0.82535  0.764582  0.795685            1                                                Source: author’s calculations                 Concept of β-convergence                 The results of β-convergence are in Table 4. All the values in the table are negative. It                means that there is a convergence in the bond markets of the selected countries. The p-                values  are  mainly  less  than  chosen  significance  level  of  α=0.01,  therefore  we  can                contribute the models as significant.                                                                             199","Table 4 β Coefficients                                1/00 – 4/04       5/04 – 7/07       8/07 – 3/09      4/09 – 12/17                                                                            P-                              Coeff.  P-value  Coeff.  P-value  Coeff.  value  Coeff.  P-value                                      1.11e-                                                                                      -                                                                                            3.89e-                                                                    -                                                  -                                                        1.68e-                       EST  -0.758      021     0.842  07  ***  0.586      0.866     1.145  12  ***                                        ***                                      1.49e-                                                                                            5.47e-                                                                    -                                                        1.78e-                                                                                      -                                                  -                       LAT  -0.359      044     0.742  09  ***  0.765     0.9308    1.215  06  ***                                        ***                                      2.42e-      -     9.87e-      -                 -     6.49e-                       LIT  -0.895      045     0.824     015     0.832   0.1020    1.057   07 ***                                        ***               ***                                                Source: author’s calculations                The only exception is in all selected countries (Estonia, Latvia and Lithuania) in the period                of financial crisis (8/07-3/09).                Relatively high values of β coefficient indicate that individual financial markets of observed                economics  integrated  relatively  easily  with  the  German  market.  The  β  coefficient  was                relatively low in Lithuania during the financial crisis. It means that Lithuania started to                diverge and in the period after financial crisis started quickly converge to benchmark. The                divergence may be caused by increased nervousness of financial market participants and                the associated increase in the volatility of market assets. Both investors and investment                services  providers  in  fear  of  their  liquidity  position  restricted  their  market  activities,                including cross-border activities and integration process more or less weakened.                 The absolute values of the β coefficients are close to one for all the countries in the period                after financial crisis. It means that the levelling of newly arising differences between the                chosen country and the Germany is fast.                The tests of normality and homoscedasticity are shown in the Table 5. For the evaluation                of  the  normality  test  is  probably  the  easiest  to  observe  the  result  from  graph  of  the                assumed  normal  distribution  in  comparison  to  the  actual  distribution  of  residues  and                analyse p-values of Chi-square test. We test the hypothesis H0: Residuals are normally                distributed,  against  the  hypothesis  H1:  Residuals  are  not  normally  distributed,  the                significance level of α was chosen as 0.01. If the p-value is greater than α then we cannot                reject the H0, therefore the residuals are normally distributed.                For the testing of heteroscedasticity we chose the White’s test. We test the hypothesis H0:                Constant variances of residuals – homoscedasticity, against H1: Heteroscedasticity. The                significance level of α was chosen as 0.01. If the p-value is greater than α then we cannot                reject H0, therefore it contributes homoscedasticity.                                     Table 5 Tests of Normality and Homoscedasticity                              1/00-4/04           5/04-7/07           8/07-3/09          4/09-12/17                             P-    P-value      P-     P-value      P-     P-value      P-     P-value                           value              value               value                value                          norm.    heteros.   norm.    heteros.   norm.    heteros.   norm.    heteros.                    CZ     0.378    0.277     0.150     0.633      0.178    0. 475     0.225     0.345                    SK     0.827    0.597     0.592     0.258      0.267     0.526     0.268     0.236                    PL    0.0204    0.157     0.448     0.270      0.367     0.873     0.194     0.275                                                Source: author’s calculations                 5 Conclusions                In this paper we have discussed the selected aspects of bond integration of Estonia, Latvia                and Lithuania. The aim of this paper was to analyse the influence of the EU accession and                                                            200","financial  crisis  to  convergence  and integration  of  the  bond yields  and  bond markets  of                selected countries.                 Are the bond markets of the selected countries group convergent? Answering this question                requires  a  surprising  amount  of  preliminary  work.  According  to  all  used  methods  the                markets converge more after the financial crisis then before, however they converge for                the whole observed period of time. Surprisingly all methods showed and confirmed lower                pace of integration in Lithuania in the period of financial crisis, which rose in the period                after the financial crisis. The divergence in the period of financial crisis may be caused by                increased nervousness of financial market participants and the associated increase in the                volatility of market assets.                 Future  research  could  be  extended  to  a  wider  examination  of  integration  of  the  stock                markets. It would be interesting to test if there would be a change in results when using                data of EU15 as an average of the oldest members of European Union instead of Germany                as the most stable economy.                 Acknowledgments                 Support of Masaryk University within the project MUNI/A/1092/2017 Financial stability and                economic development is gratefully acknowledged.                 References                 Adam K. et al. (2002). Analyse, Compare and Apply Alternative Indicators and Monitoring                Methodologies  to  Measure  the  Evolution  of  Capital  Market  Integration  in  the  European                Union. Palermo: University of Palermo, CSEF.                Babecký J. et al. (2007). Financial Integration of Stock Markets among New EU Member                States and the Euro Area. ČNB Working Paper Series No 7. Prague: Czech National Bank,                2007.                Baele L. et al. (2004). Measuring Financial Integration in the Euro Area. ECB Occasional                Paper Series No 14. Frankfurt am Mein: European Central Bank.                Bloomberg (2018). 10-year Government Bond Yields. Retrieved from: Terminal Bloomberg.                 Česká  národní  banka  (2012).  Analýza  stupně  ekonomické  sladěnosti  České  republiky  s                Eurozónou. Praha: Česká národní banka.                                                                                            201","A Relationship between Gender and Age Diversity among Board                                        Members and Firm Performance                                                                              2                                                               1                                             Lenka Janošová , Petr Mikuš                                                    1  Masaryk University                  Faculty of Economics and Administration, Department of Business Economics and Management                                           Lipová 41a, 602 00 Brno, Czech Republic                                             E-mail: lenkajanosova@mail.muni.cz                                                     2  Masaryk University                  Faculty of Economics and Administration, Department of Business Economics and Management                                           Lipová 41a, 602 00 Brno, Czech Republic                                                E-mail: 62765@mail.muni.cz                 Abstract: This paper provides an evidence that age diversity in managing and supervisory                boards among Czech joint-stock companies has impact on firm´s performance indicators.                Age  diversity  is  becoming  widely  discussed  topic  due  to  population  ageing,  which  is                reflected in corporate governance area as well. Age diversity can influence decision-making                process, risk attitude or structure of assets, that´s why it has impact on overall profitability                of businesses.  The main goal of our paper is to test possible relationship between age                composition of supervisory and executive board members in Czech stock companies and                firm performance. The initial sample included 364 companies with Czech domicile operating                in manufacturing industry, especially in IT industry. Randomly chosen sample is composed                of 70 Czech companies from IT industry. For this sample we gained basic data describing                the  number  of  board  members,  their  age,  gender  as  well  as  current  key  performance                indicators  published  financial  statements  (ROA,  ROS,  liquidity,  indebtedness).  We  use                these indicators to assess the relation between age diversity and profitability through basic                statistic and mathematic methods.                Keywords: age diversity, gender diversity, Key Performance Indicators (KPI), ROA, ROS                JEL codes: M14, M21, M12, L25, L22                  1 Introduction and literature review                With  an  increasing  rate  of  globalization,  a  growing  public  interest  and  increasing                competitive pressure, an interest in the efficiency and functioning of corporate governance                is increasing. Although it's been decades since Eastern European markets opened their                boarders for free trade and transited from a centrally-planned to a market economy, there                are still considerable differences between the operation of businesses in Western and post-                communist  countries.  The  sale  of  assets  to  private  hands  did  not  ensure  its  effective                management. Administrative functions should focus on setting targets and corresponding                policies to fulfilling the interests of owners.                Currently,  the  most  widely  used  system  in  Central  Europe  is  the  continental-European                model, including the existence of three bodies: the General Meeting, the Supervisory Board                and Board of Directors. This model was recently the only possible in the Czech Republic.                From the above it is clear that the a way how the business will prosper (ie. to achieve the                desired financial performance) depends on the decisions of firm´s  organs that are made                up of groups of people of different ages, nationalities, gender and having different ways of                professional  development,  other  education  and  experience  .  This  heterogeneity  in  the                group is called diversity, Pelled (1999) distinguishes demographic diversity (= degree to                which  the  organization  is  heterogeneous  with  respect  to  demographic  attributes)  and                functional  diversity  of  backgrounds  (=  degree  of  heterogeneity  in  education  and                experience).                The corporate governance is rather new term, and the number of studies is rising in these                days. A topic of corporate governance is very broad and consists of legal setting of the                companies in a country and, in many cases, the free-will setting of a company management                                                              202","and corporate governance rules. In the Czech Republic, there is no strict or mandatory                script  for  corporate  governance.  When  thinking  of  the  connection  between  corporate                governance and the performance, the numbers of scientific papers is rather low. This article                is new contribution to this topic from the very actual point of view of age diversity and age                in the company – effectively connecting two actual topics – corporate governance and the                age management in the ageing environment of the European Union as well as an area of                gender issue among board members.                The problem in the research, and this is proven in many studies setting and researching                often  very  different  factors,  is  to  set  the  proper  variables.  Those,  that  influence  the                performance most significantly. In our paper, we have focused on the age and gender                diversity among the managing and supervisory boards in the companies as bearer of the                strategic  management  in  the  companies  and  thus  one  of  the  factors  of  the  corporate                governance.  Our  contribution  is  to  research  on  the  sample  of  the  Czech  companies,                whether the age and gender diversity in the company’s bodies have an effect on the key                performance indicators.                Performance measurement                The word performance is widely used in all fields of business management. Nevertheless,                performance as a term is not precisely explained. Dictionary explanation of the term varies                and is frequently defined from the concrete point of view. One of many definitions (Neely                et al., 2003), (Wholey et al., 1996) of performance is the one of Richard and Devinney                (2009):                Organizational performance encompasses three specific areas of firm outcomes:                (a) financial performance (profits, return on assets, return on investment, etc.);                (b) product market performance (sales, market share, etc.);                 (c) shareholder return (total shareholder return, economic value added, etc.).                In the Czech Republic, the financial performance is the most suitable, as the financial data                are  believed  to  be  most  precise,  although  there  are  some  limitations  –  tax  policies  of                company etc. The product market performance is hard to observe, as we have chosen the                general IT companies in the Czech Republic market and the sales or market share are not                part of the financial statements, those are base of our research. Shareholder return is not                usable in the Czech Republic because most of the companies in the sample are not publicly                traded and there is no evidence of sharing the profit.                Financial  performance  of  the  company  is  usually  depicted  using  key  performance                indicators.  In  this  paper  there will  be consistency  with  most used traditional indicators                mentioned by Hult (2008) ROA, ROE, ROS. In addition, there will be used liquidity ratios                and indebtedness as an indicator of financial responsibility and stability.                Construction of the performance indicators is standardized, using EBIT in both ROA and                ROS. For the liquidity ratio, the sum of short term liabilities with short term loans and                financial aids will be used. For the indebtedness, there are two ratios, general indebtedness                and the investor´s risk. We will use only general indebtedness is rate of equity another                sources as a measure for the stability of the company.                                       Table 1 Key Performance Indicators Overview                 KPI                       Target                  Characteristics                  ROA                       maximization            performance                  ROS                       maximization            performance                  Liquidity (current ratio)  Optimization  towards  Carefulness  approach  –  insurance                                           optimum (adequate)      against market risk                                                              203","General indebtedness      Optimization  towards  Carefulness approach – risk of control                                           optimum (adequate)      loss                                    Source: own creation based on literature mentioned above                 Corporate governance and performance                State or find a concrete factor, that has an important or key impact on performance of the                companies is not trivial. General problem of humanities is the number of factors influencing                the operations of the company. Many studies proof relation of corporate governance and                the  performance  of  the  companies  expressed  by  financial  indicators.  This  linkage  was                declared  specifically  in  Bebczuk’s  (2005)  research  of  Argentinian  companies  and                Gruszczynski’s (2006) research in Poland. More generally the impact of good corporate                governance is expressed in Gompers’s, Ishii’s and Metrick’s (2003) research paper, Bhagat                and Bolton’s (2008) study and predominantly Milstein report of good corporate governance                in general. In all studies it is declared, that the good corporate governance, regardless the                meaning  of  adjective  good,  doesn’t  mean  higher  performance,  but  bad  corporate                governance means undoubtedly lower performance. In these studies, authors were using                the EVA or Tobin q. In the study of Ooihi a Lecomt (2013), the principle of the stock market                valuation was used. These approaches are not feasible in the Czech Republic, due to low                number of companies being publicly traded on stock market – the financial data will be                used instead.                Corporate  governance  as  an  operation  –  good  managing  of  the  company includes  also                taking managerial risk, decision making process and in this area predominantly targeting                of  the  KPI  in  the  company.  This  process  might  be  influenced  by  the  nature  and  the                composition  of  management  boards  in  the  companies  –  the  bearers  of  the  “strategic”                corporate government. In this paper we focus mainly on age and gender composition of                the boards in the selected Czech companies.                Gender and Age diversity                Discussion about importance of demographic characteristics of board members is coming                to the researchers´ attention. A Ratio of women participating at the labour markets has                been getting bigger since Second World War. So, gender diversity is not only the current                topic in the management area, but also in overall society. Heidrick and Struggles (2007)                proved  in  their  report  that  women  proportion  at  the  European  labour  markets  are                increasing in the last years, from 5% in 2001 to 8.4% in 2007. But we are still not at the                same level as American companies and big differences among European countries exist.                The Scandinavian countries are widely known for their progressive approach in involving                older people and women into the working process at higher positions. However, in the                central-European context, gender and age diversity is out of the primal research focus of                most researchers. There are some studies aiming at gender diversity, because it is easer                for statistical study rather than age diversity, which is hard to define. An interest in gender                diversity is also supported by European policy, which is trying to introduce women quotas                for executive and supervisory bodies of companies at the level of 20%. (Matsa, Miller,                2013). Campbell (2008) provides us two reasons, why women should be included among                members of companies´ bodies more often. At first, there are ethical arguments. Excluding                women from executive and supervisory bodies only because of gender can be considered                as  discrimination.  And  no  firm  wants  to  build  up  an  image  of  low  Corporate  Social                Responsibility. Secondly, economic arguments are related to the assumption that every                company should choose for each job position the most suitable candidates, who reflected                all requirements for that position. If you exclude women, your financial performance can                decline, since you didn´t have to choose the best candidate. So the higher diversity can                bring a competitive advantage for a company.                Age diversity is getting more popular with ageing tsunami topic. According to the higher                percentage of older people (above 50 years) at the labour markets, employment, inter-                generation  teams  operation  or  their  engagement  in  organizational  culture  is  becoming                interesting  for  researchers.  Robinson  (1997)  presents  that  company´s  costumers  are                                                             204","diverse as well, so more diverse working teams can understand needs of your target groups                easier.  As  a  result  you  can  provide  more  customize  products  and  penetrate  additional                markets.  Second,  it  is  argued  that  diversity  increases  creativity,  innovation  and  new                approaches for problem solving. In addition, a process of knowledge creation and sharing                is encouraged by members´ diversity, which is extremely important in a case of original,                not  repeated  tasks  requiring  high level  of  creativity  for  solving.  (Van Knippenberg  and                Schippers, 2007). At third, it is argued that diversity can support problem-solving process                as  the  variety  of  perspectives  and  opinions  are  emerging  from  more  diverse  board                members  bring  more  alternatives  for  evaluation.  An  understanding  of  high  complex                system, which undoubtedly business environment is, can improve decision making process.                Decision will be right with higher probability. It may also lead to improvement of firm´s                image as well as benefits from customer behaviour. (Smith et al., 2006)                Based on literature overview provided in previous text, we propose these hypothesis and                research questions:                 1) Firm´s performance is diminishing with increasing age among members of the company                bodies.                2) Firm´s performance is diminishing with higher ratio of women among members of the                company bodies.                3) Firm´s security is stronger with higher ratio of women among members of the company                bodies.                4) Firm´s security is stronger with higher age among members of the company bodies.                 2 Methodology                Our initial sample included 364 companies with Czech domicile operating in manufacturing                industry, especially in IT industry. Randomly chosen sample is composed of 71 Czech joint-                stock companies from IT industry. Two of examined companies had no data in evidence,                thus  the  final  number  of  researched firms is 69. For  this  sample  we  gained  basic  data                describing the number of supervisory and managing board members, their age, gender as                well as current key performance indicators (ROA, ROS, liquidity, indebtedness) for selected                fiscal year 2012. We have chosen this year after first data scan, in 2012 there is the highest                completeness of required data. All data had to be obtained manually from public business                register, since in the Czech Republic there is no central database, where everybody can                download  mentioned  data  automatically  (as  it  is  in  American  business  environment).                That´s also the main reason, why we need to handle with lower sample than it is usual in                American research literature.                Gender diversity is calculated as the proportion of women to the total number of members                on the supervisory and managing board. Age diversity is problematic for statistic testing                since there is no clearly defined way how to measure it. So, we decided to make an average                age from all managing and supervisory board members for each examined company. To                asses firm performance we used various Key Performance Indicators  - return on assets                (ROA), return on sales (ROS), current ratio (CURRAT), indebtedness (DEBT). For testing                the relationship between age and gender diversity and firm performance, we used Kendall                tau. All calculation were accomplished by software IBM SPSS, version 23.                 3 Results and Discussion                In the table 2 we can find the basic characteristics of our research sample. Return of assets                is around 7.4%, which is slightly higher than average rate in the Czech Republic. Return                of sales is 6.5% that number doesn´t deviate from common range. Average age among                managing and supervisory boards´ members is around 50. This number is lower than in                German companies (Janošová et al., 2016), since German promotion system is based on                senior  principle,  it  means  you  build  your  career  in  one  company,  over  time  you  are                promoted according your experiences. Women share in Czech boards around 23 % falls                into  extremely high figure,  typical more  for  Scandinavian  countries  rather  than  central                European.                                                             205","Table 2 Basic Sample Description                                                 Sample Statistics                                                                                          Std. Error                                             N             Mean        Std. Deviation      Mean                           ROA              69             ,0743          ,42572          ,05125                           ROS              69             ,0654          ,36887          ,04441                         CURRAT             69           22,6951         77,04534         9,27517                          DEBT              69            2,9738         23,14120         2,78587                          Avrage            69           50,0329          5,58147         ,67193                       Womenshare           69             ,2309          ,21710          ,02614                                                  Source: own computation                For the hypothesis testing, we have chosen the Kendall tau, which is suitable for testing of                association of interval and ordinal variables. The results are provided in the table 3.                                                 Table 3 Kendall Tau Test                                                                                          Women       Avr.                                                  ROA       ROS     CURRAT  DEBT          share      age                                                                 **                 ROA          Correlation          1,000     ,734      0,011    0,126       0,052    0,049                              Coefficient                              Sig. (2-tailed)                0,000     0,897    0,127       0,564    0,555                              N                       69        69        69       69           69      69                                                       **                 ROS          Correlation          ,734      1,000     0,008    0,125       0,087    0,000                              Coefficient                              Sig. (2-tailed)      0,000               0,926    0,130       0,336    0,996                              N                       69        69        69       69           69      69                  CURRAT       Correlation          0,011     0,008     1,000        -        ,204   -0,063                                                                                                 *                                                                                    **                              Coefficient                                      ,251                              Sig. (2-tailed)      0,897     0,926              0,002       0,023    0,446                              N                       69        69        69       69           69      69                                                                                                **                 DEBT         Correlation          0,126     0,125    -,251     1,000      -,309   -0,017                                                                           **                              Coefficient                              Sig. (2-tailed)      0,127     0,130     0,002                0,001    0,840                              N                       69        69        69       69           69      69                 Women        Correlation          0,052     0,087     ,204         -       1,000  -0,004                                                                            *                                                                                    **                 share        Coefficient                                      ,309                              Sig. (2-tailed)      0,564     0,336     0,023    0,001                0,966                              N                       69        69        69       69           69      69                 Avr. age     Correlation          0,049     0,000    -0,063  -0,017       -0,004    1,000                              Coefficient                              Sig. (2-tailed)      0,555     0,996     0,446    0,840       0,966                              N                       69        69        69       69           69      69                  **. Correlation is significant at the 0.01 level (2-tailed).                 *. Correlation is significant at the 0.05 level (2-tailed).                                                   Source: own computing                 Of  the  association,  the  awaited  association  is  between  ROA  and  ROS.  These  two  key                performance indicators are functionally connected. The same logical connection is among                                                            206","the current ratio and indebtedness. When talking about the aim of the article, it must be                assumed, that average age has no connection to any of observed dependent variables. The                second,  share  of  women  in  the  bodies  of  the  companies  has  significant  correlation                coefficient  to  current  ratio  and  indebtedness,  which  means  that  higher  percentage  of                women between board members will result in lower level of firm indebtedness. Similar                score can be found for a relationship between women share and current ratio. With raising                women share, current share is also heightening. Both of the key performance indicators                are part of the firm security.                4 Discussion and Conclusion                 Age diversity and gender diversity were studied in the way as researched by Byrnes and                Miller (1999), ea. in the connection to risk-taking tendencies. The meta-analysis resulted                in support of the idea, that male participants are less risky avoidant than females and the                variety also fluctuated according to respondents´ age. On the contrary, Bonem (2015)                states, that the older people see more danger in ethical and health problems, but in the                social area they can accept more risk. The social area can be connected to the decision-                making process through negotiating and propagation of own opinion on problems in the                company and finding solution. In our research, age diversity among the average age of the                bodies´ members in the Czech IT companies showed no significant results. Average age of                the members was around 50 years, what might be surprised in IT sector. The question is,                what is the nature of these people being in such innovative sector and if holding the position                in  bodies  of  the  company  means  also  furthering  and  strengthening  the  corporate                governance principle of transparency – the owners are also managers in the company and                have control.                Rolison (2003) states that “Financial risk taking reduced steeply in later life for men but                not for women”. As there are not direct studies of connection between managerial style                and  gender/age,  this  study  can  be  approximation  of  financial  risk  of  the  company                management. The older the managers are, the more conservative the managerial decision                should be, unless there are women at power in the board of director.                The main purpose of this research study was to present age and gender diversity topic in                connection with key performance indicators. The initial research sample comprised from                Czech joint-stock companies operating in IT industry, with Czech headquarters. Average                and  gender  diversity  was  chosen  as  a  topic  based  on  theoretical  studies  mentioned  in                literature  review,  which  consider  these  types  of  diversity  as  possible  factor  of  firm´s                competitive advantage. Age and gender structure was can potentially have influence on                corporate performance, in case of this article measured using ROA, ROS, indebtedness and                current ratio. Because we primarily proceeded from more general theories, which provide                evidences about the positive effects of diverse teams (irrespective of the specific diversity                type), demonstrating themselves in an increased level of innovation, creative environment                and knowledge sharing, our research interest did not limit on age diversity, but also that                of gender.                Our findings are restricted by the size and selectivity of research sample, which do not                allow us to analyse data by more advanced statistical methods used in western literature.                Most of Czech joint-stock companies are not publicly traded, so the number of possible                tests is limited. Furthermore, the key performance indicators used are only some kind of                the possible analysis instruments and by its very nature may be distorted by unexpected                external or internal circumstances.                Within the sample of IT companies operating in the Czech Republic examined here, we can                conclude that gender has significant impact on firm´s indebtedness and current ration.                Lower indebtedness and higher current ration contribute to better security of company,                which may be a competitive advantage within crisis time, but too expensive in boom years,                since that firm policy binds financial resources. Concerning age diversity, we cannot prove                any significant results. So, we could not prove any bias between board members´ age and                key performance indicators. For future researches, it will be necessary to include other                industries  or  pick  out  bigger  sample  from  IT  industry.  It´s  also  possible,  that  IT  area                                                            207","demonstrates  slightly  different  results  than  other  fields  from  manufacturing  industry,                because  of  greatly  unstable  and  creative  environment.  Therefore,  we  should  conduct                research in a few areas to compare results and prove trend.                 References                Bebczuk,  R.  (2005).  Inter-american  Development  Bank.    Corporate  governance  and                ownership: measurement and impact on corporate performance and dividend policies in                Argentina. Working paper.                 Bhagat,  S.,  Bolton,  B. (2008).  Corporate  governance  and  firm  performance.  Journal  of                Corporate Finance, vol. 2008(14), pp. 257-273.                Bonem, E. 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Niepodległości 10,61-875 Poznań, Poland                                               E-mail:m.kaczala@ue.poznan.pl                 Abstract:  Rationales  for  government  intervention  in  agricultural  insurance  market  are                widely approved. The main aim of this study is to identify the relationship between the                crop insurance uptake and different government intervention on the Polish market. The                local legislation restrictions as well as data available have been analyzed. The measure of                crop  insurance  uptake  was  the  amount  of  insured  arable  land.  The  most  important                instruments identified are: insurance obligation, premium subsidies and the possibility of                insuring crops against a single risk (abolition of risk packages).                Keywords: crop insurance, subsidies, demand, state intervention, Poland                  JEL codes: Q14, R28, G22, H76, K12                 1 Introduction                 Agricultural insurance dates back several hundred years. In Europe it started as early as                the end of the 17  century, when mutual insurance against hail was introduced; in the 19                                 th                                                                                                          th                century it was offered in several European countries and in the USA (Mahul and Stutley,                2010). In the past few decades, development of this type of insurance measured according                to the volume of gross written premium and the range and scope of insurance products                has intensified, which is connected with growing state activity in this market sector (Smith                and Glauber, 2012).                In the USA the first state-subsidised crop insurance scheme was implemented in 1938. In                the following years it was modified, which resulted in the growing commitment of the state                (Goodwin and Smith, 2013). In 1939 governments introduced subsidised crop insurance                in Japan and Brazil, in 1943 in Jamaica (UNCTAD 1994), and in 1959 in Canada. European                countries began running similar programmes as of the 50’s, e.g. in Austria in 1955, in Italy                in 1970, in Spain in 1980 and in France as late as 2005 (from 1965 France ran a state-                sponsored  insurance  programme  called  the  National  Guarantee  Fund  for  Farming                Calamities  (FNGCA)),  and  in  Poland  in  2006  (Enjolras,  Capitanio,  and  Adinolfi,  2012;                Enjolras  and  Sentis,  2011;  Kaczała  and  Wiśniewska,  2015;  Mahul  and  Stutley,  2010;                OECD, 2011).                The subject literature points at the three types of reasons for government intervention with                regard to agricultural insurance, i.e. market failures, disparity in the availability of private                insurance between regions and crops and reporting damage more promptly. Market failures                result from three phenomena, generally present in the insurance market and in agricultural                insurance  in  particular:  moral  hazard  (Coble  et  al.,  1997),  Goodwin,  2001),  adverse                selection problems (Skees and Reed, 1986; Just, Calvin, and Quiggin, 1999) and systemic                risk (Kleindorfer and Klein, 2003).                 State intervention in the  crop  insurance  market  may  take  various forms, i.e.  premium                subsidy,  reassurance  backing  for  insurance  companies,  covering  administrative  or                operating  costs  or  providing  support  with  reference  to  infrastructure  needed  for  data                collection connected with risk assessment. Several forms of support are likely to be used                simultaneously within the framework of the same programme. WTO guidelines determine                acceptability  of  a  given  form  of  intervention,  along  with  CAP  regulations  for  the  EU                members as well as local legislations.                The main aim of this study is to identify the relationship between the crop insurance uptake                and various forms of government intervention on the Polish market.                                                             210","2 Methodology and Data                To solve the problem, a legal and functional point of view will be applied. In order to identify                intervention mechanisms one must analyse local legislation as well as superior regulations                (WTO and the EU). Due to the fact that both of these superior regulations are well known                and described in the subject literature, and Poland has been obliged, since its accession to                the European Union, to conform to the legal regulations in force in the EU, and the EU                members must comply with standards set by the WTO as for state intervention on the                agricultural market – the present analysis focuses on local regulations. The description is                complete with the analysis of the available data, which has been obtained from the Ministry                of  Agriculture  and  Rural  Development  (MARD),  the  Central  Statistical  Office  of  Poland                (GUS)  and  state  budget  implementation  reports.  Application  of  advanced  methods  of                statistical analysis is quite limited, due to a brief period of analysis determined by data                accessibility  and  a  high  level  of  data  aggregation.  Auxiliary  calculations  have  been                conducted  by  means  of  Excel  and  Gretl.  The  measure  of  crop  insurance  uptake  is  the                amount of insured arable land.                 3 Results and Discussion - Main instruments of government support on                the agricultural insurance market in Poland                Historical overview until 2005                State intervention has been seen on the agricultural insurance market for a very long time.                It had two forms: establishing or very tight control of insurance companies which would                offer this type of insurance and introduction of obligatory insurance.                The beginnings of the agricultural quasi-insurance funds in the form of brotherhoods date                               th                back to the 17  century, when representatives of several villages located on the island                between the rivers of Vistula and Nogat along with people from towns near Elbląg and                Gdansk, set up a brotherhood which guaranteed a payment of cash as a compensation for                flood or fire depending on the amount of cultivated farmland (Szczęśniak, 2006). During                the partitions period, i.e. from 1772 to 1918, three independent insurance systems were                established,  in  the  Prussian,  Russian  and  Austrian  partition  respectively.  Agricultural                insurance was offered in each, at different time and in various scopes, and all of them were                tightly controlled by the respective state authorities. Hence, in the Prussian partition in                1785 the Kwidzyń Society introduced obligatory insurance against fire which encompassed                rural property (only the property of the gentry was exempt from it); in 1790 a Gentry and                Landowners’ Fire Society was founded, in 1804 a fire society for villages in South Prussia,                followed by further establishments in Prussia (Szczęśniak, 2006a). Starting from 1873 it                was possible to insure crops, and obligatory insurance against hail became available in the                                    th                beginning of the 20  century. During the period of the Duchy of Warsaw (1807-1815) the                Prussian organisations were dismissed and in 1807 the Fire Society for Towns and Villages                came into being, introducing obligatory insurance against fire for villages (gentry property                was exempt) (Szczęśniak, 2006b).                In the Kingdom of Poland (also known as Congress Poland, being the Russian partition                from 1815) the above society was closed down and replaced by a new one, which imposed                the duty of insuring all land property against fire (except for inherited property) and in                1833 it was made possible (through another society) to insure farming machines, loss of                shipped goods, mill and oil press machinery, crops and farm animals. In 1843 the insured                risks  also  included  hail  and  cattle  plague  (Szczęśniak,  2008).  In  the  Austrian  partition                insurance against fire existed from 1860, against hail from 1864 and cattle plague was first                insured in 1906 (Szczęśniak, 2009).                 When Poland regained independence (1918), in 1921 obligatory insurance against fire was                introduces  for  farm  buildings,  and  starting  from  1929  in  particular  voivodships  it  was                possible to insure crops against hail and animals against plague. (Stroiński, 1989).                After the Second World War and the change in the economic system it was assumed as a                rule that the whole farm property should be insured. Gradually, from 1947, obligatory crop                                                             211","insurance  was  introduced  for  rye,  barley,  wheat  and  oats,  and  with  time  the  scope                encompassing crops was broadened to include maize, potatoes, buckwheat, fodder plants,                meadows  and  pastures.  Insurance  against  hail  and  flood  was  obligatory  (from  1972)                although  voluntary  insurance  also  covered  damage  done  by  spring  frost,  winterkill,                hurricane or drought (only in 1957-58). The terms of crop insurance assumed full cover of                the loss, considering a 10% integrated deductible. Later, if the crop was entirely damaged                in the early stages of growth, the level of loss was established according to the level of                insurance amount. Apart from crops, the farmer also had an obligation to insure buildings                against fire, movable property (livestock and inventory, household movables, reserves)                and farm animals against plague. In the 1970’s obligatory third-party liability insurance for                farm owners was introduced, which transfers onto the insurance company financial liability                for  unintentional  damage  caused  by  the  farmer  while  performing  vocational  activities.                Obligatory insurance types (eventually, there were nine) were contractless, (concluded by                ex  lege)  and  the  premium  was  included  in  the  tax.  Until  1984  the  obligation  only                encompassed private farms with the farmland exceeding 0.5 ha, (which was an important                exclusion, as PGR-s (state-owned collective farms) accounted for 10% of the land in 1950                and 20% in 1989). After 1984 the scope of obligatory crop insurance shrunk, as industrial,                seed  and  horticultural  plants  were  excluded  from  the  obligation,  and  were  covered  by                voluntary insurance. From that year, too, as a result of legal changes which allowed more                insurance companies to be set up in Poland, the number of entities offering agricultural                insurance grew from one to three.                 After the collapse of communism in 1989 obligatory agricultural insurance was abolished,                all except two types: firstly, insurance of farm buildings against fire or any other random                incidents  which  oblige  the  insurance  company  to  cover  the  costs  of  rebuilding  or                refurbishment of the damaged premises and secondly, third-party liability insurance for                farmers. Therefore, the number of crop and animal insurance contracts fell dramatically                (table 1).                   Table 1 Number of Insurance Contracts Before and After the System Transformation                                Insurance  of  Property            Crop              Livestock                       Years  farm buildings  insurance           insurance         insurance                               (thousands)       (thousands)      (thousands)       (thousands)                       1985  3 098               3 098            3 098             20 273                       1986  2 966               2 966            2 966             22 936                        1987  2 901               2 901            2 901             21 139                       1988  2 886               2 886            2 886             22 155                            1997  1 486               354              75                202                       1998  1 449               373              61                187                       2001  1 357               393              45                98                                                   Source: Rojewski 2012                 The period from 2006 to 2017                 Introduction in September 2005 of the act on subsidised crop and animal insurance under                the rule of which sales started in 2006, made it possible to initiate several government                intervention instruments  on  the  market,  such  as:  premium  subsidies (level  of  subsidy,                maximum tariff levels which entitle farmers to a subsidy) introduction of obligatory crop                insurance, a possibility to insure crops against one risk or a whole package of risks, defining                the subjective scope of subsidised insurance (type of crop), defining the entities eligible                for a subsidy, defining the maximum insurance amount, subsidies for insurance companies                in the case of excessive damage caused by drought and support for drought monitoring in                Poland.  All  of  these  actions  have  significantly  increased  the  scope  of  insurance  cover.                Initially, only 3% of all crops were covered by subsidised insurance, while eleven years                later it was as much as 31% (data from Ministry of Agriculture and Rural Development).                                                            212","Figure 1 Amount of Farmland Covered by Subsidised Crop Insurance in 2006-2017                                                                 Source: MARD and CSO data for the years 2006-2017                 Firstly, subsidies were offered with reference to premiums paid by the farmer for one policy                annually. Over the first three seasons, i.e. until 3 June 2007 subsidies amounted to 40%                for cereals, maize, rapeseed, turnip rape, potatoes, and sugar beets and 35% for potatoes                or sugar beets; next, until 2016 it was 50% for all insured crops, and from 2016 onward                65% for all crops. Subsidy was allocated on condition that the tariff did not exceed the                established limit. In this way a limit was set for the price which entitled the farmers to the                subsidy. It was connected with limiting the state budget spending as well as prevented                insurance companies from raising the prices because of the subsidies. In 2006 the upper                limit of tariff accounted for 3.5% of the insurance amount, and if it should be exceeded,                the subsidy was not granted. From April 2007 the limit was raised to 5% of the insurance                amount  for  certain  crops,  such  as  winter  rapeseed,  outdoor  grown  vegetables,  hops,                tobacco, fruit trees and shrubs, strawberries or legumes, while for other crops (cereals,                maize, spring rapeseed, turnip rape, potatoes or sugar beets) it remained at 3.5%. If a                farmer  exceeded  the maximum  tariff  by less  than  1  percentage  point, they  received  a                subsidy up to the maximum tariff, and if it was more than 6% of the insurance amount,                they received no subsidy at all.  This regulation was abolished in July 2015 for outdoor                vegetables and fruit trees and shrubs. In 2017 new maximum tariffs were introduced - 9%                of the insurance amount, and in the case of crops grown on poor quality soils 12% (class                5) or 15% (class 6). Initially, exceeding this limit caused the subsidy to be withdrawn. Four                months later, however (i.e. before the spring season began) the regulation was changed.                When the tariffs were exceeded, (calculated both for the whole package of ten possible                risks and for the collective tariffs for selected risks) subsidies were reduced according to                the percentage of tariff increase, with exclusion of tariffs for drought and winterkill. The                above rule was slightly modified for fruit trees, shrubs and strawberries, in the case of                which  exceeding  of  the  tariff  entailed  a  subsidy  according  to  maximum  rates.  The                correlation between the insured area and the average amount of subsidy (in percentage)                is moderately positive (r-pearson=0.57, p-value=0.025).                It is not statistically relevant (p-value >0.1) for cereals, rapeseed, fruit trees and shrubs,                tobacco, strawberries and hops. It is moderately positive for maize (r-pearson=0.62, p-                value=0.03),  legumes  (r-pearson=0.66,  p-value=0.03),  outdoor  vegetables  (r-                pearson=0.61,  p-value=0.04)  and  it  is  strong  and  positive  for  sugar  beets  (r-                pearson=0.75, p-value=0.01) The correlation between the insured area and the average                maximum  tariff  is  weak  and  positive  (r-pearson=0.40,  p-value=0.1).  It  is  statistically                relevant and rather positive for maize (r-pearson=0.52, p-value=0.06), sugar beets (r-                                                             213","pearson=0.65,  p-value=0.02),  legumes  (r-pearson=0.61,  p-value=0.04),  and  outdoor                vegetables (r-pearson=0.61, p-value=0.04).                 The value of annual premium subsidies and average subsidy per 1 ha can be seen in table                2. Although since 2011 premium subsidies have exceeded the planned annual limits, it was                only in 2016 that sales were reduced significantly due to lack of financing from the budget                (the subsidy pool dried out virtually a few days after the beginning of the autumn season                as a result of increase in tariffs after the experiences connected with 2015 winterkill). This                justifies the lack of correlation between the volume of planned subsidies and the insured                area  (p-value=0.15).  The  level  of  actual  subsidies  is  in  turn  moderately  positively                correlated with the size of the insured area (r-pearson=0.67, p-value=0.01). A statistically                relevant, positive and moderate correlation can be observed for cereals (r-pearson=0.48,                p-value=0.08),  maize  (r-pearson=0.64,  p-value=0.02),  rapeseed  (r-pearson=0.58,  p-                value=0.04),  legumes  (r-pearson=0.54,  p-value=0.07),  outdoor  vegetables  (r-                pearson=0.56, p-value=0.06), and a very strong one for sugar beets (r-pearson=0.75, p-                value=0.01).                           Table 2 State Budget Subsidy for Crop and Animal Insurance System                                                                             Subsidies  for  insurance                                                          Average                          Crop and animal insurance       subsidy  per  1   companies  for  excessive                          subsidies (millions of PLN)                       damage        caused       by                                                          ha (PLN)          drought (millions of PLN)                          actual          planned                           actual                planned                 2006     9,861           55               31,6            -               -                 2007     39,348          59,902           68,4            209,902         210                 2008     97,596          168,472          53,3            545             545                  2009     131,139         150              46,7            150             150                 2010     96,679          108,47           34,0            193,147         300                 2011     126,141         100              41,6            100             100                 2012     162,412         103,8            59,0            68,641          100                 2013     164,407         103,8            48,4            80              80                 2014     161,363         100,717          49,3            99,5            100                 2015     173,177         100,717          61,3            100             100                 2016     207,03          100,717          88,5            100             100                 2017     397,9           725              121,6           n.d.            100                                 Source: Budget implementation report for the years 2006-2017                 The connection between the volume of subsidies and the amount of insured arable land is                clear and dependent on the subsidy level.                Another  instrument  was  the  introduction  of  obligatory  insurance  for  farmers  who  had                obtained direct subsidies for arable land in the previous year. The obligation refers at least                to half of the area sown with crops which can be covered by state-subsidised insurance                and must apply to at least one of the five occurrences: drought, flood, hail, winterkill or                spring  frost.  Introduction  of  this  requirement  was  directly  enforced  by  the  European                Commission decree (1857/2009) which pointed at the connection between meeting the                insurance  requirement  and  the  possible  ad  hoc  assistance  offered  by  the  state.  This                instrument,  although  initially  intended  for  a  different  purpose,  appeared  to  be  very                effective in boosting popularity of crop insurance. Considering that there were no other                relevant changes in the regulations at that time, introduction of obligatory insurance can                be treated as the main cause of more than twofold growth in the amount of insured land.                It must be stated, though, that farmers fulfilled this new obligation by purchasing insurance                against the cheapest type of risk, namely hail.                                                             214","The element which exerts an immediate influence on the price of crop insurance and, in                presence of maximum tariffs, on accessibility and level of subsidies, is the possibility of                insuring  against  single  risks.  Initially,  subsidised  crop  insurance  encompassed  whole                packages  of  risks,  such  as:  hurricane,  flood,  torrential  rain,  hail,  lightning,  landslide,                avalanche, drought, winterkill and spring frost (at the very start, during the first season,                the list also included fire and explosion). In April 2007, however, a choice of a single risk                to be insured against was enabled. Also, several times during the term of the act being in                force definitions of some occurrences (drought, spring frost and winterkill, among others)                were altered. This certainly resulted in an increase in access to insurance, although one                has to remember that the 84%-growth in crop-insured area was also connected with the                rise in the level of premium subsidy.                A few times, especially at the start of the subsidy system, the range of crops which could                be subsidised, was broadened. Initially, subsidies were granted for insurance of cereals,                maize, rapeseed, turnip rape, potatoes, or sugar beets i.e. essential/basic crops. From the                beginning of 2007 subsidsed insurance could be used for hops, vegetables, fruit trees and                shrubs  and  in  April  2007  the  list  was  complemented  with  tobacco,  strawberries  and                legumes. Nevertheless, broadening the range of crops did not contribute to increasing the                amount of insured land. The six initial crops accounted for at least 95% of the insured area                throughout the analysed period. It must be said, however, that the structure of the insured                crops changed despite the stable structure of sown plants. For instance, in 2006 the area                of insured rapeseed made up 60% of the whole insured land, while in the years 2009-2017                30% on the average (MARD, 2006-2017). The drop in this crop insurance is particularly                visible in 2015-16, when the winter package tariffs grew significantly. Cereals, despite the                initial 39%, constituted an average 56% from 2009 to 2017.                Obtaining a crop insurance subsidy depends on assuming the insurance amount which does                not  exceed  the  maximum  insurance  amount  per  1  hectare  of  a  given  crop  which  is                regulated  annually  by  the  appropriate  minister  of  agriculture.  Maximum  insurance                amounts, which depend on the changes in market prices of a given crop (in the case of                fruit trees and shrubs it is connected with the increase in the value of plantings) were                introduced from 2008 and underwent changes in the analysed time period of up to 100%,                the average change referring to a given crop from 3% to 17% (respective decrees by the                MARD, 2007-2017). Their introduction was supposed to prevent overestimating insurance                amounts by insurance companies. Reaching this goal, however, affected adversely the size                of the insured area for cereals (r-pearson=-0.5, p-value=0.07) and maize (r-pearson=-                0.6,  p-value=0.03),  which  can  be  understood  in  the  context  of  subsidy  limits  set  for                insurance companies. Interestingly, an increase in maximum insurance amount turned out                to  be  very  strongly  correlated  for  legumes  (r-pearson=0.94,  p-value=0.00).  This  has                probably allowed in the recent years to adapt the insurance amount to the actual market                value of a given crop and thus has made this insurance more attractive.                The  state  has  also  defined  the  subjective  scope  of  subsidy  beneficiaries  –  initially  the                eligible farms were larger than 1 ha. However, in April 2007 this regulation changes and                subsidies were  available only up to 300 ha belonging to one agricultural producer. The                regulation was abolished in August 2008. The definition of an agricultural producer was                also  changed  several  times.  In  April  2007  the  subsidy  was  limited  to  entities  which                                                                                2                maintained the status of a small or medium-sized enterprise  which excluded from the                subsidy system a whole range of enterprises, including strategic ones (e.g. owned by the                Agency  of  Rural  Property).  Finally,  after  negotiations  with  the  European  Commission,                starting from July 2015 larger farms were also admitted into the system, after they had                met certain requirements. The impact of the above changes on the amount of the insured                area is difficult to assess due to the lack of available data on the structure of the insured                entities.                   2  As understood in the attachment I to the Commission decree (EU) 70/2001 of 12 January 2001 r. on application                of art. 87 and 88 EU Treaty with reference to government aid for SME (Dz.Urz. WE L 10 z 13.01.2001, p. 33,                with amendments.; Dz.Urz. UE Polskie wydanie specjalne, ch. 8, vol. 2, p. 141).                                                            215","We also have to remember that the state can affect the supply side of the market. The                only insurance  companies  which  are  allowed  to  offer  subsdised insurance  are  the  ones                which have signed an agreement with the appropriate minister of agriculture and who meet                statutory requirements. In reality, this tool did not serve as a means to limit the number                of entities. When subsidised insurance was introduced onto the Polish market, there were                only three insurance companies offering crop insurance and they all were admitted into the                system.  In  the  2011/2012  season  another  insurer  offered  crop  insurance,  but  it  soon                abandoned the market. In 2017 the number of companies grew to five entities. The small                number  of  insurance  companies  involved  constitutes  a  serious  problem  in  subsidised                insurance development.                 Another instrument used by the state is a specific subsidy for insurance companies in the                case of excessive damage caused by drought (table 1). The subsidy amounts to 60% of                the difference between the total sum of compensation paid within a single calendar year                for  damage  caused  by  drought  and  the  amount  which  accounts  for  90%  of  the  total                premiums paid in a single calendar year with respect to compulsory or subsidised crop                insurance contracts (calculated in total with the subsidy). The solution, which was intended                to encourage insurance companies to cover the risk of drought after risk “packages” had                been abolished, came to nothing. In fact, there is no data referring to the amount of land                insured against drought, but it may suffice to look at the figure concerning the number of                signed insurance policies which include this risk. It decreased systematically from over                13.5 K in 2006 to a meagre 90 contracts  in 2015 (the most recent available data from the                Ministry of Agriculture and Rural Development, justification of the bill, 2015).                4 Conclusions                 Intervention  instruments  applied  on  the  agricultural  insurance  market  come  in  a  large                variety: premium subsidies (level of subsidy, maximum tariff levels which entitle farmers                to a subsidy) obligatoriness of crop insurance, a possibility to insure crops against a single                risk or a whole package of risks, defining the objective scope of subsidised insurance (type                of  crop),  defining  the  entities  eligible  for  a  subsidy,  defining  the  maximum  insurance                amount, subsidies for insurance companies in the case of excessive damage caused by                drought and support for drought monitoring in Poland.                Until 1989 the only explicitly used instrument was obligatory insurance. Nonetheless, we                must not forget that the whole insurance system at that time was financed, managed and                controlled by the state. Obligation was reintroduced in 2008, after approximately 15 years,                under completely different market conditions. This clearly affected the amount of insured                land, albeit well below the expected levels. This is due to the relatively weak sanctions                which usually were not executed.                After the introduction of premium subsidies, i.e. from 2006, a systematic growth in the                amount of insured land could be seen, although the correlation between these figures is                rather moderate (r-pearson=0.67, p-value=0.01). Importance of the subsidy level in the                context of increasing the amount of insured land was contingent on the type of crop. It                mostly affected insurance of sugar beet sowings, and, to a slightly smaller extent, maize,                legumes and outdoor vegetables. For the same crops, raising the upper premium limits,                which were not exceeded, also had a stimulating effect.                 The possibility of insuring crops against a single risk (abolition of risk packages) was at                least partly responsible for the 84%-growth of insured area in 2007. Unfortunately, precise                assessment of this influence is impossible due to lack of data. For the same reasons, it is                also difficult to calculate the implications of admission of large enterprises into the system                of  subsidies.  However,  it  is  possible  to  state  that  increasing  the  objective  scope  of                subsidised insurance (the types of insurable crops) resulted in only a slight increase in the                amount of insured sowings.                The maximum level of insurance amounts, regulated by the government was relevant to                the amount of insured cereal, maize, and legume sown land; in the first two cases it had                a de-stimulating effect, while in the latter one it increased the insurance levels. One may                                                             216","presume, however, that the negative correlation in the case of cereals and maize may                become more feeble in the near future because the premium subsidy levels have been                raised  a  few  times;  hence,  accepting  larger  insurance  amounts  will  not  have  to  imply                limiting the amount of insured land.                The specific subsidy for insurance companies which has been aimed at covering excessive                costs of drought impact, and thus was to encourage insurance companies to offer cover                for this risk turned out to be a complete failure.                The  above  analysis  of  the  efficiency  of  government  intervention  instruments  on  the                insurance market should be extended – as the next step – to encompass the effectiveness                analysis. Such an analysis would look at the aspects (effectiveness for whom?), and would                call for identification of the purpose of using certain instruments (what for?). One of such                aims is, as one can suppose, the problem analyzed here, i.e. the extent of crop insurance                which could achieve an assumed level (how high and why this high?). Such considerations                on  effectiveness  would  also  have  to  refer  to  the fact  that  application  of the presented                instruments may lead to (and indeed it does) a different impact than the intended one and                in  various  areas  (sowings  structure,  environmental  impact,  capacity  to  reestablish                production potential etc.).                 References                Central  Statistic  Office  (2006-2017).  Rocznik  statystyczny  rolnictwa.  Retrieved  from:                http://stat.gov.pl/obszary-tematyczne/roczniki-statystyczne/roczniki-statystyczne/                rocznik-statystyczny-rolnictwa-2017,6,11.html.                Coble,  K.  H.,  T.  O.  Knight, R.  D.  Pope,  Williams,  J. R.  (1997).  An  Expected-Indemnity                Approach  to  the  Measurement  of  Moral  Hazard in  Crop  Insurance.  American  Journal  of                Agricultural Economics, vol. 79(1), pp. 216–26.                 Enjolras, G., Capitanio, F., Adinolfi, F. (2012). The Demand for Crop Insurance: Combined                Approaches for France and Italy. Agricultural Economics Review 13(1), pp. 5-22.                Enjolras, G, Sentis, P. (2011). Crop Insurance Policies and Purchases in France. Agricultural                Economics, vol. 42, pp. 475–486.                Goodwin, B. K. (2001). Problems with Market Insurance in Agriculture. American Journal                of Agricultural Economics, vol. 83(3), pp. 643–649.                Goodwin, B. K, Smith, V. (2013). What Harm Is Done by Subsidizing Crop Insurance?.                American Journal of Agricultural Economics, vol. 95(2), pp. 489–497.                Just, R. E, Calvin, L., Quiggin, J. (1999). Adverse Selection in Crop Insurance: Actuarial                and Asymmetric Information Incentives. American Journal of Agricultural Economics, vol.                81(4), pp. 834–49.                Kaczała, M., Wiśniewska, D. (2015). Risks in the Farms in Poland and Their Financing  –                Research Findings. Research Papers of Wrocław University of Economics (Prace Naukowe                Uniwersytetu Ekonomicznego we Wrocławiu), (381), pp. 98–114.                Kleindorfer, P. R., Klein, R. W. (2003). Regulation and Markets for Catastrophe Insurance.                Advances in Economic Design, Berlin, Heidelberg: Springer, pp. 263-279.                Mahul, O., Stutley, Ch. (2010). Government Support to Agricultural Insurance: Challenges                and Options for Developing Countries. Washington, DC: World Bank Publications.                MARD, the Ministry of Agriculture and Rural Development - data regarding crop insurance.                Ministry of Finance (2006-2017). Budget implementation report for the years 2006-2017.                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Secretariat (UNCTAD/SDD/INS/1/Rev.1), United Nations publication.                                                                                                       218","Structural Evaluation of Securities’ Rates of Return in Selected                              Business Sectors on New York Stock Exchange                                                                                     2                                                          1                                      Marcin Kalinowski , Grzegorz Krzykowski                                                 1  WSB University in Gdansk                                     Finance and Management Faculty, Finance Department                                          Grunwaldzka 238A, 80-180 Gdansk, Poland                                               E-mail: mkalinowski@wsb.gda.pl                                                  2  WSB University in Gdansk                                     Finance and Management Faculty, Finance Department                                          Grunwaldzka 238A, 80-180 Gdansk, Poland                                               E-mail: gkrzykowski@wsb.gda.pl                 Abstract:  The  share  prices  behaviour  on  the  financial  market  is  subject  of  numerous                researches. Technical and fundamental analyses as well as theory of behavioural finance                are attempting to demonstrate certain patterns in the share price behaviour of enterprises                quoted  on  the  stock  exchange.  The  aim  of  this  work  is  to  evaluate  the  behavioural                heterogeneity  of  rates  of  return  on  shares  in  selected  sectors  in  the  American  market                (NYSE). The research hypothesis of this work is the claim that share price behaviour, and                rates  of  return  on  shares  are  sectors  specific  rather than  global  share market  specific.                Therefore, it is vital that we should conduct the analysis of the homogeneity of rates of                return  on  shares  of  enterprises  from  diverse  industries.  The  research  is  based  on  an                enterprises representing four selected sectors from the American market (energy, trade,                finance, health) has been instrumental in pursuing that goal. Detailed analyses have been                conducted  with  the  use  of  data  mining  methodology.  The  work  has  demonstrated  the                existence of behavioural heterogeneity of rates of return on shares in selected industries.                The future research in this domain ought to concentrate on conducting a thorough analysis                of homogeneity structure in selected industries so as to determine some regularities in the                behaviour share prices in the particular industries.).                Keywords: rate of return, market behaviour, stock market, NYSE                 JEL codes: G100, G140, G150, G170, G190                 1 Introduction                The behaviour of share prices on the market is subject to continual research. Technical and                fundamental  analyses  as  well  as  theory  of  behavioural  finance  are  attempting  to                demonstrate certain patterns in the share price behaviour of enterprises quoted on the                stock exchange.                Diverse models of functioning of the market endowed with a forecast potential are the                outcome of such analyses.                 The aim of this work is to evaluate the behavioural heterogeneity of  rates of return on                shares in selected sectors (industries) in the American market (NYSE).                The research hypothesis of this work is the claim that share price behaviour, and rates of                return on shares are industry specific rather than global share market specific. Therefore,                it is vital that we should conduct the analysis of the homogeneity of rates of return on                shares of enterprises from diverse industries.                The  research  into  enterprises  representing  four  selected  industries  from  the  American                market  (energy,  trade,  finance,  health)  has  been  instrumental  in  pursuing  that  goal.                Synthetic  price indexes  for  particular  sectors  and  representative  enterprises  have  been                created so as to carry out reliable research. Detailed analyses have been conducted with                the use of data mining methodology.                 The problem of functioning of financial markets is the subject of continuous research in                economic  sciences.  Numerous  theoretical  and  empirical  studies  tend  to  explain  the                                                             219","operation mechanisms on the markets, their place in the global market, the determinants                of development at national and international level. Numerous theoretical models describing                the functioning of financial markets are formulated. A special place in economics occupies                classical financial theory, which assumes the rationality of investors in making investment                decisions.  Investors  -  according  to  this  theory  -  make  decisions  that  maximize  their                benefits, provided the information is properly interpreted.                 Investors'  investment  decisions  are  inseparable  connected  with  exposure  to  risk.                Determining investor preferences and evaluating investment options are the starting point                for any traditional equity valuation model.                The risk problem occurs in portfolio theory. It has become one of the basic theories of                making rational investment decisions in the stock market under uncertainty. The decisions                are made there due to two criteria: income and risk. H. Markowitz, Nobel Laureate is the                creator  of  classic  portfolio  theory.  He  presented  his  concept  for  the  first  time  in  1952                (Markowitz, 1952). The market where investors use Markowitz's portfolio theory as a tool                for selecting an investment is described by the Capital Asset Pricing Model (CAPM). It is a                continuation  of  Markovitz's  theory.  The  CAPM  model  can  be  considered  as  a  valuation                model as it determines what is the level of return rate on the sustainable market. The                CAPM theory was created in the mid-1960s. Its creators were: W. Sharpe (Sharpe, 1964),                J. Lintner (Lintner, 1965), J. Mossin (Mossin, 1966) and J. Treynor (Treynor,  1961). W.                Sharpe, for the CAPM was awarded the Nobel Prize in Economics in 1990. The Capital Asset                Pricing Model was repeatedly criticized because it is impossible to identify in reality the                market portfolio underlying the CAPM model (Fama, French, 1992), (Fama, French, 1996),                (Roll, 1973). As a consequence, CAPM modifications and new pricing models for financial                instruments using different valuation principles were introduced in subsequent years. An                example of a capital market model that differs from the CAPM model is the Arbitrage Pricing                Theory (APT). This pricing model was announced in 1976 by S. Ross (Ross, 1976). The                APT model assumes the existence of law of one price on the financial markets and thus the                functioning of the arbitration mechanism.                 The different reasons for price changes on the stock market is described in the financial                markets information efficiency theory. The determinant of investor behavior in the financial                market is, according to this theory, information. The beginning of efficient market theory                can  be  seen  in  1965,  when  P.  Samuelson  argued  that  stock  price  volatility  was                unpredictable when all market participants had full access to information and where the                information was reflected in prices (Samuelson, 1965). However, the creator of the market                efficiency  theory in the  modern form is  E.  Fama.  According  to this  author,  an  efficient                market is a market where prices always fully reflect the information available (   (Fama,                1970). Market information efficiency is an important element in evaluating these markets.                It allows, by analyzing price behaviour, to evaluate the behaviour of investors in stock                markets. It also makes it possible to assess the adequacy of valuation of shares on the                market based on fundamental data.                 2 Methodology and Data                 The subject of the analysis are daily share prices at closing in the group of companies of                the industry under consideration. Four sectors have been selected for the analysis: energy,                trade,  financial  and  health.  Four  companies  have  been  selected in  every industry.  The                energy  industry  is  represented  by  Exxon  Mobil  Corporation  (XOM),  General  Electric                Company (GE), Chevron Corporation (CVX), Petro China Company Limited (PTR). From the                trade  industry  Anheuser-Busch  Inber  (BUD),  Coca-Cola  Company  (KO),  Pepsico  (PEP),                Phillip Morris International Inc. (PM), has been selected. Financial industry is represented                by  JP  Morgan  Chase  \&  Co.  (JPM),  Wells  Fargo  \&  Company  (WFC),  Bank  of  America                Corporation (BAC), HSBC Holdings (HSBC) and the health industry by Johnson \& Johnson                (JNJ), Pfizer Inc. (PFE), Novartis (NVS), Merck \& Company (MRK). The data comes from                NYSE in the period from 2005-02-25 to 2016-11-02. The quotes for the two companies                commenced at a later period. PM is quoted from 2008-03-17 and BUD from 2009-07-01.                Within this period, 2944 of daily quotes were obtained for the 16 analyzed companies. As                                                             220","share prices are too diversified in terms of nominal value, the research has been based on                return rates expressed in per cent                                             +1  −                                                         (1)                             +1  =              100,      = 0,1, … ,2943.                                                +1                The construction of the scaled return rate within the group of companies in the                industry                The  scaled  return  rate  for  the  industry  is  based  on  the  return  rates  for  the  selected                companies within the industry and has the form of a weighted value of the rates. For the                needs of this article this indicator will be called industry index (I2). The analytical form of                the indicator of the daily combined return rate for the industry (I2) is as follows:                                                    ∑                                                                                 (2)                               (  )                                                         =0                where    is the number of companies in the industry,      is the return rate for the index                                                                         (  )                   (   = 1, … ,   ) at the specific day and the coefficients α are nonnegative and sum to unity. In                case of the analyzed industries the number of companies in every industry amounts to                                                                                           1                four, thus always     =  4. If for example we assume equal weights (i.e.    =   for    = 1,2,3,4),                                                                                                                                                                                     4                than the weighted index will be the mean value of return rates for the industry. The weights                may be selected arbitrarily but usually are conditioned by the importance of the industry                itself and additionally by the structure delineating the significance of each and every of the                companies for the industry. The industry index, which is one-dimension transformation of                the return rates in the industry, should be independent of the impact of those factors and                take into consideration solely the internal structure of the return rates. In the analyses                presented, the index construction for the industry should consider the long observation                period  and  should  be  comparable  across  the  industries.  The  proposed  procedure  of                construction  for  the  scaled  return  rate  is  based  on  the  assumption  that  the  distance                measure between the daily return rates for the companies in the industry fits their one-                dimensional counterparts.                It  is  assumed  that  each  daily  observations  of  return  rates  in  the  industry  is  the                    dimensional vector. The data is presented in the geometrical form as a cloud of 2943 points                in the four dimensional Euclidean space. At the subsequent stage, the measure of distance                between the points in ℝ  is given by the following formula (Euclidean distance)                                                                                                                           2                                          (  )                             ,    = √ ∑(   (  ) −    )  ,                                                 (3)                                                                                                                     =1                                                                                                     (  )                where the indicators   ,     = 1, … ,2943 are the indicators of the daily observations and     and                                                                                                                        (  )                    are the empirical return rates of the     index in the days of    and    indexes. In the                                                                                                                                            procedure of multidimensional scaling, we strive to find the linear transformation   : ℝ  →                 ℝ  meeting the optimality conditions of differences in distance between the points in ℝ                                                                                                                              1                and ℝ . The daily return rates for the    index day for the entire industry will be approached                      1                                                                                  as a vector and the marking     = (   (1) , … ,    (  ) ) , will be adopted where the operation \"  \"                                                                                                                                                                                         means  the transposition  of the vector.  The linear  transformation      yields  the vector  of                return rates     into the number      ⃘    , where the operation \" ⃘\" signifies the scalar product                                                                                                                                                           of  vectors.  Thus,  the  name  of  the  transformation  has  been  identified  with  the  vector                determining this transformation. Additionally, let us introduce the symbol for the distance                of linear transformation    for the return rate vectors in the days of the    and    indexes                                                                                                          (4)                                                                                                           ̂  = |     ⃘    −      ⃘    |.                                                                                                                  ,                                                              221","Formally the values     are distances between return rates in the industry on different days                                       ,                       ̂                and     are distances between return rate in one-dimensional sub-space of ℝ . The idea of                                                                                                                        ,                  multi-dimensional scaling consists in finding such linear transformation    so that the value                                                                         2                                          ̂ 2                           = ∑  ∑(     ,    −    )                                             ,                                 =1    =1                                                                   (5)                is the minimal. This means selection of an appropriate transformation (coefficient vector                   for    = 1,2,3,4) so that the distances between return rates in the industry on different days                could  possibly  be  precisely  approximated  by  one-dimensional  distances.  The  vector                determining  optimizing  linear  transformation  is  obtained  from  the  empirical  covariance                                                             (  )                matrix  of  return  rates  for  the  companies     , = 1, … ,2943)  of  indexes     = 1, … ,   ,  which  is                                                                               marked ∑. The covariance matrix ∑  (of the p×    dimension) has the real and non-negative                eigenvalues. Then making use of spectral decomposition of the matrix we discover that                one  -  dimensional  sub-space  determining  the  scaled  return  rates  is  generated  by  the                eigenvector of the maximal. Thus                                                                                        ̂ 2                          min       { ∑  ∑(   2  −    )} =     ,                                            ,                                                    ,                                                          1                              ∈ℝ                                                                          (6)                                    =1    =1                where vector     is the eigenvector of the covariance matrix ∑  that is ∑     =     where     is                                                                                         1                                                                                              1                               1                                                                                                        1                the maximal eigenvalue of the covariance matrix ∑. Additionally, the eigenvector has been                selected in such a way so that it is normalized to unity and the project vector of the return                rates on one-dimensional space generated on this vector has the positive covariance with                each and every vector of empirical return rates for the companies within the industry.                Finally,  the  vector          ⃘   1   (   = 1, … ,2943)is  the  vector  of  the  scaled  return  rates (industry                indexes). Let us observe that the index form is given by formula (1) where weight vector                     for     = 1,2,3,4  is     ,  vector,  that  is  the  eigenvector  of  the  covariance  matrix                                       1                                   ∑  corresponding the maximal eigenvalue. The construction of the industry index is based                on the vector of return rate observations of the companies within the industry and generally                the index values change as raw data changes for example by the increase in the amount                of data.                The multidimensional scaling procedure is well known and frequently applied, but for tasks                of a different nature. It is one of the basic procedures comprising data mining methods                and  is  aimed  at  dimensionality  reduction  or  cluster  analysis.  Technically,  the                multidimensional  scaling  procedure  is  analogical  to  Hotelling’s  Principal  Component                Analysis  (1934).  Proofs  of  the  quoted  values  can  be  found  in  numerous  monographs                (Krzanowski, 2000), (Krzanowski, Marriott, 2010), (Mardia, Kent, Bibby, 1979), (Ripley,                1996).                 3 Results and Discussion                Considering  the industry indexes in the  entire  analysed  period  of  observation  we  have                noticed a minute dispersion level of the industry indexes (see table 1).                                  Table 1 Descriptive Statistics of the Industry Indexes                 Industry       N       Mean     SD       Min      Q05      Q50        Q95        Max                 index                 Energy         2939    0.060    3.202    -23.939   -4.855   0.108     4.561      24.887                 Trade          1848    0.117    1.827    -9.030   -2.883   0.128      3.096      10.352                 Finance        2942    0.076    5.003    -45.336   -6.397   0.037     6.211      51.369                  Health         2942    0.058    2.180    -15.747   -3.276   0.089     3.303      21.853                                                    Source: own research                                                               222","The calculations indicate that regardless of the differences in the maximal values of indexes                their extreme quantiles (order 5% and 95%) do not exceed 6,5%.                 The aim of the research analysis is recreation of changes of the industry indexes in the                observation period. It means the researchers are striving to find moments in time in which                behavioural changes in the industry indexes are conspicuous.                The regression analysis is the first analysis aimed at evaluating return rates’ behaviour. It                has been observed that the regression line is a straight line almost identical to the abscissa                in each  of  the industries (equation     = 0). As  shown in table  2  the  slope  and intercept                coefficients in all industries are insignificantly different than nought. However, quantiles of                5% and 95% order for residuals do not exceed 2%.                              Table 2 Statistical Characteristics of Industry Regression Lines                   Industry        Min      Q05      Q50       Q95       Max      Slope        Intercept                  index                  Energy          -24.02   -1.47    0.06      1.54      24.81    0.473        -0.000028                  Trade           -9.18    -1.08    0.01      1.12      10.18    1.025        -0.000058                   Finance         -45.40   -1.62    -0.04     1.47      51.30    -0.171       0.000016                  Health          -15.79   -1.02    0.02      1.08      21.81    -0.271       0.000022                                                    Source: own research                 Applying simple regression analysis (CDA - confirmatory data analysis) it may seem that                the industry indexes are insignificant. It is not the case, however, and indexes are endowed                with structural changeability resulting from fluctuations in stock market. This changeability                is  relatively  minor  in  comparison  with  the  aggregate  of  observation  containing                approximately  2.5   .  pieces  of information. Analytical tools  which  hinge  on  parametrical                procedures are not precise enough to detect minute dependencies.                The detailed industry index analysis is to be carried out with the application of the basic                methods belonging to the set of exploratory data analysis (EDA) tools, frequently referred                to as data mining. Tree-base method adopted for our research appear in the literature on                the subject matter. First formal algorithms were presented in 1963 (Chambers, Hastie,                1993).  Main  outcomes  are  to  be  found  in  Brieman’s  monographs  of  1984  (Breiman,                Friedman, Stone, Olshen, 1984).                 The method of recreating the structure of changes in the industry index is, in theory, based                on the so called quantization process. At the first stage, we pinpoint a moment (date)                separating observations into an earlier and a later one in relation to a given date. A moment                in time is selected in a way which enables minimising the sum of variance of observations                in two analysed aggregates. The same mode of operations is performed at subsequent                stages.  The  process  may  be  conducted  until  final  single  measurements  are  obtained.                However, the image of results obtained would be illegible. For this reason it is imperative                that we should introduce a selection of parameters allowing for termination of the process.                Two conditions have to be adopted in the applied procedure. It has been assumed that                each  group  at  each  stage  of  division  includes  at  least  seven  days  of  observations.                Additionally, it has been agreed that 180-day periods are indivisible. The standard division-                stopping procedure based on the relative variance in division related to the variance before                and after division has not been applied.                Please consider the following point graphs of variability in the industry indexes. Average                index values in selected time periods have been attached to the industry indexes.                                                                 223","Figure 1 Changeability of the Energy Industry Index (EI2)                                                                      Source: own research                                Figure 2 Changeability of the Trade Industry Index (TI2)                                                                      Source: own research                                 Figure 3 Changeability of the Finance Industry Index (FI2)                                                                      Source: own research                                                                       224","Figure 4 Changeability of the Health Industry Index (HI2)                                                                      Source: own research                     Figure 5 The Structure of Behavioural Homogeneity of Return Rates' Indexes in                                                  Researched Industries                                                                             Source: own research                 Figure 5 presents the structure of changes in industry indexes (I2) in the entire observation                period. The conducted research leads to the conclusion that the changeability structure of                industry  indexes  is  heterogeneous  in  the  time  in  the  analysed  industries.  The  energy                industry is first to manifest the industry index instability. The middle ground belongs to the                health  and  finance  industries.  Evidently,  the  trade  industry  is  last  to  demonstrate  the                instability phenomenon. The final comparative analysis of instability requires rescaling of                changes in industry indexes to achieve the same scale. The detailed pictures of changes in                industry indexes is presented in figures from 1 to 4.                 4 Conclusions                This article attempted to evaluate the behavioural homogeneity of return rates on shares                based  on  American  stock  exchange  (NYSE)  and  taking  into  account  divers  industries                represented particular enterprises.                The research carried out proves that:                 1.  There  appears  to  be  behavioural  homogeneity  of  industry  indexes  (at  the  level  of                regression analysis).                2.  Behavioural  heterogeneity  in  selected  industries  has  been  discovered  owing  to  the                application of the analysis incorporating data mining tools.                                                             225","3. The energy industry has been first to reveal behavioural instability. Subsequently, it has                appeared in the health, finance and trade industries.                 4. Despite the lengthy research period the instability of industry indexes appeared at a                similar time in all industries (2007-2010). Please note that this time period overlaps with                the global recession in financial markets.                5. In addition it is worth noticing that behavioural instability of trade and health industries                manifested itself at the end of the research time period (IX-X 2016). It justifies an increase                in risk related to the next phase of crisis in the financial market. It may be correlated with                the global political situation.                 The work has demonstrated the existence of behavioural heterogeneity of rates of return                on shares in selected industries. The future research in this domain ought to concentrate                on conducting a thorough analysis of homogeneity structure in selected industries so as to                determine some regularities in the behaviour share prices in the particular industries.).                 References                 Breiman,  L.,  Friedman,  J.,  Stone  Charles,  J.,  Olshen,  R.  A.  (1984).  Classifcation  and                Regression Trees. Florence: Wadsworth.                 Chambers, J. M., Hastie, T. J. (1993). Statistical Models in S. London: Chapman \& Hall.                Fama, E. F. (1970). Effcient capital markets: A review of theory and empirical work. Journal                of Finance, vol. 25(2), pp. 383-417.                 Fama, E. F., French, K. R. (1992). The cross-section of expected stock returns. Journal of                Finance, vol. 47(2), pp. 427-465.                Fama, E. F., French, K. R. (1996). The capm is wanted, dead or alive. Journal of Finance,                vol. 5(5), pp. 1947-1958.                Krzanowski, W. (2000). Principles of Multivariate Analysis: A User's Perspective. New York:                Oxford University Press.                Krzanowski, W., Marriott, F. H. C. (2010). Multivariate Analysis v. 1 (Kendall's Library of                Statistics). New York: John Wiley \& Sons.                Lintner, J. (1965). The valuation of risk assets and the selection of risky investments in                stock portfolios and capital budgets. Review of Economics and Statistics, vol. 47(1), pp. 13-                37.                Mardia, K. V., Kent, J. T., Bibby, J. M. (1979).  Multivariate analysis. London‐New York‐                Toronto‐Sydney‐San Francisco: Academic Press.                Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, vol. 7(1), pp. 77-91.                Mossin, J. (1966) Equilibrium in a capital asset market. Econometrica, vol. 34(4), pp. 768-                783.                Ripley,  B.  D.  (1996).  Pattern  Recognition  and  Neural  Networks.  New  York:  Cambridge                University Press.                 Roll, R. (1973). A critique of the asset pricing theory’s tests: Part. 1: On past and potential                testability of the theory. Journal of Financial Economics, vol. 4(2), pp. 129-176.                Ross, S. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory,                vol. 13, pp. 341-360.                Samuelson, P. (1965). Proof, that properly anticipated prices fluctuate randomly. Industrial                Management Review, vol. 6(2), pp. 41-49.                Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions                of risk. Journal of Finance, vol. 19(3), pp. 425-442.                Treynor, J. L. (1961). Market Value, Time and Risk (Unpublished Manuscript).                                                             226","Stability Prerequisites of the Czech Republic Banking System                                            (Cybernetic Point of View)                                                    František Kalouda                                                   MASARYK UNIVERSITY                                Faculty of Economics and Administration, Department of Finance                                          Lipová 41a, 602 00 BRNO, Czech Republic                                                 E-mail: email@university.cz                 Abstract: The paper is devoted to further description and analysis the selected stability                aspects of the Czech banking system (as a cybernetic system), with special focus to the                influence of the managed or controlled system (commercial banks).The objective of the                paper is to clarify the implications of the parameters of the regulator (Czech central bank)                to behavior the managed/controlled system (Czech commercial banks). The paper draws                on data published by the Czech National Bank (CNB, http://www.cnb.cz) as well as on data                from  literature  and  from  previous  studies  of  the  author.Methodology  of  the  paper  is                principally based on the economic cybernetics utilization in terms of study the stability of                the Czech banking system as a cybernetic one. The usual description, literary research,                comparison  and  analytic-synthetic  methods  are  used  here  as  well.The  main  expected                results of the paper relate to the linkages between stability of the Czech banking system                as  a  whole  and  behavioral  parameters  (characteristics)  of  the  controlled  system                (commercial  banks)  -  still  not  explored.  Conclusions  of  the  paper  in  a  significant  way                redefine the options of the central bank in general (this means not only Czech central bank)                concerning the stability control of the banking system.                Key words: banking system, controller, stability, controlled system                JEL codes: C62, D22, E43, G21, G38                 1 Introduction                 The stability of the cybernetic system in general is result of interaction of the controlled                system/plant and controller.                Their standard interconection in form of the simplest block diagram is a closed-loop (or                feedback) control system with negative feedback (see Figure 1).                In  our  case  we  are  specifically  monitoring  stability  of  the  Czech  banking  system.  The                controlled  system  (plant)  is  system  of  Czech  commercial  banks  (hereinafter  Czech                commercial  banks).  As  the  controller  performs  Czech  National  Bank  (hereinafter  CNB),                Czech Central Bank.                We examine the stability of the Czech bankin system through relationships between the                indicators (or variables) discount rate and commercial rate.  The reason for choosing these                indicators  is  the  CNB´s  view  that  commercial  rate  can  be  managed  (with  sufficient                precision) by a discount rate.                The formal independence of the CNB leads to significant modification of the standard block                diagram of a closed-loop or feedback control system (see Figure 2). The feedback control                system is preserved indeed but its ability of target behaviour is in the absence of command                variable w(t) - and implicitly also variable e(t) error - at least disputable.                 The other consequences of the CNB´s independence are here ignored.                                                                   227","Figure 1 Block Diagram of a Closed-loop (feedback) Control System – Classic Form                                                                                        vx(t)                                    e(t)                                                                u(t)                  w(t)                                                                             controlled                              SP                  controller                 system                                                                             (plant)                                  - y(t)                   Legend:                w(t) ……… command variable                e(t) …….…error                u(t) ……… manipulated variable                v x (t) …… disturbance variables                y(t) ……… controlled variable                SP  ……… summing point (e(t) = w(t) – y(t))                    Source: Adapted from Švarc et al. (2011, pp. 38, 341), Stefani, R.T. et al (2002, p. 75)                   Figure 2 Block Diagram of a Closed-loop (feedback) Control System – Consequence of                                                    CNB Independence                                                                                   vx(t)                              e(t)                                                           u(t)                                                                       commercial                        SP                     CNB                    banks                                           (controller)               (plant)                               - y(t)                    Legend:                w(t) …..command variable                            – unknown/missing signal/information, it cannot be                                                                                     interpreted                e(t) ….…error                                               -                         - „ -                u(t) ….…manipulated variable                v x (t)…. disturbance variables                y(t) ……controlled variable                SP ….….summing point (e(t)=w(t)–y(t))        - unknown component, it cannot be defined                Source:  Own construction of the author based on Švarc et al. (2011, pp. 38, 341), Stefani, R.T. et                                                      al. (2002, p. 75)                 The past results of identifying the parameters of commercial bank system (Balátě (2004),                Fikar, Mikleš (1999), Kubík et al (1982), Švarc (2003) and Švarc et al (2011)) are relatively                convincing.  The  dynamic  behaviour  of  this  system  is  analyzed  using  the  step  function                response  of  the  banking  system  h(t).  The  step  function  (or  transient)  response  of                commercial bank system h(t), as it has been analyzed to date ((Kalouda (2014a), (2014b),                (2014c), (2015)), leads us to the partial conclusion that (from a dynamic point of view)                system of commercial banks can be considered as proportional (static) system element.                                                               228","Nevertheless, the available data does not rule out a tendency of this system to oscillations                with a potential risk of destabilizing the system ((Kalouda (2014a), (2014b)).                 It is clear that the risks of the system oscillating into an unstable regime is too large to be                ignored. The step function response of the Czech banking system in its different periods                are, after all, not identical to each other (Kalouda, 2015), which evokes a risk that the                stability of the system may be lost.                 This is  why this  paper is  devoted to  examining the  dynamical  behavior  of  the  banking                system in the Czech Republic with special focus to its stability. In this context the objective                of the paper is to clarify the influence of controller´s (CNB) parameters on behaviour of                the controlled system (Czech commercial banks, plant).                 2 Methodology, Data and Model Specification                 The limited extend of the paper makes impossible to provide a detailed presentation of all                methodological procedures that led to the results presented below. Therefore, we will limit                ourselves to the brief overview of the most important topics discussed, in connection with                the relevant sources.                Methodology                 The key importance of the methodological input for this paper results first of all from the                interdisciplinary nature of the issues studied. On the other hand they are also relevant in                this  context  the  researches  previously  carried  out  and  the  publications  of  the  results                achieved, to which this paper is closely tied.                The methodical basis of the paper are the usual methodological procedures:                       ●    description,                     ●    analysis,                      ●   comparison, and                      ●   synthesis.                In addition, however, the specific topic and the objective of the paper virtually forced the                application  of  methodological  tools  which  have  been  less  frequently  used  in  the  given                context. These include, in particular:                       ●     technical cybernetics, explaining the principles of automatic control in the form of                feedback  systems  represented  by  Balátě  (2004),  Fikar  and  Mikleš  (1999),  Houpis  and                Sheldon (2014), Kubík et al. (1982), Švarc (2003), Švarc et al. (2011),                      ●    economic cybernetics, transforming the methodology of technical cybernetics into                the environment of economic systems, as discussed by Allen (1971), Šerý (2010), Kalouda                and Svítil (2009), and Kisačanin and Agarval (2001) and                     ●  financial business management in the form of applications of its access to economic                cybernetics, as illustrated by Kalouda (2015, 2016).                Data                 This paper is primarily based on freely available data published by the Czech National Bank                (CNB)  at  http://www.cnb.cz/cs/financni_trhy/penezni_trh/pribor/rok_form.jsp,  and  at                http://www.cnb.cz/cnb/STAT.ARADY_PKG.STROM_DRILL?p_strid=0\&p_lang=CS,                    to                which we link here. This data is taken from the period 31/01/2004 until 30/09/2013. The                values  of  the  variable  discount  rate  and  commercial  rate  are  monitored  (see  Figure  3                Commercial rate = f (discount rate)).                                                                    229","Figure 3 Commercial Rate = f (discount rate)                       6                                                                              discount rate  commercial rate                      5                      v                      4                     a                     l                     u                     e                      o  3                     f                      r                     a                     t  2                     e                      1                       0                         1.1.2004  1.5.2004  1.9.2004  1.1.2005  1.5.2005  1.9.2005  1.1.2006  1.5.2006  1.9.2006  1.1.2007  1.5.2007  1.9.2007  1.1.2008  1.5.2008  1.9.2008  1.1.2009  1.5.2009  1.9.2009  1.1.2010  1.5.2010  1.9.2010  1.1.2011  1.5.2011  1.9.2011  1.1.2012  1.5.2012  1.9.2012  1.1.2013  1.5.2013  1.9.2013                                                                 time                    Source: Prepared by the author using data from ARAD - Time series system - CNB. on-line]                 Retrieved from: http://www.cnb.cz/cnb/STAT.ARADY_PKG.STROM_DRILL?p_strid=0\&p_lang=CS                                                  Accessed on 26/11/2013]                The above mentioned data are essentially unusable for the given purpose in their original                form. The necessary modifications are described and justified in Kalouda (2017).                Model Specification                In this case, the modelled object is the banking system of the Czech Republic. We model                the processes of managing cost of capital at the level of businesses (commercial rate) by                the CNB using the discount rate. The model of this system is the graphical representation                of the dependence of the commercial rate on the discount rate.                This relatively simple model is, in principle, based on the assumption that the requirement                for the linearity of the modelled system is met (Švarc et al., 2011). Therefore we only                consider  the  data  that  complies  with  the  linearity  condition,  i.e.  the  reactions  the                commercial rate response to a rise in the discount rate.                However, this model is able to describe the expected non-linearities that are typical for the                banking system with sufficient accuracy. The relative simplicity of the model used is thus                not an obstacle to its use for describing the situation and identifying the problems in order                to obtain the reference characteristics of the examined system (Fikar and Mikleš, 1999).                 3 Results and Discussion                As  mentioned  above,  results  of  previous  research  lead  to  the  conclusion  that  (from  a                dynamic point of view) system of Czech commercial banks (or plant) can be considered as                proportional  (static)  system  element.  Its  dynamic  behaviour,  however,  can  be  very                different from the usual concept of stability.                Proportional (static) system element/plant – dynamics of behaviour                Behaviour of system elements (plant) can be described by using the step function response                h(t). Its shape determines the parameters of the system elements, in this case parameters                of the Czech commercial banks. Depending on these parameters, step function response                h(t) may take the form as shown in Figure 4.                                                               230","Figure 4 Variants of the Step Function Response h(t)                                                Legend:                h 0  ....... step response of an proportional element without inertia (an ideal proportional element)                h 1 ....... step response of an first-order proportional element with inertia (aperiodic first-order                            element)                h 21 ……. step response of an second-order aperiodic element (criticaly damped)                h 22 ……. step response of an second-order limit aperiodic element (over-damped)                h 23 ……  step response of an second-order oscilating element (under-damped)                h 24 …..  step response of an second-order conservative (lossless) element (on the threshold of                           instability, un-damped)                Source: Adapted from Balátě, J. (2004, pp. 57-58, Figure 3.21), Poley, R. (2014, p.18, Table 1.10)                 Behaviour of system element (plant) is criticaly significant to the stability control of the                system (in this case  the CR banking system). It is defined by the nature of the system                element.                The dynamic behaviour of the Czech commercial banks testet previous research (Kalouda,                2015).  We  can  be  considered  proven  that  this  system  is  able  (considering  unit  step                response h(t)) to respond by all above-mentioned ways.                Proportional (static) system element controller (as a technical system)                 From the above, it is obvious that the nature of the system element (plant) is important                for the selection of the controller as well. „The choice of the structure or type of controller                is therefore largely  determined  by  the  nature  of  the  system  element  (plant).“  (Balátě,                2004, p. 221).                 The  basic  characteristics  (parameters)  theoreticaly  applicable  controllers  (as  technical                systems) are shown in Table 1.                                 Table 1 Theoretically Applicable Controllers (Overview)                   controller                          e(t)            quality   stabilization                  P controller                        stable            good                  I controller                     temporary            good                  PI controller                          0         better then  I controller                  PD controller                         0          better then  P controller                  PID controller                        0             the best                                                              231","Legend:                P controller     – proportional controller                I controller     – integral controller                PI controller   – proportional plus integral controller                PD controller  – proportional plus derivative controler                PID controller – proportional plus integral plus derivative controller                  Source: Adapted from Balátě, J. (2004). Automatické řízení, Praha: BEN, pp. 155-156, Švarc, I.,                 Matoušek, R., Šeda, M., Vítečková, M. (2011). Automatické řízení, 2  ed. Brno: CERM, pp. 97-100                                                                               nd                The last step is assigning the controller to the Czech commercial banks system. Theory of                the technical cybernetics (automatic control) is in this case relatively unambiguous – only                higher  type  controllers  (PI,  PD  a  PID) are  suitable  for  higher  order  systems (it  means                second-order or higher) – see Table 2.                        Table 2 The Order of the Controlled System (Plant) and Suitable Controller                  plant /system element                                     controller                                                                     I          P        PI    PD         PID                  proportional element without inertia            /               /          /   /     /                 (zero-order ideal proportional                 element)                 aperiodic first-order element                      /      /          /     /         /                 (proportional)                 second-order aperiodic element                                      /      /         /                 (both proportional and integral)                 second-order limit aperiodic element                                /      /         /                 (both proportional and integral)                 second-order oscilating element                                     /      /         /                 (both proportional and integral)                 second-order conservative                                           /      /         /                 (lossless) element (on the threshold                 of instability)                  Source: Adapted from Balátě, J. (2004). Automatické řízení, Praha: BEN, pp. 99,222, Švarc, I.,                                                                               nd                 Matoušek, R., Šeda, M., Vítečková, M. (2011). Automatické řízení, 2  ed. Brno: CERM, pp. 56-57                Proportional (static) system element/plant – CNB as a controler                It schould be considered that controller is in this case a group of human individuals                (Board of CNB). With all the advantages and disadvantages. The competence of the                Board members are exceptional and, on the other side of hand, the risk of erroneus                decisions is also exceptional.                It is obvious, that full analogy of technical and economic systems (with a strong human                aspects - i.e. behavioral) is not possible. Nevertheless Allen (1971, p. 270) emphasizes in                these terms unequivocal opinion: „There is necessary only the formal similarity to                anticipate that the methods used in technics will be suitable for economic models too.“ In                this context we assume, that this condition is in this case met (with sufficient accuracy                off course).                 This issue will be (among others) the subject of the further research activities.                4 Conclusions                 Cybernetic modeling the behavior of the Czech banking system is a long term deal on the                finance department of the ESF MU – more systematically we are dedicated to the topic                during  the  last  five  years.  In  overwhelming  majority,  the  results  obtained  are  very                interesting and regularly break the myths about the CNB´s management potential.                                                                232","In this paper, we discuss the stability of the Czech banking system with regard to the                capital cost management processes.                 The focus of our interest is the choice of controller, which would guarantee the stability of                the Czech banking system. Result of the research is as follows:                     ● system of Czech commercial banks acts as a proportional first/second-order system,                    ● this system faces potential instability due to inappropriate management  intervention,                    ● the controller of this system should have the control potential of the entire range of                      controllers (namely type I, P, PI, PD and PID controllers), preferably in the form of a                      PID controller.                The subject of the further research will be:                    ● detailed description and analysis of the technical aspects of the matter (transmission                      coefficient k, damping ratio ξ, time delay TD etc.),                    ● transformation of the technical cybernetics approaches to stability management into                      the approaches of the economic cybernetics in order the stability control of the Czech                      banking system (with a strong human aspects), and                    ● objectivization of the need for CNB interventions, which is a particularly sensitive                      issue, because off theoretical knowledge available on this subject (so far unique), are                      truly  alarming!  For  the  proportional  controlled  system  it  is  true  that:  “…  after                      deflection from the equilibrium state, they are able to theoretically always achieve a                      new  equilibrium  state  without  any  intervention  (connection)  of  the  controller.”                      (Balátě, 2004, p. 113).                  Acknowledgments                This  paper  was  written  with  support  from  the  project  „Behavioral,  Knowledge  and                Economic  Aspects  Affecting  the  Decision-Making  Subjects  (MUNI/A/0753/2017  -                Behaviorální, znalostní a ekonomické aspekty ovlivňující investiční rozhodování subjektů).                 References                Allen, R. G. (1971). Matematická ekonomie. Praha: Academia.                 Balátě, J. (2004). Automatické řízení. Praha: BEN.                Fikar, M., Mikleš, J. (1999). Identifikácia systémov. Bratislava, STU Bratislava.                Česká  národní  banka  (2018).  Sazby  PRIBOR  –  roční  historie.  Data  retrieved  from:                http://www.cnb.cz/cs/financni_trhy/penezni_trh/pribor/rok_form.jsp.                Česká  národní  banka  (2018).  Data  retriever  from:  http://www.cnb.cz/cnb/STAT.ARADY                _PKG.STROM_DRILL?p_strid=0\&p_lang=CS                 Houpis,  C.  H.,  Sheldon,  S.  N.  (2014).  Linear  Control  System  Analysis  and  Design  with                MATLAB. 6th ed. New York: CRC Press                 Kalouda, F. (2014a). Řízení ceny kapitálu centrální bankou jako kybernetický proces. In:                Recenzovaný sborník mezinárodní odborné konference Hradecké ekonomické dny 2014.                Hradec Králové: Gaudeamus, vol. 4(1), pp. 83-88.                Kalouda,  F.  (2014b).  The  Impact  of  Discount  Rate  on  Commercial  Rates  in  the  Czech                Republic: The Cybernetic Approach. In:  Proceedings of the 11th International Scientific                Conference European Financial Systems 2014. Brno: Masaryk University, pp. 307-313.                Kalouda, F. (2014c). The hysteresis erosion of the discount rate control potential in CR                conditions – behavioral solution. In: Managing and Modelling of Financial Risks. Ostrava:                VSB – Technical University of Ostrava, pp. 329-334                Kalouda, F. (2015). The Banking System of the Czech Republic as a Cybernetic System –                the  Unit  Step  Response  Analysis.  In:  Proceedings  of  the  12th  International  Scientific                Conference European Financial Systems 2015. Brno: Masaryk University, pp. 253-261.                                                             233","Kalouda,  F.  (2016).  Fenomén  stability  bankovní  soustavy  ČR  v řízení  úrokového  rizika                podniku.  In:  Managing  and  Modelling  of  Financial  Risks.  Ostrava:  VSB  –  Technical                University of Ostrava, pp.  379-386                Kalouda, F. (2017). Negative interest rates – consequence staying in error?         (empirical                evidence).  In:  Proceedings  of  the  14th  International  Scientific  Conference  European                Financial Systems 2017. Brno: Masaryk University, pp. 290-299.                 Kalouda, F., Svítil, M. (2009). The Commercial Bank such as the Cybernetic System in                Conditions  of  the  Global  Financial  Crisis.  In:  Management,  Economics  and  Business                Development in European Conditions. Brno: FP VUT, pp. II.24-II.31.                 Kisačanin, B., Agarval, G. S. (2001).  Linear Control Systems with solved problems and                MATLAB examples. New York: Kluver Acadamic/Plenum Publishers.                 Kubík, S., Kotek, Z., Strejc, V., Štecha, J. (1982). Teorie automatického řízení I, Lineární                a nelineární systémy, Praha: SNTL.                Poley.  R.  (2014).  Control  Theory  Fundamentals.  2.  ed.  Marston  Gate:  CreateSpace                Independent Publishing Platform.                Stefani, R. T., Shaian, B., Savant, C. J. Jr., Hostetter, G. H. (2002). Design of Feedback                Control Systems. 4. ed. New York: Oxford University Press.                Šerý. M. (2010). Vliv diskontní sazby na úrokové sazby komerčních bank v České republice.                Brno: Mendelova univerzita, Provozně ekonomická fakulta. Bachelor’s Thesis.                Švarc, I. (2003). Teorie automatického řízení. Brno: FS VUT.                Švarc, I., Matoušek, R., Šeda, M., Vítečková, M. (2011). Automatické řízení. 2nd ed. Brno:                CERM.                                                                                                         234","How the Changes in Exchange Rate Affect the Turkish Economic                                 Growth under Inflation Targeting Regime?                                                      Özcan Karahan                                              1  Bandırma Onyedi Eylül University                               Faculty of Economics and Political Sciences, Economics Department                                     Merkez Yerleşkesi, 10200, Bandırma-Balıkesir, Turkey                                             E-mail: okarahan@bandirma.edu.tr                 Abstract:  In  the  literature,  the  conventional  view  argues  that  there  is  a  positive                relationship between exchange rate and economic growth since an increase in exchange                rate stimulates the volume of net exports. However, Structuralist Economists show that                there  may  be  an  inverse  relationship  between  exchange  rate  and  economic  growth.                Particularly  in  developing  countries,  the  input  structure  of  production  depends  on  the                imported capital and intermediate goods, so an increase in the exchange rate negatively                affects  the  economic  growth  by  making  imported  production  inputs  more  expensive.                Turkey, leaving exchange rates free to fluctuate, have adopted Inflation Targeting (IT) as                a monetary regime since 2001 and thus exhibited a genuine experience to be analysed the                role of exchange rate in economic growth. Accordingly, using quarterly data from 2002 to                2017  and  employing  Johansen  cointegration  and  Granger  causality  tests,  the  nexus                between exchange rate and economic growth has been investigated in this study. Empirical                findings indicate that, as argued by Structuralist Economists, there is a negative causal                relationship from exchange rates to economic growth. From the policy perspective, it can                be concluded that monetary policy applications in Turkey should provide both price and                exchange rate stability together under inflation targeting regime.                Keywords: exchange rate, economic growth, Time-Series Model                 JEL codes: F31, F43, C22                 1 Introduction                The economic effects created by exchange rate changes have been the subject of constant                debate in the literature. With the spread of the financial liberalization period, discussions                about the effects of the exchange rate on the macroeconomic variables have increased                even more. Recently, deep attention has been given to examination of the link between                exchange  rate  and  economic  growth.  Thus,  the  effect  of  exchange  rate  changes  on                economic growth is one of the important research issues in the literature today.                Conventional view argues a positive causality relationship from exchange rate to economic                growth.  According  to  this  approach,  an  increase in  exchange  rate  stimulates economic                growth via enlarging the volume of net export. Rising exchange rate affects the relative                prices of domestic and foreign goods and hence increases exports and decreases imports.                In  other  words,  depreciation  of  the  local  currency  directs  the  demand  for  foreign                commodities to local products. As a result, when the exchange rate raises, it will enhance                economic growth by promoting net exports. Thus, devaluation can be used as an effective                policy tool to encourage economic growth.                However,  the  Structuralist  Economists  suggest  that  devaluation  policy  may  have  a                constrictive  effect  on  the  economies  of  developing  countries  in  particular.  The                manufacturing process of developing countries is largely based on imported inputs such as                machinery and intermediate goods. Thus, with the increases in the exchange rate, the                costs of imported input and accordingly the cost of production increase significantly. As a                result, increased production costs will slow production and in this case leads to a decrease                in total supply and economic growth. Therefore, in developing countries there is a negative                causality from exchange rate to economic growth.                                                                235","To sum up, the transmission mechanism between exchange rate and economic growth can                be examined in two respects. In other words, it is appropriate to determine the impact of                exchange rate on economic growth through two channels. According to the conventional                view,  the  exchange  rate  increase  has  a  positive  effect  on  the  economic  growth  by                increasing the net export volume. Thus, there is a positive causality relation from exchange                rate  to  economic  growth.  On the  contrary,  the  Structuralist Economists  argue  that  the                exchange  rate  increase,  especially  in  developing  countries,  has  a  negative  effect  on                economic  growth  since  exchange  rate  increase  reduces  the  imported  production  input                which is intensively used in the production process. For this reason, they assert that there                is an inverse relationship between exchange rate and economic growth.                 The aim of this paper is to make a contribution to the debate in the literature indicated                above.  Accordingly,  the  relationship  between  exchange  rate  and  economic  growth  in                Turkey will be analysed using the Johansen cointegration and Granger causality tests for                the quarterly data between 2002 and 2017. Following the economic crisis in 2001, within                the framework of Inflation Targeting Regime, the exchange rate in Turkey was allowed to                float. Thus, Turkey provides a genuine experience to be analysed the role of exchange rate                in  economic  growth.  The  rest  of  paper  is  arranged  as  follows.  Section  2  reviews  the                empirical  literature  focusing  on  the  relationship  between  exchange  rate  and  economic                growth. Section 3 explains the data and methodology. Section 4 presents empirical results.                Final section concludes and makes some policy implications.                  2 Literature Review                There  are  two  different  hypotheses  in  the  literature  about  how  exchange  rate  affects                economic growth. In other words, there are two different perspectives on the effect of                exchange rate on economic growth. Conventional approach asserts that rising exchange                rate positively affects economic growth while Structuralist view argues that rising exchange                rate brings about a contraction in the economy.                According to Conventional view, an increase in exchange rate creates a positive impact on                economic growth via enhancing the volume of net export. The main reason for the increase                in economic growth with net exports here is the change in the relative prices of domestic                and  foreign  goods  after  the  increase  in  exchange  rates.  Namely,  depreciation  of  local                currency decreases the prices of domestic goods while makes the prices of goods abroad                much more expensive. Thus, depreciation of exchange rate firstly increases the volume of                net export and then growth rate of economy. That means devaluation can be used as a                policy  tool  promoting  economic  growth.  Accordingly,  the  positive  impact  of  currency                depreciation on economic growth has been widely documented in the literature. Indeed,                mainstream  studies  have  mostly  produced  the  econometric  results  concerning  with  the                positive correlation from exchange rate increase to net export and economic growth.                Domaç (1997), for the period 1960-1990, examined the relationship between exchange                rate and economic growth in Turkey. Using regression analysis, he found that unexpected                devaluations are a positive effect on output. Thus, he determined that the contractionary                devaluation hypothesis is not valid in Turkey. Narayan and Narayan (2007) investigated                the effects of devaluation for the Fiji economy between 1970-2000. They employed the co-                integration  method  and  found  that  the  devaluation  in  the  short  and  long  term  led  to                widening effect for the Fiji economy. They found that 10% devaluation increased the output                by 2.3% or 3.3%. Rodrik (2008) examined the relationship among economic growth and                real exchange rates using a data set from 188 countries between 1950 to 2004. Rodrik                found  a  systematic  positive  relationship  between  growth  and  depreciation  in  the  real                exchange rate. However, the increase in the depreciation of the real exchange rate has                only caused to economic growth only in some developing countries. According to him, this                relationship does not apply to developed rich country economies.                Di Nino et al. (2011) analysed the connection between exchange rate and economic growth                in Italy. They concluded that there is a positive relationship between undervaluation and                economic growth for a dataset covering the period 1861–2011. In addition, the authors                also  showed  that  undervaluation  supported  growth  by  increasing  exports,  especially  in                                                            236","high-productivity sectors. Chen (2012) focused on the role of exchange rate in economic                growth and the convergence of growth rates in Chinese provinces. Data set between 1992                and 2008 for 28 provinces were tested with dynamic panel data estimation. He found that                real exchange rate increase has a positive effect on the economies of provinces. Aman et                al. (2013) investigated the relationship between exchange rate and economic growth in                Pakistan for the period 1976–2010 by employing two, three stage least square techniques.                They showed that exchange rate has a positive impact on economic growth through the                promoting export and import substitute industry. Habib et al. (2017) analysed the impact                of movements in the real exchange rate on economic growth based on five-year average                data for a panel of over 150 countries in the post Bretton Woods period. They determined                that  a  real  depreciation  raises  annual real  GDP  growth.  Thus,  the  results  revealed  the                broader economic effects of devaluation for developing countries.                As seen in the literature, many studies have found that the rising exchange rate ratio has                a positive impact on the economy and thus supported the devaluation policy for economic                growth. However, Structuralist economists have argued that the rising exchange rate has                a  restrictive  effect  on  economic  growth,  especially  in  developing  countries.  Therefore,                devaluation policy creates confining impact on the growth of developing economies (Bird                and  Rajan  2004:143-144).  The  negative  impact  of  increases  in  exchange  rates  on                economic  growth  arises  from  the  restrictive  effects  on  imported  input.  The  production                structure of developing countries is mainly dependent on the imported input. Therefore,                the  increases  in  the  exchange  rate  create  a  negative  impact  on  economic  growth  by                decreasing the imports of raw materials, intermediate goods and investment goods. That’s                why the devaluation policy produces positive outcomes in some developed countries while                it results in negative consequence on economic growth in developing countries (Hallwood                and Macdonald, 2003:421).                Furthermore, it has been argued that import affects growth in developing countries by                transferring of new knowledge or technology from abroad. Thus, import affects economic                growth in developing countries by not only providing production input but also enhancing                the  domestic  technological  capability.  Imported  machinery  and  intermediate  goods                generates the channels for diffusion of new technology among countries. Therefore, it is                clear that import have the impact on developing countries’ growth by raising technological                capacity of economy through transferring new knowledge. The important role of imports                in technological diffusion has been extensively documented in the literature. (Lee 1995;                Mazumdar 2001; Keller 2004).                In  conclusion,  rising  exchange  rate  which  causes  imports  to  be  restricted  decreases                economic  growth  by  both  reducing  the  availability  of  production  input  and  technology                transfer. Accordingly, many studies in the literature have shown that depreciation of the                local currency caused to a contraction in the economies of developing countries. Bahmani-                Oskooee et al. (2002) examined the transfer mechanism from exchange rate to economic                growth in 5 Asian countries using the monthly data between 1976 and 1999. The empirical                results of Johansen cointegration and Granger causality tests indicated that depreciation                of local currencies have a negative impact on economic growth in Asian countries. Galindo                and Montero (2005) investigated the causality relationship from exchange rate to economic                growth  by  using  panel  data  relating  to  9  Latin  American  countries.  Empirical  results                indicated that rising exchange rate in the Latin American countries which have high foreign                debt ratio negatively affects economic growth. Yiheyis (2006) investigated the interaction                among the exchange rate and economic growth in 20 African countries for the term 1981-                1999. The results of unbalanced panel data analysis indicated that depreciation of local                currencies creates a shrinking effect on economic growth.                Blecker and Razmi (2008) examined the impact of devaluation on economic growth in 17                developing countries for the period 1983-2004. Results of dynamic panel data analysis                (Generalised Method of Moments) showed a negative relationship between exchange rate                and  economic  growth.  As  a  result,  the  authors  emphasize  that  contractionary  effect  of                devaluation for developing countries compared to industrialized countries. Vaz and Baer                (2014) employed a panel model using unbalanced panel data covering 1995-2008 for a                                                             237","sample  of  39  countries.  They  showed  that  depreciation  of  currency  in  Latin  American                countries  causes  a  nominal  cost  increase  in  production  process  and  hence  decreases                economic  growth  rate  rather  than  raises  it.  Çelik  et  al.  (2017)  investigated  transfer                mechanism from exchange rate to economic growth via panel data analysis using cross-                sectional data between 1995 and 2014 for 12 transition economies in Eastern Europe and                Middle  Asia.  The  results  of  study  showed  that  an  increase  in  exchange  rate  result  in                economic downturn. However, they also indicated that real exchange rate movements do                not turn out to be the major variable in explaining the economic growth.                 3 Data and Methodology                 The aim of the study is to examine the relationship between exchange rate and economic                growth using quarterly data between 2002-Q1 and 2017-Q4. The variables of exchange                rate (ER) and economic growth rate (EG) are sourced from Electronic Data Delivery System                in Central Bank of the Republic of the Turkey. Empirical analysis begins by checking the                stationary statute of data set by using Dickey and Fuller and Phillips-Perron Tests. Then,                the long run relationship between inflation and interest rate is analysed by using Johansen                (1988) Cointegration Test. Finally, we examine the causality relationship between inflation                and interest rate by operating Granger (1988) Causality Test based on Error Correction                Model.                The stationarity analyses of the variables were performed with the ADF (Augmented Dickey                Fuller-ADF) and PP (Philips Perron) unit root tests. The constant model used in the unit                root tests developed by Dickey and Fuller (1981) can be shown as equation 1. Test results                determined  by  estimation  of  these  model  are  evaluated  in  terms  of  critical  values  of                MacKinnon and the null hypothesis (   :    = 0 ) is tested against the alternative hypothesis                                                      0                 (   :    ≠ 0 ).  The  null  hypothesis  indicates  that  the  series  is  not  stationary,  while  the                  1                 alternative hypothesis suggests that the series is stationary.                       Y = β + α Y    + ∑ m  Y t−i  + ɛ                                                                        (1)                                                     t                                          i−1                              0                                     t−1                         t                The unit root test developed by Phillips-Perron (1988) is based on the model determined                in Equation 2. In this equation T determines the number of observation while t indicates                time. Test results determined by estimation of these model are evaluated in terms of critical                values  of  MacKinnon.  The  null  hypothesis  (   :    = 0 )  is  tested  against  the  alternative                                                                 0                hypothesis (   :    < 0 ). The null hypothesis states that the series contain unit root while the                              1                 alternative hypothesis states that the series do not contain unit root.                       Y =  + λY t−1  +  (t − T/2) + u                                                                      (2)                              0                         t                                         1                                                      t                The cointegration test developed by Johansen and Juselius (1990) is suitable for examining                the long-term relationship between variables after determining that the data are stable at                the  same  level.  This  method  makes  an  analysis  of  cointegration  over  the  system  of                simultaneous equations generated by using variables that become stable at the same time                when  the  difference  is  taken.  In  the  framework  of  this  methodology  a  VAR  (Vector                Autoregressive) Model with p degree in Equation 4 is operated.                       Y = μ +  AY t−1  + … … . +A Y  + e                                                                      (3)                                                      t                                              p  t−p                        t                Equation 3 can be rearranged as in equation 4 by taking the first difference.                       Y = μ +  Π  Y t−1   + ∑ p−1  Γ  Y t−i  + e                                                                  (4)                                                         t                                               i                         t                                           i=1                In Equation 4, Π is the parameter indicating the long term relation. Thus, the cointegration                hypothesis is analyzed through the equation which is Π = α β. So, α and β (pxr) represents                a two-dimensional matrix. The matrix α shows the rate of improvement of the deviations                from the long-run equilibrium of the variables while β denotes the cointegration vector                showing the long-term effects of the variables in the equilibrium relations. The number of                cointegration vectors is examined in the framework of Trace Statistics and Maximum-Eigen                Statistics. Thus, both tests investigate long term associations by determining how many                cointegrated vectors are among the variables.                                                            238","Trace Statistics can be calculated from the formula indicated in Equation 5. Here, the null                hypothesis (   :    ≤    − 1 ) is tested against the alternative hypothesis (   :    ≥    ).                               0                                                                                          1                       λ trace (r) = −T  ∑ m   ln (1 − λ )                                                                         (5)                                      i=r+1                                                   i                Max-Eigen Statistics can be calculated from the formula indicated in Equation 6. Here, the                null hypothesis (   :    ≤    − 1 ) is tested against the alternative hypothesis (   :    ≥    ).                                  0                                                                                             1                             (r, r + 1) = −T ln(1 − λ r+1 )                                                                          (6)                       λ max                If the cointegration relation is found between the variables used in the analysis, then the                VECM (Vector Error Correction Model) method should be used when the dynamic effects                are examined in the next stage. Accordingly, the models to be used in the causality test                developed by Granger (1988) are defined in Equation 7 and 8 below, including the error                correction term.                        Y = β + ∑ m  α Y t−i  + ∑ m  β X t−k  + λ ECT t−1  + u                          (7)                          t                              0                                                    k                                                              1                                                                        t                                   i=1                                       i                                                k=1                       X = ɥ + ∑  m  ɥ X t−i  + ∑ m  δ Y t−k  + λ ECT t−1  + v                          (8)                          t                                                                        t                                                             2                                       i                              0                                                    i                                                k=1                                   i=1                In equations, the coefficients of the error correction term (ECT)    and    show the rotational                                                                                       2                                                                                1                speed of X and Y variables to equilibrium relation. Error correction coefficients are expected                to  be negative  and  statistically  significant.  In  the framework  of  Granger  causality  test,                whether the α and δ coefficients in the equations are meaningful is tested by the standard                F-test.  Accordingly,  by  equation  (7)  Y  Granger  causes  X  if  the  null  of  either  ∑ m  β =                                                                                                        k                                                                                                    k=1                0 or λ = 0 is  rejected.  On the  other  hand,  by  equation  (8),  X  Granger  causes  Y if  λ is                     1                                                                                                        2                significant and ∑ m  δ are jointly significant.                                     i                                 k=1                4 Results and Discussion                Before running the econometric analysis of the model, we test whether time series used in                the model are stationary or not. Accordingly, we check whether time series of economic                growth rate (EG) and exchange rate (ER) are stationary by using Augmented Dickey Fuller                (ADF) and Philips Peron (PP) tests. Table 1 presents the results of unit root tests. Findings                show that all variables are not stationary at level. However, by first differentiation all series                become  stationary  since  the  calculated  test  statistics  exceed  the  critical  values.  In                conclusion, both economic growth (EG) and exchange rate (ER) series are integrated of                the same order at I (1).                                            Table 1. Results of Unit Root Tests                                                            ADF                        PP                                             (Augmented Dickey Fuller)            (Philips Peron)                       Variables                 Level          First Difference       Level   First Difference                      EG                      -3,5311      -6,5466*          -2,4321           -4,5675**                      ER                      -2,8346      -5,4184*          -1,8778     -3,1134**                           Not: * and ** indicate the significance level at 1 % and 5 %, respectively.                                 Maximum lag length is specified by considering Akaike Information Criterion (1).                                                       Source: Author                After determining that both the series of exchange rate (ER) and economic growth (EG)                are integrated of the same order at I (1), we employ Johansen cointegration test in order                to investigate the long run relationship between the variables.  The results of Johansen                Cointegration analysis are presented in Table 3. Results show that the computed value of                the both Trace Statistic and Max-Eigen statistic are more than the critical value at 5% level                of significance. Therefore, the null hypothesis that there is no co-integrating vector was                rejected.  Thus,  the  results  of  both  cointegration  test  implies  that  there  is  long  run                relationship between exchange rate and economic growth in Turkey.                                                                239","Table 3 Johansen Cointegration Tests Results                                                Trace Statistics     Maximum Eigenvalue Statistics                            Null                         Hypothesis        Test     % 5 Critical           Test      % 5 Critical                                         Statistic     Value             Statistic        Value                            r = 0        26.8643      19.7432           20. 6784        16.3467                            r ≤ 1         7.5431      8.7854             7.5431          8.7854                                                       Source: Author                 Given the long run relationship among the variables, Granger causality test based on the                VECM (Vector Error Correction Model) has been employed in order to detect the direction                of relation between the variables. The results of Granger causality test are presented in                Table-4. According to results, we reject the null hypothesis that exchange rate (ER) does                not Granger cause economic growth rate (EG). Thus, findings reveal that causality runs                from  exchange  rate  (ER)  to  economic  growth  (EG)  while  the  reverse  causation  is  not                confirmed.                                              Table 4 Granger Causality Test                                      Null                      F-             p-           Decision                                  Hypothesis                Statistic       Value                    ER does not Granger cause EG             3.1253*       0.0180           Reject                     EG does not Granger cause ER            0.9811         0.4831         Not Reject                       Not: * indicates the significance level at 5 %. Maximum lag length is specified by Akaike                               Information Criterion (1).                                                       Source: Author                 Finally, in order to show short-run dynamics between exchange rate and economic growth,                the results of VECM estimation are presented in Table 5. The coefficient of error correction                term is statistically significant and negative as we expected. Findings also indicated that                exchange rate affects economic growth negatively as confirmed by the sign of the related                coefficient (ΔER t-1).                               Table 5 Estimation Results of Vector Error Correction Model                         Variables           Coefficient              t Value           Probability                          Constant              0.134**                 1.649             0,024                            EC t-1             -0.281*                 -3,195             0,001                           ΔER t-1             -0.133**                -2.078             0.031                           ΔEG t-1              0.621*                  3,841              0.000                             Not: *, ** indicates the significance level at 1% and 5 %, respectively.                                              2                             Diagnostic Tests: Adj. R  = 0.312; F test = 4.502 =.025); D.W = 2.034                                                       Source: Author                Overall, empirical results indicated that there is a unidirectional negative causality from                exchange rate to economic growth. These finding supports structuralist view asserting that                depreciation of local currency have a contractionary impact on economic growth. It seems                that depreciation of Turkish Lira restricts imports that create the appropriate conditions for                economic growth by providing machinery, equipment and raw materials needed as the                production  inputs.  In  terms  of  policy  implementation,  empirical  findings  also  give                significant insights for  the  effectiveness  of  Inflation  Targeting  Policy  adopted in  Turkey                since 2001. According to this, since the exchange rate is one of the significant determinants                of  economic  growth  in  Turkey,  special  importance  should  be  given  to  exchange  rate                management under Inflation Targeting Regime. In other words, Inflation Targeting Policy                in Turkey should fulfil a difficult task by successfully providing both price and exchange                rate stability in the same time.                                                              240","5 Conclusions                In the literature, there are two basic approach related to transfer mechanism between                exchange rate and economic growth. Conventional view argues that rising exchange rate                stimulates  economic  growth  via  enlarging  the  volume  of  net  export.  Contrarily,                Structuralist view claims that there is a negative relationship between exchange rate and                economic growth in developing countries in particular. After an increase in exchange rate,                in the developing countries that manufacture on the basis of external inputs, the cost of                production increases and consequently the economic growth is adversely affected. In the                line with the views indicated above, we examine the relationship between exchange rate                and economic growth in Turkey using Johansen co-integration and Granger causality tests                for the quarterly data from 2002 to 2017.                The  findings  of  Johansen  cointegration  test  show  that  exchange  rate  and  income  are                significantly cointegrated in the long run. Estimation results of Granger causality test and                Vector Error Correction Model indicated a negative causality relationship from exchange                rates  to  economic  growth.  Together  all  of  these  findings  provide  an  econometric                confirmation  for  structuralist  view  arguing  that  the  rise  in  the  exchange  rate  causes  a                contraction  in  economic  activity.  From  the  policy  perspective,  instead  of  completely                liberalizing the exchange rate in the framework of inflation targeting strategy adopted since                2011, policy makers in Turkey have to prevent the upside movements in the exchange rate                by taking into consideration its negative effect on economic growth.                 Acknowledgments                 This work was supported by Scientific Research Projects Coordination Unit of Bandırma                Onyedi Eylül University. 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International Review of Applied Economics, vol. 20(1), pp. 21–45.                                                                                                    242","The Effect of Bank Credit on the Current Account Balance in Turkey                                                                        2                                                                                        3                                                   1                                  Özcan Karahan , Metehan Yılgör , Hakan Öndes                                              1  Bandırma Onyedi Eylül University                                Faculty of Economics and Political Sciences, Economics Department                                     Merkez Yerleşkesi, 10200, Bandırma-Balıkesir, Turkey                                             E-mail: okarahan@bandirma.edu.tr                                                 2  Bandırma Onyedi Eylül University                              Faculty of Economics and Political Sciences, Econometrics Department                                     Merkez Yerleşkesi, 10200, Bandırma-Balıkesir, Turkey;                                              E-mail: myilgor@bandirma.edu.tr                                                3  Bandırma Onyedi Eylül University                              Faculty of Economics and Political Sciences, Econometrics Department                                     Merkez Yerleşkesi, 10200, Bandırma-Balıkesir, Turkey                                              E-mail: hondes@bandirma.edu.tr                 Abstract: Like another developing country, one of the most important problems in Turkish                economy  is  the  large  current  account  imbalance  which  continues  permanently.  Past                experiences  in  the  Turkish  economy  indicated  that  persistent  large  current  account                imbalances  make  financial  system  more  fragile  and  cause  serious  currency  crises.                Therefore,  examining  the  determinants  of  current  account  deficit  in  Turkey  becomes  a                significant research topic. Accordingly, this study aims to analyse the impact of bank credit                expansion on current account balance in Turkey. Covering the quarterly data between 2004                and  2017  the  relationship  between  bank  credit  and  current  account  balance  has  been                investigated in the long run and the short run by ARDL Model. Empirical results show that                there is a causal relationship from credit growth to current account deficit. It seems that                credit expansion has been one of the leading reasons behind the growing current account                deficits in Turkey. Thus, the results indicate that policy-makers in Turkey should take into                account the credit expansion while keeping the current account balance as the primary                objective to provide financial system soundness and economic stability.                Keywords: bank credit, current account deficit, ARDL model                 JEL codes: E51, F32, C22                 1 Introduction                Along with the liberalization process of the 1980s, real and financial economic relations                among the countries have increased. In this process, the current account balance which                demonstrates domestic residents’ transactions with foreigners in the goods markets had                been  critical  for  the  developing  economies  from  various  aspects.  Even  if  developing                countries have  the  opportunity  to finance  current  account  deficits  by  attracting  foreign                capital flows from international financial markets, national economies with high levels of                current account deficits in the turbulent global market environment have become more                vulnerable to external economic conditions. For this reason, high level of current account                deficit has been accepted as an indicator that show the imbalance in the economy and                country's risk increase.                Thus, the fight against the current account deficit can be seen as one of the main policy                objectives  for  developing  countries.  In  this  context,  studies  aiming  to  determine  the                dynamics  of  current  account  deficits  have  begun  to  take  an  important  place  in  the                literature. Economists have identified many factors that cause the current account deficit                in developing countries. However, in recent years, economists have focused on the credit                expansion as the basic dynamic of current account deficit. Accordingly, expansion of bank                credit leads current account balance via increasing total consumption. Thus,  total credit                volume in banking sector regarded as a driving force behind the growth of the current                account deficit. Therefore, economic policies towards controlling the volume of bank credit                have crucial importance for developing countries with high current account deficits.                                                            243","Turkey is  also  among  the  countries  facing  with  the  problem  of  current  account  deficit.                Indeed,  the  current  account  deficit for  Turkish economy  was  a  chronic  macroeconomic                problem  which  had  generated  financial  instability  and  economic  crisis  in  the  past.                Therefore, changes in the current account deficit and its determinants are on the top topics                of  agenda  in  Turkish  economy  today.  Accordingly,  this  study  examined  the  dynamics                determining the current account balance focusing on the role of credit expansion in Turkey.                More specifically, the aim of our study is to explore the impact of credit expansion in Turkey                on current account balance during the period of 2004- 2017 by employing ARDL model.                The paper proceeds as follows: Section 2 discusses the related literature briefly. Section 3                presents  the  data,  econometric  methodology  and  results.  Final  section  concludes  and                makes some policy implications.                 2 Literature                 Looking at the literature, it seems that one of the most important macroeconomic problems                in developing countries is current account deficit. It also seems that credit volume has                been  shown  among  the  basic  determinants  of  current  account  deficit  by  most  of  the                economists. It can be theoretically asserted that an increase in bank credit would have an                unbalancing  effect  on  the  current  account  deficit  via  stimulating  import  in  developing                countries. Accordingly, bank credits increase total domestic consumption, which results in                increasing volume of imported consumption and investment goods.                Credit expansion or an increase in total domestic credit volume means that economic actors                within  the  country  have  an  additional  income  and  purchasing  power.  This  additional                purchasing power is expected to increase both domestic demand and external demand                since it is almost impossible to assess all of the consumer demand within the country. The                increase  in  consumption  demand  for  the  foreign  market  directly  increases  the  current                account deficit. The increase in demand for the domestic market also leads to a current                account deficit indirectly by increasing the imports of investment goods such as capital and                intermediary  goods  in  developing  countries.  As  is  known,  the  production  structure  in                developing  countries  is  overly  dependent  on  the  import  of  foreign  intermediate  and                machinery equipment. That means firms in developing countries produce on the basis of                imported inputs. For this reason, the increase in domestic demand due to the expansion                of bank credits and the start of additional production of companies cause the increase of                imported input production, which results in the increase of the current account deficit.                 There  are  numerous  empirical  studies  analysed  whether  there  is  a  causal  relationship                between  bank  credit  and  current  account  deficits.  It  seems  that  total  credit  volume  in                banking sector is a driving force behind the growth of the current account deficit. Brissimis                et  al.  (2012)  studied  the  main  macroeconomic  factors  that  shaped  current  account                development in Greece over the period from 1960 to 2007. They found that credit growth                was pivotal in explaining the fall of the private saving rate and thus a factor deteriorating                of  the  current  account.  Ekinci  et  al.  (2015)  analysed  the  determinants  of  the  current                account  balance  for  the  annual  dataset  covering  the  period  from  1991  to  2011  in  49                countries including industrial and developing ones. They employed generalized method of                moments (GMM) procedure in the framework of dynamic panel data analysis and reported                a significant deterioration in the current account balance in case of an increase in the credit                growth.  The  acceleration  of  loan  growth  has  a  deteriorating  effect  on  current  account                balance especially in emerging countries. Buyukkarabacak and Krause (2009) investigated                the impact of household and firm credit on current account deficit using data between 1990                and 2011 in 18 emerging economies. Employing panel data analysis, it was concluded that                household credit is negatively but firm credit is positively correlated with the trade balance.                In other words, consumer credit and producer credit have sharply different impacts on the                trade balance.                 Concerning with the case of Turkey, Göçer, İ. (2013) investigated the relationship between                total domestic credit and current account deficit in Turkey. The results of cointegration test                with  multiple  structural  breaks  using  quarterly  data  over  the  period  1992—2013                determined that an increase in the domestic credit volume is a factor in increasing the                                                             244","current account deficit. Demirhan (2014) analysed the effect of credit growth on current                account deficit using quarterly data ranging from 1998 to 2015. Empirical results of ARDL                model showed that credit growth have a significant impact on current account deficit in the                short  run  and  the  long  run.  Thus,  it  was  concluded  that  credit  growth  is  taken  into                consideration as policy instruments improving current account balance in Turkey. Soydan                (2016)  empirically  analyzed  the  relationship  between  current  account  balances  and                domestic  credit  dynamics  in  the  case  of  Turkey.  The  results  of  ARDL  bounds  testing                methodology  showed  the  causality  between  domestic  credit  and  external  balance.                Accordingly, he concluded that policymakers should designed a policy towards bring credit                growth down in order to reduction in current account deficit. Karahan and Uslu (2016)                investigated the relationship between credit by which commercial bank to private sector                and current account deficit covering the period of 2005-2015. Employing ARDL model, it is                asserted that credit volume is statistical significant and positively affects current account                deficit in the short and long run. Therefore policymakers in Turkey should slow down credit                volume in response to current account deficit in order to ensure financial stability.                Looking  at  the  literature  it  seems  that  some  studies  decompose  bank  credit  while                examining the impact on external balance in Turkey. Telatar (2011), using the quarterly                data between 2003 and 2010, examined the impact of different bank credit on current                account  deficit.  He  found  that  there  is  no  significant  causality  relation  to  the  current                account from the total loans but there is a positive causality relation to the current account                from the consumer loans. In other words, he showed that consumer credits, rather than                total  credits,  contribute  to  current  account  deficit.  Turgutlu  (2014)  also  decomposed                consumer loans into sub-categories as real estate loans, vehicle loans and other loans to                investigate their individual effects on the current account balance of Turkey over the period                2000-2013. The results of ARDL model indicated that real estate loans and other loans                negatively affected the current account balance. Alioğulları et al. (2015) investigated the                relationship between consumer and commercial loan growths and current account deficit                in Turkey. Regression analysis using quarterly data between 2003 and 2015 revealed that                consumer  loan  growth  negatively  and  statistically  significantly  affects  current  account                balance while commercial loan growth has no statistically significant effect. Finally, Dücan                (2016) investigated the impact of the increase in consumer credit on current account deficit                using quarterly data covering the period between 2009 and 2015. Granger causality test                and impulse response analysis and variance decomposition based on the VAR model were                carried out. Empirical findings showed that the increase in consumer loans seems to be                quite high share in the expansion of current account deficit.                 Contrary  to  studies indicated  above,  some  studies found no  relationship  between  bank                credit  and  externa  balance  in  Turkey.  For  example,  Gacaner  and  Saygılı  (2014)  used                quarterly  data for  the  period  of  1998-2013  to identify  the  determinants  of the  current                account  deficit  in  Turkey.  When  the  variables  affecting  the  current  account  deficit  are                evaluated together, they determined that the effects of variables representing monetary                aggregates like bank credit are weak. Thus, their results showed that the limitations on                the loans are not enough to reduce current account deficit problem. Akçayır and Albeni                (2016) examined the link between the volume of loans and the current account deficit in                Turkey. Conducting Toda-Yamamoto test and ARDL Model for quarterly data set covering                the period between 1992 and 2014, they found that the impact of bank credit on current                account deficit is very weak. Therefore, they argued that it is impossible to think of the                total domestic credit volume as the single most powerful cause of the current deficit in                Turkey. Thus, it is not anticipated that policies towards restriction of bank credit expansion                will be effective over the elements that increase current account deficit.                 3 Data, Methodology and Empirical Results                 The aim of this section is to empirically analyse the relationship between credit expansion                and the current account deficit in Turkey. We use quarterly data covering the period 2004-                Q1 and 2017-Q4. The amount of bank credit consists of all kinds of loan given to firms and                household except credit cards. The current account deficit (CAD) and total bank credit (BC)                variables used in the model is obtained as ration of current account deficit and bank credit                                                            245","data to Gross Domestic Product (GDP). In addition, the logarithms of the series were taken                and  each  data  are  shown  as  LBK  and  LCAD.  All  of  the  data  set  is  compiled  from  the                Electronic  Data  Distribution  System  (EDDS)  in  Central  Bank  of  the  Republic  of  Turkey                (CBRT).                Empirical analysis begins by conducting the unit root tests such as ADF and PP in order                check the stationary status of the variables. Later, the relationship between the series of                bank credit and current account balance has been examined by using the autoregressive                distributed lag (ARDL) model popularized by Pesaran, et al. (1996), Pesaran (1997) and                Pesaran and Shin (1999). ARDL model does not impose a restrictive assumption that all                the variables under study must be integrated of the same order. This model also allows us                to get consistent and robust results both for the long-run and short-run relationship among                the variables even if he sample size is small. In the framework of ARDL model we follow                three-step procedure.                Firstly, cointegration relationship between the series of bank credit and current account                balance  has  been  examined  by  using  Bond  Test.  In  the  framework  of  bounds  test,                calculated F-statistics is compared with the critical value tabulated by Pesaran (1997) and                Pesaran et al. (2001). If F-statistics exceeds the upper critical value, the null hypothesis                of a no long-run relationship can be rejected. If the test statistic falls below a lower critical                value, the null hypothesis is not rejected. However, if the test statistic falls between these                two bounds, the result is inconclusive. When the order of integration of all the variables is                I (1), the decision is made based on the upper bound. If all the variables are I (0), then                the decision is made based on the lower bound.                In the second step, if there is evidence of a cointegration among the variables, the long-                run model indicated in Equation 1 is estimated. It is expected that coefficient of bank credit                (λ) is positive and statistically significant.                       LCAD =  + ∑     n  β  LCAD   t−1 + ∑ n  λ  LBC  t−1  + ɛ                          (1)                                                            i=1                                                                i                                                                             t                             t                                   1                                        i=1                                             i                In the third step, based on the finding related to a long run relationship, error correction                model (ECM), which indicates the speed of adjustment back to long-run equilibrium after                a  short-run  disturbance is  estimated.  The  standard  ECM involves estimating  the model                indicated in Equation 2. It is expected that coefficient of error correction  term (ECT) is                negative and statistically significant                       LCAD = Υ + Ø ECT      t−1  ∑ n  ɥ    CAD  t−i + ∑ n  α  LBC  t−i  + ɛ              (2)                                         0                                    1                              t                                                                         i=1                                                                             i                                                                                           t                                                        i                                                    i=1                Unit Root Test                Firstly,  to  determine  the  order  of  integration  for  each  variable  we  use  the  Augmented                Dickey-Fuller  (ADF)  and  Phillips  Perron  (PP)  tests.  The  first  step  of  the  empirical                methodology  is  finding  the  order  of  integrations  of  the  data.  Accordingly,  in  order  to                indicate the stationary statute of data set related to bank credit (LBC) and current account                deficit (LCAD) we perform Augmented Dickey Fuller (ADF) and Philips Peron (PP) unit root                tests. The results of the Augmented Dickey-Fuller and Phillip Perron tests are presented in                Table-1. Bank credit (LBC) is integrated to the order of one I (1), while the current account                deficit (LCAD) is also integrated to the order of one I (1). Findings show that all variables                are  not  stationary  at  level  but  both  series  become  stationary  after  taking  their  first                difference. Thus, unit root test results show that all time series in the model have same                order I (1).                                                                   246","Table 1 Results of Unit Root Tests                                                           ADF                           PP                                         (Augmented Dickey Fuller)               (Philips Peron)                           Variables         Level     First Difference       Level   First Difference                             LBC         -1,274       -3,332**             -1,676              -2,679*                             LCAD        -2,159       -5,488***            -2,028         -2,242*                      Note: *, ** and *** denote statistical significance at the 10 %, 5 % and 1 % levels,                      respectively. Maximum lag length is specified by considering Akaike Information Criterion (2).                                                      Source: Authors                 Cointegration Tests                In  this  section  we  employ  cointegration  tests  which  are  the  standard  tools  in  order  to                investigate  the  linear  combination  of  time  series  variables.  In  the  framework  of  ARDL                model, bounds test is applied to indicate whether bank credit (LBC) and current account                deficit (LCAD) have a long run relationship or not.                 In the framework of bounds test for co-integration, we firstly determine lag order as  5                based  on  Akaike  Information  Criterion.  Accordingly,  bounds  test  is  employed  and  the                results are shown in Table-2. As can be seen from the Table calculated F-statistic (6.333)                is  higher  than  the  upper  bound  critical  value  at  a  5%  level  of  significance  (4.390)  as                reported by Pesaran et al. (2001). This implies that the null hypothesis of no cointegration                is rejected at 5%. Therefore, there is a cointegrating relationship among the bank credit                and current account deficit. In other words, the result of the bounds test indicates that                there  is  a  stable  long-run  relationship  between  the  bank  credit  and  current  account                balance.                                            Table 2 ARDL Bound Test Results                          F statistics                             Critical Values                         (k=1, n=55)                1%                   5%                  10%                                              I (0)      I (I)      I (0)     I (I)     I (0)     I (I)                            4,90                                             3.143      3.670      3.790     4.190     5.377     6.047                                               R        Adj. R    LM Test      BP        JB       Wald                                                2                                                             2                          Diagnostic                             Test             0.81       0.75       2.45      1.05      1.07                                                                   (0.10)    (0.42)    (0.58)     4.90                     Note: k is the number of independent variables while N indicates the number of observations.                             Critical values are taken from Narayan (2005).                                                      Source: Authors                Results of Long Run Estimation                 After indicating the presence of cointegration among the variables, in this part of study we                estimated  the  coefficients  indicated  the  long  run  relationship  between  bank  credit  and                current account deficit. The empirical results of the long-run model obtained by ARDL (6,5)                based on Akaike Information Criterion are presented in Table-3. The impact of the bank                credit  on  the  current  account  deficit is  positive  and  statistically  significant.  The  results                indicate that the bank credit is an important determinant of current account deficit. Every                1% increase in bank credit yields average 0.231% deterioration in the current account                balance. Thus, the results confirm the theory asserting that a rise in bank credit increases                the total consumption and will therefore increase imported consumption and investment                goods and deteriorate the current account balance.                 To ascertain the goodness of fit of the ARDL model, the results of diagnostic and stability                tests also have been presented in Table 3. Test results of LM, Breusch-Pagan and Jarque-                Bera  indicate  that  there  is  no  problem  of  the  serial  correlation,  heteroscedasticity,                normality, associated with the model.                                                              247","Table 3 Estimated Long-Run Coefficients for ARDL (6,5)                       Variables                    Coefficients               Prob.                       LCAD(-1)                     1.955 ***                  0.000                       LCAD(-2)                     -2.087 ***                 0.000                       LCAD(-3)                     1.653 ***                  0.000                       LCAD(-4)                     -1.179 **                  0.010                       LCAD(-5)                     0.686 **                   0.044                       LCAD(-6)                     -0.196                     0.191                       LBC                          0.231 **                   0.038                       LBC(-1)                      -0.634 **                  0.039                       LBC(-2)                      1.085 *                    0.077                       LBC(-3)                      -1.213 *                   0.086                       LBC(-4)                      1.083 *                    0.099                       LBC(-5)                      -0.547 **                  0.030                       C                       Long Term Coefficients                       C                            0.258 **                   0.035                       BC                           0.231 **                   0.040                       Diagnostic Test                       R 2                           0.95                       Adj. R 2                     0.94                       LM test                      2.02 [0.16]                       Breusch-Pagan                0.95 [0.50]                       Jarque-Bera                  1.70 [0.42]                          Note: *, ** and *** denote statistical significance at the 10 %, 5 % and 1 % levels,                          respectively.                                                      Source: Authors                 Results of Short Run Estimation                The results of the ECM obtained by ARDL (5,6) based on Akaike Information Criterion are                presented  in  Table-4.  Estimated  coefficient  of  bank  credit  is  positive  and  statistically                significant. Every 1% increase in bank credit yields average 0.212% deterioration in the                current account balance. It seems that the magnitude of positive impact of bank credit on                the  current  account  deficit  is  smaller  than  that  of  the  long-run  impact  (0.231%).  This                finding indicates that the impact of change in bank credit on current account balance is                stronger in the long run compared to short run.                 Error correction term (ECT-1) is one lagged value of the residuals from long-run ARDL model                and the coefficient of it is the speed of adjustment towards long term equilibrium path. The                coefficient of the lagged error term (ECT-1) is negative and highly statistically significant                as we expected. The significance of error correction term shows the causality in at least                one direction. The coefficient of -0.579 indicates the rate of convergence to equilibrium,                which implies that deviation from the long-term equilibrium is corrected by 57.90 % over                each quarter. We apply a number of diagnostic tests to ascertain the goodness of fit of the                error correction model. Findings of LM and Breusch-Pagan confirm that there is no serial                correlation and heteroskedasticity, respectively. The model also passes the Jarque-Bera                normality test which suggests that the errors are normally distributed.                 Summing up the empirical results, it can be inferred that there is reasonable evidence                depicting both positive long-run and short-run relationship between the bank credit and                current account deficit in the case of Turkey. In other words, results of ARDL model indicate                that the credit volume of Turkish Economy is statistical significant and positively affects                current account deficit in the short and long run. The policy implication is that credit growth                is taken into consideration as policy instruments improving current account balance and                hence macroeconomic stability in Turkey. For example difficulties in the current account                balance can be corrected through monetary policies restricting the volume of bank credit.                                                               248","Table 4 Estimated Error Correction Model                       Variables                       Coefficients             Prob.                       LCAD(-1)                       1.615 ***                0.000                       LCAD(-2)                       -1.875 ***               0.000                       LCAD(-3)                       1.453 ***                0.000                       LCAD(-4)                       -1.294 ***               0.000                       LCAD(-5)                       0.795 ***                0.004                       LCAD(-6)                       -0.450 ***               0.002                                                            **                       LBC                            0.212                    0.038                       LBC(-1)                        -0.332 **                0.044                       LBC(-2)                        0.652 **                 0.029                       LBC(-3)                        0.504 **                 0.024                       LBC(-4)                        0.414                    0.107                       ECT(-1)                         -0.579 *                 0.084                       C                               -0.008                   0.353                       Diagnostic Test                       R 2                             0.82                       Adj. R 2                        0.76                       LM Test                         1.44 [0.25]                       Breusch-Pagan                   1.63 [0.12]                       Jarque-Bera                     1.32 [0.51]                         Note: *, ** and *** denote statistical significance at the 10 %, 5 % and 1 % levels,                          respectively.                                                      Source: Authors                 4 Conclusions                Continuity high current account deficit has been one of the crucial economic problems in                Turkey  from  past  to  now.  Namely,  an  increase  in  current  account  deficit  has  been                considered  as  significant  factor  raising  country  risk  and  leading  to  economic  crisis.                Therefore, it is crucial to indicate the dynamics of current account imbalance for Turkish                economic agenda. Accordingly, this article is an attempt to examine the short and long-                run relationship between the bank credit and current account balance in the case of Turkish                economy. The autoregressive distributed lag (ARDL) model is applied to quarterly data for                the  period  2004  to  2017  in  order  to  investigate  whether  a  causal  relationship  exists                between the bank credit and current account balance.                 The result of the bounds test indicates that there is a stable long-run relationship between                the bank credit and current account balance. The estimated coefficients in the short run                and long run ARDL models show that the volume of bank credit is positively related to the                current  account  deficit.  Thus,  the  results  provide  an  evidence  that  bank  credit  play  a                stronger role in determining the behaviour of the current account deficit in Turkey. This                finding also shows that unbalancing impact of bank credit expansion on current account is                crucial  factor  determining  macro-economic  risks  in  Turkey.  Thus,  management  of  the                volume of bank credit is crucial for designing policies aimed at macroeconomic stability.                That  means  the  monetary  policy framework  of  Central  Bank  of  the Republic of  Turkey                (CBRT)  should  take  into  account  the  volume  of  bank  credit  while  keeping  the  current                account balance and macroeconomic stability together.                 References                Akçayır, Ö., Albeni, M. (2016). Impact of Credit Expansion on the Current Account Deficit                in Turkey: Bound Test Approach. Çankırı Karatekin University, Journal of The Faculty of                Economics and Administrative Sciences, vol. 6(1), pp. 557-583.                 Alioğulları, H. Z., Başkaya, Y. S., Bulut, Y. E. and Kılınç, M. (2015). The Relationship of                Consumer and Comercial Loans with Current Account Deficit in Turkey. Research Notes In                Economics, no: 2015-19/17.                                                              249","Brissimis,  S.  N.,  Hondroyiannis,  G.,  Papazoglou,  C.,  Tsaveas,  N.  T.,  Vasardani,  M.  A.                (2012). Current Account Determinants and External Sustainability in Periods of Structural                Change. Economic Change and Restructuring, vol. 45, pp. 71-95.                Buyukkarabacak, B., Krause, S. (2009). Studying the Effects of Household and Firm Credit                on the Trade Balance: The Composition of Funds Matters, Economic Inquiry, vol. 47, pp.                653-666.                Demirhan, B. (2014). The Impact of Credit Growth and Real Exchange Rate Appreciation                on  Current  Account  Deficit  in  Turkey:  ARDL  Bounds  Testing  Approach.  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An Autoregressive Distributed Lag Modeling Approach to                Cointegration Analysis, In: Strom, S. Holly, A. Diamond, P., eds., Centennial Volume of                Rangar Frisch. Cambridge: Cambridge University Press.                Pesaran, M. H., Shin, Y., Smith, R. J. (2001). Bounds Testing Approaches to the Analysis                of Level Relationships. Journal of Applied Econometrics, vol. 16(3), pp. 289-326.                Soydan, A. (2016). Does Credit Growth Cause Current Account deficits? Evidence from                Turkey. Advances in Economics and Business, vol. 4(2), pp. 102-116.                Telatar, E. (2011). Determinant of Current Account Deficit in Turkey and the Relationship                between Bank Credit and Current Account Deficit. The Journal of Bankers, vol. 78, pp. 22-                34.                 Turgutlu, E. (2014). Current Account Balance Implications of Consumer loans: The case of                Turkey. International Journal of Applied Economics, vol. 11(2), pp. 55-64.                                                                     250","Relations between IFRS Adoption and Financial and Non-financial                Measures of Economic Entities, Empirical Results from the Selected                                          Countries of Western Europe                                                                                 2                                           Marcin Kędzior , Dorota Kędzior                                                            1                                               1  Cracow University of Economics                                  Faculty of Finance and Law, Financial Accounting Department                                           ul. Rakowicka 27, 31-510 Cracow, Poland                                              E-mail: kedziorm@uek.krakow.pl                                               2  Cracow University of Economics                                   Faculty of Finance and Law, Corporate Finance Department                                           ul. Rakowicka 27, 31-510 Cracow, Poland                                               E-mail: kedziord@uek.krakow.pl                 Abstract: The aim of the article was to analyze the relationship between the application                of IFRS by economic entities and financial and non-financial measures of enterprises in the                selected countries of Western Europe. 461 companies from Western European countries                were analyzed. The article discusses the issues of preparing financial statements according                to IFRS. The subject of the detailed analysis were correlations between the IFRS application                and  profitability,  liquidity  ratio,  assets  turnover,  investment  intensity,  total  debt  ratio,                company  size,  company  growth,  international  activity,  market  share  and  industry                concentration. The higher level of risk, lower liquidity, lower turnover of assets, higher                capital  intensity,  higher  company  size,  age  of  the  business  unit,  broader  international                operations and higher market shares in companies applying IFRS in selected countries of                Western Europe were verified empirically.                 Keywords: IFRS adoption, European Union, financial ratios, non-financial ratio, accounting                harmonization                JEL codes: M41, M42, M21, L6, L2                 1 Introduction                 International Financial Reporting Standards have been globally recognized and accepted in                accounting. They should be considered as the most mature form of harmonization of local                accounting standards applied in particular countries, and they have been implemented in                over 120 countries. The scope of their application varies significantly, however they are                most commonly applied on stock exchanges. Within the area of the EU, all listed companies                preparing consolidated financial statements are obliged to comply with their standards. In                some  countries,  it  is  required  to  apply  them  in  all  entities,  both  listed  and  unlisted                (Bulgaria), whilst in other countries they are allowed, yet not mandatory for the financial                reports  of  unconsolidated  listed  companies  (Great  Britain),  or  finally  in  some  other                countries  their  application  for  the  mentioned  above  companies  is  prohibited  (Hungary,                Sweden, Spain). Within the confines of the EU, voluntary use of standards in the countries                of Western Europe has been increasing, even though the dynamics of that increase has                somehow slowed down (Kędzior, 2015).                The application of IFRS is beneficial for many participants in the market. The standards                enhance the quality of financial reporting; it is more precise, submitted on time, it better                protects the interests of minority investors, largely eliminates  disparities between local                accounting standards, lowers the cost of financial information for the investors (Ball, 2006).                Nonetheless, the application of IFRS proves to be costly for many entities as it requires                advanced training sessions, and numerous enterprises encounter problems in the practical                application  of  standards.  In  many  cases,  intensive  investment  in  the  area  of  IT  is                prerequisite  (Kim  et  al.,  2012).  Numerous  research  papers  have  shown  the  conducive                impact of IFRS on the cost of capital, risk (Daske, 2006; Daske et al., 2008), profitability,                liquidity (Persakis and Iatridis, 2017; Grabiński and Kędzior, 2006), minimizing earnings                management  (André  et  al.,  2012),  and  alike.  This  paper  aims  at  analyzing  the                                                             251","dependencies between IFRS application by the economic entities and the chosen financial                and non-financial company evaluation ratios in the selected countries of Western Europe.                The  analysis  will  focus  on  the  relationships  between  IFRS  application  and  profitability,                liquidity  ratio,  assets  turnover,  investment  intensity,  total  debt  ratio,  company  size,                company  growth,  company  age,  international  activity,  market  share  and  industry                concentration.  The  analysis  of  relationships  between  asset  turnover,  market  share,                industry concentration and IFRS should be considered as a novelty in the literature on that                subject.                2  The  application  of  IFRS  versus  financial  and  non-financial  gauges                applied by economic entities                 In  literature,  various  dependencies  between  the  application  of  IFRS  and  the  selected                financial  and  non-financial  factors  were  analyzed.  Bessieux-Ollier  and  Walliser  (2012)                considered  the  size  of the  company,  book  value  of  fixed  assets versus total assets,  or                sectorial affiliation. Bova and Pereira (2012) empirically verified indebtedness, company                size, ROE, the percentage of shares held by foreign investors, the number of companies in                the industry, book-to-market ratio, dividend yield. In turn, Gassen and Sellhorn (2006)                analyzed  the  dependencies  between  IFRS  and  international  exposure,  company  size,                dispersion  of  ownership  and  recent  IPOs.  Renders  and  Gaermynck  (2007)  scrutinized                microeconomic  factors  and  country  level  factors.  This  paper  will  focus  on  profitability,                liquidity  ratio,  assets  turnover,  investment  intensity,  total  debt  ratio,  company  size,                company  growth,  company  age,  international  activity,  market  share  and  industry                concentration (compare: Gupta et al., 2017).                 Apparently the companies that apply IFRS are characterized by higher profitability (Pichler                et al., 2018). Business entities that use IFRS should be considered as entities with lower                risk,  a  larger  scope  of  accounting  disclosures,  hence  also  often  a  lower  cost  of  capital                (Daske, 2006; Daske et al., 2008; Jaruga et al., 2007; compare: Cuijpers and Buijink,                2005). In most cases, the application of IFRS  translates into higher quality of financial                reporting in comparison to the local standards. For that reason, the entities applying IFRS                can expect a better access to external financing (Kędzior, 2012; similarly, Grabiński et al,                2013).  On  the  other  hand,  it  should  be  noted  that  earnings  smoothing  is  made  more                difficult by the application of high quality reporting standards (IFRS). Consequently, it is                likely that companies demonstrating high, or consistently high profitability level will decide                to  apply  IFRS  (André  et  al.,   2012).  In  line  with  the  theory  of  political  costs,  highly                profitable  business  entities  should  account  for  their  high  profitability  by  additional                disclosure and special transparency which is feasible with the application of IFRS.                Companies  which  show  higher  levels  of  risk  will  be  more  prone  to  apply  IFRS.  This  is                primarily attributable to the necessity of lowering the corporate risk through the application                of  high  quality  financial  reporting.  Higher  quality  of  such  reporting  allows  individual                investors to compete with the institutional investors on capital  markets, particularly the                less informed investors. Financial reporting according to IFRS reduces the cost and risk of                information  for  investors,  hence  they  may  require  lower  rate  of  return  on  stock  (Ball,                2006). The application of IFRS facilitates risk assessment for the investors (Fijałkowska                and Jaruga-Baranowska, 2007) and also conveys more information e.g. on the investment                risk (Adamik-Citak, 2011).                Low  liquidity  companies  will  strive  for  voluntary  application  of  IFRS.  Excessively  low                liquidity  ratios  increase  the  risk for  a  company  and they may  even lead  to  bankruptcy                (Maślanka,  2012;  Maślanka,  Mazur-Maślanka,  2017).  The  application  of  high  quality                accounting standards (IFRS) may become the way for lowering the risk. IFRS ensure better                information about company’s liquidity relative to local standards (Adamik-Citak, 2011). In                view of Grabiński and Kędzior (2007) those companies that apply IFRS show lower liquidity                level. They can afford it due to lower risk. However, in the opinion of the authors those                differences are negligible. Large companies showing excess liquidity do not mostly require                external capital, hence the application of global accounting standards is not for them of                major importance.                                                             252","Companies characterized by high operational efficiency will be less likely to apply IFRS.                The higher asset turnover, the more efficient use of assets which may translate into higher                profitability of an entity (Okwo et al., 2012). It seems that those companies which display                higher operational efficiency are more likely to obtain external financing at a lower cost.                By this token, they will not strive to implement IFRS at all cost. Companies characterized                by  high  asset turnover  are mostly  smaller  entities  which  do not  possess  the resources                necessary for IFRS implementation (Chi and Padgett, 2006).                High capital intensity is to be found most often in those companies which practice local                accounting standards rather than global solutions such as IFRS (compare: Renders and                Gaeremynck, 2007). Capital intensity is to be understood as the share of fixed assets in                the balance sheet total. The companies with a high share of fixed assets are considered to                be relatively safe; their operation is predictable, and book value closer to the market value.                In most cases, fixed assets do not stand for higher disclosure since their value is relatively                stable and it does not involve “buying options on future” (Hope et al., 2006;  Bessieux-                Ollier  and  Walliser,  2012).  In  addition,  oversight  of  long  term  fixed  assets  is  not  as                important as a large share of current assets. Hence, the application of IFRS in these entities                is less likely (Dumontier and Raffournier, 1998; André et al.,  2012).                Indebted  companies  are  more likely  to  disclose  financial  and  non-financial information.                Higher level of risk requires more disclosure to lessen the informative asymmetry between                people who are outside and inside the company. In this way it is possible to lower the cost                of additional oversight and agency costs to be borne by the owners. Higher cost of capital                for  the  indebted  companies  may  be  lowered  by  voluntary  disclosure  of  additional                information (IFRS) (compare: Li, 2010). Economic entities encumbered with higher risk                (high indebtness) are more willing to implement IFRS to mitigate the cost of capital. High                quality accounting standards may be conducive for forming proper relations between the                owners and creditors. Credit terms are more likely to be complied with and less prone to                manipulation  when  high  quality,  rigorous  accounting  standards  (IFRS)  are  being                implemented.  By  the  same  token,  monitoring  the  company  from  the  perspective  of                creditors as well as the owners will be more efficient once high quality accounting standards                are up and running.                 Large companies are more prone to apply IFRS since the costs of their implementation are                relatively lower for large entities (Bessieux-Ollier and Walliser, 2012). Large entities are                obliged to disclose more financial information, hence their clear preference for IFRS. The                cost of disclosing successive information is declining as that cost is spread over a bigger                amount of sold products or services (Cuijpers and Buijink, 2005). Operating in line with                the  theory  of  political  costs,  large  entities  should  disclose  greater  volume  of  financial                information. Large entities are often financed with external capital and that is why they are                concerned about securities exchanges reactions and they prefer the accounting standards                that are common there (IFRS). By disclosing more information large entities do not worry                about  losing  their  competitive  advantage.  In  conclusion,  large  entities,  stable  on  the                market,  should  apply  high  quality  (credible),  generally  accepted  accounting  standards                (compare: Gassen and Sellhorn, 2006).                Companies with a relatively high growth potential are more likely to apply IFRS. For obvious                reasons, those companies will be more dependent on the external capital to finance their                dynamic growth. One of the prerequisites for the acquisition of capital at lower cost will be                decreasing information asymmetry with the increased disclosure guaranteed by IFRS. IFRS                warrant high quality of financial reporting (Fitó et al., 2012). The external investors will                become more interested in financing a company with a high growth potential once the risk                of  financial  reporting  is  mitigated,  and  information  asymmetry  contained  (André  et                al., 2012).                Again, the dependencies between the age of a company and application of IFRS are not                quite clear. Younger companies are, as a rule, more flexible, and they are characterized by                greater  development  potential  or  the  management  more  open  to  changes.  Greater                development potential translates into a greater demand for the external capital (Bassemir,                2012). It is necessary to show that the conducted activity and the financial performance                                                             253","are credible, hence the application of IFRS seems more likely. Nonetheless, the process of                their application is complex, costly and time consuming, and it calls for qualified manpower                and expertise. Quite often, advanced training courses allowing proper implementation of                IFRS are a must, and they are necessary for older companies. Such companies are, as a                rule, larger entities, well established on the market, and simply required to apply high                quality financial reporting (Şenyiğit, 2014). It seems natural that the above attitude will                prevail and older companies will more eager to apply IFRS.                The companies engaged in the international activity will be more likely to apply IFRS since                they  are  more  accepted  on  capital  markets.  Quite  frequently,  the  companies  involved                internationally are required to produce financial reports for the local markets. Their cost                can be lowered with a uniform application of consistent, standardized accounting standards                (IFRS). By the same token, the cost of auditing financial reports may be also mitigated.                What is more, IFRS reports provide wider information on changing currency exchange rates                which  are  crucial  for  international  companies  (Adamik-Citak,  2011).  International                operations mean that international investors can also invest in enterprises. It is also more                common  to  hire  Board  or  Supervisory  Board  members  from  abroad.  Due  to  their  little                knowledge  about  local  accounting  standards  they  will  prefer  globally  recognized,  high                quality accounting standards represented by IFRS (Francis et al., 2008; Bova and Pereira,                2012).                The companies which have a large share in the market are called industry leaders in many                categories.  This phenomenon concerns the competitive advantage related to operating,                financial and investment activities. The largest companies on the market are concerned                about their image in the eyes of many stakeholders, so they will be especially interested                in the application of widely recognized and reputable accounting standards (IFRS). The                companies which have a large share in the market are characterized by high innovation                and  susceptibility  to  change  in  many  areas  (e.g.  replacing  local  standards  with  IFRS)                (Goddard et al., 2005). As large economic entities they will be more likely to use IFRS. It                should also be noted that financial statements based on IFRS provide important information                on market risk, which is important for these entities (Adamik-Citak, 2011).                The  concentration  of  shares  in  the  industry  usually  means  a  very  intense  competitive                struggle  between  economic  entities  (Bennenbroek  and Harris,  1995).  Often, 4-5  of  the                largest enterprises in the industry control a large part of the market. It means large barriers                to entry and exit from the market (Kotha and Nair, 1995). Competition between individual                entities  is  not  limited  only  to  operational  activities,  but  also  financial  ones,  including                competition for external sources of financing, in particular for share capital. The use of                IFRS  is  a  way  to  gaining  equity  capital  and  authenticating  your  business  and  financial                reporting.  Acquiring  share  capital  at  a  lower  cost  may  facilitate  competitive  struggle,                especially in industries with large capital expenditures.                 For purposes of empirical analysis the following hypotheses have been developed:                H1 – ROE is higher in companies using IFRS                H2 – level of risk is lower for economic entities using IFRS                H3 - liquidity ratio is higher in companies that adopted IFRS                H4 - assets turnover is lower for corporations using IFRS                H5 – investment intensity is lower in companies that implemented IFRS                H6 - total debt ratio is higher in companies using IFRS                H7 – bigger companies implement IFRS                H8 - company growth is higher in companies using IFRS                H9 – older companies use IFRS                H10 - international activity is greater in economic entities that introduced IFRS                H12 - industry concentration is higher in companies using IFRS                The definitions of particular variables included in the analysis are listed below:                    •   IFRS (1;0); when a company applies IFRS, the variable takes ”1”, if it applies local                       standards (local GAAP) then the variable takes ”0”,                    •   ROE = 100%*net result/equity capital,                                                            254","•   liquidity = current liabilities/current assets,                    •   capital intensity = fixed assets/total assets,                    •   asset turnover = operating activity revenue/value of assets,                    •   total debt ratio = value of short and long-term liabilities/ total liabilities,                    •   risk = EBIT standard deviation for the last 4 years,                                                                            3                    •   company size = ln (revenue from operating activity) ,                    •   company age = the number of years in business,                    •   growth potential = annual increase of revenue from operating activity in %,                    •   international activity = the number of branches abroad,                    •   concentration  of  shares  in  the  industry  =  the  value  of  revenue  from  operating                       activity  of  the  four  largest  branches  in  the  industry/  total  revenue  of  operating                       activity of all entities in the industry,                    •   market share = revenue from operating activity of a specified entity/total revenue                       from operating activity of all entities in the industry.                 3 Results and Discussion                The analysis included 461 listed companies from Germany, Great Britain, Finland, Italy and                France, and the empirical data were collected for 2012- 2016. In the test sample, p of                Shapiro-Wilk test was below 0.05, hence the analysis was conducted with the application                of Mann-Whitney test (Table 1 and 2). P values below 0.05 indicate those variables which                are significantly related with the accounting practice.                 Table 1 The Dependencies Between IFRS Application by Economic Entities and Financial                                                         Measures                                   Accounting                   Variable                        Average     SD    Median      Q1       Q3         p                                    practice                                   Local GAAP        -6.73   62.37     5.39     -2.75    13.16                     ROE                                                                         p=0.222                                      IFRS           -3.21   63.27     6.55     -5.86     14.8                                   Local GAAP       39529    239726    1568    487.63    5336                     Risk                                                                        p<0.001                                      IFRS           7186    313698    4976    1584.29   19032                    Liquidity       Local GAAP        0.87     4.88     0.49     0.27      0.75   p<0.001                     ratio            IFRS           0.77     1.96     0.59     0.38      0.81                     Assets         Local GAAP        1.19     0.88     1.10     0.80      1.47   p<0.001                   turnover           IFRS           1.02     0.63     0.93     0.64      1.23                                    Local GAAP        0.40     0.22     0.39     0.23      0.54                  Investment                                                                     p<0.001                                      IFRS           0.47     0.19     0.47     0.33      0.61                   Total debt       Local GAAP        0.78     3.25     0.53     0.37      0.66   p=0.656                     ratio            IFRS           0.59     2.41     0.52     0.37      0.67                                              Source: Author’s own elaboration                 In the analysis of IFRS application and the selected financial measures, it should be noted                that liquidity ratio values are significantly higher for  those companies which apply local                accounting  standards.  Hence,  the  assumption  is  confirmed  that  companies  with  lesser                liquidity and higher risk will seek to reduce it through the use of IFRS. It was noted that                capital intensity was significantly higher among those companies which applied IFRS, hence                the  increased  demand  for  long  term  capital  requires  the  application  of  IFRS.  The                dependency  of  IFRS  on  profitability  and  indebtness  turned  out  to  be  insignificant.  In                addition, it was noted that companies encumbered with higher risk are more likely to apply                IFRS  (IFRS  reduce  information  cost  for  the  investors).  In  turn,  asset  turnover  ratio  is                   3  Company size can be also defined as, value of assets or number of employees.                                                            255","significantly  higher  for  those  companies  which  apply  local  accounting  standards.  High                operating efficiency lowers the risk of a company and the cost of its capital.                 Table 2 presents the dependencies between the selected non-financial factors and IFRS                application.  In  terms  of  statistics,  the  age  of  companies  applying  IFRS  is  significantly                higher. This  might  be  attributable  to  the fact  that  older  companies  have the  resources                allowing  application  of  IFRS.  In  turn,  companies  applying  IFRS  are  substantially  bigger                which possibly stems from the fact that such companies require more capital from the                external sources and need to remain accountable to the social and economic environment.                Companies applying IFRS have greater share in the market. Such entities, as business                leaders,  are  obliged to  sustain the image  of  their  company  and  the  quality  of  financial                reporting. For this reason, they are more likely to apply IFRS. International operations are                conducted  to  a  wider  extent  in  entities  applying  IFRS,  which  are,  for  understandable                reasons, more accepted or even required for entities operating internationally (globally).                The  relationship  between  IFRS  and  the  concentration  of  market  shares  and  growth                opportunities proved to be statistically insignificant.                 Table 2 The Dependencies Between IFRS Application by Economic Entities and Selected                                                  Non-financial Measures                                  Accounting                   Variable                        Average     SD     Median     Q1       Q3         p                                    practice                   Company         Local GAAP        10.70    2.40    10.54     9.24     12.01   p<0.001                     size             IFRS           11.74    2.38    11.59     10.30    13.02                    Company         Local GAAP        14.35    73.92    5.24     -8.44    18.44   p=0.341                    growth            IFRS           18.52   349.53    4.96     -8.34    16.37                    Company         Local GAAP        45.85    70.79   23.00     11.00    54.00   p<0.001                     age              IFRS           44.25    41.68   30.00     14.00    58.00                  International     Local GAAP        1.70     1.47     1.39     0.69      2.56   p<0.001                    activity          IFRS           2.69     1.51     2.56     1.61      3.66                                    Local GAAP        3.66     10.76    0.19     0.04      2.18                 Market share                                                                    p<0.001                                      IFRS           6.29     12.97    0.92     0.16      4.69                   Industry        Local GAAP        84.05    11.01    86.6     78.11    92.73   p=0.478                 concentration        IFRS           84.19    9.92     84.8     78.22    91.38                                              Source: Author’s own elaboration                 4 Conclusions                IFRS  should  be  considered  the  most  mature  attempt  to  harmonize  local  accounting                standards.  The  introduction  of  IFRS  has  undoubtedly  become  the  biggest  revolution  in                accounting history in over 120 countries. They affect many spheres of activity, such as,                among  others,  the  cost  of  capital,  risk,  profitability,  liquidity,  the  quality  of  financial                reporting,  the  phenomenon  of  profit  management,  etc.  This  paper  focused  on  the                dependencies  between  IFRS  application  and  the  selected  financial  and  non-financial                measures  of  economic  entities’  performance  such  as  profitability,  liquidity  ratio,  assets                turnover, investment intensity, total debt ratio, company size, company growth, company                age, international activity, market share and industry concentration.                 This  paper  has  shown  that  the  production  sector  of listed  companies,  operating  in  the                selected countries of Western Europe and applying IFRS,  demonstrate higher risk, lower                liquidity, lower assets turnover, higher capital intensity, and they are relatively older and                larger entities more actively engaged in the international activity. In addition, they control                larger market share than those entities which do not apply IFRS. Therefore, the relationship                between IFRS and liquidity assets turnover, company ages, company, international activity                and  market  share  should  be  verified  positively.  Thus,  dependencies  with  non-financial                                                            256","variables are more predictable than with financial variables. The dependencies between                IFRS and risk and investment capacity have been verified negatively. A higher level of risk                was  observed  in  companies  using  IFRS.  Thus,  in  the  surveyed  company  population,                reducing  the  total  risk  of  a  company  by  preparing  the  high  quality  of  the  financial                statements is not significant. Public manufacturing companies listed on the stock exchange                are  not  characterized  by  a large level of  risk, hence  the introduction  of  IFRS  does  not                significantly reduce the risk. In companies using IFRS, the capital intensity is higher. An                important factor for these companies may be valuation of fixed assets according to fair                value recommended by IFRS.                 References                Adamik-Citak,  M.  (2011).  Porównanie  informacji  zawartej  w  sprawozdaniu  finansowym                prezentowanym  według  IFRS  ze  sprawozdaniem  finansowym  sporządzonym  zgodnie  z                polskim prawem bilansowym. 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Accounting  and  Management  Information  Systems, vol.  13,  pp.                449–465.                                                                258","Comparison of Gulka’s Model and Model IN05                                 on Sample of Slovak Industrial Companies                                                                                   2                                                             1                                        Natália Klempaiová , Mária Bohdalová                                              1  Comenius University in Bratislava                                  Faculty of Management, Department of Information Systems                                          Odbojárov 10, Bratislava, 820 05, Slovakia                                           E-mail: natalia.klempaiova@fm.uniba.sk                                               2  Comenius University in Bratislava                                  Faculty of Management, Department of Information Systems                                          Odbojárov 10, Bratislava, 820 05, Slovakia                                            E-mail: maria.bohdalova@fm.uniba.sk                 Abstract: In this paper, we analyse two modern bankruptcy models on samples of selected                Slovak industrial enterprises. IN05 is a well-known model in Slovakia that gives a high                percentage of reliability in predicting the financial health of Slovak companies. The Gulka’s                model was created in 2016 and its popularity is rising. The aim of this paper is to compare                IN05  and  Gulka’s  models  and  determine  which  of  them  is  more  suitable  for  Slovak                enterprises. Analyses were based on real data from the latest available financial statements                of  industrial  enterprises  for  five  industries  typical  of  Slovakia:  Automotive,  Electricity,                Agriculture and Forestry, Construction, Mechanical Engineering. We have selected private                domestic  companies  with  limited  liability.  In  the  case  of  companies  in  bankruptcy  or                restructuring,  we  used  the  latest  available  financial  statements  before  the  date  of                bankruptcy or, respectively, management of restructuring. Using contingency tables, we                compared the number of correct predictions for both models - the Gulka’s model, created                by  the  logistic  regression  method  (LOGIT  model)  and  the  IN05  index  created  by  the                Multidimensional Discrimination Analysis (MDA method). We predicted the correct forecast                when the company was actually in bankruptcy and the model rated it or if the company                was not bankrupt and the model ranked it as prosperous. Both models are very useful in                the conditions of the Slovak Republic. For Slovak industrial enterprises, the Gulka´s model                is more reliable. In general, the model's highest ability to leverage is in the country where                the model was created or for the industry for which it was created.                Keywords:  Index  IN05,  Gulka’s  model,  corporate  financial  health  forecast,  credit  and                bankruptcy models                JEL codes: C35, M10, G32                 1 Introduction                 Predictive ability of any bankruptcy model depends on time, location, industry for which it                was developed, and also on the used method. Our research focuses on modern bankruptcy                models  that  can  estimate  the  financial  situation  of  Slovak  companies  as  accurately  is                possible.  In  this  paper,  we  focused  on  Slovak  or  Czech  models  because  the  economic                situation is similar in both countries (Diheneščíková and Hičák, 2011).                There  are  several  Slovak  bankruptcy  models.  For  example,  Chrastinová´s  CH-index                (Chrastinová, 1998) or Gurčík’s model (Gurčík, 2002) are aimed for agriculture.  Binkert                (Binkert,  1999)  developed  bankruptcy  model  for  joint  stock  companies,  Hurtošová                (Hurtošová, 2008) created model for personal and legal entities accounting for double-                entry  accounting  and  Gulka’s  model  (Gulka,  2016)  was  developed  for  Slovak  trading                companies.  Only Gulka´s model could easily differentiate prosperous and unprosperous                businesses. In the study by (Ďurica and Zvaríková, 2017), models created for V4 countries                were compared, either through Multidimensional Discrimination Analysis (MDA) or logistic                regression (LOGIT models).                                                              259","Based on the above, we decided to look more closely at the perspective for future Gulka´s                model and to compare it with the frequently used the Czech index IN05 (Neumaierová and                Neumaier,  2005),  which  also  proved  to  be  successful  in  Slovakia  (Bohdalová  and                Klempaiová, 2017). Our aim is to compare and analyse the predictive power of the IN05                model  and  Gulka´s  model  on  samples  of  selected  Slovak  industrial  enterprises  and  to                determine which of them is more suitable for Slovak companies.                 The Gulka´s model was obtained using a logistic regression method. The math entry is as                follows (Gulka, 2016):                                 0,0216−0,6131∗  1−0,0068∗  2−0,0293∗  3−0,0011∗  4+0,0240∗  5+0,0317∗  6−1,0663∗  7                          =                                                                                             (1)                           1 +    0,0216−0,6131∗  1−0,0068∗  2−0,0293∗  3−0,0011∗  4+0,0240∗  5+0,0317∗  6−1,0663∗  7                where                 X1  –  Quick  Ratio  –  (Financial  accounts  /  (Short-term  liabilities  +  Short-term  financial                borrowings + Current bank loans))                 X2 – Working Capital Turnover = ((Revenue from sold goods + Manufacture) / Working                capital;  while  WC  is  Current  assets  –  Short-term  liabilities  –  Short-term  financial                borrowings – Current bank loans)                X3 –Financial Accounts Ratio (in %) – (Financial accounts / Total Assets)                 X4 – Self-financing Ratio (in %) – (Equity / Total Assets)                X5 – Credit load (in %) – ((Fixed Bank loans + Short-term financial accommodations) /                Total Assets)                X6 – Share of liabilities to state institutions (in %) – ((Payables from social insurance +                Tax liabilities and grants) / Total Assets)                X7 – Return on Assets from the perspective of EBITDA – (EBITDA / Total Assets; while                EBITDA = Profit from economic activity + Depreciation + Residual cost of the sold long-                term assets and material – Revenue from the sale of long-term assets and material)                 p –probability the company will go bankrupt during the next 12 months                if 1 ≥ p ≥ 0,50, an enterprise is heading to bankruptcy                 and if 0,50 > p ≥ 0, the enterprise is not heading to bankruptcy                Index IN05 was created by modifying Altman's methodology for the conditions of Czech                businesses using a multidimensional discriminatory analysis:                                                                         Ý                                           05 = 0,13 ∗  + 0,04 ∗   + 3,97 ∗     + 0,21 ∗    + 0,09 ∗                              (2)                                                Ú                                       +     Ú                where                A – Assets respectively Liabilities                CZ – Foreign Sources                EBIT – Earnings before Interest and Taxes – (Profit before tax + Interest expense)                Ú – Interest Expense                VÝN – Total Returns                OA – Current Assets                KZ – Short-term Liabilities                KBÚ – Short-term Bank Loans and Borrowings                If IN05 > 1.6, an enterprise creates worthiness with probability 67%.                 The area enclosed by 0.9 ≤ IN05 ≤ 1.6 is an area of unmatched results (grey zone).                If IN05 < 0.9, an enterprise is at risk of bankruptcy with probability 86%.                                                              260","If interest rates in the EBIT / Ú indicator approach zero, a problem arises. The Neumaiers                recommend limiting the value of this indicator to 9 (Neumaierová and Neumaier, 2005).                  2 Methodology and Data                To analyze Gulka´s model and IN05 index, we have selected five sectors that make up a                significant part of GDP in Slovakia and are typical for Slovakia. These are privately owned                domestic limited liability companies in the automotive, electrical, agricultural and forestry                sectors, construction and machinery.                 We have drawn the data from the databases of Finstat, L.t.d.(2018). After filtering the five                sectors according to Finstat´s classification, legal form and type of ownership, a database                with 31 431 enterprises was created. Of these, 380 companies were bankrupt with a known                bankruptcy date or authorization for restructuring.                 We removed empty rows in the indicators needed to calculate p in Gulka´s model. We also                removed rows with businesses missing the IN05 value, as the database contained already                calculated index values of IN05 and its indication. The database was also cleared from                companies  where  assets  were  not  attributable  to  liabilities  and/or  were  found  to  be                negative  in  assets,  stocks,  financial  accounts,  bank  loans,  sales,  etc.  Unlike  Gulka,                however, we ignored extreme variability in the values of financial indicators. After these                adjustments, the number of businesses decreased to 661, of which 15 were in real failure.                We manually searched and added data from the last published financial statements before                the  date  declared  as  bankruptcy  or  restructuring  authorization  for  businesses  in                bankruptcy. We mostly used data from one to two calendar years before the date with                public known bankruptcy. Subsequently, companies with missing indicator values required                to calculate the value p in Gulka´s model or with some of the above accounting anomalies                were removed again. Final database sample consisted of 649 businesses, out of which 10                were in real decline.                Subsequently, calculations and the new cut-off thresholds for the IN05 index include only                two areas in Gulka´s model, in contrast to IN05 model, to determine the prosperity of the                business. We calculated the average from the upper (1.6) and the lower (0.9) limits for                IN05  model  (1.25)  to  compute  new  cut-off  thresholds  similarly  as  Gulka  used  for  the                Altman model (Gulka, 2016). For modified IN05 model, there was a cut-off threshold equal                to 1.25. If the calculated value was higher than 1.25, the company was prosperous, and                businesses with a value of less than or equal to 1.25 were non-prosperous. For Gulka´s                model, there was a cut-off threshold of 0.50.                We have calculated individual ratios (X1 – X7) for all companies according to equation (1)                and then we have calculated the probability p. There were situations when the denominator                was zero when we calculated the probability  p. Therefore, we had to discard four more                companies from the database for which it was impossible to calculate their p values. The                final database contained 645 businesses, 10 of them were actually bankrupt. After checking                duplicity of data, we could proceed with the analysis of both models.                This  database  was  used  to  find  out  how  many  businesses  the  model  labelled  as                \"prosperous\" in a group of truly prosperous businesses or non-prosperous in a group of                companies  in  real  decline.  Models  can  be  compared  based  on  the  number  of  correct                answers  individually  in  each  group.  Given  an  uneven  number  of  prosperous  and                unsustainable businesses in the database, we will not be able to determine whether the                model  is  more  successful  in  predicting  bankruptcy  or  predicting  business  prosperity                (whether the model has a tendency to include an enterprise rather to prosperous or non-                prosperous companies).                If we would like to determine the success or failure of the models based on a simple sum                of the total correctly evaluated companies or the total error rate (census of type I and type                                                             261","II errors), we would have to use a sample with the same number of prosperous and non-                prosperous businesses. Otherwise, we could get a distorted conclusion. If the first model                was compared to the other model, it would have a higher tendency to classify businesses                as  thriving,  it  would  show  a  higher  success  rate  than  the  second  model  in  an  uneven                database with more realistically prosperous businesses. In reality, however, the second                model  could  be  more  successful.  Based  on  this  fact,  we used  a  database in which the                number  of  businesses  in  real  decline  (10)  and  the  number  of  real  prosperous                businesses (10)  was  the  same.  For  each  bankruptcy  or  restructuring  of  companies,  we                assigned a truly prosperous company with the same SK-NACE business class rating and                approximately  the  same  size  category,  with  the  main  criteria  being  the  number  of                employees, after which other size criteria were compared.                 The analysis was performed using MS Office Excel software.                 3 Results and Discussion                We began analysis with the case when businesses were real prosperous (their count was                635) or bankrupt (their count was 10). Table 1 shows the number of prosperous businesses                and businesses in bankruptcy or restructuring in total and in individual sectors. There are                no companies in the automotive and mechanical engineering sector among companies in                decline.                                                   Table 1 Initial State                  Bankruptcy and                        Number of             % of        % of Superior                 Restructuring                         enterprises           Total             row                 Yes                                        10                1.55            1.55                 Electricity                                 1                0.16            10.00                 Agriculture and Forestry                    3                0.47            30.00                 Construction                                6                0.93            60.00                 No                                        635               98.45           98.45                 Automotive                                  3                0.47            0.47                 Electricity                                19                2.95            2.99                 Agriculture and Forestry                  172               26.67            27.09                 Construction                              407               63.10            64.09                 Mechanical Engineering                     34                5.27            5.35                 Total                                     645              100.00           100.00                               Source: Own results processed based on data from www.finstat.sk                 We  found  out  which  model  predicted  more  business  prosperity  in  a  group  of  truly                prosperous businesses and bankruptcy in a group of truly non-prosperous businesses. The                results  for  IN05  model  with  the  modified  cut-off  boundary  and  Gulka´s  model  are                summarized in Table 2.                In the group of truly prosperous businesses (635), IN05 model with a modified cut-off                threshold identified 397 (62.52% of 635) companies in bankruptcy or restructuring and                238 (37.48% of 635) companies as prosperous. Gulka´s model identified 322 companies                as  non-prosperous  and  313  as  prosperous in this  group.  In this  comparison, it had  75                correct predictions more than IN05 model. In the group of companies that were bankrupt                (10), both models agreed. They predicted bankruptcy to 9 businesses and 1 was identified                as prosperous, and it was for the same company.                                                                   262","Table 2 Comparison of Prediction - IN05 Model with Cut-off Line 1.25 and Gulka´s Model                                      IN05 model with modified                   Gulka´s model                                          cut-off line (1.25)                 (cut-off line 0.50)                 Bankruptcy        Number of       % of       % of      Number of      % of        % of                 and               enterprises     Total    Superior  enterprises      Total     Superior                 Restructuring                                 row                                 row                 Yes                   10          1.55       1.55          10         1.55        1.55                 Bankruptcy             9          1.40       90.00         9          1.40       90.00                 Prosperity             1          0.16       10.00         1          0.16       10.00                 No                   635         98.45      98.45         635        98.45       98.45                 Bankruptcy            397        61.55       62.52        322         49.92      50.71                 Prosperity            238        36.90       37.48        313         48.53      49.29                 Total                645        100.00      100.00        645        100.00     100.00                               Source: Own results processed based on data from www.finstat.sk                 From the 397 enterprises that IN05 model with a modified cut-off threshold identified as                bankrupted in a group of real prosperous companies, Gulka´s model marked 254 as non-                prosperous and 143 as prosperous. From the 238 enterprises that the revised IN05 model                estimated as prosperous companies, both models agreed on 170 businesses. The others                (68) were classified by Gulka´s model to be non-prosperous companies (Table 3).                           Table 3 IN05 Model with Modified Cut-off line versus Gulka´s Model                                 Bankruptcy          Number of        % of        % of                                 and                enterprises       Total     Superior                                 Restructuring                                     row                                 Yes                     10           1.55         1.55                                 Bankruptcy               9           1.40        90.00                                 Bankruptcy               9           1.40        100.00                                 Prosperity               1           0.16        10.00                                 Prosperity               1           0.16        100.00                                 No                      635         98.45        98.45                                 Bankruptcy              397         61.55        62.52                                 Bankruptcy              254          39.38       63.98                                 Prosperity              143          22.17       36.02                                 Prosperity              238         36.90        37.48                                 Bankruptcy               68          10.54       28.57                                 Prosperity              170          26.36       71.43                                 Total                   645         100.00      100.00                               Source: Own results processed based on data from www.finstat.sk                 It means that from the 322 companies that Gulka´s model estimated as a non-prosperous                in a group of truly prosperous companies, IN05 model with a changed cut-off threshold                marked 68 companies as prosperous and 254 as non-prosperous. From the 313 enterprises                that Gulka´s model identified as prosperous, 143 as non-prosperous and 170 prosperous                companies were determined by modified model IN05 (Table 4).                 We also looked at whether the business was indeed prosperous or non-prosperous if the                model marked it so. The analysis has been done on a sample of 20 companies filtered from                the original database with 645 companies. 10 companies were bankrupt and 10 were not                bankrupt.  The  sample  consisted  mostly  of  the  construction  industry.  It  did  not  include                Automotive  and  Mechanical  Engineering  industries  because  there  are  no  bankrupt                companies in the original database with 645 enterprises.                                                             263","Table 4 Gulka´s Model versus IN05 Model with Modified Cut-off Line                                 Bankruptcy          Number of        % of        % of                                 and                enterprises       Total     Superior                                 Restructuring                                     row                                 Yes                     10          1.55%       1.55%                                 Bankruptcy               9          1.40%       90.00%                                 Bankruptcy               9          1.40%       100.00%                                 Prosperity               1          0.16%       10.00%                                 Prosperity               1          0.16%       100.00%                                 No                      635        98.45%       98.45%                                 Bankruptcy              322        49.92%       50.71%                                 Bankruptcy              254         39.38%      78.88%                                 Prosperity               68         10.54%      21.12%                                 Prosperity              313        48.53%       49.29%                                 Bankruptcy              143         22.17%      45.69%                                 Prosperity              170         26.36%      54.31%                                 Total                   645       100.00%  100.00%                               Source: Own results processed based on data from www.finstat.sk                 IN05 model with an amended cut-off marked 16 out of 20 enterprises to be bankrupt, of                which there were 9 companies in bankruptcy or restructuring and 7 were not. 4 businesses                were  declared  prosperous,  of  which  1  was  actually  bankrupt  and  3  were  actually                prosperous. As can be seen in Table 5, the same is valid for Gulka´s model.                                   Table 5 Comparison of Prediction for 20 Companies                                      IN05 model with modified                   Gulka´s model                                          cut-off line (1.25)                 (cut-off line 0.50)                                    Number of      % of       % of      Number of      % of        % of                                    enterprises    Total    Superior   enterprises     Total     Superior                                                              row                                  row                 Bankruptcy             16        80.00      80.00          16         80.00      80.00                 Yes                     9        45.00      56.25          9          45.00      56.25                 No                      7        35.00      43.75          7          35.00      43.75                 Prosperity              4        20.00      20.00          4          20.00      20.00                 Yes                     1         5.00      25.00          1          5.00       25.00                 No                      3        15.00      75.00          3          15.00      75.00                 Total                  20       100.00     100.00          20        100.00     100.00                               Source: Own results processed based on data from www.finstat.sk                 However, the predictions of the two models differ slightly. From the 16 companies that                were predicted by IN05 model with the modified cut-off threshold as bankrupt, there were                only  15  marked  as  non-prosperous  by  Gulka´s  model.  1  business  was  identified  as                prosperous, and it also corresponded to the reality. Modified IN05 model classified this                business as non-prosperous. From the 4 companies that IN05 model with the changed cut-                off  boundary  classified  as  prosperous,  1  was  classified  as  non-prosperous  by  Gulka´s                model.  However,  this  business  was  prosperous.  From  3  other  companies  classified  by                Gulka´s model to be prosperous, 2 were prosperous and 1 bankrupt. The company that                was bankrupt and was among those four companies was evaluated as prosperous by both                models (Table 6).                 Based on the results of the analyses, we can evaluate Gulka´s model as slightly more                successful in comparison to IN05 model with the changed cut-off-border. In the group of                                                             264","truly prosperous businesses, more businesses were rated as prosperous, in the group of                companies in real decline, the situation was balanced in both models. When investigating                whether the business was actually bankrupt or prospering, both Gulka’s model and IN05                model with cut-off boundary resulted in 7 false negatives (error of type II). Both models                resulted in 1 false positive (error of type I). In this investigation, the status of both models                was balanced.                  Table 6 IN05 Model with Modified Cut-off Line versus Gulka´s Model – 20 Companies                                                         Number of        % of        % of                                                       enterprises       Total     Superior                                                                                      row                              Bankruptcy IN05               16          80.00        80.00                              Bankruptcy Gulka              15          75.00        93.75                              Yes                            9           45.00       60.00                              No                             6           30.00       40.00                              Prosperity Gulka               1           5.00         6.25                              No                             1           5.00       100.00                              Prosperity IN05                4          20.00        20.00                              Bankruptcy Gulka               1           5.00        25.00                              No                             1           5.00       100.00                              Prosperity Gulka               3          15.00        75.00                              Yes                            1           5.00        33.33                              No                             2           10.00       66.67                              Total                         20          100.00      100.00                               Source: Own results processed based on data from www.finstat.sk                 4 Conclusions                In our analysis, Gulka´s model came out slightly more successful than the IN05 model,                but it is questionable to declare it as more successful than the IN05 model. In practice,                nobody uses IN05 model with a modified cut-off boundary because it was only used to                make the comparison of both models feasible. The advantage of Gulka´s model is that it                was created by a method of logistic regression (generally considered to be more accurate                than methods of multidimensional discriminatory analysis), it is newer than IN05 index and                it is created based on Slovak businesses data (IN05 index is a Czech model) including                industrial companies (IN05 index was based on Czech industrial enterprises). These factors                could cause that Gulka´s model is more successful than IN05 index in the conditions of the                Slovak Republic and therefore more suitable for Slovak companies.                 References                Binkert,  C.  H.  (1999).  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Ekonomika Management Inovace,                vol. 9(3), pp. 30-38.                                                                                                                    266","The Efficiency of Health Systems in OECD Countries with Novel                                                  DEA Methodology                                                                                2                                                               1                                           Kristína Kočišová , Jakub Sopko                                                    1  Technical University                                 Faculty of Economics, Department of Banking and Investments                                         Němcovej 32, 040 01 Košice, Slovak Republic                                              E-mail: kristina.kocisova@tuke.sk                                                     2  Technical University                                 Faculty of Economics, Department of Banking and Investments                                         Němcovej 32, 040 01 Košice, Slovak Republic                                                E-mail: jakub.sopko@tuke.sk                 Abstract:  The  main  purpose  of  this  paper  is  to  determine  the  relative  scores  of  the                efficiency of health systems in the OECD countries. The data used in the analysis of this                paper has been obtained from the official OECD database and we have been analyzing the                data between 2005 and 2015. We use the health indicators to point out the performance                of healthcare systems of individual OECD countries. The efficiency results are obtained by                Data Envelopment Analysis (DEA). DEA is an analysis of the efficiency of production units,                which give us the efficiency score of the specified individual units. DEA is also used for the                examination of the efficiency in the services sector. In this way, we can point to the level                of  efficiency  of  the  health  systems  of  the  individual  OECD  countries.  Traditional  DEA                methods are limited in some cases with regard to the nature of the selected variables.                Emrouznejad and Amin (2009) point to the limitations of DEA models using the indicators                expressed as ratio variables. In our paper, we will use the novel method for assessing                efficiency  –  Dynamic  Network  Data  Envelopment  Analysis  (DNDEA),  applied  by  Ozcan                and Khushalani (2016).                Keywords: public health, Data Envelopment Analysis, DNDEA, OECD, efficiency                JEL codes I15, H51, C61                  1 Introduction                The process of shaping individual sectors of the national economy can be monitored on the                basis  of the  characteristic  changes  that  have  been  deepening in the  given  period.  The                national economy represents the basic cycle of economic processes, which includes four                basic economic sectors: the primary, secondary, tertiary and quaternary. A major change                in the development of the national economy has led to deindustrialization – the shift of                labor force from the industrial sector. The beginning of this process has gradually become                the  fastest  growing  and  currently  one  of  the  most  important  sectors  of  the  national                economy,  the  service  sector.  Important  fields  of  the  tertiary  sector  include  health  and                education. These areas of service sector can be quantified by a number of methods. The                results being known as determinants of health and education. The educational level of the                country and the adequate health status of the population affect the development of the                regions.  In  most  countries,  we  still  see  poor  regions  and  regions  with  poor  healthcare                infrastructure.  Often  these  problems  are  associated  with  inefficient  financing  in  the                country. At the present we are often confronted with the increasing trend of investment                and innovation in the service sector. The service sector is known as a faster developing                sector compared to other. Health and education are areas in which the country in recent                years invested considerable percentage of funds. Therefore, we can further consider the                analysis of the efficiency of inputs and outputs of health and education policy. Among the                best-known and most widely used methods of measuring efficiency in the service sector                are Data Envelopment Analysis models (DEA). In our work we try to estimate the efficiency                of health systems based on data obtained from the OECD (Organisation for Economic Co-                operation and Development) database using the DEA analysis.                                                               267","The paper is organized as follow: In the section 2 a preview of the relevant literature is                provided.  In  the  section  3,  the methodology  and  data  are  described  and  the section 4                presents the key findings and results.                 2 Literature preview                Several authors address issues of efficiency in the healthcare sector. The OECD and the                World Health Organization (WHO) offer several publications and reports (2017, 2018) that                tell us about health care situation. The literature overview on efficiency in the health sector                is also shown in Table 1.                Tandon et al. (2001) in their work point out that with the increase of health expenditure                per capita, overall results of the country have increased. They analyze 191 countries and                the relative efficiency score of the health expenditure. Countries with a high incidence of                AIDS and HIV had a low level of efficiency. As the worst countries were: Malawi, Botswana,                Namibia, Zambia and Zimbabwe, while the best performing countries were: Italy, France,                San Marino, Malta, Oman. Sinimole (2012) analyzes the efficiency in 180 WHO countries.                The  results  show  that  45  out  of  180  countries  were  efficient. Using  the DEA  model, it                estimates  the  relative  efficiency  of  WHO  countries,  pointing  to  five  types  of  indicator                groups: health status, health system, risk, health service coverage, demographic socio-                economic situation and statistics and the author uses data, which belong under the groups                of health status and health service coverage.                              Table 4 DEA Models Specification Used in the Reviewed Studies                                                           Paper                                                      Data \& Methods                               Inputs \& Outputs                                  Results                                                Retzlaff-Roberts et. al. (2004)                                               27 OECD countries; DEA models                 Inpatient   Beds,   MRI,   Physicians,   Health   Australia,  UK,  Sweden,  Turkey,  Spain,  Norway,                 expenditure, Tobacco use, Gini coefficient    Mexico,  Australia,  Canada,  Japan,  France,  Ireland,                 Outputs:                                      Greece,  Korea  are  efficient.  Therefore,  they  have                 Infant mortality, Life expectancy             found that the country can be technically efficient or                                                               inefficient at any level of health outcomes.                                               Spinks and Hollingsworth (2009)                                   28 OECD countries, DEA \& Malmquist index, 1995 and 2000                 Expected  years  of  schooling  under  current   Efficient countries:                 conditions  excluding  education  for  children   In  1995  –  Spain,  Japan,  Korea,  Greece,  Mexico,                 under five, Unemployment rates as percentage   Turkey.                 of  total  workforce,  GDP  per  capita,  Health   In 2000 – Spain, Japan, Switzerland, Korea, Iceland,                 expenditure per capita                        Turkey, Mexico, Greece.                 Outputs:                 Life expectancy                                                     Hadad et al. (2013)                                31 OECD countries, DEA models and multivariate linear regression                 Physicians  density,  Inpatient  bed  density,   9  countries  (Australia,  Canada,  Israel,  Italy,                 Health  expenditure,  GDP,  Fruit  and  vegetable   Luxembourg,  Spain,  Sweden,  Switzerland,  and  the                 expenditure.                                  United Kingdom) as efficient.                 Outputs:                 Life  expectancy,  Infant  mortality;  Control                 variables:  Fat  intake,  Health  expenditure,                 Unemployment,    Gini   index,   Environmental                 health score, Organizational arrangements, Use                 of disease management programs, Gatekeeping,                 Performance related payment incentives                                                    Jeremic et al. (2012)                                        27 European countries, Distance based analysis                 Nurses,  Physicians,  Dentists,  Public  and  total   Cyprus  and  Ireland  have  a  most  efficient  health                 health expenditure, Beds                      system. New model for evaluating the efficiency score                 Outputs:                                      is presented.                 Life expectancy, Healthy life expectancy; Adults,                 under 5, infants, mothers mortality rate                                                      Sinimole (2012)                                               180 WHO countries, DEA models                 Nurses, Physicians, Health expenditure        45  out  of  180  countries  are  relatively  efficient  in                 Outputs:                                      providing  health  services.  12  out  of  48  efficient                                                             268","Adult,  infant,  neonatal  and  under  5  mortality   countries  are  EU  countries.  Most  countries  are                 rate, immunization coverage                   countries  with  low  input  variables  (e.g.  density  of                                                               doctors,  expenditure  on  health)  but  these  countries                                                               produce high output values.                                             Tajnikar and Došenovič Bonča (2007)                                      16 EU countries, DEA models and group comparison                 Physicians,  Beds,  Discharges,  Consultations,   Health  care  provision  is  more  efficient  in  countries                 Health expenditure                            where  the  private  ownership  of  providers  is                 Outputs:                                      predominant  and  where  health  care  systems  follow                 Life  expectancy  (male,  female),  Mortality  rate,   the social insurance model.                 Discharges, Physicians consultations                                                   Samut and Cafri (2015)                  29 OECD countries between 2000 - 2010, DEA models, Malmquist Productivity, Panel Tobit Model                 Beds, Physicians, Nurses, MRI, CT             6 countries were fully efficient in 2010. Fully efficient                 Outputs:                                      during period 2000-2010 were Mexico, Turkey and the                 Discharge  rates  from  all  hospitals,  Infant   UK. Japan, Iceland, France and Belgium have under                 survival rate.                                average efficiency scores for all the years.                 Independent variables: GDP, Health expenditure                 (public  and  private),  educational  expenditure,                 Public hospital, Private hospital, Life expectancy                                                Ozcan and Khushalani (2016)                               34 OECD countries between 2000 and 2012, Dynamic Network DEA                 Alcohol   and   tobacco   consumption,   Obese   14  out  of  34  countries  underwent  reform  between                 population,   Public   and   private   health   period.  Average  overall  Malmquist  score  for  these                 expenditure, CT, Physicians, Hospital beds    countries  was  0.98.  Austria,  Canada,  US,  Mexico,                 Outputs:                                      Turkey and Norway improved efficiency scores of the                 Life  expectancy  at  birth  (male  and  female),   overall health system over time.                 Discharges,  Consults.  Carry  over  variables:                 Mortality  (maternal  and  infant),  Health  status.                 Medical  care  division:  Immunization,  Cancer,                 Screening-breast and cervical cancer.                                                  Behr and Theune (2017)                                               34 OECD countries, DEA models                 Beds,  Physicians,  Nurses,  Cataract  surgery,   Germany  was  inefficient  at  producing  surgeries.                 Coronary artery bypass graft, Transplantation of   Denmark  –  the  country  with  the  highest  sum  of                 kidney,   Health   expenditure,   GDP,   Gini   weighted  inputs.  Japan  was  the  most  inefficient                 coefficient,  Alcohol  and  tobacco  consumption,   country in analysis of lifestyle and mortality. Norway,                 Obesity.                                      Switzerland,  Luxembourg  and  USA  –  extremely low                 Outputs:                                      efficiency scores in the analysis of life expectancy and                 Infant   mortality,   30-day   mortality   after   health expenditure.                 admission  to  hospital  for  ischemic  stroke    and                 for acute myocardial infarction per 100 patients;                 Life expectancy at birth                                                Source: prepared by authors                 Samut and Cafri (2016) use the Malmquist index and the regression analysis extended by                the Tobit model in addition to the DEA analysis. The authors try to estimate the efficiency                and the changes in the efficiency of key determinants of health systems on the sample of                29 OECD countries between 2000 and 2010. 6 countries were fully effective in 2010. Fully                efficient during the period 2000-2010 were Mexico, Turkey and the UK. Japan, Iceland,                France and Belgium have under-average efficiency scores for all years.                 3 Methodology and Data                One of the most well-known methods for estimating efficiency in the healthcare sector is                Data Envelopment Analysis. Efficiency is in this case in the form of ratio of health output                to health input. The goal is to minimize the input side given the certain volume of outputs                or to maximize the output given a certain volume of inputs. Data Envelopment Analysis is                a  non-parametric  method  that  estimates  the  efficiency  of  production  units.  Koopmans                (1951), Debreau and Arrow (1954) and Farell (1957) have provided a basic concept for                DEA development with their articles. Debreau and Arrow (1954) and Farell (1957) have                introduced the score of technical efficiency rate. In the next period, DEA was the subject                of several authors. The further development of this method was provided by Charnes et                al.  (1978)  who  introduced  the  CCR  (Charnes,  Cooper,  Rhodes)  model  based  on  the                constant returns to scale and Banker et al. (1984) developed the BCC (Bankers, Charnes,                Cooper)  model  based  on  the  variable  returns  to  the  scale  (VRS).  The  Network  DEA                                                             269","represents a new approach to the assessment of the efficiency, which is needed on the one                hand to understand the past success of the organization and on the other hand for planning                to  the  future.  The  network  DEA  helps  identify  the  specific  factors  which  point  to  the                inefficiency of the decision making units (DMUs) as a whole (Kao, 2017).                Dynamic Network Data Envelopment Analysis (DNDEA) specification                Standard DEA models are often referred as a “black box” models, because they do not take                into account the internal factors and structure of DMUs. Tone and Tsutsui (2009) in their                analysis  present  new  types  of  Dynamic  Network  DEA  models,  taking  into  account  the                internal  or  linking  structure  of  DMUs.  Färe  and  Grosskopf  (1997,  2000)  present  their                network of DEA models with various types of structure. The DNDEA model can evaluate                the overall efficiency, dynamic change in the periodic efficiency and dynamic change in the                divisional  efficiency.  In  the  health  sector,  the  advantage  of  DNDEA  is  that  it  assesses                efficiency separately in public health and also medical care provision. DMUs have carry-                over variables that take into account a positive or negative factor in the previous period                (Ozcan and Khushalani, 2016). Kawaguchi et al. (2014) use DNDEA with estimating the                efficiency of Japanese hospitals.                Consider n DMUs (j=1,…,n), which consist of K divisions (k=1,…,K) over T time periods                (t=1,…,T). The number of inputs and outputs to division k represent mk and rk, respectively.                The set of links Lkh point from division k to division h by (k,h) and the observed data are                as follows (Kawaguchi et al. 2014):                 x   R + (i =  , 1   , m k ; j =  , 1   , n ;k =  , 1   , K ;t =  , 1   ,T ) represents input resource I to DMUj                  t                  ijk                for division k in the period t;                  t                 y rjk   R + (r =  , 1   ,r k ; j =  , 1   ,n ;k =  , 1   , K ;t =  , 1   ,T )   represents  output  product  r  from                DMUj for division k in period t. We treat the outputs, which are undesirable, as inputs to                division k.                 z t j (kh ) t   R + ( j =  , 1   ,n ;l =  , 1   , L kh ;t =  , 1   ,T )   define  the  linking  intermediate  products  of                DMUj from k to h in period t                   t                                       l                 z ( t , +  ) 1   R  ( =  , 1   ,n ; =  , 1   , L  ; =  , 1   , K ; =  , 1   ,T  −  ) 1   represents  carry-over  of                             j                                                              t                                                   k                  jk t    +                      k                DMUj.  DMU   o (o = , 1   , n                                         ) P  can be expressed as follows:                               t                        x t ok  =    t k  + s t−  ( k   ) t                                          ,                               k                                     ko                               t                                         ,                        y t ok  =   t k  + s t+  ( k   ) t                               k                                     ko                        e  t k  =1  ( k   ) t                                                         (1)                                   ,                           , 0 s t−   , 0 s t+   , 0  ( k   ) t                         t                                               ,                               ko                                       ko                         k                where                  t k  = (x ,  x ,  t nk  ) R mk n and  k t  =  (y ,  y ,  t nk ) R r k  n   represent  input  and  output  matrices                                                                                                                                                 t                        t                        k 1                                                    k 1                              + t                and s ko − t  and s  represent input and output slacks, respectively.                             ko                In the “as input” link value case, the linking activities are treated as input to division and                excesses are accounted for in the input inefficiency:                        z t o ( )inkh  =   t ( kh) in  t k  + s t o( kh) in  ( ( )inkh  =  , 1   , linkin k  )   (2)                and in the “as output” link value case, the linking activities are treated as output from the                preceding division and shortages are accounted for in the output inefficiency:                        z t o ( )outkh  =   t ( kh) out  t k  − s t o( kh) out  ( ( )outkh  =  , 1   , linkout k  )   (3)                                                            270","where s t o( kh) in   R  L ( kh) in  represents slacks and is non-negative, linkin  represents the number                                                                                  k                of “as input” links and s t o( kh) out  R  L ( kh) out   represents slacks and is non-negative and linkout                                                                                                          k                is the number of “as output” links from k division.                 Carry over categories can be expressed as follows:                           good                                                                           ;                        z  (  , t t + ) 1  =  n  z (  , t t + ) 1   t  − s (  , t t + ) 1  (k = , 1   , ngood  k;  ) t                          ok l      j=1  jk l good  jk  ok l good  l      k                           bad                                                                        ;                        z  (  , t t + ) 1  =  n  z (  , t t + ) 1   t  + s (  , t t + ) 1  (k = , 1   , nbad  k;  ) t                          ok l     j=1  jk l bad  jk  ok l bad  l      k                           free                                            t                                                                       ;                        z  (  , t t + ) 1  =  n  z (  , t t + ) 1    +  s (  , t t + ) 1  (k = , 1   , nfree  k;  ) t      (4)                         ok l      j=1  jk l  free  jk  ok l  free  l  k                           fix                                                               ;                        z  (  , t t + ) 1  =  n  z  (  , t t + ) 1  t  (k = , 1   , nfix  k;  ) t                          ok l     j=1  jk l  fix  jk  l      k                        s (  , t t + ) 1    s , 0  (  , t t + ) 1   0  and  s (  , t t + ) 1  :  free  ( k   ) t                                                                ;                         ok l good  ok l bad      ok l free                The objective function for the overall-efficiency can be expressed by the following program:                                                                   − t      s t          (  + t t,  ) 1                                       T  t   K  k   −    1         m k s iok  +  linkin k  o( kh l )  in  +  nbad k s oklbad                                              k                                                                             )                                                                           (                                  = t 1 W     =1 w 1  m  + linkin  + nbad      = i 1  x t   kh l z t   =1  z (  + t t,  ) 1          (5)                                                                                                                          =1                                                                                       k l                          = min                k      k     k      iok       o( kh l )  in  oklbad                                                                      + t       t            (  + t t,  ) 1                                    T  W t    K  w 1     1           r k s rok  +  linkout k  s o( kh l )  out  +  ngood k  s ok lg ood                                               k                                            +                                                                   r                                            k      k       k     rok       o( kh l )  out  ok lg ood                                              k                                                                           (                               = t 1   =1   m  + linkout  + ngood     =1  y t   kh l ) 1 = z t   =1  z (  + t t,  ) 1                                                                                          k l                          t                where, W  (t=1,…,T) represents the weight to period t and w  represents the weight to                                                                                k                division  k.  To  keep  the  structure  of  the  paper,  we  will  not  report  and  present  further                calculations for period efficiency, divisional efficiency and period-divisional efficiency. In                our analysis we used for evaluating the efficiency score the mathematical program DEA                Solver Pro 13.                4 Results and Discussion                In our analysis we used Dynamic Network Data Envelopment Analysis model with Constant                Returns to scale for 35 OECD countries between period 2005-2015. We consider health                systems of OECD countries as a DMU. In our paper, the health system consists of two                parts: public health system and medical care. In our model we used equal weights for both                divisions (0,5 for public health and 0,5 for medical care). Characteristics of inputs and                outputs and the definition of all variables, which belong to the divisions are presented in                the Table 2. The inputs and outputs have been used in several studies, including basic                health determinants as well as medical care characteristics (Ozcan and Khushalani, 2016).                                             Table 5 Definitions of Variables                                   Variable                                     Definition                                                 PUBLICH HEALTH DIVISION                                                           Inputs                 1 Alcohol consumption                         Annual consumption of pure alcohol in liters, per                                                               capita, 15+                                                               Tobacco consumption, % of population 15+ who are                 2 Tobacco consumption                                                               daily smokers                 3 Health expenditure                          Current expenditure on health, per capita, US$                                                               (current prices, current PPPs)                                                          Outputs                 4 Life expectancy                             Life expectancy, Total population at birth, Years                                                        CARRY OVER                                                            Bad                                                               Number of deaths of children aged under 1 year of                 5 Infant mortality                                                               age, per 1000 live births                                                           Good                                                               Percentage of the population, aged 15 years old and                 6 Health status                                                               who report their health to be \"good/very good\"                                                            271","LINKS TO MEDICAL CARE                                                               Diphtheria, Tetanus, Pertussis; % of children                 7 Immunization                                immunized                                                               Breast cancer screening, survey data; % of females                 8 Screening-breast cancer                                                               aged 50-69 screened                 9 Screening-cervical cancer                   Cervical cancer screening, survey data; % of females                                                               aged 20-69 screened                                                  MEDICAL CARE DIVISION                                                           Inputs                                                               Computed Tomography scanners, total, Per million                 10 Medical Technology                                                               population                 11 Employment                                 Nurses + physicians per 1000 population                 12 Beds                                       Beds per 1000 population                                                          Outputs                 13 Discharges                                 Discharges per 100 000 population                 14 Consults                                   Number of consultation per capita                                                      BAD CARRY OVER                                                               The number of new cancer cases per 100 000                 15 Cancer                                                               population                                                Source: prepared by authors                Efficiency  scores  for  selected  OECD  countries  are  presented  in  the  Table  3.  The  table                consists of four important columns: overall efficiency score, public health, medical care                and Malmquist score, in given period 2005-2015. The overall Malmquist score above 1                point out to the improvement in the efficiency between period in OECD countries.                                  Table 6 Efficiency Scores for Selected OECD Countries                 Country            Overall score       Public Health     Medical Care      Malmquist                 DMU                2005     2015     2005     2015    2005     2015     Overall   Rank                 Australia          0,7706   0,8576   0,8104   0,9721   0,7245   0,7406   1,1916   8                 Austria            1        1        1        1       1        1        0,9246    29                 Belgium            0,6538   0,6399   0,7788   0,715   0,5659   0,5809   0,9113    31                 Canada             1        1        1        1       1        1        1,1959    6                 Chile              1        1        1        1       1        1        1,0045    22                 Czech Republic     1        1        1        1       1        1        0,9247    28                 Denmark            0,6975   1        0,7478   1       0,6552   1        1,6831    2                 Estonia            1        0,8273   1        1       1        0,6866   0,744     35                 Finland            1        1        1        1       1        1        0,9778    25                 France             0,743    0,6585   0,7722   0,7045   0,7182   0,6195   0,8565   33                 Germany            0,7178   0,7773   0,7348   0,7468   0,698   0,814    1,1039    10                 Greece             1        1        1        1       1        1        1,2054    5                 Hungary            1        1        1        1       1        1        0,9712    26                 Iceland            1        1        1        1       1        1        0,978     24                 Ireland            0,5137   1        0,7249   1       0,4119   1        1,9942    1                 Israel             1        1        1        1       1        1        1,0268    20                 Italy              1        1        1        1       1        1        1,047     16                 Japan              1        1        1        1       1        1        0,9185    30                 Korea              1        1        1        1       1        1        0,902     32                 Latvia             1        1        1        1       1        1        1,0187    21                 Luxembourg         0,6368   0,7627   0,685    0,7269   0,5976   0,7978   1,1263   9                 Mexico             1        1        1        1       1        1        1,0887    12                 Netherlands        1        0,7083   1        0,7674   1       0,6602   0,8152    34                 New Zealand        1        1        1        1       1        1        1,1933    7                 Norway             0,7932   0,8232   1        1       0,6416   0,6719   1,042     17                 Poland             0,8119   1        1        1       0,6776   1        1,0903    11                 Portugal           1        1        1        1       1        1        0,9563    27                 Slovak Republic    1        1        1        1       1        1        1,0331    19                 Slovenia           1        1        1        1       1        1        0,9872    23                 Spain              1        0,9449   1        0,8897   1       1        1,0807    13                 Sweden             1        1        1        1       1        1        1,3612    3                 Switzerland        0,4826   0,4922   0,6616   0,6179   0,3698   0,406   1,0406    18                 Turkey             1        1        1        1       1        1        1,2665    4                 United Kingdom     1        0,9408   1        0,8832   1       1        1,0769    14                                                             272","United States      1        1        1        1       1        1        1,0544    15                 Average            0,9092   0,9266   0,9404   0,9435   0,8874   0,9136   1,0798   -                                                Source: prepared by authors                 The average Malmquist score for the medical care division for the selected countries is                1,1535 and for the public health division is 1,0315. The best score in the improvement of                the  efficiency  showed  Ireland,  Denmark,  Sweden  and  Turkey  over  time.  Based  on  the                Malmquist score, it is important to mention, that the improvement of the overall efficiency                score was driven by better score in efficiency of the medical care as to public health system                in Ireland, Denmark, Sweden. The improving of medical care should to be the challenge                for Turkey. The same results we can see in other countries. The better improvement we                can see in medical care division over time (19 countries vs 16 countries).                                   Table 7 Projections of Inputs for Selected Countries                                                                   Average % difference between actual and                 Inputs                                                                   projected values                                                      PUBLIC HEALTH                 Alcohol consumption                               -6,6714                 Tobacco consumption                               -3,1449                 Health expenditure                                -3,6071                                                       MEDICAL CARE                 Medical technology                                -9,1394                 Beds                                              -7,7206                 Employment                                        -1,4031                                                Source: prepared by authors                 In the Table 4 we present the projections based on DNDEA model for inputs of our sample.                This table shows how should to be reduced the inputs to public health system and medical                care  system  based  on  the  projected  values.  These  projections  represent  average  %                difference between actual and projected values for achieving the efficiency. From the table                can be seen that the inputs to the medical care should to be reduced more than the inputs                to the public health system.                 5 Conclusions                In this paper we used DNDEA model for assessing the efficiency of health system by the                constant returns to scale for selected OECD countries between period 2005-2015. The aim                of  the  paper  was  to  compare  the  health  systems  results  based  on  DNDEA  model.  The                DNDEA model consisted of a selection of inputs and outputs for two divisions: the public                health system division and the medical care system division. The results of our analysis                could be useful for better understanding of current health situation of health policy makers                in  selected  countries.  Within  the  divisions  we  estimated  the  efficiency  based  on  15                variables.  The  results  of  our  analysis  point  to  improvements  in  health  care  in  OECD                countries. The presented results of efficiency also have some limitations. In the case of                missing data in a given year, we included the data that was closest to the given year,                which, of course, leads to a bias in the results. Inputs and outputs in health system are                very complex. In the literature, there are a number of the other determinants and variables                of  health  system  or  medical  care  system,  which  can  be  used  in  DNDEA  or  other  DEA                models. The results of either DNDEA models or traditional DEA models are mainly affected                by the choice of input and output variables. By including other variables in our model, we                can achieve different results.                                                                   273","Acknowledgments                This work was supported by the Slovak Scientific Grant Agency as part of the research                project VEGA 1/0794/18.                 References                Banker, R. D., Charnes, A., Cooper, W. W. (1984). Some models for estimating technical                and scale inefficiencies in data envelopment analysis. Management science, vol. 30(9), pp.                1078-1092.                 Behr, A., Theune, K. (2017). Health System Efficiency: A Fragmented Picture Based on                OECD Data. PharmacoEconomics-Open, vol. 1(3), pp. 203-221.                Charnes, A., Cooper, W. W., Rhodes, E. (1978). 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World Health Organization.                                                                                                                         275","Household Deposits’ Availability for Credit Institutions                                     during Tensions on Funding Markets                                     – A Case of the Euro Area Countries                                                    Katarzyna Kochaniak                                               Cracow University of Economics                                                   Department of Finance                                         27 Rakowicka Street, 31-510 Cracow, Poland                                         E-mail: katarzyna.kochaniak@uek.krakow.pl                 Abstract:  The  EU  regulations  indicate  household  deposits  as  stable  funding  for  credit                institutions under stress and in the long run. However, the recent financial crisis revealed                runs in the euro area, including ones that threatened financial stability. They encouraged                to analyse the changes in the levels of deposits placed by households with credit institutions                in individual member countries in the period of distress in funding markets. The study aims                to  analyse  the  fluctuations  in  the  levels  of  household  deposits  and,  in  particular,  to                recognise regularities and irregularities in this respect. It applies regression models with                time series data on household deposit totals for individual countries in the euro area. The                results display considerable cross-country differences in the formation of deposit levels in                the period analysed. For several member states, the trend could be recognised only in                specified sub-periods. Only for some of the countries, the linear trend described a general                long-term direction of changes in household deposit totals, confirming its stability in the                entire period. In most member states, seasonal fluctuations in deposit totals occurred, but                they  related  to various  months. The  study  allowed  identification  of  unexpected  deposit                outflows as well, which resulted, among other reasons, from financial problems of credit                institutions. On the other hand, the adjustments in deposit guarantee schemes fostered                households’ confidence in these entities and led to the unexpected increases of the deposits                in selected countries.                Keywords: banks’ liquidity, household deposits, banks’ debt funding                 JEL codes: G21, G32                 1 Introduction                The financial crisis has revealed previously underestimated weaknesses of funding models                of credit institutions in the EU. They resulted from the growing reliance on wholesale funds,                including short-term, and diminishing the importance of retail deposits (Song and Thakor,                2007; Borio, 2009; Brunnermeier, 2009; Fender and McGuire, 2010; Merc et al., 2012;                van Rixter and Gasperini, 2013; UKNF, 2017). The period of distress in funding markets                led to the revision of the opinion on wholesale funds, drawing attention to their sensitivity                to the prevailing conditions. This new, critical view has been strengthened by the EU post-                crisis liquidity standards which emphasise the stable nature of retail deposits, both under                stress and in the long run (ESRB, 2012; EBA, 2013; European Parliament and Council,                2013; EC, 2015). However, the experience of the last financial crisis provides examples of                increased vulnerability of household deposits as well.  They were recognised in Belgium,                Cyprus, Greece, Spain, Ireland, and Portugal (ECB, 2009; Banco de Espana, 2012; Merck                et al., 2012; Central Bank of Cyprus, 2013; Constancio, 2013; Whelan, 2013; National                Bank of Greece, 2015).                The  literature  pays  little  attention  to  the  formation  of  household  deposit  totals  and  its                significance  for  the  liquidity  of  credit  institutions  in  countries  of  the  single  regulatory                background.  This  study  aims  to  fill  the  existing  gap  and  recognise  regularities  and                irregularities in this respect. Moreover, it refers the empirical results to the EU post-crisis                regulatory  stance  about  deposit  stability.  The  paper  answers  the  following  research                questions:                    •   Were the aggregated levels of household deposits characterised by a general (long-                       term) direction of changes in individual countries in the period analysed?                                                             276","•   Could the seasonal changes in household deposit totals be recognised in the euro                       area member states despite the ongoing destabilisation?                    •   Were there any unexpected deposit fluctuations during the period analysed?                The  paper  is  organised  as  follows.  Section  2  describes  the  research  method  and  data                applied  in  the  study;  Section  3  presents  the  results  of  the  study;  Section  4  contains                conclusions.                  2 Methodology and Data                The  formation  in  time  of  household  deposit  totals  (Dt)  is  an  economic  process  that                comprises  the  general  trend (Tt),  the economic  cycle  (Ct),  seasonal  variation  (St),  and                irregular component (εt). It can be described as follows:                          =    +    +    +    .                                                         (1)                                                                                                                                                                                               Due to the length of the period analysed, the study allows determining the trend, seasonal                fluctuations,  and  autoregressive  component.  The  trend  defines  a  general,  long-term                direction  of  changes  in  deposit  totals,  the  seasonality  is  confirmed  by  at  least  one                significant parameter of the seasonal variable, and the autoregressive process occurs when                the current value of the examined feature results from its value in previous periods. These                issues are relevant to assess the formation of household deposit totals in the euro area                countries. The study applies the following model (Kufel, 2013):                                      m−1                         t                                             it                                    j                        D =  r  a j t +  d i M +  p   s e t− s  +  e ,                               (2)                                                           t                             j=0      i=1       s=1                where Dt is the value of household deposits in the sector of credit institutions of a given                country (in EUR million) in the t-th unit of time. The unit of time refers to the subsequent                months of the analysed period while the value of deposits reflects their value at the end of                the month. The first part of the equation relates to jth-degree polynomial whose argument                is  the  time  index  (t)  to  isolate  the  appropriate  trend.  The  second  segment  captures                seasonal fluctuations in deposit values using dummy variables (Mi) identifying the months                from January to November (December is the basis for comparison). The third part refers                to autoregressive residues from the model (3); a, d, ρ - structural parameters of the model.                                       m−1                         t                         D =  r  a j t +  d i M + e                                                     (3)                                   j                                            it                                                 t                             j=0      i=1                The OLS is applied to estimate of the structural parameters of the models. However, GLS                is  used  in  the  case  of  heteroscedasticity  (Kufel,  2013),  according  to  heteroscedasticity                consistent covariance matrix (HCCM). The goodness-of-fit of the models to empirical data                is tested by the Akaike criterion (AIC), the Schwartz-Bayes information criterion (SBC),                and  the  coefficient  of  determination  (R )  (Gorecki,  2010).  The  variance  inflation  factor                                                         2                (VIF) is used to check the collinearity of independent variables, and the White's test to                verify the homoscedasticity of the variance (Kufel, 2013). The t-Student test is applied to                assess the significance of structural parameters of the models, while the Doornik-Hansen                test is to assess the normality of the distribution of residues (Kufel, 2013).                The  study uses monthly  data  on household  deposit totals  placed  with  credit institution                sectors  of  the euro  area.  They  are  derived  from the ECB's  and  national  central  banks'                database, \"Our statistics\", for the period of distress in funding markets. In most cases, this                time span lasts from August 2007 to December 2014. However, in the case of Cyprus,                Greece,  Portugal,  and  Italy,  it  is  extended  until  December  2016  due  to  the  continuing                funding problems of domestic credit institutions. The limited data for Estonia and Latvia                led to the adoption of shortened periods for them (from January 2008 to December 2014                and from September 2010 to December 2014, respectively).                                                                  277","3 Results and Discussion                The analysis of the formation of household deposit totals in individual euro area countries                was carried out using the model (2). In the case of Cyprus, Ireland, and Luxembourg, its                specification was only possible in sub-periods, which referred to August 2007 - December                2012, January 2009 - December 2014, and May 2008 - December 2014, respectively. Due                to the severe economic and financial problems of Greece, the analysis conducted for this                country was limited to the estimation of a trend for specified sub-periods. In its case, the                reliance  on  international  aid  programs  and  the  persisting  political  problems  triggered                immediate  reactions  by  depositors,  who  were  withdrawing  the  deposits  from  domestic                banks. The above limitations of the study prove significant cross-country differences in the                formation of deposit totals in the euro area.                The first critical issue in this study was the choice of the polynomial degree of the time                variable. The linear trend was adopted in the models for most countries (i.e., Belgium,                Cyprus, Estonia, Finland, France, Germany, Ireland, Italy, Latvia, the Netherlands, and                Spain).  Intercept  and  parameter  estimate  of  the  time  variable  informed  about  the                differences  in  the  availability  of  household  deposits  for  credit  institutions  among  the                countries regarding their initial levels and average monthly changes in the period analysed.                However, an upward trend was recognised in all of them except for Ireland, where for the                sub-period December 2009 to December 2014, the trend was negative. Concerning the                remaining countries - Austria, Lithuania, Luxemburg, Portugal, Slovakia, and Slovenia -                second-degree polynomial for the time variable was adopted. The German sector certainly                stood out with the intercept reaching almost EUR 1,500,000,000,000 and the coefficient of                time variable exceeding EUR 5,600,000,000. Also, the French sector distinguished itself                with the intercept equal to over EUR 950,000,000,000 and the trend coefficient of almost                EUR 3,400,000,000. Subsequently, the attention should be drawn to Italian sector (over                EUR  750,000,000,000  and  EUR  2,400,000,000  respectively)  and  the  Spanish  sector                (almost EUR 650,000,000,000 and EUR 1,500,000,000 respectively). On the other hand,                there were relatively small countries with significantly lower deposit levels like Estonia with                the  intercept  of  slightly  over  EUR  3,000,000,000  and  trend  coefficient  equal  to  EUR                27,000,000,  Latvia  with  values  of  almost  EUR  4,000,000,000  and  EUR  22,000,000                respectively,  or  Malta  with  the  values  of  almost  EUR  6,000,000,000  and  almost  EUR                30,000,000 respectively. On the basis of the levels of the intercept it can be stated that                with the beginning of the crisis, the availability of this funding was generally adequate to                the size of domestic sectors of credit institutions.                The  parameter  estimates  confirmed  the  occurrence  of  seasonal  changes in the  deposit                levels  in  all  countries  analysed,  except  for  Greece  (Table  1).  Regarding  the  dummies                identifying individual months, at least three of them were statistically significant in each                model. The least susceptible to seasonal fluctuations were the deposits placed in Austria,                France,  Ireland,  and  Luxembourg.  It  should  be  emphasised  that,  in  Austria,  the  most                significant monthly outflows of deposits accounted for only 1.4% of the average value of                the deposits in the analysed period. While comparing the seasonality of deposit totals in                each month, it can be stated that in January they were close to those in December (the                basis for comparison). In January, a seasonality was recognised only in Belgium, Lithuania,                Malta,  the  Netherlands,  Slovakia  and  Slovenia. However, for  the  Slovenian  sector,  this                amplitude was the highest during the entire year. Most models (except for Austria, France,                and Luxembourg) confirmed the seasonal fluctuations of the deposits in July. Regarding                Estonia and Portugal, they led to utmost changes in their availability for credit institutions.                Moreover, in June, household deposit totals were subject to significant adjustments. The                positive ones were observed in Belgium, Cyprus, Estonia, Finland, the Netherlands, and                Spain. This phenomenon resulted from bonus payments to employees before the summer                vacation season, among other factors (DeNederlandscheBank, 2016). It is worth noting                that September was a month of substantial negative seasonal changes in the deposit levels.                The limited availability of this funding occurred then in, e.g. Austria, Germany, Italy, Latvia,                Lithuania,  and  Portugal.  The  same  could  be  concluded  regarding  November,  when  the                deposits outflowed from the sectors of credit institutions in, e.g. Belgium, Cyprus, Estonia,                Finland, France, Ireland, Italy, Lithuania, Slovakia, and Spain. In October, large deposit                                                            278","withdrawals were observable in, e.g. Finland, France, Germany, Italy, Portugal, Slovakia,                and Spain. For selected countries, the statistical significance of the parameter estimates of                the dummies representing seasonal changes in deposit totals from January to November                allowed estimating the ones that occurred in December each year. They led to conclusions                about deposit outflows at the end of each year from credit institutions located in Belgium,                Cyprus, Estonia, Finland, Luxembourg, the Netherlands, and Slovenia. They were caused,                among other factors, by increasing expenditures of households during the Christmas and                New Year period, as well as estimations of their capital gains taxes based on deposit levels                at the end of the year (in the case of the Netherlands). In turn, the deposit accumulation                in  December  was  recognised in  Germany,  Italy,  Latvia,  Lithuania,  Malta,  Portugal,  and                Slovakia..                In  selected  countries  of the euro  area,  solely  positive  or  negative  seasonal  changes in                deposit levels could be observed (Table 1). The only negative estimates were recognised                in the models for Austria, Germany, Ireland, Italy, Lithuania, Latvia, and Malta. Thus, they                informed  about  the  largest  availability  of  household  deposits  for  credit  institutions  in                December  each  year.  The  opposite  could  be  concluded  regarding  Luxembourg,  the                Netherlands, and Slovenia. In the Netherlands and Slovenia, the elevated amounts of the                deposits  were  recorded  in  most  months.  The  results  in  Table  1  also  draw  attention  to                Belgian, Cypriot, Estonian, Finnish and Slovakian households who in general displayed an                increased propensity to accumulate the deposits in the first half of a year and to withdraw                them in the second.                In  selected  credit  institution  sectors,  certain  irregularities  regarding  deposit  formation                could be identified. They related to sudden inflows or outflows of the deposits in monthly                periods.  Due  to  their  occurrence,  the dummies  relating  to  specific  months  entered  the                models and allowed estimating the dimension of these phenomena. Such adjustments were                carried  out  for  Belgium,  Cyprus,  Estonia,  the  Netherlands,  Lithuania,  Latvia,  Germany,                Slovakia, Slovenia, and Italy. In Lithuania, October 2008 and December 2011 emerged as                months  characterised  by  deposit  outflows  equal  to  EUR  414,500,000  and  EUR                581,700,000, and December 2014 as a month with an increased value of the deposits by                EUR 419,600,000. It should be emphasised that both negative changes resulted from a                loss  of  confidence  of  depositors  (households)  in  credit  institutions  caused  by  the                bankruptcy of Snoras (one of the largest banks in Lithuania) and liquidity problems of the                sector, including its limited access to market funding (IMF, 2009). On the other hand, a                substantial increase in the deposits in December 2014 should primarily be linked to the                accession of Lithuania to the euro area. In Slovakia, December of 2008 turned out to be                the month of massive unexpected inflows of the deposits as well as October and November                of 2008. The abandonment of the limit within the deposit guarantee scheme could explain                the unusually high level of the deposits in the last quarter of 2008. In Belgium, irregular                units of time with negative effects on deposit totals became apparent in December 2011                and May 2014 due to the problems of Dexia, including a rescue package provided for this                bank by the governments of Belgium, France, and Luxembourg. In Cyprus, the last month                of  2016  turned  out  to  be  distinctive  due  to  a  vast  increase  in  the  aggregated  level  of                deposits  resulting  from  the  boosted  confidence  of  depositors  in  credit  institutions  and                elevated interest rates in this country (IMF, 2017). In the Netherlands, December 2014                represented a month of reduced deposit totals due to households looking for alternative                investment  opportunities  of  a  higher  rate  of  return  (DeNederlandscheBank,  2016).  In                Latvia,  November  2011  and  December  2013  significantly  differed  from  the  remaining                months. Regarding the first one, the decreased level of deposits resulted from households’                reaction  to  the  insolvency  of  JSC  Latvijas  Krājbanka  and  commencement  of  deposit                repayments from the national deposit guarantee scheme. In December 2013, the value of                households' deposits, in turn, was higher and could be explained by the adoption of the                single currency by this country. It is worth noting that it was the most substantial increase                in deposits since the beginning of the financial crisis. In Germany, irregularities in deposit                totals were recognised in October 2008. The remarkably higher level of deposits resulted                from the increased limit in the deposit guarantee scheme. In Italy, unexpected significant                deposit inflows happened in October, November, and December 2008. The last quarter of                                                             279","2008  can,  therefore,  be  seen  as  conducive  to  the  accumulation  of  deposits  by  Italian                households, which resulted from the elevated guarantee limit. In Estonia, irregular positive                changes  in  deposit  totals  were  recognised  in  December  2010  due  to  a  new  deposit                guarantee  limit  and  Estonia's  entry  into  the  euro  area.  However,  January  2013  was                identified as a month of a statistically significant decrease in deposit totals caused by low                interest rates, which discouraged households from holding them.                          Table 1 Seasonal Fluctuations in the Total Value of Household Deposits in Plus                            and Minus in Individual Months in the Sectors of Credit Institutions                                                          Months                Country      1      2      3       4      5      6      7       8      9      10      11                AT            ×     (−)    ×       ×      ×      ×      ×       (−)!   (−) !!   ×     (−)                BE           (+)    (+)    (+)     (+)    (+) !!   (+) !   (+)   ×     ×      ×       (−) !!                CY           ×      (+)    ×       (+)    (+) !   (+) !!   (+)   ×     ×      ×       (−)!!                DE           ×      ×      (−)     ×      ×      (−)    (−)     (−)    (−) !!   (−) !   ×                EE           ×      (+)    ×       (+)    (+)    (+) !   (+) !!   (+)   ×     ×       (−) !!                ES           ×      ×      ×       ×      ×      (+) !!   (+) !   ×    (−)    (−) !!   (−) !                FI           ×      ×      ×       (+) !   (+)   (+) !!   (+)   (+)    ×      (−) !   (−) !!                FR           ×      ×      ×       (+) !!   ×    ×      ×       ×      (−)    (−) !   (−) !!                IE           ×      ×      (−)     ×      ×      ×      (−) !   ×      ×      ×       (−) !!                IT           ×      (−)    (−)     ×      ×      ×      (−)     (−)    (−) !   (−) !!   (−)                LT           (−)    (−)    (−)     (−)    (−)    (−)    (−)     (−)    (−) !!   (−)   (−) !                LU           ×      (+) !!   (+)   (+)    (+) !   ×     ×       ×      ×      ×       ×                LV           ×      ×      (−)     (−)    (−)    (−)    (−)     (−) !!   (−) !   (−)   ×                MT           (−)    (−)    (−) !   (−)    (−) !!   (−)    (−)   (−)    (−)    (−)     ×                NL           (+)    (+)    (+)     (+)    (+)    (+) !!   (+) !   (+)   (+)   (+)     ×                PT           ×      ×      (−)     (−)    (−)    ×      (+) !!   ×     (−) !   (−) !!   ×                SI           (+) !!   (+) !   (+)   ×     (+)    (+)    (+)     (+)    ×      ×       ×                SK           (+) !   (+) !!   (+)   (+)   ×      ×      (−)     (−)    (−)    (−) !   (−) !!                   Notes: (+) - positive parameter estimate, (-) - negative parameter estimate, (!!) - the highest                        amplitude of seasonal fluctuations among all positive/negative monthly amplitudes,                  (!) – second the highest amplitude of seasonal fluctuations among all positive/negative monthly                                                        amplitudes.                                    Source: own study based on results from the model (2).                 The  last  part  of  the  model  (2)  refers  to  the  autoregressive  component.  It  allowed                concluding about the statistical significance of developments in deposit totals from past                months for  the  current level  of  deposits in  the  countries  analysed  (Table  2). The links                between  the  levels  of  the  deposits  in  the  current  month  and  previous  month  were                recognised as the sole one in the models for Austria, Belgium, Cyprus, Spain, Lithuania,                Luxembourg,  Malta  and  Slovakia.  Regarding  the  remaining  countries,  the  influences  of                more  distant  months  were  observable  as  well.  They  could  result  from  the  flows  of                household saving deposits.                  Table 2 Statistically Significant Delays of Residues from Models (2) of the Formation of                          Household Deposit Totals in Credit Institution Sectors of the Euro Area                                                  Delay in months                Country      1      2      3      4     5      6      7      8      9     10    11     12                AT           (+)    ×      ×     ×      ×      ×      ×      ×     ×      ×      ×      ×                BE          (+)     ×      ×     ×      ×      ×      ×      ×     ×      ×      ×      ×                CY          (+)     ×      ×     ×      ×      ×      ×      ×     ×      ×      ×      ×                DE          (+)     ×     (+)    (−)    ×      ×      ×      ×     ×      ×     (+)    (−)                EE          (+)     ×      ×     ×      ×      ×      ×      ×     ×      ×      ×      ×                ES          (+)     ×      ×     ×      ×      ×      ×      ×     ×      ×      ×      ×                FI          (+)     ×      ×     (−)    ×      ×      ×      ×     (+)   (−)     ×      ×                FR          (+)     ×     (+)    (−)    ×      ×      ×      ×     ×      ×      ×      ×                                                             280","IE          (+)     ×      ×     ×      ×      ×     (−)     ×     ×      ×      ×      ×                IT          (+)     ×      ×     ×      ×      ×      ×      ×     ×      ×     (+)     ×                LT          (+)     ×      ×     ×      ×      ×      ×      ×     ×      ×      ×      ×                LU          (+)     ×      ×     ×      ×      ×      ×      ×     ×      ×      ×      ×                LV          (+)     ×     (+)    ×      ×      ×      ×      ×     ×      ×      ×      ×                MT          (+)     ×      ×     ×      ×      ×      ×      ×     ×      ×      ×      ×                NL          (+)     ×      ×     ×      ×      ×      ×      ×     (+)   (−)     ×      ×                PT          (+)     ×      ×     (−)    ×      ×      ×      ×     ×      ×      ×      ×                SI          (+)     ×      ×     ×      ×      ×      ×      ×     ×     (−)    (+)    (−)                SK          (+)     ×      ×     ×      ×      ×      ×      ×     ×      ×      ×      ×                          Notes: (+) - positive parameter estimate, (-) - negative parameter estimate.                                    Source: own study based on results from the model (2).                 4 Conclusions                The  results  of  modelling  the  process  of  household  deposit  allocation  in  the  euro  area                countries allowed to refer them to the EU single regulations, which emphasise the stable                nature of retail deposits under stress and in the long run. They served to formulate the                following conclusions:                •   The process of deposit allocation in credit institution sectors of the euro area was not                    uniform in the period of tensions in funding markets. The differences in the availability                    of household deposits in the long term, as well as in its monthly fluctuations, drew                    attention to the limited adequacy of the single regulatory stance regarding the euro                    area countries.                •   In the models for most countries, the time variable was linear. Only in their cases can                    we,  therefore,  conclude  the  constancy  of  the  general  direction  of  the  value  of  the                    deposits  in  the  period  analysed.  Increasing  deposit  totals  in  the  sectors  of  credit                    institutions of these countries confirmed their long-term availability and, thus, their                    stability as funding for credit institutions, despite the evolving destabilisation.                •   In  the  remaining  sectors,  the  direction  of  changes  in  household  deposit  totals  was                    described with a second-degree polynomial. Thus, regarding the entire period analysed,                    it was possible to distinguish two sub-periods characterised by the opposite direction                    of changes in the availability of the deposits for credit institutions. In one of them, a                    gradual  decrease  in  deposit  totals  was  observed,  emphasising  the  need  for  credit                    institutions to rely more on funding from other sources, which were difficult to obtain                    on a dysfunctional financial market. Thus, the changes observed in deposit totals were                    not in line with the regulatory stance on the availability of deposits in the long run.                    Regarding the second subperiod, the deposit inflows were conducive to their availability                    for  credit institutions.  These  results led  to  conclusions  that  the  general  direction  of                    changes in deposit totals might be responsible for the modifications of the models of                    funding of credit institutions throughout the study period.                •   In most sectors of credit institutions in the euro area, the levels of household deposits                    were  subject  to  seasonal  fluctuations.  It  should  be  noted  that  the  recognised                    fluctuations  occurred  on  a  monthly  basis  and  had  a  repetitive  nature  despite  the                    instability.  The  changes  in  deposit  totals  were  therefore  predictable  and  regular                    throughout  the  analysed  period  regardless  of  the  phase  of  the  financial  crisis.  The                    positive  mid-year  and  end-year  fluctuations  were  revealed  in  selected  countries.  It                    should be emphasised that in some sectors, the amplitude of fluctuations in the level                    of deposits was negative and occurred, among others, in December. The knowledge on                    the seasonality of changes in deposit values—in particular, those related to outflows—                    is essential for liquidity management in credit institutions.                •   In  the  models  for  selected  countries,  irregular  time  units  were  identified  in  which                    excessive  flows  of  household  deposits  resulted  from  extraordinary,  unpredictable                    events.  The  sudden  increase  in  deposits  was  caused  by,  among  other  phenomena,                    increased trust in governments and public institutions, both domestically and within the                    euro area. They occurred in selected sectors after the announcement of changes in                    deposit guarantee schemes, referring to the increase or abandonment of the limits, as                                                             281","well as after the accession of selected countries to the euro area. However, attention                    should  be  paid,  in  particular,  to  these  irregular  time  units  that  identified  sudden                    outflows  of  deposits.  They  occurred,  among  other  situations,  under  the  lack  of                    depositors' confidence in credit institutions. All the runs identified in this study related                    to large credit institutions and were visible at the country level. In their case, the limited                    stability of household deposits under stress should be therefore concluded.                •   In all credit institution sectors, the availability of household deposits in a given month                    was positively affected by their increasing value in the previous month. This conclusion                    seems  essential  for  the  management  of  short-term  liquidity  of  credit  institutions,                    according to the LCR standard.                  Acknowledgments                The paper presents the results of a study carried out under a research project financially                supported  by  the  National  Science  Centre  in  Poland  (grant  number:  DEC-                2013/11/D/HS4/04056).                 References                 Banco de Espana (2012). Analysis of recent changes in bank deposits in Spain. 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The Good, the Bad and the Ugly. Retrieved                from: http://www.karlwhelan.com/Papers/Whelan-IrelandPaper-June2013.pdf.                                                                                          283","Motives of Households for Saving in Poland                                             Anna Magdalena Korzeniowska                                          Maria Curie-Skłodowska University in Lublin                                         Faculty of Economics, Department of Banking                                     Pl. Marii Curie-Skłodowskiej 5, 20-031 Lublin, Poland                                          E-mail: anna.korzeniowska@umcs.lublin.pl                 Abstract: According to J. M. Keynes' theory of saving in the economy and households'                behaviour followed by M. Browning and A. Lusardi (1996), the main motives for people to                save are the precautionary motive, the life-cycle motive, the intertemporal substitution                motive, the improvement motive, the independence motive, the enterprise  motive, the                bequest motive, the avarice motive and the down payment motive. This article examines                which motives prevail in the decisions of Polish households in terms of saving. A sample of                223  working  respondents  was  taken  under  consideration.  Each  of  them  represents  a                household  from  Lubelskie  Voivodeship  (Poland).  The  research  proves  that  the                precautionary  motive has  the  highest  ranking  in  respondents'  opinions followed  by  the                independence motive. The down payment motive was ranked lowest, closely followed by                the enterprise motive. Additionally, analysis of correlation between motives was carried                out. In most cases, there is a statistically significant correlation between saving motives.                However,  although  the  respondents  rated  saving  motives  highly,  their  impact  on                respondents' saving behaviour is lower than can be assumed.                Keywords: saving motives, saving behaviour, personal saving                JEL codes: D14, E21                 1 Introduction                 Saving is a process of putting away some resources to preserve them for future use. The                process can be run either consciously or unconsciously. The latter occurs, e.g. when at the                end of a certain period, some unplanned surplus is available. The saving process is the                subject of intense consideration in different trends of economics. Many researchers and                scientists are interested enough in saving and its motives to include it as part of their                economic theories. Among them were J. M. Keynes, who focused on current income, F.                Modigliani, the author of the life-cycle hypothesis of saving and M. Friedman, who worked                on  the  permanent  income  hypothesis.  J.  Duesenberry  and  a  large  group  of  other                researchers  state  that  the  saving  behaviour  of  households  is  determined  mostly  by                psychological  and  sociological  determinants.  Duesenberry  emphasizes  that  saving                behaviour of an individual is influenced by that person's peers. Others focus on personal                traits such as aversion to risk, habits and attitudes as factors of saving. They often take                into consideration socio-economical qualities of savers, e.g. gender, age, location, income,                etc. These points of view were presented in the works of G. Katona, who also suggested                that past experience in saving influences the likelihood of doing it in the future (Devaney,                Anong, Whirl, 2007).                The  aim  of  this  article is to  analyse  which motives  determined  by  Keynes, in  terms  of                saving, prevail in the decisions of Polish households.                 2 Saving motives in theories of economy                 In his work published in 1981, Alf Lindqvist pointed out that socioeconomic variables and                those variables specifically that are important in economic theories may not be related to                bank  saving.  During  the  regression  analysis,  the  variables  assumed  to  be  important                predictors of saving showed no predictive value (Lindqvist, 1981). This research inspired                other scientists to search for and analyse the determinants and motives of saving.                On  the  basis  of  Maslow’s  hierarchy  of  needs,  some  researchers  studied  motives  that                influence saving behaviour simultaneously (Xiao, Noring, 1994). In 1998, Boeree ranked                                                             284","saving motives by their importance, from lowest to highest: physiological (basic), safety,                security,  love/societal,  esteem/luxuries  and  self-actualization  (Devaney  et  al.,  2007).                Generally, when the lower level needs are met, an individual one tends to move up to                higher  ones  in  the  hierarchy.  The  perception  of  importance  and  significance  of  saving                motives depends mostly on age and wealth of individuals (Yao, et. al., 2011)                Following J. M. Keynes' theory of saving in economy and households' behaviour with regard                to  this,  M.  Browning  and  A.  Lusardi  (1996)  produced  a  list  of  eight  household  saving                motives  with  one  addition:  the  precautionary  motive,  the  life-cycle  motive,  the                intertemporal substitution motive, the improvement motive, the independence motive, the                enterprise motive, the bequest motive, the avarice motive and the down payment motive.                However, research scientists had already observed the need to analyse these motives while                considering heterogeneity of members of populations and changes that can occur in the                motives of the same person over the long run (Browning, Lusardi, 1996).                The  importance  attributed  to  saving  motives  can  determine  or  at  least  influence  the                household  saving  rate.  Additionally,  the  relative  contribution  of  the  saving  motive  to                household saving changes with age. What is more, the importance that households attach                to different saving motives is correlated with observed saving behaviour (Schunk, 2009).                But scientist also determined that there is vast heterogeneity between households from                different regions, cultures and age groups when it comes to the hierarchy of saving motives                and the strength of their influence on saving (Canova et al., 2003).                 3 Goals and motives of saving in Poland                 In research carried out at the beginning of 2018, a sample of 223 working respondents                was considered. Each represented a household from Lubelskie Voivodeship (Poland). The                respondents differed in gender, age, location and the level of education. They also declared                the size of their household measured by the number of members, and they were asked to                assess their financial situation. In all, 57% of respondents were women, and more than                55% of respondents were 26 to 35 and 36 to 45 years old. The same percentage, 30%,                was  recorded  for  those  respondents  living  in  rural  areas  and  in  cities  with  more  than                200,000  citizens.  Those  having  one  to  three  other  members  in  their  households,                respectively,  were  within  a  group  of  26%  to  28%  respondents.  As  for  their  economic                situations,  approximately  50%  assessed  it  as  average.  Almost  80%  hold  permanent                employment,  while  12%  run  their  own  businesses  or  were  self-employed.  In  all,  35%                graduated from high school, 24.5% hold bachelor's or engineering degrees and 34.5%                were university graduates.                The respondents were asked to choose all their goals of saving from the provided list (Fig.                1). The largest group (39.01%) identified their saving goal as preserving money for their                children.  The  Pearson  coefficient  between  this  goal  and  bequest motive is  r=0.346  (p-                value=0.000; with significance threshold 0.01). The next three goals indicated by more                than  30%  of  the  respondents  were  renovation  of  housing  estate,  reserve  for  random                situations  and  providing  security  in  old  age.  The  second  one  is  correlated  with  the                precautionary  motive  with  r=0.165  (p-value=0.033,  sign.=0.05),  and  the  last  one  is                correlated with the life-cycle motive with r=0.166 (p=0.023, sign.=0.05).                Both  the  improvement  and  the  independence  motives  are  statistically  significantly                correlated with purchasing own housing, with r=0.218 (p=0.003, sign. 0.01) and r=0.159                (p=0.036, sign. 0.05), respectively. Although only 8.97% of the respondents declared they                were saving for opening or developing their own business, that goal is strongly correlated                with the enterprise motive with r=0.270 (p=0.000, sign.=0.01).                                                                  285","Figure 1. Goals of Saving (Multiple Choice)                                            rehabilitations   0,044843049                     for the development of your own business    0,089686099                                   fixed fees (e.g. housing)      0,098654709                                             treatment            0,107623318                                 purchase of durable goods            0,139013453                               purchase of a housing estate            0,147982063                                   without special purpose             0,152466368                                       for other purposes               0,161434978                   reserve for current consumption expenditure           0,170403587                                              holidays                    0,179372197                                       security in old age                             0,304932735                            reserve for emergency situations                             0,322869955                               renovation of the house, flat                              0,33632287                              securing the future of children                                  0,390134529                                                     0%   5%   10%  15%  20%   25%  30%  35%  40%   45%                                      Source: own calculations on the basis of questionnaire                 The  research  proves  that  the  precautionary  motive  has  the  highest  ranking  in  the                respondents’ opinions, with the average mark 7.21 in the ascending scale from 0 to 10,                followed  by  the  independent  and  improvement  motives  with  marks  of  6.87  and  6.80,                respectively  (Fig.  2).  The  lowest  ranking  refers  to  the  down  payment  motive  with  an                average assessment of only 3.87, closely followed by the enterprise motive with an average                mark  of  4.01.  The  standard  deviation of  the  mark for  both  these latter motives is the                highest, which indicates large differences in respondents’ opinions about these motives'                importance.                  Figure 2. Importance of Saving Motives (0—not important, 10—extremely important)                          7,210762332  5,439461883  6,116591928  6,798206278  6,869955157  5,856502242                   8                   7                   4,98206278                   6                                                4,013452915    3,874439462                           2,587564406   2,841025719    2,653299226  2,694645662                   5              3,125859692    3,008931853          3,262066006  3,215299798  3,60896014                   4                    3                                                                                           average                   2                                                                                           standard deviation                   1                    0                                              Source: own calculations on the basis of questionnaire                 The relatively high value of averages indicates the importance of these saving motives for                Polish households. However, the low correlation between saving motives and actual goals                                                               286","of saving may imply that either households do not follow their motives or other important                factors influence the choice of saving goals.                 Furthermore,  analysis  of  correlation  between  saving  motives  was  carried  out.  In  most                cases, there is a statistically significant correlation between saving motives (Table 1). The                highest  correlation  (r=0.654)  occurred  between  the  life-cycle  motive  and  the  bequest                motive, followed by the correlation between the improvement and independence motives                (r=0.537)  and  the  independent  and  precautionary  motives  (r=0.501).  However,  no                significant correlation was found between the down payment motive and the precautionary,                improvement or independence motives. There is also a lack of correlation between the                enterprise and precautionary motives and between the bequest and avarice motives.                             Table 1. Pearson’s Coefficient Between Saving and Saving Motives                                           precautionary   motive   intertemporal   substitution   motive   avarice motive   life-cycle   motive   improvement   motive   independence   motive   enterprise   motive   bequest motive   down   payment   motive                        precautionary             1    .300   .219   .471   .501   .366   .147  .291   .123                                                           **                                                                                  **                                                    **                                                                   **                                                                           **                                                                                                  **                motive                intertemporal          .300      1     .384   .454   .345   .234   .397   .373   .237                                                                                                  **                                                                                          **                                                                   **                                                                           **                                                           **                                            **                                                                                  **                                                                                                          **                substitution motive                                            **                                                                                  **                                                                                                          **                                                                   **                avarice motive         .219   .384       1    .245   .182   .259   .159       .137  .197                                                    **                                                                           *                                                                                           *                                            **                                                                                                          **                                                                           **                                                           **                                                    **                                                                                                  **                                                                                  **                life-cycle motive      .471   .454   .245        1    .424   .309   .332   .654   .236                                                                                          **                improvement            .501   .345   .182   .424         1    .537   .307   .223   .147                                                                   **                                                            *                                                    **                                            **                                                                                                  **                                                                                  **                                                                                          **                motive                independence           .366   .234   .259   .309   .537         1     .220   .174     .152                                                                                                  *                                                           **                                            **                                                                           **                                                    **                                                                                          **                                                                   **                motive                                                                           **                                                                   **                                                                                                  **                                                                                  **                                                                                                          **                enterprise motive       .147  .397   .159   .332   .307   .220          1    .306   .203                                                            *                                                    **                                                                                   *                                                                           **                                                                                                          **                                                                                          **                                                    **                                            **                                                                   **                bequest motive         .291   .373   .137  .654   .223   .174   .306            1    .256                down payment                        **     **      **                     **      **                motive                  .123  .237   .197   .236   .147        .152  .203   .256        1                *  - refers to p=0.05                **  - refers to p=0.01                                   Source: own calculations on the basis of the questionnaire                4 Relationship between saving and its motives                To compare saving behaviour of the respondents with their saving motives, cross tables                and  the  correspondence  analysis  with  symmetrical  normalisation  were  calculated.                Correspondence analysis allows presenting results in graphic form as a chart of dispersion                in which the points present particular categories of variables. The results proved to be                statistically significant only for three motives: precautionary, improvement and life-cycle.                In the case of both the precautionary and improvement motives, the first two dimensions                of the correspondence analysis cover more than 70% of the results. While the importance                of motives was assessed on an ascending scale from 0 to 10, the list of answers to the                question about saving behaviour was:                    •   I do not save,                    •   I spend everything on current needs, do not put off anything, do not invest (spend                       everything),                    •   I usually devote everything to current needs, but from time to time, I manage to                       save something (save from time to time),                                                            287","•   in addition to current expenses, I try to save some funds on regular basis (try to                       save regularly),                    •   I save a certain amount of money in addition to current expenses each month (save                       regularly),                    •   I allocate funds for consumption and savings in advance, but I have no specific                       purpose to save and plan to use financial instruments (save without a goal), and                    •   in advance, I allocate funds for consumption and savings, which I invest in planned                       financial instruments (planned saving).                The analysis shows that Polish households do not save with a proper plan. The largest                group consisting of 34.08% of the respondents are those who try to save regularly. Another                group of 25.11% of the respondents saves only from time to time after having satisfied                their  basic  needs.  Certain  amounts  of  money  are  regularly  put  away  by  17.49%.  The                respondents allocating set amounts of income to saving in advance are in the minority. In                all, 6.73% save intentionally but without a goal or a saving plan, and 5.38% have some                saving strategy. More than 11% of the respondents do not save at all.                The  analysis  reveals  various  patterns  in  households'  behaviour.  In  the  case  of  the                precautionary motive, both the 0 and the 2 marks stand out, which indicates evidently that                this motive is extremely important and is placed close to those who make an effort to save                regularly (Fig. 3). Some who save regularly set amounts of money, but they do not have                fixed  goals,  so  they  tend  to  follow  this  attitude,  while  the  rest  of  them  seem  to  have                assessed the importance of the precautionary motive as an average “5” within the ranking.                The  respondents  who  allocate  their funds  in  advance  and have  plans to  utilise  specific                financial  instruments  to  increase  their  wealth  and  those  who  manage  to  save  some                amounts from time to time (which in G. Katona's classification would be contractual saving                and residual saving, respectively)  (Fisher, Anong, 2012), the precautionary motive has                varied importance. Some give it a rather low mark of “3”, while others attributed to it                higher ratings of “7” and “9”. The largest group of regular savers is very close to mark “8”.                Interestingly enough, the declaration about not saving in the case of the precautionary                motive is close to “0”, while the improvement motive is closer to ratings “3” and “4” (Fig.                4).                  Figure 3. Correspondence Analysis of the Saving Behaviour vs the Precautionary Motive                                                                       2                                      chi =80.497, p-value=0.04 (60 degrees of freedom)                      Source: own calculations with the usage of IBM SPSS on the basis of the questionnaire                                                              288","In  Fig. 4,  which  represents the  correspondence  between  the  saving  behaviour  and  the                improvement  motive,  we  can  observe a  very  distinctive  pattern.  The  more  regular  the                saving,  the  more  important  the  improvement  motive  is,  with  the  exclusion  of  planned                saving. The improvement motive includes saving for different real assets and services such                as  real  estate  satisfying  the  housing  requirements  of  the  household,  a  car,  up-to-date                furnishings and household equipment, and an education in the form of extra courses or                studies. The implied need to save with the intention to spend money on specific planned                goals forces savers to allocate their money regularly or at least semi-regularly. The analysis                proves a real correspondence of saving behaviour with the improvement motive.                     Figure 4. Correspondence Analysis of Saving Behaviour vs Improvement Motive                                                                    chi2=87.245, p-value=0.012 (60 degrees of freedom)                        Source: own calculations with the usage of IBM SPSS on the basis of questionnaire                        Figure 5. Correspondence Analysis of Saving Behaviour vs Life-cycle Motive                                                                     2                                     chi =87.783, p-value=0.011 (60 degrees of freedom)                        Source: own calculations with the usage of IBM SPSS on the basis of questionnaire                                                             289","The first two dimensions in the correspondence analysis of saving behaviour vs the life-                cycle  motive  cover  roughly  65.8%  of the  results  (Fig. 5).  This  motive  of  saving is  not                important for people who do not save. It is also one of the less important saving motives                for  these  who  allocate  and  invest  their  saving  with  a  plan.  Out  of  all  correspondence                analysis  between  saving  behaviour  and  the  importance  of  the  life-saving  motive,  the                answers are the most concentrated in one place on the figure. This suggests that although                life-cycle  motive is  not the  most important  for  respondents, it is  considered  significant                regardless of their saving behaviour. The results may also suggest that Polish households                do not know how or do not have proper instruments to save in the long term because the                life-cycle motive requires low risk stable instruments such as insurance policies, pension                plans, and long-term deposits.                 5 Conclusions                 These  findings  indicate  that  Polish  households  understand  the  importance  of  saving  to                provide financial security for themselves and their descendants. They are aware of the                implications of saving inspired by the improvement motive. They also know how to gain                financial independence through that activity. The relatively low standard deviation of their                assessment  of  saving  motives’  importance  suggests  the  high  compatibility  of  the                respondents’ opinions in that mater.                Although  the  analysed  saving  motives  are  considered  essential  in  the  respondents’                assessment, their opinions do not translate into saving goals and other aspects of saving                behaviour. Out of the list of 14 saving goals, only four were identified by more than 30%                of the respondents, seven others were viewed by from 10% to 20% of the households                representatives  and  the last  four  were  chosen  by less  than  10%.  These  figures  clearly                suggest that the knowledge and opinions of the importance of saving are not supported by                real activity. This statement is strengthened by the data representing behaviour of the                respondents in terms of saving, which clearly reflect that Polish households save mostly                occasionally  and  without  employing  any  strategy  such  as  using  adequate  financial                instruments.                The  results indicate  a  rather  disturbing  picture  of  Polish  households'  saving  behaviour.                Despite  the  fact  that  the  Polish  financial  market  is  highly  developed,  to  the  level  that                recently analysts stopped considering Poland an emerging market, Polish households do                not  take  full  advantage  of  that  fact.  Many  households  store  their  saving  in  liquid                instruments or in cash even if their goal of saving is long term. The lack of translating                saving motives into actual saving behaviour can be influenced by many, not just economic,                factors because households are under social and cultural constraints. Nevertheless, both                financial and educational institutions should intensify their efforts in educating individuals                to adjust their offers of financial instruments to the needs of households.                References                 Browning,  M.,  Lusardi  A.  (1996).  Household  Saving :  Micro  Theories  and  Micro  Facts.                Journal of Economic Literature, vol. XXXIV(December), pp. 1797–1855.                Canova, L., Maria, A., Rattazzi, M., Webley, P., Rattazzi, A. M. M., Webley, P. (2003). The                hierarchical  structure  of  saving  motives.  Journal  of  Economic  Psychology.  DOI:                http://doi.org/10.1016/j.joep.2003.08.007.                Devaney, S. A., Anong, S. T., Whirl, S. E. (2007). Household Savings Motives. Journal of                Consumer  Affairs,  vol.  41(1),  pp.  174–186.  DOI:  http://doi.org/10.1111/j.1745-                6606.2006.00073.x.                 Fisher, P., Anong, S. (2012). Relationship of Saving Motives to Saving Habits. Journal of                Financial  Counseling  and  Planning,  vol.  23(1),  pp.  63–79.  Retrieved  from:                https://www.afcpe.org/assets/pdf/v23_j4.pdf.                 Lindqvist,  A. (1981).  A  note  on  determinants  of  household  saving  behavior.  Journal  of                Economic  Psychology,  vol.  1(1),  pp.  39–57.  DOI:  http://doi.org/10.1016/0167-                                                             290","4870(81)90004-0.                Schunk,  D.  (2009).  What  Determines  Household  Saving  Behavior?  An  Examination  of                Saving  Motives  and  Saving  Decisions.  Jahrbücher  Für  Nationalökonomie  Und                Statistik/Journal of Economics and Statistics Bd, vol. 229(2294), pp. 467–491. Retrieved                from: http://www.jstor.org/stable/23813451.                Xiao, J. J., Noring, F. E. (1994). Perceived Saving Motives and Hierarchical Financial Needs.                Financial Counseling and Planning, vol. 5, pp. 25–45.                Yao, R., Wang, F., Weagley, R. O., Liao, L. (2011). Household Saving Motives: Comparing                American and Chinese Consumers. Family and Consumer Sciences Research Journal, vol.                40(1), pp. 28–44. DOI: http://doi.org/10.1111/j.1552-3934.2011.02086.x.                                                                                                                               291","The Relationship between Saving and Debt in Polish Households                                                                                           2                                                                   1                                Anna Magdalena Korzeniowska , Wojciech Misterek                                         1  Maria Curie-Skłodowska University in Lublin                                         Faculty of Economics, Department of Banking                                      Pl.Marii Curie-Skłodowskiej 5, 20-031 Lublin, Poland                                          E-mail: anna.korzeniowska@umcs.lublin.pl                                          2  Maria Curie-Skłodowska University in Lublin                                         Faculty of Economics, Department of Banking                                      Pl.Marii Curie-Skłodowskiej 5, 20-031 Lublin, Poland                                           E-mail: wojciech.misterek@umcs.lublin.pl                 Abstract:  It  can  be  assumed  that  having  saving  and  credit  is  mutually  incompatible,                because the logics requires paying down of any debt if one has financial surpluses. This is                because the cost of servicing debt is much higher than the benefits of interest on saving.                As the observation of real household behaviour proves the opposite, the aim of this paper                is to analyse the relationship between saving and debt of households, which is illustrated                by the case of citizens of Lubelskie Voivodeship (Poland). The research conducted on a                group of 223 working representatives of households shows that more than 50% of persons                with  bank  loans  also  have  saving  that  exceeds  in  its  amount  a  3-month  income  of  a                respondent.  It  is  worth  noting,  however,  that  among  persons  assessing  their  financial                situation as good or very good, the percentage of such persons is higher than in the case                of people assessing their financial situation as bad or rather bad. In the latter group, many                more people are holding only credit products. The research has also shown that people                with saving are more likely to use car loans and credit cards, while people who do not save                are more likely to use cash credits and cash loans. In both groups, the percentage of people                with mortgages is comparable.                Keywords: personal finance, credit instruments, saving level                JEL codes: D14, G21                 1 Introduction                 Consumption  spending is  the  most important  component in the  expenditure method  of                calculating the gross domestic product. Therefore, the number of research topics connected                with  consumption  is  not  surprising.  Scientists  research  the  level  of  consumption                expenditure as part of national demand, the structure of the consumption on a micro level                and  sources  of  financing  consumption.  The  standard  theory  claims  that  the  level  of                consumption depends on a disposable income because the basic decision of the household                is how to divide the income into consumption and savings when the latter is understood as                postponing consumption. From this perspective, households’ debt is treated as accelerating                consumption in time that in consequence will be paid off during coming periods by using                saving. The intertemporal aspect of saving is fundamental for understanding households'                financial behaviour (Börsch-Supan and Essig, 2003).                The commonly shared approach in the analysis of the household's wealth (which  is co-                integrated with income and consumption ((Dreger and Reimers, 2011)) on a micro level is                to focus on net wealth, whereby the household’s liabilities are deducted from its assets                (Kukk, 2014). But while drawing proper conclusions, there is a problem with heterogeneity                of household assets across countries because there are noticeable differences in real estate                ownership, which is an important part of real assets. Another problem is that the same net                wealth can be calculated from various combinations of assets and liabilities. That is why in                another approach, researchers compare only financial assets with liabilities. It is claimed                that households use credit to smooth their income, but the process is affected by credit                constraints. There is also a relationship between credit constraints and selection of financial                assets (Wang, 2016).                                                               292","Scientists  are  also  interested  in  households  with  income  that  is  lower  than  their                expenditures. The question arises how households deal with these expenses. Usually the                sources of financing over-expenditure are wealth/past saving, formal credits and loans,                informal loans in the form of assistance from family and/or friends and unpaid bills  (Le                Blanc, Porpiglia, Teppa, Zhu, and Ziegelmeyer, 2015).This prompts to further analysis on                the financial situation of vulnerable households (Ampudia, van Vlokhoven, and Żochowski,                2016).                There  is  little  to  none  simultaneous  research  on  both  saving  and  debt  of  households.                Usually debt is used only as a background for saving opportunities of individuals or it is                treated as a constraint to save, especially if we take into consideration the debt-to-income                ratio of households, which in the European Union is much higher than 100% (Rocher and                Stierle, 2015).                It can be assumed that having saving and credit mutually exclude each other, because the                logics require paying down of any debt only if financial surpluses are generated. This is                because the cost of servicing debt is much higher than the benefits of interest on saving.                However,  the  observation  of  real  household  behaviour  proves  the  opposite.  This  is                particularly  the  case  with  the  precautionary  and  the  life-cycle  motives  of  saving.  For                example, according to the Polish Central Bank, the value of financial assets of households                at the end of the 1st quarter of 2017 was 1 953,7 bln PLN, while at the same time, the                value of financial debt was 701 bln PLN (Kolasa, 2017). Therefore, this paper analyses the                relationship between saving and debt of households in Lubelskie Voivodeship (Poland).                 2 Description of the methodology and the research sample                 This paper researches financial aspects of households' functioning in Lubelskie Voivodeship.                The  research  was  run  by  the  scientists  from  the  Faculty  of  Economics  of  Maria  Curie-                Skłodowska  University  in  Lublin  under  the  patronage  of  the  research  staff  of  the                Department  of  Banking.  The  research  was  carried  out  with  the  use  of  the  direct                questionnaire survey methodology. The questionnaire was completed by 223 professionally                active  persons  representing  households.  Among  the  respondents,  the  majority  had                employment contracts or some other similar form of employment, and they account for                79.37% of the research sample. A significant proportion of the respondents run their own                businesses (12.11%) or farms. The remaining respondents (1.35%) most often combined                a few forms of employment. Although the questions referred to the financial standing of                the whole household, the structure of the respondents affected the answers only to a small                extent. The research group was dominated by women (56.95%) and persons aged 36-45                (30.49%).There  were  slightly  fewer  persons  aged  26-35  (26.46%)  and  46-55                (21.52%).The least numerous were respondents up to the age of 25 (13.01%) and those                56 and older (8.52%). The research was carried out in Lubelskie Voivodeship, which is the                sample included  30%  of  households in  towns  with  more  than  200  000 inhabitants  and                villages, 20% in towns up to 20 000 inhabitants as well as between 20 000 and 200 000                inhabitants. The research covered households of various sizes. A similar proportion, i.e. at                the level of 27%, was recorded for two- , three- , and four-person households. Slightly                over 12% of the research group were households consisting of a minimum of five persons,                and only 6.3% were single person households.                To  sum  up,  the  structure  of  the  research  sample  fully  reflects  the  structure  of  the                inhabitants of the examined region both in terms of demographic and social factors.                 3 Comparative analysis of holding saving and credit                With the right assumption, saving and credit are mutually exclusive because the logics                require repaying debt when some financial surpluses are generated. It results from the fact                that the cost of debt service is much higher than benefits from interest rates for savings.                                                             293","However, the research shows that over 30% of persons with bank credits hold saving that                exceeds a three-month income of a respondent. If the research was to take into account                also those persons with saving lower than a three-month income, it would appear that a                group of respondents with both credits and saving increases to more than 50%. Therefore,                it seems appropriate to verify the characteristics of this group of persons and the nature                of the debt they hold.                 Analysis of the data in Table 1 shows that a financial standing is essential for holding debt                or saving instruments. It is clear that the higher assessment of a household's financial                standing, the higher the number is of individuals holding saving or credit together with                saving. Within the group, which is of key importance for the research, i.e. those persons                who hold both credit and saving, 40% assess their financial standing of their households                at a good or very good level, and only 6% said their situation is bad or very bad. This                structure is very similar to the structure of persons holding only saving. For comparison,                among persons with credits only, only 20% of persons assess the financial situation of their                households at a good or very good level, and nearly 30% of those declared it is bad or                rather bad.                     Table 1 Structure of Respondents by Their Individual Assessment of a Household's                                                    Financial Standing                  Financial            Only credit       Only saving       Credit and       Neither credit                 standing                                                   saving           nor saving                                           6.7%              1.2%             0.0%               0.0%                 Bad                                         (1.6%)            (0.0%)           (0.0%)             (3.0%)                                         20.0%              3.7%             6.0%              10.0%                 Rather bad                                         (9.4%)            (1.7%)           (5.9%)             (9.1%)                                         53.3%             43.2%             53.0%             70.0%                 Average                                        (56.3%)           (41.4%)           (50.0%)           (54.5%)                                         20.0%             40.7%             34.2%             20.0%                 Good                                        (29.7%)           (43.1%)           (35.3%)           (30.3%)                                          0.0%             11.1%             6.8%               0.0%                 Very good                                         (3.1%)           (13.8%)           (8.8%)             (3.0%)                   * the data in parenthesis are given for the assumption that saving must account for minimum                                             three-month incomes of a household                                      Source: Own study based on questionnaire surveys                 Giving the assumption that saving should account for minimum three-month incomes of a                household (the data in parentheses in Table 1), the reliances indicated are also confirmed.                Yet, the discrepancies between the answers from persons only with credits and possessors                of credits and saving or only savings are slightly smaller.                 The  data  presented  in  Table  2  identify  a  similar  reliance  between  a  saving  instrument                and/or  credit  and  income  per  capita  in  the  household.  It  becomes  evident  that  the                respondents holding both credit and saving and those with saving only are wealthier than                the respondents who have only credit. In the first two groups, the proportion of persons                with incomes higher than 2400 PLN per person is between 21.7-28.6%, and those earning                the least (less than 1 600PLN per person) account for 40%, while among the persons with                credit only, the proportions are -12.7% and 54.5%, respectively.                Therefore, while the financial standing of a household has an effect on saving, it has no                effect on debt. The data in Fig. 1 shows that the level of saving does not affect holding or                not holding credit. It is clear that for all the groups of respondents with different levels of                saving, the proportion of persons declaring bank debt is similar, and its level is 46.9%-                                                               294","67.5%.  What  is  also  essential  is  the  fact  that  it  is  difficult  to  identify  any  trend  here,                whether it is an uptrend or downtrend.                    Table 2 Structure of Respondents with Credit or Saving by Income per Capita in a                   Household (for the analysis, it was assumed that saving must account for minimum                                           three-month incomes of a household)                     Income per                    capita in a                                           Credit and       Neither credit                  household (in        Only credit      Only saving         saving           nor saving                       PLN)                 0-799                    10.9%              2.0%           6.7%                8.0%                  800-1599                 43.6%              38.8%          33.3%               52.0%                 1600-2399                32.7%              30.6%          38.3%               36.0%                  2400-3199                10.9%              18.4%          10.0%               0.0%                 > 3199                   1.8%               10.2%          11.7%               4.0%                   * the data in parenthesis are given for the assumption that saving must account for minimum                                             three-month incomes of a household                                      Source: Own study based on questionnaire surveys                     Figure 1 Percentage of Persons Holding Credits with Different Levels of Household                                                          Saving                      between 1-3 months income                                                 67.50%                      between 3-6 months income                                  46.90%                       between 0.5-1 year income                                      53.60%                         between 1-3 year income                                               66.70%                            above 3 year income                                   50.00%                                            0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%                                       Source: Own study based on questionnaire surveys                  Hence, if the income (wealth) of a household is a determinant of saving but not credit, it                is worth verifying what affects decisions to use credit. It is then fairly justified to compare                types of credit typical for the groups of respondents with different levels of saving. It should                allow diagnosing why persons holding saving hold credit  at the same time. As the data                from  Table  3  show,  the  amount  of  saving  influences  a  particular  type  of  credit.  These                dependencies, however, may vary because for some types of credit, an increase in saving                causes this very type of credit to be more common, while for others, an increase in saving                results in a decrease of the percentage of persons holding this type of credit.                                                                295","The first group, for instance, has credit cards that are used by only several per cent of                households with no saving or with saving equivalent to an annual income at maximum;                more than 25% of households hold saving in excess of their annual income. This is clearly                determined  by  a  type  of  credit.  People  mostly  use  credit  cards  for  convenience  to                temporarily improve their creditworthiness and to transfer debt service burden to another                period. Consequently, economic rationality, which says that the cost of credit is higher than                the interest rates on saving, is of minor importance. What is more, the cost of debt service                in this case is low. That is why so many persons despite having some funds in their bank                accounts decide to use a credit card to increase their comfort while clearing transactions.                Similarly, it applies to car credits. In this case, only a few households with no or a low level                saving use this form of debt. Yet, among individuals with saving higher than their annual                income, the frequency of this type of debt is higher and ranges from several to several                dozen per cent. Also in this case, the purpose of the credit affects the situation. Car credit                is often used by persons only because they would like to split the burden of purchase of a                given means of transport over time, not because they are not able to set aside sufficient                resources for a one-off transaction. Additionally, not less important is the influence of car                manufacturers  themselves,  which  encourage  buy  new  vehicles  by  offering  very  cheap                credits. Thus, wealthy persons, despite having saving, decide to purchase a new car by                instalments and take advantage of car credit. This type of debt is less popular with less                wealthy persons and therefore holding less saving may mostly result from the fact that                such persons decide to buy second-hand cars for which car manufacturer offer no cheap                credits.                 Table 3 Percentage of Households with Different Levels of Saving and Holding Particular                                                      Types of Credit                   Level of saving in                  monthly income of                    Between       Between       Between       Between                         a household                      1-3           3-6           6-12         12-36                                         No saving      months        months        months        months                                                         income        income        income        income                 Credit type                  Mortgage                  24.0%         27.5%         18.8%        32.1%         27.8%                  Credit card               16.0%         17.5%         6.3%         14.3%         27.8%                 Credit  line,    e.g.  in                 savings           and                 transaction                 account                   8.0%          3.8%          6.3%          0.0%          0.0%                  Car credit                 0.0%          8.8%          3.1%          7.1%         11.1%                  Credit/Cash loan           20.0%         15.0%         15.6%         3.6%         11.1%                  Consumer credit            4.0%          10.0%         6.3%          3.6%         11.1%                  Student credit             4.0%          1.3%          3.1%          0.0%          0.0%                  Consolidation loan         0.0%          1.3%          0.0%          0.0%          5.6%                                        Source: Own study based on questionnaire surveys                A reverse situation is the case of cash credit/loan. This form of funding is more commonly                used by households with low saving, i.e. less wealthy ones, because it is a form of debt                                                             296","used mainly to replenish current cash flow shortages when there is no other way to provide                funds. Although these credits are very expensive, there are persons who decide to choose                them  because  they  have  no  other  alternative  and  also  because  interest  rates  on  their                saving are lower than the cost of the debt.                Student credit is relatively commonly used by households with low saving. However, age                is a decisive factor in this case. Young persons who use this form of credit to finance their                education are obliged to repay it in the first years of their employment. Consequently, such                debt is still serviced even though some surpluses in saving are systematically generated.                 It is worth noting that in the case of some credits, the frequency of their use is comparable                for persons both with high and low saving. This is the case for mortgages, which are used                to split very high payments into a few or a few dozen years. Rare is the case when saving                allows paying such a high debt off in one payment. As a result, this form of debt is chosen                both by wealthy persons with high saving and those with lower income and saving. It would                seem reasonable to repay this debt from saving, but persons with higher income and saving                do  not  do  this  because  they  want  more  comfort  of  functioning  and  safety  (so-called                precautionary saving). The same applies to a credit line in the current account, except that                it is affected not by the amount of debt but by its form. This credit is used while using a                bank account, which is why so many persons take this opportunity, no matter how high                their incomes and saving are.                 4 Conclusions                 It  is  natural  to  assume  that  holding  saving  and  credit  is  mutually  excluded  because                logically, one is expected to repay debt when financial surpluses are generated. As the                research shows, it does not always hold true because more than 30% of persons with bank                credit are able to hold saving that exceeds their three-months income, and another 20%                hold some lower saving despite holding debt. This mainly results from the fact that financial                standing of a household is a determinant of saving but not credit. Consequently, the higher                the income generated by a household, the higher its saving is. It does not mean, however,                that such a household has lower debt. This is influenced by the purpose for which credit is                taken because many types of credit are offered for our convenience. Although we bear the                costs of its service, we try to take advantage of it because it makes our functioning in the                market much easier. We often decide to take credit despite holding saving because of an                attractive commercial offer, e.g. car credit. Still, some other reasons are for mortgages,                which allows a high one-off payment to be split over time. Although we are sometimes able                to  generate  surpluses,  we  decide  to  put  aside  some  saving  rather  than  make  an  early                payment of credit. These are psychological factors that decide that our priority is to hold                free financial resources for safety, not to reasonably repay debt.                 References                 Ampudia, M., van Vlokhoven, H., Żochowski, D. (2016).  Financial fragility of euro area                households.  Journal  of  Financial  Stability, vol.  27(December  2016),  pp.  250–262. DOI:                http://doi.org/10.1016/j.jfs.2016.02.003.                Börsch-Supan, A. (2000). International Comparison of Household Savings Behaviour : A                Study  of  Life-Cycle  Savings  in  Seven  Countries.  Discussion  Papers.  Retrieved  from:                http://ideas.repec.org/p/mnh/vpaper/1016.html.                Börsch-Supan,  A.,  Essig,  L.  (2003).  Household  Saving in  Germany:  Results  of  the  first                SAVE      study.     NBER     working      paper,     no.    9902.     Retrieved     from:                http://www.nber.org/papers/w9902.                 Dreger, C., Reimers, H.-E. (2011). The long run relationship between private consumption                and wealth: common and idiosyncratic effects. Portuguese Economic Journal, vol. 11, pp.                21–34. DOI: http://doi.org/10.1007/s10258-011-0075-y.                                                            297","Kolasa, A. (2017). Sytuacja finansowa sektora gospodarstw domowych w I kw. 2017 r.                Warszawa: Departament Analiz Ekonomicznych NBP, Nr 03/17.                Kukk,  M.  (2014).  Distinguishing  the  Components  of  Household  Financial  Wealth :  the                Impact of Liabilities on Assets in Euro Area Countries Merike Kukk. Eesti Pank. Working                Paper Series, no. 2.                Le Blanc, J., Porpiglia, A., Teppa, F., Zhu, J., Ziegelmeyer, M. (2015). Working Paper Series                Household saving behaviour and credit constraints in the euro area.                Rocher, S., Stierle, M. (2015). Household saving rates in the EU: Why do they differ so                much? No. 2015.01, Vol. 8022.                 Von  Kalckreuth,  U.,  Eisele,  M.,  Le  Blanc,  J.,  Schmidt,  T.,  Zhu,  J.  (2012).  The  PHF:  a                comprehensive  panel  survey  on  household  finances  and  wealth  in  Germany.  Retrieved                from: https://www.econstor.eu/bitstream/10419/59999/1/719704995.pdf                Wang, G. (2016). Credit Constraints and Household Selection of Financial Assets.  Open                Journal of Social Sciences, (February), pp. 47–56.                                                                                                                         298","The Selected Aspects of Foreign Trade in Central European                                                       Countries                                                                                 2                                                              1                                          Jaroslav Kovárník , Eva Hamplová                                                1  University of Hradec Králové                               Faculty of Informatics and Management, Department of Economics                                   Rokitanského 62, 500 03  Hradec Králové, Czech Republic                                              E-mail: jaroslav.kovarnik@uhk.cz                                                 2  University of Hradec Králové                               Faculty of Informatics and Management, Department of Economics                                   Rokitanského 62, 500 03  Hradec Králové, Czech Republic                                                E-mail: eva.hamplova@uhk.cz                 Abstract: This article deals with the selected aspects of foreign trade development in the                countries  from  central  Europe,  namely  the  Czech  Republic,  Poland,  Slovakia,  Hungary,                where these countries belong into Visegrad Four, and Germany, because countries of V4                have usually strong connections with Germany. The article continues with analysis from                other articles presented on other conferences, and it analyses development since 2000,                and it focuses on selected aspects of foreign trade development, because foreign trade is                crucial part for sustainable development for almost all countries around the world (it is part                of GDP formula in open economy). The article analyses development of foreign trade with                goods  and  with  services,  where have been  discovered  relatively  surprising  results (the                weakest economy in terms of services has been Germany), but consequently the article                analyses  relations  between  export  development,  foreign  trade  development,  and  GDP                development.  In  theoretical  point  of  view,  export  is  considered  to  be  independent  on                domestic  product,  and  economically  weak  countries  are  usually  considered  to  be  more                dependent on export (in terms of GDP development). This fact has been proved during                analysis, where especially V4 countries are strongly dependent on export. However, the                final impact of net balance of foreign trade on GDP is relatively weak in all V4 countries,                which is relatively surprising fact again. Detail description of results is in this article.                Keywords: Central Europe, Czech Republic, export, foreign trade, import                JEL codes: F43, O11                 1 Introduction                Foreign trade is very important for every state all around the world because it is, among                others, part of macroeconomic Gross Domestic Product (GDP) formula. In other words, it                can either improve the GDP level (in case that export is higher than import) or worsen it                (in the opposite case). This basic theory has been explained in many books and articles,                for example in Andrews, Bernake, \& Croushore (2011), or Samuelson \& Nordhaus (2010).                Nevertheless, foreign trade is important for every country because of other reasons as                well.  In  almost  all  countries  worldwide,  foreign  trade  helps  to  solve  the  proportionality                problem, where only few countries have all necessary resources in quantities required for                economic  development.  Foreign  trade  also  has  demonstrative  effect,  where  the  export                program is kind of indicator of the level of economic development, and it also helps create                an image of developed country. Foreign trade has also other benefits, such as support of                peaceful cooperation among partners, reducing of the risk of conflict, growth of education,                and others.                From above mentioned text is clear that foreign trade is very important for every country.                The importance of foreign trade has been evaluated in other articles by other authors, for                example Baier, Bergrstrand \& Feng (2014), Cieslik, Bieganska \& Sroda-Murawska (2016),                Do, Levchenko \& Raddatz (2016), Fracasso \& Marzetti (2015), Giordano \& Zollino (2016),                Gladkov (2016), or Vannoorenberghe (2014). Authors themselves already analysed the                                                               299","topic  of  foreign  trade  several  times,  for  example  in  Kovárník  \&  Hamplová  (2016),  or                Kovárník \& Hamplová (2017).                 The  Czech  Republic  is  a  member  state  of  Visegrad  Four,  and  all  other  member  states                (namely  Poland,  Slovakia,  and  Hungary)  are  very  important  business  partners  for  the                Czech  Republic.  Situation  in  Visegrad  Four  countries  has  been  also  analysed  by  other                authors, for example by Zdražil \& Kraftová (2012). However, the most important business                partner  for  the  Czech  Republic  is  Germany,  where  this  economy  has  a  very  important                position as a business partner for all V4 countries.                The aim of this article is to analyse the selected aspects of foreign trade in above mentioned                countries, namely in the Czech Republic, Poland, Hungary, Slovakia, and Germany. Firstly,                this  article  analyses  the  development  of  GDP  in  selected  countries,  because  of  the                importance of foreign trade for this indicator. The development of foreign trade in terms                of goods and in terms of services in these countries is next topic for analysis. Consequently,                the relation between export and GDP and between foreign trade and GDP are analysed as                well. The aim is to verify the hypothesis whether the development of both GDP and foreign                trade of these countries is similar or not, and also to verify whether especially small and                relatively weak economies are more open and more dependent on foreign trade.                 2 Methodology and Data                Covered period of time is 2000  – 2017, where data were obtained in general available                database Eurostat and calculated by authors (Eurostat a, 2018, Eurostat b, 2018).                Methods  of  comparison  and  comparative  analysis  have  been  used.  Moreover,  for  the                verification  of  above  mentioned  hypothesis  about  openness  of  economy  can  be  used                several different calculations. One of the most frequently used calculations measures the                relation between export and GDP, where more open economies usually have high ratio.                Another possible calculation uses turnover of foreign trade (summary of both export and                import) on GDP.                On the other hand, the importance of foreign trade for GDP creation can be evaluated by                the size of net balance on GDP, because in the theoretical point of view, only net exports                are part of GDP formula in open economy.                 3 Results and Discussion                The Analysis of GDP Development                Based on the fact that the Czech Republic has currently around 10.5 billion of inhabitants,                Hungary around 9.8 billion, Slovakia around 5.4 billion, Poland more almost 38 billion, and                Germany more than 82 billion, it is quite obvious that the level of GDP in billions of euro is                the highest in Germany, second highest in Poland, next in the Czech Republic, in Hungary,                and Slovakia is on last position.                However, it is better to use the level of GDP per capita for comparison. According to this,                Germany  still  has  the  highest  level,  but  the  Czech  Republic  is  on  the  second  position,                Slovakia is the third, Hungary on the fourth place, and Poland is the last. With respect to                this information is good to add one interesting fact. Even if the development in the number                of inhabitants in each country have not been steady, this number grew in the all analysed                countries except Hungary (comparison of the number of inhabitants in the years 2000 and                2017).                Deep analysis of GDP development shows that in all analysed countries was significant                decrease in this indicator in the year 2009 (both in absolute value and in per capita) as a                result  of  global  economic  crisis.  However,  the  after  crisis  development  is  different.                Germany has been growing since 2009 and it managed exceed pre-crisis year already in                2010. The Czech Republic was growing between 2009 – 2011, it was decreasing between                2011 – 2014, and it has been growing again since 2014. Moreover, it managed to exceed                pre-crisis year in 2011, but the level of GDP per capita in this country decreased under the                                                              300","pre-crisis year during the decrease, and it exceed the pre-crisis level again in 2015. Poland                was on the last position in 2008, it had been growing since 2009 to 2015, it dropped a little                in 2016, and it has grown again in 2017. It exceeded pre-crisis year in 2011, and in the                year 2012 (but only in this year) it exceeded Hungary. Hungary has been growing since                2009 with one exception in 2012. It was on the fourth position before crisis and it is on the                fourth position again in 2017. Slovakia has been growing, as well as Germany, since 2009,                and it also managed to exceed pre-crisis year within one year.                Following Fig. 1 shows the development of GDP per capita in analysed countries.                                      Figure 1 GDP Development (Euro per Capita)                   40000                  35000                  30000                 25000                  20000                  15000                  10000                   5000                      0                        2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017                              Czech Republic      Germany        Hungary        Poland       Slovakia                              Source: own calculations based on Eurostat a (2018), Eurostat b (2018)                Foreign Trade Development – Trade with Goods                 The analysis of foreign trade development shows relatively different results than the GDP                development analysis. The development of net balance in terms of goods recalculated per                capita  is  described  in  following  Fig.  2.  Recalculation  per  capita  is  necessary,  because                absolute values of net balance in Germany are significantly higher than in other countries                and  mutual  comparison  of  all  five  countries  in  absolute  values  is  difficult.  After  the                recalculation, Germany is still on the first position, but the differences are lower.                First  interesting  fact  is  that  in  the  first  analysed  year  (2000)  all  countries  except  of                Germany had negative trade balance, which means they had higher imports than exports.                Even if current trade balance is positive in all analysed countries, the development has                been quite irregular. Germany had surplus more than 64,000 billion in 2000, and it had                surplus around 268,000 billion in 2017. The second highest surplus in 2017 had the Czech                Republic, where this surplus is little bit more than 9,000 billion.                As was already mentioned, Germany has been in surplus and it has had the strongest                position  from  all  analysed  countries.  However,  the  mutual  comparison  of  V4  countries                shows interesting results. The Czech Republic had the worst position in 2000 (the highest                deficit  per  capita,  while  in  absolute  amount  had  Poland  worse  result),  but  it  has  been                growing (with few exceptions), and currently the Czech Republic has the second highest                surplus (after Germany). Exactly opposite development has been in Slovakia. This country                had the best result from V4 countries in 2000 (both in absolute amount and per capita),                but it has the second worst result in 2017 in absolute amount and the third highest values                in  terms  of  net  balance  per  capita  (after  Germany  and  the  Czech  Republic).  Really                interesting fact is that in 2009, during the economic crisis, was net balance decreasing in                                                             301","Germany, while it was increasing in all other analysed countries. Moreover, the net balance                grew in Germany in 2010, while it decreased in all analysed countries. The development                of foreign trade with goods per capita is described in the following Fig. 2.                                   Figure 2 Foreign Trade with Goods (Euro per Capita)                   3500                   3000                  2500                  2000                  1500                  1000                   500                     0                        2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017                   -500                  -1000                              Czech Republic      Germany        Hungary        Poland       Slovakia                              Source: own calculations based on Eurostat a (2018), Eurostat b (2018)                Foreign Trade Development – Trade with Services                 The analysis of foreign trade with services shows completely different results. Surprisingly,                the only country which has been in deficit for all analysed period is Germany. All other V4                countries has been in surplus, with only few exceptions in case of Slovakia. However, the                development  is  quite  irregular  in  all  countries,  with  several  increases  and  decreases.                Nevertheless, the deficit in Germany in 2017 is lower than in 200, but on the other hand,                surpluses in the Czech Republic and in Slovakia decreased, while surpluses in Poland and                in Hungary increased. The development (again recalculated per capita) is described in Fig.                3.                                  Figure 3 Foreign Trade with Services (Euro per Capita)                    800                   600                    400                   200                     0                        2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017                  -200                  -400                   -600                  -800                               Czech Republic      Germany        Hungary        Poland       Slovakia                              Source: own calculations based on Eurostat a (2018), Eurostat b (2018)                 The Relation between Export and GDP Development                As  was  mentioned  before,  one  of  the  aims  of  this  article  is  to  verify  the  openness  of                analysed countries, where one of the possible tools (indicators) for this evaluation is the                                                             302","ratio between export and GDP. Open economies usually have high value of this ratio, where                more closed economies have lower values. Openness is typical for small and developing                economies, where more closed are usually large and developed economies.                The share of export on GDP (in percent) is described in following Fig. 4.                                            Figure 4 Export on GDP Ratio (%)                     1                 0,9                 0,8                  0,7                  0,6                 0,5                 0,4                  0,3                 0,2                      2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017                              Czech Republic      Germany        Hungary        Poland       Slovakia                              Source: own calculations based on Eurostat a (2018), Eurostat b (2018)                 It  is  quite  obvious  that  hypothesis  about  openness  is  verified.  Despite  the  fact  that                Germany is not only the largest, but also the most powerful economy (in this article), its                export on GDP ratio has been below 50% during the whole analysed period. This share is                low also in Poland (in 2000, Poland had lower value than Germany, but in 2017, Poland                had a little bit more than 50%), where this economy cannot be considered as powerful one                (GDP per capita is the lowest, see Fig. 1), but it is large country (it has second highest                number of inhabitants, and second highest value of GDP in absolute amounts).                All other analysed countries have had higher values. In 2000, the Czech Republic had this                ratio under 50%, but it is around 80% nowadays. Hungary had this ratio more than 66%                in  2000,  but  its  development  has  not  been  steadily,  and  nowadays  is  Hungary  on  the                second position with the export on GDP ratio around 90%. Slovakia had this ratio around                54% in 2000, but it has been growing relatively fast (compared to other countries), and                this ratio is more than 96% in 2017.                This can be explained in that way that small and weak economies are more dependent on                foreign countries than large and / or more powerful economies. Of course, exceptions can                exist,  where  powerful  economies  are  dependent  on  foreign  trade,  or  less  powerful                economies  are  not  so  dependent  of  foreign  trade,  even  if  they  are  relatively  small.                However,  this  is  not  an  example  of  analysed  countries.  Less  dependent  seems  to  be                Germany (powerful and large country) and Poland (not powerful, but large country with a                lot of inhabitants), where the Czech Republic, Slovakia, and Hungary (relatively small and                weak economies) seem to be more dependent on foreign trade.                 Relation between Foreign Trade and GDP Development                Previous chapter analyses the openness of analysed economies with one of the possible                tools, namely export on GDP ratio. The results have been not surprising, but this chapter                is analysing the impact of foreign trade in national economy. It other words, in this chapter                is the analysis of net balance of foreign trade on GDP. As was mentioned in previous text,                in  the  formula  of  product  in  open  economy  can  be  found  net  exports,  which  can  be                calculated as a difference between exports and imports in particular economy in analysed                year. The analysis in this chapter describes how many percent from GDP is presented by                                                             303","net balance of foreign trade (net export). The results of this analysis are described in the                following Fig. 5.                                  Figure 5 Net Balance of Foreign Trade on GDP Ratio (%)                     0,1                   0,08                    0,06                    0,04                   0,02                      0                        2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017                   -0,02                   -0,04                    -0,06                    -0,08                              Czech Republic      Germany        Hungary        Poland       Slovakia                              Source: own calculations based on Eurostat a (2017), Eurostat b (2017)                 It is quite obvious that all countries except Germany had been dealing with negative trade                balance.  That  means  that  foreign  trade  was  not  helping  generate  the  GDP,  but  it  was                worsening it. The Czech Republic turned into positive balance in 2004, Hungary in 2007,                Slovakia in 2012, and Poland in 2013.                Analysis of each country can be done as follows. Germany is relatively closed economy                (previous chapter), but despite of this fact, the foreign trade generates more than 7.6%                of GDP. With respect to the fact that GDP in this country is significantly higher than in other                analysed countries, net balance of foreign trade in Germany is also significantly bigger than                in other countries. Even if Germany is not so dependent on foreign trade, it can generate                a lot from it.                Even if  Hungary  turned into  positive  balance in  2007, it  had  the  highest  ratio  in  2017                (almost 8%). Hungary had also the second highest export on GDP ratio. It can be explained                in  that  way  that  Hungary  is  relatively  open  economy,  where  foreign  trade  is  currently                helping create the product in this country.                The third highest ratio has the Czech Republic (little bit more than 7%), where this country                has had also the third highest export on GDP ratio. The explanation in this country can be                same as in case of Hungary.                Poland is relatively closed economy (relatively low export on GDP ratio), and foreign trade                in this country is not so much participating on creation of product, where it generates only                little bit more than 4%. On the other hand, it is not so surprising result based on the fact                that Poland is relatively closed economy.                As far as Slovakia is concerned, the results in this country are relatively surprising. Despite                the fact that Slovakia has the highest export on GDP ratio (it is the most open economy),                the foreign trade on GDP ratio is the lowest. Even if Slovakia is relatively dependent on                foreign  trade,  it  is  not  generating  product  in  this  country.  In  other  words,  Slovakia  is                exporting a lot, but it is also importing and the final impact of foreign trade on GDP is low.                                                                  304","4 Conclusions                The aim of this article was to analyse the selected aspects of foreign trade countries from                central Europe, namely in the Czech Republic, Poland, Hungary, Slovakia, and Germany.                Firstly, this article analyses the development of GDP in selected countries, secondly the                development of foreign trade in terms of goods and in terms of services. Consequently,                the relation between export and GDP and between foreign trade and GDP are analysed as                well.                The  analysis  of  GDP  development  shows  relatively  expected  results.  Germany  is  the                strongest evaluated economy; it has the highest level of GDP not only in absolute value,                but also in recalculation per capita. The second highest GDP in absolute value has Poland,                because it is  the  second largest  economy, where  after  recalculation  per  capita  has  the                second highest GDP the Czech Republic, and Poland is the weakest.                 In terms of trade with goods is Germany again the strongest economy, net balance has                been  in  surplus  in  whole  analysed  period,  and  even  after  recalculation  per  capita  has                Germany serious lead. All other analysed countries have been dealing with deficit for some                time, they are all in surplus now, but for example in case of Poland is this surplus really                small. The second highest surplus is in the Czech Republic.                The analysis of trade with services shows relatively surprising results. All members of V4                have been in surplus for almost all period, where Germany has been in serious deficit for                whole  analysed  period.  However,  the  development  is  not  steady  at  all,  where  the  net                balance  in  some  countries  grew  (comparison  between  2000  and  2017)  and  in  other                countries decreased.                The analysis of export on GDP ratio shows again relatively expected results about openness                economy.  Germany  seems  to  be  relatively  closed  economy,  because  it  is  strong  and                powerful  country,  and therefore it is  not  so  dependent  on  foreign  countries. Relatively                closed economy seems to be also Poland, where this ratio is also low (around 50%). Poland                is not developed country, but it is relatively large country with a lot of inhabitants. Small                countries  as  the  Czech  Republic,  Slovakia,  and  Hungary,  seem  to  be  relatively  open                economies with huge dependency on foreign trade.                 However, the last analysis from this article shows again a little bit surprising results. Even                if Germany is relatively closed economy, the share of net balance on foreign trade on GDP                is relatively high in this country (more than 7%). High share can be seen also in the Czech                Republic and in Hungary, where these countries are relatively open. However, Poland has                relatively low share, but it is relatively closed economy. On the other hand, Slovakia has                also low share despite the fact that it is relatively open economy.                 To sum it up, foreign trade is very complex issue; it has a lot of aspects and factors of                influence.  It  can  be  seen  in  this  article,  where  sometimes  the  results  are  relatively                surprising, strong economy has low dependency on foreign trade, but it still has a relatively                huge influence on GDP, where in relatively open economy has the foreign trade only low                impact on GDP. The authors plan to analyse foreign trade from different perspectives in                following research.                 Acknowledgments                This study is supported by internal research project “Investment within concept Industry                4.0”, and by project IGA 2018 “Support of pedagogical work, technical equipment, and                communication with experts”, both at Faculty of Informatics and Management, University                of Hradec Kralove, Czech Republic. It was prepared in cooperation with students of Ph.D.                study at Department of Economics, namely with Ing. Vojtěch Tuček and Ing. Martin Král.                 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Comparative Analysis and Conclusions for Poland                                          Anna Krajewska, Magdalena Kapela                                              Warsaw University of Technology                                           College of Economics and Social Sciences                                            Łukasiewicza 17, 09-400 Płock, Poland                                             E-mail: Anna.Krajewskal@pw.edu.pl                                            E-mail: Magdalena.Kapela@pw.edu.pl                 Abstract: Labour  tax  burdens  consist of  two  main elements:  personal  income  tax  and                social security contribution. Both have been subjects of multiple changes in European Union                countries. The aim of the article is: 1) to present main directions of those changes, 2) to                show their influence on tax wedge in different EU countries, 3)  to show specification of                labour taxation in Poland. Tax wedge in Poland demonstrates very low progressivity, that                means it does not take into consideration income and family situation. Internal structure                of tax wedge is also untypical because of great share of social security contribution. In the                article there has been undertake an attempt to present causes of tax wedge otherness in                Poland and resulting economic and social fallouts. Also, there are postulated directions of                changes. In the paper analysis were based on Eurostat and OECD statistics and research                on labour taxation carried out in Poland.                Keywords: labour taxation, personal income tax, social security contribution, tax wedge,                redistribution of income                JEL codes: E62, E64, H20, H21, J08                 1 Introduction                 The aim of the paper is to present main directions of labour taxation in European Union.                We also intend to explain specific features of labour taxation in Poland.                The stress is put on the following issues:                       •   Evolution of labour taxation in selected EU countries,                       •   Redistributive aspects of tax wedge,                       •   Internal structure of tax wedge.                In conclusion we try to suggest directions of changes in taxation of labour in Poland.                 2 Evolution of labour taxation                The basic foundations of the modern labour taxation system in the countries of Western                Europe  were  formed  after  World  War  II  and  although  in  the  following  decades  they                underwent numerous modifications, the characteristic features of this system still persist.                Among  the  main  features  of  personal  income  tax  in  Western  European  countries                (Krajewska 2012, 88-97) should be mentioned:                1) The progressive nature of the tax reflected by the number of tax thresholds (currently                   from 2 in Ireland to 17 in Luxembourg) and the level of the lower (in the past, in the                   1970s, amounted even 3-5-10%, but now slightly higher) and upper tax rates (currently                   mostly within 40-50%, in Sweden 57.1% (Taxation Trends.. 2017, p. 30)                2) The existence of the tax-free income, which is the result of a social consensus and which                   takes into account the level of income defined as the subsistence minimum, as well as                   civilization,  cultural  factors  and  social  welfare.  This  amount  changes  along  with  the                   change in income, prices, costs of living, state of the economy.                3) The tax system indexation, automatic or steplike, consisting in adjusting the tax burden                   to the changing costs of living, and thus an increase in the amount of tax-free income                   and the amount of tax reliefs, raising the amount of income within the existing tax scale;                4) The existence of tax reliefs taking into account the family situation of taxpayers (such                   as for example the possibility of joint taxation of spouses' income, number of children,                                                              307","their age and state of health, as well as the age and state of health of the taxpayer) or                   related to the method of disbursement of income (investment, construction, education                   reliefs and donations).                The aforementioned elements of tax structures were an important tool for the redistribution                of income, especially in the post-war period of 30 years (the 50s, 60s, 70s of the 20th                century). Taxes back then were very high (as the current criteria), and the scope of reliefs                was  extensive (Krajewska  2012:94-96).  However,  at  the  same time,  the economies  of                Western  Europe  then  were  developing  dynamically.  Economic  historians  with  fondness                recall these \"golden thirty years\". Tax reforms carried out under the slogan of tax reduction                and simplification as well as reduction of tax reliefs began in the 1980s along with the                dissemination of neoliberal trends. In practice, however, it turned out that the process was                relatively slow. The redistribution of income through progression, tax allowance and reliefs,                although  perhaps  to  a  lesser  extent,  continued.  The  financial  and  economic  crisis  that                started in 2008 not only stopped this process, but also caused that the tax stimulus and                stabilizing function began to be noticed.                The anti-crisis packages included activities such as:                       •   raising the amount of tax allowance amount,                       •   reducing the lower PIT rate,                       •   implementation of a new (additional) upper PIT rate (it is worth recalling that                           under the presidential campaign in France, Francois Hollande proposed raising                           the upper PIT rate up to 75%),                       •   introduction of new reliefs or their raise,                       •   tightening the tax system.                All transforming countries in Central and Eastern Europe had to carry out thorough tax                reforms. They had to face the following choice: to imitate solutions adopted in Western                European countries (i.e. tax progression, tax allowance, tax reliefs) or to adopt their own                solutions based on simple flat taxes. In the initial period of transformation, the countries                of the Visegrád Group and Slovenia adopted solutions similar to the Western tax systems,                and the Baltic countries adopted a flat tax. However, in a quick time, the flat tax spread in                the countries of Eastern Europe. In Poland such attempts were made, but finally the tax                scale was reduced from 3 to 2 rates (18% and 32%). Slovenia was the only country in                which switching from progression to a flat tax was not considered. Progressive PIT also                occurs in Croatia.                The second component of the tax wedge are social security contributions. European Union                countries  have  not  yet  developed  such  a  consistent  system  as  in  the  case  of  income                taxation. Differences between countries are very large.                The  amount  of  social  security  contributions  depends  on  public  programs  offering  social                security for citizens, financed from these sources, as well as on the age structure of the                society. The increase in the level of benefits observed in economically developed countries,                accompanied  by  the  aging  of  the  society,  means  that  the  tax  burdens  is  growing.  For                example, in Sweden until the mid-1960s, the social security contribution was 7% of gross                wages, in 1986 it increased to 36% so almost five times (Andersson, Norman, 1987, p.                144) and showed an upward trend in the following years – up to 38.4% in 2010 (European                Commission… 2012, s. 159).                It is worth noting that relatively recently, social security contributions were not collected                in Denmark, Finland and Australia. An even now, those contributions constitute only a few                percent of the tax burden imposed on labour. Still an exception – a country, where there                are no mandatory insurance premiums is New Zealand. This means that funds financed for                retirement  and  disability  pensions  payments  come  from  income  taxes,  which  in  these                countries are therefore high.                Social security contributions burden employees and employers. The classic system that                evenly burdens the employees and employers with contributions (\"fifty-fifty\" or in similar                proportions) has now been preserved only in a few countries: in Germany, Switzerland,                Luxembourg, the USA, Turkey, Korea, and also in Poland (OECD, 2013). The distribution                                                            308","of  the  burden  of  employees  and  employers  with  contributions  was  subject  to  changes                depending on the bargaining position and the power of both parties. During the 30 years                after the war, strong trade unions were trying to transfer the burden of contributions more                and more to employers. However, with the strengthening of the neo-liberal ideology, the                position of employers grew, which in turn favoured transferring the burden of insurance                contributions on employees. However, this process was quite slow. The global crisis, which                began in 2008 and the increase in unemployment, especially among young people, caused                that in many countries social security contributions, especially for young and unqualified                employees, showed a declining trend.                In empirical research on the tax wedge, the most attention is paid to its height and impact                on the labour market. Most often the conclusion that is drawn is that a high tax wedge                reduces  the  demand  for  labour  and  leads  to  an  increase  in  unemployment  (Dolenec,                Laporsek 2010, pp. 356-357; Góra et al. 2006; Tvrdon 2011; Wojciechowski 2008). Hence                the conclusion is that the tax wedge should be lowered because it will allow to reduce                unemployment. The research shows that the negative effects of the tax wedge affect the                unqualified and low-paid employees the most, because their remunerations are less flexible                than the ones of employees with high earners. Thus, a high tax wedge may slightly affect                the employment decline of highly qualified employees, while it creates high unemployment                among low-qualified employees. Thus, the macroeconomic consequences of the tax wedge                depend on the qualification structure of employees. They are stronger in countries with a                large share of low-qualified employees and lower in those where their share is low (Góra                et al., 2006).                3 Tax wedge redistributive aspects in EU countries                 In the Eurostat and OECD statistics, the tax wedge is defined as a percentage ratio of taxes                imposed on labour to the total labour costs borne by the employer. The numerator of this                fraction  therefore  includes  the  following  elements:  Income  tax  of  natural  persons                performing a paid work, social security contributions paid by the employer, social security                contributions  paid  by  the  employee  and  other  taxes  related  to  the  employment  of  the                employee. In turn, labour costs are: gross wages (net wages + personal income tax +                contributions paid by employees), contributions covered by the employer and other taxes                related to employment (OECD, 2014).                The tax wedge can be presented in the form of the following formula:                       Tax wedge =   taxes imposed on labour  · 100%                                    (1)                                         labour costs                Tax  burdens  imposed  on  employees  depend  to  a  large  extent  on  the  amount  of  their                earnings and the family situation (number of children, working or non-working spouses).                For  this  reason,  for  the  analysis  of  the  tax  wedge level, the  most  common  are  single,                childless persons earning 67% of the average remuneration. This level of remuneration is                treated as the median of average remuneration due to the concentration of high earnings                in the upper deciles. In turn, the median is the basis for determining the remuneration                defined  as  low  pay  earners.  In  the  OECD  and  Eurostat  statistics,  it  is  assumed  that                remunerations  defined  as  low  are  less  than  2/3  of  the  remuneration  median  for  the                economy.                 Over the past 20 years, the tax burden of low-paid work fluctuated (Figure 1). In some                countries  they  have  decreased.  In  others,  they  slightly  increased.    Comparing  the  tax                wedge in 1996 and 2016, it turns out that with a general decrease in tax burdens in 8 EU                countries, the tax wedge showed an upward trend. The fastest tax wedge increase took                place  in  Malta  (increase  by  7.5  percentage  points).  However,  it  should  be  taken  into                account that it is the country with the lowest taxes (19% in 2016 only, and in 1996 only                11.9%). During the period of the crisis after 2008, in some countries there was a pressure                to raise taxes in order to reduce the budget deficit. This concerned, among others countries                such as Greece, Spain and Portugal, where international organizations, in exchange for                financial assistance, forced governments to take actions to discipline public finances.                                                              309","Figure1 Tax Wedge for a Single, Childless Person Earning 67% of Average Wages in EU                                                      Countries (%)                   60                 50                 40                 30                 20                                                                                                   1996 r.                 10                  0                                                                                2016 r.                       Hungary  Belgium  Germany  France  Austria  Latvia  Sweden  Italy  Czech Rep.  UE-27  Lithuania  Slovak Rep.  Slovenia  Romania  Finland  Estonia  Portugal  Greece  Spain  Poland  Denmark  Bulgaria  Luxembourg  Netherlands  United Kingdom  Ireland  Malta                                        Source: Eurostat – Data Explorer, Tax Wedge on Labour Costs,                                 http://appsso.eurostat.ec.europa.eu/nui/print.do (11.05.2018)                 The tax wedge is calculated for various types of households differentiated in terms of family                income,  number  working  persons  in  the  family  and  their  income,  and  the  number  of                children.                  Table 1 Tax Wedge for a Single, Childless Person, Earning 67% and 167% of Average                                           Wages in Selected EU Countries (%)                                                        2000                          2017                     Wyszczególnienie                       Difference                    Difference                                           67%  167%                      67%  167%                                                               (p.p)                         (p.p)                           Ireland          18      39          21         21      38         17                        Luxembourg          30      44          14         29      44         15                            Italy           44      51          8          41      54         13                           Belgium          51      63          11         47      60         12                           Finland          43      53          10         37      49         12                                            44      52          9          43      54         12                           France                              …              …       …          …          …       …           …                       Czech Republic       41      45          4          41      45          5                      Slovak Republic       41      45          5          39      44          4                           Estonia          40      43          3          38      40          2                            Latvia          42      44          3          42      44          2                           Poland           37      39          2          35      36          1                          Hungary           51      59          8          46      46          0                                           Source: Own study based on OECD 2018,                             http://stats.oecd.org/Index.aspx?DataSetCode=AWCOMP (11.05.2018)                 A    comparison    of   the   tax   wedge     amount    of   single,   childless   persons,                earning 67% and 167% of the average wage in selected EU countries (Table 1) and the                analysis  of  changes  that  took  place  in  2017  compared  to  2000  lead  to  the  following                conclusions:                1) In the Western Europe countries, the tax burden on labour is high. At the same time,                however, in most of these countries, differences between the tax wedge of persons earning                67% and 167% of the average wage are usually several percentage points.                                                               310","2) The attention is paid by the relatively high level of tax wedge for low paid earners in                Eastern European countries, at the same time with not very high taxes for higher paid                earners, which means that the labour tax system to a small extent in these countries is                used to redistribute income. In the Czech Republic, Estonia, Slovakia and Hungary a flat                tax applies. In Poland, although there are two rates: PIT-18% and 32%, the income of                over 98% of taxpayers is settled at a lower rate, and the tax allowance amount is also low.                This  causes  that  the  difference  between  taxes  of  persons  earning  67%  and  167%                accounted for only 2 percentage points in 2000 and 1 percentage point in 2017. It put                Poland in the penultimate place in the EU before Hungary, where both groups of employees                pay equal labour taxes.                 3) In the last several years, changes in labour taxation have been aiming towards lowering                the tax wedge and increasing the redistributive function of labour taxation. To the largest                extent,   such    activities   were   undertaken     in   Western    European     countries                and  were  a  reaction  to  the  global  crisis  initiated  in  2008.  An  important  element                of anti-crisis packages were changes in the income taxation system aimed at increasing                global  demand,  such  as  raising  tax  allowance  amount,  implementing  or  extending  tax                reliefs addressed mainly to large families and disadvantaged families, reducing the lower                rates of PIT. The situation on the labour market also forced a change in social security                contributions.  As  part  of  the  fight  against  unemployment,  mainly  among  younger  and                poorly educated people, actions were taken to lower the contributions (Krajewska, 2012,                pp. 215-224).                An  important  element  of  the  structure  of  labour  taxation  is  to  take  into  account                the taxpayer's ability to pay and his/her family status. It is possible, among others through                the use of pro-family relief system. The experience of European Union countries on taking                into account pro-family aspects in the tax system is very diverse. This is evidenced by the                data included in Table 2 presenting a tax wedge for a single person, childless, earning 67%                of the average wage and a single person receiving the same remuneration, but raising two                children in 2000 and 2017.                 As far as labour taxation system, in Poland, the family situation of the taxpayer has not                been taken into account for many years. For example, in 2000, the difference between the                tax burden of a single person earning 67% of the average wage, childless and with two                children, was only 7 percentage points. This defect of the tax system has been repeatedly                pointed out in Poland (Kryńska 2014, Rekas 2012, Nadolny 2009, Ślesicka 2011). In 2007                a relief based on a tax deduction was introduced, but it was not beneficial for people with                                                                                                    4                low incomes (Budlewska, 2016, pp. 729-730). Only changes introduced in 2014  made                Poland the best in this respect compared to EU countries. The next such example is Ireland,                where the payment of grants for children made that net wages were higher than gross                wages.                 Clear tax preferences related to bringing up children are also found in the tax systems of                Denmark, Luxembourg and the Netherlands, as well as Slovenia. The lowest level of family                redistribution was marked in Greece, where single people paid taxes only by 4 percentage                points smaller than those bringing up two children.                  Table 2 Tax Wedge for a Single Person Earning 67% of Average Wedge, Childless and                                    Having Two Children in Selected EU Countries (%)                 Specification                       2000                               2017                                        without      with 2    Difference   without    with 2   Difference                                        children    children     (p.p)      children  children     (p.p)                       Poland              37         30           7          35        -21         56                       Ireland             18          -1          19         21        -17         38                   4 Starting from 2014, the portion of the pro-family relief, which is not deducted due to insufficient tax, is refunded                to  the  taxpayer,  however,  in  the  amount  not  higher  than  the  sum  of  social  security  and  health  insurance                contributions deducted in the tax return.                                                            311","Denmark              38         12           26         34         6          28                      Slovenia             43         13           30         40         13         27                    Luxembourg             30          4           26         29         5          24                    Netherlands            42         26           16         31         7          24                          …                …           …           …           …         …          …                  Czech Republic           41         13           28         41         22         19                      Belgium              51         36           15         47         33         14                  Slovak Republic          41         25           16         39         28         11                       Finland             43         28           15         37         26         11                      Sweden               49         40           9          41         34          7                       Greece              36         35           1          37         33          4                                           Source: Own study based on OECD 2018,                             http://stats.oecd.org/Index.aspx?DataSetCode=AWCOMP (11.05.2018)                 4 Internal structure of tax wedge                The tax wedge consists of two main parts: personal income tax of persons receiving income                from work (PIT) and insurance contributions charged to employees and employers. Other                taxes burdening work are found only in some countries and, moreover, they are rarely                more than 1% of the entire tax wedge. They will therefore be omitted from this analysis.                               Figure 2 Tax Wedge Structure in EU Countries in 2015 (%)                  100                  80                  60                  40                  20                    0                        Austria  Belgium  Bulgaria  Czech Rep.  Croat  Cyprus  Denmark  Estonia  Finland  France  Greece  Spain  Netherlands  Ireland  Lithuenia  Luxembourg  Latvia  Malta  Germany  Poland  Portugal  Romania  Slovak Rep.  Slovenia  Sweden  Hungary  United Kingdom  Italy  UE-27                                      personal income tax  empoyees contribution   employer contribution                                    Source: Own study based on: Rogers, Phillipe (2015, p. 7)                 Graph 2 presents data on the internal structure of the tax wedge in 2015. Tax reforms                have  been  carried  out  for  years  in  EU  countries,  although  they  were  based  on  similar                principles,   they   had   to   take    into   account   historical   legacy   and   social                conditions. For this reason, the labour taxation systems are very diversified in the European                Union.  The internal structure of the tax wedge is also diversified.                Here are two extreme examples: Cyprus and Denmark. In Cyprus, not only, as it already                was referred to earlier, the tax wedge was very low in 2015 (19.2%), but the internal                structure of labour taxation was also atypical.  Income tax accounted for only 1.9% of the                labour cost (gross pay plus contributions covered by the employer). Admittedly, income                taxation is progressive with rates of: 0, 20, 25, 30 and 35%, but at the same time a high                tax  allowance  amount  (EUR  19.500)  caused  that  this  tax  was  hardly  noticeable  by  an                employee  earning  an  average  income.  Also,  social  security  contributions  burdened  the                employer  more  than  the  employee.    In  turn,  Denmark  is  unusual  for  another  reason.                Income taxes are high and, in addition, account for almost 98% of the tax wedge. Income                tax  consists  of  two  parts:  a  progressive  central  tax  supporting  the  state  budget  and  a                municipal, a flat tax contributing to the local budget and the church, set by local authorities                                                             312","at a quite high level (22-28%). In turn, the social security contribution is low (2.1% of the                total labour tax burden).                 In Western European countries, as a rule, the share of income tax in the tax wedge is                higher  than  in  the  new  Member  States.  In  Poland,  this  share  is  maintained                at a particularly low level, in 2015: 14.1% total labour tax burden (compared to an average                of 30% to EU-28) This can be explained by referring to the beginnings of taxation reform                in Poland. Then, enterprises paid 20% tax on payroll fund and high contributions (45% of                the payroll fund for social security + 3% for the Labour Fund).  In 1992, a personal income                tax with three rates was introduced: 20%, 30% and 40%. The first tax rate (20%) was so                defined that it included over 90% of taxpayers. It allowed to introduce the universal income                tax efficiently and without social resistance.  The rates for health and social insurance have                remained  at  the  previous  level.  This,  however,  meant  a  serious  limitation  of  the                redistributive  function,  and  also  led  to  a  distortion  of  the  internal  structure  of  the  tax                wedge.                 The high level of tax wedge in Poland, accompanied by its low progressivity and incorrect                structure leads to many negative effects, among others:                    •   unemployment in the group of people with low earnings,                    •   malfunctioning of the labour market and development of the shadow economy in                       order to avoid paying taxes and contributions,                    •   deepening of poverty among people with low earnings.                 5 Conclusions                 Analysis of statistical data on labour taxation in European Union countries makes it possible                to formulate the following conclusions:                1) In Poland, the average level of the tax wedge is, against the background of the European                Union countries, moderate. However, it is characterized by very low progressivity, taking                on the account the level of income, and until 2014 also the taxpayer's family situation.  It                is unfavourable for professional groups with low qualifications and low earnings, because                it  is  difficult  to  enter  the  labour  market  and  it  is  difficult  to  maintain  on  it.  It  seems,                therefore,  appropriate increasing  the  progressiveness  of income  tax,  among others,  by                raising the amount of tax allowance and increasing the number of tax thresholds.                2) There are large differences in the internal structure of the tax wedge in the European                Union.  The high share of contributions in the total tax wedge and the resulting negative                consequences for the labour market caused that in many countries measures have been                taken to reduce the tax burden of contributions, especially for some groups of employees                – unqualified low-income earners, taking their first job, etc.                3)  In  Poland,  the  structure  of  the  tax wedge  is  atypical.  Poland  was  on  the  last  place                among EU countries in terms of social security contributions percentage in total tax wedge                (85.5%). The high share of contributions is badly assessed by Polish entrepreneurs as it                leads to an increase in labour costs, it lowers the competitiveness of Polish enterprises and                leads to the development of grey zone. At the same time, however, reducing the share of                social security contributions in relation to budget revenues from taxes and to GDP is difficult                due  to  the  situation  on  the  labour  market  and  relations  of  people  who  work  and  pay                contributions  to  non-working,  pensioners.  Possible  changes  would,  therefore,  have  to                relate not only to contributions, but to the entire taxation system.  More often, in this                context, attempts are being made to discuss the concept of a single tax on labour.                 References                Andersson K., Norman E. (1987). Capital taxation and neutrality. Lund: Lund University.                 Budlewska  R.  (2016).  Ulga  na  dzieci  jako  instrument  realizacji  polityki  prorodzinnej  w                Polsce.  In:  Oeconomica,  Annales  Universitatis  Mariae  Curie-Skłodowska.  Lublin,  vol.  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Luxembourg:                Publications Office of the European Union.                 Tvrdon  M.  (2011).  Taxation  on  Labor  in  the  Economic  Union.  Retrieved  from:                http://www.wseas.us/e-library.                 Wojciechowski  W.  (2008).  Skąd  się  bierze  bezrobocie.  Zeszyty  Forum  Obywatelskiego                Rozwoju, vol. 2.                                                                                                314","Taxation of Consumption in Poland. Redistribution and Fiscal                                                         Effects                                                            1                                        Anna Krajewska , Katarzyna Osiecka                                                                                  2                                          1 College of Economics and Social Sciences                                              Warsaw University of Technology                                            Łukasiewicza 17, 09-400 Płock, Poland                                             E-mail: anna.krajewska@pw.edu.pl                                           2 College of Economics and Social Sciences                                              Warsaw University of Technology                                            Łukasiewicza 17, 09-400 Płock, Poland                                            E-mail: katarzyna.osiecka@pw.edu.pl                 Abstract: Consumption taxes constitute important source of budgetery revenues. In time                of crisis the role of those taxes had raised – the rates of VAT and excise rose. The aim of                this paper is to present those tendencies, and next to show their redistribution and fiscal                results, mainly on Polish case. Redistribution tax effects are verified by 1) presentation of                empirical research findings concerning VAT and excise burdens on households in Poland,                in cross-section decyl groups and distinct social-economic groups.  2)  comparison Gini’s                coefficient before and after taxation and transfers in Poland and in secected EU countries.                Analysis  of  fiscal  effects  of  consumption  taxation  is  based  on  1)  demonstration  of                correlation  between  contribution  of  indirect  taxed  in  GDP  and  shadow  economy  in  EU                countries, 2)  showing dependency between level of taxes and shadow economy by the                example of tobacco market in Poland. Last chapter of the article shows actions taken in                Poland aimed on insulating tax system.                 Keywords: consumption taxes, VAT, excise, redistribution, shadow economy                JEL codes: H20, H21, H25, H31, E62                 1 Introduction                The aim of this paper is to analysis the role of consumption taxes in budget revenues and                to show their redistribution and fiscal effects.                We start with statistical data showing big differences in the structure of budget revenues                in EU countries and presentation of empirical research concerning impact of consumption                taxes on redistribution of income. We also use Gini coefficients to present redistributive                effects of tax and social policy. Moreover the relation between taxation of consumption and                the size of the shadow economy is analysed. The stress is put on tabacco market in Poland.                In conclusion we show actions taken in Poland aimed on insulating tax system.                 2 Taxation of consumption in European Union and Poland. Statistical data.                 On average, in the EU-28 countries, consumption taxes account for about 30% of total                budgetary  revenues,  but  their  share  in  the  budgets  of  individual  countries  is  highly                diversified.    In  the  1970s  analyses  of  the  structure  of  taxes  in  Western  Europe  were                conducted  and  the  historical  character  of  these  differences  was  clearly  emphasized  -                countries with a \"southern\" and \"northern\" tax mentality were distinguished [Hansmeyer,                1977, p. 570].  In countries with the \"southern\" mentality, the size of shadow economy is                high,  and  the  efficiency  of  the  tax  administration  is  low.    In  this  situation,  expanding                consumption taxes, especially excise duty, can feed to the budget with tax revenues that                \"escape\" direct taxes.  In turn, in countries with the \"northern\" tax mentality, tax collection                is higher, the share of taxes in GDP higher, and moreover, the state budget may be more                based on income taxes.  The first group included Greece, Italy, Portugal and Spain, while                the second included Sweden, Denmark, Germany, Austria and Belgium.  EU actions aimed                at  harmonizing  VAT  and  selected  excise  products  were  aimed  at  both  creating  better                conditions for the free movement of goods and services, as well as to achieve a better                                                             315","balance between direct and indirect taxes. Tax and shadow economy details for different                countries are shown in Table 1.                                            Table 1 Taxes and Shadow Economy                 Country              Total taxes/GDP        Consumption             Shadow      economy                                      (2015)                 taxes/Total  taxation  (2017)                                             a)                                                                                             b)                                                             (2015)                                                                     a)                 Germany                      38,6                    28,5                   10,4                 Belgium                      45,1                    29,3                   15,6                 Czech Republic               34,3                    36,5                   14,1                 Poland                       32,5                    40,1                   22,2                 Romania                      28,0                    47,6                   29,6                 Bulgaria                     29,0                    53,5                   26,3                            Source: a) European Commission 2017, p. 193; b) Schnider 2017, p. 154                 After the enlargement of the EU by 10 new members in 2004, and in the following years                by 3 next members (Bulgaria, Romania and Croatia), there were again large differences in                the  structure  of  budget  revenues.  The  new  member  states  have  a  clearly  \"southern\"                structure of budget revenues.  In 2015, indirect taxes in Bulgaria accounted for 53.5% of                total tax revenue and 47.6% in Romania, while only 28.5% in Germany and 29.5% in                Belgium.                The reasons why indirect taxes have become the most important source of the budget                revenues result from economic difficulties which Poland and other transforming economies                had to face.  Revenues from the public sector were diminishing fast, the fiscal apparatus                was inefficient and the tendency to avoid taxes was high.  It was considered that VAT and                excise duty, despite its drawbacks, could best guarantee the security and stability of public                finances (Owsiak, 1997, p. 369 and others).                In Poland, in the years 2003-2015, indirect taxes were of 41.0-44,3% of total tax revenues                (including obligatory social security contributions). The EU-28 average was 33.7-35.1%,                so this means that in Poland the share of indirect taxes was by 6-7 percentage points                higher. On the other hand, in the Czech Republic, indirect taxes accounted for 36.5% of                total tax revenues, i.e. they were close to the EU-28 average.                The share of tax revenues from the sale of tobacco products and alcohol (VAT + excise                duty)  in  the  budget  revenues  from  taxes  is  also  significantly  different:  from  9.6%  in                Bulgaria to 1.2% in Germany. The budget revenues of several other new EU members from                these taxes are relatively high:  Romania 5.8%, Estonia 5%, Lithuania 5%, and Poland                5.1% (European Commission 2017, p. 195).                            Table 2 VAT Standard Rates in the EU Member States, 2008-2017                  Country           2008  2009  2010  2011  2012  2013  2014  2015  2016  2017                 Belgium           21      21     21     21     21      21     21     21      21     21                 Bulgaria          20      20     20     20     20      20     20     20      20     20                 Czech Republic    19      19     20     20     20      21     21     21      21     21                 Denmark           25      25     25     25     25      25     25     25      25     25                 Germany           19      19     19     19     19      19     19     19      19     19                 Estonia           18      20     20     20     20      20     20     20      20     20                 Ireland           21      21,5  21      21     23      23     23     23      23     23                 Greece            19      19     23     23     23      23     23     23      23     24                 Spain             16      16     18     18     18      21     21     21      21     21                 France            19,6  19,6  19,6      19,6  19,6  19,6  20         20      20     20                 Croatia           22      22     23     23     25      25     25     25      25     25                 Italy             20      20     20     20     21      21     22     22      22     22                 Cyprus            15      15     15     15     17      18     19     19      19     19                 Latvia            18      21     21     22     22      21     21     21      21     21                 Lithuania         18      19     21     21     21      21     21     21      21     21                                                             316","Luxembourg        15      15     15     15     15      15     15     17      17     17                 Hungary           20      25     25     25     27      27     27     27      27     27                 Malta             18      18     18     18     18      18     18     18      18     18                 Netherlands       19      19     19     19     19      21     21     21      21     21                 Austria           20      20     20     20     20      20     20     20      20     20                 Poland            22      22     22     23     23      23     23     23      23     23                 Portugal          20      20     21     23     23      23     23     23      23     23                 Romania           19      19     24     24     24      24     24     24      20     19                 Slovenia          20      20     20     20     20      22     22     22      22     22                 Slovakia          19      19     19     20     20      20     20     20      20     20                 Finland           22      22     23     23     23      24     24     24      24     24                 Sweden            25      25     25     25     25      25     25     25      25     25                 United Kingdom  17,5  15         17,5   20     20      20     20     20      20     20                 EU-28             19,5  19,9  20,5      20,8  21,1  21,5  21,5       21,6  21,5  21,5                                           Source: DG Taxation and Customs Union                 Consumption generally falls during the crisis, leading to a reduction in the share of indirect                tax revenues.  In this situation, the state can react in two ways: lowering consumption                taxes in order to stimulate global demand or raise VAT and excise rates in order to maintain                budget  revenues.    During  the  recent  crisis  of  2008-2010,  the  first  of  these  types  of                activities was used very rarely.  In 2008, Portugal lowered the standard VAT rate by 1                percentage point and in 2009 temporarily by 2.5 percentage points. The VAT was raised in                the United Kingdom (but in the next year, the previous rate was returned), and in 2010 by                0.5 percentage points the VAT was raised in Ireland.  In total, since 2008, a standard VAT                rate has been raised in 20 out of the 28 EU countries (Table 2). To the largest extent, the                standard VAT rate was raised in Hungary (from 20% to 27%) and in Greece (from 19% to                24%).  In 2017, standard VAT rates were: 18% in Malta and 19% in Germany and 25% in                Denmark, Sweden and Croatia.  The 23% VAT rate in Poland is therefore high for European                standards.    In  Poland,  the  standard  VAT  rate  was  raised  (from  22  to  23%)  in  2011,                announcing that it will be valid for one year, at most two-three years. But this situation                has lasted for 8 years and nothing foreshadows change.                 In EU countries, VAT ensures about 60% of revenue from consumption taxes (European                Commission, 2017, p. 22).  It should be expected that the increase in VAT rates should                positively affect the increase of budget revenues from this tax.  Data presented in Table 3                indicate  that  there  are  no  major  changes  in  the  share  of  VAT  in  tax  revenues  of  EU                countries.                   Table 3 The Share of Consumption Taxes in the Budget Revenue from Taxes (in %)                                                          Total consumption taxes                            Country                                            2003      2005      2008      2010      2012     2015                        EU-28               28,7      28,6      27,6      28,7      28,6     28,7                        Portugal            38,2      41,0      37,6      37,4      37,7     35,7                        Ireland             36,9      37,0      36,3      35,5      33,5     33,3                        Italy               25,3      26,2      24,3      25,7      25,7     25,8                        Belgium             24,3      24,7      24,0      24,5      23,3     22,8                        Czech Republic      27,8      30,2      30,0      32,7      35,1     34,8                        Poland              37,1      37,9      37,6      39,4      36,7     35,2                        Croatia             50,2      49,2      46,5      47,5      49,0     50,1                        Bulgaria            45,1      51,2      53,7      52,7      53,2     50,8                        Slovakia            34,3      37,9      34,7      34,6      32,8     31,9                                         Source: European Commission 2017, p. 193                 In  addition  to  raising  VAT  rates,  excise  duty  rates  (in  particular  on  fuels,  tobacco  and                alcohol) have been raised many times in most EU countries (European Commission, 2011,                p. 34).                                                              317","3 Impact of consumption taxes on the redistribution of household income.                The high share of consumption taxes in total tax burdens, the increase in the standard VAT                rate and the increase in excise duty rates in Poland have a negative impact on the situation                of households, as:                     1)  it leads to a decrease in disposable income, which means that they can buy less                       products and services,                    2)  it causes socially undesirable redistributive effects, because consumption taxes due                       to their regressive nature lead to an increase in the diversification of household                       incomes in various cross-sections,                    3)  the decline in real incomes and the increase in prices caused by the increase in                       taxes prompts people to make purchases in the grey market, where products may                       be of inferior quality, come from an illegal source, may threaten your health and                       expose you to fraud, etc.                Interesting  research  on  the  redistributive  consequences  of  taxation  of  individual                consumption in Poland in the years 1995-2006 was carried out by Dobrowolska (2008).                The main source of information was the unpublished GUS data from Household Budget                Surveys. The data were aggregated up to the sixth level of grouping (subcategories) of the                Polish  Classification  of  Products  and  Services.  The  following  conclusions  arise  from  the                statistical and econometric analyses:                    1)  In  the  analysed  period,  household  burden  with  indirect  taxes  increased                       systematically.    Average  burdens  of  approximately  7.48%  in  1995  increased  to                       11.23% in 2006.                    2)  Indirect taxes are the biggest burden for the poorest households, those in the 1st                       decile of income group. The average income burden with indirect taxes (for the                       period 1995-2006) was 11.88%, while in the X group they were lower than in other                       decile groups and amounted to 8.87%                    3)  The regressivity of indirect taxes is increasing. This is evidenced by the deepening                       difference between the burdens in the I and X decile group. In 1995, the burdens                       of  households  in  the  X  decile  group  were  2.9  percentage  points  lower  then  the                       burdens of households from the I decile group. In 2006, differences in tax burdens                       increased to 5.28 percentage points.                When analysing the distribution of households' burdens with indirect taxes, Dobrowolska                distinguishes  five  socio-economic  groups:  households  of  employees,  households  of                employees using farms, households of farmers, households of self-employed entrepreneurs                and households of pensioners.  The analysis of the distribution of tax burdens in this cross-                sections show that:                    1)  to the greatest extent, indirect taxes are borne by households of farmers (15.5%                       in 2006) and households of employees using farms; this is because these farms,                       apart from consumer goods, purchase VAT and excise duty products and services                       used for agricultural production;                    2)  the burden on self-employed entrepreneurs’ is relatively high (11.38%), which can                       be partly explained by the higher level of consumption of this social group;                    3)  households of employees are burdened with indirect taxes to the smallest extend                       (10.5%) and pensioners (9.83%);                    4)  it turns out that in the analysed period, the burdens of households with the lowest                       incomes (pensioners) were growing faster (169.78%) and the slowest (139.29%)                       growth was that of the wealthiest households of self-employed entrepreneurs.                 The next empirical studies conducted by Dziura (2015) concern households' excise duty                burdens for the years 2004-2012. The obtained results confirm previous observations, but                at  the  same  time  bring  new  threads  resulting  mainly  from  the  analysis  of  not  only                household budgets, but also statistical data on shadow economy, mainly illegal trade in                fuels, smuggling and illegal production of tobacco and alcohol.                The observations that especially deserve underlying are:                                                             318","1)  Poorer  households  are  more heavily  burdened  with  excise  duty  than  the  richest                       households (e.g. in 2004, this burden constituted 2.27% of net income in the first                       decile  group,  while  in  the  X  group  it  was  much  less  -  1.79%),  but  the  spread                       between these groups, during the analysed years reduced (in 2004 it was 0.48 pp,                       and in 2012 it decreased to 0.29 pp), i.e. the degree of regressivity is weakening.                    2)  Fiscal burdens of consumption with excise duty from year to year are decreasing                       (except in 2006  and  2008-2009,  when  there  was  a  slight increase  compared  to                       previous years).  This applies to both decile groups and types of households.                    3)  To  the  greatest  extent,  the  households  of  self-employed  entrepreneurs  and                       pensioners are burdened with the excise duty                 Dziura  explains  those  trends  by  referring  to  changes  in  the  consumption  structure  of                various groups of households. The increase in excise tax on tobacco products and alcohols                to a greater extent stimulated wider use of the shadow economy by poorer households,                farmers and pensioners, and the increase in excise tax on motor fuels increased the excise                duty burden on self-employed entrepreneurs' households.                The  redistributive  effects  of  tax  policy can  be  measured  by  comparing  Gini  coefficients                before tax and transfers, as well as after taxes and transfers. The data presented in Table                4  show  that  taxes  and  transfers  are  the  main  tool  for  redistribution  in  Germany  -  the                difference in Gini coefficients in the analysed years was usually 24-28 pp. In Belgium, the                difference in Gini's coefficient remained at a similar level of 20-21 pp, that is at a slightly                higher level than in the Czech Republic, where taxes and transfers allowed for a reduction                of the coefficient by around 20 pp. The redistribution tools were much weaker in Poland                and Romania, and worst in Bulgaria, where since 2007 the high Gini coefficient, sometimes                exceeding 50%, thanks to fiscal and social policy, was reduced by only a dozen (14-15)                percentage points.                  Table 4 Gini Coefficients Before and After Tax and Transfers in Secected EU Countries                                                          (in %)                             200    200    200    200    201     201    201    201    201     201    201                  Country    6      7      8      9      0       1      2      3      4       5      6                                               Gini coefficients before tax and transfers                 Belgium     48,7  46,7  48,0  46,6  46,5  47,1  47,7  47,1  47,6  48,0  48,8                 German      55,2  54,4  56,0  54,4  55,4  55,5  54,4  56,4  57,7  56,4  55,5                 y                 Poland      53,0  51,4  49,8  48,2  47,9  47,8  47,5  47,7  47,9  47,9  46,7                 Czech       45,2  44,9  44,1  43,7  43,8  44,1  44,5  44,1  45,0  44,9  44,9                 Republic                 Bulgaria    52,8  50,4  49,7  46,4  46,7  48,1  47,5  49,1  50,8  51,6  52,8                 Romania            55,6  54,7  54,0  53,2  53,4  52,9  53,8  53,2  53,2  53,3                                                Gini coefficients after tax and transfers                 Belgium     27,8  26,3  27,5  26,4  26,6  26,3  26,5  25,9  25,9  26,2  26,3                 German      26,8  30,4  30,2  29,1  29,3  29,0  28,3  29,7  30,7  30,1  29,5                 y                 Poland      33,3  32,2  32,0  31,4  31,1  31,1  30,9  30,7  30,8  30,6  29,8                 Czech       25,3  25,3  24,7  25,1  24,9  25,2  24,9  24,6  25,1  25,0  25,1                 Republic                 Bulgaria    31,2  35,3  35,9  33,4  33,2  35,0  33,6  35,4  35,4  37,0  38,3                 Romania            38,3  35,9  34,5  33,5  33,5  34,0  34,6  35,0  37,4  34,7                                Difference between Gini coefficients before and after tax and transfers                 Belgium     20,9   20,4   20,5   20,2   19,9    20,8   21,2   21,2   21,7    21,8   22,5                 German                 y           28,4   24,0   25,8   25,3   26,1    26,5   26,1   26,7   27,0    26,3   26,0                 Poland      19,7   19,2   17,8   16,8   16,8    16,7   16,6   17,0   17,1    17,3   16,9                                                              319","Czech                 Republic  19,9     19,6   19,4   18,6   18,9    18,9   19,6   19,5   19,9    19,9   19,8                 Bulgaria    21,6   15,1   13,8   13,0   13,5    13,1   13,9   13,7   15,4    14,6   14,5                 Romania            17,3   18,8   19,5   19,7    19,9   18,9   19,2   18,2    15,8   18,6                                           Source: www.epp.eurostat.ec.europa.eu                4 Taxation of consumption and the size of the shadow economy                 The  concept  of  shadow  economy  is  difficult  to  define  precisely  and  unambiguously.                Generally  speaking,  this  term  covers  economic  activities  carried  out  outside  the  state                registration and regulation, hence not subject to taxation. Therefore, the shadow economy                includes both legal actions, but not reported to the tax office, as well as actions on the                borderline of the law, as well as illegal and even mafia activities.                In the transforming economies, the shadow economy has already appeared in the initial                phase of the transformation period, accounting for 23.4% of GDP in the countries of Central                and Eastern Europe and 32.9% in the countries of the former USSR. At the turn of 2000-                2001, the share of the shadow economy in the countries of Central and Eastern Europe                increased  to  29.2%,  in  the  former  USSR  countries  up  to  44.8%,  in  some  countries                (Azerbaijan, Kazakhstan) exceeding even 60% (Schneider, 2010).                The  main  reasons  for  the  rapid  development  of  the  shadow  economy  in  transforming                economies include: high tax burden that persuades individuals and businesses to evade                taxes; high social security contributions that encourage private employers, especially those                who  do  business  to  a  small  extent,  to  employ  illegally  and  weak  and  inefficient  tax                administration system that makes tax collection low, and sanctions for tax evasion - low                and inconsistently enforced (Krajewska, 2012, pp. 296-298).                According to a recent Schneider researches (2017), the share of shadow economy in GDP                in the EU-28 countries was estimated at 29.6% (Bulgaria), 26.5% (Croatia) and 26.3%                (Romania) to 8.2% (Luxembourg) and 7.1% (Austria), with an EU-28 average of 17.1%.                In Poland, estimations of the size of the shadow economy are published by the Central                Statistical Office (CSA) and the Institute of Economic Forecasts and Analyses (IEFA), which,                when examining the shadow economy, covers a wider group of entities than the Central                Statistical Office. Both institutions indicate that the size of shadow economy is decreasing.                According to the CSA, the share of the shadow economy in GDP is falling from 13.3% GDP                in 2014 to 13.2% in 2017, and as estimated to 12.9% in 2018, and according to IEFA,                respectively, from 19.5% GDP up to 18% (Fundowicz, Łapiński, Wyżnikiewicz, 2018, pp.                21-22).                Among the most important factors affecting the reduction of the shadow economy in recent                years can be considered (Fundowicz, Łapiński, Wyżnikiewicz, 2018, p. 23):                    1)  favourable condition of the economic situation, encouraging enterprises to conduct                       activities not hidden from the tax office;                    2)  government action aimed at sealing the tax system, and in particular with regard                       to VAT collecting (mainly in the fuel sector);                    3)  effective fight against illegal activities - limitation of production and trade in drugs                       and  designer  drugs,  decrease  of  cigarette  smuggling,  elimination  of  illegal                       gambling.                As an example, the market for tobacco products can serve to show the negative impact of                the  increase  in  excise  tax  on  budget  revenues.  The  shadow  economy  is  particularly                extensive there and covers two areas. The first one is the smuggling of cigarettes from the                eastern border (Russia, Belarus, Ukraine) and their illegal sale in Poland. The second one                is the illegal production of counterfeit goods from well-known brands and placing them on                the market without applicable excise marks or illegal exports to Western Europe (mainly                Germany, Great Britain, Sweden and Italy).                The big difference between the price of cigarettes in Poland and the eastern border is a                serious incentive to smuggle tobacco products. For example, in 2015, the price of a packet                                                             320","of cigarettes in Poland was PLN 12.85, while PLN 4.65 in Russia, 3.26 PLN in Belarus and                PLN 3.20 in Ukraine (Skwirowski, 2016). Such a large difference in prices makes smuggling                a  deal  for  both  organized  gangs  and  small  smugglers,  so-called  \"Ants\"  -  most  often                unemployed,  pensioners  crossing  the  border  several  times  a  day.  Customs  officers                estimate that only in the Warmian-Masurian voivodship there are about 10,000 smugglers.                This  is  confirmed  by  the  data  of  the  Central  Statistical  Office,  which  shows  that  the                unemployment rate in most border-crossing entities with border crossings is decreasing                much slower than the average in their voivodships, and slower than in poviats neighbouring                with border counties (Fundowicz, Łapiński, Wyżnikiewicz, 2018, p. 19). This means that                the proximity of the border is not conducive to take up legal activities.                 On  the  other  hand,  the  illegal  production  of  tobacco  products  is  encouraged  by  low                production costs, simple technology, high taxation (VAT and excise duty account for over                80% of the price of cigarettes in Poland) and the possibility of illegal exports to Western                European countries. High profits from illegal production make possible penalties in the case                of detecting illegal activities are not perceived as onerous.                In the opinion of experts, the shadow economy on the tobacco market in Poland grew                rapidly - from 12.3% in 2008 to 21% in 2012 and 25% (Poniatowski, Głowacki 2018).                According  to  some  estimates  it  reached  25-30%  (Association  of  Entrepreneurs  and                Employers 2016).                To a large extent, the development of the shadow economy on the tobacco market results                from the rapid increase in excise tax rates, especially in 2010-2014. During this period,                the  increase  in  excise  duty  rates  exceeded  the  pace  required  by  the  EU's  obligations                regarding the harmonization of excise tax on tobacco products. The level of excise taxation                required by the EU until 2018 has been reached or even exceeded in Poland already in                2014.  However,  this  decision  has  had  a  negative  impact  on  excise  duty  on  tobacco                products. In 2013-2015, excise tax revenues decreased in absolute terms, and in 2016                they increased slightly, but the percentage of total excise tax on tobacco products in total                tax revenues was still lower than in 2010 (Table 5).                        Table 5 Budget Revenues from Excise Tax on Tobacco Products in Poland                              Tax  income  (in  PLN  Revenues from excise tax on tobacco products                             bilion)                in PLN bilion              in %                     2010            222,5                     17,4                       7,8                     2011            243,2                     18,3                       7,5                     2012            248,3                     18,6                       7,5                     2013            241,6                     18,2                       7,5                     2014            254,8                     17,8                       7,0                     2015            259,7                     17,8                       6,9                     2016            273,1                     18,5                       6,9                       Souce: Own study based on the Statistical Yearbooks of the Central Statistical Office                 5 Conclusions                The Polish tax system is very much based on consumption taxes. The share of VAT and                excise duties in budget revenues from taxes is high. The standard VAT rate (23%) is one                of the highest in EU countries. The rates of excise tax are also high. This leads to negative                effects, both for households (reduced disposable income, and the regressivity of indirect                taxes which deepens social disparities), as well as for the economy (a large share of the                shadow economy with its negative consequences).                In  recent  years,  various  measures  have  been  taken  in  Poland  to  strengthen  the  fiscal                function  of  consumption  taxes,  e.g., increasing  the  collection  of  VAT  and  excise  taxes,                limiting  cash  turnover  and  replacing  it  with  non-cash  transactions,  implementing  a                monitoring system for transporting sensitive goods (the obligation to report the carriage                of  so-called  \"sensitive\"  goods)  to  and through  Poland to  the  electronic  register  and its                ongoing updating, also introducing legislative changes to limit the shadow economy on the                                                             321","tobacco  market.  The  effects  of  these  activities  are  now  visible  and  according  to  EU                estimates in 2015, the so-called VAT gap was 23.9% of potential revenues, while the EU                average was 12.8%.  In 2017, the gap was at the level of 14%, which is close to the                average recorded in the EU. In Poland, VAT revenues increased by 0.4% of GDP, while only                France,  Malta,  Cyprus  and  the  Netherlands  managed  better  with  the  VAT  collection                (Cieślak-Wróblewska 2018).                 The impact on reducing the negative redistributive effects of VAT and excise duty is a wider                problem,  going  beyond  the  reform  of  indirect  taxes.  These  should  be  comprehensive                activities, including an increase in the redistributive income tax function (increase in the                amount  of  tax-free  income,  increasing  tax  progression),  reform  of  social  security                contributions, as well as the introduction of cadastral tax.                 References                Cieślak-Wróblewska, A. (2018). Polska szybko nadrabia straty podatkowe. Rzeczpospolita.                Dobrowolska,  B.  (2008).  Ekonomiczne  konsekwencje  opodatkowania  konsumpcji                indywidualnej w procesie integracji z Unią Europejską. Łódź: Uniwersytet Łódzki.                Dziura, E. (2015).  Obciążenia gospodarstw domowych podatkiem akcyzowym w Polsce.                Uwarunkowania,  konsekwencje,  perspektywy.  Lublin:  Uniwersytet  Marii  Curie-                Skłodowskiej.                 European Commission (2011). Taxation trends in the European Union. Focus on the crisis.                The main impacts. Retrieved from: http://ec.europa.eu/eurostat/web/products-statistical-                books/-/KS-EU-11-001?inheritRedirect=true                European  Commission  (2017).  Taxation  trends  in  the  European  Union.  Luxembourg:                Publications Office of the European Union.                 Fundowicz, J., Łapiński, K., Wyżnikiewicz, B. (2018). Szara strefa 2018.  Warszawa: IPiAG.                GUS (2010-2016). Roczniki Statystyczne. Warszawa: GUS.                Hansmeyer, K. H., Mackscheidt, K. (1977). Finanzpsychologie. Tübingen: Hdb. d. Fin.                 Krajewska, A. (2012). Podatki w Unii Europejskiej. Warszawa: PWE.                Owsiak, S. (1997). Finanse publiczne. Teoria i praktyka. Warszawa: PWN.                Poniatowski, G., Głowacki, K. (2018).  Wpływ produkcji wyrobów tytoniowych na polską                gospodarkę. Warszawa: Centrum Analiz Społeczno-Ekonomicznych.                Schneider,  F.  (2017).  Implausible  Large  Differences  of  the  Size  of  the  Underground                Economies in Highly Developed European Countries? A Comparison of Different Estimation                Methods.        Retrieved        from:        http://www.econ.jku.at/members/Schneider                /files/publications/2017/EstShadEc_OEC D/countries.pdf                 Schneider, F. (2010). The Size and Development of the Shadow Economies and Shadow                Economy Labour Force of 22 Transition and 21 OECD Countries: What Do We Really Know?.                In:  Referat  na  konferencję  Unofficial  Activities  in  Transition  Countries:  Ten  Years  of                Experience. Zagrzeb: Friedrich Ebert Stiftung, Institute za Javne Financije, pp. 18-19.10.                 Skwirowski, P. (2016). Jak walczyć z Szarym paleniem. Gazeta Wyborcza.                Szara  strefa  to  nieuczciwa  konkurencja  (2016).  Warszawa:  Związek  Przedsiębiorców  i                Pracodawców.                                                                        322","Problems in the Banking Sector                                                        Jan Krajíček                                                     Masaryk University                                   Faculty Economics and administration, Department Finance                                           Lipová 41a, 602 00 Brno, Czech Republic                                               E-mail: krajicek@econ.muni.cz                 Abstract:  The  paper  dedicated  to  some  problems  in  the  banking  sector.  Banks  have                different problems. Are some different between great, middle and small banks. Conditions                that  affect  it  and  the  possibilities  for  further  development.  Emphasis  placed  on  the                development of interest rates and their impact on the economy and management of banks.                The decline in interest rates would itself be a problem for banks such as resources grow in                the banking sector. Growth in deposits not only in banking, but in the financial sector, and                has its consequent reflection in both the declining risk aversion and the need to create new                products  and  services.  The  decline  in  risk  aversion  also  reflected  in  the  design  of  new                products, where risk is transferred to raise immediate profits problems are dependent on                the size of the bank. Large banks have different problems than medium and small banks,                due to their client focus.                Keywords: bank, loans, revenue, risk                JEL codes: C15, E37, E42, G20, G21                 1 Introduction                 Management of Balance sheet credits his structure forms the crux of bank management                and is an essential part of the financial management of the bank. It's a way of managing                the balance sheet structure of the bank, which is an overview of the bank's assets and                sources of its financing (Krajicek 2017).                 Management of assets and liabilities refers to the bank as a whole. Bank to manage bank                risk  diversification,  risk  limitation  specifying  upper  and  lower  volumes  borders  while                respecting the criteria of profitability and bank profitability. When management assets and                liabilities of the bank always acts in the space defined by the central bank and must respect                its  actions  regarding  prudent  banking  and  monetary  policy.  While  it  is  necessary  to                understand the management of assets and liabilities as a whole, management liability has                its  rules  and  peculiarities,  by  analogy  Asset  Management  which  is  followed  by  risk                management.                Influence of credit risk on the bank's economy:                 -   It has a direct impact on the bank's profit, which reduced as a result of provisioning,                    write-off of loans granted, the creation of reserves and lower interest income of the                    bank;                -   It is closely related to liquidity:                    -    Instead of becoming a liquid asset from an illiquid asset, the balance remains an                         illiquid item,                    -    Interest payments for missing resources drain bank liquidity,                    -    The resources used in the credit relationship not valued as a result of interest                         payments,                    -    The credit principal was not returned, causing the bank's cash flow,                -   There is also a fundamental relationship between credit risk and the risk of interest                    rate movements. When changing interest rates to the detriment of the borrower, the                    credit risk increases.                -   The ultimate effect of credit risk growth is the pressure to reduce staffing. A lower                    number of employees reduces labor costs and consequently increases undistributed                    earnings during the year and profit at the end of the year. There has a positive impact                    on bank regulation indicators.                                                              323","The risk of a loan portfolio in the area of credit operations of a bank is necessary to manage                and monitor not only the risk of specific loans but also the risk of the entire loan portfolio.                The more diversified the structure of the loan portfolio, the lower the bank's credit risk.                High concentration per client, an economically linked group of clients, to one economic                sector or region, the bank's credit risk is always substantially increased.                 2 Methodology and Data Banking sector                For  the  stable  development  of  the  bank  is  a  necessary  qualified  asset  and  liability                management, which form the focus of bank management and is an essential part of the                financial management of the bank. It's a method of managing the balance sheet structure                of the bank, which is an overview of the bank's assets and sources of its funding, with the                aim of maximizing profits.                 To bank management also reflects the macroeconomic effects of regulation of the banking                sector in particular, as currently applicable to Basel II and Basel III, banks are implanted                Slovik and Cournede (2011).                The  paper  mainly  used  for  data  analysis,  which  is  available  in  the  system  ARAD  CNB                (2018).  The  analysis  is  focused  exclusively  on  the  Czech  Republic  for  which  data  are                available over a sufficiently long time series, which allows the adoption of conclusions. Use                of a literature search is limited primarily to the basic literature, which dedicated to banks                and their evaluation.                The  decisive  is  considered  primarily  an  analysis  of  profits  about  the  development  of,                particularly interest rates, interest rates and risks in the banking sector. For purposes of                evaluating risk, development again used data from ARAD CNB (2018).                 The  crucial  focus is  not  on  how  to  analyze the  current  situation in  the  banking  sector,                especially the excess of liabilities reflected in the financial results of individual bank groups.                As the basis for the analysis used data ARAD CNB (2018). Their classification is according                to  the  size  of  banks  on  small,  medium  and  large  banks.  Development  of  receivables                demonstrates the following figure.                                           Figure 1 Receivables in Banks Total                                                                     Source: Own processing on basis data ARAD CNB (2018)                                                               324","Figure 2 Receivables in Great Banks                                                                      Source: Own processing on basis data ARAD CNB (2018)                                           Figure 3 Receivables in Middle Banks                                                                     Source: Own processing on basis data ARAD CNB (2018)                                                                    325","Figure 4 Receivables in Small Banks                                                                        Source: Own processing on basis data ARAD CNB (2018)                 3 Results and Discussion                The  analysis  is  based  exclusively  on  available  data  published  by  the  CNB.  It  focuses                exclusively on the Czech Republic, for which data are available in sufficiently long time                ranges, which allow me to take relevant conclusions.                 The banking sector has a very balanced development of receivables without default, which                evolves in line with total receivables, due to a significant increase in receivables. At the                same  time,  there  is  a  slight  decrease  in  receivables  in  default  and  loss  receivables.                However, it is fundamentally different if we focus on banks related to their client orientation                and size. The size of the bank also determines their orientation.                The current developments, when interest rates on deposits have declined significantly, with                the current excess of liquidity in Czech banks, are strongly reflected in their orientations.                Especially small banks are trying to find a trick on the market. Critical for them is the                provision of retail loans. Here, however, they are beginning to get more and more into                battles with non-bank providers of consumer credit.                Large banks have the advantage both in their client portfolio and thus not endangered.                The key problem remains with medium-sized banks that are trying to compete with large                banks by taking corporate clients and pushing for consumer credit.                The current favorable development in the portion of the loan in default in the loan portfolio                also influenced by the growth in the absolute value of the loan portfolio. The portion of                defaulting credit also reflected in the relatively short credit expansion period, particularly                in the area of retail loans and housing loans. With a prolonged credit expansion period, risk                aversion to banks will decline as a result of high deposits. It can assume that this will result                in a significant increase in default credit.                The following charts document developments in the banking sector, and it is likely that                small banks will be expected to expect problems in the future.                                                               326","4 Conclusions                Development of economic indicators in the banking sector at the current low-interest rates                depends mainly on the orientation of the bank, the result of the woods reach small banks.                For further development of the banking sector and individual banks, it is crucial:                     •   Client orientation of the bank, whether the bank-oriented to corporate clientele,                       retail, mortgage lending,                    •   The size of the bank, which closely linked to the ability of the bank to focus on client                       demand,                    •   The size of the bank at the same time also has a decisive influence on the Bank's                       potential for further development.                     •   In particular, small banks are expected to increase outstanding loans, which are                       related to their expansion policy.                 Acknowledgments                 Support  of  Masaryk  University  within  the  project  MUNI/A/0753/2017  is  gratefully                acknowledged.                 References                 Heffernan, S. (2005). Modern Banking. 1.rst. Ed. Chichester: John Wiley \& Sons.                Krajicek,  J.  (2017).  Economics  and  Management  of  Banks.  3rd  upgraded  and  revised                edition. Brno: Masaryk University, Faculty of economics and administration, Department                of Finance. Study texts.                Slovik,  P.,  Cournede,  B.  (2011).  Macroeconomic  Impact  of  Basel  III.  OECD  Economics                Department Working Papers, vol. 844.                CNB  (2018).  The  time  series  Database.  Retrieved  from:  http://www.cnb.cz/cnb/STAT.                ARADY_PKG.STROM_DRILL?p_strid=0\&p_lang=CS                                                                                                  327","The Impact of Behavioral Finance on the Financial Performance                                                   of an Enterprise                                                                                 2                                                          1                                          Peter Krištofík , Michaela Novotná                                                    1  Matej Bel University                                  Faculty of Economics, Department of Finance and Accounting                                       Tajovského 10, 975 90 Banská Bystrica, Slovakia                                               E-mail: peter.kristofik@umb.sk                                                     2  Matej Bel University                                  Faculty of Economics, Department of Finance and Accounting                                       Tajovského 10, 975 90 Banská Bystrica, Slovakia                                              E-mail: michaela.novotna@umb.sk                 Abstract: Behavioral finance is an increasingly accepted approach to explaining human                behavior  in  the  market.  Traditional  financial  theories  treat  financial  markets  and  their                participants as rational subjects. This paper focuses on behavioral finance and its impact                on Slovak medium and large enterprises. It points to different perceptions of rationality in                classical economics  and  behavioral  economics.  The  aim  is  to  create  a  linear  regression                model and to investigate the impact of deviations from the rationality on the company's                financial performance, measured through ROA and ROE. The paper employs a primary data                source: a questionnaire survey, completed by 33 corporate managers. It tests six linear                regression models. These show that deviations from rationality, and age affect the financial                performance of enterprises.                Keywords:  behavioral  economics,  behavioral  finance,  deviations  from  rationality,                regression models, financial performance of enterprises                JEL codes: G02, C58                 1 Introduction                Traditional financial theories treat participants in financial markets as rational subjects.                They  assume that if new information  emerges  on  the  market,  entities  will  adjust  their                preferences  and  attitudes  rationally.  They then  take  decisions that  are in line  with  the                theory of maximizing their personal benefit. The theory of rational behavior thus appears                to  be  clear,  well  defined,  standing  on  logical  foundations.  It  would  be  sufficient  if  its                forecasts were confirmed by practice. However, after many years and especially in today's                chaos on the financial markets, it is clear that such theories cannot be fully applied to the                real world of people, especially in terms of individual investor behavior.                This  is  related  to  the  emergence  of  behavioral  economics  and  behavioral  finance.  The                central theme of these disciplines is precisely economic decision-making - the way in which                we decide, the possible causes of our action, and possibly its inconsistency with economic                rationality.                Behavioral approaches used to address the variety of financial problems are referred to as                behavioral finance. Behavioral finance represents a new approach to financial markets that                has emerged in response to a totally inappropriate model of rational behavior.                Kahneman  and  Tversky  (1979)  describe  several  classes  of  choice  problems  in  which                preferences  systematically  violate  the axioms  of expected utility  theory.  In  the light  of                these observations they argue that utility theory, as it is commonly interpreted and applied,                is not an adequate descriptive model. They propose an alternative account of choice under                risk. This is prospect theory: a response to the inability of the theory of expected utility to                explain certain phenomena that seem to conflict with its underlying assumption that people                behave  perfectly  rationally.  Prospect  theory  suggests  that  there  may  be  limitations  to                human  thinking  that  lead  to  a  systematic  violation  of  the  underlying  assumptions  of                probability theory.                                                              328","Psychology and neurology have shown that a person does not always only act rationally.                In addition to reason, emotions are important for behavior, even to a greater extent than                logical considerations. Most decisions are made on the basis of intuitive approaches that                arise from personal preferences and character traits. Some deviations from rationality are                called adaptable, because they allow people to adapt to certain situations and take more                rapid and hence efficient solutions. Other deviations from rationality arise as a result of                people failing to develop proper mental mechanisms to solve certain problems or using                mechanisms that are ill suited to dealing with these specific problems (Pilch, 2014).                As Pilch (2014) states, deviations from rationality can occur at any stage of the decision-                making process, starting from the form in which the new information is presented, and                continuing until the decision is taken. The decision-making process has several phases,                each of which can be changed significantly.                 Furthermore, we may sort the deviations from rational behavior on whether they result                from incorrect information processing, or from the personal characteristics of the investor.                Accordingly, we divide them into cognitive and emotional (Baláž, 2006).                Cognitive deviations from rational behavior are based on incorrect collection, analysis, and                interpretation of information. They can be corrected by learning, or by appropriate financial                counseling (Pilch, 2014a).                Emotional deviations from rationality in thinking and acting are conditioned by emotional                factors,  especially  by  desire  and  concern.  Not  every  emotion  is  an  expression  of                irrationality. Emotions, however, support the adoption of fast and economical decisions.                These  are  especially  beneficial when  the  problems  are  very  complex  and  their  rational                solution  would  take  more  time  than  is  tolerable.  In  certain  situations,  emotions  can                suppress rational thinking and result in mistaken or irrational behavior.                 2 Methodology and Data                The aim of the paper is to point out the importance of psychological aspects in decision-                making  and  the  dependence  between  the  financial  performance  of  an  enterprise  and                deviations from rational behavior. Based on the results of empirical studies, we assume                that  the  performance  of  a  company measured  through  the ROA  and  ROE has  a  strong                impact on cognitive, emotional and mixed deviations from rationality.                 We  used  a  primary  data  source,  specifically  a  questionnaire  survey.  The  aim  of  the                questionnaire was to determine the presence of selected deviations from rationality on the                part of the respondents. We chose the most frequent deviations from rationality, and the                questions were formulated on the basis of various previous research. The questionnaire                consisted of closed questions with one choice of answer, and closed questions with multiple                choices of answer. The questionnaire was completed by 33 corporate managers. The group                of managers comprised 26 men (78.79%) and 7 women (21.21%). All the managers who                worked for the addressed companies had work experience of over 12 months.                 After evaluation, we created regression models, where the dependent variable was ROA or                ROE,  and  the  independent  variables  were  individual  distortions,  age,  and  respondent                gender.                 The ROA was calculated as the share of the economic result before tax and the deduction                of interest (referred to as EBIT), and total assets. We measured ROE as a share of net                profit and equity.                We will use data from the results of the questionnaire survey and the information we have                been  able  to  obtain  from  our  financial  statements  about  the  companies'  financial                performance.                 3 Results and Discussion                 We began with a regression model with the ROA indicator as the dependent variable. In                the first model we only examined the effect of deviations from rationality on this indicator                                                             329","and we call it Model 1. Firstly, we evaluated the statistical significance of the individual                variables entering the model. For all our models we tested the hypotheses:                 H0: βi = 0, the variable is statistically insignificant,                H1: βi ≠ 0, so the variable is statistically significant, it is not zero.                For Model 1, the p-value of all indicators is less than the significance level α = 0.05, hence                we not reject H0, and all the variables are statistically significant. Then we evaluate the                sign  of  each  of  the  regressive  coefficients.  A  negative  sign  \"-\"  represents  indirect                proportionality,  and  a  positive  sign  \"+\"  represents  direct  proportionality.  If  we  have  a                negative sign for all regression coefficients, this implies that if the number of  individual                deviations  from  rationality  increases,  economic  performance,  measured  by  the  ROA                indicator, will decrease. Our first linear regression model, Model 1 has the form:                           =  0.29549 + (−0.03030                                      ) + (−0.03172                               ) +                                                          (−0.02707                                                                           ) + (−0.02895                         ) +                               (−0.03491                                                                ) + (−0.02862                                   1) +                               (−0.02824                                   2) + (−0.03213              1) + (−0.02361              2) +                               (−0.03203       ℎ                       )                                (1)                 The  importance  of  all  models  as  a  whole  was  tested  by  the  F-test.  We  tested  the                hypothesis:                H0: β1 = β2 = β3 = β4 = 0,                 H1: non H0.                Model 1 as a whole can be evaluated as statistically significant because the p-value of the                F-statistic is 6.3E-143, and thus we reject the null hypothesis about the non-importance                                                                                            2                of the model. Furthermore, the model as a whole is evaluated using the R  coefficient of                determination. It states, after multiplying by 100, the percentage variability that can be                explained by the variability of the independent variables in a given model. The closer the                                                                                                    2                coefficient of determination is to 100%, the better the model. Our Model 1 has an R  value                of 84.39%. So approximately 84.39% of ROA indicator changes depend on deviations from                rationality. The disadvantage of this coefficient is that if we add parameters, its value will                increase.  This  disadvantage  is  removed  by  calculating  the  adjusted  (corrected)                determinant coefficient R adj. This adjusts for the number of variables, and like R  the higher                                                                                               2                                         2                its value, the better the model. In our Model 1 it is 83.97%.                In the second model, Model 2, we examine the impact of all deviations from rationality,                age and gender on the ROA indicator. First, we again evaluated the statistical significance                of each variable. The P-value of the statistical gender variable is very high and therefore,                at all current levels of significance, the zero hypothesis is not rejected and the gender                variable is statistically insignificant. All other p-values are less than the significance level                α  =  0.05,  so  we  can  reject  the  H0  hypothesis  and  all  other  variables  are  statistically                significant. For the variable age, there is a positive sign, which is a direct dependence, and                therefore with the manager's increasing age, the financial performance of the business                measured through the ROA indicator also increases. The sign of variables that represent                deviations  from  rationality  are  negative,  indicating  indirect  dependence.  Based  on  the                calculated coefficients, Model 2 will have the form:                          =  0.13285 + 0.00355        + (−0.02528                                      ) +                                                          (−0.02754                               ) + (−0.02250                                                                           ) +                               (−0.01997                         ) + (−0.02724                                                                ) +                               (−0.02055                                   1) + (−0.02415                                   2) +                               (−0.02573              1) + (−0.01430              2) + (−0.02578       ℎ                       )              (2)                The P-value of the F-statistic equals 5.6E-145, the H0 hypothesis is rejected, the model as                a whole is statistically significant. The value of the determination coefficient is 85.22%.                Compared to Model 1, this value is higher, and so we can say that Model 2 has a better                predictive  power  than  Model  1.  Similarly,  the  value  of  the  adjusted  determination                coefficient has risen to 84.74%. In Model 2 we have one statistically insignificant variable,                                                             330","so we have compiled a third model in which we have omitted this statistically insignificant                variable, we call it Model 3. All p-values of the coefficients were lower than the significance                level α = 0.05, which means that we reject the H0 hypothesis and all the variables are                statistically significant. The sign of variables that represent deviations from rationality is                again  negative,  that  is,  the  indirect  dependence  between  ROA  and  deviations  from                rationality. The sign of the variable age is positive, and therefore it is the direct dependence                of the ROA variable and the variable age. Model 3 takes the form:                          =  0.13177 + 0.00356        + (−0.02523                                      ) +                                                           (−0.02750                               ) + (−0.02240                                                                           ) +                                 (−0.01995                         ) + (−0.02722                                                                ) +                                 (−0.02055                                   1) + (−0.02407                                   2) +                                 (−0.02569              1) + (−0.01431              2) + (−0.02574       ℎ                       )       (3)                For Model 3, the p-value of the F-statistic was 3.9E-146, which means that we reject the                H0 hypothesis and the model as a whole is statistically significant.                                               2                The determination factor of R  is 85.22% for Model 3, which is the highest value of this                indicator so far. Similarly, the value of the adjusted determinant increased to 84.78%. We                can say that Model 3 is the best designed and most powerful of the models.                 In the second case, we will build models where the ROE indicator is the dependent variable.                This indicator is  not  as  complex  as ROA,  so  we expect  the  results  will  differ.  First, we                constructed  a  model  called  Model  4.  In  this  model,  we  examined  the  impact  of  the                deviations from rationality on the financial performance of a business, measured through                the  ROE.  All  ten  deviations  enter  the  model.  In  this  case,  we  have  variables  that  are                statistically  insignificant.  It  is  a  variable  of  aversion  to  loss,  mental  accountancy  1,                shortsightedness 2. Variable mental accountancy 2 has a boundary p-value, which means                that  at  the level  α  =  0.05  we  would  also  consider it  statistically insignificant.  The  sign                before the coefficients is again negative, even in this case it is an indirect dependence, and                therefore with the increasing deviations, the value of the ROE indicator will decrease. The                model as a whole, despite the insignificant parameter, can be evaluated as statistically                significant         as          the          p-value          of          the          test                F-statistic is 1E-57. H0 hypothesis is rejected. Model 4 has the form:                       y =  0.63449 + (−0.10012 Overconfidence bias) + (−0.02879 Aversion to loss) +                        i                                (−0.04685 Aversion to loss and willingness to risk) + (−0.11064 Availability) +                                (−0.07312 Overestimate of low probabilities) + (−0.04005 Mental accounting 1) +                                (−0.04284 Mental accounting 2) + (−0.05804 Myopia 1) + (−0.01176 Myopia 2) +                                (−0.07045 Anchoring effect)                                             (4)                                               2                The determination coefficient R  is 54.63% for this model, which is much less compared to                                                                                                  2                models built on the ROA indicator. Even the adjusted determination coefficient R adj has a                low value compared to previous models, at only 53.41%. We can say that Model 1, which                has the same independent variables but is constructed on the basis of the ROA indicator,                has a better predictive power than Model 4.                As  with  the  ROA  indicator,  we  also  constructed  an  ROE  model  in  which,  besides  the                deviations from rationality, we included variables for age and gender. This model is called                Model 5. Even in this case, the model has statistically insignificant variables. These are the                variables of aversion to loss, aversion to loss and willingness to risk, overestimation of                small probabilities, mental accountancy 1, mental accountancy 2, myopia 1 and myopia 2.                The  P-value  of these  variables is higher  than  α  =  0.05  and  we  do  not  reject  the  zero                hypothesis  about  the  statistically  insignificant  variables.  The  sign  on  deviations  from                rationality is again negative, that is, indirect dependence. Direct dependence is on age and                gender. Model 5 thus has the form:                       y =  0.03623 + 0.01447 Age + (−0.07914 Overconfidence bias) +                        i                                 (−0.01139 Aversion to loss) + (−0.02689 Aversion to loss and willingness to risk) +                                 (−0.07430 Availability) + (−0.04206 Overestimate of low probabilities) +                                 (−0.00766 Mental accounting 1) + (−0.02511 Mental accounting 2) +                                                             331","(−0.03165 Myopia 1) + (−0.01408 Myopia 2) + (−0.03316 Anchoring effect) +                                  0.00667 Gender                                                        (5)                 Despite  the  statistically  insignificant  variables  in  the  model,  the  p-value  of  the  test                F-statistic is very low at 5,87E-60, and thus we reject the null hypothesis and the model                as a whole is statistically significant.  The value of the determination coefficient in this case                is 55.85%, which is higher than in Model 4, but is still smaller than in the previous models.                Even the adjusted coefficient value is low, namely 55.39%. Even so we can say that Model                2, which has the same independent variables, has better explanatory power than Model 5.                If  there  are  statistically  insignificant  variables  in  the  model,  the  professional  literature                recommends that one deletes such variables, beginning with the least significant, and re-                estimating at each stage. This procedure leads to Model 6, which has only four variables:                age, overconfidence bias, availability and anchoring effect. Model 6 has the form:                        y =  0.25386 + 0.01900 Age + (−0.09263 Overconfidence bias) + (−0.07966 Availability) +                        i                                 (−0.03979 Anchoring effect)                                            (6)                Dependence  is  again  indirect  in  deviations  from  rationality;  in  the  age  it  is  a  direct                dependence. The model as a whole is also evaluated as statistically significant. The P-value                of F-statistic is 1.22E-65, so we reject the null hypothesis about the non-importance of the                model as a whole. The value of the determination coefficient is 56.82%, which is higher                than the previous values, but is still lower than in the models we used for the ROA indicator.                The same is true for the adjusted coefficient of determination, which is 55.42%.                 4 Conclusions                After designing all the models and evaluating them, we can conclude that psychological                aspects have an impact on the performance of the business. As we have seen, there were                statistically significant variables in each model that represented deviations from rationality.                Psychological aspects affect company performance measured through ROA and ROE.                In  addition,  entrepreneurs  as  investors  are  influenced  by  financial  factors  other  than                rational expectations and the assumption of an efficient market. This has been confirmed                in the design of linear regression models.                 We  can  also  say  that  there  is  an  indirect  dependence  between  the  investor's  financial                decision-making measured by ROA, ROE, and cognitive deviations from rationality. We can                confirm  this  hypothesis  on  the  basis  of  coefficient  markers  in  each  model  that  clearly                demonstrated  the  indirect  dependence  between  deviations  from  rationality  and  the                financial performance of the business, measured through ROA and ROE.                Monitoring  deviations  from  rationality  and  linking  them  to  companies`  financial                performance is not widely studied. Indeed, our research is unique. Until now all behavioral                finance research has focused only on individual investor behavior.                Traditionally  oriented  economists  question  the  methods  used  in  behavioral  economics,                especially experiments and tests. They point to the fact that people often say one thing                but  do  another.  However,  if  experiments  and  tests  are  performed  professionally,  their                results are comparable in different situations and environments (Baláž, 2006).                Richard Thaler sampled several thousand volunteers and found that patterns of behaviour                are determined by mental abbreviations, resulting in systematic and predictable mistakes                (Thaler, 2008).                Kahneman and Tversky (1984) have been able to systematically question the traditional                theory of rational theory in their numerous experiments.                Behavioral economics provides us with a number of practical results that can be used to                better understand behavior. By recognizing its radical findings people can correct mistakes                and  achieve  greater  value  in  their  decisions.  As  the  scope  of  behavioural  economics                expands  and  more  anomalies  are  explained,  more  people  will  become  aware  of  its                implications and their decision-making will improve.                                                              332","Asked  whether  the  financial  adviser  should  try  to  suppress  or  deviate  from  traditional                rationality, Pompian (2006) based his answer on two key factors. The first is the wealth of                the investor. According to the author, the advisor should adjust to the deviations of richer                investors but try to suppress deviations by investors with only small assets. The logic of                this  advice  is  that  while  deviations  from  traditional  rationality  may  threaten  a  client`s                standard of living, the impact is relatively less important the richer the client. The second                factor is the nature of the deviations from rationality. If cognitive deviations predominate,                the  advisor  should  try  to  suppress  them  as  the  explanation  and  the  provision  of                comprehensive information can minimize investor cognitive deviations. On the other hand,                emotional deviations are mostly related to the nature and impulsivity of the investor and                are  very  difficult  to influence.  Scheme  1  below illustrates  the  principles  of  adapting  or                suppressing deviations.                             Figure 1 Illustration of Adaptation or Suppression of Deviations                                                 High level of wealth                                                 Suppression                                           and adaptation   Adaptation                             Cognitive                            Suppression                Emotional                      deviations            Suppression    and adaptation              deviations                                                    Low level of wealth                                        Source: Own processing, following Pompian, 2006                 References                Baláž, V. (2006). Rozum a cit na finančných trhoch. Bratislava: VEDA.                 Kahneman, D., Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk.                Econometrica, vol. 47(2), pp. 263-292.                Kahneman, D., Tversky, A. (1984). Choice, values and frames. The American Psychologist,                vol. 39(4), pp. 341-350.                Pilch, C. (2014). Seriál: Odchýlky od racionality pri investovaní. 2. časť – Niektoré vybrané                odchýlky. FOR FIN Odborný mesačník pre financie a investovanie, vol. 1(6), pp. 1-7.                 Pilch, C. (2014a). Seriál: Odchýlky od racionality pri investovaní. 6. časť. FOR FIN Odborný                mesačník pre financie a investovanie, vol. 1(11), pp. 1-7.                 Pompian, M. (2006). Behavioral Finance and Wealth Management. New Jersey: John  Wiley                \& Sons.                Thaler, R. (2008). Mental accounting and consumer choice. Marketing Science, vol. 27(1),                pp. 15-25.                                                                333","Using of Non-financial Data in Predictive Models                                                                        2                                                                                             3                                                    1                             Zuzana Kubaščíková , Miloš Tumpach , Zuzana Juhászová                                            1  University of Economics in Bratislava                             Faculty of Business Informatics, Department of Accounting and Auditing                                          Dolnozemska 1, 852 35 Bratislava, Slovakia                                            E-mail: zuzana.kubascikova@euba.sk                                             2  University of Economics in Bratislava                             Faculty of Business Informatics, Department of Accounting and Auditing                                          Dolnozemska 1, 852 35 Bratislava, Slovakia                                               E-mail: milos.tumpach@euba.sk                                             3  University of Economics in Bratislava                             Faculty of Business Informatics, Department of Accounting and Auditing                                          Dolnozemska 1, 852 35 Bratislava, Slovakia                                             E-mail: zuzana.juhaszova@euba.sk                 Abstract: Forecasting the company's future economic situation arose in the early 20th                century.  First  of  all,  a  multidimensional  discriminatory  analysis  was  used  to  construct                prediction models, later replaced by logistic regression. The new challenge in predicting                financial development is neural networks representing a more reliable financial forecast                compared to mathematical and statistical methods. The neural network, by mimicking the                capabilities of human brain neurons, is capable of modeling the course of dependencies                between individual indicators and results. A disadvantage of the original prediction models                is also the low range of empirical accounting data and the fact that they are focused only                on financial data. The introduction of the financial statements registers led to the possibility                of free access to full data from the financial statements, which opens the door to new                possibilities in scientific research. For the purpose of this paper an annual report of 20                selected companies were tested. The aim of this paper is to accept or reject the claims that                non-financial  „narrative”  data  could  be  also  used  for  the  assessment  of  the  financial                position and financial performance of the companies. The results of our sentiment analysis                supported  the  hypothesis  that  financially  distressed  companies  use  a  different  tone  of                language in their annual reports compared to financially stable companies. These findings                confirmed the relationship between the tone which managers use in constructing annual                report narratives, and the financial performance of the company. Therefore, it is advisable                to incorporate non-financial data into the forecasting models.                Keywords:  financial  statements, financial  analysis,  neural network,  deep  learning,  data                mining                JEL codes: M49                 1 Introduction                The goal of financial analysis is to recognize what is bad and good for the business, which                can cause problems in the future, and vice versa, to identify its strengths that can be relied                upon  in  the  future  (Šlosárová,  2014).  The  forecasting  of  the  financial  situation  of                enterprises is considered to be a relatively young area of scientific research, which dates                back to the 30s of the 20th century. At the beginning experts predicted the future financial                situation by comparing financial ratios. Beaver (1966) examined the financial indicators on                the  basis  of  a  one-dimensional  discriminatory  analysis.  There  are  known  following                prediction  models  -  Altman  (1968),  Deakin  (1972),  Ohlson  (1980),  Taffler  (1982)  or                Zmijewski (1984).                At the end of the 1960s, multidimensional discriminatory analysis began to be used for                forecasting. Based on the multidimensional discriminatory analysis, it was the first model                of the Altman (1968) predictive model for publicly-traded joint stock companies (the so-                called Model Z-score) which some other theoreticians and practitioners have already been                                                              334","taking  in  the  short  time,  Deakin  (1972)  and  Blum  (1974).  In  the  1980s,  logistical                regression was gradually brought to the forefront of financial forecasting research, which                gradually  replaced  multidimensional  discriminatory  analysis.  First,  he  used  logistic                regression to create a model to predict the future financial situation of Ohlson (1980).                Prognosis of the future situation of a business entity through methods of multidimensional                discriminatory analysis and logistic regression is very widely used, but in reality, when                predicting company failures, these methods have certain limitations that largely result from                their very nature. One of the basic assumptions of multidimensional discriminatory analysis                and  logistic  regression  is  dichotomic  dependent  variability.  What  requires  groups  of                companies with a good financial situation and an unsatisfactory financial situation to be                clearly defined and clearly distinguishable. In practice, however, this assumption is very                difficult.  The  problem  is  the  very  definition  of  defaulting  companies,  from  which  the                classification  of  the  companies  on  which  the  prediction  model  is  based  depends.  Most                models  use  a  sample  composed  of  two  priority  groups  \"unsuccessful\"  and  \"successful\"                companies. Models constructed on the basis of multidimensional discriminatory analysis                and logistic regression have limitations that relations between financial ratios are unstable                over time and, due to changes in inflation, economic cycles in a given country, interest                rates. instability over time leads to a change in the set coefficients of the ratios or changes                in the boundary classification values. The disadvantage of these approaches is also the low                scope of empirical accounting data, moral wear and tear in countries where these analyzes                were directly related and further low usability in the conditions of the Slovak Republic, as                they use data which have a low prediction ability in the Slovak environment or some data                are not available at all (for example, market price of shares). These facts should lead to                regular testing of the model and its re-verification, if necessary.                One of the first studies dealing with the comparison of neural networks (NN) with classical                mathematical  and  statistical  techniques  in  forecasting  the  financial  development  of                enterprises was the Odom and Shard study of 1990. Neural networks represent one of the                areas of machine learning. By machine learning, we mean a set of methods and approaches                that allow the machine to learn. The system's knowledge gains from the training set of                data.  This  system  (in  our  case  the  neural  network)  should  also  have  the  ability  to                generalize.                In 2015, Blanco-Oliver published a collection of study authors focusing on creating a model                for micro-accounting units. They introduced non-financial information into the model and                constructed it using neural networks. In this study, we conclude that predicting bankruptcy                through  neural  networks  can  achieve  greater  predictive  power  and  lower  cost  of                classification  errors  than  with  logistic  regression  and  the  introduction  of  non-financial                variables  improves  the  predictive  precision  of  models.  According  to  this  study,  the                introduction of neural network access and the introduction of non-financial variables are                two important means to improve the predictive precision of predictive models (Blanco-                Oliver, Irimia-Dieguez, Oliver-Alfonso, Wilson, 2015).                 A very large number of published studies to compare classical mathematical and statistical                methods  with  neural  networks  confirm  the  conclusion  that  neural  networks  are  better                suited  to  predicting  bankruptcy  and  have  significantly  higher  predictive  precision  than                logistic regression or multidimensional discriminatory analysis. The introduction of non-                financial or macroeconomic variables into the model through independent indicators greatly                improves the predictive precision of the model and the use of any method to model. It is                not necessary to note that questions about modeling through neural networks such as the                integration of data mining methods, the selection of suitable parameters, the selection of                functions, a wide range of different techniques, etc., which are currently not universally                and sufficiently relevant, are still open based on previous research, and should therefore                do research in the future to answer these questions.                Machine learning is widely used nowadays to review contracts, leases, invoices, and other                documents.  The  adoption  of  machine  learning  within  the  accounting  profession  is  still,                admittedly, at an early stage. To accelerate the wider use of this technology, it is necessary                to create economies of scale by integrating its cognitive capabilities in the areas of textual                                                            335","analysis,  voice  recognition,  image  and  video  parsing,  and  judgment  support  into  the                financial analysis and audit process. This article discusses how the cognitive capabilities of                machine learning of non-financial data from annual reports could be applied to financial                analysis and predictive models and to enable improve decision making.                 2 Methodology and Data                For the purpose of this paper we analyzed annual reports of companies divided into two                groups based on Altman's bankruptcy prediction model: financially distressed companies                likely to go bankrupt within the following two years and financially stable companies with                the high probability to survive. This model proposed bankruptcy based on financial ratios                based  on  current  financial  results.  We  examined  whether  the  qualitative  information                contained in annual report narratives indicated the bankruptcy alongside this quantitative                information.  Thus,  we  analyzed  whether  companies  likely  to  go  bankrupt  employed  a                different tone of language compared to financially stable companies.                For this purpose, we selected 20 companies out of the Fortune 1000 list (Fortune, 2018).                We divided them into two equal groups according to Altman's Z-score obtained from the                Factiva database portal. Further, in order to obtain the data, we collected annual reports,                10-K filings, of U.S. companies from the EDGAR database (EDGAR, 2018). In total, we                examined 20 annual reports for the fiscal year 2016.                  Table 1: Companies Classified into the \"Distress Zone\" According to Altman's Z-score                   Rank  Company                                  Z-score  Industry (SIC)                  1.      Anadarko Petroleum Corp.                   0,71  Crude  Petroleum  and  Natural                                                                           Gas                  2.      Apache Corp.                               0,55  Crude  Petroleum  and  Natural                                                                           Gas                  3.      Caesars Entertainment Corp.               -0,24  Hotels and Motels                  4.      Darling Ingredients                        1,53  Animal and Marine Fats and Oils                  5.      Freeport-McMoRan, Inc.                     0,22  Copper Ores                  6.      Leucadia National Corp.                    0,46  Investors, NEC                  7.      Post Holdings, Inc.                        1,06  Cereal Breakfast Foods                  8.      The ADT Corp.                              0,99  Security Systems Services                  9.      WestRock Co.                               1,35  Die-Cut  Paper  and  Paperboard                                                                           and Dardboard                  10.     Windstream Holdings, Inc.                  0,62  Telephone Communications                                       Source: Factiva Companies and Executives, 2018                The following table lists the \"safe zone\" companies, the industries in which they operate,                and their Altman's Z-score:                    Table 2: Companies Classified into the \"Safe Zone\" According to Altman's Z score                    Rank  Company                          Z-score  Industry (SIC)                    1.      Applied Materials, Inc.                 4,17  Semiconductors and Related                                                                     Devices                    2.      Campbell Soup                      3,19  Canned Specialties                    3.      Cisco Systems                        3,14  Telephone and Telegraph                    4.      Citrix Systems, Inc.                   3,83  Pre-packaged Software                    5.      Exxon Mobil                        3,72   Petroleum Refining                    6.      Johnson \& Johnson                   5,09  Pharmaceutical Preparation                    7.      McDonalds Corp.                    5,25  Eating and Drinking Places                    8.      PepsiCo, Inc.                      4,01  Bottled and Canned Soft Drinks                    9.      Phillips 66                          3,56  Petroleum Refining                    10.     The Walt Disney Co.                  4,5  Motion Picture and Video Production                                       Source: Factiva Companies and Executives, 2018                                                            336","Although the companies were selected across various industries, we attempted to choose                similar  industries  within  both  the  safe  zone  and  distress  zone.  This  would  help  us  to                prevent the situation where industry-specific attributes would undermine the results as                some words are more characteristic for some industries than others. Thus, the companies                within both groups operated in telephone communication, the food sector, entertainment,                petroleum refining, and security systems and software.                 To assess the sentiment used in annual report narratives, we processed and analyzed this                data with the LIWC2015 (Linguistic Inquiry and Word Count) textual analysis program.                This program analyses text on various sentiment categories, including positive emotions,                negative emotions, certainty, risk focus and others.                 Linguistic  Inquiry  and  Word  Count  (LIWC)  is  a  text  analysis  computer  program  that                provides output in the form of a percentage of the words contained in the text from the                categories mentioned. For example, if the text contains 1,000 words, the dictionary might                find 50 words which occur in the text express positive emotion, so then it allocates score                5 to positive sentiment. Thus, within each category the score ranking from 0 to 100 may                be attributed to the specific text. The more words the software analyses, the more reliable                the results are. As our samples contained texts ranging from 7,000 to 22,000 words, we                consider the results obtained to be reliable (LIWC, 2018).                We employed this computer-assisted dictionary in order to analyze the sentiment aspect                of  annual  report.  Each  of  the  companies  was,  firstly,  analyzed  individually  on  several                categories  of  sentiment.  Then  we  synthesized  the  results  obtained  for  \"distress  zone\"                companies and \"safe zone\" companies in order to get the full picture about the differences                between these two.                  3 Results and Discussion                Although the idea of artificial neural networks dates back to the 1950s, such networks                could not be called real artificial intelligence until recent advances in computational power                and  data  storage  enabled  the  development  of  deep  neural  networks  that  model  the                structure and thinking process of the brain. The hidden layers of a deep neural network                automatically  “learn”  from  massive  amounts  of  data  (especially  semi-structured  or                unstructured  data)  received  by  the input layer  (e.g.,  also images,  annual  reports,  text                files), recognize data patterns in more and more abstract representations as the data is                processed and transmitted from one hidden layer to the next, and classify the data into                predefined categories in the output layer.                Deep learning algorithms further enrich financial analysis by identifying related concepts                or topics, recognizing entities (e.g., people, place, events, companies), extracting emotions                (e.g., anger, joy, sadness, disgust), and understanding subject-action-object relationships.                In addition, they can link concepts to a document and tag them accordingly. Deep learning                technology—an  emerging  form  of  artificial intelligence that  can  be  trained  to recognize                patterns in vast volumes of data that would be impossible for humans to process. This still                evolving technology represents a way to utilize big data to create supplementary audit                evidence that improves the effectiveness and efficiency of audit automation and decision                making.                For  example,  financial  analysts  can  select  data  attributes  in  order  to  predict  bankrupt                (auditors  can  predict fraud);  the  selected  attributes  are  then  combined  with  traditional                financial or nonfinancial data fields to develop a new deep learning prediction model. Deep                learning  performs  as  an  appropriate  prediction  algorithm  in  this  case  because,  by                introducing the extracted attributes, the number of predictors is much larger than what a                traditional machine-learning algorithm could process. For each assertion, the output of the                model could be the predicted risk level or suggested follow-up tests, depending upon the                nature and the label of the training data.                For the purpose of this paper, we analyzed the companies according to the Altman Z-score                to find out whether the qualitative information expressed in narrative reporting differs                between companies with a high probability of bankruptcy and those in a financially stable                                                            337","position. Thus, we could assume that the annual report narratives of financially distressed                companies show sentiment which is more negative and more   uncertain compared to the                financially healthy companies.                We  found  out  that  financially  healthy  companies  achieved  a  slightly  higher  score  on                positive sentiment compared to distressed companies (2,79 vs. 2,77). When we examined                more deeply the companies themselves, we found that companies from the distressed                group  which  had  suffered  a  loss  in  fiscal  year  2015  employed  less  positive  and  more                negative  emotions  than the  distress-  classified  companies  which, however,  achieved a                profit.                However, when we further examined the negative emotion elements, we could see that                companies  with  probable financial  problems  tended  to  employ  more  anxious language                compared to the other group (0,26 vs. 0,21). This may be due to the fact that distressed                companies  are  worried  about  the future  because  even  though  they  might  not  fall into                financial problems yet, it is likely that they will experience them within the next two years.                Thus,  they  expressed  their  concerns  through  this  category  of  words  even  before  the                bankruptcy  or  insolvency  appears.  However,  when  we  considered  only  the  companies                which already suffered a loss in fiscal year 2015 from the distressed group, these have a                much lower level  of  anxiety  compared  to  the  rest  of  the  group  (0,20 vs.  0,30)  and  a                slightly higher level of certainty (0,79 vs. 0,76).                Interestingly, the financially distressed companies ranked lower on two almost opposing                categories: certainty (0,77 vs. 0,84 achieved by the safe group) and tentativeness (1,79                vs. 1,94 achieved by the safe group). Even though these results are relatively surprising,                distressed companies with a poorer financial performance were found to be more certain                and, at the same time, more uncertain compared to the better-performing companies. It                might be caused by the fact that these companies, on the one hand, connect uncertain                terms with the financial situation and, on the other hand, the certain terms with activities                to handle the poor financial situation. Therefore, in our case, we could use the reasoning                that  financially  distressed  firms  are  less  tentative  in  terms  of  handling  their  financial                situation, for example, implementing new strategies, restructuring programs, and other                initiatives  in  order  to  prevent  the  possible  bankruptcy.  However,  they  are  still  more                uncertain referring to their future financial situation compared to companies from the safe                zone which can be almost sure they will not experience bankruptcy during future years.                Then the five additional categories were examined. Those are Affiliation, Achievement,                Power,  Reward  focus,  and  Risk focus. All  of  them  belong to  LIWC2015  categorization.                They could provide important clues about what specifically drives company performance                and/or which categories the managers would like to place the highest emphasis on in their                reporting while assessing company financial performance and future trends.                Companies  within  the  safe  zone  achieve  a  higher  score  on  the  first  four  categories  -                affiliation (2,6 vs. 2,5), achievement (1,8 vs. 1,65), power (2,74 vs. 2,69), reward (1 vs.                0,65). The financially distressed companies only outperform them on the risk category                (0,96 vs. 1,09). The power category, on which both groups score the highest (2,74 and                2,69)  reveals  interest  in  status  and  dominance.  However,  this  category  is  likely  to                comprise words like \"boss\", \"president\", or \"strong\".  Thus, it is obvious that such words                appear in the annual reports of the companies. Therefore, this category does not have a                huge explanatory power for the purpose of our analysis.                What  is,  however,  more  interesting  is  the  fact  that  financially  distressed  companies                already see the higher risk connected with their financial performance, and we can see                that this is reflected in the texts of their narratives. Therefore, this analysis might help us                to predict the financial performance of the firms to some extent. The full picture which                combines the insight into all of these five categories together could, therefore, provide us                with some evidence about the worsening financial performance of a company expressed                in the textual part of the annual reports.                                                                338","4 Conclusions                The financial data comprised in annual reports are important indicators of current financial                performance  and may  also indicate  future financial  development via  the  application  of                prediction models such as Altman´s Z- score. However, they do not provide us with insight                into all of the circumstances that led to these results, information about future trends,                development  and  managers’  expectations.  For  this  purpose,  the  qualitative  data  is                especially useful.                For the purpose of this paper the annual reports of 20 selected companies were tested.                Companies were randomly selected from database Fortune 1000. Their Altman Z-score                was  obtained  from  the  Factiva  database.  We  have  chosen  companies  across  various                industries. Firstly, we focused on the tone used by managers when discussing company                performance,  position  and  future  trends  in  annual  reports.  For  this  purpose,  was  used                computerized text analysis program.                Although annual report narratives are more subjective compared to the financial data, we                suggest they may be indicative of future financial developments, as demonstrated by the                language managers use to disclose information. In this study we used a textual analysis                software in order to accept or reject the claims, that non- financial \"narrative\" data could                also be used for the assessment of the financial position and financial performance of a                company.  Our  analysis  focused  on  the  Management  Discussion  and  Analysis  section                (MD\&A) of the 10-K reports. Here managers comment on financial performance, but also                express  predictive  statements  about  future  trends,  expectations  and  challenges.                Therefore,  the  information  comprised  in  these  sections  indicate  future  financial                performance.                The results of our sentiment analysis supported the hypothesis that financially distressed                companies use a different tone of language in their annual reports compared to financially                stable  companies.    These  findings  confirmed  the  relationship  between  the  tone  which                managers use in constructing annual report narratives, and the financial performance of                the  company.  Therefore,  it  is  advisable  to  incorporate  non-financial  data  into  the                forecasting  models.  This  can  be  incorporated  into  prediction  models  based  on  neural                networks.                However,  the  research  presented  has  its  limitations  that  could  subsequently  affect  the                reliability and accuracy of the results achieved and on which our recommendations are                based it  can  be  possible  to  eliminate  the  drawbacks  by using  neural  networks  but this                requires a bigger data sample and automated input data processing.                 Acknowledgments                This article is an output of the project of the Scientific Grant Agency of the Ministry of                Culture of the Slovak Republic and Slovak Academy of Sciences (VEGA) no. 1/0512/16                (2016-2018) “Retention and Development of Business Property of Company as a Relevant                Tool for Sustainable Development.”                 References                 Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate                bankruptcy. Journal of Finance, vol. 23(4), pp. 589-609.                Blanco-Oliver,  A.,  Irimia-Dieguez,  A.,  Oliver-Alfonso,  M.,  Wilson,  N.  (2015).  Improving                Bankruptcy  Prediction  in  Micro-Entities  by  Using  Nonlinear  Effects  and  Non-Financial                Variables. Finance a Uver: Czech Journal of Economics \& Finance 2015, vol. 65(2), pp.                144-166.                Deakin,  E.  (1972).  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In:                Proceedings  from  IJCNN  International  Joint  Conference  Neural  Networks.  pp.  163-168.                Retrieved      from:      https://www.researchgate.net/publication/224755073_A_Neural                _Network_Model_fo r_Bankruptcy_Prediction                Ohlson, J. A. (1980). Financial ratios and probabilistic prediction of bankruptcy. Journal of                Accounting Research, vol. 18(1), pp. 109-131.                 Ondrušová, L. (2016). Company in crisis form the point of view of accounting. Journal of                East European Science and Research: scientific peer-reviewed journal, vol. 8(1), pp. 338-                345.                Šlosárová, A. (2014). Analýza účtovnej závierky. Bratislava: Ekonóm.                Taffler,  R.  J.  (1982).  The  Z-score  approach  to  measuring  company  solvency.  The                Accountant`s Magazine, pp. 22-24.                Zmijewski,  M.  E.  (1984).  Methodological  issues  related  to  the  estimation  of  financial                distress prediction models. Journal of Accounting Research, vol. 22, pp. 59-82.                                                                                                                340","The Role of Apparent Signs of Financial Distress in Test Samples                               and Verification Samples of Bankrupt Models                            Michal Kuběnka, Renáta Myšková, Josef Novotný, Vít Jedlička                                                   University of Pardubice                    Faculty of Economics and Administration, Institute of Business Economics and Management                                            Studentská 84, 532 10, Czech Republic                       E-mail: michal.kubenka@upce.cz; renata.myskova@upce.cz; josef.novotny@upce.cz;                                                 vit.jedlicka@student.upce.cz                 Abstract: Financial bankrupt models are characterized as quite accurate and above all                very  fast tools  for  quantitative  evaluation  of  financial  health  of  company.  The  creators                report the accuracy of the predicted bankruptcy usually in the range of 70 to 90%. But the                problem of bankruptcy models is the test sample on which the models were created. The                sample affects the predictive power of these models.  Usually indicated accuracy rate differs                from the real predictive power of these models. The financial distress of certain businesses                may be obvious even without the use of bankruptcy models. Apparent signs of financial                distress may be insolvency, negative equity, VAT unreliability, negative economic result                for several years in a row. Survey conducted by more than 270 companies has shown that                more  businesses  with  apparent  signs  of  financial  distress  in  the  sample  increase  the                reported accuracy of the bankruptcy model. The research carried out also has determined                the real accuracy of selected bankruptcy models on the standard sample of Czech firms                and also on a sample of companies where companies with obvious signs of financial distress                were eliminated. Due to the modification of the test sample subsequently the accuracy of                the selected models changed radically.                Keywords: bankrupt models, financial distress, prediction                 JEL codes: G32, M10, C38                 1 Introduction                The legislation on insolvency proceedings in the Czech Republic is contained in Act no.                182/2006 Coll., “On Bankruptcy and Methods of its Settlement”, as amended (hereinafter                                                                        st                the  Insolvency  Act).  This  Act  entered  into  force  on  1   January  2008  and  replaced  the                previous  regulation  contained  in  Act  no.  328/1991  Coll.  The  position  of  insolvency                administrators in charge of this issue is regulated by Act no. 312/2006 Coll. on Insolvency                Administrators, as amended. According to the Insolvency Act, it is possible to solve not                only if financial failure has already occurred but also the imminent bankruptcy. Insolvency                act (§3) defines the concept of bankruptcy with three conceptual features  - plurality of                creditors, the existence of repayable obligations for more than 30 days and the inability to                perform financial obligations.                            Table 1 Insolvency Proposals in the Czech Republic in 2010-2017                                   Year            Corporates       Individuals       Total                                   2010                5559            10559          16118                                   2011                6753            17600          24363                                   2012                8398            23830          32228                                   2013                6021            30888          36909                                   2014                3563            31577          35140                                   2015                3004            29349          32353                                   2016                2438            27067          29505                                   2017                1803            21343          23146                                           Source: CreditReform 2012, 2015, 2017                 Table 1 shows that insolvency proposals are tens of thousands each year. Therefore, it is                important  to  prevent  a  situation  where  this  situation  occurs  in  one's  own  business  or                                                              341","business  partner.  Timely  disclosure  of  incoming  bankruptcy  may  cause  avoidance  of                bankruptcy or minimize losses as a result of the bankruptcy of a business partner.                 Bankruptcy models are based on the assumption that an enterprise has been showing signs                of bankruptcy for years and years before it has gone bankrupt. A prediction of financial                bankruptcy in advance could thus avert an imminent bankruptcy. Attempts to find a simple                and yet accurate model that would be able to classify future financial decline appears in                the middle of the last century. The break was 1968, when prof. Altman (Altman, 1968)                created a bankruptcy model Z score using multivariate analysis. This model is based on                financial data contained in account books. Therefore, the input data is easily accessible and                subsequent enterprise application is possible for the general public. His model works with                five financial ratios. Relatively soon it was followed by other specialists, who also used                other mathematical and statistical methods. For instance, for creation of his bankruptcy                model J. A. Ohlson used the logit linear probability in 1980 as the first one (Ohlson, 1980).                In the year 1985 the factor analysis was used in order to get independent variables for the                logit model (Zavgren, 1985). Later the progress has led to methods of artificial intelligence                that mainly use the neural networks (NN) for creation of prediction models since nineties                of last century. Tam and Kiang (Tam, 1991), (Tam \& Kiang, 1992) belong to pioneers of                NN usage. Particular methods (MDA vs. Logit vs. NN) of models creation were compared                many times. The results show NN as the most suitable method as proven by (Pendharkar,                2005) (Liang, 2005) (Rafiei, Manzari and Bostanian, 2011). After the passage to the market                economy (nineties 20th century) the bankrupt models also started to origin in the Czech                Republic and Slovak Republic in order to predict the company bankruptcy. These models                should  regard  the  market  specificity  of  these  countries.  The  model  (index)  IN95                (Neumaierová \& Neumaier, 2002) has appeared as the first one, being designed as the                creditor´s model, as i tis mostly used for subjects in the creditor´s position (banks and                business partners). In 1999 the same authors brought the so-called ownership´s model,                named  IN99.  Its  function  consists  in  the  prosperity  prediction  based  on  the  positive                economic  value  added  (EVA).  In  2001  they  created  the  model  IN01  that  connected                properties of both previous models, i.e. it predicts the bankruptcy as well as the prosperity.                Just in the year 2005 it was updated to the version called index IN05 (Neumaierová, 2005).                There is a lot of models for earnings prediction (e.g. Hou \& Van Dijk \& Zhang, 2012; Sheng                \& Thevenot, 2012; Duspiva \& Novotný, 2012; Banker \& Chen, 2006) but only IN05 predicts                EVA.  Before  the  economic  crisis  two  models  focused  on  the  agriculture  appeared  in                Slovakia. It was CH-index from 1998 (Chrastinová, 1998) and G-index from 2002 (Gurčík,                2002).  After  the  economic  crisis  in  2008  only  few  bankruptcy  models  appeared  at  the                territory of the Czech Republic and the Slovak Republic, i.e. P´model (Delina \& Packová,                2013) and the bankruptcy Index of Karas and Režňáková (IKR) (Karas \& Režňáková, 2013).                Čámská emphasizes that the application of these types of model is „user friendly as they                do  not  require  any  specific  mathematic  and  statistic  knowledge  of  the  user“  (Čámská,                2013). The authors of model IKR determine the model accuracy 91.71% (calculated as the                weighted average of sensitivity and specificity). The authors of model  P´ determine the                bankruptcy prediction accuracy 21.26% and the bankruptcy prediction return at 71.84%.                Kuběnka and Slavíček (Kuběnka and Slavíček, 2014) claim that although prosperity and                bankrupt  models  were  created  differently,  their  construction  is  similar,  which  means  a                combination  of  ratios  and  assigned  weights  of  importance.  Financial  diagnostic  and                prediction models vary predominantly in their targeting. However, despite a long history                of  these  models  there  are  still used  groups  of  simple  ratios for  economic  and  financial                stability analysis, e.g. (International Monetary Fund; Černohorská \& Linhartová, 2013).                Let´s suppose that the bankruptcy model is accurate at maximum when applied in the                region  (country)  of its  origin  (due  to  the  differences  of  accounting  methods (see  more                Honková,  2015),  market  environment,  etc.).  Let´s  also  suppose  that  the  market                environment in the Czech Republic and the Slovak Republic is still very close. Thus we shall                test just last two mentioned models (IKR and P´model) in order to define their accuracy on                sample of companies with and also without of apparent signs of financial distress. Altman´s                Z score models are the most famous in the world. That is why one of them is also tested.                                                              342","The goal of this survey is to answer the question whether a sampling pattern on which the                accuracy of the model is validated by its structure does not affect the resulting accuracy                of the model. The investigation will determine how far the specified accuracy of the models                changes after eliminating the obvious bankruptcy features.                2 Tested models                 The  original  Altman  Z-Score  of  1968  was  designed  for  publicly  traded  joint  stock                companies.  On  the  other  hand,  the  Z'score  (1983), which  was  published in  1983,  was                compiled  for  public  limited  companies  and  non-publicly  traded  shares.  Below  is  a                modification of the previous model where the X4 indicator has been altered from the market                value of the equity to the book value of the equity. The stated accuracy of this model is                82% (18% error of type I.). The model has the following form (Altman, 1993):                        Z´= 0,717*X1 + 0,847*X2 + 3,107*X3 + 0,420*X4 + 0,998*X5                                     (1)                where:                      X1 = working capital / total assets;                      X2 = retained earnings / total assets;                      X3 = earnings before interest and tax / total assets;                      X4 = book value of equity / total assets;                      X5 = sales / total assets.                 Creditworthy enterprises should have a score of more than 2.90, and on the other hand                enterprises  in  default  below  1.23.  The  results  between  boundary  values  (1.23;  2.90)                cannot be clearly explained.                Index  of  Karas  and  Režňáková  (IKR)  is  one  of  the  newest  bankruptcy  model  with                different  structure  of  variables  calculation.  All  known  bankruptcy  models  (based  on                author´s knowledge) use 4 ratio indexes at minimum, whereas IKR use only two of them.                The first one (X2) is the assets turnover and the second one (X3) is the ratio of quick assets                and sales. In addition, it contains the variable of absolute amount (X1) that represent the                value of total assets in EUR. The authors of index (Karas \& Režňáková, 2013) created the                model based on the sample of 880 financially stable and 628 bankrupted companies. Data                were  drawn  from  the  accounting  statements  from  the  period  2007  to  2012.  All  1508                companies belonged to the processing industry, based on their business activity, (NACE                rev. 2, section C: Manufacturing).                In their text the authors (Karas \& Režňáková, 2013) state that the model construction is                based on the connection of linear discrimination analysis and the Box-Cox transformation                variables. The model is shown as follows:                     IKR = 1.841* (X 1 +16783.91) 0.02941 −1  + 1.112* (X 2 +1) −0.35627 −1 *13.55* (X 3 +1.112) −2.97955  – 17,319   (2)                                      0.02941                 0.35627              2.97955                where:                         = value of total assets (EUR)                     1                        = turnover of total assets                     2                        = quick assets a sales ratio                     3                The border limit was determined by the 0 value. Then the company with achieved value                IKR > 0 should be financially healthy and with IKR < 0 business goes bankrupt.                Delina and Packová (2013) proposed their own bankruptcy model using the ration indexes                used in analyses models (Z-score, Creditworthiness Index, IN05) and regression analysis.                The so-called P´ model has the following form:                    P´  model  =  2.86  –  0.0001278     +  0.04851     +  0.2136     –  0.000071                                                                                           3                                                          1                                                                          2                                                                                                          4                    + 0.0001068    – 0.0006116                                                          (3)                                                  4                                   1                where:                        = (financial assets – short-term liabilities) / (operating expenses –depreciations)                     1                                                            343","    = retained earnings / total assets                     2                        = profit before interests and taxes / total assets                     3                        = registred capital / (long-term + short-term liabilities)                     4                        = cash flow / total liabilities                     1                        = earnings before taxes / total operating revenue                     4                The evaluation scale does not contain the interval of non-specified values of P´ model. The                critical limit for  the  company  classification is  at  the  value  2,856.  When  P´< 2.856  the                company  tends  to  bankrupt,  when  P´  ≥  the  company  is  financially  healthy  and  the                bankruptcy probability is very low.                 3 Research sample description                I was found 273 companies with defined parameters. This sample was used for finding of                current accuracy of selected tested model (test no. 1). The sample consist of the companies                operating  in  the  manufacturing  industry,  in  bankruptcy,  who  had  available  financial                statements both in the year of bankruptcy and in the previous year, in order to be able to                monitor the possible occurring negative events even in previous years.                 In general, the following may be considered as negative events: bankruptcy, execution,                insolvency,  claim,  enforced  execution,  liquidation,  extinction,  negative  equity,  VAT                unreliability or loss for several consecutive years. And the last one was one of the main                indicators  of  bankruptcy,  along  with  negative  own  equity  and  negative  economic                performance observed on the sample being tested.                Out of the original 273 companies, 135 companies were eliminated on the basis of the                observed loss for several consecutive years, the negative equity and the negative economic                result. For the rest of 138 companies selected bankruptcy models were applied, detected                accuracy evaluated (test no. 2).                                    Table 2 Frequency of Occurrence of Negative Events                                Negative events                    Absol. frequency           Relat.                                                                                           frequency                   Loss for several consecutive years                      34                  25%                   Negative equity \& negative economic                     41                  30%                   result                   Negative economic result                                63                  47%                   Negative equity                                         82                  61%                   Total                                                  135                 100%                                                   Source: Own research                 4 Results                Test no. 1 consisted of applying models to a whole sample of enterprises (273 pcs) in                bankruptcy.  P'  model  evaluated  correctly  as  bankrupt  203  businesses,  it  is  74.36%  of                sample. This has become the most successful model in testing. Only 71.44% (28.56% type                I.  error)  assigned  creators  accuracy  of  the  bankruptcy  prediction.  Sample  application                without apparent signs reduced the model's accuracy by 6.59% to 67.77%.                Z´ score evaluated 185 companies (67.77%) in bankrupt, which is less than the creator                states (82.00%). The least successful was the IKR model, where the authors report accuracy                of 69.91% and test no. 1 indicates accuracy 62.27%, i.e. 170 enterprises classified as                bankrupt. Rank in success of prediction in test no. 1 is as follows:                    •   P´model (74.36%)                    •   Z´score (67.77%)                    •   IKR (62.27%)                 The reliability interval π for these results can be, according to Pacáková (2003), determined as                follows.                                                              344","  (1−  )                 (1−  )                          (   −     ∝ ∗ √    <     <     +     ∝ ∗ √   ) = 1 −                                                                      (4)                              1−                      1−                                        2                       2                where:                    •  p – found current accuracy of models (74.36%, 67.77%, 62.27%)                    •  n – the size of the base π, means number of companies in sample,                    •  α – determined at the level of 5%,                 Table 3 states original accuracy stated by author, current accuracy checked in test no. 1,                confidence interval of current accuracy and accuracy without apparent signs checked in                test no. 2.                                   Table 3 Accuracy With and Without Apparent Signs                     Model       Creator´s          Current          Confidence           Without                                  accuracy          accuracy           interval       apparent signs                   Z score         82.00%            67.77%         62.23; 73.31           58.70%                   IKR             69.91%            62.27%         56.52; 68.02           61.59%                   P´model         71.84%            74.36%         69.18; 79.54           67.77%                                                   Source: Own research                 Accuracy  without  apparent  signs  was  stated  after  reduction  of  research  sample  to 138                companies. As a result, the resulting order of accuracy will change as follows:                    •   P´model (67.77%)                    •   IKR (61.59%)                    •   Z´score (58.70%)                                     Figure 1 Success of Business Failure Prediction                                                                                         Source: own                 Accuracy of Z´score and P´model in test no. 2 (without apparent signs) is out of confidence                interval  of  accuracy  checked  in  test  no.  1.  It  means  that  usage  of  visible  features  of                bankruptcy leads to distortions in the stated accuracy of the models. In case of Z´score                and P´model it was confirmed with statistical significance. Success of prediction of IKR has                also  fallen  specifically  from  62.27%  to  61.59%  but  without  confirmation  of  statistical                significance.                                                              345","5 Conclusions                Every day many subjects need to evaluate in fast manner the financial health of business                partners, loan applicants, debtors, etc. To this purpose there have been developed many                failure prediction models. However their accuracy depends on many factors. On this basis                authors have set themselves the task to answer the question whether a sampling pattern                on which the accuracy of the model is validated by its structure does not affect the resulting                accuracy of the model.                In order to meet this goal the classification of companies in 2009-2013 was confronted                with the fact that these companies went bankrupt one year later.                IKR model showed the worst prediction power in test no. 1, where was used a research                sample of 273 companies.  It correctly predicted a business failure in 62.27% of cases                based  on  the  data  available  a  year  earlier.  Order  accuracy  has  changed  within  the                application to the reduced sample (without apparent signs) in test no. 2. Model moved                from third to second place.                P´ model was the best in bankruptcy predicting, namely in 74.36% of cases in test no. 1.                In test no. 2 the accuracy fell down to 65.94%. This change is statistically significant. This                model is the most successful also in test no. 2.                 Z´ score showed average accuracy in test one. This model has the worst results in test                no. 2. From point of view of authors are more important the results of test no. 2 with the                sample without of apparent signs.                The investigation has shown that the sample structure has a key impact on the reported                accuracy of the bankruptcy model.                 Acknowledgment                 This contribution was made with the financial support of SGS_2018_013.                 References                Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate                bankrupcy. Journal of Finance, vol. 23(4), pp. 589 – 609.                Altman. E. I. (1993). Corporate Financial Distress and Bankruptcy: A Complete Guide to                Predicting and Avoiding Distress and Profiting from Bankruptcy. New York: John Wiley and                Sons Inc.                Banker, R., Chen, L. (2006). 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Journal of Business Finance and Accounting, vol. 12(1), pp. 19–45.                                                                      347","The Anti-Tax-Avoidance Measures in the EU – from Denials to Wide                                                      Acceptance                                                      Jana Kubicová                                             University of Economics in Bratislava                                      Faculty of National Economy, Department of Finance                                    Dolnozemská cesta 1, 852 35 Bratislava, Slovak Republic                                               E-mail: jana.kubicova@euba.sk                 Abstract:  In  July  2016  the  European  Commission  adopted  the  Anti-Tax  Avoidance                Directive (ATAD). The Directive lays down common harmonized rules of several kinds of                anti-tax avoidance measures. Those rules are not brand new, in contrast, they have been                well known for years in the national tax systems around the world. This paper presents                survey of the former key decisions of the Court of Justice of the European Union concerning                national  exit  taxation  provisions  and  compares  them to the  current  ATAD  exit  taxation                provisions.  The  legal  analysis  suggests  that  several  former  Court  of  Justice  of  the  EU                decisions considerably differ from the adopted ATAD exit taxation rules. This shift might                suggest that judgements of Court of Justice of the European Union and tax policy of the                European Union institutions might not provide enough consistency and legal certainty.  The                paper  hypothesizes  four  unspoken  reasons  which  might  lay  behind  introduction  of  the                common harmonized exit taxation rules in the EU.                Keywords: tax avoidance, exit tax, Court of Justice of the European Union, harmonization                JEL codes: H26, K2, K34, K42, M21                  1 Introduction                In  July  2016  there  was  adopted the  Directive  laying  down  rules  against  tax avoidance                practices that directly affect the functioning of the internal market (EC, 2016)  (hereinafter                ATAD). The ATAD introduces common harmonized provisions of several anti-tax avoidance                measures with aim to tackle and eliminate aggressive tax planning practices. Namely they                are: limitations to the deductibility of interest, also known as thin capitalization rules, exit                taxation, taxation of controlled foreign companies’ rules, rules to tackle hybrid mismatches,                and general anti-abuse rule (GAAR). Those rules are not brand new, in contrast, for years                they have been integral part of the national tax systems around the world, especially in                the most developed countries, including several “old” European Union Member States.                The Preamble of the ATAD lists several objectives of the ATAD, they are:                     •   to ensure that tax is paid where profits and value are generated;                    •   to restore trust and the fairness of tax systems and allow governments to effectively                       exercise their tax sovereignty;                    •   to find common yet flexible solutions at the EU level consistent with the OECD BEPS                       conclusions;                    •   effective and swift coordinated implementation of the anti-BEPS measures at the                       EU level;                    •   to ensure good functioning of the internal market;                    •   to discourage tax avoidance practices and ensure fair and effective taxation in the                       Union in a sufficiently coherent and coordinated fashion;                    •   to strengthen the average level of protection against aggressive tax planning in the                       internal market;                    •   to establish rules applicable to all taxpayers that are subject to corporate tax in EU                       Member States;                    •   to lay down rules against the erosion of tax bases in the internal market and the                       shifting of profits out of the internal market (EC, 2016).                For those who have been carefully watching development of international income taxation                rules,  including  willingness  of  nations  and  the  European  Commission  to  adopt  anti-tax                avoidance  measures  applicable  on  corporate  income  taxation,  the  adoption  of  ATAD                                                            348","measures by European institutions must be a surprise. This is because it is not so long ago,                when the European Commission had been quite aware when it came to the elimination of                tax  barriers  within  the  European  Union  internal  market.  For  years  the  European                Commission and the Court of Justice of the European Union watched cautiously compliance                of  the  national  anti-tax  avoidance  measures  with  fundamental  rights  and  freedoms                guaranteed by the now Lisbon Treaty. In fact, it is not so long time ago, when the European                institutions were quite reluctant to accept existing national anti-tax avoidance measures.                Several  judgements  of  the  Court  of  Justice  of  the  European  Union  state  that  national                provisions governing exit taxation, thin capitalization rules or taxation of controlled foreign                companies rules violate fundamental freedoms, mostly freedom of movement, freedom of                establishment,  freedom  of  payments,  and  freedom  to  provide  services,  all  of  them                guaranteed  by  the  EU  law.  Though,  currently,  almost  out  of  the  blue,  the  European                Commission changed its mind and decided to lay down common harmonized rules of anti-                tax avoidance measures and adopt ATAD.                 Above in this paper there are listed formal reasons to introduce common harmonized anti-                tax avoidance measures and adopt the ATAD. This paper aims to go further, dive deeper,                and search for other, not publicly articulated reasons that might explain the sudden change                of mind of the European Commission and Court of Justice of the EU concerning presence                of the anti-tax avoidance measures in the national tax systems of the EU Member States.                The aim of this paper is to demonstrate that that there are thoughtful differences in the                attitudes of the European Commission and Court of Justice of the European Union towards                presence and application of the anti-tax avoidance measure, namely exit taxation, before                and after 2016. This paper is also inquiring the actual reasons that might lay behind the                abrupt adoption of the ATAD measures.                To reach the goals, I adopt the legal analysis method and analyze judgements of the Court                of  Justice  of  the European Union  that focus  on  national  exit  taxation legislation.  I  also                search for those EU Member States that are likely to be mostly affected by the obligatory                adoption of the common harmonized anti-tax avoidance measures.                The paper proceeds as follows: the second section presents details of the legal analysis                procedure, and the third section presents results of the legal analysis, namely how exactly                the Court of Justice of the EU had evaluated national exit taxation provisions before ATAD                was adopted. This section also presents brief description of the currently adopted common                harmonized  exit  taxation  provisions  laid  by  the  ATAD.  The  fourth  section  discusses                unspoken reasons for lying down common harmonized exit taxation measure. Finally, there                is  conclusion  which  summarizes  findings  and  points  out  some  open  questions  to  be                answered by future research.                 2 Methodology and Data                As I have already mentioned earlier, this paper aims to show that there are significant                differences in the approaches of the European Commission and the Court of Justice of the                European Union towards presence and application of the exit taxation provisions before                and  after  2016.  To  make  those  differences  observable,  I  compare  current  and  former                attitudes  of  the  European  Commission and  the  Court  of  Justice  of  the  EU towards  exit                taxation.                To show former attitude of the EU institutions, I take a list of judgements of the Court of                Justice of the EU about national exit taxation provisions launched before 2016, and subject                those judgements to legal analysis where I am looking how the Court evaluated national                exit taxation provisions before 2016. What I am interested in is to find, whether former                non-common non-harmonized national exit tax provisions were pronounced as being in                line  with  then  EU  law,  or  whether  they  were  declared  as  national  provisions  that  had                violated the EU law.                Legal analysis of judgements of the Court of Justice of the European Union consists of three                consecutive questions that must be answered in hierarchical order. They are as follows:                                                             349","Question 1 focuses on the compliance and possible violation of the EU law.                           •   Do  interrogated  national  exit  tax  provisions  restrict  some  fundamental                              freedom guaranteed by the Treaty of the European Union or any provision                              of the Secondary EU Law?                    Question 2 focuses on the justification of the national exit taxation rules.                           •   Can be the interrogated national exit tax provisions justified if the answer                              on the question 1 is affirmative?                    Question 3 focuses on the proportionality of the national exit taxation rules.                           •   If the answers on questions 1 and 2 are affirmative, are the interrogated                              national exit tax provisions proportional to the goals they intend to pursue?                To show current attitude of the EU institutions towards exit taxation, I briefly describe exit                taxation provisions as laid down by the ATAD in 2016.                Before running legal analysis I demonstrate presence of the exit taxation provisions in the                national tax systems of the EU Member States. The aim is to indicate those European Union                Member States that had not had any experience with such provisions before the common                harmonized exit taxation rules were adopted in 2016.                 3 Results                Let us first examine presence of the exit taxation rules before the ATAD was introduced. It                is obvious (see Table ), that national exit taxation rules were incorporated in the national                tax systems of the most developed countries around the world. Out of the 35 OECD Member                States there were 20 with national exit taxation rules, while 15 OECD Member States did                not have exit taxation rules before 2016. Twenty OECD Member States had national exit                taxation rules, out of them  thirteen  were most developed, “old” EU Member States. In                contrast, before 2016 fifteen EU Member States did not have national exit tax provisions                in their national tax systems, out of them thirteen post-communist countries. They were                namely: Bulgaria, Croatia, Czech Republic, Cyprus, Estonia, Hungary, Latvia, Lithuania,                Malta, Poland, Romania, Slovakia and Slovenia. At the same time thirteen countries, all of                them old EU MSs and members of the OECD had national exit taxation rules (Table ).                      Table 1 Presence of the National Exit Tax Rules in the EU Member States before                                                   Adoption of the ATAD                                         presence of national exit taxation rules                                      yes                                           no                                          OECD       EU MS                              OECD       EU MS                                            MS                                           MS                  1           AUS        1          1           1          CZE        1           1                  2           BEL        1          1           2          EST        1           1                  3           DNK        1          1           3          GRE        1           1                  4           FIN        1          1           4          HUN        1           1                  5           FRA        1          1           5          LAT        1           1                  6           DEU        1          1           6          POL        1           1                  7           IRE        1          1           7          SLO        1           1                  8           ITA        1          1           8          SVK        1           1                  9           LUX        1          1           9          SWE        1           1                  10          NLD        1          1           10         AUL        1           0                   11          PRT        1          1           11         CHL        1           0                  12          ESP        1          1           12         KOR        1           0                  13          GBR        1          1           13         MEX        1           0                                                             350","14          CAN        1          0           14         NZL        1           0                  15          ICE        1          0           15         TUR        1           0                  16          ISR        1          0           16         BUL        0           1                  17          JAP        1          0           17         HRV        0           1                  18          NOR        1          0           18         CYP        0           1                  19          SWZ        1          0           19         LIT        0           1                   20          USA        1          0           20         MAL        0           1                                                                21         ROM        0           1                                  Source: Author's own compilation based on (Deloitte, 2017)                 The presence of the national exit taxation rules in the some EU Member States before 2016                created three major difficulties:                    •   First, national exit taxation rules in individual EU Member States become subject to                       investigation of the Court of Justice of the European Union because they had been                       accused of violation of fundamental freedoms guaranteed by the EU law.                    •   Second,  national  exit  taxation  rules  had  varied  among  the  EU  Member  States                       making their tax regimes more unlike and different.                    •   Third, some EU Member States did not have any exit taxation rules. This situation                       might  have  triggered  tax  structuring  and  tax  competition  within  the  European                       Union.                Table 2 presents a list of judgements delivered by the Court of Justice of the European                Union from 2002 to 2016; the subject matter of them was national exit taxation legislation.                In fourteen cases out of sixteen the Court of Justice of the European Union or European                Commission stated that national exit taxation rules violated or did not comply with the EU                law  provisions.  The  affected  EU  Member  States  were  Netherlands  (4  times), France  (3                times), Deutschland (3 times), Spain (2 times), Portugal (2 times), and Denmark (1 time).                These results suggest that national exit taxation rules were not, in general, welcomed by                European Union institutions, either because they were alleged from violating the EU law,                or they were found not justifiable or proportional.                     Table 2Chyba! Nenalezen zdroj odkazů. List of Judgements of the CJ EU and                             Infringement Proceedings Regarding the National Exit Tax Rules                        Short case name                 Case No       Defen    Answers of the Court of                                                                      dant     Justice of the European                                                                               Union                                                                               Q1        Q2        Q3                  1     A Oy                            C-292/16      FIN      yes       na        na                  2     Jacob                           C-327/16      FRA      yes       na        na                  3     Picart                          C-355/16      FRA      no        na        na                        Trustees of the P Panayi                  7                                     C-646/15      GBR      yes       na        na                        Accumulation                        Verder Lab Tec GmbH \& Co                  5                                     C-657/13      DEU      no        na        na                        KG v Finanzamt Hilden                  6     DMC Beteiligungsgesellschaft   C-164/12       DEU      yes       yes       yes                  7     EC v Denmark                    C-261/11      DNK      yes       na        na                  8     EC v Spain                      C-64/11       ESP      yes       na        na                  9     EC v Netherland                 C-301/11      NLD      yes       na        na                  10    National Grid Indus BV          C-371/10      NLD      yes       yes       no                  11    EC v Portuguese Republic        C-38/10       PRT      yes       yes       no                  12    EC v Portuguese Republic        C-38/10       PRT      yes       na        na                                                             351","13    EC v Spain                      C-269/09      ESP      yes       na        na                  14    EC v Germany                    C-269/07      DEU      yes       na        na                  15    Mr. N                           C-470/04      NLD      yes       na        na                  16    Lasteyrie du Saillant           C-9/02        FRA      yes       na        na                  Legend: Q1 states for Question 1. Q2 states for Question 2. Q3 states for Question 3. The exact                  wording of all three questions is specified in the Section 2 in this paper.                                              Source: Author's own compilation                 In the contrast of the previous judgements concerning the exit taxation legislation in some                European Union Member States, the European Commission adopted Directive laying down                rules against tax avoidance practices that directly affect the functioning of the internal                market where it lays down common harmonized exit taxation provisions. They are placed                in the Article 5 of ATAD, and they go as follows:                     •   A taxpayer shall be subject to tax at an amount equal to the market value of the                       transferred assets, at the time of exit of the assets, less their value for tax purposes.                    •   The  tax  shall  be  imposed  if  a  taxpayer  transfers  assets,  or  its  tax  residence  or                       business carried in one EU MSs to another EU MSs.                    •   If  certain  conditions  are  met,  the taxpayer  shall be  given  the  right  to  defer  the                       payment of an exit tax by paying it in instalments over five years.                    •   If taxpayer defers the payment, interest may be charged in accordance with the                       legislation of the EU MSs.                    •   If there is demonstrable and actual risk of non-recovery, taxpayers may also be                       required to provide a guarantee as a condition for deferring the payment.                    •   The taxpayer shall not be given the right to defer the payment if the legislation of                       the EU MSs provides for the possibility of recovery of the tax debt through another                       taxpayer.                    •   If  a  taxpayer  defers  payment,  the  deferral  of  payment  shall  be  immediately                       discontinued  and  tax  debt  becomes  recoverable  immediately,  if  the  transferred                       assets or business carried are sold or otherwise disposed of or transferred to the                       third country.                    •   The EU MSs of destination shall accept the value established by the EU MSs of the                       taxpayer (EC, 2016).                4 Discussion                 Apart  from  formal  reasons  listed  earlier  in  this  paper,  in  my  opinion  it  is  possible  to                hypothesize four not articulated reasons why common harmonized exit taxation rules were                adopted by the European Union institutions.                First, the reason why the European Union institutions changed their mind concerning the                applicability of anti-tax avoidance measures is that the situation in the  European Union                changed, and also the European Union goals have changed. While before 2016 the highest                priority was to develop and build the common internal European Union market ensuring                fundamental freedoms and no tax barriers, after 2016, when the European Union common                market has been already successfully built, the European Union institutions prioritize other                goal: protection of the tax revenues by eliminating the aggressive tax planning practices.                Second, the reason which lays behind the ATAD measures is to unify, harmonize anti-tax                avoidance measures so that former existing national measures are unified on the European                Union level.                 Third,  not  only  the  ATAD  Directive  does  harmonize  formerly  existing  national  anti-tax                avoidance measures. In addition the ATAD Directive forces those European Union Member                States that did not have such measures before 2016, to adopt them now. This means, that                the European Union latest attendants (EU-12) must introduce exit taxation measures into                their national tax systems. The material effect of adoption of the ATAD is that by 2016                unknown  exit  taxation  provisions  must  be  adopted  by  twelve  European  Union  Member                States. To my knowledge there is no impact analysis on how incorporation of the common                                                             352","harmonized  exit taxation  rules  might  affect  economics  and  foreign investment  flows in                those  countries.  Yet,  one  may hypothesize  that incorporation  of  the  exit  taxation  rules                might  make  those  countries  less  competitive  because  they  are  likely  to  lose  a  tax                advantage originating from soft or no anti-tax avoidance legislation they offered before                2016.                Fourth, anti-tax avoidance measures, including exit taxation rules were adopted shortly                after  the  financial  crisis  2008  had  erupted  and  smoothed  over.  The  European  Union                institutions that were formerly reluctant to adopt those measures changed its mind.  In                order to protect tax revenues from decline, the EU decided to launch those measures.                 5 Conclusions                This paper provides analysis of the several former key decisions of the Court of Justice of                the European Union (formerly European Court of Justice) concerning national exit taxation                rules and compares those former decisions with the exit taxation provisions introduced by                the ATAD.                In  order  to  compare  former  Court  of  Justice  of  the  European  Union's  decisions  with                measures adopted by the ATAD Directive, I employed the legal analysis method.                The legal analysis shows, that number of the former Court's decisions concerning national                exit taxation measures were identified as violating the EU law and not being in compliance                with the law provisions. Notwithstanding prior judgements of the Court of Justice of the                European Union, and in contrast to some of them, the European Commission laid down                common harmonized exit taxation rules. This shift might suggest that judgements of Court                of Justice of the European Union and tax policy of the European Union institutions might                not provide enough consistency and legal certainty.                The following questions remains open: what might be the financial effect of the introduction                of the common harmonized exit taxation rules? How will business, multinational companies                and wealthy individuals react? Will they anticipate exit taxation rules and decide not to                settle there their future businesses, headquarters, permanent establishments and assets                in the European Union? Will they prefer to settle out of the European Union to eliminate a                risk of exit taxation payable later on?  How much potential tax revenues will the European                Union lose due to such potential outflow?                 Acknowledgments                 This  paper  is  1,0  result  of  the  research  project  supported  under  the  scheme  VEGA                1/0776/16 Taxation of Financial Sector and Harmonization Tendencies in the EU funded by                the Ministry of Education, Science, Research and Sport of the Slovak Republic.                 References                Brocke,  K.  V.,  Muller,  S.  (2013).  Exit  taxes:  The  Commission  versus  Denmark  Case                Analysed against the Background of the Fundamental Conflict in the EU: Territorial Taxes                and an Internal Market without Barriers. EC Tax Review, vol. (6).                 Cédelle , A. (2016). The EU Anti-Tax Avoidance Directive: A UK Perspective. 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Man, F., \& Tiiu, A. (2011). Contradiction Views of Exit Taxation under OECD MC and TFEU:                Are Exit Taxes Still Allowed in Europe?. Intertax, vol. 39(12), pp. 621-622.                Parleani, G. (2005, July). Relocation and Taxation: The European Court of Justice Disallows                the French Rule of Direct Taxation of Unrealised Gains. European Company and Financial                Law Review, vol. 1(3), pp. 379-389.                Wattel,  P.  (2012).  Exit  Taxation  in  the  EU/EEA  Before  and  After  National  Grid  Indus.                International Tax Notes, pp. 371-372.                                                                                                             354","Financial Crisis Influence on Tax Mix Changes                                        1                                                                                                       4                                                                              3                                                          2                   Gabriela Kukalová , Lukáš Moravec , Jakub Ječmínek , Barbora Martásková                                           1  Czech University of Life Sciences Prague                            Faculty of Economics and Management, Department of Trade and Finance                                         Kamýcká 129, 165 21 Praha, Czech Republic                                                E-mail: kukalova@pef.czu.cz                                            2  Czech University of Life Sciences Prague                            Faculty of Economics and Management, Department of Trade and Finance                                         Kamýcká 129, 165 21 Praha, Czech Republic                                                 E-mail: moravec@pef.czu.cz                                            3  Czech University of Life Sciences Prague                            Faculty of Economics and Management, Department of Trade and Finance                                         Kamýcká 129, 165 21 Praha, Czech Republic                                                E-mail: jecminek@pef.czu.cz                                            4  Czech University of Life Sciences Prague                            Faculty of Economics and Management, Department of Trade and Finance                                         Kamýcká 129, 165 21 Praha, Czech Republic                                             E-mail: matejkova.barca@gmail.com                 Abstract: The aim of the study is to evaluate the reaction of tax policy makers to financial                crisis in the short term based on the statistical analysis of consequent tax mix changes in                the EU member states. The paper describes the changes in tax structure of EU members,                particularly  focusing  on  the  period  2008  –  2009.  The  analysis  verifies  the  statistical                significance  of  the  financial  crisis  influence  on  the  changes  in  tax  structure  of  the  EU                member  states.  The  post  crisis  changes  statistical  significance  evaluation  is  realized                particularly by using the paired t-test for defined groups of taxes and levies comparing the                development nature in the period before and after the financial crisis in 2008 based on the                data issued by EUROSTAT following the methodology ESA 95 valid in the period analyzed.                The Statistica 12 software is used. The results show the statistically significant changes in                tax mix as result of the slowed down economies in 2008 and also as the result of the stress                on  different  group  of  taxes,  applied  by  the  policy  makers,  based  on  the  tax avoidance                resistance of different group of taxes.                 Keywords: tax mix, tax structure, tax policy, EU, financial crisis                JEL codes: H20, H30, H68, E62                 1 Introduction                The tax mix informs about the tax structure in the given country, respectively to what                share of taxes the given country gives priority. The structure of the tax mix of each country                corresponds to their traditions and long-term fiscal policy. Discussing optimal tax structure                is still a topical issue today. The main subject of the debate is the relationship between                direct and indirect taxes in the tax system. Some countries are of the opinion that the tax                burden imposed on the taxpayer should be largely exercised by its consumption through                indirect taxes. This would ease the taxation of income, it would have a motivating effect                on the job, and it would avoid taxpayers' general dislike to pay taxes.                An optimal tax mix is therefore the subject of a number of studies. The share of direct and                indirect taxes in the tax mix is reported in contributions of Cremer, Pestieau and Rochet                (2001). Eckerstorfer (2014) studied the optimal tax mix under asymmetric information in                a two-type model, when individuals make relative consumption comparisons. Iosifidi and                Mylonidis (2017) investigated effective tax rates that are directly comparable across OECD                countries and over time. In their contribution, they point out that what matters from a                redistributive standpoint is the tax mix rather than the tax rates in isolation from the rest.                Huang and Rios (2016) investigated the optimal combination of a linear consumption tax                                                             355","with a non-linear income tax for redistributive purposes. Kubátová and Štefanský (2016)                in their contribution deal with the typology classification of tax systems of 28 EU member                states in 2012 by applying variables of the tax quota and tax mix (shares of particular                taxes in GDP).                The  tax  structure  of  the  EU  states  also  affects  the  tax  competition,  but  above  all  the                approximation of tax rules, tax coordination and harmonization. Vintila, Onofrei and Tibulca                (2014) in their study present the evolution of taxation convergence tendencies within the                European Union, using Sigma-convergence and cluster analysis. The aim of another study                was  to  quantify the impact  of  single  taxes  and  tax  competition  on  the  convergence  of                taxation  in  the  EU  (Bušovská  and  Bušovský,  2016).  Delgado  and  Presno  (2017)                investigated tax convergence in 15 EU member states (data from 1975 to 2011). Bušovská                (2014) in her contribution focused on the issue whether the tax systems in the EU member                states  are  converging  in  the  context  of  tax  burden,  tax  mixes  and  implicit  tax  rates.                Harmonization also penetrates other areas such as financial accounting. Hinke (2011) in                his study analysed the harmonisation trends of the European Union, and used this analysis                for a prediction of the development of financial accounting in the future.                 Many authors in their studies assess the relationship between economic growth and the                structure of taxes. Changes in the OECD tax mix in response to economic growth between                1980-1999 were examined by Tosun and Abizadeh (2005). According to Macek (2015),                labor taxation (personal income taxes and social security contributions) is the most harmful                for economic growth.                The  tax  mix  also  affects  the  tax  burden  in  given  country.  As  mentioned  in  European                Commission (2017) the ration of tax collection (including social contribution) to GDP in EU-                28 remain higher related to other well-developed countries. However, economists mostly                agree that increasing tax burden discourages economy and implicates higher deadweight                loss  (Kubátová,  2009).  Taxpayers'  behavior  is  also  related  to  the  tax  burden.  Hinke,                Zborková and Černá (2014), in their contribution, among others, notes the impact on the                behavior of economic entities - legal persons. The subject of the study are also the possible                social impacts of changes in the distribution of direct and indirect taxes (Smart, 2002).                The aim of our study is to evaluate the reaction of tax policy makers to financial crisis in                the  short term  based  on  the  statistical  analysis  of  tax  mix  changes in  the EU  member                states.  The paper focuses mainly on the changes in 2008-2009.                 2 Methodology and Data                Our research focusing on the impact of the financial crisis on the tax mix is a part of a                wider study of the development of EU tax mixes. The states of the then EU-27 were divided                into  four  groups  according  to  geographical  location  -  Nordic,  Southern,  Western  and                Eastern (Table 1). The former post-communist states, including the Baltic States, have                been ranked among the eastern states, which have undergone a gradual transition from a                centrally planned economy to a market one in the past two decades.                   Table 1 Classification of EU-27 States into Groups According to Geographic Location                  ⎯      Group                                     ⎯      States                 Northern States (N)    Denmark (DK), Finland (FI), Sweden (SE)                 Southern States (S)    Italy (IT), Cyprus (CY), Greece (EL), Malta (MT), Portugal (PT),                                        Spain (ES)                 Western States (W)     Austria (AT), Belgium (BE), France (FR), Germany (DE),                                        Ireland (IE), Luxembourg (LU), Netherlands (NL),                                        United Kingdom (UK)                 Eastern States (E)     Bulgaria (BG), Czech Republic (CZ), Estonia (EE), Hungary (HU),                                        Latvia (LT), Lithuania (LV), Poland (PL), Romania (RO),                                        Slovenia (SI), Slovakia (SK)                                                     Source: own work                                                              356","The development of the tax mix of the EU countries has been monitored since 2004, when                the European Union expanded at the most. Both Bulgaria and Romania were included in                the analysis of the development of the EU average mix of taxes and individual groups of                countries. (These countries were candidates for accession in 2004 and are members of the                EU since 2007). The data used in our research follow the methodology of the European                System  of  Accounts  (ESA  95),  which  was  in  force  in  the  monitored  period  (European                Commission, 2014).                The average share of selected taxes and social insurance revenues of the EU-27 tax mix                (including Bulgaria and Romania in 2004-2006) and the tax mix of the established groups                of countries were monitored between 2004 and 2012 (4 years before the transfer of the                crisis to Europe (2008) and another four years after the transfer to Europe). The evolution                of shares in the tax mix is shown in charts 1, 2 and 3. The average EU-27 tax mix, the                average of tax mixes of individual groups of countries and the real values of the tax mix                of all member states were considered in these analyses. The assessment of the change in                tax  mixes  because  of  the  financial  crisis  was  carried  out  in  the  then  EU-27  countries                (without Croatia, which became a member of the EU in 2013).                 Based  on  the  analysis  of  the  development  of  the  share  of  individual  taxes  and  social                insurance revenues on the tax structure of the EU-27 states, significant changes in the tax                mix between 2008 and 2009 were found (Table 2).                An analysis was subsequently carried out to determine whether the financial crisis had a                statistically significant effect on the structure of the tax mix of the 27 EU Member States                in 2008-2009. The analysis was carried out by testing selected taxes (personal income tax,                corporate  income  tax,  Value  Added  Tax,  consumption  taxes)  and  social  security                contributions.  The  data  normality  was  verified  by  the  Kolmogorov-Smirnov  test  at  the                significance level of 0.05.                The  statistical  test  using  Student  paired  t-  test  was  used  to  assess  the  impact.  The                measurement  was  carried  out  on  a  selected  sample  of  the  selective  aggregation;  the                measurements were  compared  before the  change in  the  share  of the  tax  or the  social                revenues (2008) and after the change (2009). Testing against each other is always subject                to a zero and alternative hypothesis. The following zero hypotheses were formulated:                    • H01: The value of the average share of personal income tax in total tax revenues in                       2008-2009 did not change, the financial crisis did not have a statistically significant                       effect on possible changes in the structure of the tax mix.                    • H02: The value of the average share of corporate income tax in total tax revenues in                       2008-2009 did not change, the financial crisis did not have a statistically significant                       effect on possible changes in the structure of the tax mix.                    •  H03:  The  value  of  the  average  share  of  social  security  contribution  in  total  tax                       revenues remained unchanged in the period 2008-2009, the financial crisis did not                       have a statistically significant effect on possible changes in the structure of the tax                       mix.                    • H04: The value of the average share of value added tax in total tax revenues in 2008-                       2009 did not change, the financial crisis did not have a statistically significant effect                       on possible changes in the structure of the tax mix.                    •  H05:  The  value  of  the  average  share  of  consumption taxes in total tax  revenues                       remained unchanged during the period 2008-2009, the financial crisis did not have                       a statistically significant effect on possible changes in the structure of the tax mix.                The  result  of  the  test  was  decided  based  on  the  comparison  of  the  p-value  with  the                significance  level  α=0.05.  Analytical  software  Statistica  12  was  used  for  statistical                calculations.                 3 Results and Discussion                 The assessment of the evolution of average shares of selected taxes and social security                contributions in the tax mix of the EU-27 countries and the established groups of countries                took place in the period 2004-2012.                                                            357","Average shares of selected taxes and social security contributions in the tax mix                Based  on  the  analysis  of  the  evolution  of  average  shares  of  selected  taxes  and  social                security contributions in the EU-27 tax mix and the established groups of countries, it was                found that the biggest changes occurred between 2008-2009.                Figure 1 Shares of Selected Direct Taxes in the EU-27 Tax Mix and Groups of States (%)                                 (left: Personal Income Tax, right: Corporate Income Tax)                             PERSONAL  INCOME TAX                         CORPORATE  INCOME TAX                 45                                           14                 40                                                              12                 35                                                              10                 30                 25                                            8                 20                                                               6                 15                 10                                            4                     2004 2005 2006 2007 2008 2009 2010 2011 2012  2004 2005 2006 2007 2008 2009 2010 2011 2012                                                                          EU-27    N     S     W     E                          EU-27     N      S     W      E                                       Source: European Commission (2014), own work                 The average share of personal income tax in the tax mix is long-term the highest in the                case of a group of Nordic countries (Figure 1, left: Personal Income Tax). The high share                is mainly due to Denmark, which finances retirement benefits from this tax, and causes                some distortion for the entire Nordic group. Changes in personal income tax have been                around 0.5% for the long-term, the only exception was a 1.3% decrease for the eastern                group after 2008. Since then, the share of the Nordic and Eastern countries has declined,                with a slight increase in the western and southern countries. Figure 1 (right: Corporate                Income  Tax)  shows  the  variability  of  the  share  of  corporate  income  tax,  which  is  an                important tool for tax competition among states. Since the outbreak of the financial crisis,                it has been possible to observe a significant drop in shares for all groups of countries after                2007. Between 2007 and 2010, a number of countries experienced significant changes in                tax rates, exemptions, or additional tax. A significant drop of the share of corporate income                tax in the tax mix occurred in the Eastern Group of States. Between 2008 and 2019, the                share decreased by 1.6%.                  Figure 2 Share of Social Security Contributions in the EU-27 Tax Mix and Groups (%)                                                   SOCIAL  SECURITY  CONTRIBUTIONS                                        40                                        35                                         30                                        25                                        20                                        15                                        10                                            2004 2005 2006 2007 2008 2009 2010 2011 2012                                                  EU-27       N       S       W        E                                ⎯                                       Source: European Commission (2014), own work                 The share of social security contributions is the lowest in the Nordic countries (Figure 3).                                                              358","South and West countries show almost identical trends and jointly copy the development                of the average curve of all EU-27 member states. The Eastern Group of States is well above                the average of the other groups. Even though the average shares of the groups varied very                slightly,  there  were  significant  changes  in  individual  countries  between  2008-2012.                Example of major changes are Lithuania (+9%), Cyprus (+5.6%) and Poland (+4.6%).                The largest growth rate was recorded for the Eastern group of countries by 5.6%.                   Figure 3 Share of Selected Indirect Taxes in the EU-27 Tax Mix and Groups of States                                  (%) (left: Value Added Tax; right: Consumption Taxes)                                                           ⎯                                     VAT                       15         CONSUMPTION  TAXES                 30                                       ⎯                                                               13                                                          ⎯                 25                                            11                                                          ⎯                                                                9                 20                                                          ⎯     7                                                           ⎯                 15                                             5                     2004 2005 2006 2007 2008 2009 2010 2011 2012  2004 2005 2006 2007 2008 2009 2010 2011 2012                                                          ⎯                       EU-27       N       S      W        E          EU-27      N       S      W        E                                       Source: European Commission (2014), own work                The average share of VAT in the EU-27 tax mix and individual groups changed most in                2008-2010  (Figure  3,  left:  Value  Added  Tax).  Between  2008  and  2009  there  was  a                decrease in the share (except for the Nordic countries); on the contrary, between 2009                and 2010 the share of VAT in the tax mix increased, especially for the group of southern                and eastern countries. The average share of consumption taxes in the tax mix increased                between 2008-2009 for all groups of countries (Figure 3, right: Consumption Taxes). The                largest increase in the share is evident in eastern countries (1.4%), while in the Nordic                group  increased  by  only  0.3%.  The  average  share  of  consumption  taxes  in  the  EU-27                increased by 0.8%.                Changes in average share of taxes and social security contributions across EU-27 countries                are shown in Table 2. Based on these data are tested statistical hypotheses.                       Table 2 Share of Selected Taxes in the EU-27 tax mix (%) in 2008 and 2009                                  Personal        Corporate     Soc. security                Consumption                 Group  State                                                      VAT                                 income tax       income tax    contributions                    taxes                                2008    2009     2008    2009  2008  2009  2008  2009  2008  2009                 N      DK        52.6    55.2     6.9     4.9    2.0     2.0  21.0  21.3      6.6     6.8                        FI        30.9    31.2     8.1     4.7  28.0     29.9  19.5  20.2      7.7     8.0                        SE        35.8    35.1     6.3     6.4  18.2     17.4  20.0  20.7      5.8     6.2                 S      IT        27.4    27.2     7.2     5.7  31.4     31.9  13.9  13.3      4.5     4.9                        CY        13.0    11.2    18.4    18.4  20.1     24.6  27.4  26.0      8.6     9.1                        EL        15.0    16.3     7.8     8.1  34.7     34.2  22.7  22.1      7.2     8.4                        MT        17.8    19.7    18.4    18.3  17.9     17.7  23.3  22.9      9.0     8.8                        PT        17.0    18.4    11.1     9.2  26.8     29.1  25.6  22.9      8.3     8.8                        ES        22.3    23.0     8.6     7.5  37.4     40.5  15.5  13.5      6.6     7.2                 W      AT        24.4    23.4     6.2     4.4  33.6     35.0  18.2  18.9      5.8     5.8                        BE        28.4    28.2     7.6     5.6  31.5     33.4  15.7  16.0      4.7     4.9                        FR        18.1    18.0     6.3     3.0  37.7     39.9  16.5  16.4      4.9     5.1                        DE        23.2    23.1     6.9     5.0  39.1     40.3  18.3  19.0      6.6     6.8                        IE        27.9    27.8     9.8     8.7  18.1     20.5  24.6  22.7      8.3     9.6                        LU        21.7    20.6    14.3    14.7  28.3     29.8  16.8  17.3      9.8     9.7                                                             359","NL        18.4    22.4     8.8     5.6  37.0     36.2  18.5  18.3      6.0     6.0                        UK        28.5    30.0     9.6     8.0  18.1     19.4  16.9  16.4      8.6    10.1                 E      BG        28.4    28.2     9.8     8.8  24.1     26.6  33.8  31.1  18.4       18.8                        CZ        10.7    10.8    12.2    10.5  45.1     44.5  19.7  20.6      9.5    10.9                        EE        19.5    16.0     5.1     5.2  36.6     36.9  24.9  24.8  10.4       14.1                        HU        19.0    18.5     6.5     5.4  34.0     32.9  19.3  21.3      8.3     8.9                        LT        21.8    20.4    10.9     5.9  28.3     32.0  23.0  22.5  11.0       13.9                        LV        21.3     3.6     8.9     6.0  31.3     42.0  26.0  24.2      9.9    11.4                        PL        15.6    14.6     7.9     7.2  33.0     35.7  23.4  23.4  13.0       11.9                        RO        12.1    13.1    10.7     9.9  33.3     34.9  28.2  24.7      9.6    11.8                        SI        15.7    15.7     6.7     4.9  37.7     40.1  22.8  21.6      9.0    11.1                        SK         9.4     8.5    10.7     8.8  40.7     43.6  23.8  23.4      9.3     9.8                 EU-27            21.4    21.2     9.3     7.8  29.8     31.5  21.5  20.9      8.4     9.2                                       Source: European Commission (2014), own work                Testing of statistical hypotheses                 The Paired Student´s T – Test was applied to verify the hypothesis as the T – Test confirms                the  (non)existence  of  statistically  significant  differences  between  the  averages  of  two                selected groups of items.                            Table 3 Paired t-test: Personal Income Tax, Corporate Income Tax                                               Personal Income Tax               Corporate Income Tax                                                2008           2009               2008           2009                      Average share        21.35256337  21.18591463    9.326267333  7.810643407                 Number of observations                  27                                27                             t                     0.408179472                       6.176503512                        t crit. (26)                   1.706                             1.706                            p                      0.686483998                         0.000002                            α                           0.05                              0.05                                       Source: European Commission (2014), own work                 According  to  the  performed  calculations,  the  zero  hypothesis  H01  (Table  3)  can’t  be                rejected.  Thus, it is valid  the  assumption  that  as  regards  the  personal income  tax the                financial crisis did not have a statistically significant effect on a moderate reduction of the                share of tax in the total tax mix for the 27 EU member states. Changes that have occurred                in individual Member States may in part be attributed to the financial crisis, but its overall                impact has not been statistically confirmed.                The zero hypothesis H02 can be rejected. And we accept an alternative hypothesis. It was                confirmed the assumption that the financial crisis had a statistically significant impact on                the share in the tax mix of 27 EU member states. Regarding corporate income tax, we can                unequivocally confirm the significant impact of the crisis on the reduction of the share. At                the mean value of each period, we can see a demonstrable decrease of the share in total                tax revenue.                                    Table 4 Paired t-test: Social Security Contributions                                                             Social Security Contributions                                                                 2008           2009                                       Average share        29.78052329    31.51421167                                  Number of observations                  27                                             t                         - 3.892                                         t crit. (26)                   1.706                                             p                          0.0010                                             α                           0.05                                       Source: European Commission (2014), own work                                                              360","On  the  basis  of  the  above  data  (Table  4),  the  zero  hypothesis  H03  of  statistical                insignificance  is  rejected.  The  financial  crisis  had  a  statistically  significant  effect  on                increasing the share of social insurance in the overall tax mix. In the monitored period, the                mean value increased significantly.                                    Table 5 Paired t-test: VAT and Consumption Taxes                                                         VAT                      Consumption Taxes                                               2008              2009            2008             2009                     Average share         21.45299111      20.91473407      8.426352185     9.215474815                Number of observations                   27                                27                            t                       2.161352387                      -3.985292671                       t crit. (26)                     1.706                            1.706                           p                        0.040057407                      0.000485778                           α                            0.05                              0.05                                       Source: European Commission (2014), own work                 Based  on  the  calculations  performed,  the  zero  hypothesis  H04  (Table  5)  is  rejected.                According  to  the  alternative  hypothesis,  the  financial  crisis  had  an  overall  statistically                significant  effect  on  the  VAT  share in  the  EU-27  tax mix.  The largest  differences  were                reached in the group of eastern countries, the biggest rise of the shares recorded Hungary                and  the  Czech  Republic,  while  Romania  and  Bulgaria  experienced  the  most  significant                decline.                It also follows from the calculations that the zero hypothesis H05 can be rejected (Table                5). An alternative hypothesis was adopted when the financial crisis had a demonstrably                statistically significant impact on the share of consumption taxes in the tax mix of the 27                EU member states. Year-on-year comparison has seen a significant change in mean values.                The impact of the financial crisis on the change of the share of individual taxes in the tax                mix has not yet been evaluated, according to available sources. Changes in the structure                of the tax mix are often assessed in terms of setting rates (Iosifidi and Mylonidis, 2017) or                setting up an optimal tax mix (e.g. Cremer, Pestieau and Rochet, 2001; Eckerstorfer, 2014;                Huang  and  Rios,  2016).  The  impact  of  economic  growth  (eg  Macek,  2015;  Tosun  and                Abizadeh,  2005)  was  also  studied.  Široký  and  Maková  (2014)  verified  the  relationship                between the growth rate of the gross domestic product and the public deficits on one hand                and the change of VAT rates on the other hand in 27 European Union Member States within                the period of 2008-2013. The influence of tax systems harmonization and convergence                (Bušovská and Bušovský, 2016; Delgado and Presno, 2017 and others) was also evaluated.                The influence of tax mix changes and tax rates modifications on taxpayers and consumers                behavior was confirmed by Hinke, Zborková and Černá (2014) and Krzikallová (2016). The                conclusions  of  the  above-mentioned  studies  are  in  line  with  the  presented  results                confirming the crisis in 2008 changed the tax policies globally.                 4 Conclusions                An analysis of the impact of the financial crisis on the structure of the EU-27 tax mix has                produced interesting results. Changes in average tax shares or social security contributions                are apparent between 2008-2009. However, these changes cannot be attributed solely to                the financial crisis. The share of a tax in the tax mix is also affected by other factors, such                as a change of the rate.                In  the  case  of the  personal income  tax,  the  test  showed  that  the  share  changes  were                statistically insignificant. This was due to changes that only occurred in a small number of                countries and in the upshot, became equal among themselves. Since 2009, the share of                this tax has declined, potentially due to the fact that the member states adopted a number                of measures to support national economies. The measures consisted in narrowing the bases                of  personal  revenue  tax  and  extending  tax  reliefs  in  order  to  better  reflect  the  social                position  of  taxpayers.  Statistically  significant  was  the  impact  of  the  financial  crisis  on                corporation tax. Between 2008-2012, the share of this tax in the average tax mix of EU-                                                             361","27 countries decreased by approximately 21%. Changes in social security contributions                were  evaluated  as  statistically  significant.  The  share  of  social  insurance,  despite  the                financial crisis, was the only one of all tax groups and contributions continued to grow                slightly. Testing of value added tax and consumption tax showed changes in both sub-                groups of indirect taxes, the changes resulting from the financial crisis were statistically                significant.  For  both  taxes,  a  number  of  measures  was  adopted,  including  extensive                adjustments in tax legislation, particularly in the area of increase of tax rates. Value added                tax,  particularly  in  southern  countries  severely  affected  by  financial  crisis,  has  seen                frequent increases in tax rates. The eastern countries then manifested themselves in the                introduction of a new, further reduced tax rate.                  Acknowledgments                This paper is a partial result of the research project No. 20171032 - Czech tax gap as a                consequence of profit shifting quantification and its factors identification, supported by the                Internal Grant Agency (IGA) of Faculty of Economics and Management, CULS Prague.                 References                Bušovská, M. (2014). Convergence of Tax Burden, Tax Revenues and Implicit Tax Rates in                the European Union Member States. Ekonomický časopis, vol. 62(4), pp. 363-376.                Cremer, H., Pestieau, P., Rochet, J. C. (2001). Direct versus indirect taxation: The design                of the tax structure revisited. International Economic Review, vol. 42(3), pp. 781-799.                Delgado,  F.  J.,  Presno,  M.  J.  (2017).  Tax  Evolution  in  the  EU:  A  Convergence  Club                Approach. Panoeconomicus, vol. 64(5), pp. 623-643.                Eckerstorfer, P. (2014). 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Transylvanian Review of Administrative Sciences, issue 41E, pp. 213-223.                                                                                                                          363","Environmental Risk Insurability Issues – the EU Perspective                                                   Malwina Lemkowska                                         Poznań University of Economics and Business                                        Faculty of Economics, Department of Insurance                                         Al. Niepodległości 10, 61-875 Poznań, Poland                                          E-mail: malwina.lemkowska@ue.poznan.pl                 Abstract:  The  aim  of  this  paper  is  to  evaluate  insurability  of  environmental  risk.                Juxtaposition of the attributes of both environmental  risk and insurance market against                risk insurability factors will be the evaluation criterion. Two groups of insurability criteria                have been identified: endogenous and exogenous ones. Exogenous criteria of insurability                refer to the attributes of the risk itself. Exogenous ones are connected with the features of                the insurance market and its participants, especially the ability of the insurance companies                to deal with the specific risk. The level of environmental risk insurability is relatively low.                It is because of the problem with  fulfilment of both endogenous and exogenous criteria.                The occurrence, which results in environmental damage is very often not sudden, random                and extraordinary. The probability of accident and probable maximum loss are difficult to                estimate.  Additionally,  environmental  risk  results  often  in  catastrophic  loss.  Although                insurance  companies  provide  relatively  high  capacity,  they  have  problem  to  create                sufficiently large insurance portfolio.                Keywords: environmental insurance, environmental risk, insurability of risk                JEL codes: G22, Q50, K21                 1 Introduction                 Evaluation of risk insurability is the basis of insurers’ operations. The purpose of this article                is  to  assess  the  insurability  of  environmental  risk.  Insurability  is  an  changeable                phenomenon, firstly, because of continuous evolution of risks, and secondly, technological                progress  concerning  insurance  offer.  Along  with  an  increasing  number  of  threats  in                economic  reality  risk  is  becoming  an  ever  more  complex  notion.  Ultimately,  it  causes                entrepreneurs to acknowledge more readily the types of risks which traditionally did not                use  to  be  subject  to  insurance  (Swiss  Re,  2017).  Development  of  data  processing                technologies  as  well  as the  evolution  of  the  methods  of insurance  products  creation is                continually increasing insurers competencies in covering formerly uninsurable risks.                 2 Methodology and data                Research  findings  presented  in  this  paper  are  the  outcome  of  review  of  the  subject                literature on risk insurability criteria, on the one hand. On the other, an overview of legal                acts  on  environmental  law  allows  to  identify  the  attributes  of  environmental  risk.  The                criterion  for  insurability  evaluation  will  involve  a  juxtaposition  of  the  attributes  of                environmental risk and insurance market against risk insurability factors.                3 Results and discussion                 Environmental risk                The environmental risk is understood narrowly in the article, i.e. environmental liability                risk.  The  above  results  from  economic  practice:  environmental  insurance  is  mainly                concerned with legal liability insurance. In the EU the scope of this liability is determined                by Community legal acts, international conventions, (nuclear and oil-related damage) and                member countries’ legal acts.                 Features of insurable risk                Thanks to the review of the subject literature one can distinguish more than ten risk factors                of insurability. Scientists propose a variety of criteria to categorise them. Fedor (2004)                names  11  features  of  risk insurability and  divides  them into  two  groups:  absolute  and                                                            364","relative ones. The first one contains the conditions which absolutely have to be met to                make the risk insurable (i.e. it must be a future and random event, independent of the                insured’s will, assessable and capable of being classified within a similarly characterised                set). The other one consists of features which determine insurability in a non-definitive                way  (appropriate  frequency  of  occurrences,  price,  susceptibility  of  an  occurrence  to                phenomena  related  to  information  asymmetry  i.e.  moral  hazard,  adverse  selection).                Berliner  (1982),  in  turn,  identifies  actuarial  criteria  of  insurability,  which  require                independence  of  risks  and  reliable  estimation  of  loss  probabilities  (randomness  of  loss                occurrence), manageable maximum possible losses per event in terms of insurer solvency                (maximum possible loss), moderate average loss amounts per event (average loss per                event),  a  sufficiently  high  number  of  loss  events  per  annum  (loss  exposure)  and  no                excessive information asymmetry problems (i.e. moral hazard, adverse selection). Berliner                also  distinguishes insurability  factors  determined  by  markets. He lists  there: insurance                premium  level  (adequate  to  the  scope  of  risk,  affordable),  limits  of  insurance  cover                (acceptable), insurance sector capacity (sufficient). Furthermore, social conditions of risk                insurability identified by Berliner refer to public policy and the legal system  - insurance                cover should not contradict them (Biener, Eling, Wirfs, 2015). Rejda (2014) presented the                concept of ideally insurable risk. Hence, he turns one’s attention to gradability of risk by                giving the example of a blaze as an ideally insurable risk and unemployment as an instance                of uninsurable risk. Vaughan and Vaughan (2008) mention “ideal” elements of insurable                risk. These are: sufficiently large number of homogenous exposure units to make the losses                reasonably predictable, definite and measurable loss produced by the risk, fortuitous or                accidental loss and non-catastrophic loss. Outreville (1997), in turn, points out that ideally                insurable risk is pure, static and particular. Even if, however, ideal insurability criteria are                not met, a given risk may not remain uncovered (Vaughan and Vaughan, 2008). According                to Faure (2001), this can be explained by the concept of ‘insurer ambiguity” (Kunreuther,                Hogarth, Meszaros, 1993). Its authors assert that an insurer may offer insurance cover                against the types of risk which, due to their lack of compliance with measurability and                predictability  criteria,  are  considered  as  uninsurable.  In  such  case  the  insurer  charges                additional  premium.  Therefore,  Faure  (2001)  does  not  refer  to  insurability  criteria  and                points to factors which determine it in a positive or negative way.                 All the above factors could be categorized as endogenous toward risk, i.e. features of                the risk itself, or as exogenous (the attributes of a particular insurance market). A future                occurrence which is sudden, random, independent of one’s will, measureable, capable of                being classified within a similarly characterised set, extraordinary, resulting in reasonable                damage  and  statistically  independent  can  be  said  to  meet  the  endogenous  criteria  of                insurability.  Exogenous  conditions  of  insurability  are  met  if  insurers  manage  to  control                moral hazard and adverse selection, have sufficient insurance capacity, offer acceptable                limits and price for the cover which are in line with their tendency to accept risk, and act                in favourable circumstances relating to public policy and legal system.                Gradability of insurability categories                The concept of insurability may be interpreted in two ways. Firstly, a risk is insurable if                insurers  supply  cover;  secondly,  insurability  is  contingent  on  its  doctrinal  criteria                (endogenous attributes of risk), (Knight, 1921). In both cases insurability is a gradable                category. Assuming the first approach, B. Berliner uses abstract terms of objective and                subjective insurability (Berliner, 1985). The first one refers to declarations made by all                potential insurers operating in the area of the given risk category that they will cover this                risk  (objectively insurable  risk).  Subjective insurability, in  turn, is linked  with  only  one                individual insurer’s readiness to cover a risk. This implies that between the area of objective                insurability and objective uninsurability there is a space where only a few insurers offer                protection. It determines the degree of this risk’s insurability.                In  the  case  of  insurability  viewed  from  the  other  perspective  (determined  by  the  risk                attributes) this category remains gradable, just as the assessment of particular features of                this risk can be gradable as well (e.g. more or less easily measureable). Changeability of                                                               365","insurance supply as well as the evolution of the assessment of particular risk features in                time determine the dynamics of the degree of insurability.                 Environmental risk insurability – endogenous approach                Environmental  risk  is  insurable  within  the  area  of  subjective  insurability.  Only  a  few                property insurers offer environmental products. One can put hypothesis that environmental                risk does not meet all the criteria for endogenous insurability.                If it comes to the first premise for risk insurability (insurance accident has to realise in                future), it raises doubts in the context of the natural environment. The “future” attribute                may  refer  to  different  factors  of  risk:  firstly,  it  may  imply  the  event  resulting  in                environmental damage (peril); secondly, to the damage itself; thirdly, its disclosure and                finally  the  claim  on  the  damage  to  be  remedied.  The  timespan  between  the  damage                occurrence and the claim being made may be significantly large. This may be caused by                                                       5                                                                                6                the long stage of the damage latency , lack of peril peculiarity , gradual accumulation of                                                                    7                the damage (serial damage, accumulative damage) . What is more, the long timespan may                cause more likely the damage resulting  from a reaction of primary pollutant with other                substances, either natural or emitted by other entities. Additionally, the future event should                be  sudden  (Guevara,  Deveau,  2012),  which  cannot  be  assigned  to  the  attributes  of                                      8                accumulative damage .                In order to overcome insurability barriers companies may prepare “claims-made” products                (Swiss Re, 2009). Making a claim – as an insurance accident – meets the condition of being                a future and sudden event. Nonetheless, evaluation of the insured’s past operations within                underwriting process becomes a challenge to insurers.                Occurrence, which can result in damage should be random (many entities may face it and                it is impossible to point at the particular entity affected by the risk) and extraordinary.                Randomness  refers  not  only  to  the  event  occurrence  itself,  but  also  its  timing  and                consequences. The nature of environmental damage which results from business activity                raises doubts about both randomness regarding the event itself and extraordinariness of                risk,  especially in the  case  of  business  sectors  which  are  particularly  dangerous  to  the                environment,  often  requiring  special  permits  to  run  business  operations.  Although                European regulations (article 8 section 4 of ELD  make it possible to abolish environmental                                                                )                damage  liability  (so  called  permit  defence),  the  ultimate  decisions  about  liability                exemptions are made by individual member states. Seven states have decided to levy the                burden of liability for environmental damage on entities operating within the permit’s scope                (Belgium, Bulgaria, Germany, Hungary, Poland, Romania and France), (Bio Intelligence                Service  in  association  with  Stevens\&Bolton  LLP ,  2009;  Faure,  Grimeaud,  2000)9 .  As  a  result,                randomness of the event becomes reduced, at the most, to the time of damage occurrence                or to its implications, while extraordinariness often becomes eliminated (Bio Intelligence                Service in association with Stevens\&Bolton LLP   , 2009 ). The problem arises especially with                respect  to  insurance  against  dispersed  pollution  effects,  where  pollution  amasses  from                various sources or reacts with other (external) chemical substances and only then does it                generate  environmental  damage.  The  European  regulations  assume  that  as  long  as                pollution  can  be  assigned  to  a  particular  company  or  a  confined  conglomerate  of                businesses, administrative liability arises. Moreover, polluters have to consider a constantly                 5  A direct damage (e.g. water contamination) is discovered often through indirect damage                (e.g. a drop in a particular fish species population in the contaminated area).                6  Environmental damage is generated not only by so called direct pollution occurrences                (explosion, container leakage), but also non-pollution occurrences, e.g. flood, blaze.                7  E.g. river contamination caused by filtering of rainwater through artificially fertilised soil.                8  Inability to meet the criterion of “suddenness” was the reason for denial of compensation                payments for environmental liability in the past in the USA (Naylor, Dybdahl, 2012).                9  The authors emphasized, that businesses acting in accordance with regulatory standards                are not exempt from environmental damage liability. Hence, a socially effective protection                of natural resources may require an implementation of higher standards than the legally                established ones.                                                            366","growing likelihood of claims on the grounds of civil law. A lot of attention has been paid to                the court sentence concerning Kivalina, an Eskimo village in Alaska which filed a claim                against numerous entities’ insurers who had based their operations on coal power. Global                warming, resulting from coal used as fuel, led to melting of the ice which protected the                village from storms. Virginia state court, having analysed the insurance contracts signed                by the polluters, pronounced lack of insurance cover for the occurrence described. This                does not mean, however, that there are no legal grounds for a non-insurance claim against                the authors of the damage and there will be no demand for cover in the future.                Pollution  which  naturally  results  from  a  particular  business  activity  and  is  socially                acceptable (in particular, as proven by a legal permit), which is a compromise between                                                                                                         10                economic and environmental interests within the concept of sustainable development ,                cannot meet another insurability criterion, i.e. independence of the insured’s will.                Two reversed cycles of insurers’ operations (i.e. distribution prior to production and price                setting prior to defining costs) touch upon the issue of risk measurability criterion. The                basis for premium calculation should be a data base containing records of past random                events (probabilistic risk, qualified as insurable). Estimative risk, in turn is difficult to insure                (Knight, 1921). Environmental risk is in a great part an estimative one. There are at least                three reasons for this. Firstly, the level of environmental knowledge still does not make it                possible  to  assess  the  impact  of  polluting  substances  on  the  natural  environment  and                human life and health. Secondly, although environmental damage has long been included                in the regime of the polluter’s liability, in the past it was hardly ever followed through                (especially in the area of the environment as a common good). The third reason is that the                polluter’s liability is continually broadened and – according to ELD - polluters are not only                held  responsible  for  essential  remedial  actions  but  also  for  compensational  and                complementary actions. The problem of measurability appears in particular in connection                with environmental liability for  actions,  as  for  which,  according  to  the  present  state  of                technical and scientific knowledge, adverse effect on the environment has been impossible                                                                                         )                to predict (so called state-of-the-art defence), (article 8 section 4 of ELD . In this area, the                risk  cannot  be  categorised  either  as  probabilistic  or  estimative  risk.  It  is  totally                unmeasurable and consequently, endogenously uninsurable. The ELD allows exemption of                the above from the polluters’ liability, nonetheless, many countries, including Poland, have                given up this right.                Environmental risk may materialise with reference to a large number of entities. Although                the ELD pronounces administrative liability only with regard to professional activities, and                additionally, in the case of damage done to waters and land surface, limits the subjective                scope to the activities enumerated in the appendix, member states are eligible to expand                and do expand the regulation. From the point of view of insurance, though, what matters                is the size of the group which presents demand for insurance cover. Unfortunately, the                European Commission’s study shows a very low level of demand for insurance (European                Commission, 2016). Main reasons for this situation are: lack of public reports informing                about  environmental  damage  materialisation  and  lack  of  adequate  execution  of                environmental liability.                 Environmental risk may generate catastrophic damage. First of all, it is caused by large                mobility  of  environmental  damage.  Secondly,  the  scope  of  environmental  liability  is                systematically  broadened  through  regulations.  A  special  example  of  the  latter  is  the                analysis  conducted  by  the  French  government.  The  study  concerned  two  instances  of                environmental damage which occurred in 1996 and 1997. The costs of remedying these                two  cases  according  to  the  legal  status  of  the  time  amounted  to  $  67  K  and  16  K                respectively. Analyses showed that if such damage were to happen at present – under the                ELD  –  the  costs  of  remedying  would  skyrocket  to  $  636  K  and  $  6.4  m  (Institute  of                Sustainable Development,  2010;  ACE Group,  2011).  History  presents  a  whole  array  of                occurrences generating exorbitant costs of remedying them. It is enough to mention just                  10  The most problematic from the point of view of insurance is chronic pollution, besides                diffuse pollution, long-distance pollution and historical pollution (Swiss Re, 2003).                                                            367","a few: the Boliden's Doñana (Spain) accident in 1999 (approx. € 240 m (El Pais, 2010)),                Kolontar, Hungary (approx. € 65 m (ACE Group, 2011; European Commission, 2016));                Moordijk,  The  Netherlands  (€  65,4  m  (European  Commission,  2016)).  Despite  the                continuous growth of insurance capacity (European Commission, 2016) the sector itself is                clearly unwilling to cover catastrophic damage (Insurance Europe, 2013). The analyses                show that the average cost of remedying amounts to € 42 K. The average is calculated                after the extreme events have been excluded – the few occurrences where the costs of                remedying  exceeded  €1m.  Member  states  have  recorded  five  extreme  events:  two  in                Hungary and the Netherlands where the costs of remediation amounted to € 65 m, and                three others – in Spain, Greece and Sweden – where the damage cost ranged between €                1m – € 10 m (European Commission, 2016).                Environmental risk insurability – exogenous approach                 Not all local environmental insurance markets are working efficiently. The reasons for this                situation  should  be  sought  within the exogenous indicators  of insurability i.e.  the  ones                which describe the attributes of the supply and demand sides of the market as well as its                legal and political surroundings (the ability to control moral hazard and adverse selection;                insurance capacity, determined primarily by insurers’ capital resources, and also by legal                requirements concerning the above).                Occurrence of moral hazard is a common problem of insurance companies, especially in                liability insurance in the  actio  directa  system.  Controlling moral hazard  is  possible  and                efficient  as  long  as  it  is  allowed  by  the  know-how  and  organisational  qualities  of  the                insurers, market-related expectations of insurers toward potential policyholders, market-                                                                                           11                verified environmental risk management standards and legal regulations . An essential                way of controlling adverse selection, in turn, is tailoring the premium level to the individual                risk profile of the protection-seeking entity. It is difficult because of a limited demand for                environmental insurance, caused by poor insurance and environmental awareness.                It  is  worth  emphasizing  that  it  is  often  not  the  insurers  who  hinder  the  market                development. The research conducted in 2014 by Insurance Europe points at a relatively                high  insurance  capacity  of  the  market.  Liability  insurance  cover  against  environmental                damage (in the scope defined by the ELD) is usually offered at the level of guaranteed                sums from € 1 m to € 5 m. Insurance capacity in some markets, however, reaches far                higher, up to € 50 m, and in some individual cases - even more (Insurance Europe, 2014).                If one should set the capacity declared by insurers against the size of the environmental                damage  registered in  Europe, it  can  be  assumed  that  even in  the  case  of  catastrophic                events (e.g. Kolontar, Hungary) it would have been possible to sign insurance contracts                for sums close to the damage values. A great diversity in the markets is the crucial barrier:                large  insurance  claims  may  be  paid  in  Germany,  France  or  Great  Britain.  Developing                markets’ capacity is still rather limited and barriers on the demand side are an obstacle to                cross-border cover purchases. However, entities which seek to insure against large risks                must  be  aware  that  insurers  are  not  going  to  offer  limitless  cover  for  environmental                damage, as its size can exceed the whole industry’s capacity (Insurance Europe, 2013).                Public  policy  and  the  legal  system,  as  subsequent  insurability  factors,  increase  the                importance of environmental damage liability issues in the global scale. The concept of                society’s  sustainable  development  is  becoming  a  contemporary  paradigm.  Since  1987,                when the notion of sustainable development (United Nations, 1987) was coined, it remains                the  benchmark  for  creation  of  societies’  development  strategy  (European  Commission,                2010;  Decision  No  1386/2013/EU).  Legislations  in  particular  countries  have  been                gradually,  but  with  a  growing  determination,  introducing  preventive  and  repressive                measures as well as regulating the legal duty to remedy environmental damage.                A legal system which defines the notion of environmental damage and liability, pre-defines                environmental risk. The doctrine points at two basic areas where certain traits of the legal                  11  E.g. the structure of the obligatory insurance system, which enables the insurer to refuse                to supply insurance cover.                                                            368","system generate greatest problems relating to covering environmental liability. These are:                 changeability  (Ad-Hoc  Industry  Natural  Resource  Management  Group,  2009) and                regulatory transfer of uncertainty connected with the cause of environmental damage to                the potential insured (Faure, 2001). Changeability refers to both the content of regulations                and their interpretation. In the first case a risk which arises concerns retroactive change,                i.e.  introduction  of  obligation  to  remedy  the  damage  which  was  not  encompassed  by                obligation to be remedied at the time of insurance contract signing. In the other, a change                in interpretation of laws leads to increasing polluters, and their insurers’ environmental                liability.  In 2004 the ELD introduced  an  unprecedented  – in most  member  countries  –                                                                                                         12                obligation to remedy damage inflicted on waters, protected species or natural habitats ,                and additionally included a requirement concerning preventive measures. The burden of                both restoring the environment and rectifying so called functional damage is extremely                challenging. In the first case, it may be very difficult to establish the initial state of the                environment. In the other case, the steps taken should become the compensation for the                fact  that  some  elements  of  the  environment  cannot  perform  their  functions  (Ad-Hoc                Industry  Natural  Resource  Management  Group,  2009).  The  relatively  small  number  of                occurrences which undergo the ELD regime still results in fairly high uncertainty as for their                interpretation. This in turn causes the size of liability to be less predictable.                The  transfer  of  uncertainty  connected  with  the  cause  of  environmental  damage  to  the                potential insured takes place under the risk-based liability regime. This is what the ELD                includes  when  it  comes  to  the  obligation  to  remedy  the  damage  caused  by  entities                generating an increased probability thereof. Moving forward from the general rules (fault                principle) to the risk principle is a factor which limits insurability. Uncertainty connected                with  the  source  of  the  environmental  damage  cause  is  assigned  to  the  insured,  and,                consequently, their insurer as well. As a result, the latter has the obligation to pay the                compensation even if the entity covered has not caused the damage (Faure, 2001).                 4 Conclusions                If insurability were to be measured only according to the market accessibility of insurance                cover for environmental risk, it would have to be categorised as insurable. Nevertheless,                the assessment of environmental risk through the prism of doctrinal criteria of insurability                indicate the low degree of insurability. What makes environmental risk less insurable than                other risk categories is the difficulties with defining environmental risk as measureable,                extraordinary and independent of one’s will, followed by the fact that it often materialises                as a catastrophe. The above is additionally exacerbated by instability in legislation and its                interpretation as well as low insurance awareness. The few available research studies of                environmental risk insurability have shown that in European countries no or insufficient                insurance  cover  refers  to  damage  done  to  protected  species  and  natural  habitats                (juxtaposed with a relatively high level of insurance against water and soil pollution) as                well as environment impairment caused either gradually or by activity for which permission                has  been  previously  granted.  Also,  significantly limited insurance  offer  covers  activities                related to new technologies or activities generating an increased threat of environmental                damage (i.e. activities connected with GMO, nuclear power, waste management and the                use of plant protection products in farming), (Bio Intelligence Service in association with                Stevens\&Bolton LLP  , 2009; Swiss Re, 2007) .                 The analysis of the degree to which insurability criteria are met serves as the basis for                creating an action catalogue for the state or a business sector in order to enhance it (e.g.                building data bases, creating estimation models, raising legal and insurance awareness                through  educational  projects).  Such  actions  will  most  probably  remain  inefficient  with                regard to non-insurability of environmental damage resulting from regular (non-accidental)                operations of business entities. Economic development will always generate environment                impairment, partly an uninsurable one. The private insurance sector will not embark on                  12  The directive also imposes a duty to remedy the damage on the soil surface, but in this                respect, most countries already had some regulatory experience.                                                            369","covering catastrophic damage, either. Environmental risk management in this area must                be focussed on instruments of preventive impact on the scope of potential damage.                 Aknowledgments                 This research was undertaken as part of the project: Private insurance and environmental                management  systems  complying  with  ISO  14001  as  tools  of  environmental  risk's                management - analysis of relations and was funded by a grant of National Science Centre,                Poland (2016/23/D/HS4/02654)                 References                 ACE  Group  (2011).  A  Guide  to  Environmental  Liability  in  Europe.  Retrieved  from:                https://www2.chubb.com/benelux-nl/_assets/documents/strategic-risk-guide-to-                environmental-liability-in-europe.pdf                Ad-Hoc  Industry  Natural  Resource  Management  Group  (2009).  Financial  Security  and                Insurance Aspects of the European Union Environmental Liability Directive. White Paper.                Brussels.                Berliner, B. (1985). Large risks and Limits of Insurability. 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John                Willey \& Sons.                                                                                                  371","Assessment of Factors Influencing of Final Corporate Income Tax                           Liabilities in Selected Sectors in the Czech Republic                                                                                     2                                                              1                                      Karolina Lisztwanová , Iveta Ratmanová                                                      1  VSB-TU Ostrava                                         Faculty of Economics, Department of Finance                                      Sokolská třída 33, 702 00 Ostrava, Czech Republic                                             E-mail: karolina.lisztwanova@vsb.cz                                                       2  VSB-TU Ostrava                                         Faculty of Economics, Department of Finance                                      Sokolská třída 33, 702 00 Ostrava, Czech Republic                                               E-mail: iveta.ratmanova@vsb.cz                 Abstract: The Czech government uses corporate taxation to support desirable corporates`                behaviour, for example the willingness to invest or the elimination of impact of corporates`                risk etc. The object of the paper is to assess the impact of changes of the statutory tax                rate, to emphasize the impact of the items reducing tax base and the influence of the tax                discounts on the total tax liability. First of all, main attention is devoted to the evaluation                of the impact of tax losses, research and development expenses, expenses connected with                donation activities and investment incentives of the selected sectors during period 2005-                2016 with using data provided by Ministry of Finance of the Czech Republic. The method                of pyramidal decomposition of the final tax liability is used, so that we could state and                compare the changes of above-mentioned items moreover functional method is applied in                case of identification of multiplicative relationship among defined individual items. As far                as  our  results  are  concerned,  we  are  able  to  emphasize  the  items  that  influenced  the                changes of the corporate income tax liability of the selected sectors the most. We can state                that the changes of the individual adjustments of tax base are the most significant in the                case of all sectors, but on the other hand, when it comes to the rest of the items their                impact differs.                Keywords: corporate income tax, tax base, pyramidal decomposition, functional method,                individual sectors                JEL codes: H20, G30, K34, H25, G39                 1 Introduction                Direct taxation is closely tied with taxpayers` income. Corporate income tax as one kind                of direct taxes uses this fact in order not only to create governments` tax revenues, but                to support desirable activities or behaviour of individual enterprises too. Among activities,                which  can  be  in  the  centre  of  tax  policy  interests,  we  can  include  e.g.  research  and                development expenses, elimination of impact of tax losses, gratuitous transactions and                reliefs decreasing final tax.                Generally, it can be claimed that it is necessary to know value of corporate`s accounting                profit or difference between incomes and expenses to determine final tax. However, in                most countries the way, how to calculate final tax is not so simple. Because of this, not all                corporate`s revenues and not all corporate`s expenses may influence total tax. There are                some specific kinds of revenues, which should be excluded from total sum of revenues and                there are some other specific kinds of expenses which must not influence final tax or which                may be taken into account only respecting certain limit. Moreover, countries exist, which                use different other kind of tax base and not only income decreased with expenses.                In the case of the Czech Republic, processing of assessment of final tax is derived, first of                all from accounting profit i.e. from difference between income and expenses, which are                adjusted  respecting  if  they  are  or  not  being  recognized  as  taxable.  Detail  of  their                assessment must respect information of section mark 23, 24 and 25 of The Act. 586/1992                                                               372","Coll., on Income Taxes. The following figure briefly summarizes the prevailing structure of                the tax calculation in practice.                             Figure 1 Structure of tax calculation and settlement of tax liability                           Earnings Before Tax (EBT)     +    Increasing of EBT   -     Decrasing of EBT  ±     Specific Adjustments of EBT                             Separate Tax Base § 20b              Adjusted Tax Base I.   -          Deductions § 20, § 34                                                                           Adjusted Tax Base II.                                                                                 .                               Withholding Tax Rate                                 Tax Rate                                   Tax                             Tax    -             Tax Reliefs § 35, §35a, §35b                                                     Total Tax                                                    Amount of tax advances                                                              Underpayment                                                  or Overpayment of tax                    Source: Authors` processing according data of the Act no. 586/1992 Coll., on Income Taxes                 Regarding the previous figure 1 amounts, which are able to increase or to decrease profit                or  loss  influence  determining  of  tax  base  possibly  of  tax  loss.  Deductions  pursuant  to                section mark 20 or 34 moreover reduce tax base. In addition, the final tax is calculated                respecting tax rate and tax reliefs. Moreover, it is clear from the figure that the certain                part of the tax base exists which is taxed separately with withholding tax rate.                 As it can be seen, there are many facts, which can influence the final total tax. It is clear,                if data are available, it is not problem to identify value of these facts. If their value is                known, it is not problem to assess their impact on the total tax too.  The impact of individual                items influencing value of the total tax respecting total corporates‘data between years 2005                - 2015 have been already made (Lisztwanová, Ratmanová, 2017). The goal of this paper                is  to  concentrate  on  determining  and  assessment  corporates`  tax  data  with  regard  to                individual  selected  sectors  during  period  2005  –  2016  and  to  identify  the  impact  of                indicators on changes of the total tax. The length of the observed period was chosen with                regard to data availability.                 2 Methodology and Data                It would be certainly interesting to find out, how individual adjustments, deductions and                reliefs influenced the final tax of corporates and their development in time. Of course, the                very easy way, how to evaluate their impact, it is to calculate their ratio to the total tax                value.    Notwithstanding,  determination  of  above-mentioned  impact  will  be  stated  with                analysis  of  variances  which  can  be  considered    to  be  more  sophisticated  process.  In                addition, it can be said that the analysis of variances is applied, if there is possibility to                identify data during certain period and if partial components of analysed item exist.                If the total final tax is considered to be the top indicator, relationships among individual                items  influencing  the  total  tax  can  be  described  with  the  way  mentioned  in  figure  2                (Lisztwanová, Ratmanová, 2017). It is clear that figure 2 should be similar to figure 1.                Nevertheless, figure 2 brings the possibility to emphasize the impact of selected corrections                of the tax base with section mark 20 and 34 of The Act. 586/1992 on the final tax. Above                that, the final form of the created pyramidal decomposition corresponds to the structure                of the available data. Generally, it can be claimed that is possible to identify decomposition                of the total tax  and  to calculate power of influence of  individual indicators.   It means                that changing of the top indicator can be explained with changes of individual items where                individual relationships are expressed mathematically.                                                             373","Figure 2 Pyramidal decomposition of total tax liability                                                                      Total Tax Liability                                                             Tax             -           Reliefs                                          Adjusted TB II.     ·          Tax Rate                           Adjusted TB I.     -            IRTB                              Tax Loss         +             R\&D            +          Donations                                  Source: Authors` processing according data of Finanční správa                  When it comes to the detail of figure 2 it is clear that mathematical expression of individual                relationships among indicators can be stated through additive or multiplicative operation.                In the case of additive operation, the following formula can be determined                       ∆x =    ∆a i  ∙ ∆y ,                                                                                            (1)                         a i         x                              ∑ ∆a ii                where Δai = ai,1 – ai,0, ai,0, and ai,1 is the value of the indicator i respecting starting (0)                and ending (1) state.                Referring  to  (Lisztwanová,  Ratmanová,  2017)  the  multiplicative  operations  can  be                calculated with the functional method, which expresses the combined simultaneous impact                of the all indicators explaining their influence on the top indicator (Zmeškal, Dluhošová,                Tichý,  2004).  Respecting  the  multiplicative  operation  between  two  indicators,  the                influences can be formulated as                               1          1                       ∆x   =   ∙ R  ∙ (1 + ∙ R ) ∙ ∆y ,                                                                       (2)                                                    x                         a 1       a 1    2   a 2                              R x                                1             1                       ∆x   =     ∙ R  ∙ (1 + ∙ R ) ∙ ∆y .                                                                          (3)                          a 2        a 2          a 1    x                               R x            2                where R    and Rx are relative changes of indicators.                        a i                So that one could assess the impact of individual components of the final tax as the top                indicator of the pyramidal decomposition, we used data provided by Finanční správa that                provides annual value of the earnings before taxation (EBT), the tax base, and the items                reducing tax base, the tax reliefs and the final total tax.                 3 Assessment of Impact of Indicators of Selected Sectors                Concerning the total corporates` tax data, the final power of impact of above-mentioned                                 Table 1 Total power of influences of individual indicators                                                                                                 % of                          05-06   06-07   07-08   08-09   09-10   10-11   11-12   12-13   13-14   14-15   15-16   Sum   sum   Order                          mil.   mil.   mil.   mil.   mil.   mil.   mil.   mil.   mil.   mil.   mil.                          CZK   CZK   CZK   CZK   CZK   CZK   CZK   CZK   CZK   CZK   CZK   mil. CZK                                        -     -                 ΔTTL    17,662  23,335  19,143  27,202   -2,928  -1,203   1,512   5,300  11,206  11,326   7,191                 Reliefs   731   820   1,994   129   902   732   1,569   9,672   689  10,206   105   27,550   12,11   4                 STR     11,112    0  21,481   6,664   6,268   0   0   0   0    0   0.000   45,525   20,01   2                 ATB I.   26,392  22,169   6,700  20,921   3,466   3,942   2,524  18,514   8,835   1,623   6,791   121,875   53,56   1                 Tax Loss   3,449   2,202   6,465   308   1,137   3,829   5,723   2,979   3,000   223   363   29,679   13,04   3                 R\&D       249   215   87    60   345   549   120   359   56   273   177   2,492   1,10   5                 Dona-                 tions      87   0.21   21    6    15   35     3   204    4     7    35    418   0,18   6                                                                                         227,538                                 Source: Authors` calculation according data of Finanční správa                                                               374","indicators  on  the  total  tax  has  been  already  defined  (Lisztwanová,  Ratmanová,  2017).                Table 1 presents these findings. The changes of data between individual periods, the total                tax as the topic indicator of pyramidal decomposition and analysis of variances have been                used  to  determine  impact  of  changes  of  individual  indicators  on  the  top  indicator.                Respecting the way of processing and concerning on data of the last column of the table                1, it is clear which changes affected the most the development of the total tax. It was                adjusted tax base I. On the contrary, the changes of donations caused changes of total tax                at least in the selected periods. The following table 2 contains value of changes of the total                tax with regard to selected sectors. The main attention has been concentrated on following                sectors: agriculture (A), mining and quarrying (B), processing industry (C), production and                distribution of electricity (D), construction sector (F) and finance and insurance sector (K).                The sectoral designation with individual letters corresponds to the designation used by the                financial administration of the Czech Republic.                              Table 2 Change of total tax liability according selected sectors                          05-06   06-07   07-08   08-09   09-10   10-11   11-12   12-13   13-14   14-15   15-16                         mil. CZK   mil. CZK   mil. CZK   mil. CZK   mil. CZK   mil. CZK   mil. CZK   mil. CZK   mil. CZK   mil. CZK   mil. CZK                 Sector A   216.5   278.5   -159.3   -527.9   772.4   1,031.6   100.4   284.5   721.7   -658.3   -43.4                 Sector B   -17.6   487.1   325.8   -2,276.1   1,105.9   165.6   -1,320.3   396.2   -600.7   108.1   -753.2                 Sector C   1,360.8   10,425.9   -11,511.7  -12,887.9   4,235.4   452.9   1,356.3   1,534.6   11,573.0   3,581.2   4,519.5                 Sector D   5,202.1   1,043.4   2,675.5   -735.8   -929.2   -3,199.2   2,905.6   -947.8   -2,663.4   -435.0   513.4                 Sector F   839.3   1,259.1   -1,016.4   -673.5   -427.8   -1,737.0   -621.0   241.9   178.6   1,881.5   9.2                 Sector K   -1,042.5   2,175.1   -3,579.5   2,212.9   -3,722.6   1,366.4   4,848.4   -798.5   284.9   1,711.6   -1,162.4                                 Source: Authors` calculation according data of Finanční správa                 Because it is obvious, that changes of the total tax differ among individual sectors, data of                selected sectors respecting period between years 2005 – 2016 have been tested. The way                of  processing  was  the  same  as  of  data  for  all  businesses  respecting  created pyramidal                decomposition and using analysis of indicators variances between years. In the following                text, sector findings are always presented with two ways. First, information in mil. CZK                expresses  value,  which  caused  increasing  or  decreasing  of  the  total  tax  between  two                periods.  It is  clear  that  changes  of  the  total  tax liability  were  not  only influenced  with                increasing of all items. It means that changes of indicators` influences could be positive or                negative.  Although,  in  order  to  determine  the  overall  ranking  of  impacts  of  individual                indicators, their absolute values have been used. This information can also be presented                as the absolute value of individual indicators for the individual sector. Similarly, as in the                table 1 information about final rank of individual indicators, respecting sectoral data are                added. Thanks to this, it is easy to determine and later to compare results of selected                sectors to each other.                      Table 3 Total power of influences of individual indicators of agriculture sector A                        05-06  06-07  07-08  08-09  09-10  10-11  11-12  12-13  13-14  14-15 15-16  Sum  % of sum Order                        mil. CZK mil. CZK mil. CZK mil. CZK  mil. CZK  mil. CZK  mil. CZK mil. CZK mil. CZK mil. CZK mil. CZK mil. CZK                 Reliefs  5,279  5,279  5,279  5,279  5,279  5,279  5,279  5,279  5,279  5,279  5,279  58,073  0,86  4                 STR    119,311  0,000 241,009  70,347  80,827  0,000  0,000  0,000  0,000  0,000  0,000  511,494  7,53  3                 ATB I.  334,934 476,641 150,674 508,940 1 006,186 1 093,595  20,266 225,563 735,661 706,465 23,862 5 282,787  77,79  1                 Tax Loss  9,284 172,496 216,568  34,232  147,686  67,002 116,653  60,077  8,052  48,181 20,215  900,446  13,26  2                 R\&D     1,198  3,685  3,747  0,518  0,133  1,066  0,643  0,812  1,824  2,517  3,213  19,355  0,29  5                 Donations  1,970  4,051  1,722  2,724  1,176  0,523  0,463  2,162  2,247  1,114  0,570  18,723  0,28  6                                                                                         6 790,877                                 Source: Authors` calculation according data of Finanční správa                 According to data of the first sector – agriculture it can be seen that individual adjustments                of tax base I. influenced the total tax of corporates the most. The tax loss was the following                indicator that influenced observed changes the most. Vice versa, the impact of donations                was the least.                When it comes to the sector of mining and quarrying, it was observed the same result as                in  the  case  of  the  agriculture  sector.  The  biggest  influence  of  ATB  I..  The  expenses                connected with research and development t caused changes of the total tax liability the                leas. Surprisingly, changes of statutory tax rate brought changes of the final tax about in                the second rank.                                                             375","Table 4 Total power of influences of individual indicators of sector of mining and                                                        quarrying B                         05-06  06-07  07-08  08-09  09-10  10-11  11-12  12-13  13-14  14-15  15-16  Sum  % of sum Order                        mil. CZK mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK mil. CZK  mil. CZK mil. CZK  mil. CZK                 Reliefs  2,353  2,377  2,215  1,237  0,347  0,896  4,098  0,356  1,118  0,936  0,273  16,205  0,16  5                 STR    285,194  0,000  563,424  155,775  135,879  0,000  0,000  0,000  0,000  0,000  0,000 1 140,272  11,48  2                 ATB I.  228,510 499,313  901,865 2 116,057 1 214,764 181,889 1 349,011 401,614 599,317 188,069 644,648 8 325,057  83,84  1                 Tax Loss  43,470  17,332  5,700  8,446  23,300  22,105  9,189  2,332  2,560  84,091 112,887  331,414  3,34  3                 R\&D     0,045  0,029  0,015  0,059  0,137  0,018  0,058  2,815  1,355  0,955  1,373  6,858  0,07  6                 Donations  6,718  2,686  9,188  2,926  3,895  39,354  33,718  0,627  0,905  4,102  5,411  109,530  1,10  4                                                                                         9 929,336                                 Source: Authors` calculation according data of Finanční správa                 Judging  the  impact  of  indicator  that  defines  changes  of  the  total  tax  in  the  case  of                processing industry it was verified the most important role of individual adjustments of tax                base I. too. For the second time one can see, that the individual reliefs followed indicator                ATB I. in influencing of the total tax. The indicator with the least impact is the same as in                case of agriculture sector, i. e. donations influenced changes of the total tax the least.                    Table 5 Total power of influences of individual indicators of processing industry C                       05-06  06-07  07-08  08-09  09-10  10-11  11-12  12-13  13-14  14-15  15-16  Sum  % of sum Order                       mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK                Reliefs  922,600  501,501  1 996,656  151,628  818,572  960,508 1 579,082 2 212,027  2 213,781  661,642  19,004  12 037,002  11,48  2                STR    3 042,872  0,000  5 731,350  1 455,081  1 336,389  0,000  0,000  0,000  0,000  0,000  0,000  11 565,692  11,03  3                ATB I.  5 889,013 10 723,455  8 924,789  11 096,440  8 603,456 1 091,781 1 839,875  470,506 15 003,251 3 639,962 3 344,885  70 627,414  67,36  1                Tax Loss  398,337  191,189  1 166,825  558,197  1 876,418  593,081 1 185,453  2,254  1 187,255  751,175 1 064,204  8 974,388  8,56  4                R\&D     114,335  15,897  32,253  36,665  327,061  253,368  99,068  204,793  24,138  161,872  140,005  1 409,455  1,34  5                Donations  50,071  28,696  13,236  33,543  9,640  18,114  9,165  0,170  53,332  13,626  10,574  240,168  0,23  6                                                                                           104 854,117                                 Source: Authors` calculation according data of Finanční správa                The sector of production and distribution of electricity does not differ from previous sectors.                Individual adjustments played significant role in determining of changes of the total tax                liabilities. Nevertheless, one can identify for the second time the important impact of the                tax  loss  generated  by  corporates  of  this  sector.  Surprisingly,  respecting  the  observed                period,  this  sector  is  associated  with  the  least  impact  of  expenses  on  research  and                development.                   Table 6 Total power of influences of individual indicators of sector of production and                                                distribution of electricity D                        05-06  06-07  07-08  08-09  09-10  10-11  11-12  12-13  13-14  14-15  15-16  Sum  % of sum Order                        mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK                 Reliefs  1,297  1,488  1,546  0,430  0,349  1,540  0,981  0,780  1,077  0,186  0,317  9,990  0,024  5                 STR    772,534  0,000  1 956,110  755,415  751,354  0,000  0,000  0,000  0,000  0,000  0,000  4 235,414  10,174  3                 ATB I.  6 036,184 994,079  4 742,141  102,426  318,348 3 206,311 2 890,840 2 697,786 5 983,323  550,410 1 305,705 28 827,553  69,248  1                 Tax Loss  59,820  26,848  118,143  56,691  160,902  24,714  36,115 3 656,317 3 332,180  101,760  800,787  8 374,277  20,116  2                 R\&D      1,033  0,034  0,117  0,021  1,227  0,871  0,474  1,938  0,937  0,643  2,165  9,460  0,023  6                 Donations  4,101  21,054  5,909  25,656  18,787  29,432  19,925  11,881  12,118  12,820  10,948  172,630  0,415  4                                                                                          41 629,325                                 Source: Authors` calculation according data of Finanční správa                 Regarding the calculation and data of table 7, it can be confirmed that ATB I. the most                influenced changes of the total tax followed with changes of the statutory tax rate in the                case of the construction sector. Vice versa, this one is the sector with the least impact of                donations activities on the final tax and it is the same as in the case of processing industry.                 Table 7 Total power of influences of individual indicators of sector of construction sec.  F                         05-06  06-07  07-08  08-09  09-10  10-11  11-12  12-13  13-14  14-15  15-16  Sum  % of sum Order                       mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK                 Reliefs  25,608  20,000  10,316  162,871  103,519  6,335  115,845  4,374  6,007  1,761  0,052  456,689  3,56  4                 STR    540,629  0,000  1 057,191  349,137  340,301  0,000  0,000  0,000  0,000  0,000  0,000  2 287,257  17,83  2                 ATB I.  1 595,329 1 235,412  121,974  72,400  155,641  1 672,865  787,016  337,722  342,929 2 027,653  197,144  8 546,086  66,64  1                 Tax Loss  135,204  108,735  80,099  22,685  73,887  34,046  19,975  108,620  174,923  139,809  210,080  1 108,062  8,64  3                 R\&D     46,219  62,684  76,472  67,051  38,485  43,732  26,388  6,279  18,659  0,242  0,610  386,822  3,02  5                 Donations  8,394  2,384  4,114  0,685  0,029  7,258  3,846  2,098  2,091  4,795  4,348  40,043  0,31  6                                                                                          12 824,959                                 Source: Authors` calculation according data of Finanční správa                                                            376","Even in the latter case we can identify adjustment of tax base I. as the indicator which the                most changed the total tax liability of finance and insurance enterprises. The tax loss is                the  indicator  with  the  second  rank  in  assessing  indicators  and  regarding  expenses  of                research and development, it can be observed the least impact on the total tax in selected                periods.                 Table 8 Total power of influences of individual indicators of finance and insurance sec.K                         05-06  06-07  07-08  08-09  09-10  10-11  11-12  12-13  13-14  14-15  15-16  Sum  % of sum Order                       mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK  mil. CZK                Reliefs  96,302  6,320  50,185  25,349  7,712  25,888  0,039 12 067,365  1 289,760 10 773,281  2,760  24 344,961  20,54  3                STR    1 258,895  0,000  2 071,006  729,066  723,095  0,000  0,000  0,000  0,000  0,000  0,000  4 782,062  4,03  4                ATB I.  5 420,789 1 622,054  4 987,609 2 994,045  8 097,862  7 787,080  399,142 12 125,904  3 119,177  9 024,365  893,916  56 471,942  47,64  1                Tax Loss  5 528,149  563,820  6 454,254  38,717  5 074,876  6 334,863 5 275,074  703,392  1 944,701  50,520  291,093  32 259,458  27,22  2                R\&D      2,092  2,816  4,327  13,853  2,684  105,899  6,365  38,759  8,351  13,772  7,304  206,222  0,17  6                Donations  14,777  7,270  12,712  24,894  13,125  5,822  21,214  192,412  161,274  0,562  12,528  466,590  0,39  5                                                                                           118 531,234                                 Source: Authors` calculation according data of Finanční správa                 4 Conclusions                The following table 9 provides summarized information about the power of influences of                individual indicators respecting data of selected sectors. Generally, it can be claimed that,                thanks to methods of the pyramidal decomposition and thanks to the analysis of variances,                it has been verified respecting input data that individual adjustments of the tax base I.                affected  changes  of  the  corporates`  final tax  regarding  selected  sectors  the most.  The                influence  of  changes  in  rates  of  the  statutory  tax  itself  cannot  be  overlooked  either.                Comparing individual sectors, it can be confirmed that reliefs play the most important role                in  the  case  of  the  processing  industry.  The  agriculture,  the  sector  of  production  and                distribution  of  electricity  and  the  finance  and  insurance  sector  are  sectors  with  the                important  influence  of  the  tax  loss  in  observed  period.  Expenses  of  research  and                development affected situation of the agriculture sector, the processing industry and of the                construction sector the most. The last one – the gratuitous activities play important role in                changes of the total tax during period 2005- 2016 in the case of the sector of mining and                quarrying and in the sector of production and distribution of electricity.                                Table 9 Ranking of indicators' impacts in individual sectors                                  Sector A     Sector B     Sector C     Sector D     Sector F     Sector K                 Reliefs                4            5            2            5            4            3                 STR                    3            2            3            3            2            4                 ATB I.                 1            1            1            1            1            1                 Tax Loss               2            3            4            2            3            2                 R\&D                    5            6            5            6            5            6                 Donations              6            4            6            4            6            5                                 Source: Authors` calculation according data of Finanční správa                 In addition to determining the absolute magnitude of the influence, the direction of this                influence can be assessed and compared too. This direction is presented in table 10. It is                clear that the frequency of occurrences of the effect of increasing or decreasing the overall                tax burden within the sectors between the various items has been identified.                Regarding  the  influence  of  the  statutory  tax  rate,  it  can  be  only  observed  decreasing                impact.  Generally,  it  can  be  claimed,  that  decreasing  of  statutory  tax  rate  causes                decreasing of the total tax liability of all sectors. The impact of the rest of indicators were                so unequivocal. However, in most cases, the indicator adjusted tax base I. has contributed                to  the  increasing  of  the  tax  burden  for  individual  sectors.  The  tax  loss  contributed  to                reduction in the year-on-year tax burden on the sectors in most cases; surprisingly in the                case of the agriculture and processing industry, the tax loss was more likely to increase                the  tax  change.  As  regards  to  the  impact  of  changes  of  research  and  development                expenditures and their impact on changes in overall tax liability for individual sectors, no                clear impact on the increase or decrease it has been demonstrated with usage of methods                                                             377","of  pyramidal  decomposition  and  analysis  of  variances.  However,  results  of  individual                sectors, in the case of the agriculture sector can be confirmed that if the total tax increases,                research  and  development  expenditures  reduce  this  increasing.  Unfortunately,  this                unambiguous  relationship  cannot  be  confirmed  for  other  sectors.  When  it  comes  to                donations as the indicator which changes influenced development of the sector`s total tax,                this one more often contributed to reduction of the sector`s tax liability. The last factor -                reliefs - generally can be confirmed that except the processing industry, this one decreased                the total sector`s tax.                 Table 10 Frequency of direction of influence of indicators' impacts on individual sectors                                  Sector A     Sector B    Sector C    Sector D     Sector F    Sector K                                    -    +      -    +      -     +      -    +      -    +      -     +                   Reliefs         6      5     9     2     3     8     6     5      6     5     8     3                   STR             4      0     4     0     4     0     4     0      4     0     4     0                   ATB I.          5      6     4     5     3     8     4     7      6     5     6     5                   Tax Loss        5      6     8     3     5     6     6     5      7     4     6     5                   R\&D             7      4     4     7     8     3     6     5      4     7     5     6                   Donations       8      3     4     7     5     6     5     6      6     5     6     5                                 Source: Authors` calculation according data of Finanční správa                In conclusion, it has been verified the possibility to determine factors influencing the final                tax of corporates of the individual selected sectors through the usage of the pyramidal                decomposition method with regard to the selected top indicator. Moreover, it has been                verified that analysis of variances can be used to determine power of impact of individual                indicators of the pyramidal decomposition on the final total tax. Last but not least, with                regard to the real data of selected sectors for the certain period, factors that most or the                least affected the change of the tax liability were identified. However, with regard to the                input  data,  it  was  unreservedly  confirmed  with  the  analysis  of  the  variances  that  tax-                deductible items or tax reliefs have always contributed to the tax reduction.                  Acknowledgments                This paper was supported by the SGS Project VŠB  – TU Ostrava SP2018/154 \"Finanční                rozhodování podniků a finančních institucí za rizika”.                References                 Act no. 586/1992 Coll., on Income Taxes, as amended.                Dluhošová,  D.,  Zmeškal,  Z.,  Richtarová,  D.,  Valecký,  J.  (2010).  Finanční  řízení  a                rozhodování podniku. 3th ed. Praha: Ekopress.                Finanční  správa  (2017).  Analýzy  a  statistiky:  Daňová  statistika  k  roku  2005-2015.                Retrieved    from:     http://www.financnisprava.cz/cs/dane/analyzy-a-statistiky/danova-                statistika.                Finanční správa (2017). Údaje z výběru daní: Vývoj inkasa vybraných daní v ČR v letech                1993-2016.       Retrieved     from:     http://www.financnisprava.cz/cs/dane/analyzy-a-                statistiky/udaje-z-vyberu-dani.                Kubátová, K. (2015). Daňová teorie a politika, 6th ed. Praha: Wolters Kluwer.                Lisztwanová, K., Ratmanová, I. (2017). Assessment of Impact of Items Reducing Tax Base                and  Tax  on  Total  Amount  of  Corporate  Income  Tax  in  the  Czech  Republic  in  Selected                Periods. In: Proceedings of the 14th International Scientific Conference European Financial                Systems 2017. Brno: Masaryk University, pp. 498-507. ISBN 978-80210-8609-8.                OECD (2017). Tax on corporate profits. Retrieved from: https://data.oecd.org/tax/tax-on-                corporate-profits.htm#indicator-chart.                                                              378","Široký, J. (2008). Daňová teorie s praktickou aplikací. 2nd ed. Praha: C. H. Beck.                Zmeškal, Z., Dluhošová, D., Tichý, T. (2004). Financial Models. 1st English ed. Ostrava:                VSB-Technical University of Ostrava, Faculty of Economics.                                                                                                                                             379","Financial Performance of Czech Privately-Held Firms with Multiple                                                        Owners                                                              1                                                                              2                                            Ondřej Machek , Aleš Kubíček                                              1  University of Economics, Prague                                   Faculty of Business Administration, Department of Strategy                                     nam. W. Churchilla 4, 13067 Prague 3, Czech Republic                                               E-mail: ondrej.machek@vse.cz                                               2  University of Economics, Prague                                   Faculty of Business Administration, Department of Strategy                                     nam. W. Churchilla 4, 13067 Prague 3, Czech Republic                                                 E-mail: ales.kubicek@vse.cz                 Abstract: Privately-held firms represent the vast majority of firms worldwide. When owned                by multiple shareholders, the owners become a social group who interact with each other,                which affects the behavior of the firm and its financial performance. The goal of this paper                is to explore the financial characteristics of Czech privately-held firms with multiple owners.                Using a sample of 28,972 Czech limited liability firms, and after controlling for firm size,                age, and industry affiliation, we found that the presence of multiple owners has a negative                effect on asset turnover. However, the number of shareholders as such seems to have no                effect  on  profitability,  debt  use,  and  liquidity.  Instead,  sole  proprietorships  seem to  be                empirically  different  from  firms  with  two  or  more  owners,  as  they  have  a  significantly                greater liquidity.                Keywords: multiple owners, performance, privately-held firms, Czech Republic                JEL codes: M10, G34                  1 Introduction                The relationship between ownership structure and financial performance has been subject                to many studies over the last few decades. The existing body of research focused primarily                on structural aspects, such as ownership concentration, the proportion of family owners,                the proportion of institutional, private, government ownership, as well as the number of                owners (Uhlaner et al., 2015).                Most of the existing research on corporate governance has been carried out in countries                with strong legal protection and developed financial markets. The existing studies focused                mostly  on  publicly  listed  firms  (McCahery  and  Vermeulen,  2010)  and  in  corporate                governance research, a particular emphasis has been placed on the relationship between                ownership structure and performance; however, the studies presented inconsistent results                (Uhlaner, 2008).                Although publicly traded firms are important players in the global business, privately-held                firms represent the vast majority of firms worldwide. This form of businesses encompasses                all  possible  kinds  of  firms,  including  small  family-run  businesses,  but  also  large                international corporations, joint ventures, etc.                Agency theory assumes divergence of interests between owners and managers because of                the separation of ownership from control. Managers pursue their self-interests (Jensen and                Meckling, 1992), which creates the potential for management’s opportunism and leads to                agency costs. Hence, agency theorists proposed mechanisms that can protect shareholders                against management’s opportunism.                In publicly listed firms, governance is mostly formal (contractual), whereas in privately-                held firms, governance becomes relational (Mustakallio et al., 2002). Hence, the findings                on corporate governance in publicly traded firms cannot be simply applied to privately-held                firms.                                                               380","Moreover,  it  can  be  assumed  that  privately-held  companies  are  also  heterogeneous                regarding  governance  mechanisms.  Firms  with  a  smaller  number  of  owners,  especially                those  owned  by  the  same  family,  will  rely  more  on  informal  (relational)  governance                mechanisms than firms with more dispersed ownership (Nordqvist et al., 2014; Uhlaner et                al.,  2007).  It  is  no  wonder  that  recent  authors  emphasized  the  need  for  studying  the                owning groups in corporate governance. This need is even more important when we realize                that business-owning groups are a worldwide phenomenon; according to Uhlaner (2008),                excluding publicly-traded firms, nearly half of all firms are owned by two or more people.                When multiple owners are present in a firm, they become a social group of people who                “are aware of, interact with, and influence each other” (Uhlaner, 2008). The dynamics of                owning group is assumed to affect the behavior of firms and their performance. Due to                data unavailability, it is extremely difficult to study the dynamics of owning groups per se;                however, existing databases allow for studying the effect of number of owners on various                dimensions of financial performance. The goal of this paper is to explore the effects of the                number of shareholders on financial ratios (profitability, liquidity, asset management and                debt use) of Czech privately-held firms.                 In past studies, the number of owners has often been used as a measure  of ownership                dispersion  or  firm  size.  The  academic  literature  presents  several  findings  on  financial                characteristics of single-owned firms and firms with multiple owners.                As to the relationship between the number of shareholders and profitability, the literature                presents inconsistent evidence. A larger number of owners may result in higher agency                costs, but at the same time, profit maximization may require a diffused ownership structure                (Demsetz, 1983). Steijvers et al. (2006) found that in family firms, the number of owners                had a positive effect on performance if the largest shareholder has an ownership share                between 45% and 60%. On the other hand, a later study by Lappalainen and Niskanen                (2012)  found  that  an  increase  in  the  number  of  owners  reduced  firm  profitability.                Performance  may  also  be  measured  using  asset  management  ratios,  such  as  asset                turnover. This financial ratio has often been used as an inverse proxy for agency costs                (Singh and Davidson, 2003; Fleming et al., 2005). In this context, Florackis (2008) found                that larger UK firms had a lower asset turnover than small firms.                 As  the  ownership  becomes  dispersed,  the  free-rider  problem  can  occur.  While  few                shareholders will have a strong incentive to incur the costs of management monitoring, in                larger owning groups, individual shareholders will be less active. Limited monitoring will                result in higher agency costs (Ang et al., 2000). Hence, we expect that increasing agency                costs will result in worse performance, in particular:                       H1: The number of owners negatively affects profitability.                       H2: The number of owners negatively affects asset turnover.                 Dalbor et al. (2004) found that the number of owners was positively associated with the                amount of total debt in small restaurant firms. Similarly, Newman et al. (2013) found that                single-owner SMEs had a lower propensity to take on total debt than SMEs with multiple                owners. The idea that firms with multiple owners use more debt financing can be justified                by several reasons. First, larger firms are more likely to have better access to external                financing. On the other hand, smaller firms without formal governance mechanisms are                likely to rely on internal financing (Young et al., 2008). Second, using increasingly more                debt increases the risk of financial distress. Small firms are often more averse to risk,                because their personal wealth, as well as the wealth of their owners’ families, is directly                connected to the success of their firm; also, small business owners want to avoid the loss                of control over their firms (Mishra and McConaughy, 1999). Hence, we hypothesize that:                       H3: The number of owners positively affects the level of debt.                We found no study evaluating the effect of the number of owners on firm liquidity (the                ability to settle short-term liabilities). However, it can be assumed that larger firms, which                are more likely to have multiple owners, are less risk-averse (Misra et al., 2005); a more                aggressive financial policy can lead to keeping less amount of liquid assets, thus making                                                             381","firms  with  multiple  owners  adopt  a  riskier,  but  potentially  more  profitable  position.                Therefore, we expect that:                        H4: The number of owners negatively affects liquidity.                As far as we know, no similar study has been carried out in the Czech Republic. Several                authors  focused  on  the  relationship  between  ownership  concentration  and  performance                (Konečný  and  Částek,  2016;  Machek  and  Kubíček,  2018).  In  this  paper,  we  are  not                interested in the effects of the amount of stock owned by individual investors, but in the                number of owners, which is a straightforward indicator of the size and complexity of owning                groups.                 The rest of this paper is structured as follows. We present the methodology and data used                in this study. Then, we present and discuss the results. Finally, concluding remarks are                presented.                 2 Data and Methodology                To  get  financial  data  on  privately-held  firms,  we  used  the  Bureau  van  Dijk’s  Amadeus                database.  We  selected  all  Czech limited liability  firms  (s.r.o.)  with  one  or  more  known                shareholders,  and  with  known  data  on  the  relevant  financial  ratios.  To  eliminate                subsidiaries of foreign firms, we selected only firms located in the Czech Republic, who are                owned by one or more named individuals or families, owning together between 50% and                100%. The final sample contains 28,972 firms and financial data for 2014-2016. Table 1                presents the industry affiliation of firms in the sample.                                     Table 1 Industry Affiliation of Firms in the Sample                 Industry name (abbreviated)                                   Number of        Relative                                                                                  firms        frequency                 Agriculture, forestry, fishing, mining, quarrying                       812       2.80%                 Manufacturing                                                         4,747      16.38%                 Electricity, gas, steam, air conditioning, water                        437       1.51%                 supply, sewerage, waste management                 Construction                                                          3,527      12.17%                 Wholesale and retail trade; repair of motor                           7,539      26.02%                 vehicles and motorcycles                 Transportation and storage                                            1,091       3.77%                 Accommodation and food service activities                               766       2.64%                 Information and communication                                         1,295       4.47%                 Real estate activities                                                1,611       5.56%                 Professional, scientific and technical activities                     3,978      13.73%                 Administrative and support service activities                           838       2.89%                 Other                                                                 2,331       8.05%                 Total                                                               28,972    100.00%                              Source: Own analysis based on Bureau van Dijk’s Amadeus database                 To test the effect of the presence of multiple owners on financial performance, we employed                correlation and linear regression analysis performed in Stata 14.                There are four models with four explained variables:                     •   Return  on  equity  (net  income  over  shareholder’s  funds)  as  a  measure  of                       profitability.                    •   Asset turnover (total sales over total assets) as a measure of asset management                       efficiency.                    •   Gearing (liabilities over equity) as a measure of financial leverage (level of debt).                    •   Current ratio (current assets over current liabilities) as a measure of liquidity.                 The  main  explanatory  variable  is  the  number  of  owners.  Besides  that,  we  control  for                industry  affiliation  using  eleven  dummy  variables  (since  financial  characteristics  differ                                                             382","across  industries).  The  analysis  also  controls  for  firm  size  as  measured  by  the  natural                logarithm of total assets (to capture size-related effects such as returns to scale), and firm                age since the date of incorporation (to capture maturation effects).                Standard regression diagnostics were applied. Possible multicollinearity has been evaluated                using the variance inflation factors (VIF). However, no serious issues have been found. In                all  four  models,  means  of  residuals  are  very  close  to  zero.  Because  according  to  the                Breusch-Pagan  test,  the  models  are  heteroskedastic,  we  used  regression  with  robust                standard  errors.  Finally,  according  to  the  Durbin-Watson  test,  there  is  no  evidence  of                autocorrelation in our data.                It also makes sense to compare sole proprietorships (firms owned by a single shareholder)                with firms owned by two or more shareholders. For this purpose, Student’s t-test with                unequal variances was used.                 3 Results and Discussion                Table 2  present  the  descriptive  statistics  for the individual  dependent  and independent                variables.                                               Table 2 Descriptive Statistics                                                                                Mean      Standard                                                                                         deviation                       Number of owners                                          1.752        1.370                       Firm age                                                 12.994        7.147                       Firm size (ln of total assets)                            5.166        1.705                       Return on equity (ROE)                                   16.114       35.252                       Asset turnover (AT)                                       5.674       12.825                       Current ratio (CR)                                        5.317        7.887                       Gearing (GE)                                             47.281       95.804                              Source: Own analysis based on Bureau van Dijk’s Amadeus database                 As to the number of owners, there are 15,187 sole proprietorships, 9,620 firms are owned                by two owners, and 2,623 firms are owned by three owners. Many firms are also owned                by four owners (883), five owners (309) and six owners (115). The number of firms owned                by more than six owners is rapidly decreasing.                 Table  3  present  the  Pearson  correlation  coefficients  between  the  dependent  and                independent variables. The correlation analysis suggests there is a negative bivariate linear                relationship between the number of owners and firm age and size (both significant at the                0.01 level), but also a negative relationship between the number of owners and profitability                and asset turnover (significant at the 0.01 level). No significant correlation between the                number of owners and liquidity/use of debt has been found.                                                Table 3 Correlation Matrix                              Owners       Age         Size       ROE         AT         CR          GE                 Owners             1                 Age           .181**           1                 Size          .061**     .361**           1                 ROE         –.015**     –.149**     .067**           1                 AT          –.040**     –.089**    –.041**      .016**           1                 CR             –.007      –.019    –.168**    –.036**     –.159**           1                 GE              .002    –.051**     .170**    –.019**      .065**    –.122**            1                 Note: ** - significant at 0.05, Owners = number of owners, Age = firm age, Size = firm size, ROE                            = return on equity, AT = asset turnover, CR = current ratio, GE = gearing                              Source: Own analysis based on Bureau van Dijk’s Amadeus database                 Table 4 displays the regression results for four models with different response variables.                The results support the hypothesis that the number of owners in a firm has a negative                                                            383","effect on asset turnover (significant at the 0.05 level). Contrarily to what we expected,                return on equity, current ratio and level of debt seem to be unaffected by the number of                owners.                                                Table 4 Regression Results                       Dependent variable           Return on         Asset      Current     Gearing                                                     equity        turnover      ratio                      Intercept                     10.185**       67.653**     9.272**    43.314**                      Number of owners                .029          -.139**       .057       -.793                      Size                          3.160**         -.188**     -.821**    11.199**                      Age                            -.922**        -.159**      .052**     -1.696**                                      Note: * - significant at 0.1, ** - significant at 0.05.                                             Industry dummies are not displayed.                              Source: Own analysis based on Bureau van Dijk’s Amadeus database                 To compare firms with one owner with firms with multiple owners, Table 5 displays the                results of Student’s t-test with unequal variances. There are 15,187 firms with one owner                and 13,785 firms with multiple owners in the sample. Significant differences have been                found in terms of asset turnover and current ratio (in both cases, firms with one owner                exhibit a greater mean than firms with multiple owners). Single-owned firms also used less                debt, but the result was not statistically significant.                                             Table 5 Results of Student’s t-test                                                             Mean                         t statistic                                                Single-owned     Multiple-owned                                                    firms             firms                 Return on equity (ROE)                16.330              15.877                   1.092                 Asset turnover (AT)                    6.038               5.273               5.073***                 Current ratio (CR)                     5.554               5.055               5.374***                 Gearing (GE)                          46.672              47.953                  -1.136                                 Note: *** - significant at the 0.01 level, two-tailed significance                              Source: Own analysis based on Bureau van Dijk’s Amadeus database                 To  summarize  the  results,  we  found  convincing  evidence  that  the  number  of  owners                negatively affects asset turnover (H2). Not enough evidence on the effects of number of                owners on profitability (H1), debt use (H3) and liquidity (H4) has been found.                The results support the hypothesis that agency costs increase along with the number of                owners. This can be explained by the occurrence of the free-rider problem (Ang et al.,                2000),  which  means  that  when  the  number  of  shareholder  increases,  the  individual                shareholders  become  less  active  and  are  unwilling  to  carry  the  costs  of  management                monitoring. As a result, the asset turnover will decrease. This finding, however, does not                apply to return on equity. Although the mean ROE of single-owned firms was higher, the                difference is not statistically significant. It may also be that ROE is actually not a good                inverse  measure  of  agency  costs;  several  authors  considered  them  to  be  different                constructs (see e.g. Lins et al., 2010).                Our results failed to support the hypotheses H3 and H4. The number of owners does not                directly affect liquidity of firms and their use of debt. A higher use of debt can be attributed                rather to the firm size (larger firms have better access to external finance) rather than to                the number of shareholders.                However,  it  seems  that  single-owned  firms  are  empirically  different  from  firms  with                multiple owners as such, but the very number of owners does not play a significant role. A                greater risk aversion of firms with a one owner is manifested in greater liquidity of such                firms.  Keeping  more  liquid  assets  acts  as  a  “financial  pillow”  and  reduces  the  risk  of                financial distress.                                                               384","However, this study also has limitations. In the Czech Republic, only few privately-held                firms  have  a large  number  of  owners.  The  groups  of  firms  corresponding  to  individual                numbers of shareholders are disproportional in terms of size. For example, only one firm                had  24  shareholders,  and  no  firm  had  29  shareholders.  These  data  issues  reduce  the                possibilities  of  statistical  inference,  since  the  information  about  the  group  means  is                distorted. A larger number of observations for various groups of firms could provide more                reliable  results.  We  also  had  to  rely  on  the  credibility  of  data  extracted  from  the  BvD                Amadeus Database. However, this database has been used my many other authors in their                research.                 4 Conclusions                The goal of this paper was to explore the effects of the existence of multiple owners of                financial performance of Czech privately-held firms. The results support the hypothesis                that the number of shareholders negatively affects asset management efficiency. However,                no significant effect of the number of owners on return on equity, liquidity and debt use                has been found. Instead, we argue that single-owned firms are different from firms with                multiple owners, as they tend to employ more liquid assets to reduce the financial risk.                However, further research is needed in this area. Some authors, such as Chrisman et al.                (2004),  emphasized  the  need  for  measuring  not  only  economic  (financial),  but  also                noneconomic  performance  (the  achievement  of  noneconomic  goals,  such  as  the  firm                reputation,  contribution  to  society,  but  also  family-centered  goals  in  family  firms).                Moreover,  future  research  should  address  the  question  how  exactly  the  owning  group                dynamics (such as shared vision, commitment to the organization, quality of relationships)                affects the performance of firms and what are the antecedents and the driving mechanisms                of these effects.                 Acknowledgments                This paper has been written with the support of the Czech Science Foundation, project no.                GA17-10948S  „Privately-held  Firms  with  Multiple  Owners:  The  Role  of  Family  and                Responsible Ownership“.                References                 Ang, J. 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T., Narasimhan, C. (2005). Salesforce compensation: An analytical                and empirical examination of the agency theoretic approach. Quantitative Marketing and                Economics, vol. 3(1), 5-39.                 Mustakallio, M., Autio, E., Zahra, S. A. (2002). Relational and contractual governance in                family firms: Effects on strategic decision making. Family business review, vol. 15(3), pp.                205-222.                 Newman, A., Borgia, D., Deng, Z. (2013). How do SMEs with single and multiple owners                finance  their  operations  differently?  Empirical  evidence  from  China.  Thunderbird                International Business Review, vol. 55(5), pp. 531-544.                Nordqvist, M., Melin, L. (2001). Exploring the dynamics of family firms: linking corporate                governance to strategy change. In: 17th EGOS Colloquium. Lyon, pp. 5-7.                Singh,  M.,  Davidson,  W.  N.  (2003).  Agency  costs,  ownership  structure  and  corporate                governance mechanisms. Journal of Banking \& Finance, vol. 27(5), pp. 793-816.                 Steijvers,  T.,  Voordeckers,  W.,  Vandemaele,  S.  (2006).  Ownership,  governance  and                financial performance in small and medium-sized family firms. In: RENT XX Conference.                Brussels.                Uhlaner, L. M., Floren, R. H., Geerlings, J. R. (2007). ̳Owner Commitment and Relational                Governance in the Privately-Held Firm: An Empirical Study. Small Business Economics, vol.                29(3), pp. 275-293                Uhlaner,  L.  M.  (2008).  The  role  of  ownership  in  governance:  A  neglected  focus  in                entrepreneurship and management research. Breukelen: Nyenrode business universiteit.                Uhlaner L. M., Berent-Braun M. M., Flören R. (2015). The influence of ownership social                capital on the performance of privately-held firms: A lagged effect. In: Family Enterprise                Research Conference (FERC). Burlington: University of Vermont, June 4-7.                Young,  M.  N.,  Peng,  M.  W.,  Ahlstrom,  D.,  Bruton,  G.  D.,  Jiang,  Y.  (2008).  Corporate                governance in emerging economies: A review of the principal–principal perspective. Journal                of Management Studies, vol. 45(1), pp. 196-220.                                                                        386","Spatial Inequality in Ownership and Activity of Banking Sector:                                                    Case of Russia                                                      Marina Malkina                                       Lobachevsky State University of Nizhni Novgorod                                         Institute of Economics and Entrepreneurship                                         Macroeconomics and Microeconomics Center                                               Bolshaya Pokrovskaya street 37                                               603000 Nizhni Novgorod, Russia                                                  Е-mail: mmuri@yandex.ru                 Abstract: Our study is aimed at evaluation of spatial inequality in ownership and activity                of  commercial  banks  of  Russian  Federation  in  2001-2016.  The  level  of  interregional                inequality in banking sector was assessed for its assets, financial results, attracted deposits                and provided loans, which were calculated both per capita and as their ratio to GRP of                regional economies. To eliminate the effect of price level on the variables in time series we                determined their real values using the cumulative GRP-deflator indices. The inequality in                spatial distribution of assets, liabilities and financial results of banking sector was evaluated                by means of the weighted coefficients of Gini and variation and tested for - and -types                convergence. We found significant growth of the spatial concentration of ownership and                management  of  assets  in  Russian  banking  sector  accompanied  by  low  and  cyclical                concentration of financial results (net profits) within the sector, which meantime increased                noticeably last year. At the same time, banking activity demonstrated much lower and                sometimes diminishing regional disparities. First of all, we observed decrease in spatial                inequality  in  the  level  of  lending  to  enterprises  and  population,  and  the  both  types  of                convergence were proved for them. This trend was opposite to the growth of interregional                disparities  in  the  average  personal  deposits  and  almost  nil  change  in  inequality  of  the                deposits of enterprises. We concluded that discrepancy between distribution of ownership                on assets and actual implementation of banking activity, in combination with high interest                rate  (cost  of  capital),  contributed  to  further  redistribution  of  financial  resources  of  the                periphery in favor of the center and prevented the improvement of financial position of                backward regions.                Keywords: region, banking system, unevenness, assets, convergence                JEL codes: G21, R12                 1 Introduction                The spatial inequality in the distribution of property and activities in the banking sector is                a fairly new area of economic research. Previous studies in the field of spatial economics                and  new  economic  geography  focused  mainly  on  interregional  inequality  in  GDP,                employment, incomes of the population etc.                Studies  of  the  banking  sector’s  spatial  location  have  been  less  common  and  usually                concerned the analysis of expansion of global banks and crowding out of domestic banks                in regions (Lee and Eun-Joo, 2017), or the processes of convergence of various countries’                financial markets (Niţoi and Poche, 2016). Some researchers also investigated influence of                financial deepening and banks development on regional economic growth (Hasan et al.,                2009; Belke et al., 2016; Asteriou and Spanos, 2018) and income inequality (Haan and                Sturm,  2017;  D’Onofrio  et  al.,  2017). Other  scientists  examined  relationships  between                convergence in the banking sector and indicators of economic development. Thus, testing                of the Turkish regions for conditional β-convergence (Birkan and Akdogu, 2016) did not                provide evidence that alignment of regional financial imbalances led to an evening-out of                regional gross value added per capita.                A number of studies were dedicated to analysis of the spatial distribution of the Russian                banking sector’s activity, its causes and consequences. For example, (Malkina, 2017) used                various indices for assessment of spatial inequality in the banking sector of Russia, which                                                             387","allowed to determine the level and dynamics of interregional disproportions in provision of                Russian regions with banking services. Other authors (Ageeva and Mishura, 2017) analyzed                the determinants of the level of development of regional banking systems in Russia and                obtained evidence that it was affected by both economic and institutional factors, such as                the republican status, distinctive national and ethnic characteristics and degree of regional                authorities’ independence. The researchers also distinguished regional, multi-regional and                federal  banks  with  extended  branch  network  in  Russia  and  explored  their  competitive                advantages  and  weaknesses  (Rykova  et  al.,  2017).  In  the  work  by  (Valiullin  and                Merzlyakova, 2011) the authors concluded that the territorial expansion of Moscow and                other large banks contributed to the development of competition in the Russian banking                sector.                The aim of our current study is to evaluate the degree of interregional unevenness in the                distribution  of  assets,  profits,  loans  and  deposits  of  population  and  enterprises  in  the                banking sector of Russia in dynamics, to test all these parameters for sigma- and beta-                convergence and to compare inequality in distribution of the property and management                and the actual implementation of banking activities in Russian regions. We expect that this                study should shed light on the dynamics and conservation of regional imbalances in the                Russian banking sector.                2 Methodology and Data                 Our study is based on the official data provided by the Central Bank of Russia embracing                the indicators of activity of all credit institutions (banks and their branches) in 80 Russian                regions in 2001-2016. The analysis also involved the data of the Federal State Statistics                Service of Russian Federation on the average population, gross regional product (GRP),                GRP’s volume and deflator indices for Russian regions in the period reviewed.                We studied two types of spatial distribution of banking activity. The first type of spatial                distribution,  related  to  property  and  general  management  in  the  banking  system,  was                assessed on the basis of the data on banks registered in the regions, including value of                their assets, net financial results (profits less losses), deposits and loans. The second type                of spatial distribution, concerned the actual implementation of banking activities, including                attracted deposits and issued loans, was evaluated on the basis of information on all credit                institutions  operating  in  the  given  territories:  the  headquarters  and  branches  of  banks                registered in the regions and the branches of other banks headquartered in other regions.                This type of distribution covers activities of both regional banks and cross-regional banks.                 Since the economies of Russian regions differ significantly in scale, the level of banking                activity in Russian regions was determined on the basis of relative indicators, such as: the                ratio of annual personal deposits or loans to the average population of the regions; the                ratio of assets, financial results, annual deposits or loans provided to enterprises to GRP of                the regions. To eliminate the influence of the inflation factor, the procedure for deflating                the nominal variables was carried out using the cumulative GRP deflator indices. Stock                variables, determined on a certain date, were transformed into flow variables by calculation                of their chronological mean values in the correspondent years.                 The interregional inequality of bank assets and profits, deposits and loans was measured                using two indices:                - the Gini coefficient (based on M. Brown formula):                                 n                           =                        G 1   −    i (X  − i 1  + X  i  ,  )                                         (1)                                = i 1                where   i   - the share of i-th region in total population of country (for indicators related to                population), or the share of i-th region in the total real GRP or assets (for indicators related                                         n                to legal entities);  X i  =  x i   - the cumulative share of regions from the 1st to the i-th in                                         = i 1                                                            388","total value of indicator (loans, deposits, assets or profits), provided that all the regions are                ranked    in   ascending    order    of   its   per   capita   or   relative   value    y i  ,                                n                and  x i  =  i y i    i y i  ;                                = i 1                - the coefficient of variation:                                 n                                    ( −                        CV  =     y i    )  2   ,                                                  (2)                                   i                                = i 1                                                                  n                where    - the country average of  y i  , and   =   i y i  . This coefficient was also used for                                                                  = i 1                testing banks activities in the regions for sigma-convergence.                For the purpose of testing the banks activities in the regions for beta-convergence, we                used the procedure proposed by (Baumol, 1986) and then developed in (Barro and Sala-i-                Martin,  1992).  This  approach  involves  the  construction  of  a  spatial  dependence  of  the                average annual growth rate of the examined indicator on its initial value:                        1  ln( y /  y )  =   +   ln( y ) +  ,                                      (3)                        T     iT  i0    1    2     io    i                 where  y  0 i   - per capita or relative value of correspondent indicator in i-th region in the                initial year of the study, t=0;  y iT   - its value in the final year of the study, t=T; T - total                number of years in time series;  i   - residuals of the model.                 The above equation can also be written in the following form:                         (y /  y i0 )  T / 1  =  0  y   io  2   + u i  ,                             (4)                          iT                where   =  exp( 1 )    and u =  exp( i ) .                                           i                         0                The inverse relationship in this test confirms the existence of -convergence, that is, the                catching-up of the backward regions with advanced regions by the examined parameter.                3 Results and Discussion                 First of all, we should outline some relevant trends in the banking sector of the Russian                Federation in the studied period. One of these trends was a sharp reduction in the number                of banks, which was caused by the recession in economy and the tightening of banking                regulation  in  Russia.  At  the  beginning  of  2001,  there  were  1311  credit  institutions                registered in various regions of Russia, and 3973 branches of credit institutions, of which                1095 were located in the same regions as their headquarters. Thus, in 2001 the share of                own credit institutions in the regions accounted for 47.1% of all operating institutions. In                early 2017, the number of registered credit institutions in the regions decreased to 623,                the number of their branches - to 1098, of which 124 were located in the regions of the                headquarter organizations. Thus, over 16 years the share of own credit institutions in the                regions decreased to 43.4%, i.e. by 3.7 percentage points.                Another important trend was the expansion of Moscow banks in the regions. However, the                crisis phenomena and tightening of regulation affected these banks as well. Some of large                federal  banks,  in  order  to  optimize  activities,  closed  a  part  of  their  regional  branches.                However,  on  average  this  did not  reduce  the  share  of the  banking  market  serviced  by                Moscow banks. Table 1 demonstrates the processes of concentration of banks and their                assets incorporated in Moscow in comparison with other economic indicators of the capital                city.                                                             389","Table 1 The Share of Moscow in Some Economic Indicators, %                                                         2001  2004  2007  2010  2013  2016                     Total population                     7.0     7.4     7.8    8.0     8.4     8.6                     Total GRP                           20.0    19.5    19.3    17.7    18.9    18.5                     Number of registered banks*          n/a    50.3    49.9    49.3    51.7    52.2                     Managing banking assets*             n/a    83.9    84.4    86.1    88.8    90.6                                               Note: *at the beginning of year                 Source: author’s own calculations based on the data officially provided by the Bank of Russia and                                    the Federal State Statistics Service of Russian Federation                 Now we can proceed to analysis of the levels of two types of spatial inequality in the Russian                banking sector measured by means of the formulas 1-2.                 Figure 1 with an embedded table represents the results of calculation of the Gini coefficient                of interregional inequality for the indicators of banks registered in the regions. First of all,                this  figure  demonstrates  a  high  level  of  interregional  differences  in  the  distribution  of                property  of  bank  assets.  Moreover,  for  the  deposits  and  loans  of  both  population  and                enterprises,  this inequality  appeared  to  be even higher  than for  all  assets  on  average.                Among  them,  the  maximum  level  of  spatial  inequality  was  observed  for  the  personal                deposits per capita, attracted by home regional banks, the minimum level - for the ratio of                corporate loans to GRP provided by these banks.                 The growth of inequality indices of all examined variables evidenced the concentration of                property  in  the  banking  sector  of  Russia.  Thus,  the  unevenness  of  assets  distribution                relative to GRP increased by 9.3% for 14 years, whereas the interregional inequality in the                per capita loans provided by regional banks to population increased by 18.1% for 13 years.                For  banks  registered  in  the  regions,  a  minimum  increase  in  inequality  in  2003-2016,                namely 2%, was observed for personal deposits per capita.                 Figure 1 Interregional Inequality in Liabilities and Assets of Banks Registered in Regions                                                   Source: author’s own calculations based on the data provided by the Central Bank of Russia and                                the Federal State Statistics Service of Russian Federation (FSSS)                 At the same time, very interesting data were obtained for spatial inequality of the ratio of                net financial results to bank assets (the so called return on assets). The interregional Gini                coefficient of this indicator was moderate until recently, although highly fluctuating, but it                                                             390","soared up in 2016. Moreover, we identified two cycles of regional banks inequality in ROA                within the period of 2003-2016. The first cycle, covering 2003-2010, reached a bottom                point in 2006 (when G=0.026), which separated the downward and upward waves, and                had a peak in 2010 (when G=0.116). The second cycle, lasting during 2010-2016, reached                a minimum in 2012 (G=0.038) and showed a maximum in 2016 (G=0.458). However,                more than 83% of the four-year growth occurred in the last year under study, when the                crisis in Russian banking sector advanced.                Figure 2  shows the  results  of  calculation  of  the  Gini coefficient for  spatial inequality  of                actual banking activity in Russian regions. This activity embraces the performance of both                regional banks and their home branches and subsidiaries of cross-regional banks registered                in  other  regions,  among  which  the  dominant  role  belongs  to  Moscow  banks.  The                comparison of figures 1 and 2 discovers a more even distribution of attracted deposits and                provided  loans  in  the  regions  than  their  distribution  according  to  the  place  of  banks                incorporation.  Moreover,  in  contrast  to  the  observed  increase  in  the  concentration  of                banking assets ownership, the location of banking activity demonstrated a reduction in                regional disparities.                 This  tendency  was  mainly  related  to  the  average  loans  to  population,  for  which  the                interregional Gini coefficient declined by 63% during the period under review. To a lesser                extent, it manifested itself for the level of loans to firms, which interregional Gini coefficient                diminished by 24.3%. For these types of assets, the coefficient of variation decreased by                67.1% and 31.3% respectively, which proved the significant sigma-convergence of Russian                regions in terms of lending.                For the loans to both population and enterprises, the hypothesis of the beta-convergence                was also confirmed. The dependence between the level of loans to population in 2001 and                the rate of its growth in 2001-2016 in Russian regions was described by a function with a                negative slope:                  1  ln(y   / y  ) =  . 0  1869−  . 0  0494 ln(y  )        / 1  15  =  −  . 0  0494  2  =                15     iT   0 i    . 0 (  0027 )  . 0 (  0017 )  io  ;   y iT  / y i 0    . 1 2055 y io  ;  R  . 0  912  .                                                                                        Figure 2 Interregional Inequality in Actual Banking Activity                                              Source: Author’s own calculations based on the data provided by the Central Bank of Russia and                                the Federal State Statistics Service of Russian Federation (FSSS)                 This confirms approaching of backward regions to advanced regions on average in terms                of the issued loans per capita. Indeed, the highest growth rates of the personal loans per                                                             391","capita were observed exactly in the regions with their lowest initial levels, namely in the                North Caucasian republics of Chechnya (where they increased 22067 times over 16 years)                and Ingushetia (224.5 times), the southern border republics of Kalmykia (76 times) and                Tyva (71 times), as well as in the Far Eastern republic of Sakha-Yakutia (75 times). On the                contrary, the lowest growth rate of personal loans per capita (6.4 times) was found in                Moscow, which used to be among the leaders by this indicator in 2001 and ranked second                after the Magadan Region.                The relationship between the level of loans to enterprises in 2001 and its growth rate for                2001-2016  was  slightly  weaker,  compared  to  that  for  loans  to  population,  but  also                significant and negative by sign:                                                                             1  ln(y   / y  ) =  −  . 0  0708−  . 0  0589 ln(y  )  ;   y   / y    / 1  15  =  . 0  9317 y  −  . 0 0589  ;  R 2  =  . 0  698 .                15     iT   0 i    . 0 (  0126 )  . 0 (  0044 )  io    iT  i 0    io                                                                                    The largest increase in loans to enterprises was again indicated in the subjects of the North                Caucasus Federal District - the Chechen (250 times) and the Karachay-Cherkess republics                (16 times), and the subjects of the Volga Federal District - the republics of Mordovia (25                times) and Mari El (17 times). In 2001, these regions also demonstrated one of the lowest                levels of lending to enterprises. However, the growth rates of corporate loans in backward                regions turned out to be scattered, that can be attributed to the specifics of their economic                structure, spatial location and institutional environment. It should also be noted that in 7                out of 80 regions the level of corporate loans even declined, and in all of them, with the                exception of the Komi Republic, the initial level of lending was relatively higher than the                national average.                 For  the  deposits  of  both  population  and  firms  attracted  in  the  regions,  the  level  and                dynamics of spatial inequality turned out to be fundamentally different. First of all, the                interregional inequality of  bank deposits noticeably exceeded interregional inequality of                bank loans across the time. However, similar to loans, the spatial inequality of corporate                deposits significantly surpassed the spatial inequality of personal deposits. Furthermore,                unlike  loans,  which  demonstrated  regional  convergence,  regional  disparities  in  the                population's per capita deposits increased on average (according to the Gini coefficient, by                12.8%), and regional differences in the level of corporate deposits almost did not change.                Meanwhile, according to the coefficient of variation, interregional inequality for both types                of  deposits  increased  (by  22.1%  for  personal  deposits  and  by  11.7%  for  corporate                deposits). This testifies existence of the sigma-divergence of Russian regions in terms of                deposits.                Despite  the  evidence  of  a  general  divergence  of  Russian  regions  in  terms  of  attracted                deposits, the correspondent test confirmed the existence of beta-convergence for personal                deposits. Indeed, the relationship between their level of 2001 and its average growth rate                in 2001-2016 can be expressed by the functions:                  1  ln(y   / y  ) =  . 0  1688−  . 0 0424 ln(y  )     / y    / 1  15  =  . 1 1838 y  −  . 0  0424  ;  R 2  =  . 0  8759                15     iT   0 i    . 0 (  0028 )  . 0 (  0018 )  io  ;   y iT  i 0    io           .                                                                         The largest increase in deposits was again observed in the regions with their lower initial                levels: in the republics of Chechnya, Ingushetia, Kabardino-Balkaria (all are the subjects                of the North Caucasus Federal District) and in the Altai Republic (located in the Siberian                Federal  District).  The  smallest  growth  of  personal  deposits  per  capita  (1.9  times)  was                observed in the Magadan Region, which was ranked first by this indicator in 2001. For                corporate deposits, the beta-convergence was not confirmed.                 The revealed mismatch between sigma- and beta- convergence for deposits of population                is consistent with findings by other researchers who discovered a similar phenomenon in                other spheres (Young, Higgins, Levy, 2008; Dvoroková, 2015).                Figure  2  also  shows  the  change  in  regional  unevenness  of  net  per  capita  deposits  of                population  (deposits  minus  loans)  and  net  relative  loans  of  enterprises  (loans  minus                                                             392","deposits  ratio  to  GRP).  For net  deposits  of  population  the inequality initially increased,                peaked in 2007, and then declined. Eventually, it increased by 11.1% according to the Gini                coefficient  and  by  17.5%  according  to  the  coefficient  of  variation.  For  net  loans  to                enterprises the inequality changed cyclically and ultimately decreased by 33.7% according                to the Gini and by 38.5% according to the CV. In addition, the tests evidenced the absence                of beta-convergence for both net personal deposits and net corporate loans.                 Thus, we found a discrepancy between growing disparities in the regional distribution of                ownership  of  bank  assets  and  reducing  disparities in  provision  of  Russian  regions  with                banking services, primarily lending. At the same time, the average gap between regions in                the level of corporate deposits did not decrease. Besides, the gap of regions in the average                personal deposits even widened, albeit these deposits were redistributed in space in favor                of poor regions.                 How can we explain the discrepancy? In our view, one of the reasons of this phenomenon                is the high  real interest  rate,  which largely  contributes to  the  preservation  of  financial                backwardness of regions - net debtors and supports further improvement of the position                of  regions  -  net  creditors.  Moreover,  the  redistribution  of  property  rights  of  financial                resources in favor of federal banks leads to redistribution of banks’ incomes and risks in                this direction as well.                 4 Conclusions                 Our study is dedicated to comparative analysis of interregional inequality in ownership and                management of banking assets, on the one hand, and implementation of actual banking                activities, on the other hand. For this purpose we evaluated inequality in spatial distribution                of banking assets, profits, deposits and loans to population and enterprises relatively to                distribution of population, GRP and banking assets in regions. The interregional inequality                was assessed for both credit organizations incorporated on a certain territory and credit                organizations operated there. The level of spatial inequality was evaluated in dynamics by                means of the weighted Gini coefficient and the coefficient of variation and tested for sigma-                and beta-convergence using the technique developed by Baumol and Barro \& Sala-i-Martin.                As a result, we found a significant increase in the spatial concentration of ownership and                asset management in the Russian banking sector and the growing expansion of Moscow                banks to other regions. Meanwhile, these processes were accompanied by the alignment                of actual banking activities in the regions. The difference between the level of borrowing                of enterprises and the population from the banking system has significantly reduced, and                for corporate and personal loans both types of regional convergence were confirmed. On                the  contrary,  the  deposits  of  population  per  capita  demonstrated  an  increase  in                interregional imbalances. However, the sigma-divergence of regions by personal deposits                was accompanied by their beta convergence by this type of liabilities. Further, the deposits                of  enterprises  showed  only  sigma-divergence,  according  to  the  coefficient  of  variation.                Ultimately, we came to the conclusion that the opposite tendencies in the distribution of                property and actual activity in the banking sector, combined with a high real interest rate,                could lead to a further redistribution of the financial resources of the periphery in favor of                the federal center and prevent the equalization of financial conditions of Russian regions.                 References                Ageeva, S., Mishura, A. (2017). Institutional Factors to Assess the Spatial Development of                Regional Banks. Region: Economics and Sociology, vol. 2 (94), pp. 52-75, in Russian.                Asteriou,  D.,  Spanos,  K.  (2018).  The  Relationship  between  Financial  Development  and                Economic Growth during the recent Crisis: Evidence from the EU. Finance Research Letters,                in press.                Barro,  R.J.,  Sala-i-Martin,  X.  X.  (1992).  Convergence.  Journal  of  Political  Economy,                vol. 100, pp. 223–51.                                                               393","Baumol, W.J. (1986). Productivity Growth, Convergence, and Welfare: What the Long-run                Data Show. American Economic Review, vol. 76(5), pp. 1072-1085.                 Belke,  A.,  Haskamp,  U.,  Setzer,  R.  (2016).  Regional  bank  efficiency  and  its  effect  on                regional growth in “normal” and “bad” times. Economic Modelling, vol. 58, pp. 413-426.                Birkan, A. O., Akdogu, S. K. (2016). The geography of financial intermediation in Turkey:                1988–2013. The Annals of Regional Science, vol. 57(1), pp. 31–61.                Dvoroková, K. (2015). Sigma Versus Beta-convergence in EU 28: do they lead to different                results?  In:  Proceedings  of  The  5th  MAC  2015.  Prague,  pp.  88-94.  Retrieved  from:                http://www.wseas.us/e-library/conferences/2014/Tenerife/ECONMATH/ECONMATH                -13.pdf/.                D’Onofrio, A., Minetti, R., Murro, P. (2017). Banking development, socioeconomic structure                and income inequality. Journal of Economic Behavior \& Organization, in press.                Haan, J., Sturm, J.-E. (2017). Finance and income inequality: A review and new evidence.                European Journal of Political Economy, vol. 50, pp. 171-195.                 Hasan, I., Wachtel, P., Zhoud, M. (2009). Institutional development, financial deepening                and  economic  growth:  Evidence  from China.  Journal  of  Banking  \&  Finance,  vol.  33(1),                pp. 157-170.                Lee,  E.-J.  (2017).  Intra-  and  inter-regional  portfolio  diversification  strategies  under                regional  market  integration:  Evidence  from  U.S.  global  banks.  International  Review  of                Financial Analysis, vol. 54, pp. 1-22.                Malkina, M.Yu. (2017). Uneven provision of Russian regions with banking services. Finance                and Credit, vol. 23, issue 36(756), pp. 2136-2158, in Russian.                Niţoi,  M.,  Poche,  M.  M.  (2016).  Testing  financial  markets  convergence  in  Central  and                Eastern Europe: A non-linear single factor model. Economic Systems, vol. 40(2), pp. 323-                334.                Rykova, I. N., Gubanov, R. S., Dansonova, B.S. (2017). Regional banking market in Russia:                theory and practice. Bankovskoe delo [Banking], vol. 8, pp. 24-33, in Russian.                Valiullin, K. K., Merzlyakova, S. I. (2011). Tendencies towards the spatial concentration of                the  Russian  banking  sector.  Studies  on  Russian  economic  development,  vol.  22(5),                pp. 526-534.                 Young, A.T., Higgins, M.J., Levy, D. (2008). Sigma Convergence versus Beta Convergence:                Evidence from  U.S.  County-Level  Data.  Journal  of  Money,  Credit  and  Banking,  vol.  40,                pp. 1083-1093.                                                                                        394","The Use of Backtesting in Assessment of the Value-at-Risk on                        Unites States, Great Britain and German Capital Markets                                        1                                                            2                                                                                          3                      Tomáš Meluzín , Marcin Fałdziński , Michał Bernard Pietrzak , Adam P.                                              Balcerzak , Marek Zinecker                                                                            5                                                         4                                               1  Brno University of Technology                                             Faculty of Business and Management                                         Kolejní 2906/4, 61200  Brno, Czech Republic                                               E-mail: meluzint@fbm.vutbr.cz.                                            2  Nicolaus Copernicus University in Toruń                                        Faculty of Economic Sciences and Management                                           Ul. Gagarina 13a, 87-100 Toruń, Poland                                                    E-mail: marf@umk.pl                                            3  Nicolaus Copernicus University in Toruń                                        Faculty of Economic Sciences and Management                                           Ul. Gagarina 13a, 87-100 Toruń, Poland                                               E-mail: michal.pietrzak@umk.pl                                            4  Nicolaus Copernicus University in Toruń                                        Faculty of Economic Sciences and Management                                           Ul. Gagarina 13a, 87-100 Toruń, Poland                                               E-mail: adam.balcerzak@umk.pl                                                5  Brno University of Technology                                             Faculty of Business and Management                                         Kolejní 2906/4, 61200  Brno, Czech Republic                                               E-mail: zinecker@fbm.vutbr.cz                 Abstract: For many years, a progressive globalization process has been observed, which                translates into an increase in interdependencies between world economies. This growing                level of interdependence affects the functioning of economies significantly. The greatest                impact of mutual connections can be observed on the world capital markets. Therefore,                the  issue  of  the  identification  and  measurement  of  market  risk  becomes  an  important                problem related to the functioning of capital markets. Value at Risk is a metric that allows                the risk of loss for selected assets to be assessed. The research objective of this article is                to assess the quality of Value at Risk applied to measure the risk of the DJIA, DAX, and                FTSE stock indices. The VaR estimation was carried out in the time period 2000-2012,                where the DCC-GARCH model with the conditional Student’s t-distribution was used. To                evaluate VaR quality, the backtesting procedure was used, within which we used the Juc,                Jind, and Jcc tests.                Keywords:  capital  market,  value-at-risk,  backtesting,  DCC-GARCH  model,  conditional                variance                JEL codes: G15, C58                 1 Introduction                A progressive globalization process has been observed since the 1990s. This translates into                an increase in interdependence between world economies and their respective markets.                This phenomenon is becoming a significant factor affecting the efficiency of financial and                banking systems in providing capital for growth of real sphere (Meluzin et al., 2017; 2018a;                2018b; Kubiszewska, 2017; Balcerzak et al., 2017; Gavurova et al., 2017; Dusatkova et                al., 2017, Dvorsky et al., 2017; Ivanová, 2017). Therefore, it translates into countries’                competitiveness  and  socio-economic  situation  (Cheba  and  Szopik-Depczyńska,  2017;                Zygmunt, 2017).                                                                395","The potential instability of international financial markets, which can be the result of these                interdependencies, can have especially strong and negative influence for lower developed                economies, which can suffer from rapid capital flows even in the case of effective national                governance, or prudent fiscal and monetary policy (Sanusi et al., 2017; Mihóková et al.,                2017; Simionescu et al., 2017; 2018)                The most dynamic growth as well as the variability of mutual dependencies can be observed                in the case of financial markets, which is confirmed by the empirical research (Heryan and                Ziegelbauer,  2016;  Cevik  et  al.,  2017;  Fałdziński  et  al.,  2017;  Pietrzak  et  al.,  2017a;                Vukovic et al., 2017). The identification and measurement of market risk resulting from                the  links  between  markets  is  an  important  indication  for  domestic  decision-makers                representing  various  financial  institutions  (Khan  et  al.,  2016;  Navid  and  Shabantaheri,                2017; Ahmed et al., 2018). Therefore, the issue of market risk management becomes a                significant problem related to the functioning of capital markets. The greatest risk of loss                relates  to  those  assets  that  are  listed  on  capital  markets,  where  their  capitalization  is                systematically increasing (Meluzin and Zinecker, 2016).                Value at Risk is a metric that allows the risk of loss for selected assets to be assessed. The                major research objective of the proposed article is to assess the quality of Value at Risk                applied to measure the risk of the DJIA, FTSE, and DAX stock indices. The VaR estimation                was  carried  out in the time  period  2000-2012,  where  the  DCC-GARCH model  with the                conditional  Student’s  t-distribution  was  used.  To  evaluate  VaR  quality,  the  backtesting                procedure was used, within which we used the Juc, Jind, and Jcc tests.                 2 Methodology and Data                The Value at Risk (VaR) metric is commonly used to assess risk for financial institutions                (Smolović et  al,  2017).  The  risk  assessment  concerns the  financial  assets held  and, in                accordance with the determined VaR, the institution must make appropriate reserves to                secure  potential  losses.  This  means  that  Value  at  Risk  belongs  to  the  group  of  risk                measures.  VaR  estimation  is  performed  based  on  the  knowledge  of  the  distribution  of                returns of selected financial asset and allows determining the probability of loss occurrence.                Determining the distribution parameters is a challenging task due to the volatility of the                variance  heteroscedasticity  and  due  to  the  occurrence  of  interdependencies  between                markets and assets. Suitable modelling of returns distribution can be performed using the                DCC-GARCH model (see Szumilo et al., 2018; Zinecker et al., 2016; 2017), which is defined                by means of the following formulas:                           =    +    ,    |     −1 ~  (0,    ),    =                                     (1)                                                                                                                                                                                                                                                                                                                                                                                                                                                       =         {   },                                                              (2)                                                                                                                       ,    =    +       2  +         ,  −1                                        (3)                                                                                                                    ,  −1                          =                                                                             (4)                             −                                                                                                                       =         {   } −1/2            {   } −1/2                                    (5)                                                                                                                                                                                                ′                                                    ̅                                                                =    +        −     −    +        −   ,    =   (1 −    −   ),                     (6)                                                                                  t                 Y t  - multivariate process of returns,   - conditional t-distribution with    > 2 degrees of                freedom, μ  t - the vector of conditional means of returns,  H t  - the conditional covariance                matrix,   t i,   - i-th  equation of the conditional mean, h  t i,   - the conditional variance equation                for  i-th  returns,    where  i = 1,..., N  ,     ,    ,      the  parameters  of  the  conditional  variance                                                                                                                                                                                                   equation,  R t   -  conditional  correlation  matrix,  Q t   -  quasi  correlation  matrix,  R  -  the                unconditional covariance matrix of the standardize errors which can be estimated or set as                ̅   1            ,   ,    the parameters of the conditional correlation equation.                            ′                   = ∑                          =1                       Aielli  (2009)  shows  that  the  estimation  of      as  the  the  empirical  covariance  matrix  is                                                             ̅                inconsistent. So, we decided to estimate the unconditional covariance matrix.                                                            396","Value-at-Risk (VaR) is a commonly used risk measurement technique. It can be applied to                assess  the  probability  of  an  asset loss,  where  different levels  of  probability  and  of  the                potential loss are assumed. Value at Risk can be defined as the amount of loss of an asset                (portfolio) whose probability of reaching or exceeding it in a given time interval is equal to                a given tolerance level. If the quantile of the distribution of the value of the asset (portfolio)                    is determined using the formula below:                                            (   ≤    ) =                                                                   (7)                                                then Value at Risk can be defined as follows:                              = −      ,                                                                (8)                                                     where  r is  the  return,      is  the initial value  of  the  asset  (portfolio),  and     is  the                                                           tolerance level (see: Dowd, 2005).                Within the first step of the procedure of VaR estimation we need to assume the distribution                of the asset returns. The decided to estimate the DCC-GARCH model, and then use the                parameter estimates to compute VaR. The use of the DCC-GARCH model allows taking into                account the autocorrelation of returns and the properties of the variance heterogeneity in                the form of autocorrelation of returns squares, as well as conditional correlations. Based                on  the  results  of  the  parameter  estimation  of  the  DCC-GARCH  class  models,  VaR  is                estimated by applying the following formula:                                                                                                       = −     +1 +ℎ   +1                                                        (9)                                             where       +1  is a one period ahead forecast of conditional mean, ℎ   +1   is a one period ahead                forecast of conditional variance and     is the q-quantile of conditional distribution.                                                                        The second step of the VaR determination procedure, requires evaluating the quality of its                estimates. After Value at Risk estimation its quality should be assessed. The VaR measure                is tested for two properties: unconditional coverage and independence property. The first                one  assumes  that  the  expected  number  of  hits  (cases  when  returns  are  greater  than                estimated  VaR)  given  the  assumed  coverage level.  The  second  one  states  that  the  hit                process  is  independent.  For  this  purpose  the  VaR  estimates  are  compared  to  the                realizations of the asset returns. In the article, in order to perform backtesting, we used                the unconditional coverage test Juc, the test of independence Jind, and the conditional                coverage test Jcc (Candelon et al., 2011). Candelon et al. (2011) show that the statistical                power of these tests are greater than standard test used for backtesting.                 3  Results and Discussion                Following the determined research objective of this article regarding the assessment of the                quality of Value at Risk, the study will be conducted based on three stock indices,  i.e.,                DJIA, FTSE and DAX. The selection of indices will allow us to solve the problem of assessing                the risk measurement related to the functioning of the US, UK and German capital markets.                The study was conducted in the time period 2000-2012. Logarithmic returns were used to                estimate the DCC-GARCH model and VaR. Returns were calculated based on the levels of                selected indices and data was obtained from the Yahoo Finance website.                 In  accordance  with  the  presented  procedure  for  VaR  estimation,  in  the  first  step  the                distribution of returns of selected stock indices should be determined. For this purpose, the                parameter  of  the DCC-GARCH  model  with  conditional  t-distribution  was  estimated.  The                initial analysis of individual DJIA, FTSE and DAX indices indicated the correctness of the                GARCH(1,1) model estimation for each of them. This information was used to determine                the  model  specification,  where  the  DCC-GARCH(1,1)  model  was  adopted.  Therefore,                estimation of the model parameters was carried out and the results obtained are presented                in Table 1. The analysis of the parameters estimates of the model indicates a good fit to                the empirical data, which means that this model should describe the distribution of returns                correctly.  All  parameters  related to  the  conditional variance  and  conditional  correlation                equations were found to be statistically significant at the 5% significance level. For each of                the equations of the conditional variance, the sum of    +     is less than 1. The estimates                                                                                                                                                                                                                    397","of  the  parameter  v  of  the  t-distribution  confirms  the  occurrence  of  thick  tails  in  the                distribution of the returns.                  Table 1 The Results of the Estimation of the Multivariate DCC-GARCH Model Parameters                                          The conditional variance equations                                Parameter                    Estimate         Std. error        p-value                                    (DJIA)                    0.0168          0.0037           0.0000                                  1                                  1   (DJIA)                  0.0605          0.0060           0.0000                                  1  (DJIA)                   0.9312          0.0064           0.0000                                  2  (DAX)                    0.0114          0.0027           0.0000                                  2  (DAX)                    0.0704          0.0080           0.0000                                   2  (DAX)                    0.9204          0.0084           0.0000                                  3   (FTSE)                  0.0124          0.0027           0.0000                                  3   (FTSE)                  0.0696          0.0073           0.0000                                  3   (FTSE)                  0.9213          0.0076           0.0000                                                               8.9725          0.7264           0.0000                                         The conditional correlation equation                                 Parameter                    Estimate         Std. error        p-value                                                              0.0198          0.0033           0.0000                                                              0.9746          0.0054           0.0000                                                                0.5242          0.0691           0.0000                                     12                                                               0.8190          0.0304           0.0000                                     13                                                               0.4784          0.0728           0.0000                                     23                                                  Source: own calculations.                 Therefore,  it  was  decided  to  use  DCC-GARCH  model  to  estimate  Value-at-Risk.  The                procedure  consisted  in  estimating  DCC-GARCH(1,1)  model  for  last  2000  observations                applying rolling window approach. The first model was estimated based on the initial 1000                observations, and each subsequent model was based on the data window, shifted one-day                session  forward.  In  this  way  2000  one-day  ahead  VaR  forecasts  were  obtained.  Each                forecast is a measure of the risk of incurring a particular loss at a given probability level.                Thus, market risk forecasts were obtained for each of the three indices.                Then, in accordance with the assumed objective of the article, the quality of VaR obtained                was evaluated on the basis of the backtesting procedure. The results of the binominal tests                are presented in Table 2. The analysis of the results obtained based on the Juc, Jcc, and                Jind  tests  indicates  the  correctness  of  VaR  in  terms  of  unconditional  coverage  and                independence for  all three indices. For  most lags, there is  no  reason  to  reject  the  null                hypothesis  at  the  5%  significance  level,  which  indicates  that  the  VaR  properties  are                appropriate for each of the tests. Only in two cases the null hypothesis is rejected, for DAX                Jcc test and for DJIA Jind test.                             Table 2 Results for Juc, Jcc, Jind Tests for 95% Confidence Level                                        Statisti     Simulated                   Statisti    Simulated                  Index      Test                                     Test                                          c          p-value                       c         p-value                           Juc(p=1)    1.3534         0.2403        Jind(p=1)  0.0095         0.7993                           Jcc(p=2)    4.9370         0.0784        Jind(p=2)  4.7987        0.0216                   DJIA                           Jcc(p=3)    7.2218         0.0594        Jind(p=3)  6.5493        0.0280                           Jcc(p=4)    7.3700         0.0764        Jind(p=4)  6.5811        0.0424                                                             398","Jcc(p=5)    7.4074         0.0919        Jind(p=5)  6.7672         0.0519                           Jcc(p=6)    7.4083         0.1126        Jind(p=6)  6.8188         0.0672                           Juc(p=1)    3.8365         0.0447        Jind(p=1)  0.0088         0.8841                           Jcc(p=2)    4.8683         0.0817        Jind(p=2)  0.9527         0.2899                           Jcc(p=3)    6.4406         0.0748        Jind(p=3)  3.1778         0.1133                   FTSE                           Jcc(p=4)    8.1640         0.0631        Jind(p=4)  4.8119         0.0837                           Jcc(p=5)    9.5744         0.0547        Jind(p=5)  5.7938         0.0746                           Jcc(p=6)  10.5593          0.0552        Jind(p=6)  6.3225         0.0838                           Juc(p=1)    4.8896         0.0276        Jind(p=1)  0.0086         0.8951                           Jcc(p=2)    6.5668         0.0433        Jind(p=2)  1.7289         0.1479                           Jcc(p=3)    8.3263         0.0415        Jind(p=3)  3.7312         0.0820                   DAX                           Jcc(p=4)    9.4476         0.0420        Jind(p=4)  4.1612         0.1009                           Jcc(p=5)    9.8601         0.0492        Jind(p=5)  4.1717         0.1409                           Jcc(p=6)    9.9473         0.0596        Jind(p=6)  4.1787         0.1831                Bold values means rejection of the null hypothesis based on the 5% significance level.                                              Source: Source : own calculations.                 4 Conclusions                 The article addresses the issue of the identification and measurement of market risk of                assets  listed  on  capital  markets.  The  increasing  level  of  interdependence  between                economies affects the situation on capital markets significantly; therefore the problem of                market risk becomes a major problem related to their functioning. A commonly used tool                for  assessing  the  risk  of  losses  for  selected  assets  is  the  Value  at  Risk  measure.  The                research objective of the article was to estimate Value at Risk for selected DJIA, DAX and                FTSE stock indices, as well as to assess the quality of the determined values of VaR. In                order to verify the properties of VaR, the backtesting procedure was applied, in which the                Juc test, Jind test and Jcc test were used. 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Oeconomia Copernicana, vol. 8(4), pp. 505-521.                                                                                          401","Changes in Consumer Credit Market in Slovakia                                                                               3                                                                                             4                                             1                                                               2                             Michal Mešťan , Maroš Malíček , Matej Žofaj , Ivan Králik                                                    1  Matej Bel University                                  Faculty of Economics, Department of Finance and Accounting                                       Tajovskeho 10, 975 90  Banská Bystrica, Slovakia                                               E-mail: michal.mestan@umb.sk                                                    2,4  Matej Bel University                        Faculty of Economics, Department of Public Economics and Regional Development                                       Tajovskeho 10, 975 90  Banská Bystrica, Slovakia                                     E-mail: maros.malicek@umb.sk , ivan.kralik@umb.sk 4                                                                  2                                                     3  Matej Bel University                                  Faculty of Economics, Department of Tourism and Hospitality                                       Tajovskeho 10, 975 90  Banská Bystrica, Slovakia                                                E-mail: matej.zofaj@umb.sk 4                 Abstract:  Slovak  households  have  been  among  the  fastest-growing  debtors  of  all  EU                countries in the past 10 years. Consumer credit regulation in Slovakia over the last 3 years                has considerably tightened the terms of consumer credit provision not only for banks but                especially for non-bank entities that have had to undergo relicensing. In this article, we                focus on the analysis of consumer behavior and the structure of consumer credit granted                to households by banks and non-bank entities, taking into account the volumes of new                loans and their prices, expressed in interest rates and annual percentage rate of charge.                At the same time, we evaluate the real effect of the tightened regulation on the consumer                credit market for households in Slovakia.                Keywords: banks, non-banks, annual percentage rate of charge, interest rates, consumer                credit.                 JEL codes: E51, P36, G21, G23, H31                1 Introduction                 The consumer credit market is an integral part of the market economy. It allows economic                entities with a shortage of capital to obtain the necessary resources to finance and satisfy                their needs from economic subjects with surplus capital. The basic entity operating in the                consumer credit market are banks. In addition to banks, non-bank entities are also active                in this market. At present, the consumer credit market is also made up of other entities                that do not have a banking license and are referred to as non-bank entities. According to                Milne and Parboteeah (2016), due to the development of sharing economic industry the                number  of  so-called  Peer-to-peer  lending  platform  increased  in  recent  years.  Those                platforms apart from traditional regulation, combines demand and supply with consumer                credit. Economic developments in recent years is coupled with lower interest rates and less                strict regulation. It has enabled consumer credit to be extended to a wider range of people,                leading to significant household indebtedness over the last nine years in Slovakia.                There  are  several  academic  contributions  that  deal with the  credit market in  Slovakia.                Specific features of the Slovak banking sector can be studied in Horvátová (2014) and an                impact of the crisis on the financial system of Visegrad countries is explored in Lawson and                Zimková  (2009).  Majority  of  studies  focused  on  a  profitability,  technical  efficiency  and                copetiveness of the banks operating in Slovakia, e. g. Boďa and Zimková (2015), Boďa,                Farkašovský and Zimková (2016), Palečková (2015), Zimková (2014), Horvátová (2017).                Only some of them reflect also the quality of the banking services, e.g. Boďa and Zimková                (2013), or Farkašovský and Pinter (2014). On the contrary, the papers on the financial                services  of  non-banking  sector  are  scare  in  the  academic  literature.    The  aim  of  our                contribution is to fill this gap as we aim to research the structure of consumer credit granted                to households by both, banks and non-bank entities.                                                              402","In this article, we focus on the analysis of consumer behavior and the structure of consumer                credit  granted  to  households  by  banks  and  non-bank  entities,  taking  into  account  the                volumes of new loans and their prices, expressed in interest rates and annual percentage                rate of charge. At the same time, we evaluate the real effect of the tightened regulation                on the consumer credit market for households in Slovakia.                 2 Regulation of Consumer Credits in Slovakia                Rights and obligations related to the provision of consumer credits on the basis of consumer                credit agreements has been regulated in Slovakia via separate legislative act since 2010.                The area of consumer credit has been regulated by the Act No 129/2010 Coll. on consumer                credits and other credits and loans for consumers and amending certain laws.                The framework of the consumer credit regulation between the years 2010 and 2015 could                be  characterized  as  a  soft  approach  towards  the  rights  and  obligations  of  market                participants, where the providers has not been limited in entering the market or product                design. However, this soft approach has turned out to be too liberal for providers and led                to many complaints from the side of consumers on applied practices and unfair conditions.                This  market  development  has  led  to  the  demand  for  a  new  regulatory  framework  on                consumer credit, which became effective since April 2015 (and some parts afterwards).                 However, the Act on consumer credits is not the only legislative framework regulating the                consumer credit market in Slovakia. Additional legislative acts that have significant impact                on the consumer credit market, consumer credit products and additional conditions, rights                and obligations could be summarized as follows:                     •   § 52 - § 54 of the Civil Code,                    •   Act  No.  129/2010  Coll.  on  consumer  credits  and  other  credits  and  loans  for                       consumers and amending certain laws,                    •   Act No. 266/2005 Coll. on the protection of consumers in respect of the distance                       marketing of financial services (and amending certain laws)                    •   Act No 250/2007 Coll. on consumer protection.                The Civil Code as well as Act on Consumer credits points at the Regulation No 87/1995                Coll. of the Government of the Slovak Republic implementing certain provisions of the Civil                Code, where the price regulation of the consumer credit products is presented.                 Thus, we can generalize, that the consumer credit regulatory framework consists from five                main legislative acts, which form the basis for organization of the consumer credit market                and product design, distribution, pricing and market participant behavior.                Regulatory changes in 2015                In order to explain the regulatory changes after the April 2015, we have structured the                text into several areas defining the consumer credit market:                    •   authorization (licensing) of the consumer credit providers (non-banking providers),                    •   price regulation (ceiling),                    •   pre-contractual obligations of providers,                    •   evaluation of creditworthiness and ability to repay the consumer credit,                    •   details of the consumer credit agreement, • information obligations for the purpose                       of regulation and supervision.                Main regulatory changes in 2016                Main regulatory changes in 2016 have  been focused on tackling the issue of increased                speed  of  indebtedness  of  households.  Legislative  changes  defined  under  the  Act  No.                90/2016 Z.z. on Mortgage loans have changed the definition of consumer credit where also                credits used for financing real estate where no real estate collateral is required fall under                the scope of consumer credit. At the same time, for these types of consumer credits there                is no limit for repayment, while for other types of consumer credit a maximum of 10 years                is defined as the maximum length for which consumer credit can be offered.                                                              403","However,  significant  influence  on  the  consumer  credit  market  have  had  the  Act  No.                299/2016 Z.z., which changes the Act on Consumer Credit. The dominant part of the act                focuses on the introduction of measures oriented on the obligation of a creditor to assess                the creditworthiness of the consumer before the consumer credit is approved as well as                during the whole contract duration. The creditworthiness assessment is based on simple                formula comparing the net monthly income of a consumer (“A”) and minimum standard                living  costs  (“B”),  costs  of  a  credit  provided  to  a  consumer  (“C”)  and  other  obligatory                monthly payments (“D”). Thus, the consumer credit can be provided under the condition                that                          ≥    +    +                                                                  (1)                 At the same time, creditor has to take into account the impact of interest increases and                shall not take into account expected increase of future consumer´s income if this is not                plausibly  shown.  The  above  mentioned  Act  also  introduced  the  regulatory  power  for                National Bank of Slovakia to issue detailed regulation on how to calculate all items A till D                as well as the power to gather statistical data on provided consumer credit from credit                providers on a regular basis and other oversight powers including the fines for consumer                credit providers for breaching the obligations.                Main regulatory changes in 2017                Legislative  changes  in  2017  have  been  focusing  on  further  tightening  the  introduced                formula where the minimum living standard costs has been increased by a coefficient of                1,2  and  thus  increased  the  overall  limit  for  consumer  creditworthiness  assessment.                National Bank of Slovakia has issued its regulations which significantly influenced the ability                to  provide  consumer  credits.  At  the  same  time,  National  Bank  of  Slovakia  has  started                preparing  its  regulation  on  overall  limits  implied  on  consumer  credit  providers.  These                regulations are expected to be effective starting July 2018.                 In the context of the above-mentioned legislative changes, we will look at the development                of the consumer credit market in Slovakia in the context of banks and non-bank entities                and the impact on the structure of the market.                 3 Development and Changes in Consumer Credit Market in Slovakia                Economic development not only in Slovakia but also in the surrounding countries of the V4                region  is  characterized  by  a  positive  and  stable  growth  rate  since  the  outbreak  of  the                financial and economic crisis in 2009. Information about real GDP growth rates in selected                countries  is  shown  in  Figure  1.  Positive  Economic  Growth  is  linked  to  the  growth  of                economic  activity,  which  is  manifested  by  increased  production  of  companies  and                household consumption. Economic developments in the last nine years have been coupled                with  the  extensive  fiscal  stimulus  of  individual  governments,  supplemented  by  an                expansionary monetary policy not only by the European Central Bank (ECB) but also by                other central banks outside the Eurozone or in the world. Expansionary monetary policy                has been associated with a reduction in key interest rates, which currently reach a zero                level. The cost of credit has fallen and has become more accessible to consumers. The aim                was to promote economic growth and increase consumption of households and companies.                Household  indebtedness  can  be  characterized  as  the  ratio  of  household  debt  to  gross                domestic product or as a ratio to their net disposable income. From our point of view, it is                more  appropriate  to  indicate  household  indebtedness  as  a  ratio  to  the  net  disposable                income  of  households.  Figure  2  shows  the  evolution  of  indebtedness  to  household  net                disposable  income  between  2006  and  2016.  Among  the  countries  surveyed,  Slovak                households recorded the highest increase in indebtedness, which grew at 32,59 % in 2006                to 73,53 % in 2016. Higher the level of indebtedness has only the Germans among the                countries surveyed. In 2016, it reached 93,41 %, a decrease of 12,31 pp compared to                2006.                                                               404","Figure 1 Real GDP Growth in Selected Countries                  0,12                   0,1                  0,08                   0,06                   0,04                  0,02                     0                    2006    2007   2008    2009   2010    2011   2012   2013    2014   2015    2016   2017                 -0,02                  -0,04                  -0,06                 -0,08                                  Czech republic     Hungary       Poland        Slovakia      Germany                                                    Source: Eurostat, 2018                                 Figure 2 Household Total Debt of Net Disposable Income                  1,2                    1                  0,8                   0,6                  0,4                  0,2                     0                       2006    2007   2008    2009    2010   2011    2012   2013    2014   2015    2016                               Slovakia       Czech republic     Poland        Hungary       Germany                                                    Source: Eurostat, 2018                 Figure 3 shows the change in household indebtedness of individual EU countries between                2006  and  2016  and  2009  to  2016.  In  both  cases,  Slovak  households  are  among  the                countries with the highest growth. Between 2009 and 2016, household indebtedness grew                by  103,45  %,  up  from  345,80  %  between  2006  and  2016.  The  increase  in  household                indebtedness in EU countries was only 19,04 % in the first case, and in the other 10,60 %                and in the Eurozone countries it was 19,95 % respectively 5,85 %. The significant increase                in household indebtedness in Slovakia was mainly due to its low level compared to the EU                or  Eurozone  countries.  Stable  economic  growth,  disposable  income  growth,  and  low                interest rates have created conditions for increasing households' lending capacity, which                has also led to an increase in their indebtedness.                                                                 405","Figure 3 Changes in Household Indebtedness in EU Countries in Two Selected Tie                                                          Periods                        3,5                       3                       2,5                       2                      1,5                        1                      0,5                       0                     -0,5                                                            Changes in household indebtedness between 2006 - 2016                                                           Changes in household indebtedness between 2009 - 2016                                                    Source: Eurostat, 2018.                 Figure 4 shows an increase in the volume of loans granted to households, especially in the                case of housing loans. Housing loans recorded an increase of 544,0 % over the monitored                period, consumer credit 429,21 % increase and other types of loans 300,45 %. The growth                of  consumer  credit  provided  mainly  by  banks  is  due  to  the  growth  of  housing  loans.                Individuals very often, in the absence of their own savings, finance the purchase of real                estate  with  consumer loans.  Indeed, the  rise in long-term indebtedness in  the form  of                housing loans is problematic from the point of view of the regulator of the National Bank                of Slovakia, especially in the period of low interest rates. At present, the limit for lending                for housing has been reduced from 90 % to 80 % of the value of the property. Therefore,                an individual need not the amount of 10 % but 20 % of the property price either in the                form of own savings or by the drawing of consumer credit.                  Figure 4 Structure of Loans Providing Household and Volume of Failed Loans in Slovakia                                   in Monthly Bases from January 2006 to March 2018                        35000                                                                         0,06                         30000                                                                                                      0,05                        25000                                                                                                      0,04                        20000                                                                         0,03                   In million €  15000                                                                0,02                           10000                                                                                                       0,01                         5000                           0                                                                          0                                Consumer loans   Housing loans   Other types of loans   Volume of failed loans (right axis)                                            Source: National bank of Slovakia, 2018.                                                               406","The  aim  of  the legislative  changes mentioned in  the  second  part  of  this  article  was  to                tighten the criteria for the provision of consumer credit by banks and non-bank entities.                Figure 5 shows the development of the number of banks and non-bank entities providing                consumer credit in Slovakia between 2014 and 2018 on a quarterly basis. Throughout the                monitored period, we can see a slightly stable development in the number of banks that                provided consumer credit. Their number oscillated between 16 and 18. In the case of non-                bank entities, the development is more variable. We observe a stable number of non-bank                entities in 2014, which began to decline from 1Q 2015. From the original 45 entities, this                figure dropped to 30 in the 3Q 2015. In this period, the entities operating on this market                had  to  go  through  licensing  under  the  auspices  of  the  National  Bank  Slovakia,  if  they                wanted  to  stay  on  the  consumer  credit  market.  A  more  significant  drop in  9 non-bank                corporations is recorded in the 4Q 2015 when we can see that the consumer credit market                has stabilized, and the market has remained subject to tighter consumer credit conditions.                Between 1Q 2016 and 4Q 2017, the growth in the number of non-bank entities was stable                and oscillated around 19-23 subjects. The new stricter conditions on the consumer credit                market since 2018 have caused a further decline in the number of non-bank entities in the                1Q 2018 to 17 subjects.                       Figure 5 Number of Entities Operating in Consumer Credit Market in Slovakia                       50                      45                      40                     Number of entities  30                      35                        25                      20                      15                      10                       5                       0                                                          Banks       Non-banks                          Source: National bank of Slovakia and Ministry of Finance Slovak republic, 2018.                 In terms of the number of entities operating in the consumer credit market, the tightening                of  the  terms  of the  consumer lending was  reflected  mainly in the  decline  of non-bank                entities. Through Figure 6, we look at the volume of newly granted consumer credit by                banks and non-bank entities from 1Q 2013 to 1Q 2018. Figure 6 also shows the average                APRC  average  for  consumer  loans  provided  by  banks  and  non-bank  households  in  the                reference  period.  This  increase  in  household  indebtedness  is  particularly  noticeable  for                bank entities. The share of newly granted consumer credit by banks increased over the                period under review compared to consumer credit provided by non-bank entities. In the                case of non-bank entities, the decrease is visible especially since the 1Q 2015, when the                number  of  non-bank  entities  in  the  Slovak  market  was  significantly  reduced  and  the                conditions  for  the  provision  of  consumer  credit  were  tightened.  The  volume  of  newly                provided consumer credit by non-bank loans decreased from the level of 231,05 mil. € in                3Q 2015 to the current 84,45 mil. € in 1Q 2018. In the case of banks, we see an increase                from the level of 844,51 mil. € to 860,98 mil. €.                A diametrically different development is observed when looking at the average APRC. In                the case of banks, we notice significantly lower levels of the average APRC throughout the                reference period, which dropped from 15,26 % in 1Q 2013 to 8,43 % in 1Q 2018. In the                case of non-bank entities, we see a significantly more volatile development of the average                                                            407","APRC. In 2013 it oscillated between 69,19 % and 76,62 %. In 2014 it began to decline and                ranged from 33,78 % to 66,88 %. In 2015, we see again the decrease in the average                APRC, which in the following year 2016 shows values ranging from 23,22 % to 34,75 %.                In 2017, the decline and convergence of the average APRC on consumer credit provided                by non-banks to the average APRC covered by banks continued.  For the 1Q 2018, the                average APRC was 15,12 %, which is 6,69 % more for banks.                      Figure 6 Development of Average APRC and Amount of Newly Granted Credit to                       Household (in million €) by Banks and Non-banks from 1Q 2013 to 1Q 2018                       0,9                                                                            1200                     0,8                                                                            1000                    Average Annual Percentage Rate of Charge (APRC)  0,6                            800  Amout of newly granted credit (in million €)                     0,7                       0,5                                                                                                    600                     0,4                     0,3                                                                                                    400                     0,2                     0,1                                                                            200                       0                                                                             0                             Banks amount of newly granted credit (in million €) (right axis)  Non-banks amount of newly granted credit (in million €) (right axis)                           Banks average APRC (left axis)              Non-banks average APRC (left axis)                                              Source: authors’ elaboration, 2018.                From the available public sources, it is not possible to obtain information on the number of                clients who draw consumer loans in one year, so we do not discuss this area in the article.                 4 Conclusions                In the article we analyze in detail developments in the consumer credit market in Slovakia                with  regard  to  a  tightening  regulation in  the last three years.  Slovak  households  have                increased  their  indebtedness  most  significantly  since  the  European  Union.  Narrow                regulation  of  the  consumer  credit  market  has  in  recent  years  significantly  reduced  the                number of non-bank entities operating in this market. The gap between consumer credit                costs in APRC significantly decreased, which helped reduce the number of non-bank entities                and clear the market from the so-called Predatory consumer credit providers. From the                perspective of the client, we can talk about positive legislative changes between 2015 and                2017, which created the conditions for fair consumer credit provision. In our article, we                could not fully evaluate the impact of the legislative changes affecting 2018, which lead to                a more rigorous approach to assessing borrowers' credit capacity. That is why we want to                continue in the field of research in this area. We would like to extend the survey with an                analysis  of  the  profitability  of  companies  operating  in  the  consumer  credit  market,                supplement this list of companies with peer-to-peer lending companies, and map out the                relationship between consumer credit growth, executions and personal bankruptcies.                 References                Boďa,  M.,  Zimková,  E.  (2013).  Service-oriented  efficiency  of  Slovak  banks.    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DOI:                10.1016/S2212-5671(14)00405-5.                                                                                                     409","The Relationship between Debt and Equity in Social Economy                                           Organizations – Polish Case                                                     Grzegorz Michalski                                               Wroclaw University of Economics                                                   Katedra Pracy i Kapitału                                          Komandorska 118, 53-345 Wroclaw, Poland                                            E-mail: Grzegorz.Michalski@ue.wroc.pl                 Abstract:  In  that  paper  is  presented  the  model  based  on  data  about  debt  to  equity                relationship, collected from 5500 Polish social economy organizations in comparison to data                from over 112000 European social economy organizations. Capital structure is an indicator                that testify about level of financial risk. Polish social economy entities covered by Amadeus                database, reports different debt to whole capital relationship customs. The levels of debt                to whole capital reported in financial statements covered by Amadeus database in social                economy entities in V4 and European countries were higher in the time financial risk 2007                - 2012 period than in Polish social economy entities. Polish social economy organizations                are important part of general social policy in Polish economy. They realize important aims                in healthcare, in education and many other socially important areas. Considering efficiency                of social economy organizations, should be remembered that from the donor perspective,                is important the way the managing team uses resources of the social economy organization                and if it is used in the most effective way. The social economy organization efficiency should                be considered in the context of the risk. The one from the most important way to be out                from business is the debt to equity relation. In paper are considered relations between                debt measures and efficiency measures. That relation is also illustrated for Polish social                economy organizations data.                 Keywords: equity, capital structure, nonprofit organizations, efficiency of social economy                organizations                JEL codes: G30, G31, G32                 1 Introduction                 Grizzle  et.  al.  (2015)  examine  organizational  factors  that impact the level  of  operating                reserves in social economy organizations and explore the relationship of operating reserves                with organizational demographics and financial health variables (Grizzle et. al., 2015). T                Kuo et. al. (2014) noticed that Magnus et al. (2003) and Yetman (2007) argued that debt                financing  would  have  a negative impact  on  the  charitable  donations  of  social economy                entities (Kuo et. al., 2014). Kuo et. al. (2014) investigate the relationship between debt                financing and donation levels and according to Kuo et. al. (2014) empirical results, was                proved  that  debt  financing  has  both  a  crowd-in  effect  and  crowd-out  effect  on  social                economy entities donations. Kuo et. al. (2014) show that the crowd-in effect tends to exist                in low debt ratio social economy entities, and the crowd-out effect is often found to exist                in  not-for-profit  social  economy  entities  with  higher  debt  ratios  (Kuo  et.  al.,  2014).                Tuckman and Chang (1993) analyses why social economy entities accrue debt and whether                the  funds  they  borrow  are  used  productively.  Work  of  Tuckman  and  Chang  (1993)                distinguishes between productive, problematic, and deferred debt. Tuckman and Chang                (1993) examine the pervasiveness of social economy debt and the relation between this                debt and social economy financial health and they find that over 70 percent of the social                economy entities hold debt, the distribution of this debt is highly concentrated, and the                level of debt and leverage varies with asset size and type of activity what is in one accord                with findings of that paper (Tuckman, Chang, 1993). Social economy entities with higher                debt levels are financially healthier than those with lower levels (Tuckman, Chang, 1993),                (Gavurova et. al., 2018)                                                                410","Prentice (2016a) explores the organizational and environmental factors that affect social                economy financial health (Prentice, 2016a). Turner et. al. (2015) focused on the differential                use of debt financing among forprofit and social economy entities. Forprofit entities use                significantly  and  substantially  more  debt  than  social  economy  entities  (Turner  et.  al.,                2015). Calabrese (2011) analyzed the static trade-off and pecking order capital structure                theories and applied to social economy organizations. Calabrese (2011) also considers how                social economy entities adjust their debt to  equity relation over time. Calabrese (2011)                indicates that social economy capital structure choices are best explained using the pecking                order theory, in which internal funds are preferred over external borrowing (Calabrese,                2011). Rosen and Sappington (2016) investigate the decisions of social economy entities                to issue debt and they test whether the expected value and uncertainty of a social economy                entity nonfinancial income affect its capital structure (Rosen, Sappington, 2016). Rosen                and Sappington (2016) find that debt to equity increase is negatively related to both the                expected value and the uncertainty (Rosen, Sappington, 2016).                Szymanska  and  Jegers  (2016)  theoretically  describe  social  enterprises  taking  into                consideration their main aims and they point out the direction which social enterprises                should follow in order to obtain the highest value of their objective functions (Szymanska,                Jegers, 2016). Wedig (1994) points that social economy entities are similar to proprietary                firms except that their financial residual is expensed on a philanthropic activity which is                similar to a dividend-in-kind for a donnors (Wedig, 1994). Wedig (1994) shows how the                constraint against paying cash dividends affects the intertemporal paths of capital structure                and argue that the social economy entities dividend-in-kind are similat to dividends in for-                profit firms. Social economy entities are risk averse over cash flows and fund balance and                behaves like a risk averse consumer rather than a risk neutral firm (Wedig, 1994). Wedig                (1994) used a dynamic model to derive closed form expressions for the time paths of debt                ant  in  comparison  with  empirical  data  Wedig  (1994)  confirms  the  hypothesis  of  risk                aversion.                Long (1976) finds that debt to equity decisions must be based on many inputs—including                financial valuation, which has not traditionally been applied in the social economy entities                sector (Long, 1976). Reiter et. al. (2000) claims that in capital structure decisions of social                economy entities play the same rules as in for profit entities (Zietlow, 1989).                Copeland  and  Smith  (1978)  make  suggest  that  social  economy  entities  that are  donor                funded  have  the  primary  objective  of  donor  utility  maximisation  to  ensure  that  the                resources  provided  by  the  donor  are  utilised  in  the  most  efficient  manner  possible                (Copeland, Smith, 1978), (Strydom, Stephen, 2014). Upadhyay et. al. (2015) studied the                relationship between the social economy entity profitability and cash tied in operational                activity (Upadhyay et. al., 2015; Blendinger, Michalski, 2018).                Rauscher and Wheeler (2012) claims that increased financial pressures on social economy                entities have elevated the importance of working capital management. Efficient working                capital  management  allows  social  economy  entities  to  reduce  their  holdings  of  current                assets  and  cash  inflows  can  be  used  to  reduce  borrowing  (Rauscher,  Wheeler,  2012).                Rauscher and Wheeler (2012) examine the relationship between social economy entities                profitability  and  their  performance  at  managing  accounts  payable  (Rauscher,  Wheeler,                2012). Singh and Wheeler (2012) investigate used data for 1,397 bond-issuing, not-for-                profit US entities for 2000 to 2007, and Singh and Wheeler (2012) analyzed the relationship                between social economy entities performance at managing the revenue cycle and their                profitability and ability to build equity capital (Singh, Wheeler, 2012). Singh and Wheeler                (2012) model four different measures of profitability and equity capital as functions of two                key  financial  indicators  and  their  results  indicated  that  higher  amounts  of  revenue  in                relation  to  a  social  economy  entity  assets  were  associated  with  statistically  significant                increases in equity capital (p < 0.01 for all four models). Singh and Wheeler (2012) claims                that social economy entities that generated more revenue per assets invested reported                improved  financial  performance  (Singh,  Wheeler,  2012).  Statistically  significant  link                existed between lower revenue collection periods and equity (p < 0.01 for three models;                p < 0.05 for one model): social economy entities that collected faster on their revenue                                                             411","reported  larger  equity  values  (Singh,  Wheeler,  2012).  Findings  of  Singh  and  Wheeler                (2012)  means  that  social  economy  organization  can  advance  the  financial  viability  by                improving profitability and enabling equity growth (Singh, Wheeler, 2012).                Wheeler and Smith (1988) show that the appropriate discount rate for evaluation of capital                expenditures depends on risk, leverage, cost-based reimbursement. Method presented by                Wheeler and Smith (1988) can be used to account for these effects that is both practical                and consistent with theory (Wheeler, Smith 1988). Wacht (1978) deals with the financial                problem  of  integrating  debt  financing  and  fund-raising  campaigns  in  social  economy                equities. Objective of Wacht (1978) findings is the model of the capital budgeting process                for social economy entities. Social economy institutions, as claims Wacht (1978) cannot                use orthodox cost-benefit tests because they are inappropriate and impractical because of                the multi-dimensional character of the capital structure decision (Wacht, 1978), (Bem et.                al., 2017).                Trussel  (2012)  claims  that  a  capital  structure  used  by  social  economy  entities  is  an                important  determination  of  financial  risk.  Trussel  (2012)  indicates  that  there  is  no                difference in the amount of leverage between the two institutional types od NGO's. Social                economy and social economics entities have unique financing mechanisms which do not                impact the relative amount of debt and equity in their capital structures (Trussel, 2012).                Woronkowicz (2016) investigates social economy financial vulnerability metrics resulted                from the effect of a capital facilities project. Woronkowicz (2016) uses data for a sample                of social economy organizations and models the relationship between financial vulnerability                indicators and facilities investments. The findings of Woronkowicz (2016) are evidence for                fact that investments in facilities  are  associated  with the  costs  of  debt  associated  with                facilities projects and influence social economy finances. The Woronkowicz (2016) findings                have implications for the financial management of social economy organizations costs of                capital (Woronkowicz, 2016).                 Wacht (1984) claims that characteristics of social economy entities prevent the transfer                and  successful  application  of  standard  financial  management  solutions  to  financial                management decisions in the social economy context (Wacht, 1984). Such characteristics                include a dual management structure composed of professional and financial managers                (Wacht, 1984),  restrictions on the disposition of assets and earnings, and the constant                threat of illiquidity as the result of the uncoupling of organizational goals and cash flows                (Wacht, 1984). The theory of financial management separates the financial management                goals  from  the  professional  goals  (Wacht,  1978).  A  social  economy  entity  can  survive                financially through time while its professional manager pursues utility‐denominated goals                delineated by the organization's tax‐exempt status (Wacht, 1978).                 Tuckman and Chang (1992) claims that social economy decisionmakers have an incentive                to earn and accumulate surpluses. Tuckman and Chang (1992) developed a behavioral                model and used to derive a demand function for equity. Tuckman and Chang (1992) applied                such model to a national sample of 6168 charitable social economy entities and establish                the  hypothesis  that  social  economy  decisionmakers  consciously  plan  to  increase  their                organization's equity (Tuckman, Chang, 1992).                Prentice (2016b) claims that financial measures are used in social economy research to                predict funding opportunities. The findings of Prentice (2016b) suggest that using debt to                equity measures in social economy entities do not guarantee to find the searched answer                (Prentice, 2016b).                Social economy organization may be defined as the entity that is concerned in its actions                about realization of social value adding mission. Such mission is realized thanks to sources                collected thru donations from donors (Michalski, 2016b). Donor is an individual (person,                firm,  other  entity)  who  appreciate  social  value  generated  by  realization  of  the  mission                realized  by  social  economy  organization.  Such  appreciation  results  in  supporting  social                economy organization by donor’s donations. Donation is a supporting of social economy                organization actions amount of money, other assets or volunteer work that donor delivers                to  supported  social  economy  organization  (Michalski,  2016a).  Presented  discussion                                                             412","contributes  in  corporate  finance  theory  in  its  narrower  area  concerned  about  social                economy organizations model of financial management in financial liquidity with efficiency                measures as the context. That context is seen by some as controversial, especially from                technical  point  of  view.  Some  claim  that  social  economy  finance  and  its  managerial                decisions in them, are not different from for-profit business decisions (Hansmann, 1987,                Jegers, 2011; Michalski, 2016c, Gavurova, Korony, 2016). Such position is only partially                correct. Sloan et al. and Wedig et al. use with modifications financial management portfolio                theory to social economy organization financial management (Sloan et al., 1988; Wedig                1994, Wedig et al. 1996, Jegers, Verschueren, 2006; Soltes, Gavurova, 2015). In the paper                is used the model of financial debt management in social economy organizations from the                perspective that states the fundamental financial target of social economy organization is                the best financially effective implementation of the mission that cause the donors support                for the social economy organization (Leone, Van Horn, 2005; Eldenburg, 2011; Gavurová,                Šoltés,  2014). Social economy organization financial debt management decisions need to                take in account relation between future effects in the context of risk as debt financing is a                specific  form  that  increase  financial  risk  (Soltes  2010,  Bem,  Michalski,  2015).  That                perspective is close to creation of for-profit firms value (Michalski, 2016a; Chapelle, 2010;                Siedlecki, Bem, 2016).                 2 Results and Discussion                Polish  social  economy  entities  covered by  Amadeus  database,  reports  different  debt to                whole capital relationship customs. Social economy organization managing team decision                about the financial debt level policy, is a balance of gaining new opportunities to serve thru                realization of the mission. That kind of decision shapes the level and quality of financial                debt (Michalski, 2012). Paraphrasing Keith Smith and James A. Gentry observations, is                possible to observe that Robichek et al. (Gentry, 1988; Robichek et al., 1965; Smith, 1973)                tell about risk involved to financial debt level decisions, which must be accepted by financial                institutions  pledging  of  financial  debt  level  of  the  nonprofit  organization.  Keith  Smith                (Smith, 1973; Gentry, 1988) predicted and Michalski (Michalski, 2008; Michalski, 2012)                showed how portfolio theory may be used to decrease financial debt level risk. Debt to                equity could be viewed in portfolio context as presented by Friedland (1966; Gentry, 1988).                Pringle and Cohn (1974; Gentry, 1988) tried to adapt the CAPM theory to capital elements.                Bierman and Hausman (1970; Gentry, 1988) discuss the granting policy of organization                and shows that financial debt level policy requires balancing the future sales gains against                possible  losses.  Lewellen,  Johnson  and  Edmister  (Lewellen,  Johnson,  1972;  Lewellen,                Edmister, 1973) explain how and why traditional devices used for monitoring financial debt                level should be changed by new and better ones. Freitas (Freitas, 1973) shows relation                between debt and risk during financial debt level management. The question discussed in                presented paper concerns the making decisions by nonprofit organizations in financial debt                level area in connection with efficiency measures (Michalski 2016b).                  Table 1 The Relationships between Debt and Whole Capital in European, V4 and Polish                                                  Social Economy Entities                                        2015     2014    2013    2012    2011     2010     2009      2008                   EUR               36,20%  17,16%  17,75%  19,25%  30,66%    48,29%   60,39%    50,27%                   V4                78,38%  45,92%  46,89%  50,17%  59,97%    69,66%   66,74%    64,24%                   PL                17,82%  11,53%  11,99%  12,68%  18,56%     7,66%     8,07%    7,84%                          Source: Database Amadeus product of Bureau van Dijk, [date: 2017 MAY 15]                As we can observe in Table 1. and Figure 1, the levels of debt to whole capital in social                economy entities in V4 and European countries were higher in the time financial risk 2007                - 2012 period. That levels in Polish social economy entities looks different and that is an                interesting observation, which illustrate general tendency of Polish social economy entities                covered by Amadeus Database (probably also it is the truth also for whole population for                Polish social economy entities).                                                               413","Figure 1 DEBT to Capital Invested (Debt + Equity) Levels in Chosen European Social                                                  Economy Organizations                                            Source: own study based on data from 112000 social economy organizations reported in Database                                   Amadeus product of Bureau van Dijk, [date: 2017 MAY 15]                 There  is  difference  in  treating  the  entitlements  of  people  who  control  organizations  in                prohibiting distribution of earnings and excess of revenues over expenses of organization:                Equity  type  capital  providers  of  for-profit  firms  can  expect  return  money  in  case  the                business  makes  excess  of  revenues  over  expenses  from  operations.  Social  economy                organizations do not have right to issue stock and equity of social economy organizations                is  issued  by  donors  and  persons  who  have  no  right  to  express  control  over  the  social                economy  organization. Equity  type  capital  providers  of  social  economy  organizations in                case  the  social  economy  organization  generates  money  or  excess  of  revenues  over                expenses from operations can consider additional support for organization but have no                possibility  to  withdraw  the  money  previously  tied  in  organization.    In  social  economy                organizations,  there  is  no  equity  capital  but  social  economy  organizations  collect  fund                capital which is an equivalent of equity capital. Fund capital is collected in social economy                organizations by earning excess of revenues over expenses, that are forced by regulation                as money which should be retained within the social economy organization. Other source                of fund capital is receiving contributions from individual persons or from private or public                entities and from for profit businesses. The last possibility to collect fund capital are money                from  grants  received  by  social  economy  organizations  from  governmental  entities.                Calabrese (2011) indicates that social economy capital structure choices are best explained                using the pecking order theory. That means that in social economy organizations internal                funds are used more likely than external borrowing.                Social  economy  organizations  act  because  expected  future  advantages  measured  by                realization of ideas and mission that are expression of donor’s vision of the world. Both                social  economy  and  for-profit  entities  have  an  aim,  which  is  a  result  of  its  owner                preferences.  For-profit  organizations  are  active  because  of  expected  future  advantages                measured in money, non-profit organizations do their business because of expected future                advantages measured by degree of realization of their mission.                Social economy organizations serve in each areas of social activities and depending on size                can  be  registered  or  not  in  internal  revenue  authorities.  Among  social  economy                organizations can be listed: educational businesses like universities or schools, healthcare                organizations like hospitals, charities working as religion institutions branches, etc.                Because of the benefits to the society generated by social economy organizations most                governments  allow  tax  exemptions  both  for  donors  and  for  the  social  economy                organizations. Such tax exemptions are usually limited only to charitable social economy                                                             414","organizations, which are listed on government records of such organizations that meet the                condition for tax exemptions. Usually governments decide that according to their policy                only the largest and the most useful for fixing society needs and most helpful for the people                of society organizations have right for tax exemptions and of course the definitions and                understanding of usefulness or helpfulness differ from country to country, and depends on                dominating philosophy or religion of local societies (Michalski, 2016b). Tax exemption for                the  social  economy  organization  could  be  for  all  money  generated  as  profit  in  these                organizations but more popular is solution where only profits that are effect of realization                of programs or activities concerned on realization of the main mission of social economy                organization are basis for tax exemption and other profits are subject of normal taxation.                Such a solution helps to prevent situation when under social economy banner is hidden full                for-profit activity not concerned on social needs and is not of help in leveling of disabilities                of weaker participants of social interaction. Usually social economy organizations to keep                tax exemption status are required to keep all excess revenues for the realization of the                mission of social economy organization. There is not a custom among governments to allow                social economy organizations to pay out money from excess of revenues over expenses to                anyone  who  normally  deserve  it  in  for-profit  entities.  Board  members,  employees  and                clients of social economy organizations are excluded from receiving money that are larger                than average expenses. Each amount of any money in social economy organizations should                be  directed  to  beneficiaries  defined  by  the  mission  of  social  economy  organization                (Michalski, 2016b).                 3 Conclusions                 Polish social economy organizations are important part of general social policy in Polish                economy. They realize important aims in healthcare, in education and many other socially                important  areas.  Considering  efficiency  of  social  economy  organizations,  should  be                remembered that from the donor perspective, is important the way the managing team                uses resources of the social economy organization and if it is used in the most effective                way. The social economy organization efficiency should be considered in the context of the                risk. The one from the most important way to be out from business is the lack of the money                for realization of the aim of organization. In paper were considered relations between debt                measures. That relation was also illustrated for Polish social economy organizations data.                Polish  social  economy  entities  covered by  Amadeus  database,  reports  different  debt to                whole capital relationship customs. Debt to whole capital levels in social economy entities                in V4 and European countries were higher in the time financial risk 2007 - 2012 period.                That  levels  in  Polish  social  economy  entities  looks  different  and  that  is  an  interesting                observation, which illustrate general tendency of Polish social economy entities covered by                Amadeus Database (probably also it is the truth also for whole population for Polish social                economy  entities).  After  2014  such  tendency  was  changed  in  Polish  social  economy                entities. Current policy of Polish  social economy entities, that allows to use too much                debt, is rather dangerous, and there is a need to increase level of understanding about risk                and its destroying influence on possibilities of realization  social economy organization                mission.                 Acknowledgments                 The presented work and results is part of monothematic cycle realized as part of grant                titled:  Determinants  of  capital  structure  in  social  economy  organizations.  The  work  is                supported by National Science Centre, and financed from the Polish budget resources in                the years 2016-2019 according to contract UMO-2015/19/B/HS4/01686 as the research                project DEC-2015/19/B/HS4/01686.                 References                Bem,  A.,  Michalski,  G.,  (2015).  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Miszczyńska                                                     University of Lodz                               Faculty of Economics and Sociology, Department of Public Finance                                             Narutowicza 68, 90-136 Lodz, Poland                                          E-mail: katarzyna.miszczynska@uni.lodz.pl                 Abstract: The performance of healthcare in Poland, compared with the EU countries, is far                from satisfactory. The indebtedness of hospitals is one of the major problems to be solved                by public administration. Hospitals as autonomous healthcare units are public units and                are  not  subject  to  market  mechanisms.  Legal  regulations  widely  determine  their                independence  but  limit  responsibility  for  its  implementation,  which  leads  to  their                uncontrolled indebtedness. That indebtedness adversely affects not only the development                of healthcare but also quality of provided medical services. Constant indebtedness of the                units creates a need for finding its determinants. The aim of the research is an attempt to                define some factors which determine indebtedness of public healthcare units (hospitals) in                Poland. The study will cover units with a similar and thus comparable profile. The study                was conducted between years 2007 – 2015 with  help of econometric modeling. It should                be stressed that the level of indebtedness of healthcare units, and at the same time, the                efficiency of their performance vary. Based on preliminary research on determinants of                indebtedness there should be mentioned the following most crucial factors: hiring medical                personnel,  average  remuneration,  bed  occupancy,  average  stay  in  hospital,  average                number of patients per bed, days of treatment per person, founders.                Keywords: health economics, indebtedness of hospitals, econometric modeling, healthcare                in Poland                JEL codes: I11, I19, P46, C23                 1 Introduction                 Public hospitals in Poland are units of the public finance sector, and thus are obliged to                manage the funds in a way that leads  to achieving the best possible results at certain                expenses.  Therefore,  it  is  decisive  to  justify  the  costs  directly  related  to  the  actions                undertaken in order to obtain the intended results.                The system of managing public funds is subject to constant transformations aimed at their                most appropriate use. The necessity of effective spending of public funds is related to the                need to ensure the financial security of public finance sector entities regardless of budget                constraints or economic fluctuations. The changes taking place in recent years in the health                care system have caused that health care units are in a new, sometimes quite difficult,                financial and economic situation. An important role was played by the changes not only in                the method of financing, but also the independence of public health care institutions, which                in turn resulted in an increase in the responsibility of the management team for the results                of  the  conducted  activity.  Constantly  growing  health  needs,  the  changing  demographic                situation resulting in an often prolonging treatment process, technological progress in the                field  of  highly  specialized  medical  procedures  and  the  associated  increase  in  patients'                awareness of the possibility of using them translates into an increase in the costs of health                care  facilities.  All  of  this  entails  the  need  to  use  methods  of  rationing  the  costs  of                functioning  of  medical  entities,  and,  as  a  consequence,  improving  the  efficiency  of                managing the resources of public health care units. A prerequisite for effective functioning                of health care institutions is the application of good management practices. The difficult                financial and economic situation of public health care units, and in particular the problem                of indebtedness, should be one of the most important problems to be solved by public                administration. Hospitals that are public health entities are companies that are not subject                to market laws. Legal regulation extensively determines their independence, and limits the                                                              420","responsibility for using it, which results in generating costs that significantly exceed the                revenues earned and, consequently, indebtedness. In some units this situation was not a                one-time  situation,  which  was  caused  by  the  conviction  that  due  to  the  nature  of  the                business and the existing situation, the situation will be repaired.                The lack  of  financial  stability  of  hospitals impacts negatively  on  the  quality  of  services                provided and, consequently, on the development of health protection. Therefore, the aim                of the article will be the identification of the factors causing the indebtedness of hospitals.                In  the  study,  a  research  hypothesis  was  put  forward  that  the  growing indebtedness  of                hospitals is determined by appropriate management of the unit, referring to the financial                and organizational sphere of the units.                In addition, the following detailed hypotheses have been put forward:                     •   H.1. Founding body has an impact on the debt of the hospital.                    •   H.2. Average length of stay affects the debt of the hospital.                    •   H.3. Changes in the current ratio affects the debt of the hospital.                    •   H.4. The amount of hospital beds has an impact on the debt of the hospital.                 2 Literature review                 Health sector, as a part of economy, is extremely important for economic development of                the country. Problems concerning the performance of public healthcare units are widely                analysed  both  by  national  centers  [Jakimowicz  2011,  Frączkiewicz-Wronka  2010]  and                foreign  ones  [Jena\&Philipson  2013,  Lupi  et  al.  2011].  Hence  efficient,  well  organized                performance  of  healthcare  units  is  crucial  not  only  from  the  perspective  of  patients’                satisfaction but also from the perspective of managers (Opolski, Dykowska, Możdżonek                2009). The performance of healthcare in Poland, compared with the EU countries, is far                from  satisfactory,  which  was  presented  by  Nojszewska’s  research  (2011  b).  Moreover,                indebtedness  of  hospitals  is  one  of  the  major  problems  to  be  solved  by  public                administration. What is more, the indebtedness adversely affects not only the development                of  healthcare  but  also  quality  of  medical  services  provided.  The  need  for  finding  the                determinants  of  their  indebtedness  is  discussed  nationwide  (Skawińska  2009;  Sowada                2012; Nojszewska 2011c i 2012; Ryć \& Skrzypczak 2011; Misiąg et. al 2008; Rój\&Sobiech                2006). Problems concerning the performance of public healthcare units are widely analysed                both by national centres (Hass-Symotiuk 2011; Nojszewska 2011a; Golinowska 2006) and                foreign ones (Alemi \& Gustafson 2006; Martin \& Smith 2012; Vanberkel et.al. 2012; Smith                \& Topol 2013; O’Neill L. et al. 2008; Carroll 2009).                The study presented herein contributes to the existing state of research by shedding light                on a new set of determinants affecting the indebtedness of public general hospitals in Lodz,                which may help in better management of these units.                 3 Methodology and data                 The aim of the study was to identify factors affecting the public hospitals’ debt. The analysis                was carried out on the basis of data regarding the operation of public general hospitals                located in Lodz. The study was based on 12 public general hospitals differentiated by the                founding body: poviat-commune, marshal’s office and university. Due to the rejection of                providing key financial and economic information for the analysis, the entities covered by                the study do not constitute all such units in Lodz.                 The  data  covers  the  period  of  2007-2015  and  was  taken  from  hospitals’  financial                statements and Voivodeship Centre of Public Health.                The list of variables is presented in Table 1.                                                                 421","Table 1 List of Variables Used in the Model                 Symbol                        Description                    Type                  l_DEBT                              Total debt ratio             Dependent variable                  l_STAY                              Mean length of              Explanatory variable                                                      hospitalization                  l_CR                           Informs about possibilities      Explanatory variable                                                    of repaying current                                                         liabilities                  l_BEDS                            Average number of             Explanatory variable                                                       hospital beds                  l_UM                            0-1 variable indicating a       Explanatory variable                                                 hospital whose founding                                               body is the marshal’s Office                  l_PG                            0-1 variable indicating a       Explanatory variable                                                 hospital whose founding                                                   body is the poviat or                                                         commune                  l_UU                            0-1 variable indicating a       Explanatory variable                                                 hospital whose founding                                                    body is the medical                                                         university                                                  Source: own elaboration                 The basic summary statistics are given in Table 2.                             Table 2 Summary Statistics, Using the Observations 1:1 - 12:9                                              (missing values were skipped)                                  Variable  MEAN        MEDIAN  MIN            MAX                                  l_STAY        1,6816     1,7115      1,0607       2,2905                                 l_CR        -0,00611    0,10118     -3,0167       1,6789                                 l_DEBT       -0,3343  -0,49678      -1,8469       1,5424                                 l_BEDS        5,6279     5,7317       3,912       7,0049                                   Variable  S.D.        C.V.       SKEW        EXCSKURT                                  l_STAY      0,26534     0,15779  -0,32992       -0,54544                                 l_CR        0,91392      149,66  -0,77339        0,54842                                 l_DEBT      0,73553      2,2002    0,59709       0,27985                                 l_BEDS      0,69504      0,1235  -0,45139        0,24437                                           Source: own calculations in Gretl software                 3 Results and discussion                 For the purpose of identification of factors affecting public hospitals’ debt econometric panel                data  modeling  was  conducted.  The  procedure  was  carried  out  on  the  basis  of  N=12                hospitals and T=9 observations over time period between 2007-2015 by means of fixed                effects panel data model (FEM). The model that is subject to estimation can be written as:                        l_DEBTtit = αi + β1 l_STAYit + β2 l_CRit+ β3 l_BEDSit+ εit,                      (1)                 where:                l_STAYit –debt ratio of i  hospital in period t,                                                             422","l_DEBTit –debt ratio of i  hospital in period t,                l_CRit – current ratio i in period t,                l_BEDSit – average number of hospital beds i in period t,                 β1, β2, β3, β4 – structural parameter of the model,                αi – fixe effect for hospital i,                εit – terror term.                The  choice  of  a  fixed  effects  model  for  hospitals  can  be  argued  in  the  following  way.                Uncontrolled differentiation of debt ratio between hospitals (i.e. intergroup differentiation                characteristic for panel data) can be a cause for heteroscedasticity of a random element                Controlling differences between units is possible thanks to using fixed or random effects                panel  models  (Dańska-Borsiak  2011).  Such  models  take  into  account  occurrence  of                constant  over  time,  unobservable  differences  between  objects  (units).  In  case  when  a                number of objects is small (N < 20), and their specification indicates that they represent                a  finite  set  of  unique  and  inimitable  categories,  it  is  preferable  to  use  fixed  effects                (Łaszkiewicz  2016).  In  this  study  a  need  to  introduce  fixed  effects  for  hospitals  was                additionally confirmed by the results of the Wald test on differing group intercepts (differing                group intercepts).                Null and alternative test hypothesis can written as:                       H0 : αi = α = const, i = 1,…, N, t = 1,…, T                                      (2)                       H1 :    i, j:  αi ≠ αj.                                                         (3)                 In accordance with the null test hypothesis there is no need to introduce specific effects                for hospitals. On the other hand, the alternative hypothesis stipulates that intercepts for                objects are not equal.                The test statistic for differing group intercepts has reached the level of 17,3523, which                proves that null hypothesis mentioning a lack of intergroup differences should be rejected                (see Table 3).                                         Table 3 Test for Differing Group Intercepts                 Test for differing group intercepts                   Null hypothesis: The groups have a common intercept                   Test statistic: F(11, 92) = 17,3523                   with p-value = P(F(11, 92) > 17,3523) = 4,88332e-018                                          Source: own calculations in Gretl software                 Therefore, the estimation of panel FEM model parameters was performed. Additionally,                test for normality of residuals was conducted, according to which there were no reasons to                reject the null hypothesis stating that a random element has a normal distribution. (see                Table 4).                                      Table 4 Normality of Residuals – Test Results                 Test for normality of residual -                   Null hypothesis: error is normally distributed                   Test statistic: Chi-square(2) = 3,333                   with p-value = 0,1889                                          Source: own calculations in Gretl software                 The graph below presents the results of normality of residuals test graphically (see Figure                1).                                                                423","Figure 1 Normality Test                 2,5                         Test statistic for normality:                                uhat15                         Chi-kwadrat(2) = 3,333 [0,1889]           N(-2,098e-015 0,23044)                      2                      1,5                         1                      0,5                         0                     -0,8      -0,6      -0,4      -0,2        0        0,2        0,4       0,6                                          Source: own calculations in Gretl software                Results of calculations obtained by means of Gretl software on the basis of fixed effects                panel model (FEM) using 107 observations are presented in the table below. The values t-                Student statistics and p – values indicate that all variables are statistically significant. The                model’s fitting is satisfactory, with R-squared of approximately 90%. Results of estimation                are presented in the table below.                                            Table 5 Results of Model Estimation.                 Model: Estimation Fixed effects using 107 observations                 12 units of cross-sectoral data were included                 Time series of length: minimum 8, maximum 9                 Dependent variable (Y): l_DEBT                                 coefficient     Std. error       t-ratio       p-value                 const             -5,50015         1,19312           -4,610       1,30e- 05      ***                 l_STAY            0,728515       0,337706         2,157         0,0336            **                 l_CR             -0,187038      0,0630249        -2,968         0,0038           ***                 l_BEDS           -0,701336       0,184723         3,797         0,0003           ***                                           Source: own calculation in Gretl software                 Based on estimations of model parameters it is clear that increase of a patient’s stay in                hospital by one unit causes, with other unaltered factors, increase in debt ratio by 0.72                unit. It is highly justified as a longer stay in hospital causes a decrease in bed occupancy                rate,  lowers  capacity,  which  as  a  result  contributes  to  an  increase  in  costs  and  thus                indebtedness.                Increase of the liquidity ratio by a unit causes a decrease in indebtedness by 0.18 unit                ceteris paribus. Therefore, when the liquidity ratio grows, it translates into a decrease in                debt ratio. Analysed hospitals are in a very bad financial condition. They often take new                liabilities to repay the previous ones. In such a situation liquidity does not improve.                                                            424","Moreover,  the  increase  of  a  number  of  hospital  beds  by  one  unit  causes,  with  other                unaltered factors, decreases in debt ratio by 0.70 unit. Hence, if hospitals have more beds                they can cure more patients and as a result they get more money from National Health                Fund.                What is more, according to the results obtained from the model, the funding body does not                have any impact on the level of hospital’s indebtedness.                 4 Conclusions                 Efficient,  well  organized  performance  of  healthcare  units  is  crucial  not  only  from  the                perspective of patients’ satisfaction but also from the perspective of managers.                The conducted study has shown dependencies between financial and non-financial ratios                as well as their impact on financial condition of hospitals measured by means of total debt                ratio.                Presented  results  indicate  that  hospitals’  indebtedness  measured  by  the  debt  ratio  is                related to an average length of patient’s stay in hospital, the current liquidity ratio as well                as  an  average  number  of  beds  in  hospital.  Obtained  results  of  estimations  of  model                parameters are marked in line with expectations.                 Based on the conducted study all detailed hypotheses have been verified. Hence:                H.1. Founding body does not have any impact on the debt of the hospital.                H.2. Average length of stay affects the debt of the hospital.                H.3. Changes in the current ratio affects the debt of the hospital.                H.4. The amount of hospital beds has an impact on the debt of the hospital.                 All in all, it should be underlined that on the basis of conducted study the main hypothesis                has been verified positively. Hence, the growing indebtedness of hospitals is determined                by appropriate management of the unit, referring to the financial and organizational sphere                of the units.                An attempt to identify factors that determine the process of units’ indebtedness, which was                presented in this paper, will be a starting point for further, more advanced studies in the                area of financial management of public hospitals in Poland.                Acknowledgments                 This article is an output from the research project Multicriteria assessment of efficiency of                public hospitals in Poland and identification of determinants of their indebtedness financed                by the National Science Centre, decision number DEC-2016/23/N/HS4/03410                 References                 Alemi, F., Gustafson, D. H. (2006). Decision Analysis for Healthcare Managers. 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OR                Spectrum, vol. 34, pp. 371-390.                                                                                426","Profitability Forecast for Assessment of Investment Project in                                                        Banking                                                     Piotr Miszczyński                                                     University of Lodz                            Faculty of Economics and Sociology, Department of Operations Research                                             Rewolucji 39 , 90-214 Lodz, Poland                                             E-mail: piotr.miszczynski@uni.lodz.pl                 Abstract: The aim of this paper is to present the results of evaluation of a bank investment                project. The main result of investment in a new banking project is the so-called a customer                portfolio that has a suitable risk structure and profitability. Therefore it is important to                estimate the profitability of current sales of currently developed banking products. Thanks                to the fast and accurate ex ante profitability estimation tool, an ongoing assessment of the                profitability of new banking products, especially loans, is possible. On this basis, price and                risk pricing decisions allows to create a reasonably profitable portfolio that affects the value                of  the  bank  being  built.  In  the  paper  there  are  the  sample  results  of  the  metamodel                profitability simulation tool presented and the accuracy measures of the obtained results.                Keywords:  simulation  metamodel,  profitability  prognosis,  investment  valuation,  bank                project evaluation                 JEL codes: C63, G17, G21, M10, M13                 1 Introduction                The issue of evaluation of investment projects is widely and multi-faceted considered in                the literature. In the classic approach, an investment can be defined as capital expenditure                in  order  to  achieve  a  positive  financial  effect in  a  given  time.  Organization in  order  to                achieve the intended effect within the intended time is defined as a project that can be                divided  into  stages:  pre-investment,  implementation  and  exploitation  (effect).                (Manikowski, 2010, pp. 7-17) In this paper, attention is focused on building the future                value (effect) of investments in the quantitative assessment of investment projects. In                projects  concerning  the  generally  understood  financial  sector,  in  particular  in  banking,                considerations  on  value  building  and  profitability  assessment  of  projects  are  of  key                importance. Creating the value of a new banking project takes place, among others by                building so-called loan portfolio (Marcinkowska, 2003). Each loan granted is characterized                by a different risk and a different level of profitability (Krysiak, Staniszewska, Wiatr, 2015].                By appropriate classification of granted loans from the point of view of price parameters                and risk assessment, one can estimate the potential revenues and costs associated with                them. The ex ante forecasts of profitability for individual loan agreements resulting from                such analysis are appropriately aggregated in order to assess the structure of the new                bundle  entering  the  portfolio.  Thus,  a  tool  is  created  thanks  to  which  it  is  possible  to                manage the process of building the value of the loan portfolio on an ongoing basis, seen                in this case as a result of a new investment project.                 This paper presents the financial assessment tools for newly created credit products as                well as the practical aspect of their use in making management decisions. The assumptions                of  the  general  model  of  single  loan  profitability  estimation  and  its  input  and  output                parameters are described first. Next, the problems related to its practical application and                the resulting need to apply the so-called metamodel to improve the process of providing                management  data.  The  concept  of  the  metamodel's  operation  was  described  and  the                several variants of its construction were compared depending on the estimation methods                used,  i.e.  decisional  trees,  random  forests,  linear  regression,  neural  networks,                interpolation. Due to the requirements for the metamodel, the measure of errors was used                for the evaluation of variants.                                                                427","2 Model, metamodel and criteria                In  the  original  financial  model  under  consideration,  profitability  for  individual  loan                agreements is calculated. The model estimates ex-ante profitability both in whole and in                individual sub-periods (months, years) of the duration of the contract. Loans starting in a                given  period  (usually  in  a  given  month)  form  the  so-called  a  bundle  whose  basic                parameters (eg capital due to a bank under a loan agreement) are modeled in the following                months until \"expiration\" (zeroing) in subsequent periods.                Assumptions                The original financial model is based on the forecast of future cash flows resulting from the                repayment schedule, interest income, assumed commission income, estimated risk costs                in particular periods and other factors resulting from the regulation of banking activity, eg                the  capital  adequacy  ratio.  The  described  model  is  deterministic  (lack  of  a  random                component causing repeatability of results with the same input parameters), while risk-                related  factors  (eg  risk  related  to  delays  in  loan  repayment)  are  estimated  in  another                (external) model and inserted into the described model as average input parameters. The                described model gives the possibility to calculate profitability for a single contract as well                as  for  an  aggregated  beam.  The model  has  been implemented in  MS Excel  due to  the                universality of this common tool.                Inputs                 The selection of input parameters to the model results both from the nature of banking                operations, which is largely regulated in Poland, as well as from market conditions and                policy in the scope of risk management (risk management aspect) and operating costs (the                aspect of operating cost optimization). Generally, parameters can be divided into several                groups, which may have different numbers depending on the level of detail of the adopted                model. And so you can distinguish a group of parameters directly related to a given loan                agreement, ie. the  basic  parameters  regarding  the loan  agreement (amount,  duration,                interest  rate,  commission,  additional  commission  components).  The  next  group  of                parameters is mainly related to operating costs in the division, for example, into the sales                channel. The next group are parameters related to risk assessment (eg customer score                category, collateral). Despite the general classification, it should be mentioned that the                model  works  in  many  aspects  and  parameters  from  a  given  group  may  affect  many                components of the model (eg. the scoring category given to the client in the application                process will affect both the assessment of the risk costs and operational costs related to                the service credit agreement).                Profitability ratio as a decision criterion                 Profitability can be examined at various levels, depending on the information the decision                maker wants to achieve. Each subsequent component taken into account when assessing                profitability may unnecessarily distort the information. And so we can consider profitability                at the first level, NIM (Net Interest Margin  - net margin), i.e. the one resulting  directly                from interest and commission income and costs. The next level includes COR costs (Costs                of Risk) resulting from national regulations and the risk management policy adopted by                the bank. The next level, ROA (Return on Assets), allows you to assess profitability after                taking into account the operating costs of the so-called OPEX (Operational Expenditures).                The last ROE (Return on Equity) expresses profitability after taking into account all of the                listed components and the capital requirement resulting directly from the bank's internal                regulations and policies. It is worth mentioning here that the capital requirement varies                depending on the purpose of the loan and possible collateral. The diagram below (Diagram                1) illustrates the successive levels of the loan profitability test.                                                                 428","Figure 1 Profitability Levels                                                        (+) Interest income                                                       (–) Interest costs                                               NIM (Net Interest Margin) ::LEVEL NIM                                                   (+) Fees and commision income                                                   (–) Fees and commision costs                                 Fees, commision and interest margin :: NRM (Net Result Margin) ::LEVEL  NRM                                                       (–) COR (Costs of Risk)                                     Margin after costs of risk :: RAM (Risk Adjusted Margin) ::LEVEL RAM                                                  (–) OPEX (Operational Expenditures)                                                ROA (Return On Assets) ::LEVEL ROA                                                        (×) capital leverage                                                ROE (Return On Equity) :: LEVEL ROE                                  Source: own study based on (Marcinkowska, 2003 pp.119-130)                 Estimating profitability using a metamodel                The concept of a metamodel in a nutshell consists in creating an alternative model to the                original model, which is based on the same batch data used in the original model, and will                generate  very  similar  results  to  those  generated  by  the  original  model.  To  estimate                metamodel parameters, the input and output data from the original model should be used                (Kaminski B., 2015a; Barton R., State P., 2010) The process of calculating profitability                without using the metamodel concept can be described by four steps: 1. The appearance                of new contracts in the database system; 2. Collection and preparation of data from a given                period (month) to calculations (semi-automated process); 3. Conversion of the profitability                of new contracts or the whole aggregate bundle (semi-automated process); 4. Aggregation                of results and report generation (semi-automated process).                 The reasons for the use of the metamodel                The main reasons for the use of the metamodel concept is the significant acceleration of                the computational process and the automation of the calculation process at the database                level. The calculation process using the \"large\" primary model is a labor-intensive process                that involves the analyst's work. In addition, due to the constraints of MS Excel, in which                the original model is implemented, it is difficult to automate at the database level. The                scheme of the profitability calculation process, which can be much more efficient thanks to                the  use  of  the  metamodel,  can  be  described  by  3  steps:  1.  The  appearance  of  a  new                contract in the system; 2. Automatic conversion of the profitability of a new contract using                a metamodel; 3. Aggregation of results and generating a report at any time based on a                \"dump\" from a data warehouse (Automatic process in any BI-Business Inteligence system)                Different approaches to building a metamodel                The  metamodel  concept  does  not impose  either  the  form  or  estimation  methods  in its                creation. In the course of work on the implementation of the metamodel concept, different                approaches were considered (described below). Due to the basic purpose of this work,                which is to indicate a new application for the metamodel concept, the calculations have                been made as simple as possible. Therefore, for the purposes of this work, an exhaustive                description of the methods has been abandoned, assuming that the most common and                classic variants of the proposed methods have been applied to well-known and described                in the literature.                                                             429","At the beginning of the research on finding the best form of the metamodel, a regressive                approach was used to analyze profitability in banking. This approach has been called the                \"magic formula\", because it was supposed to be based on a simple equation / equations                for calculating the output parameters. A multiple regression method and a neural network                method were used to implement this approach. Testing different forms and variants of the                proposed methods, the simplest single equation regression model was finally used, whose                parameters were estimated using the classical method of least squares. However, when                testing different variants of neural networks, ultimately, a multi-layered one-way network                with a linear activation function was chosen (Gajda, 2001, pp.215-244, Ripley, 2016)                The second approach has been dubbed the \"classification\" table understood as a set of                rules by which single loan agreements can be classified to a given profitability range based                on the value of input parameters. The classification tree method was used to implement                this approach (Therneau, Atkinson, Ripley, 2017) and the developed method of random                forests (Liaw, Wiener, 2015). The random forest method, which de facto generated several                hundred  decision  trees  based  on  randomly  selected  subsamples  and input  parameters,                could be applied due to the large sample size and a large number of input parameters                (Williams, 2009, p.50).                Another interesting way to solve the problem of accuracy was to use the kriging proposed                eg in the work of Bogumił Kamiński (Kamiński, 2015a). Therefore, the scope of the tested                methods was extended to include interpolation methods, in particular the kriging method.                 Criteria for estimating the results of the estimation                Another extremely important stage in the implementation of the metamodel concept was                the  development  of  appropriate  methods  for  assessing the  practical  applicability  of  the                various  approaches.  For  this  purpose,  apart  from  the  classic  methods  of  evaluating                particular models, additional comparative criteria were developed for the results of models                obtained using various methods. In the end, following assessment criteria were used:                    •    Minimization of the maximum deviation for a single case                         K →   min  ,   K =  max  y −  y ˆ n                                             (1)                                                  n                                         1                          1                         y ˆ                                           y                where:    n - the resulting value for the metamodelu;   n - the result value for the original                model                    •   Minimization of the number of outliers for a set deviation threshold δ (Kamiński B.,                       2015b)                         K 2  → min ,  K 2  =   f ( y ,)  f ( y ˆ ,  y ) =  1  dla  y n  − y ˆ n     (2)                                                                                                                      n                                                               n                                                            n                                            n                        0  dla     y n  − y ˆ n                       •    Minimization of mean average absolute error - MAPE (Gajda J., 2004)                                            1                                                           (3)                        K  → min  ,   K  =      y  − y ˆ                         3                                        3                                           N   n  n    n                    •   Observation of outliers of rest modules (Gajda J., 2002, s.234-237)                    •   Observation of the forecast-implementation graph(Gajda J., 2002, s.235, Hussain                       et al., 2002)                 The  application  of  the  criterion  of minimizing  the maximum  deviation for  a  single  case                results from the nature and purpose of the calculations. It is important that the metamodel                gives comparable results to the original model for each individual case. For the purposes                of decision-making analyzes, the results of the estimation can be aggregated in many areas                and to a different extent, therefore, possible too large discrepancies for a single case may                distort the image significantly. It is equally important that too many cases do not deviate                from the indicated acceptable deviation value, hence the criterion of minimizing the number                of outliers. The application of the other two criteria is of a control nature.                                                              430","3 Results and Discussion                Calculation  for  individual  methods  were  made  in  the  R  program  on  a  sample  of  loan                agreements launched in a new bank. Calculations for the metamodel constructed using the                kriging  method  were  used  in  the  'DiceKriging'  library.  (Roustant  et  al.,  2015).  The                calculations for the remaining methods were made using the 'Rattle' supplement (Williams                GJ, 2009) and the R program libraries implemented in this appendix: ia: Package 'rpart'                (Therneau T., Atkinson B., Ripley B., 2017), Package 'randomForest' (Liaw A., Wiener M.,                2015), Package 'nnet' (Ripley B., Venables W., 2016). The results of the estimation were                also transferred to Excel and the results obtained using different methods were compared.                Comparisons  of  metamodel  construction  methods  were  made  using  the  five  criteria                described above. In particular, criteria 1-4 (K1, K2, K3, K4) are based on the deviation                between  the  value  obtained  by  means  of  metamodel  and  the  original  model  (financial                model implemented in Excel). Because metamodel and primary model results are tested                for the ROE expressed as a percentage (%), deviations will be measured in percentage                points (pp.). The fifth criterion is based on the observation of the \"forecast - execution\"                graph where the \"forecast\" is the result obtained with the help of the metamodel and the                \"execution\" result obtained with the help of the primary model.                Comparison by means of five criteria indicates that the best accuracy was obtained by                means  of  the  kriging  method  (after  applying  the  selection  procedure  of  the  learinig                sample).  Among  other  results,  classification  methods  (classical  classification  tree  and                random forests) obtained better accuracy than regression methods, neural networks and                kriging  without  applying  the  selection  procedure  of  the  learning  sample  (in  the  charts                described as kriging_old).                Chart  1  shows  deviations  of  results  for  individual  observations  arranged  in  descending                order according to the first criterion of evaluation (criterion K1). Chart 1 shows that the                largest deviation for the kriging method is 12.2 pp for the tested sample. which is definitely                a better result than for other methods.                Chart 2 shows how many percent of the sample exceeds the set threshold for the deviation                amount, according to the second evaluation criterion (criterion K2). Chart 2 shows that                only 9% of results obtained using the kriging method deviate more than 5 pp. from values                obtained using the original model (financial model implemented in Excel)                    Figure 2 Comparison Using the Criteria for Minimizing the Maximum Deviation for a                                    Single Case and Minimizing the Number of Outliers                   the size of   4,5                  the          4                  deviation in   3,5                                                    K1(Kriging)                               3                                                        K1(Kriging_old)                              2,5                                                       K1(rpart)                               2                                                        K1(rf)                              1,5                                                       K1(glm)                                                                                        K1(nnet)                               1                              0,5                                  12,20%                               0                                                    ranking of observations                                  1  10  19  28  37  46  55  64  73  82  91  100  109  118  127  136  145  154  163  172  181  190  199  with the largest deviation                                                   Source: own calculations.                                                                     431","Figure 3 Comparison Using the Criteria for Minimizing the Maximum Deviation for a                                    Single Case and Minimizing the Number of Outliers                                    120%                    % of observation                    with deviation   100%                                              K2(Kriging)                    above the set                    accuracy threshold  80%                                            K2(Kriging_old)                                   60%                                                 K2(rpart)                                   40%                                                 K2(rf)                                   20%                                                 K2(glm)                                                  9%                                    0%                                                 K2(nnet)                                         1%  2%  3%  4%  5%  6%  7%  8%  9%  10%  11%  12%  13%  14%  15%  accuracy threshold                                                                            (acceptable deviation in pp.)                                                  Source: own calculations.                 The  general  observation  resulting  from  the  observation  of  graphs  1  and  2  indicates  a                significantly lower position of the curve for the kriging method, which proves the greater                accuracy of this method than the others.                Comparison with the mean average absolute error criterion indicates the best indicator                value for the kriging method (Figure 3). Indication with this criterion coincides with the                previous results, which suggests the correctness of the first two criteria.                                      Figure 4 Comparison with the MAPE indicator.                                                                                 0,214212378    0,214212378                                                   0,149195195                                    0,128200333                                                                  0,07753666                      0,013644745                        K3(Kriging)   K3(Kriging_old)  K3(rpart)     K3(rf)        K3(glm)       K3(nnet)                                                  Source: own calculations                Residual module charts also indicate the highest accuracy of the kriging method. In the                graphs below (graphs 4-9), a smaller number of outliers and smaller deviations for the                kriging method are clearly visible.                                                                            432","Figure 5 Deviation Modules for the Methods Being Compared.                  6                            6                            6                          |e(rpart)|                     |e(rf)|                      |e(glm)|                 4                            4                            4                 2                            2                            2                  0                            0                            0                   0         200        400     0    100  200   300   400    0         200        400                   6        |e(nnet)|           6     |e(Kriging_old)|        6      |e(Kriging)|                 4                            4                             4                  2                            2                             2                 0                            0                             0                   0         200        400     0    100  200   300   400     0         200        400                                                   Source: own calculations.                 In order to better illustrate the accuracy of the methods, the \"forecast- execution \" charts                of the results of the primary model and the metamodel obtained using the tested methods                are presented. The graphs below clearly show that the results obtained using the kriging                method are much better than the results obtained using other methods. Particularly visible                is the relatively small dispersion of kriging results beyond the line that determines the                coverage of the results of the metamodel and the main model. In turn, the linear regression                model and the neural network tend to give forecasts overestimated at the center of the                distribution and at the underestimated at its extremes. In addition, the kriging method                does not generate outliers, which is its special advantage.                          Figure 6 Graphs Matching Data from Metamodel and the Original Model                                                                  Source: own calculations in R, Rattle package                                                                     433","Figure 7 Graphs Matching Data from Metamodel and the Original Model for Kriging                                                           Source: own calculations in R                 4 Conclusions  and future research                The metamodel concept has been successfully implemented, significantly accelerating the                process  of  calculating  profitability  for  individual  credit  agreements.  The  subsequent                aggregation  of  results  allows  for  detailed  and  multifaceted  analysis  of  the  effect  of  an                investment  project  which  is  the  construction  of  a  profitable  loan  portfolio.  The  results                obtained  should  be  considered  satisfactory.  There is need to improve  the metamodel's                accuracy, the results of metamodel analyzes should have similar accuracy to the results of                the original model. As part of the work, it was assumed that for practical applications a                maximum of 5% difference between the results of the metamodel and the original model                is  required.  Of  course,  this  is  not  a  strictly  defined  limit  of  acceptability  and  can  be                interpreted differently due to the specificity of the evaluation criterion.                This work focuses on presenting the concept of solving the problem. The challenge is to                achieve satisfactory accuracy at all levels of the profitability ratios tested. The challenges                also include the need for efficient recalibration of metamodel parameters in the case of                changes in the original model that involve a change in the value of the resulting variables.                 References                Barton, R., State, P. (2010). Metamodel-Based Optimization. NSF Workshop on Simulation                Optimization.                Cicirko,  T.  (2012).  Efektywne  zarządzanie  kapitałem  banku  komercyjnego  w  Polsce  w                świetle standardów adekwatności kapitałowej. Warszawa: Oficyna wydawnicza SGH.                Gajda, J. B. (2001). Prognozowanie i symulacja a decyzje gospodarcze. Warszawa: C. H.                Beck.                 Gajda, J. B. (2002). Ekonometria praktyczna. Łódź: Absolwent.                Gajda, J. B. (2004). Ekonometria. Wykład i łatwe obliczenia w programie komputerowym!.                Warszawa: C. H. Beck.                Hornik, K., Zeileis, A. 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The R Journal, vol. 1/2, pp. 45-55.                                                                                                    435","Effects of Financial Accounting Harmonization on Agribusiness                                                          2                                                                                3                                          1                                                                                                 4                         Lukáš Moravec , Jana Hinke , Gabriela Kukalová , Zuzana Tichá                                             1,3  Czech University of Life Sciences                            Faculty of Economics and Management, Department of Trade and Finance                                               Kamýcká 129, 165 21, Praha 6                                      E-mail: LukasGM@gmail.com, kukalova@pef.czu.cz                                                 2,4  University of West Bohemia                                  Faculty of Economics, Department of Finance and Accounting                                                Univerzitní 22, 306 14, Plzeň                                        E-mail: hinke@kfu.zcu.cz, susann.t@seznam.cz                 Abstract: Financial accounting is the most important tool for determining the financial                position and performance of agricultural business. However, this is increasingly subject to                the harmonization process. The first effort to harmonize financial accounting in Europe took                place in the 1950s after the signing of the Treaty of Rome, and has been ongoing since                then. Harmonization is currently being promoted through EU directives that are changing                national  legislation. The  aim  of this  contribution  is  to  quantify  the  impact  of important                changes (in financial accounting between 2004 and 2017) on the financial position and                performance of agricultural business.The impact of the changes was quantified using the                average values of a sample of 100 selected entities across the Czech Republic (i.e., joint                stock  companies,  limited  liability  companies  and  cooperatives  with  primary  agricultural                activity). Average values are presented in the tables so that the effect of the change on                the  balance  sheet  and  profit  can  be  demonstrated.  The  data  wereobtained  from  the                collection of commercial register documents. Important changes (specific for agriculture)                have  been  found:  valuation  of  assets,  reporting  of  preferential  limits,  depreciation  of                permanent crops on foreign land and valuation of inventories of own production, reporting                a change in their amount.                 Keywords: accounting, agriculture, changes, harmonization.                JEL codes: M16, M21, M41                 1 Introduction                For  the  past  several  decades,  the  world  economy  has  been  strongly  influenced  by  the                globalization processes. The process of globalization lies in a significant increase incross-                border  activities,  particularly  the  movement  of  goods,  capital  and  the  workforce.                Businesses have the chance to operate in international markets and investors also invest                money in farawaycountries. For these investors, it is essential to gain relevant economic                data,  on  which  their  investment  decision  making  is  based.  The  relevant  sources  of                economic information include accounting data, mainly financial statements and extensive                commentary (Vallišová, Dvořáková, 2012). In the past, national accounting regulations                were  laid  downby  the  legislation  of  the  particular  country,  which  resulted  in  major                divergences. However, in a globalized environment, accounting regulations defined within                national legal frameworks are insufficient (Strouhal, Horak, Boksova, 2017). It is important                that  all  existing  (internal  and  external)  as  well  as  potential  users  get  data  about  the                financial position and performance of a company in a format that they will understand.                For that reason, the objective of this article is to describe significant changes to the Czech                legislation pertaining to financial accounting in the Czech Republic in 2004 – 2016, define                the changes that occurred as a result of harmonization, i.e., the application of Directive                2013/34/EU of the European Parliament and of the Council, and also quantify the impact                of the most significant changes on the financial position and performance of agricultural                businesses.                                                                436","2 Methodology and Data                The Desk Research method, based on acomparative analysis of the primary data in Czech                legislation, was used in compiling the description of the year-over-year changes. A sample                of 100 agricultural businesses using the system of double-entry bookkeeping according to                Czech legislation  was used  to  quantify  the impact  of  the  said  changes  on  the  financial                position,  performance  (or  turnover)  and  financial  analysis  indicators.  The  analysis  was                based on the companies’ data available in the Albertina database.                The authors first determined whatscientific research in the area of the harmonization of                accounting data and its impact on national accounting regulations has been conducted so                far. The following table details the comparison of resources published in the Web of Science                and Scopus databases.                  Table 1 Research Studies on the Harmonization of Accounting Data and the Impact on                                             National Accounting Regulations                                 Focus of the study     Type of                         Goal                    Author                                                        research                 Procházka,      Consequences of       Logistic            To provide evidence that                 D. (2017)       Accounting            regression model    subsidiaries under foreign                                 Harmonization in a                        control steadily integrate                                 Transition Country                        IFRS                 Carneiro,       Assessing             Interviews with     To confirm or rebut the                 J.,             international         key informants to  theory: it will be difficult to                 Rodrigues,      accounting            assess              achieve material                 L., Craig, R.  harmonization          accounting          harmonization in GLASS                 (2017)          in Latin America      harmonization in    countries                                                       the 13 countries                 Sforza,         Central government  Six variables         To confirm or rebut the                 V.,Cimini,      accounting            from the World      hypothesis:independent of                 R. (2017)       harmonization in EU  Bank database,       the basis for accounting the                                 member states         correlate with the  WB, the quality of                                                       quality             enforcement mechanisms will                                                       mechanisms          affect the magnitude of                                                                           adjustments                 Agasisti, T.    Public Sector         comparative         Estimation about the                 (2016) +        Accounting and        analyses            “readiness to change” of the                 Marion, B.      Auditing in Europe.                       current public administrations                 S. (2016)       The Challenge of                          in all the 14 countries                                 Harmonization                 Černý, V.       State subsidies in    comparative         To quantify of the impact of                 (2016)          the Czech Republic    analyses            reporting subsidies on                                 and in accordance                         enterprises                                 with IAS/IFRS                 Habanec,        Deferred tax          Comparative         To determine the effect of the                 P.,             materiality           analyses, time      financial reporting system on                 Bohušová,       reporting in          series analysis     deferred tax reporting                 H. (2017)       accordance with                                 Czech national                                 legislation and with                                 IFRS                 Karwowski,  Role of accounting        Review of           To identify a possible use of                 M. (2017)       in supplying          publications        accounting in studying the                                 information relative                      business models of companies                                 to the trend toward                       operating in the selected                                 convergence                               industry                                               Source: own processing (2018)                                                              437","3 Results and Discussion                The first step towards achieving the set goal is to describe the year-over-year changes in                Czech regulations pertaining to financial accounting in 2004 – 2016, which was done by                way of a comparison table summarizing the significant changes that have an impact onthe                financial  position  (balance  sheet),  performance  (profit  and  loss  statement),  and  at  the                same time the quality of the companies’ financial reporting. The last column in the table                contains anassessment of whether these changes are primarily associated with companies                conducting  agricultural  activity.  This  specification  is  important  for  the  next  step  –                quantification of the impact of the changes on the financial position and performance of                agricultural businesses.                Printed in bold in the table below are the changes that occurred due to harmonization, or                the application of Directive 2013/34/EU of the European Parliament and of the Council.                            Table 2 Comparison Table of Year-over-year Changes in Legislation                 Change/Impact on:                 Balance      Income       Quality of    Agribusiness                                                    sheet     statement      reporting      companies                                       Year-over-year changes in 2003 - 2004                 Determining the valuation             x           x              x               x                 method for a set of assets                 Change in the method of                 valuation of short-term               x           x              x                 securities, derivatives, and                 receivables for trading                 The possibility of                 depreciation of capital               x           x              x                 improvements                 The obligation for certain                 entities to account for               x           x              x                 deferred tax                                       Year-over-year changes in 2004 - 2005                 Emission allowances and                 preferential limits –                 x           x              x               x                 reporting, accounting and                 valuation                 New requirements for the                 contents of the notes to a                                       x                 financial statement                                       Year-over-year changes in 2005 - 2006                 Statement of available-for-                       x              x                 sale securities valuation                 Reporting exchange rate               x           x              x                 differences                                       Year-over-year changes in 2006 - 2007                 Deferred obligation to                 transition from single-               x           x                 entry bookkeeping to                 double-entry bookkeeping                                       Year-over-year changes in 2007 - 2008                 The possibility of reporting                 in the order of millions of                                      x                 CZK in financial statements                 Livestock reporting –                                            x                 changes in terminology                                                                438","Business and company                 share valuation in                    x                          x                 dissolved entities                 Depreciation of permanent                 crops grown on leased                 x           x              x               x                 land                 Specification of the                 accounting procedure                  x           x                              x                 related to subsidies                                       Year-over-year changes in 2008 - 2009                 Valuation of CZK 1 for                x                          x                 certain types of assets                 Component spare parts                 x                 accounted for in stock                 Accounting for capital                 funds in the event of                 x                 company transformation                 New requirements for the                 contents of the notes to a                                       x                 financial statement                 Component depreciation                x           x              x                 method                                       Year-over-year changes in 2010 - 2011                 Method of foreign currency            x           x              x                 conversion                 Depreciation period of                 goodwill and valuation                x           x                 differences on acquired                 assets                                       Year-over-year changes in 2011 - 2012                 Specification of the                                             x                 stocktaking procedure                 Creating reserves for the             x           x              x                 purpose of income tax                                       Year-over-year changes in 2015 – 2016                 Determining own costs                x            x              x               x                 incurred                 A new definition of                                              x                 reserves                 Change in inventory level                 and activation as costs                           x              x               x                 correction                 Valuation of own costs in                 connection with inventory                         x                              x                 of own products                     Source: own processing in accordance with legislation valid in the particular year (2018)                According to the table, it is clear that the largest number of changes since 2003 took effect                on January 1, 2016, as a result of the incorporation of the EU Directive into the national                legislation of the Czech Republic. On the other hand, the years 2013 and 2014 brought no                substantial changes in accounting or financial reporting.                From  the  aforementioned  information  it  can  be  inferred  that  the  specific  aspects  of                accounting in agriculture are:                    1)  the valuation method for a set of assets – change from 2004,                    2)  reporting of preferential limits – change from 2005,                                                             439","3)  depreciation of permanent crops on leased land – change from 2008,                    4)  valuation of inventory of the company’s own products and reporting changes therein                       – change from 2016.                The  effect  of  these  changes  on  the  financial  position,  performance  (or  turnover)  and                financial analysis ratio indicators of agricultural businesses will also be quantified.                Valuation of a set of assets                In agriculture, it is rather common to purchase multiple assets at the same time, without                knowing  the  price  of  each  asset  individually.  A  typical  example  is  the  purchase  of  an                orchard,  which  can  be  subdivided  into  the  real  estate  and  the  individual  trees  or                bushes,which can be accounted for as a long-term asset under \"Permanent crops\". Until                the end of 2003, the law mandated these assets to be valued as a set of assets, using the                methods applicable at the time. However, the relevant regulation did not specify a clear                procedure on how this set of assets should be valued. It was up to the individual accounting                unit how it would valuate this set of assets. In practice, the common practice was to use                the method of breaking down the purchase price based on the accounting unit’s estimate.                In 2004, this method was incorporated into the Act on Accounting, which introduced the                principle  of  proportional  breakdown  of the  purchase  price  based  on  expert  appraisal  of                individual  components.  However,  should  the  costs  of  procuring  an  expert  appraisal  be                disproportionate to the significance of the particular item, the accounting unit can valuate                this asset based on its own estimate. Among their long-term assets, accounting units report                only perennial plants – permanent crop of trees and bushes, vineyards and hop fields. For                fruit  trees  and  bushes,  the  Czech  legislation  contains  the  condition  that  they  must  be                planted  on  a  continuous  plot  of  land  of  a  minimum  area  of  0.25  hectares  and  with                aminimum density of 90 trees or 1,000 bushes per hectare. If this criterion is not met, the                plants are reported directly as costs. For vineyards and hop fields, there is no additional                restriction. Purchased fruit orchards are immediately included in long-term assets, even if                they do not bear fruit yet, i.e., they do not generate any revenue. Fruit-bearing age is                crucial  for  the  commencement  of  tax  depreciation.  According  to  tax  regulations,  only                perennial  crops  that  have  reached  fruit-bearing  age  and  will  generate  revenue  for  a                minimum  period  of  3  years  are  eligible  for  depreciation.  Permanent  crops  fall  under                depreciation category 3 with a 10-year depreciation period. The fruit-bearing age of plants                is not determined by law. It is defined by the accounting entity itself in its internal policies,                based on agrotechnological parameters. This results in a time discrepancy between the                increase in the item \"perennial crops\" and the beginning of their depreciation.                 Fruit orchards are owned only by 5 of the total number of 100 accounting entities that                made up the sample. According to the analysis of the notes to the financial statement, in                three cases the valuation methodology did not change due to the insignificance of this                item. The change in the remaining two cases is shown in the following table:                     Table 3 Impact of the Change in the Valuation Method for a Set of Assets on the                                            Financial Position and Performance                                                      Values (in K CZK)         Impact (in K CZK)                                                       Average    Adjusted     Balance      Financial                                 Item                                                        2003        2003        sheet        result                                                                                 total                     Perennial crops                    1,696      1,648.2     ↓o 47.8      ↑o 4.78                     Land                                764        811.8      ↑o 47.8     No impact                     Ratio                              69:31       67:33         -             -                            Source: own processing based on data from the Albertina database (2018)                 The table above contains the average valuation of assets in 2003 according to the original                methodology (ratio) and according to the new ratio. The table shows a decrease in the                value of perennial crops by CZK 47.8 thousand. On the other hand, the item \"Land\" saw                an increase by this value. The value of the balance sheet total thus remained unchanged.                                                              440","Given  the  depreciation  period  for  perennial  crops  (10  years),  the  depreciation  amount                decreased by CZK 4.78 thousand, which had a positive impact on the financial result.                 The  change  in  the  valuation  methodology  also  impacted  certain  financial  analysis                indicators. The formula for calculating theprofitability indicator uses the financial result as                the numerator, and as the value of the financial result increased, so did the value of this                indicator.                 Reportingpreferential limits                The  amendment  to  the  Act  on  Accounting  for  the  year  2005  made  it  mandatory  for                accounting units to report preferential limits in their balance sheet (statement of financial                position). Before that, preferential limits were reported only in off-balance sheet accounts                and in the notes to the financial statement. According to the new system, preferential limits                at the first holder are reported as long-term intangible assets (item B.I.6. Other intangible                fixed  assets)  in  the  amount  equivalent  to  the  replacement  cost  and  also  as  subsidies                received  (short-term  payables).  An  increase  in  short-term  payables  also  has  certain                negative aspects in the form of lower liquidity of the company. The new system of reporting                concerns only preferential limitsgained in 2005 or later. The quotas from before then are                recorded using the original methodology. Gaining information about the replacement cost                of this asset is very complicated. This is due to the fact that there is no registered market                for preferential limits. Therefore, it was established by a decree that should the costs of                determining  the  replacement  cost  exceed  the  significance  of  this  information,  the                accounting  unit  is  exempt  from  recording  it  in  the  balance  sheet  and  the  data  on                preferential limits are to be published only in the notes to the financial statement.                From  the  sample  of  100  accounting  units  of  agricultural  enterprises  selected  for  the                purpose of this article, only 10 reported preferential limits. Following an analysis of the                companies’ financial statements, it was discovered that only one company included this                asset in their balance sheet. The other accounting units continued to post their allocated                milk quotas only in the notes to the financial statement, mainly because they had received                no new quota in 2005.                The company that posted itspreferential limits reported in its balance sheet for 2005 a                production quota in the value of CZK 148 thousand, which it received free of charge that                year. Preferential limits in the total value of CZK 2,173 thousand, assigned before 2005,                were  posted  by  this  accounting  unit  only  in  the  notes  to  the  financial  statement.  The                obligation to post its preferential limits in the balance sheet resulted in an increase in the                balance  sheet  total  of  this  accounting unit  ofCZK  148  thousand.  However,  the  balance                between the assets and liabilities remained zero. There was an increase in the value of                long-term intangible assets and also short-term liabilities.                The increase in assets and short-term liabilities also impacted certain financial analysis                indicators. The change in the value of short-term liabilities impacts mainly the indicator of                the company’s liquidity, in which this figure represents the denominator. The increase in                the value of short-term liabilities thus caused a decrease in the value of these indicators.                The following table shows the changes in the results of the liquidity indicators of the said                agricultural business:                    Table 4 Changes in the Results of the Liquidity Ratio Indicators of the Agricultural                                                         Business                                                                     Values in 2005                                     Item                    Before the            Following                                                              change             recalculation                         Current ratio                          6.37                 5.33                         Quick ratio                            2.02                 1.97                         Cash position ratio                    0.51                 0.42                                               Source: own processing (2018)                                                                441","An increase in the value of long-term intangible assets impacted mainly the asset turnover                ratio  and  return  on  assets  ratios,  in  which  total  assets  represent  the  denominator.  An                increase in the value of long-term intangible assets thus also resulted in a decrease in the                value of these two ratios.                Depreciation of perennial crops                As of 2008, accounting units can also depreciate perennial crops that the accounting unit                manages on another owner’s land. In the notes to the financial statement, accounting units                do not post any information on the amount of land of other entities that they manage, so                for  the  purpose  of  quantification  of  the  impact  of  the  change  it  was  necessary  to  use                estimates of the value gained by way of expert discussions with the five main economists                of  the  agricultural  businesses.  According  to  their  expert  estimates,  they  manage                approximately 1/3 of their perennial crops on leased land. The quantification of the change                in depreciation is shown in the following table:                  Table 5 Quantification of the Impact of Changes in the Depreciation of Perennial Crops                                                      on Leased Land                                                      Value                 Impact of change                                                   (in KCZK)                   (in K CZK)                              Item                                                  ø in 2007      Balance sheet        Financial result                                                                      total                  Total perennial crops                  2 789          -                    -               -   - of which is on leased land            930          -            Decrease by 93                            Source: own processing based on data from the Albertina database (2018)                 An increase in depreciation affects the financial result; however, it also means a decrease                in the tax base and tax liability due to the effect of the tax shield. The change has no effect                on the balance sheet total, though it affects all the ratio indicators calculated based on the                financial result – particularly the profitability indicators.                Valuation of inventory of the company’s own products and reporting changes in                its size                 Valuation of livestock and plants procured by the company’s own activity is a major issue                in agriculture. The Act on Accounting defines the valuation of inventory or long-term assets                as equivalent to the company’s own costs. The original provisions valid until the end of                2015 gave accounting units the option to choose between the valuation of inventory of the                company’s own products solely based on direct costs, or including justifiable overheads.                Accounting units thus could choose from various valuation methods and could to a certain                extent affect their financial result and, as a result, their tax liability. The 2016 amendment                to  the  Act  on  Accounting  did  away  with  these  options  and  stated  that  valuation  must                contain  all  eligible  and  attributable  costs.  This  new  provision  contributed  to  the                harmonization of the valuation methodology in Czech legislation and the standard IAS 2 –                Inventory.  There  are  two  ways  to  valuate  inventory  by  the  company’s  own  costs.  The                valuation is either based on the actual costs incurred, or costs determined by calculation.                The research has shown that 100% of the selected accounting units in the research sample                useplan  calculation  to  determine  their  costs  for  the  purpose  of  valuation  of  inventory                created  by  the  company’s  own  activity.  This  calculation  only  expresses  the  average                production  conditions,  and  thus  also  the  average  unit  cost  in  a  particular  period.  Plan                calculations are usually carried out on an annual basis. If any significant changes occur,                these calculations can be updated during the year. As accounting units do not publish the                exact structure of their calculations, this change cannot be quantified.                Another  change  occurred  in  the  way  changes  in  the  inventory  of  the  company’s  own                products are posted. For a number of years, goods produced to stock were considered to                be revenue without being sold. Revenue was thus adjusted in relation to costs. The new                provision effective from January 1, 2016 defined the way of posting this item as costs                adjustment. This change should ensure an increase in the validity of the financial statement                                                              442","and better alignment with IFRS. This change does not affect the company’s financial result,                but it does affect its turnover. For the purpose of quantification of the impact on turnover,                the item \"change in inventory of own products\" was analyzed in the companies’ financial                statements.                Table 6 Value of the Item \"Change in Inventory of Own Products\" in the Selected Sample                                                                    Values (in K CZK)                              Value type                                                        2013        2014        2015          2016                     Maximum                            3,073       3,817       4,007         3,945                     Minimum                            1,256       1,090       1,332        1,298                     Median                             2,383       2,589       2,274        2,477                     Average                            2,481       2,623       2,516        2,719                            Source: own processingbased on data from the Albertina database (2018)                 The table above displays the values of the item \"change in inventory of own products\" in                the past four years for which there weredata available. The table implies that the transfer                of the item from revenue to cost correction resulted in agricultural companies posting CZK                2,719 thousand less in revenue in 2016. It has already been statedthat this fact does not                affect the companies’ financial results, as it is irrelevant whether the item is considered as                an increase in revenue or cost correction; however, in terms of financial analysis it will                affect all ratio indicators that are based on the value of costs or revenue (e.g., return on                costs, return on revenue, etc.).                 4 Conclusions                 The  changes  in  legislation  pertaining  to  financial  accounting  resulting  from  the                harmonization of accounting systems contribute to creating a true and accurate image of                the economic performance and financial position of all companies. Despite the major gaps                in the legislation concerning accounting in agricultural companies in the Czech Republic, it                is being harmonized with the internationally recognized accounting principles.                Acknowledgments                 This article isthe partial result of the project of the Faculty of Economics at the University                of West Bohemia entitled SGS-2017-004: Finance and Sustainable Development in Theory                and Practice, as well as the partial result of the project No. 20171032 - Czech tax gap as                a consequence of profit shifting quantification and its factors identification, supported by                the Internal Grant Agency (IGA) of Faculty of Economics and Management, CULS Prague.                 References                Agasisti, T. (2016). Public Sector Accounting and Auditing in Europe  – the Challenge of                Harmonization. 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Retrieved    from:                http://www.albertina.cz/.                                                                                                                   444","Financial Market Implied Industry Classification                                                           1                                             László Nagy , Mihály Ormos     2,3                                                   1  Department of Finance                                       Budapest University of Technology and Economics                                     Magyar tudósok körútja 2, Budapest H-1117, Hungary                                                E-mail: nagyl@finance.bme.hu                                            2  Department of Finance and Accounting                                   Institute of Business Economics, Eötvös Loránd University                                           Szép utca 2. H-1052 Budapest, Hungary                                                  E-mail: ormos@gti.elte.hu                                                  3  Department of Economics                                                     J. Selye University                                     Bratislavská cesta 3322, SK-94501 Komárno, Slovakia                                                   E-mail: ormosm@ujs.sk                 Abstract:  This  paper  introduces  the  financial  market  implied  industry  classification                standard. Besides current industry classifications we propose a spectral clustering based                quantitative  methodology.  The  main  drawback  of  current  standards  come  from  their                qualitative classification techniques which can be eliminated in this purely mathematical                concept. Calculating the market implied clusters and compering them with global industry                classification explores that market implied classification provides better statistical results.                However it also turns out that different clustering techniques provide similar classifications,                moreover, both methods determine “Real Estate” as a cluster.                Keywords: Cluster analysis, market classification, GICS, machine learning                JEL codes: G12; G15; C38                 1 Introduction                 Practitioners are using Global Industry Classification Standards (GICS) to keep track of                sector based moves. It is widely accepted that firms with similar business activities have                similar macro and micro factor sensitivities. Thus, building portfolios with names from the                same industry lets us to eliminate specific risk. Sector based bundling gives the opportunity                to tailor the macro factor exposure, understand the contamination of macro level shocks                and determine regulatory standards. Moreover, it also helps to identify firms which under                or outperform the industry hence it helps to separate systematic and non-systematic risks.                Asset  managers  use industry  classification  standards  for  their  asset  allocation  and  risk                management strategies. Moreover, industry classification is also important for regulators,                governments and labour economists to have a deeper understanding of the given state of                the economy and be able to implement policies.                GICS is the mainly used classification. The methodology was developed by Morgan Stanley                Capital  International  Inc.  (MSCI)  and  Standard  and  Poor's  (S\&P).  The  categorization                combines quantitative and qualitative techniques (MSCI 2015.) to obtain a market oriented                economically thoughtful classification standard.                In this article we introduce a spectral clustering based (Nagy and Ormos 2016.) purely                quantitative model to unveil the Financial Market Implied Classification (FMIC). Using daily                closing prices, we show that the normalized modularity cut (Bolla 2011.) implied clusters                and GICS are highly comparable. Moreover, standard Capital Asset Pricing Model based                regressions give further evidences for using spectral clustering. The results show that GICS                can be looked on as an approximation of the spectral clustering based classification. In                addition, the clusters support the latest review of GICS in which Real Estate was added as                distinct asset class.                                                               445","The article structured as follows: Section 2 is a brief overview of industry classification                standards. In Section 3 we introduce our spectral clustering based concept. In Section 4                we present the market implied clusters, compare them with groups given by GICS and                carry out regression analysis to investigate the risk explanation power of FMIC. Section 5.                summarizes the article.                 Industry Clasification Standards                Investors are using different classification techniques from long ago to identify industries                and characterize their specific behaviour. In 19th century all the market participants had                their own classifications which were mainly used to evaluate risk, understand the macro                factor sensitivities and build investment strategies (Vose 1916). However, there was no                officially  accepted  framework.  Hence,  it  was  unmanageable  to  aggregate  sector  level                sensitivities and implement economic policies.                 Standard Industrial Classification                After the Great Depression in 1937, the US Central Statistical Board (Kolesnikoff 1940.)                unified  the  different  industry  classifications  and  established  the  Standard  Industry                Classification. SIC was the main guideline of the Federal Government, banks and investors                during the 20th century, in addition, the U.S. Securities and Exchange Commission (SEC)                still uses it to its industry classification. The system had to be revised several times, in                1958, 1963, 1967, 1972, 1977 and 1987, but it still had several limitations. The main                drawbacks of SIC are that it is designed for the economy of early 20th century, it is hard                to identify new groups and follow the changes of global economy.                 North American Industrial Classification System                NAICS  is  and  industry  classification  standard  which  was  designed  to  response  to  the                increasing criticism of SIC (Executive Office of the President Office of Management and                Budget.  2017).  In  1980s  the  rapid  changes  of  world  economy  forced  the  Office  of                Management and Budget (OMB) to overhaul SIC. Thus in 1997 the Economic Classification                Policy Committee (ECPC) in Nacional the cooperation with Mexico's Instituto de Estadística,                Geografía e Informática (now the Instituto Nacional de Estadística y Geografía, INEGI) and                Statistics Canada suggested a new industry classification system which supplanted SIC.                They  agreed in that  NAICS  should  be reviewed in  every five years  to  reflect  economic                changes.  The  current  form  of  NAICS  defines  20  sectors  and  1,057  industries.  The                classification  standard  is  used  for  various  administrative,  regulatory,  and  taxation                purposes.                 Global Industry Classification Standards                 Besides the SIC and NAICS in 1999 Morgan Stanley Capital International Inc. and Standard                and Poor's created the widely used Global Industry Classification Standard. The primary                goal of S\&P was to enhance its business with introducing sector indices of S\&P 500 index.                In order to achieve this ambition an adequate industry categorization rule was needed. The                classification has a market oriented nature, which incorporates quantitative and qualitative                techniques. As GICS is used in the sub-index decomposition of S\&P 500 thus it should                follow all the changes of the market, hence it has to be reviewed at least annually. At first                GICS introduced 10 sectors, 23 industry groups, 59 industries and 123 sub-industries. The                classification was revised several times, currently it stands from 11 sectors, 24 industry                groups,  68  industries  and  157  sub-industries.  Each  sector,  namely  Consumer                Discretionary, Consumer Staples, Energy, Financials, Health Care, Industrials, Information                Technology, Materials, Real Estate, Telecommunication Services and Utilities represents an                economically understandable market segment which is the key of the popularity of the                classification.  GICS  is  the  most  famous  actively  used  standard  which  is  assiduously                reviewed by the financial market. Because, almost all U. S. market participants benchmark                their positions against the performance of GICS sector indices. Moreover, the most liquid                products in the world are the Standard \& Poor's Depositary Receipts (SPDR) ETFs which                                                             446","tracks the S\&P 500 index and sub-indices. Thus, the behaviour of GICS must be in line                with the market because like a stock it is continuously reviewed by the market and like                earning season at least annually revised by MSCI and S\&P.                In this article we propose a purely quantitative technique (FMIC) to analyse the connections                between  financial  market  and  GICS  and  study  the  efficiency  of  Global  Industry                Classification Standards.                 Spectral Clustering                The original concept of classifying the market, defining sub-groups, distinguishing sectors                raises the fundamental question: Who can judge the market?                 If we do not want to make any a priori assumptions then we have to look at the data and                dismiss other subjective classification guidelines. Mathematically it is possible to represent                a datasets as a graph, hence, we can construct an abstract network of stocks. The most                straightforward  method would  be  representing  stocks  with  nodes,  connection strengths                with weights. Thus, we can define the network with G(  ,   ) graph where    represents the                set  of  stocks  and      contains  the  connection  information.  It  is  widely  used  that  the                adjacency matrix    represents the graph, thus all the structure information is embedded                in the matrix. Note that if we normalize the sum of each row to one, then we get the                transition matrix of the random walk on the graph (Luxburg 2007.);                          =                                                                               (1)                             −1                where    represents the diagonal matrix of the sums of rows. Studying stopping times of                random walks on graphs sheds some light on the structure of the graph, because, if it                takes a long time to reach a subgraph of the graph from a given node then it would mean                that the node and the subgraph are well separated. Moreover, the largest eigenvalue of                the submatrix of    which belongs to the subgraph controls the distribution of the stopping                time;                       Prob(    ≥   ) = 1 −    ∑      =1      −1     ⋅ 1                                  (2)                                          0                where    represents the stopping time,     the initial distribution,    the submatrix of transient                                                       0                points,    defines the transition probabilities from transient to recurrent points.                Using symmetricity and spectral theorem                                                                                Prob(    ≥   ) = 1 −       ∑     Λ   −1        ⋅ 1                                 (3)                                               =1                                          0                Equation 3. shows that the spectrum of adjacency matrix, stopping times and clustering                properties are strongly connected.                In addition, the problem can be looked at from an analytical point of view. Fourier analysis                is a widely used tool in pattern recognition theory (Shi and Malik 2000). Considering an                arbitrary real valued function on the vertexes and defining the below incidence matrix led                some colour to the connections between Laplace operator and graph theory (Chung 1997.);                                 1 if v is the initial vertex of e                                = {−1  if v is the terminal vertex of e                                   (4)                                 0 otherwise                then       would be exactly the negative discrete Laplace operator because:                                                                                  (   ) =   ∑       ∼         (   ) −   (   )                                 (5)                                                                                                                                                 Notice that if we subtract the adjacency matrix from the diagonal matrix of row-sums then                we get the same operator. Thus Laplace matrix can be defined as follows;                          =    −                                                                        (6)                If  we think  of  the  adjacency matrix  as  a  noisy  matrix  and  would like  to maximize the                information content, then we get the modularity matrix;                                                              =    −                                                                          (7)                                                             447","where    represents the vector of row-sums. Normalizing the matrix gives as the normalized                modularity matrix which helps us to cluster tightly connected networks (Bolla 2011.).                                1                              −                                                                   =    2(   −      )   −1/2                                                       (8)                                           Equation 5. shows that    is the negative discrete Laplace operator hence its eigenvectors                would  be  sine  and  cosine  functions.  Analogously  to  noise  filtering  techniques  we  can                calculate  a  Fourier  approximation.  However,  we  would  like  to  optimize  the  normalized                modularity  cut  thus  we  have  to  use  the  normalized  modularity  matrix.  The  optimal                representation  of  the  original  vertices  are  the  rows  of  that  matrix  which  contains  its                columns  the  eigenvectors  of  in  absolute  sense  largest  eigenvalues  of  the  normalized                modularity matrix (Bolla 2013.);                           1       1                                  −                          −                       (   2   , … ,    2   )                                                             (9)                                                                    1                where    , …,     are the corresponding eigenvectors of |   (   )| ≥ ⋯ ≥ |   (   )| ≫ |     +1 (   )|.                                                                                                                                                                                                                 2                                                                                                                                                                                                                                                             1                        1                To unveil the market implied classification we should calculate the similarity matrix, then                determine  the  normalized  modularity,  identify  the  spectral  gap  in  the  spectrum  of  the                normalized modularity matrix and finally clustering the optimal representation with k-mean                algorithm.                The only hurdle is the calibration of edge weights. Note that a spectral clustering method                is effective if and only if in absolute sense decreasing sequence of eigenvalues goes to zero                and  gap  appears  in  the  spectrum.  Otherwise,  spectral  based  methods  cannot  give                appropriate classification. Hence, we should test different similarity measures thus we have                to calculate the spectrum of different similarity measures implied normalized modularity                and Laplace matrices and choose that which provides the best spectral properties. Ormos                and Nagy showed tightly connected financial dataset can be analysed with Gaussian square                distances.                                                                         2                          =    −||   −   ||                                                            (10)                                             Combining Fourier analysis, pattern recognition techniques, random walk and graph theory                provides us the optimal cluster property, thus spectral clustering gives us the opportunity                to find an approximation of the market implied classification.                Financial Market Implied Classification                Spectral clustering can be used to unveil the hidden market structure of stock indices. The                purely quantitative approach needs only closing prices so that the market segments are                formed by stock prices. The fundamental assumption behind the model is that at least the                weak form of market efficiency holds on a daily scale. This assumption allows intra-day                inefficiencies,  but  accepts  that  the  daily  closing  auction  forces  the  market  into  the                equilibrium.                Data                The current study identifies FMIC based on stock splits and dividends adjusted daily closing                prices between 01/01/2007 and 01/03/2017 of current S\&P 500 constituents. The data is                provided by Yahoo! Finance.                 FMIC and GICS                Calculating the spectrum of normalized Laplace and modularity matrices of Gaussian based                similarities shed some light on the structure of the network. Bolla showed that if a graph                is  dense  then  the  normalized  Laplacian  matrix  cannot  be  used  because  the  norm  of                eigenvalues  slowly  converge  to  zero.  Normalized  modularity  matrix,  however,  provides                better spectrum properties.                                                                   448","Figure 1 Spectrum of Gaussian Based Normalized Laplace Matrix                                                                  Source: own processing                 Calculating  the  spectrum  of  normalized  Laplace  matrix  displays  that  the  eigenvalues                converge  slowly  to  zero  thus  Fourier  based  approximation  techniques  cannot  be  used                (Figure 1.). It also suggests that the network structure is not scarce, different clusters                should be connected. Hence, we should optimize the information theory based Newman                Girvan cut thus we have to calculate the spectrum of normalized modularity matrix.                           Figure 2 Spectrum of Gaussian Based Normalized Modularity Matrix                                                                 Source: own processing                 The spectrum of normalized modularity matrix provides appropriate spectral properties.                Figure 2. shows that it has several large eigenvalues and the decreasing sequence of them                converges to zero. Nevertheless, the normalized Laplacian cannot be used for clustering.                All these implies that the equity index network is dense, most of the stocks are connected.                Identifying spectral gaps highlights that the optimal number of market implied clusters                would be 5, 7, 9 or 12.                Note that GICS distinguishes 11 sectors. If we calculate FIMC with 11 clusters then we get                controversial  results.  Moreover,  GICS  and  FIMC  11  classifications  are  fundamentally                different. However, spectral clustering based methodology proposes to use 12 clusters. If                we  calculate  FMIC  12  then  we  could  see  that  GICS  is  in  line  with  market  implied                classification, see Table 1.                                      Table 1 Frequency Table of GICS and FMIC 12                                                        GICS/FMIC12                          1      2      3      4     5      6      7      8     9      10    11     12                     C    0      8     33      3     0      0      0      0     0      42     0     0                     S    0      0      5      1     0      0      0      0     0      4      0     26                     E    31     2      2      0     0      0      0      0     0      0      0     0                     F    0      5      9      0     0     50      0      0     0      0      0     0                    H     0      0      5      0     0      0      0     39     0      0     16     0                     I    0      5     55      0     0      0      0      0     6      0      0     0                    IT    0      6     15     47     0      0      0      0     0      0      0     0                    M     4      1     19      0     0      0      0      0     0      1      0     0                     R    0      1      5      1     23     0      0      0     0      0      0     0                                                             449","T    0      1      4      0     0      0      0      0     0      0      0     0                    U     1      0      1      0     0      0     26      0     0      0      0     0                Notes: This table is the contingency table of GICS and FMIC 12; D denotes Consumer Discretionary,                   C: Consumer Staples, E: Energy, F: Financials, H: Health Care, I: Industrials, IT: Information                    Technology, M: Materials, R: Real Estate, T: Telecommunications Services and U: Utilities.                                                   Source: own processing                 Clusters 1, 4, 5, 6, 7 and 12 cover Energy, Information Technology, Real Estate, Financials,                Utilities and Consumer Staples respectively.                The first cluster is dominated by Energy companies. It also contains four Utilities and NRG                Energy. These firms are closely related to energy business. Thus, we could say that GICS                Energy sector can be quantitatively supported.                Information  Technology  names  are  put  into  Cluster  4.  Checking  non  Information                Technology names in Cluster 4 we see that the most of them are highly technological. The                three Consumer Discretionary companies are Amazon Inc., Expedia Inc. and Garmin Ltd.                that all carry out strongly technology based business. Equinix specialized in data and cloud                business thus putting it to a technological cluster is also in line with economic thinking.                Monster Beverage could be surprising, however, if we compare it with its peers we could                see that Monster Beverage differs from them.                Cluster  5  gives  mathematical  evidence  to  handle  Real  Estate  as  a  different  sector.                Moreover, Financials, Utilities and Consumer Staples are also separated in Cluster 6, 7 and                12 respectively.                Clusters 8, 9, 10 and 11 incorporates Health Care, Industrials and Consumer Discretionary                firms. Table 1 shows that these clusters split GICS sectors into two parts. Scrutinizing the                results we could see that names are divided along GICS sub industries (Appendix 1).                Cluster 9 is the smallest cluster which encompasses 6 Industrial firms. Studying the sub                industry  classification  we  see  that  5  companies  out  of 6  are  Airlines  and  the  outlier is                Fortive Corporation which has large exposure to aviation business.                In conclusion, we can say that FIMC 12 is in line with GICS sectors and sub-industries,                however it gives us a purely quantitative technique to identify clusters.                 The spirit of CAPM                Understanding systematic risk is essential part of asset allocation because prices can be                scrutinized only within an equilibrium model. Researchers, investors, regulators and banks                are seeking models which could distinguish systematic and non-systematic risk.                Following a purely market oriented prospect leads to the Capital Asset Pricing Model (CAPM)                which incorporates the systematic risk into the market portfolio. Several empirical studies                concluded that CAPM can be used as a benchmark, but has to be made it more precise.                Analysing risk and reward in different frameworks explores different aspects of risk. Note                that while standard deviation counts all the moves,    takes into account only that moves                which can be explained linearly with the fluctuation of market portfolio. However, investors                are  sensitive  to  losses,  filtering  out  therefore  gains  leads  to  Expected  Downside  Risk                (Ormos  and  Timotity  2016.),  in  addition,  various  information  theory  based  measures                (Ormos and Zibriczky 2014.) can be defined which are strongly connected to log-optimal                portfolio theory (Urban and Ormos 2013.).                                                           (ln         ) =    +    ⋅        +                                             (11)                               −1                All the linear models explain different aspects of systematic risk (SRM) but they can be                compared with regression statistics. Correspondingly, if we add GICS and FMIC 12 to the                regressions then we could compare them.                                                                 450","                           (ln         ) =    +           +             +                                           1                                                2                       {         −1                                                                    (12)                                                             (ln         ) =    +           +            12 +                                                   2                                       1                                −1                Table 2 shows that the FMIC 12 outperforms GICS, except when risk is characterized by                entropy and semi-variance.                                    Table 2 Regression Statistics of GICS and FMIC 12                                 p-value        R2        p-value       R2        p-value        R2                                   risk                    GICS                   FMIC 12                       Var        0.000       0.110        0.000       0.173       0.000       0.185                       Sig        0.000       0.104        0.000       0.169       0.000       0.179                    Semi-var      0.151       0.004        0.000       0.111       0.000       0.106                      CAPM        0.000       0.187        0.002       0.232       0.002       0.234                       EDR        0.000       0.143        0.000       0.210       0.000       0.227                        H         0.000       0.026        0.000       0.122       0.000       0.117                 Notes: Table 2 contains the regression statistics of different systematic risk models with GICS and                                                         FIMC 12.                                                   Source: own processing                 Notice that semi-variance is not significant and entropy could explain only 2.6% of total                variance. Hence, we could say that risk measures with high     values can be used with                                                                                2                cluster-variable like FIMC or GICS. Otherwise, there is no need adding cluster-variable to                regression, because linear terms are not explained, thus the risk measure captures the                same non-linear effect.                    Figure 3 Standard Deviation, Mean Return Plot of FMIC 12 with Zero Interception                                                                         Source: own processing                Setting the constant coefficient zero and calculating the regressions gives Table 3 which is                in line with the 2007-2017 market conditions.                        Table 3 Regression Statistics of GICS and FMIC 12 with Zero Interception                                 p-value     R2           p-value     R2          p-value     R2                                risk                     GICS                    FMIC 12                    Var           0.000       0.203        0.000       0.595       0.000       0.601                    Sigm          0.000       0.374        0.000       0.593       0.000       0.598                    Semi-var      0.000       0.253        0.000       0.565       0.000       0.562                    CAPM          0.000       0.277        0.000       0.624       0.000       0.625                    EDR           0.000       0.375        0.000       0.613       0.000       0.622                    H             0.000       0.522        0.000       0.571       0.000       0.568                               Notes: This table contains the regression statistics of Equation 12.                                                   Source: own processing                                                            451","It also can be seen that different linear based risk factors explain 20%-52% of the total                variance, but adding cluster-variables the explanatory power of the model jumps to 60%.                This means that cluster specific and linear risks explains 60% of the fluctuation.                If we generalize the baseline linear model we could specify the following regression;                                                                             (ln         ) =  ∑ 10     ⋅ (      )   +                                     (13)                                                                                =1                                 −1                Expanding the linear model with higher order terms could shed more light on non-linear                dependencies, see Table 4.                                      Table 4 Estimated R2 Statistics of Polynomial                    Risk / R2 of n-     1st        2nd         3rd        4th        5th        10th                       th order                          Var          0.203      0.365       0.495      0.537      0.567       0.582                          Sig          0.374      0.547       0.568      0.568      0.569       0.581                       Semi-var        0.253      0.348       0.411      0.454      0.477       0.518                         CAPM          0.277      0.559       0.574      0.575      0.591       0.626                         EDR           0.375      0.587       0.593      0.594      0.595       0.598                           H           0.522      0.524       0.526      0.527      0.532       0.535                 Notes: Table 4 highlights the non-linear connections between returns and systematic risk factors.                                                   Source: own processing                The results are in line with our expectations, because, adding higher order terms of linearly                inspired  risk  metrics  to  the  regression  increases  the  explanatory  power,  while  entropy                shows different behavior.                Generalizing Equation (11) we could get the following models;                         (ln         ) =            +             +                                       1                                            2                                −1                       {                                                                               (14)                                                         (ln   ) =            +             12 +                                               2                                   1                                −1                Equation  (13)  lets  us  to  distinguish  cluster  and  non-cluster  specific  higher  order                connections. The results (Table 5) show that polynomial terms explain similar effects like                GICS and FMIC 12.                         Table 5 Estimated R2 Statistics of Polynomial Regressions with FMIC 12                                        Risk/ R2 of n-th order model with FMIC 12                                    1st        2nd          3rd         4th         5th         10th                       Var        0.601       0.602        0.605       0.606       0.609       0.616                       Sig        0.598       0.601        0.604       0.605       0.608       0.614                    Semi-var      0.562       0.563        0.564       0.565       0.566       0.569                      CAPM        0.625       0.636        0.637       0.640       0.640       0.652                       EDR        0.622       0.634        0.634       0.634       0.635       0.636                        H         0.568       0.569        0.569       0.573       0.573       0.577                   Notes: This table summarizes the non-linear filter behaviour of the cluster variable (FMIC 12).                                                   Source: own processing                Tables 4 and 5 show that higher order terms do not increase the explanatory power of the                regressions. Thus, the remaining 30% part of the variance cannot be explained by liner-                based risk factors and clusters. Analysing returns with linear and polynomial regressions                show that FMIC 12 outperforms GICS, however, cluster variables explain similar non-linear                connections.                  Conclusions                Spectral clustering is an adequate technique to unveil the embedded market structure,                filter out non-linear effects and make CAPM more precise. The purely quantitative method                                                              452","gives  us  the  opportunity  to  categorize  firms  based  on  their  stock  market  returns,  in                addition, it lends some colour to Global Industry Classification Standards.                 Acknowledgements                 Mihály Ormos acknowledges the support of the János Bolyai Research Scholarship of the                Hungarian Academy.                 References                 Urban, A., Ormos, M. (2013) Performance analysis of log-optimal portfolio strategies with                transaction costs. Quantitative Finance, vol. 13(10), pp. 1587-1597.                 Timotity, D., Ormos, M. (2016). Generalized asset pricing: Expected Downside Risk-based                equilibrium modeling. Economic Modelling, vol. 52, pp. 967-980.                Zibriczky, D., Ormos, M. (2014). Entropy-Based Financial Asset Pricing. PLoS ONE, vol.                9(12).                Fan, R., Chung., K. (1997). Spectral Graph Theory. American Mathematical Society.                Shi, J., Malik, J. (2000). Normalized cuts and image segmentation. Pattern Analysis and                Machine Intelligence IEEE, pp. 888-905.                Nagy, L., Ormos, M. (2016). Friendship of Stock Indices. In: 30th Conference on Modelling                and Simulation. DOI: 10.7148/2016-0152.                Bolla,  M.  (2011).  Penalized  version  of Newman-Girvan  modularity  and their  relation  to                normalized cuts and k-means clustering. Physical review E, vol. 84.                Bolla,  M.  (2013).  Spectral  Clustering  and  Biclustering.  Learning  Large  Graphs  and                Contingency Tables. New York: John Wiley \& Sons.                 MSCI (2015). S\&P Dow Jones Indices and MSCI announce Further Revision to the Global                Industry Classification Standards (GICS) Structure in 2016. Working Paper.                Executive Office of the President Office of Management and Budget (2017). North American                Industry Classification System. Working Paper.                Vose, E. N. (1916). Seventy-five years of the Mercantile agency, R.G. Dun \& co., 1841-                1916. Brooklyn, N.Y.: Priv. Print. at the Printing House of R.G. Dun \& Co.                Von Luxburg, U. (2007). Tutorial on Spectral Clustering. Statistics and Computing, vol. 17,                pp. 395-416.                 Kolesnikoff, V. S. (1940). Standard Classification of Industries in the United States. Journal                of the American Statistical Association, vol. 35(209), pp. 65-73.                                                                                      453","The Role of Corporate Income Tax Differences on the Profit                          Shifting Behavior: Evidence from the Czech Republic                                                              2                                                                                    3                                                                                                       4                                       1                   Danuse Nerudova , Veronika Solilova , Marian Dobranschi , Marek Litzman                                               1,2,3,4  Mendel University of Brno                                             Faculty of Business and Economics,                                            Department of Accounting and Taxation                                         Zemědělská 1, 61300, Brno, Czech Republic                                            E-mail:danuse.nerudova@mendelu.cz;                                                veronika.solilova@mendelu.cz;                                               marian.dobranschi@mendelu.cz                                                  marek.litzman@mendelu.cz                 Abstract:  The  aim  of  this  paper  is  to  investigate  the  profit  shifting  behavior  of  Czech                subsidiaries owned by parent companies placed in top five EU investor countries in the                Czech Republic. We analyze the sensitivity of profit before taxation to the corporate tax                differences  between  Czech  subsidiaries  and  other  subsidiaries  owned  by  the  same                Multinational  Enterprise.  We  expect  that  the  profit  shifting  behavior  of  the  Czech                subsidiaries to be less significant compared to the existent estimates by the literature. This                hypothesis  arises  from  the  particular  tax  advantageous  position  of  the  Czech  Republic                relative to the top five EU investor countries high corporate tax rates. We adopt the Hines-                Rice  approach to  analyze  the  semi-elasticity  of  profits  before  taxation  to the  corporate                income tax differences.                Keywords: profit shifting, tax differential, semi-elasticity, effective CIT rate, tax avoidance                JEL codes: H25, H26, H32                 1 Introduction                Profit shifting issue represents a current topic due to increased globalization and mobility                of  the  capital  across  the  world  countries.  The  Multinational  Enterprises  (hereinafter  as                MNE’s) can exploit different tax rules regarding the corporate income tax rates and avoid                paying taxes on profits. Our objective is to assess the sensitivity of profits before taxation                to different tax regimes. The aim of this paper is to identify if profit shifting occurs in case                of Czech subsidiaries and what are the potential channel of shifting profits. We choose to                analyze the pre-tax profits sensitivity of Czech subsidiaries owned by MNE’s placed in the                top  five  EU  investors  countries  in  the  Czech  Republic.  We  restrict  our  sample  data  to                profitable Czech subsidiaries that are involved in manufacturing. We use Ordinary Least                Squares (hereinafter as OLS) regression to estimate the impact of corporate income tax                (hereinafter as CIT) rate on the pre-tax profits reported by selected Czech subsidiaries.                Moreover, using OLS procedure we estimate the impact of tax differential between Czech                subsidiaries and the average CIT rate of their affiliates from the same MNE group as well                as  firm-specific  input  factors  and  other  control  variables.  The  adoption  of  Hine-Rice                approach to estimate the sensitivity of pre-tax profits to tax differential seeks to investigate                whether  Czech  subsidiaries  report  less  pre-tax  income  when  their  “sisters”  companies                based elsewhere in the EU face a lower CIT rate. The paper is organized as follows. In the                first section we present a short literature review regarding the empirical literature that                analyses  profit  shifting;  in  the  second  section  we  present  the  data  used  and  the                methodology of our empirical analysis; the third section presents the results obtained and                the paper ends with concluding remarks.                 2 Literature Review                The  concept  of  profit  shifting  behavior  has  been  defined  as  the  strategic  action  of  the                Multinational Enterprises (hereinafter as MNE) to intentionally moving profits from high-                tax  jurisdictions  to  low-  or  no-tax  countries  (Dyreng,  2015;  Markle,  2015).  The  main                triggering factors of profit shifting according to Dharmapala (2014) and Markle (2015) are                the  tax  rate  differences  between  countries,  loop-holes  in  tax  laws  regarding  corporate                                                             454","income taxation, increased mobility of capital and intangible assets, political and social                instability.                 OECD (2013) points that profit shifting and base erosion phenomenon is affecting three                distinct groups: public budgets (due to the low corporate income tax revenues), domestic                companies  due  to  comparative  advantages  of  the  MNE’s  in  moving  profits  abroad  and                overall tax compliance if there is a broad perception that MNE’s can legally avoid taxation.                 The dominant approach in analyzing profit shifting behavior has been founded by Grubert                and Mutti (1991) and Hines and Rice (1994). Hines and Rice (1994) analyzed the sensitivity                of  US  MNE’s  profitability  rates  to  the  international  tax  rates  differences.  The  model                proposed  by  Hines  and  Rice  (1994)  considers  that  the  reported  profitability  of  the  US                foreign subsidiaries is a function of pre-tax earnings and local tax rates. Consequently, the                central role of the Hines-Rice approach is the tax difference between the affiliate and parent                company,  which  can  estimate  the  sensitivity  of  profits  to  tax  incentives  of  different                jurisdictions. Hines and Rice (1994) find that the pre-tax income is inversely related to the                corporate income tax rates differences between countries where the affiliates are based.                Clausing  (2009)  analyses  the  sensitivity  of  profit  shifting  due  tax  differences  between                countries. The author found that the decrease of the CIT rate by 1% by a host country                where  a  US-owned  subsidiary  is  based,  will  lead  to  an  increase  by  0.5%  increase  of                declared profit as response to lower tax burden. Clausing (2009) found that in 2004 the                financial techniques to shift profits used by US MNE’s have lowered the US CIT revenues                by 35% or 180 billion USD. The real movements of assets and employment in order to                avoid  high  CIT  rates  have  decreased  the  US  CIT  revenues  by  18%  or  80  billion  USD.                Huizinga and Laeven (2008) analyze the profit shifting behavior adopting an innovative                approach. The authors research the sensitivity of profits before taxation to the international                tax differences including also the tax differences between affiliates that are hosted in other                countries  and  are  owned  by  the  same  parent  company.  Using  firm-level  data  for  the                European MNE’s and their subsidiaries, Huizinga and Laeven (2008) found that the semi-                elasticity of pre-tax profits to top statutory tax rate is 1.43.                Dischinger and Riedel (2011) analyze the impact of corporate tax differences on location                of intangible assets within the MNE’s. The independent variable is the difference between                corporate tax rate of the analyzed subsidiaries to the unweighted tax rate average other                affiliates from the same MNE group. The authors found a semi-elasticity of 1.7. Buettner                and Wamser (2013) use the micro-level database of all German multinationals from the                German Central Bank to analyze the size of profit shifting done by German MNE’s using                the strategic intra-company loans. The author uses the internal loans between subsidiaries                owned  by  the  German  MNE’s  and  tax  differentials  between  multinational  groups.  The                results  obtained  show  that  internal  loans  tend  to  react  significantly  to  tax  differentials                between subsidiaries placed in low-tax countries.                Beer and Loeprick (2015) use the ORBIS database to analyze the main drivers of transfer                pricing and potential counter measures in the EU. Beer and Loeprick (2015) found that if                the subsidiaries have a greater ratio of intangible to total assets tend to report less profits                if the tax rate increases compared with subsidiaries with no intangibles.                Jansky and Kokes (2015) analyses the financial and ownership data of companies operating                in the Czech Republic aiming to find evidence of profit shifting. The authors also use data                regarding 4124 multinational companies that operate in Czech Republic. Jansky and Kokes                (2015) found that the MNE’s with links to tax haven countries have a positive impact on                the debt to equity ratio of the subsidiaries. Moreover, Jansky and Kokes (2016) research                the ability of MNE’s to shift profits from Czech Republic to three European tax havens,                namely the Netherlands, Switzerland and Luxembourg. The authors found that companies                linked to the above stated countries show a higher debt to equity ratio, which indicates                profit shifting through strategic use of debt.                 Markle (2016) researches the profit shifting behavior  of the MNE’s which are subject to                territorial and worldwide system of taxation. Markle (2006) finds that the MNE’s that are                subject to territorial tax regime are more prone to shift profits than the MNE’s under the                                                            455","worldwide tax regime. The results obtained by Markle (2016) show that the profits before                taxation are inversely related to the international tax differences by a coefficient of - 1.23                in case of territorial tax regime and -0.62 in case of MNE’s under the worldwide tax regime.                Heckemeyer  and  Overech  (2017)  perform  an  extensive  meta-analyze  to  provide  a                consensus estimate regarding the profits sensitivity to tax rate elasticity. The authors base                their research on 25 most important primary studies, from where they conclude that a 1%                increase into the tax rate differential between affiliates and other sister subsidiaries from                the same MNE group leads to an overall 0.8% decrease in the declared pre-tax profits.                3 Data and Methodology                 Data                The empirical analysis is based on data derived from Amadeus Database. We use firm-                level data regarding the Czech subsidiaries owned by parent companies based in top five                EU  investor  countries  in  the  Czech  Republic,  namely:  Austria,  Germany,  France,                Luxembourg and Netherlands. The period on which observations are available is between                2006 – 2015. We impose several restrictions when extracting the data from Amadeus.                Firstly we choose to analyze only the Czech subsidiaries owned by parent companies based                in top five EU investors which own at least 51% of their shares and have at least another                subsidiary elsewhere in the EU. The second condition for our data set is that we collect                data only for Czech subsidiaries involved in manufacturing. In the third step we collect data                only for those subsidiaries that have reported profit at least one year in the analyzed time                period. We exclude the non-profitable Czech subsidiaries or those subsidiaries that have                no recent financial data. In order to preserve the strongly balanced panel data, we do not                eliminate the years where the Czech subsidiaries report losses. In this situation we replace                the  negative  observations  with  zero,  since  our  interest  is  to  investigate  how  does                profitability  reacts  to  tax  differences  between  Czech  subsidiaries  and  their  “sister”                companies based in other EU countries.                The  dependent  variable  used in  our  model is  profit  before  taxation  (Pbt)  and  the  core                independent variables are: statutory corporate income tax rate (CITcz), effective corporate                income  tax  rate  (ETRcz)  in  the  Czech  Republic  and  the  tax  differential  (difftaxcz  and                diffetrcz). Following the Hines-Rice approach we also include in our model the capital and                labor variables as a method to estimate the level of “true” profit. Consequently we use the                fixed assets as a representative of capital and number of employees for labor costs. Beside                these independent variables we choose to add interest payments in order to test whether                the indebtedness of Czech subsidiaries plays a role in reporting profitability. We also include                into our model country specific factors such as GDP per capita (gdpcappp) expressed in                Purchase  Power  Parity  as  a  representative  for  degree  of  economic  development;                unemployment rate (unempl) as a proxy of economic conditions and Corruption Perception                Index (hereinafter as CPI) as a representative of the quality of legal system. The first two                control  variables  are  extracted  from  Eurostat  Database  and  the  CPI  is  derived  from                Schneider (2012, 2015). After all restrictions are imposed we were able to collect data for                only 300 Czech subsidiaries that fulfill our conditions.                The tax differential is constructed as the difference between corporate income tax rate of                the subsidiary (i.e. CIT rate in the Czech Republic) and the unweighted average corporate                income tax rate of affiliates from the same MNE group (i.e., the average corporate income                tax rate of “sister” companies and parent company). We build two distinct tax differentials                where  we  use  statutory  corporate  income  tax  rates  (difftaxcz)  and  effective  corporate                income tax rates (diffetrcz). For the analyzed Czech subsidiaries we obtain the effective                tax  rate  by  dividing  taxation  payments  to  profits  before  taxation  and  for  the  average                effective tax rates of affiliates from the same MNE group we use the data provided by                Spengel et al. (2015).                The Econometric Approach                The empirical analysis follows the traditional Hines-Rice approach which was later improved                by Huizinga and Laeven (2008), Dischinger and Riedel (2011), Beer and Loeprick (2015)                                                             456","and Markle (2016). The occurrence of profit shifting behavior can be indirectly estimated                by regressing profits before taxation to the tax differentials between the subsidiaries and                affiliates from  the  same  MNE  group  coupled  with  other independent variables.  The tax                incentives to shift profits from subsidiaries hosted in high tax countries to affiliates hosted                low-tax jurisdictions can be captured by using tax differentials while controlling for input                factors (i.e., fixed assets and number of employees). Moreover, the profit shifting activities                can  react  differently  to  statutory  corporate  income  tax  rate  than  to  effective  tax  rate.                Therefore, we intend to test whether there are differences in the sensitivity of profits before                taxation  to  statutory  and  effective  corporate  taxation.  The  regression  equations  are  as                following:                                       ,    =    +    (           ) +    (             ) +    (               ) +    (                       ) +    (   ) +                                                              ,                                                                   3                                                                             ,                                                      2                                   0                                       1                                                ,                                                                                                ,                                                                                                         ,                                                                                                    5                                                                                4                                                  +                                                     (1)                                         ,                                                                             ,    =    +    (           ) +    (             ) +    (               ) +    (                       ) +    (   ) +                                                                                                   5                                                                                                        ,                                     0                                                                                 4                                                              ,                                                                               ,                                                                    3                                                     2                                                 ,                                         1                                                                                               ,                                                   +                                                      (2)                                                                            ,                                        ,    =    +    (                   ) +    (             ) +    (               ) +    (                       ) +                                                        2                                                                 ,                                         1                                                    ,                                     0                                                                     3                                                                                ,                                                                                      4                                                                                                  ,                                     (     ,   +       +      ,                                           (3)                                       )                                                 5                                      ,    =    +    (                   ) +    (             ) +    (               ) +    (                       ) +                                                                                ,                                                                                                    ,                                         1                                                    ,                                                                   ,                                                                       3                                                                                    4                                   0                                                        2                                                 (   ) +    +                                           (4)                                                  ,                                                                                       ,                                    5                In the Eq. 1 the dependent variable is profit before taxation (logPbt) expressed in natural                logarithms.   The  core  explanatory  variable is the  corporate  income tax  rate (logCITcz)                imposed to the Czech subsidiaries expressed in natural logarithms.  The logfix and logempl                represent  the  capital  and  labor  proxies  for  input  factors.  The  country  specific  control                variables (X) are represented by GDP per capita, unemployment rate and CPI, all expressed                in natural logarithms.     represents the time effects and ϵ represents the error term. The                                                          subscript  i  represent  the  observation  for  each  Czech  subsidiary,  where  i=1…n  and  t                represent time period in yearly observations between 2006 – 2015.                In the Eq. 2 we change the main independent variable from statutory corporate income                tax rate (logCITcz) to effective corporate income tax rate (logETRcz), calculated as the                taxation payments by each Czech subsidiary divided to their profits before taxation. The                rest of variables remain as defined in Eq. 1.                In Eq. 3 we regress the profits before taxation (logPbt) to the tax differential (difftaxcz)                between  Czech  subsidiaries  and  their  affiliates  from  the  same  MNE  group  using  the                statutory corporate income tax rates. The rest of variables remain as defined in Eq. 1.                In Eq. 4 we regress the profits before taxation (logPbt) to the tax differential (diffetrcz)                between Czech subsidiaries and their affiliates from the same MNE group using the effective                corporate income tax rates. The rest of variables remain as defined in Eq. 1. We transform                the dependent and independent variables to their natural logarithm equivalents in order to                eliminate outliers and obtain stationary time series. When logarithms are used for both                dependent and explanatory variables in a regression equation, the obtained estimates can                be interpreted as elasticities. This rationale can be applied to the first two equations (i.e.,                Eq. 1 and Eq. 2). In Eq. 3 and 4 we estimate a log-level regression equation, therefore the                obtained estimates are interpreted as semi-elasticity results.                We expect that the core explanatory variables included in Eq. 1-2, namely the statutory                and effective CIT rate, will have a negative impact on the pre-tax profits reported by the                Czech subsidiaries. The tax differential included in Eq. 3-4 as main independent variable is                expected  to  have  a  negative  impact  on  pre-tax  income.  However,  considering  the  tax                advantageous position of the Czech Republic in comparison with the other EU countries in                terms of corporate income taxation, we expect that the tax differential will have a lower                impact on profits before taxation than the estimates obtained by previous studies.                                                             457","4 Results and Discussion                Before running the models proposed in Eq. 1-4, we test the stationarity of time series using                the Levin-Lin-Chu test and Fisher-type tests for panel data in STATA15 (i.e., Augmented                Dickey-Fuller test and Phillips-Perron unit-root test). All three unit root test prove that our                time series are stationary.                The results obtained from running our four regression equations are presented in Table no.                1. We used Hausman test to decide whether Fixed-effects or Random-effects model is more                appropriate for our multi-variate regression. The Hausman test showed that there are no                significant differences present between fixed or random effects assumption. Consequently                we choose to estimate our model using Random-effects assumption due to the fact that is                more efficient than fixed-effect model.                         Table 1 The Estimation Results of Generalized Least Squares Regression                 GLS Random-effects                 Dependent variable: Profit before taxation (logPbt)                 Equation                  (1)              (2)              (3)               (4)                 Independent                 Variables                 logCITcz               -2.588 ***                                        (0.559)                 logETRcz                                -0.319 ***                                                         (0.0837)                 difftaxcz                                                 0.190                                                                          (2.067)                 diffetrcz                                                                  -0.0806 ***                                                                                            (0.0172)                 logfix                 0.318 ***        0.282 ***        0.320 ***          0.319 ***                                        (0.0515)         (0.0484)         (0.0516)          (0.0512)                 logempl                0.436 ***        0.418 ***        0.435 ***          0.429 ***                                        (0.0632)         (0.0626)         (0.0635)          (0.0624)                 loginterest           -0.0535          -0.0524          -0.0542            -0.0531                                                                                 **                                                                                                    **                                               **                                                                **                                        (0.0185)         (0.0181)         (0.0186)          (0.0186)                                                                                 *                                                                                                   **                 loggdpcappp             -1.041           1.115            1.840             1.897                                        (1.192)          (0.768)          (0.747)            (0.734)                                               *                 logunempl              -0.571            -0.197          -0.0412            -0.0337                                        (0.223)          (0.246)          (0.249)            (0.246)                                                                                                   *                                                                                 *                                               *                 logCPI                  6.819            2.654            3.967              3.589                                        (3.288)          (1.906)          (1.784)            (1.739)                 Time-fixed               YES              YES              YES                YES                 effects                 N                        1585             1429             1585              1585                 R2                      0.475            0.494            0.475              0.485                Note: Standard errors in parentheses;* p < 0.05, ** p < 0.01, ***, p < 0.001.                                                 Source: Authors calculations                The results obtained by estimating the Eq. 1 show that profits before taxation are highly                sensitive to the evolution of statutory corporate income tax rate. Since the Eq. 1 is a log-                log regression, the results can be interpreted as elasticities. Therefore if Czech Republic                increases CIT rate by 1% then the profits before taxation of Czech subsidiaries owned by                top five EU investor countries will decrease by 2.58%. Looking at the firm-specific input                factors, such as capital and labor, we find that both variables are statistically significant                and have a positive impact on profits before taxation. Number of employees has a higher                impact  on  profitability  than  fixed  assets.  These estimates  show  that  Czech  subsidiaries                involved in manufacturing industry tend to be more labor intensive and rely less on capital.                                                               458","Another  interesting  result  is  shown  by  the  estimates  regarding  the  impact  of  interest                payments on profits before taxation.                 Interest payments have a negative and statistically significant impact on profitability. This                can be interpreted that the strategic use of debt can represent a possible technique of                profit  shifting.  Regarding  the  economic  development,  we  found  that  is  not  statistically                significant in Eq. 1. On contrary, the unemployment rate plays a rather weak negative                impact  on  profitability.  A  10%  increase  in  unemployment  rate  would  decrease  the                profitability  by  0.57  %.  The  decrease  of  corruption  plays  also  a  relative  weak  role  in                reporting  profitability,  where  a  10%  decrease  in  CPI  would  increase  the  profits  before                taxation by 6.8%.                 The estimates obtained from second regression equation show that the sensitivity of profits                before  taxation  to  the  effective  corporate  income  tax  rate  is  significantly  lower  in                comparison to the statutory corporate income tax rate. A 1% increase in effective CIT rate                would  decrease  the  profits  reported  before  taxation  only  by  -0.32%.  The  rest  of  the                independent variables hold the same impact on profitability as in Eq. 1, with exception of                economic  development  (loggdpcappp),  unemployment  rate  (logunempl)  and  corruption                (logCPI) which prove to be statistically insignificant.                The results obtained by estimating Eq. 3 show that the tax differential based on statutory                CIT  rate  is  statistically  insignificant.  The  company-specific  input  factors  hold  positive                impact  on  profitability,  while  the  interest  payments  have  a  negative  impact  on  profits                before taxation. The economic development is statistically significant and has a positive                impact  on  profitability,  where  a  10%  increase  in  GDP  per  capita  will  increase  the                profitability  by  1.8%.  We  found  that  unemployment  is  statistically  insignificant  and                corruption decrease plays a positive role, where a 10% decrease of corruption will lead to                a 3.9% increase in reported profits before taxation.                The estimates obtained from running Eq. 4 show that the tax differential based on effective                CIT rate between Czech subsidiaries and average effective CIT rate of the affiliates from                the same MNE group is statistically significant. Since Eq. 4 represents log-level estimation                the interpretation for diffetrcz coefficient is different. The coefficient obtained represents                the indirect or semi-elastic effect of tax differential on profits before taxation. Therefore a                1 unit change (e.g., 1 unit increase or decrease in tax differential) will produce       100%                                                                                                   1                change  in  profits  before  taxation.  The  tax  differential  is  constructed  as  the  difference                between effective CIT rate of the Czech subsidiaries and the average effective CIT rate of                their “sisters” and parent. In this context the resulted independent variable diffetrcz has                negative values due to the fact that the effective CIT rate faced by Czech subsidiaries is                smaller  than  the  average  effective  CIT  rate  imposed  on  parent  and  sister  affiliates                elsewhere  in  the  EU.  Therefore  the  interpretation  of  our  estimates  should  be  done  as                follows.  A  1  unit  increase  in  tax  differential  (i.e.,  which  translates  in  tax  differential                decrease because is negative in its’ absolute form) will lead to 8% decrease of pre-tax                profits reported by Czech subsidiaries. Because the effective CIT rate is used to compute                tax differential (diffetrcz) the interpretation should take into account the changes made in                tax  credits  by  the  EU  countries  where  the  parent  and  “sister”  affiliates  of  the  Czech                subsidiaries  are  based.  Consequently,  if  the  EU  countries  will  increase  tax  credits  or                implement new tax credits which will in turn decrease the effective tax rate faced by the                “sister” companies will lead to less profits declared before taxation in the Czech Republic.                It is important to specify that the estimates obtained by our model are significantly lower                than the estimates obtained by previous studies, such as Huizinga and Laeven (2008),                Beer and Loeprick (2015) and Markle (2015). This is due to the fact that the Czech Republic                corporate income taxation is considerable lower than the other EU countries. Therefore the                reaction of pre-tax income reported by the Czech subsidiaries to the tax differential is still                negative but smaller compared with primary estimates.                The input factors capital (logfix) and labor (logempl) have a positive impact and statistically                significant as found in previous equations. The interest payments hold a negative impact                where a 5% increase in interest payments will decrease the pre-tax income by 0.05%. An                opposite impact shows the economic development, where a 5% increase of GDP per capita                                                            459","leads  to  1.9%  increase  in  pre-tax  income.  Unemployment  rate  shows  a  statistically                insignificant effect on profits before taxation in Eq. 4 and a 10% decrease of corruption                level will increase the before tax profits by 3.5%.                 5 Concluding remarks                In this paper we analyze the sensitivity of pre-tax income to the tax differential between                Czech subsidiaries and average CIT rate of the affiliates in the same MNE group. We use                the firm-level data for the Czech subsidiaries owned by top five EU investor countries in                the  Czech  Republic.  Taking  into  consideration  that  profit  shifting  behavior  depends                significantly on the tax incentives, we run four different models to estimate the sensitivity                of pre-tax profits to corporate income taxation. In the first two models we estimate the                impact of statutory and effective CIT rate on profits before taxation. We found that the                pre-tax income of Czech subsidiaries is highly sensitive to changes in statutory than to                effective CIT rate changes. Overall the profits before taxation are inversely related to the                evolution of CIT rates. In addition to the first two models we also estimate a semi-elastic                model, where the core exogenous factor is the tax differential between Czech subsidiaries                and the average tax rate of affiliates based in other EU countries. We found that if the tax                differential  is  based  on  statutory  CIT  rate  the  results  obtained  are  not  statistically                significant. When the tax differential is based on effective CIT rate, the explanatory variable                becomes statistically significant and has a negative impact on the pre-tax income reported                by  Czech  subsidiaries.  The  results  from  fourth  model  can  be  interpreted  as  the  tax                differential decreases, due to decrease of average effective CIT rate to which the affiliates                from  the  same  MNE  group  are  liable,  then  Czech  subsidiaries  will  report  less  pre-tax                income. Consequently, this negative reaction of pre-tax profits represents a proof of profit                shifting. Moreover, we identify that the strategic use of debt represents a potential method                of profit shifting in order to avoid corporate income taxation.                 Acknowledgments                This paper was funded by the Czech Science Foundation (GACR) grant [no.18-14082S]                entitled:  “Fair  corporate  taxation:  Measurement  of  the  impact  of  the  corporate  profit                shifting on the budget of the Czech Republic”.                 References                 Beer,  S.,  Loeprick,  J.  (2015).  Profit  shifting:  drivers  of  transfer  (mis)  pricing  and  the                potential of countermeasures. International Tax and Public Finance, vol. 22(3), pp. 426-                451.                Buettner, T., Wamser, G. (2013). 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Effective tax levels using the Devereux/Griffith methodology. Project                for the EU Commission TAXUD/2013/CC/120: Intermediate report 2015. ZEW-Gutachten                und Forschungsberichte.                                                                                                             461","Monetary Policy Normalization of the European Central Bank                                                    Aleksandra Nocoń                                             University of Economics in Katowice                         Faculty of Finance and Insurance, Department of Banking and Financial Markets                                            ul. 1 Maja 50, 40-287 Katowice, Poland                                           E-mail: aleksandra.nocon@ue.katowice.pl                 Abstract: Monetary policy normalization of modern central banks is the process of ending                non-standard monetary policy initiatives and its return to the pre-global financial crisis                framework. On the other hand, it may mean a transformation of monetary policy towards                a new quality, a new monetary architecture, hitherto unknown to central banks - referred                as “new normal”. The lack of previous experience of central banks in implementation of                normalizing activities is undoubtedly a factor hindering their realization. Literature studies                indicate  a  small  number  of  scientific  studies  and  publications  in  this  research  area.                Therefore, appropriate definition and implementation of the exit strategies’ assumptions is                extremely  difficult  in  the  lack  of  theoretical  framework.  In  turn,  normalization  without                clearly formulated assumptions, implemented ad hoc, may destabilize banking sector, and                finally the whole financial system. Detailed guidelines are also crucial for the effectiveness                of the process. Appropriate identification of the normalization process, undertaken early                enough - even during unconventional policy, will avoid the negative effects of the \"exit\"                and  support  positive  results,  generated  during  the  implementation  of  non-standard                monetary policy. The main aim of the study is identification of possibilities of monetary                policy normalization of the European Central Bank. The central bank of the Eurozone still                implements extraordinary instruments (enter strategy) - in the form of quantitative easing                and zero interest rate policy, while the major central banks of the global economy have                developed  detailed  exit  strategies  assumptions.  Thus,  the  article  attempts  to  specify                detailed guidelines of the ECB normalization process, paying particular attention to balance                sheet normalization.                Keywords: monetary policy normalization, exit strategies, European Central Bank, tapering                JEL codes: E52, E58                  1 Introduction                Normalization - in general terms - is a process of returning to traditional monetary policy.                Most often, it is identified with the restoration of monetary policy to pre-global financial                crisis  framework.  On the  other  hand, it  can  mean  a transformation  of  monetary  policy                towards a new quality, so far unknown to central banks - new normal. Therefore, monetary                policy normalization is the process of implementation of the exit strategies assumptions,                precisely set by central banks. It is an attempt how to \"exit\" from non-standard monetary                policy and return to pre-global financial crisis framework. In a broader perspective, it is a                process related to withdrawal of unconventional instruments and stabilization of banking                sector  conditions.  The  assumptions  of  normalization  process  of  monetary  policy  are                therefore  defined  by  monetary  authorities  in  their  exit  strategies.  They  include                determinants of selected economy, as well as banking sector and a central bank, indicating                necessary  actions,  that  have  to  be  taken  to  restore  the  previous  monetary  policy                framework.                Monetary  policy  normalization  is  a  long-term  process  and  -  in  assumptions  –  includes                several simple operations, relating to (Yamaoka and Syed, 2010, p. 6; Pyka and Nocoń,                2017, p. 177):                       ▪      halting extraordinary interventions,                       ▪      downsizing and normalizing central bank’s balance sheet,                       ▪      selling purchased assets, if necessary,                       ▪      raising short-term interest rates.                                                               462","Expansionary monetary policy of central banks, conducted during the global financial crisis,                based on low interest rates and a high degree of Quantitative Easing, has caused that it is                very difficult for central banks to return to steady state and to plan the \"exit\" path from                extraordinary monetary policy. Moreover, they are also concerned about the consequences                of normalizing activities.                According to J. C. Trichet (Trichet, 2009), appropriate preparation for the exit from non-                standard monetary policy is a very important part of the normalization process. Central                banks must ensure that unconventional operations will be quickly withdrawn and excess                liquidity will be absorbed, when macroeconomic conditions improve. Thus, in the case of                extraordinary refinancing operations, conducted by central banks, their maturity has been                fixed in advance, while in the case of outright purchases additional decisions are required.                It should be determined whether purchased securities will be held till maturity or they will                be resold in a specific date.                Modern central banks are now faced many challenges in the area of new monetary policy                objectives  and  tools.  It  is  extremely  important  to  precisely  define  assumptions  of  the                normalization process of modern monetary policy, in particular setting aims to be achieved.                So,  the  normalization  process  is  much  more  difficult  for  central  banks  than  the  enter                strategy,  i.e.  implementation  of  unconventional  instruments.  The  lack  of  previous                experience of monetary authorities in realization of normalizing actions is also a kind of a                barrier, or a fear of smooth implementation of the  exit assumptions. Central banks can                hardly imagine  the  consequences  of  normalization,  which  additionally  slows down  their                practical actions. In the literature, in the context of normalization process, the concept of                “new normal” appears several times, as a new post-crisis monetary architecture in which                central banks will return to conventional monetary policy. However, will the new normal                be the same as the old normal? So it will be the same situation like before the outbreak of                the global financial crisis, or will be totally new quality in monetary policy, far from the                previous paradigm of central banking.                 2 Theoretical framework of normalization process of modern central                banks                 In principle, normalization of modern monetary policy includes two main aspects:                    •   increase of a level of main interest rates (normalization of interest rate policy) and                    •   restoration of central bank's balance sheet to pre-global financial crisis framework                       (normalization of balance sheet policy).                However, it is important to properly define sequencing, according to which above actions                should  be  implemented.  Thus,  except  specifying  timing  and  pace  of  the  normalization                process, sequencing of its implementation should also be identified. It means whether an                increase  of main interest  rates  should be  made  before  or  after  the  change in  size  and                structure of central bank’s balance sheet. This is particularly important when central banks                decide to raise interest rates from a very low level, seeking to tighten  credit conditions                when forecasts indicate that inflation will rise above the inflation target.                In theory, balance sheet policy can be conducted independently of interest rate policy. C.                Borio and P. Diysatat (Borio and Disyatat, 2009) indicate that decisions about main interest                rates can be made regardless of the size and structure of balance sheet. According to the                doctrine of severability, a level of key interest rates is independent from a level of bank                reserves. Therefore, decisions on a level of interest rates should not be determined by                changes  in  central  bank’s  balance  sheet.  However  in  practice,  monetary  authorities                subordinate decisions on rising interest rates to forecasts of conversion of their assets and                liabilities.                When it comes to normalization of interest rates it seems quite easy and base on gradual                and  reasonable  increases  of  central  banks’  main  interest  rates.  While  normalization  of                balance sheet might be more difficult challenge for modern central banks. The following                                                               463","potential paths of normalization of central bank’s balance sheet can be identified (Borio                and Disyatat, 2009, p. 155):                     1.  Selling Securities outright – active approach – Central banks can actively sell assets                       purchased during QEs. This will be the most effective in settings of high inflation                       and  future  unanchored  expectations  indicating  rise  for  higher  inflation  than  the                       central bank’s target, or growth overheating than central bank’s forecasts. For both                       FED and Bank of England, this approach seems unrealistic. Selling securities  will                       not only raise yields, but will also put downward pressure on asset prices i.e. both                       banks  are  targeting low  yields  while  supporting  price  stability  so  exercising this                       option has the least probability.                    2.  Maturing assets not re-invested – Passive (Gradual) approach – Central banks can                       also take the approach not to re-invest maturing assets back into the central bank’s                       expanded balance sheets. This process will allow a gradual wind down as assets                       come due. Bank of England and FED, both have indicated indirectly to follow this                       path as long as there is an output gap and threat of deflations remains.                    3.  Reinvesting maturing assets into duration for gradual equally paced long term wind                       down – Passive (Snail pace) approach - This approach allows central banks to keep                       reinvesting  maturing  assets  into  new  assets,  however,  with  a  plan  to  invest  in                       assets, which when come due, smooth the assets maturity gaps in the long run.                       This will gradually wind down the balance sheet on a longer horizon in a consistent                       and orderly manner. Also, it does not put pressure in any sector of the duration.                    4.  Keeping expanded balance sheet as a “new normal” - Central banks may accept the                       new role of having expanded balance sheet forever. Not the best of choices as many                       central bankers have openly stated that this is not their priority nor role, however,                       if circumstances of weak global economy consistently exists and inflation constantly                       undershoots banks target, both FED and Bank of England will look for alternatives                       and this may become a new norm.                In  practice,  sequencing  of  specific  stages  in  monetary  policy  normalization  might  be                modified  as  a  result  of  dynamic  changes  of  macroeconomic,  market  or  monetary                conditions. That is why central banks' flexibility in adjusting operations, by quick response                to changing conditions, is so important. Changes may follow sequencing of normalizing                actions,  some  may  be  abandoned,  and  other  initiatives  -  not  included  in  the  initial                normalization plan - may be ad hoc implemented.                 3 Towards normalization of the European Central Bank                Late implementation of quantitative easing policy (QE), public debt crisis and the prolonged                period of returning to the growth path of the Eurozone countries, caused that the European                Central  Bank  still  continues  its  non-standard  monetary  policy.  At  the  same  time,  the                assumptions of the normalization of its monetary policy have not been explicitly defined so                far.  However,  there  are  some  signs  towards  completion  of  non-standard  instruments                implementation and restoration of pre-global financial crisis monetary policy operational                framework in the Eurozone. In March 2017, the ECB reduced the volume of asset purchases                under the QE policy from 80 to 60 billion EUR per month. Further reduction of the asset                purchase program covered the period from January to September 2018, when the central                bank purchased 30 billion EUR of securities per month. At the same time, the ECB assured                that  capital from  maturing instruments  will  be  still  reinvested,  also  after  withdrawal  of                asset purchase programs. In turn, the low (zero) interest rate policy will be maintained                until total termination of QE policy.                Completion of the non-standard and unconventional initiatives implementation means a                move towards normalization of the ECB monetary policy. If the central bank of euro area                adopted the order of monetary policy normalization process defined by the Federal Reserve                System, this would mean:                    •   first of all, increasing the level of main interest rates (interest rate normalization),                    •   and  then  reducing  the  size  of  balance  sheet  (balance  sheet  normalization),  by                       cessation  of  re-investment  of  capital  from  maturing  assets  and  by  gradually                                                            464","withdrawal  of  maturing  instruments,  purchased  under  the  quantitative  easing                       policy.                 So far, the ECB has determined that it will first gradually reduce asset purchase volumes                until the QE policy will be completely withdrawn, and only then will begin to raise main                interest rates and cease to reinvest capital on maturing instruments. If it is assumed that                in the case of the ECB, normalization will mean a return to the pre-crisis policy - the so-                called old normal, it is expected:                   ▪   to raise the level of key interest rate (refinancing rate) to approximately 3%,                   ▪   to  reduce  size  of  the  central  bank's  balance  sheet  to  pre-crisis level, namely to                       around 10% of the GDP of the euro area,                   ▪   to restore pre-crisis structure of central bank’s balance sheet, so that on the asset                       side - short-term refinancing operations with banks will have the main share, while                       on the liabilities side money in circulation and reserve requirement will be dominant,                   ▪   to go back to its pre-crisis operational framework to conduct monetary policy, i.e.                       with a central role for MRO and corridor rates, an aggregate deficit of liquidity of                       the  banking  sector  relative  to  the  ECB  and  variable-rate  fixed-quantity  liquidity                       tenders.                An  important  issue  in  the  aspect  of  ECB  monetary  policy  normalization  seems  to  be                determining  the  optimal  size  of  central  bank’s  balance  sheet.  This  point  still  remains                unresolved in the scale of the whole global economy. Economists and policymakers indicate                arguments for the need to \"shrink\" balance sheet totals to pre-crisis levels and identify the                potential areas of risk, resulting from maintaining a high level of balance sheet. The first                one is the classical monetarist argument. A high level of liquidity may in fact cause a rapid                creation of credit money, an accelerator of inflation above the inflation target, which would                threaten  price  stability  -  and  thus  achievement  of  the  most  important  central  bank’s                objective (Asness et al., 2010). In theory, according to the money multiplier principle, a                relation between monetary base (M0 aggregate) and the broad money aggregate (M3)                should be relatively constant, because maintaining a higher value of reserves should enable                banks to increase lending. However, after the outbreak of the financial crisis, programs                providing  liquidity  to  banks  (through  non-standard  refinancing  operations  and  asset                purchase programs (Nocoń, 2016, pp. 141-143)) caused an increased of monetary base,                but banks did not use an additional source of capital to expand their lending, which resulted                in a disproportionately smaller increase of M3 (see Figure 1).                                  Figure 1 Base Money and M3 Aggregate (in mln EUR)                      14000000,0                      12000000,0                     10000000,0                       8000000,0                       6000000,0                      4000000,0                       2000000,0                            ,0                               01/2000  07/2000  01/2001  07/2001  01/2002  07/2002  01/2003  07/2003  01/2004  07/2004  01/2005  07/2005  01/2006  07/2006  01/2007  07/2007  01/2008  07/2008  01/2009  07/2009  01/2010  07/2010  01/2011  07/2011  01/2012  07/2012  01/2013  07/2013  01/2014  07/2014  01/2015  07/2015  01/2016  07/2016  01/2017  07/2017  01/2018                                                          M3      Base money                                             Source: own work based on ECB data                                                               465","Thus, the M3/M0 multiplier in the euro area has significantly decreased (see Figure 2). This                effect was not even compensate by the ECB decision from 2012 to reduce the reserve                requirement rate from 2% to 1%.                                             Figure 2 Money Multiplier M3/M0                      14                     13                     12                     11                     10                      9                      8                      7                      6                      5                      4                      3                      2                      1                      0                         03/2000  09/2000  03/2001  09/2001  03/2002  09/2002  03/2003  09/2003  03/2004  09/2004  03/2005  09/2005  03/2006  09/2006  03/2007  09/2007  03/2008  09/2008  03/2009  09/2009  03/2010  09/2010  03/2011  09/2011  03/2012  09/2012  03/2013  09/2013  03/2014  09/2014  03/2015  09/2015  03/2016  09/2016  03/2017  09/2017  03/2018                                              Source: own work based on ECB data                 In  the  years  of  2007-2012,  the  growth  of  the  monetary  base  (M0)  resulted  from  the                growing banks’ demand for reserves in refinancing operations, not because they wanted                to increase the volume of loans. On the contrary, they tried to secure against possible                liquidity shortage when the short-term money markets were dysfunctional. In turn in 2015,                after implementation of the asset purchase programs, the growth of monetary base was                supply-driven. The central bank of euro area increased a level of excessive  reserves to                finance asset purchases. Therefore in this case, by increasing the M0 aggregate, there was                no direct increase of banks' lending.                The second argument is that a large balance sheet of central bank and a high surplus of                reserves  in  the  banking  sector  may  reduce  commercial  banks’  incentives  for  prudent                liquidity management and allow them to rely on a central bank as a Lender of Last Resort                too much (Bindseil, 2016). If a liquidity level on the banking market is high, commercial                banks tend low willingness to finance each other on an interbank market, and therefore                mutual monitoring and assessment of other banks’ financial condition.                Finally, another side effect of having large balance sheet and a significant excess of liquidity                is the risk of lower income and increased financial losses of central banks. This is due to                the fact that a significant part of the portfolio of previously acquired assets of monetary                authorities are long-term instruments with low profitability, while liabilities are short-term,                and their interest may increase along with the normalization of central bank’s key interest                rates. Despite the fact that central banks are not institutions which main objective is to                maximize  profits,  positive  financial  results  guarantee  their  financial  and  operational                independence. Possible losses should be only a temporary problem in the period of interest                rate normalization, because in the long term if they decide to reinvest profits from maturing                assets, new investments during rising interest rates will have higher rate of return. Thus,                the spread between financial instruments on the assets side and short-term reserves on                the liabilities side will increase. However, an increase of a level of reserve requirement and                abolition of an interest rate on its funds may be one of the methods of reducing losses in                the transition period.                Analyzing the direction of central bank's balance sheet normalization, it should also be                emphasized the arguments for maintaining a large balance sheet. Greenwood et al. indicate                                                             466","that monetary authorities may strive to maintain a high level of their balance sheet due to                the financial stability objective. Maintaining high value of balance sheet total, a central                bank would provide, in the form of reserves, much needed financing for financial sector                and  the  real  economy  (Greenwood  et al.,  2016).  At  the  same time,  through  providing                higher liquidity than before the crisis in the form of reserves, central banks will be able to                displace private capital providers - debt securities issuers, in particular from the shadow                banking market. On the other hand, it should be noted that a main role of the central                bank's  market  operations  department  is  not  to  care  for  stability  of  the  whole  financial                system, but to form a level of short-term interest rates that affect the shape of the yield                curve. All in all, in order to determine the optimal size of balance sheet, the ECB should                not seek to return to its pre-crisis value and structure, but carefully consider advantages                and disadvantages of maintaining a large level of assets and liabilities, taking into account                the current situation and possible scenarios in the medium and long term.                Therefore, it is very important to assess how long it will take for the ECB to reduce its                balance sheet to pre-crisis level. If the central bank of euro area decides on a passive                approach to its normalization, and resigns from re-investment of capital from maturing                assets as well as maintains previously acquired instruments until maturity, it will last about                30 years since the expected completion of the asset purchase programs (i.e. from March                2019) to remove them completely from the ECB’s balance sheet (see Figure 3). At the                same time, it will take about 5 years to reduce the balance sheet by half, while to reduce                it by 80% - will take about 10 years.                                        Figure 3 ECB Asset Holdings (in bln EUR)                                                                  1         2         3                                                             1                                                                   4        5                                                              5                                                       4                                                 3                                            1         2                       Source: G. Claeus, M. Demertzis (2017). How should the European Central Bank ‘normalise’ its                            monetary policy? Policy Contribution, Issue No. 21, November 2017, p. 10                                                                          467","Figure 4 ECB Total Balance Sheet Size as a Percent of Euro Area GDP                                          Source: G. Claeus, M. Demertzis: How should the European Central Bank ‘normalise’ its monetary                                                    policy?, op. cit., p. 10                On the other hand, restoring the size of ECB's balance sheet in relation to euro area GDP,                taking into account the natural tendency to increase central banks’ balance sheet along                                                                                         13                with an increase of nominal GDP, requires about 14 years (see Figure 4) .                4 Conclusions                 The ECB, in reaction to the global financial crisis, implemented a set of standard and non-                standard monetary policy instruments that caused a four-fold increase of its balance sheet                total,  changed  the  operational  framework  and  the  line  of  conducting  monetary  policy.                Entering the path of growth and return of inflation in direction of the target seems to favor                actions towards normalization of its monetary policy. This process should take into account                the following issues:                     •   ensuring  predictability  of  future  monetary  policy,  by  defining  a  schedule  of                       normalizing activities, even before fully completion of asset purchase programs;                       sequencing and pace of normalization do not have to be very precisely defined, but                       this  information  seems  to  be  extremely  important  to  reduce  the  uncertainty  of                       market participants and avoid possible distortions;                    •   significant  central  bank’s  flexibility  with  regard  to  time  and  sequencing  of                       normalizing  activities;  the ECB  should not  rush  with  the  exit  from  non-standard                       monetary  policy  and  reducing  the  size  of  its  balance  sheet;  the  normalization                       process can be supported by:                           o  an increase of a level of main interest rates in the conditions of maintaining                               a large balance sheet,                           o  an increase of reserve requirement rate,                           o  reverse repo operations,                           o  the issuance of ECB’s securities to absorb excessive liquidity.                    •   a passive approach to the normalization of balance sheet by maintaining assets to                       their maturity.                 References                Asness, C., Boskin, M. J., Bove, R. X., Calomiris, C., Chanos, J., Cogan, J., Ferguson, N.,                Gelinas,  N.,  Grant,  J.,  Hassett,  K.,  Hertog,  R.,  Hess,  G.,  Holtz-Eakin,  D.,  Klarman,  S.,                Kristol,  W.,  Malpass, D.,  McKinnon,  R.,  Senor,  D.,  Shlaes,  A.,  Singer,  P.,  Taylor,  J.  B.,                 13  At the same time assuming 2% growth of GDP and inflation rate per year, as well as the fact that                short- and long-term refinancing operations (MROs and LTROs) will return to pre-crisis level.                                                            468","Wallison,  P.,  Wood,  G.  (2010).  Open  Letter  to  Ben  Bernanke.  Retrieved  from:                https://www.hoover.org/research/open-letter-ben-bernanke.                 Bindseil, U. (2016).  Evaluating monetary  policy  operational  frameworks.  Speech  to  the                symposium  on:  Designing  resilient monetary  policy  frameworks  for the  future.  Jackson                Hole, Wyoming, 26 August.                Borio, C., Disyatat, P. (2009). Unconventional monetary policies: an appraisal. BIS Working                Paper, No. 292.                Claeus, G., Demertzis, M. (2017). How should the European Central Bank ‘normalise’ its                monetary policy?. Policy Contribution, issue 21.                 Greenwood, R., Janson, S. G., Stein, J. C. (2016). The Federal Reserve’s Balance Sheet as                a Financial-Stability Tool. Prepared for the Federal Reserve Bank of Kansas City’s 2016                Economic Policy Symposium in Jackson Hole.                Nocoń, A. (2016). System reagowania współczesnych banków centralnych na niestabilność                sektora bankowego. Warsaw: Difin.                 Pyka, I., Nocoń, A. (2017). Determinanty normalizacji polityki monetarnej Europejskiego                Banku Centralnego. The Quarterly of the Collegium of Socio-Economics \"Studia i Prace\",                vol. 1 (29).                Trichet, J. C. (2009). The ECB’s enhanced credit suport. CESifo Working Paper, No. 2833.                Yamaoka,  H.,  Syed,  M.  (2010).  Managing  the  Exit:  Lessons  from  Japan’s  Reversal  of                Unconventional Monetary Policy. IMF Working Paper, WP/10/114, May.                                                                                                              469","Determinants of Non-Life Insurance Consumption                                                                        2                                                1                                                                                             3                             Tomáš Ondruška , Zuzana Brokešová , Erika Pastoráková                                             University of Economics in Bratislava                                     Faculty of National Economy, Department of Insurance                                    Dolnozemská cesta 1, 852 35 Bratislava, Slovak Republic                                              1  E-mail: tomas.ondruska@euba.sk                                             2  E-mail: zuzana.brokesova@euba.sk                                             3  E-mail: erika.pastorakova@euba.sk                 Abstract: The paper examines various personal, demographic and economic factors and                their  impact  on  the  non-life  insurance  consumption  in  the  Slovak  Republic.  Non-life                insurance is complex and abstract service that has become an increasingly important part                of  the  protection  of  individual’s  property  by  providing  a  wide  range  of  policies  for                consumers.  What  are  the  key  determinants  associated  with  the  non-life  insurance                consumption? Our results have identified most robust predictors of the non-life insurance                consumption  that  could  be  used  in  predictions  of  consumer’s  consumption  of  property                insurance. These results can help insurers to better understand their potential consumers                and to improve their acquisition and segmentation techniques.                 Keywords: consumer, insurance market, insurance consumption, determinants                JEL codes: G22                 1 Introduction                Undoubtedly, insurance  plays  an important  role in  the  modern world.  However,  overall                understanding of the insurance demand is limited contrary to individual’s common sense.                Although  insurance  demand  research  is  largely  focused  on  life  insurance,  given  the                increasing volume of assets owned by individuals or households and emerging risks that                threaten these assets is necessary to study also demand for non-life insurance.                                                                                     14                In particular, due to the lack of household-level data of insurance , there are very few                existing empirical works on non-life insurance demand. As a result, most existing studies                only use macro data. The motivation of the paper is to contribute to the understanding of                consumer  behavior  in  the  purchase  of  non-life  insurance  from  the  microeconomic                perspective.  Main  aim  of  the  paper is to identify key  determinants  associated  with  the                property  insurance  consumption,  which  could  help  insurers  to  better  understand  their                potential consumers. We do not focus on legal entities in the paper, but we will limit our                research to research of the determinants of demand for non-life insurance from the point                of view of individuals. However, in our research we only focus on the property insurance,                abstaining from various liability and compulsory insurance policies.                 The outlook of the general performance of the non-life market in Slovak Republic allows                better understanding of the specifics of this market. The performance of insurance market                is usually analyzed through three indicators: (1) volume of written premium, (2) insurance                penetration ratio (gross written premiums to GDP), and (3) insurance density ratio (gross                written  premium  per  capita).  Generally,  the  insurance  density  and  penetration  ratios                (Figure  1  and  Figure  2)  indicate  that  indicators  are  not  improving  over  time.  The                persistence of such a trend will cause that the assets of households in the Slovak Republic                will be less protected against the effects of random events.                          14  The reason for this may be the demand side represented not only by households and individuals but also by                companies as well as the obligation to buy some non-life insurance products.                                                            470","Figure 1 Non-Life Insurance Penetration in Slovak Republic – GWP as a percent of GDP                                                                             Source: AXCO \& Allianz Research                      Figure 2 Non-Life Insurance Density in Slovak Republic – GWP per capita in EUR                                                                              Source: AXCO \& Allianz Research                 Figures indicate that insurance premium remained stable over time and the variance of the                ratio was driven mainly by the changes of GDP. It signalizes that the growing GDP was not                bringing substantial growth in insurance coverage demand. The insurance density index in                Slovakia was associated with a significantly lower value of assets that can be insured in                the commercial entities and households, as well as property insurance distant priority in                the hierarchy of needs. Unfavorable indicators point to the need for research in this area                to find answers to the question of how to stimulate demand for non-life insurance.                                                              471","2 Literature Review                Determinants  that  influence  insurance  purchases  have  been  the  focus  of  insurance                research for many years. Studies define numerous variables that might be significant in                explaining the demand for insurance (Zietz, 2003). Most of the researches analyze solely                life insurance products because of the specific character of the non-life insurance products                (some policies are mandatory in many countries, e.g. MTPL). Many studies find results                conflicting  with  previous  research,  and  some  of  the  contradictions  are  shown  to  be                attributed to economic conditions of the country, demographics, or geographic factors.                 There is no doubt that extent of the research on demand for non-life insurance is low.                Especially when we take into account a number of published papers dealing with the life                insurance consumption. Both, individuals (natural persons) and companies (legal entities)                represent the demand side for non-life insurance. However, the decision to buy or not to                buy  insurance  coverage  remains  on  the  individual  itself  (or  group  of  individuals).  An                individual has to decide for himself, for another person, or on behalf of the company.                Most of the previous papers dealing with the non-life insurance demand are based on the                theoretical  models  in  which  the  purchase  of  insurance  is  viewed  as  the  exchange  of  a                certain sum of money – premium for a sum of money with uncertainty as a compensation.                Research  on  particular factors influencing  demand for  non-life insurance is  significantly                lower than the scope of papers focused on life insurance demand. The demand for non-life                insurance was examined by several authors as Szipiro (1985), Beenstock et al.  (1988),                Outreville (1990), Browne et al. (2000), Esho et al. (2004), Hussels et al. (2005) and also                Millo and Carmeci (2011), Khovidhunkit and Weiss (2005), Feyen et al. (2011) and Zhang                and Zhu (2005). A list of published studies on demand for non-life insurance is in the table                below.                                       Table 1 Non-Life Insurance Demand Papers                  Year  Author(s)  Journal                                             Title                 1985  Szipiro      The Journal of Risk and Insurance  Optimal Insurance Coverage                 1988  Beenstock,   The Journal of Risk and Insurance  The  Relationship  between  Property-                        Dickinson                                     Liability   Insurance   Premiums   and                        a Khajuria                                    Income: An International Analysis                 1990  Outreville   The Journal of Risk and Insurance  The Economic Significance of Insurance                                                                      Markets in Developing Countries                 2000  Browne,      The Journal of Risk and Insurance  International      Property-Liability                        Chung a                                       Insurance Consumption                        Frees                 2004  Esho,        The Journal of Risk and Insurance  Law and the Determinants of Property-                        Kirievsky,                                    Casualty Insurance                        Ward a                        Zurbruegg                 2005  Hussels,     Risk  Management  and  Insurance  Stimulating the Demand for Insurance                        Ward a      Review                        Zurbruegg                 2011  Millo a      Journal of Geographical Systems   Non-Life  Insurance  Consumption  in                        Carmeci                                       Italy:  A  Sub-Regional  Panel  Data                                                                      Analysis                                                      Source: authors                 The  table  illustrates  the  significant  number  of  studies  performed  compared  to  life                insurance. The reason for the lower interest in the research of determinants in non-life                insurance  is,  as  we  have  already  mentioned,  the  nature  of  non-life  and  the  frequent                presence of the obligation to buy a certain type of non-life insurance in many countries.                Despite the fact that it is not possible to create an overview of literature based on previous                research to the extent as in the case of life insurance, we will focus on a summary of the                findings of non-life insurance research. We use also results of research of life insurance                demand determinants that can help us as a proxy for non-life insurance demand. Based                                                            472","on existing theoretical and empirical literature, we identify most examined demographic                and economic characteristics that could affect the demand for non-life insurance. These                factors  are  age,  gender,  education,  dependents,  employment  status,  marital  status,                income, savings and religion.                An empirical study by Hammond et al. (1967) and theoretical paper by Campbell (1980)                both found that one of the main purposes of life insurance is to protect dependents against                financial hardship in the case of the wage earner's premature death. From the non-life                insurance  perspective, it is  also necessary  to  protect  property  as  a  possible means for                inheritance. Campbell (1980) and Burnett and Palmer (1984) argue that the protection of                dependents against financial hardships is the major force driving insurance consumption.                Chui and Kwok (2008) find that with the increasing number of dependents of a person, this                person needs to buy more insurance. Most of the previous studies have provided empirical                evidence that the consumption of insurance and the number of dependents in a country                are positively related (e.g. Hammond et al, 1967; Beenstock et al, 1989).                 Education not only lengthens the dependency period but higher level of education may lead                to a greater degree of risk aversion and more awareness of the necessity of insurance in                general (Browne and Kim, 1993). The level of an individual's education may determine                his/her ability to understand the benefits of risk management and savings (Beck and Webb,                2003).  Education  is  one  of  the  few  demographic  determinants  examined  in  studies  of                demand for  non-life insurance.  Outreville  and Esho et  al. (2004)  confirmed its  positive                impact on demand in the non-life insurance. Education is related to greater awareness of                the different types of risks and mitigation of their impact through insurance.                Age is found to be positively significant in majority of studies, e.g. Showers and Shotick                (1994), Truett  and  Truett  (1990)  and negatively  significant in  studies: Ferber  and  Lee                (1980), Chen et al. (2001). Hammond et al. (1967) found age to be a significant factor                affecting  premium  expenditures  for  insurance  for  the  low  income  and  middle  income                groups. Showers and Shotick (1994) used the age of the respondent as a proxy for the                general \"stage\" of the family unit.                There are limited papers considering the gender as the determinant of insurance demand.                Many studies confirm that women are more risk averse than men (Halek and Eisenhauer,                2001). Another recent study by Luciano et al. (2015) finds that women are less likely to                be insured than men.                Browne and Kim (1993) suggest that religious people tend to purchase less insurance,                because they perceive that buying life insurance shows a distrust of God's protection. Beck                and Webb (2003) find that insurance consumption is significantly lower in Islamic countries                than in  other  countries,  whereas  Outreville  (1996) finds  that  this  relationship  is weak.                Feyen et al. (2011) proved that the presence of Islamic individuals in the population has                the negative impact also on the development of non-life insurance market.                Employment  status  is  expected  to  be  positively  related  to  life  and  non-life  insurance                demand as a Miller (1985) reports that retirees have less insurance than active workers of                the  same  age.  Marital  status  influences  the  interest  in  insurance  positively  (Halek  and                Eisenhauer, 2001; Baek and DeVaney, 2005). Although there is not any paper directly                focused  on the  determinant head  of  household,  we included it in  our  analysis.  Several                studies (e.g. Hammond et al., 1967; Campbell, 1980) suggest that the head of household                has its unique place, which makes it suitable to an increased interest in insurance.                Income is considered as a key determinant of the insurance demand in majority of studies.                Higher income increases not only the standard of living but also wealth of individuals. As                income increases, insurance becomes more affordable. Positive impact of income on non-                life insurance  demand has  shown majority  of  the  studies that include  Beenstock  et  al.                (1988),  Outreville  (1990),  Browne  et  al.  (2000),  Esho  et  al.  (2004).  The  growth  of                individuals’ income is followed by the growth of their assets, which are the subject of non-                life  insurance.  Ultimately,  growth  in  assets  is  moving  to  the  need  for  the  additional                coverage through insurance, thereby increasing the demand for non-life insurance.                                                              473","Savings of the individuals are undoubtedly connected with the income. But the impact of                savings as a variable of the non-life insurance demand is ambiguous in previous studies.                While  Rose  and  Mehr  (1980)  argue  that  the  savings  have  a  negative  effect  on  the                consumption of insurance, Headen and Lee (1974) find the opposite. These attitudes come                from the perception of savings as a compensation of insurance or as an additional source                of funding.                 We  mentioned  only  the  most  important  determinants  of  the  demand  for  insurance                identified  by  the  previous  research.  However,  we  can  identify  other  microeconomic                determinants of the individuals’ demand for non-life insurance as bequest motive, race,                ownership of credit cards, house ownership, occupation and others.                 3 Methodology and Data                 We  use  survey  data  to  examine  determinants  of  non-life  insurance  demand.  Sample                consists of respondents of Slovak Republic aged over 18 years. At the data collection phase,                we  received  a  total  of  1044  respondents,  of  whom we  selected  by  random  selection  a                representative sample of 870 respondents, which corresponded to the distribution of the                population of the Slovak Republic by age and gender, according to data from the Statistical                Office of the Slovak Republic.                 We  use  logistic  regression  to  examine  various  determinants  of  demand  for  non-life                insurance. The dependent variable that we will model is the ownership of non-life property                insurance. As explanatory variables, we chose gender (GEN), age (AGE), savings (SAV),                education  (EDU),  head  of  household  (HEAD),  working  status  (EMPL),  marital  status                (MAR_S), dependent children (DEP) and religion (REL).                In the  first  step  we  tested  the  determinants  and  found the interdependencies  between                them to exclude multi-collinearity. The analysis of the determinants of the demand for                insurance was realized through logistic regression. We have chosen this type of regression                because  of the  categorical  nature  of  our  variables. Due  to  the  nature  of  the  explained                variables, logistic regression is a special type of generalized linear model. The moderate                association rate is present between the determinants: MAR_S and DEP; MAR_S and AGE;                INC and SOC_S. Due to the presence of a moderate association rate between MAR_S and                DEP variables, we included another variable of their cross effect MAR_S* DEP. We also                wanted to include other MAR_S * AGE and INC * SOC_S crossover variables, but the quality                of the logistics model did not improve. For this reason, we did not include these interaction                variables in the resulting model.                 4 Results and Discussion                 The non-life insurance demand model is statistically significant at the significance level                0.05. At this level of significance, several parameters are statistically significant: gender                (GEN),  age  (AGE),  marital  status  (MAR_S),  savings  (SAV),  income  (INC),  head  of                household (HEAD). Results are shown in table below.                                                                         474","Table 2 Model Parameters and Significance Tests                                         Analysis of Maximum Likelihood Estimates                                                                                    Wald                                                                         Standard  Chi-                 Parameter                                 DF  Estimate  Error      Square      Pr > ChiSq                  Intercept                                  1     0.0295    0.5112      0.0033      0.9539                 GEN          Man                           1    -0.4273    0.1739      6.0347      0.0140                 AGE1         18 - 24                       1    -1.6102    0.3605     19.9516      <.0001                 AGE2         25 - 39                       1    -0.8981    0.1945     21.3098      <.0001                  MAR_S        Married/Living in couple      1     0.7767    0.3039      6.5326      0.0106                 SAV          No                            1    -0.8340    0.2182     14.6136      0.0001                 INC1         331 - 880 €                   1     0.3592    0.3535      1.0323      0.3096                 INC2         881 - 1 500 €                 1     1.0067    0.3920      6.5954      0.0102                 INC3         over 1 500 €                  1     1.5203    0.4682     10.5459      0.0012                  EDU          Primary / Secondary           1     0.0364    0.1822      0.0399      0.8416                 HEAD         No                            1    -0.5475    0.1780      9.4561      0.0021                 EMPL_1       Employed                      1     0.3733    0.2876      1.6844      0.1943                 EMPL_2       Self-employed                 1     0.4252    0.3539      1.4437      0.2295                  DEP          No                            1    -0.4185    0.3137      1.7801      0.1821                 MAR_S*DEP  Married/Living in couple   No   1     0.5281    0.3935      1.8012      0.1796                 REL1         Nonbeliever                   1     0.1355    0.2825      0.2299      0.6316                 REL2         Believer                      1     0.0905    0.2628      0.1186      0.7306                           Model parameters: -2 Log Likelihood 893,544; AIC 927,544; SC 1008,608.                                                      Source: authors                 The  determinant  gender  (GEN)  is  statistically  significant  in  our  model.  The  chance  of                ownership of non-life insurance product by man is only 0.65 times the odds compared to                a woman. Women are therefore more interested in non-life insurance. Their higher rate of                risk aversion thus manifests itself not only in life insurance but also in the protection of                own property. The results of Gandolfi and Miners (1996) show the increased interest of                women in life insurance, and we can extend these findings to non-life insurance.                In  variable  age  (AGE),  we  have  confirmed  a  statistically  significant  impact  in  all  its                subcategories. The chance that an individual between the ages of 18 and 24 will have a                non-life insurance contract is 0.20 times the chance of an individual who is between 40                and 61 years old. Although this chance has doubled in the 25-39 age category, the odds                ratio is still 0.41 times for those aged 40 to 61. The continued growth of interest in non-                life insurance with higher age is obvious. The cause can be found primarily in the gradual                obtaining of property of individuals during their lifetime. Increasing amount of property                also generate higher need for insurance cover.                Marital status (MAR_S) is also statistically significant determinant of demand for non-life                insurance. The chance that an individual will buy the non-life insurance policy as a change                of his status from single to married increases 2.17 times. Increased interest also has its                merit. The commitments are related, among other things, to the need for own housing, or                the  acquisition  of  movable  and  immovable  property,  which  creates  prerequisites  for                widening  coverage  of  risks  in  non-life  insurance.  Individuals  are  aware  of  the  need  to                protect common values, which they can properly address through insurance.                                                               475","We can confirm the positive and significant impact of savings (SAV) on non-life insurance                interest. If the individual does not save  money, his chance of ownership of the non-life                insurance product is 0.43 times that of the person making the savings. The fact that the                individual creates savings is also related to one of the motives for handling the free means                of their use for the growth of property. This also increases the need for insurance cover.                Savings are related not only to better availability of non-life insurance but also to their                investment in the purchase of additional assets.                Income (INC) is one of the few determinants investigated in the research of demand for                non-life insurance in higher extent. Although there were more income groups in our survey,                not all of them are statistically significant. Our findings are similar to studies by Beenstock                et al. (1988), Browne et al. (2000) and Esho et al. (2004). Growth of income is also linked                to  the  growth  of  assets.  Income  is  then  used  for  consumption  of  items  that  are  not                necessary in the lives of individuals (including insurance). Revenue is also a significant                factor in our case, which is involved in modeling demand for non-life insurance.                 Determinant head of household (HEAD) is statistically significant in our model. A person                who is the head of a household has a greater chance of the ownership of non-life insurance.                These individuals often have different types of non-life insurance policies, although other                members of the household can also take part in the decision process to buy them and acts                as the main policyholder.                We do not prove the assumption of the positive impact of education (EDU) on the demand                for non-life insurance confirmed by Outreville (1990), as well as by Esho et al. (2004) in                our model. There is no significant difference between groups in the interest of non-life                insurance.  Our  model  bring  similar  results  for  other  variables  as  employment  status                (EMPL), dependent children (DEP) and religion (REL).                 5 Conclusions                 Research of factors influencing the demand for non-life insurance is in large contrast to                published  research  in  life  insurance.  While  the  interest  of  individual  consumers  in  life                insurance has been investigated in many studies, only marginal attention has been devoted                to non-life insurance. The reason is the nature of non-life insurance, the obligation to buy                some types of non-life insurance, but also the fact that the demand for non-life insurance                is also represented by legal entities. By using logistic regression and data from realized                survey, we have identified important determinants of individual interest in non-life property                insurance in the conditions of the Slovak Republic. Paper contributes to the understanding                of  consumer  behavior  in  the  purchase  of  non-life  insurance  from  the  microeconomic                perspective. As there are very few existing empirical works on non-life insurance demand                due  to  the lack  of household-level  data  of  insurance  our  contribution  enriches  existing                literature  and  we  assume  our  findings  can  motivate  more  research  in  this  field.  We                identified  following  determinants  as  statistically  significant:  gender  (GEN),  age  (AGE),                marital  status  (MAR_S),  savings  (SAV), income  (INC),  head  of household  (HEAD).  Our                results can help in addressing the marketing of insurance companies, especially by focusing                on  the  target  segment  of  potential  clients.  The  results  can  also  be  beneficial  to  policy                makers in questions about taxation of non-life insurance products. Discussed indicators of                non-life insurance market in Slovak Republic point to a low interest in non-life insurance                policies in Slovakia, and the results of our research point to groups whose decisions to buy                property insurance can be greatly influenced.                 Acknowledgments                 This paper is part of a research project No. I-18-106-00 entitled ‘Analysis of Methods and                Approaches Applicable to the Research of Demand-side Determinants in the Context of the                innovations that form the insurance market’ provided by the University of Economics in                Bratislava  and  research  project  No.  1/0827/18  entitled  ‘Experimental  investigation  of                                                                 476","factors influencing group decision-making’ provided by the Ministry of Education, Science,                Research and Sport of the Slovak Republic.                 References                 Allianz Research (2018). Allianz Global Insurance Map.                AXCO (2016). Insurance Market Report. Slovakia: Non-Life (P\&C).                Baek, E., Devaney, S. A. (2005). Human Capital, Bequest Motives, Risk, And The Purchase                Of Life Insurance. 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Risk Management                and Insurance Review, vol. 6(2), pp. 159-191.                                                                                                    478","Decomposition of ROE to Selected Financial Analysis Indicators                                                                                  2                                                            1                                         Denisa Oriskóová , Renáta Pakšiová                                            1  University of Economics in Bratislava                             Faculty of Economic Informatics, Department of Accounting \& Auditing                                       Dolnozemská cesta 1, 852 35 Bratislava, Slovakia                                               E-mail: denisaorisko@gmail.com                                             2  University of Economics in Bratislava                             Faculty of Economic Informatics, Department of Accounting \& Auditing                                       Dolnozemská cesta 1, 852 35 Bratislava, Slovakia                                              E-mail: renata.paksiova@euba.sk                 Abstract: The existence of company in a rapidly varying environment is conditioned by the                ability to respond properly to challenges arising from changing business environment and                customer  requirements.  Decisions  on  the  future  direction  of  a  company  eventually  are                translated into financial form and affect the financial health of company. Assessment and                prediction  of  financial  health  is  key  to  the  company's  management.  Data  sources  for                assessment of financial situation for internal users are not only financial statements but                also  various  internal  reports.  However,  to  external  users  are  available  only  published                financial  statements  and  annual  reports,  which  are  the  resources  of information  for                financial situation assessment as basis for decision making. The picture of the company's                financial performance is supplemented by financial analysis indicators, which should be                evaluated in their mutual contexts. Evaluation based on one indicator can lead to incorrect                conclusions, so for this reason in this paper is investigated decomposition of the return on                equity  (ROE)  to  selected  indicators.  The  aim  of  the  paper  is  to  quantify  the  impact  of                indicators, such as tax burden ratio, interest burden ratio, earnings before interest and tax                (EBIT) margin, asset turnover and financial leverage, on ROE development. In the paper                are used statistical methods, specifically regression and correlation analysis, which were                applied on the sample of the Slovak Republic engineering industry company's.  By linear                regression  model  were  found  out that to  the  development  of ROE  the  most  significant                impact has tax burden indicator and asset turnover, while the  equity multiplier has the                least significant impact.                 Keywords: return on equity, corporate finance, impact of financial indicators                JEL codes: M41, G32                 1 Introduction                The success of a company in the long run is the result of past decisions and the ability                to maintain competitiveness in a rapidly changing environment. An effort to understand                and  meet  customers'  quality  and  price  requirements  forces  companies  to  find  new                opportunities to achieve sustainable profitability (Džubáková and Kopták, 2017). To assess                the correctness of a decision, it is necessary to define what indicators are considered in                company to be a measure of success. A precondition for evaluating the development of                indicators is their comparability, so they need to be expressed in the same measurement                units.  A  comprehensive  system  of  accounting  and  reporting  is  a  relevant  source  of                comparable information in monetary terms. To assess financial performance and to predict                future  developments  the  sources  are  accounting  information  presented  in  the  financial                statements  (Lovciová,  2017).  Providing  information  for executives  and  financial                management of company is a priority role of accounting (Baštincová, 2017). Executives                and financial management are considered as internal users of information with access to                comprehensive  data  beyond  the  published  information  required  by  laws  available  for                example via the European e-Justice Portal (MacGregor and MacGregor, 2017).  External                users, shareholders and creditors make their decisions only based on published documents,                such as the annual report and financial statements. From these basic documents, using                the financial analysis instruments, can be reconsidered the financial health of the business                entity. (Benetti, 2016) Individuals remain only partly informed, which leads to a situation                                                            479","where  some  market  participants  have  more  information  than  others  (Tumpach  and                Baštincová, 2014). In any case, managers in decision making should use the information                asymmetry to maintain competitiveness. Development of information and communication                technologies  and  the  application  of  optimization  methods  for  solving  problems  ensures                greater  business  efficiency,  more  flexible  performance  of  organizations  in  the  market,                accelerating  transactional  operations,  reducing  logistics  costs  and  increasing  profits                (Mijailović et al., 2015). For comparison of company's efficiency and performance is often                used as indicator profit or loss, although alone is an inappropriate to treat as comparable                value.  The  amount  of  accounting  profit  or  loss  itself  does  not  indicate  the  size  of  the                company  or  industry  in  which  it  operates.  Comparability  of  variables  can  be  ensured                besides other conditions by financial analysis ratios. Rentability ratios are used to assess                the financial performance, which application depends on requirements and entitlements of                users. Till 70's of the 20th century the main objectives of companies were measured based                on  ratio  return  on  asset  (ROA),  later  return  on  equity  (ROE)  became  widespread                (Kharatyan et al., 2016). Company DuPont Corporation applied first the formula for the                calculation of return on assets, which was multiplying two indicators, namely the profit                margin and the asset turnover (1).                                                                                                         (1)                              =             ×                  =                                                                                                                      Return  on  assets  supplemented  by  leverage  indicator  expresses  return  on  equity  and                reflects  indebtedness  of  the  company  (2).  As  a  result  of  this  development  ROE,  as  a                relevant  indicator,  was  more  widely  applicated  than  ROA.  The  ROE  decomposition  into                individual  indicators  allow  to  assess  the  strengths  and  weaknesses  of  a  company  and                reflects more comprehensive picture than ROE itself (Prajapati and Danta, 2015). ROE has                the greatest importance from the point of view of the owners and investors as evaluates                the  profitability  of  shareholders'  equity.  (Blahušiaková,  2017;  Mareček  and  Rowland,                2017).                                                                                                        (2)                              =         ×                                         ℎ      ℎ          ´                                Based on the previous extension of profitability ratios, ROE can be calculated by multiplying                profit margin, asset turnover and equity multiplier (3, 4).                               =                                                                                                  (3)                                                                                                         (4)                              =             ×                ×                                                                          ℎ      ℎ          ´                               Extending the profit margin to another three indicators, tax burden ratio, interest burden                ratio  and  EBIT  margin  (5),  allows  more  extensive  investigation  and  understanding  of                company's various financial aspects. (Prajapati and Danta, 2015).                                                 =                                 ×                                           ×                         (5)                 The subject of further investigation will be the extension of ROE to five indicators as shown                in formula 6. The indicators asset turnover and EBIT margin capture the impact of the                investment and operational decisions of the company on overall profitability. The interest                burden ratio and equity multiplier reflect the impact of company's financing policy on ROE                as well as overall indebtedness. Last indicator, tax burden ratio captures the corporate                taxation impact on ROE development. (Kijewska, 2016).                                                                                                          (6)                              =          ×      ×       ×             ×                                                                                           ℎ      ℎ          ´                               One of the ROE components is tax burden ratio, which expresses the impact of taxes to ROE                (Grashuis, 2018). It represents the proportion of net profit to profit before taxes. The tax                                                             480","burden ratio equals to one if the amount of taxes is zero. The lower the value of the tax                burden ratio, the higher is the tax burden on the company.                 In the case of an interest burden ratio, the indicator expresses the impact of interest paid                on debt to ROE. If the amount of interest is zero, then the interest burden ratio equals                to one.  Increase in indebtedness  reflects in lower  values  of interest  burden  ratio.  Lack                of equity  caused  by  low  share  capital  and  low  undistributed  profits  from  previous                accounting  periods  stimulates  companies  to  use  debt  financing.  In  general,  the  cost                of share  capital  paid  in  the  form  of  dividends  or  profit  share  is  higher  than  the  price                of borrowed resources paid as interest. The reason is that investors are the main carriers                of the risk associated with decline of company or poor management, which can lead to lose                of  invested  capital  (Sedláček,  2017).  High  indebtedness  is  a  barrier  when  finding  new                creditors in the future. The higher level of liabilities leads to decrease of investments into                fixed assets and ultimately reduces the value of the company. (Kim, 2016)                Indicator profit before interest and taxes (EBIT) evaluate the potential for generating profit                and abstracts from the capital structure and tax burden. EBIT makes possible to compare                companies regardless of the financing strategy and the differences in the tax environment.                EBIT margin is the proportion of profit before interest and taxes to total sales.                Asset turnover represents how many units of sales accounts for unit  of asset, serves to                evaluate  the  performance  of  the  company  through  asset  use  efficiency.  Efficiency  of                company can be judged to be fair, if the ratio is higher than one, so sales are in higher                amount than assets in the given accounting period (Ďurišová and Myšková, 2010). Asset                turnover measures asset efficiency in generating revenues (Kharatyan et al., 2016).                The equity multiplier the last but not least ratio from the ROE components, expresses how                many euros of asset accounts for one euro of shareholder's equity. The higher the equity                multiplier, the higher is also the indebtedness, which means that company is more financed                by debt (Kijewska, 2016). The high level of equity multiplier is positive feature, if the return                on assets is higher, than the interest amount paid on debt. Increase in indebtedness causes                increase in  ROE (Ďurišová  and  Myšková,  2010),  which  also  results  form  the difference                between the formulas of ROE and ROA calculation.                 2 Methodology and Data                The aim of this paper is to quantify the impact of tax burden and interest burden ratios,                EBIT margin, asset turnover and equity multiplier on ROE development. Selected indicators                for year 2016 were examined on a sample of companies in the Slovak Republic engineering                industry. This industry together with the automotive industry represents the driving force                of the Slovak economy. The engineering industry has a strong historical background, holds                a stable position and is a key and still growing pillar in the Slovak industry. The data for                statistical analysis was drawn from the Financial Statements Register and the web portal                Finstat. The sample of companies was selected based on the following criteria:                    •   companies in the Slovak engineering industry and simultaneously                    •   minimum precalculated number of employees in accounting period 2016 more than                       200 and simultaneously                    •   companies with published financial statements for accounting period 2016 available                       as of 19. 3. 2018.                58  companies  met  the  selected  criteria,  whose  financial  results  presented  in  financial                statements are subject of regression and correlation analysis.                Model Specification                To determine the extent of selected components' impact on ROE linear regression model                was used. The regression model represents a simplified expression of relations between                two  or  more  variables  by  mathematic  formula  (Šoltés,  2008).  One  of  the  key  tasks                of regression  analysis  is  the  correct  identification  of  independent  (explanatory)  and                dependent  (response)  variables  (Prajapati  and  Danta,  2015).  One  of  the  regression                analysis  assumptions  is  the  independence  of  explanatory  variables,  which  is  examined                                                             481","through correlation analysis. Another requirement for regression analysis is the evaluation                of the distribution of independent variables, which should have a normal distribution. The                relationship of dependent and independent variables can be expressed by the following                linear regression model:                          =    +       +        + ⋯ +        +                                          (7)                                                                                                   0                                                                                                                                         2                                    1   1                                                                                                              2                Where β0, β1, …βk are the model parameters,                     th                xij - i  value (i = 1, 2, ..., n) of explanatory variable Xj (j = 1, 2, ..., k).                In  the  next  part  of the  paper  we  will point,  out  whether the  basic  assumptions  of  the                regression model are met, whether the selected components are statistically significant                ROE  predictors  and  we  will  determine  the  extent  of  components'  impact  on  ROE                development.                3 Results and Discussion                It is very important to find the main factors that have a significant impact on financial                performance in industry (Dluhošová et al., 2017), and therefore the ROE decomposition is                an appropriate tool to identify strengths and weaknesses in the performance of company                through  various  financial  indicators  that  consider  not  only  profitability  but  also                indebtedness, asset turnover, interest and tax burden ratio. Prior to regression analysis                indicators of descriptive statistics will be used to characterize the sample of investigated                engineering industry companies (Table 1).                                Table 1 Descriptive Statistics of ROE and Its Components                      Indicators                Average       Minimum        Maximum         Median                     ROE                            12.02         -52.90         81.08          10.35                     Tax burden                      0.80           0.28           1.11          0.78                     Interest burden                 0.01          -0.02           0.03          0.01                     EBIT margin                     3.62         -31.01         29.57           4.00                     Asset turnover                  1.41           0.23           3.42          1.40                     Equity multiplier               4.17           1.09         47.93           2.35                 Source: author’s calculations based on Finstat data retrieved on 19.03.2018 using PSPP program                 ROE, expressed as the multiplying of five indicators, has average 12.02 %, intermediate                values of all ROE's is 10.35 %, minimum ROE is -52.90 % and maximum 81.08 %. The tax                burden ratios, as mentioned above, reach value one, if the income tax equals to zero. In                the Slovak Republic all companies regardless of the economic result paid minimum tax (in                the form of a tax license) for the accounting periods ending from 2014 to 2017, which is                the reason why tax burden ratio reach values above one. We identified the minimum tax                burden at 0.28 and a maximum of 1.11, while the average is 0.80 and a median 0.78. The                interest  burden  ratio  accounts  for  much  lower  values  than  the  tax  burden  ratio.  The                maximum interest burden ratio is 0.03 and minimum -0.02. The average value and median                value is 0.01. Interest burden ratio and equity multiplier indicate higher indebtedness of                selected companies. The minimum equity multiplier is 1.09, median 2.35 and maximum                47.93. On average, companies finance one quarter (1/4.17), so 24.15 % of assets from                the  shareholder's  equity.  EBIT  margin  is  appropriate  to  compare  the  profitability  of                company,  while  abstracting  from  the  capital  structure  and  corporate  income  tax.  The                average  EBIT  margin  is  3.62,  while  median  is  4.00,  the  maximum  is  29.57  and  the                minimum value is -31.01. Asset turnover is represented by values between 0.23 and 3.42.                The difference between median (1.40) and average (1.41) is minimal. Normal distribution                as one of the assumptions of regression analysis can be evaluated based on difference                between median and average. In this case,  we have identified the smallest differences                between median and average by components tax burden ratio, interest burden ratio and                asset turnover.                                                               482","Table 2 Correlation of Return on Equity Components                    Indicators             Tax       Interest        EBIT      Asset          Equity                                         burden      burden         margin     turnover     multiplier                    Tax burden                    1        0.16         -0.13        -0.30        -0.11                                                      (0.229)        (0.335)  (0.023**)         (0.407)                    Interest burden           0.16             1        -0.08        -0.19        -0.26                                         (0.229)                     (0.552)       (0.163)  (0.046**)                    EBIT margin              -0.13         -0.08            1         0.19        -0.08                                        (0.335)         (0.552)                    (0.156)      (0.541)                    Asset turnover           -0.30         -0.19        0.19             1         0.25                                        (0.023**)       (0.163)      (0.156)                  (0.055*)                    Equity                   -0.11         -0.26        -0.08         0.25            1                    multiplier             (0.407)  (0.046**)        (0.541)     (0.055*)                      Notes: p-values are shown in parentheses besides correlations; *, ** indicate statistical                                   significance at 10 percent and 5 percent confidence levels.                 Source: author’s calculations based on Finstat data retrieved on 19.03.2018 using PSPP program                 The results of multicollinearity testing between independent variables are subject of Table                2, based on which dependence between variables can be evaluated as weak and, in most                cases,  statistically  insignificant.  At  5  %  confidence  level  there  is  a  weak  negative                relationship between the tax burden ratio and the asset turnover, as well as the weak                indirect  dependence  between  equity  multiplier  and  interest  burden  ratio.  At  10  %                confidence level we identified a weak direct dependence between asset turnover and equity                multiplier. The assumption of the independence of explanatory variables is fulfilled and we                can continue with the regression model.                                           Table 3 Regression Model Summary                     Model             R       R Square       Adjusted R          Std. Error of the                     summary                                     Square               Estimate                     Values           0.93       0.86             0.84                  7.12                 Source: author’s calculations based on Finstat data retrieved on 19.03.2018 using PSPP program                 A summary of the regression model is specified in Table 3, where R value represents the                relationship between the explanatory and response variables. In this case, R equals 0.93                and denotes the strong relationship between the variables. R square and adjusted R square                indicate how many percent of the response variable is affected by explanatory variables.                R square is applied in models with one explanatory variable, while the adjusted R square                in  case  of  several  explanatory  variables.  In  this  model  84  %  of  the  ROE  variability  is                explained by independent variables, so by ROE components.                              Table 4 Parameter Characteristics of Linear Regression Model                     ANOVA              Sum of          df      Mean Square             F       Sig.                                       Squares                    Regression         15760.82        5          3152.16          62.18       0.000                    Residual            2636.22       52           50.70                    Total             18397.04        57                 Source: author’s calculations based on Finstat data retrieved on 19.03.2018 using PSPP program                 The statistical significance of the regression model is shown in Table 4 in the last column                (p-value),  whose  value  is  0.000.  The  model  is  considered  to  be  statistically  significant                at the 5 % and 10% confidence level. We also verify the statistical significance based on                the F value, which at the 5 % confidence level at given degrees of freedom is 2.393 and                at the 10 % confidence level the 1.961 is. In the regression model the F value is 62.18,                which is much higher than the limit values at 5% and 10% confidence level, so we consider                the model to be statistically significant.                                                             483","Table 5 Regression Equation Coefficients                  Regression              Unstandardized        Standardized          t           Sig.                  model                     Coefficients         Coefficients                  coefficients             B        Std. Error       Beta                  (Constant)            -22.68         6.41          0.00           -3.54        0.000                  Tax burden             13.08         6.19          0.12           2.11         0.039                  Interest burden       234.76       225.62          0.06           1.04         0.303                  EBIT margin            2.02         0.14           0.80           14.82        0.000                  Asset turnover         7.88         1.72           0.26           4.58         0.000                  Equity                 0.88         0.15           0.34           5.99         0.000                  multiplier                 Source: author’s calculations based on Finstat data retrieved on 19.03.2018 using PSPP program                 The regression coefficients and the intercept are presented in Table 5, according to which                the regression coefficients based on p value are statistically significant at 5 % and 10 %                confidence level except for the interest burden ratio. The inclusion of interest burden ratio                into the model is important due to ROE formula decomposition. Only statistically significant                ROE  components  will  be  interpreted,  among  which  we  include  tax  burden  ratio,  EBIT                margin, asset turnover and equity multiplier. Statistically significant components of ROE                affect ROE in different extent, from which the most significant impact have tax burden ratio                and asset turnover. EBIT margin and equity multiplier have less significant impact on ROE                development at 2.02 % and 0.88 %. The intercept does not have a logical interpretation                as it is presented by negative value. The intercept represents the average value of ROE if                explanatory variables are zero. In the case of tax burden ratio one unit positive change is                responsible for 13.08 % change in ROE under remaining conditions other components.                Asset turnover generates 7.88 % change in ROE while other components remain the same.                Karathyn et al. (2016) based on the linear regression model presented similar results. The                most  significant  impact  on  ROE  development  has  asset  turnover  and  equity  multiplier                based on normal data. The authors' team examined the impact of components on ROE also                based on logarithmic data, according to which the most significant impact has the interest                burden and tax burden ratio. Prajapati and Danta (2015) examined ROE decomposition in                the Indian pharmaceutical industry, where ROE was most significantly affected by asset                turnover  and EBIT  margin. Kijewska (2016) from  five  ROE  components identified  EBIT                margin as responsible for ROE decline in the Polish metallurgy and mining industry.                 4 Conclusions                 The company's financial health is a prerequisite for future success and is in the focus of                both managers’ and owners’ attention. Financial health is assessed by various financial                indicators in terms of profitability, liquidity and activity. Owners are mostly interested in                return on equity (ROE), which measures how a company utilizes owners' capital to generate                profits. Managers’ attention is devoted to the overall picture of the financial situation and                not  just  to  the  ROE.  The  penetration  between  profitability,  indebtedness  and  activity                represents ROE decomposition to five components, which include tax burden and interest                burden ratio, EBIT margin, asset turnover and equity multiplier. The ROE components were                examined by a regression and correlation analysis based on the financial results of the                Slovak Republic engineering industry companies sample for accounting period 2016. The                aim of this paper was to quantify the impact of tax burden and interest burden ratios, EBIT                margin, asset turnover and equity multiplier on ROE development. We conclude that the                tax  burden  ratio,  EBIT  margin,  asset  turnover  and  equity  multiplier  are  statistically                significant predictors, while the interest burden ratio is a statistically insignificant predictor                of ROE. The most significant impact on ROE development has the tax burden ratio, asset                turnover  and  EBIT  margin.  We state,  that  ROE  decomposition  allows  management  to                identify weaker aspects of the company's financial performance that require more attention                and  support  to  maintain  sustainable  profitability  and  financial  health.  The  ROE                decomposition  enables  to  detect  the  reasons  of  adverse  development  by  individual                components and point out the potential opportunities for improvement.                                                            484","Acknowledgments                This article is an output of the project of  the Scientific Grant Agency of the Ministry of                Culture of the Slovak Republic and Slovak Academy of Sciences (VEGA) no. 1/0512/16                (2016-2018) Retention and Development of Business Property of Company as a Relevant                Tool for Sustainable Development.                 References                Baštincová,  A.  (2017).  Objektivizácia  výsledku  hospodárenia  vo  vzťahu  k  majetkovej                podstate  podniku:  Objectification  of  the  economic  result  in  relation  of  the  business                property.  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Brno:  Masaryk  University,  pp.                655-676.                                                                                                                   486","Internet shopping in the Czech Republic with the focus on the                            types of bought goods and services by consumers                                                   Gabriela Oškrdalová                                                     Masaryk University                                Faculty of Economics and Administration, Department of Finance                                           Lipová 41a, 602 00  Brno, Czech Republic                                               E-mail: oskrdalo@econ.muni.cz                 Abstract: The internet shopping has been developing not only all over the world but in the                Czech Republic too in the last years and it is possible to expect a future expansion. The                aim of this paper is to describe the development of the internet shopping in the Czech                Republic, introduce and interpret results of a part of own quantitative research in the field                of knowledge, skills and behaviour in the internet shopping of consumers with the focus on                the types of bought goods and services and compare these results of own research with                the results of the Czech Statistical Office’s research for the Czech Republic in the internet                shopping  field  with  the  focus  on  the  chosen  types  of  bought  goods  and  services  by                consumers. The paper’s aim is reached by using of general science methods, primarily                description, analysis, comparison, synthesis and deduction, and by using statistics methods                (methods of descriptive statistics). On the basis of data gained by the own questionnaire                research we can state, 99,4 % respondents have bought goods or services on the internet                in the last year. The most respondents have bought clothes, shoes and dress accessories                on the internet in the last year. If we focus on services, the most respondents have bought                tickets on cultural events.                Keywords: e-commerce, e-shops, internet shopping, goods, services                JEL codes: D100, L810                 1 Introduction                 The internet shopping has been developing not only all over the world but in the Czech                Republic too in the last years and it is possible to expect a future expansion. In 2016 sales                of  internet  shops  in  the  Czech  Republic  were  98  billion  CZK  and  according  to  APEK  –                Association of E-commerce the turnover of internet shops should break out the level of 100                billion CZK in 2017. (Czech News Agency – CTK, 2017) This expactation was right. In 2017                sales of internet shops in the Czech Republic increased to 115 billion CZK, it is 17 billion                CZK  more  than in  2016,  which is  the year-on-year increase  of  17,35  %.  (Czech  News                Agency – CTK, 2018)                 The aim of this paper is to describe the development of the internet shopping in the Czech                Republic, introduce and interpret results of a part of own quantitative research in the field                of knowledge, skills and behaviour in the internet shopping of consumers with the focus on                the types of bought goods and services and compare these results of own research with                the results of the Czech Statistical Office’s research for the Czech Republic in the internet                shopping  field  with  to  focus  on  the  chosen  types  of  bought  goods  and  services  by                consumers.                 2 Methodology and Data                Data for own quantitative research (in the field of the internet shopping) were collected                electronically in 2017. The target group were people studying at Faculty of Economics and                Administration of Masaryk University, number of respondents was 335. It is important to                note on the definition of the target group, that formulated conclusions can be generalized                to the population of people studying at economic colleges in the Czech Republic, for them                the research sample is representative. A part of the realized and here presented research                was focused on the types of bought goods and services. Respondents should answer the                question “What type of goods have you bought on the internet in the last year?“ and “What                type of services have you bought on the internet in the last year?” too. It was possible to                                                             487","mark  more  types  of  bought  goods  and  services  too.  Collected  data  are  evaluated  by                methods of descriptive statistics, especially by the statistic number of respondents who                have bought the chosen good or the chosen service on the internet in the last year/number                of respondents who mentioned they have bought on the internet in the last year.                To data processing the software Microsoft Office Excel was used.                Normative  and  positivist  methodologies  have  been  employed  to  reach  the  aim.                The positivist methodology is used in parts, when researched issues are described only,                not evaluated. The normative methodology is used, when it is not possible or desirable to                avoid evaluating researched facts. The paper’s aim is reached by using of general science                methods,  primarily  description,  analysis,  comparison,  synthesis  and  deduction,  and  by                using statistics methods (methods of descriptive statistics).                 3 Results and Discussion                In 2017 sales of internet shops in the Czech Republic were 115 billion CZK, it is 17 billion                CZK  more  than  in  2016,  which  is  the  year-on-year  increase  of  17,35  %  (Czech  News                Agency – CTK, 2018) and it is possible to expect a future expansion. According to Vetyška,                Executive director of APEK – Association of E-commerce, the turnover of internet shops                will rise of 15 – 20 % this year too (Association of E-commerce, 2018). In the Table 1 we                can see the development of the turnover of internet shops in the Czech Republic in years                2001 – 2017. These data confirm, that the turnover of internet shops in the Czech Republic                has been increasing for a long time.                         Table 1 Turnover of internet shops in the Czech Republic (2001 – 2017)                                              Turnover             Change per year         Change per year                              Year         (billion CZK)       (billion CZK)           (%)                              2001               1                  -                   -                              2002               2                  1                 100,00                              2003               4                  2                 100,00                              2004               7                  3                 75,00                              2005              10                  3                 42,86                              2006              14                  4                 40,00                              2007              18                  4                 28,57                              2008              22                  4                 22,22                              2009              27                  5                 22,73                              2010              33                  6                 22,22                              2011              44                  4                 12,12                              2012              51                  7                 15,91                              2013              58                  7                 13,73                              2014              67                  9                 15,52                              2015              81                  14                20,90                              2016              98                  17                20,99                              2017              115                 17                17,35                 Source: Author’s processing according to Financninoviny.cz (2011). APEK: Last year internet shops                              earned record 33 billion CZK (in Czech). Finance.cz. Retrieved from:                 http://www.finance.cz/zpravy/finance/293445-apek-internetove-obchody-loni-utrzily-rekordnich-                 33-mld-kc/; Czech News Agency – CTK (2017). This year e-shops sales will break the level of 100                         billion. But carriers are missing drivers (in Czech). Aktuálně.cz. Retrieved from:                     https://zpravy.aktualne.cz/ekonomika/trzby-e-shopu-letos-prolomi-hranici-100-miliard-                  dopravcum-al/r~8a7570aed1b811e6a78c002590604f2e/?redirected=1495194613; Czech News                  Agency – CTK (2018). Czechia belongs to the countries where on-line shopping is most popular.                                Year 2017 underlined it more (in Czech). E15.cz. Retrieved from:                 http://zpravy.e15.cz/byznys/technologie-a-media/cesko-patri-k-zemim-kde-jsou-nakupy-on-line-                                      nejoblibenejsi-rok-2017-to-jeste-podtrhl-1341730.                In 2017 the Czech internet shops’ share in the total retail sales broke out the level of 10                % for the first time (the share was 10,5 %). According to E-commerce Europe the Czech                                                             488","Republic belongs to countries with the highest share of on-line shopping in the Europe. In                2016 the Denmark was on the top of the ladder with the share of 24,0 %. The Great Britain                was second with the share of 20,9 % and the Germany third with the share of 11,2 %. In                the ladder the Ireland (the share of 10,2 %) and the Czech Republic follow. At the end of                the ladder the Bulgaria is with the share of 1,3 %. (Kolář, 2018)                 In the following section the results of own quantitative research are presented, focused on                the  types  of  bought  goods  and  services  by  consumers.  The  target  group  were  people                studying  at  Faculty  of  Economics  and Administration  of  Masaryk  University, number  of                respondents  was  335.  The  structure  of  the  sample  is  represented  in  Figure  1  (by  the                gender,  age  and  completed  education).  It is important to note  on the  definition  of  the                target group, that formulated conclusions can be generalized to the population of people                studying  at  economic  colleges  in  the  Czech  Republic,  for  them  the  research  sample  is                representative.                                             Figure 1 Structure of the sample                Basis: All respondents, n = 335                                                                                                         Source: Author                Types of bought goods and services on the internet - descriptive statistics                A part of the research was focused on internet shopping and types of bought goods and                services. Respondents should answer the question “What type of goods have you bought                on  the internet in  the last  year?“  and “What  type  of  services  have  you  bought  on  the                internet in the last year?” too. It was possible to mark more types of bought goods and                                                             489","services too. 333 respondents of 335 respondents (99,4 % respondents) mentioned they                have bought goods or services on the internet in the last year. In Figure 2 we can see                answers to the first question. The most respondents have bought clothes, shoes and dress                accessories (69,7 % respondents who mentioned they have bought on the internet in the                last year), books, newspapers, magazines, textbooks and e-books (54,4 % respondents                who mentioned they have bought on the internet in the last year) and other electronic                equipments as mobiles, TVs, cameras etc. (45,9 % respondents who mentioned they have                bought on the internet in the last year). Fewest respondents of the researched sample                answered, that they have bought computers software without games, including updates                (13,8 % respondents who mentioned they have bought on the internet in the last year),                movies and music (14,1 % respondents who mentioned they have bought on the internet                in the last year), toys and board games (14,4 % respondents who mentioned they have                bought on the internet in the last year) and e-learning materials (14,4 % respondents who                mentioned they have bought on the internet in the last year too).                    Figure 2 Types of bought goods on the internet (in detail) – descriptive statistics                What type of goods have you bought on the internet in the last year?                Basis: Respondents who mentioned they have bought on the internet in the last year, n = 333                                                                                              Source: Author                 The second question was focused on the bought types of services. As we can see in Figure                3, the most respondents have bought tickets on cultural events (79,6 % respondents who                mentioned they have bought on the internet in the last year), travel tickets and rent a car                (76,0 % respondents who mentioned they have bought on the internet in the last year)                and accommodation (62,8 % respondents who mentioned they have bought on the internet                in the last year). Fewest respondents of the researched sample answered, that they have                bought telecommunication services (23,4 % respondents who mentioned they have bought                on the internet in the last year) and tickets on sport events (26,7 %  respondents who                mentioned they have bought on the internet in the last year).                                                               490","Figure 3 Types of bought services on the internet (in detail) – descriptive statistics                What type of service have you bought on the internet in the last year?                Basis: Respondents who mentioned they have bought on the internet in the last year, n = 333                                                                          Source: Author                 Chosen types of bought goods and services on the internet - descriptive statistics                –  comparison  of  the  results  of  own  research  and  Czech  Statistical  Office’s                research for the Czech Republic                In the following section we can compare the results of own research and the results of the                research of the Czech Statistical Office (CZSO) in the field of internet shopping, focused on                the chosen types of bought goods and services on the internet in the last year. To the                research of the Czech Statistical Office it should be mentioned that respondents were 16+                years old people from the Czech Republic and formulated conclusions can be generalized                to the population of 16+ years old people in the Czech Republic, for them the research                sample is representative. For the better comparison of the results respondents of the age                group 16 – 24 years from the Czech Republic of the Czech Statistical Office’s research can                be introduced (formulated conclusions can be generalized to the population of 16  – 24                years old people in the Czech Republic, for them the research sample is representative),                because 89,6 % respondents of own research were 15 – 24 years old. More information                about the Czech Statistical Office’s research is in Czech Statistical Office (2018).                As  we  can  see  in  Figure  4,  all  chosen  types  of  everyday  consumption  goods  (food  or                groceries,  beverages,  cosmetics;  medication  and  vitamins;  clothes,  shoes,  dress                accessories) have been bought on the internet in general by more respondents of own                research  than  by  respondents  of  the  Czech  Statistical  Office’s  research  (16+  years  old                respondents) in the last year (for example according to own research 36,3 % respondents                who mentioned they have  bought on the internet in the last year have bought food or                groceries, beverages and cosmetics on the internet in the last year vs. according to the                Czech  Statistical  Office’s  research  only  21,1  %  respondents  who  mentioned  they  have                bought on the internet in the last year have bought this type of everyday consumption                goods on the internet in the last year).                The similar situation we can observe by chosen types of goods for leisure time activities                (sport  equipment;  toys,  board  games;  movies,  music,  books,  newspapers,  magazines,                textbooks and e-books) and for households (electrical devices for household as fridges,                washing machines, irons, vacuum cleaners etc.; electronic equipments and software) too.                The only exemption is other household equipment (furniture, dishes, garden equipment                etc.), but the difference by this type of goods in both groups is very small (in the Czech                Statistical Office’s research 19,9 % respondents who mentioned they have bought on the                internet in the last year have bought other household equipment on the internet in the last                year vs. in own research 19,5 % respondents who mentioned they have bought on the                internet in the last year have bought other household equipment on the internet in the last                year).                                                               491","Figure 4 Types of bought goods on the internet (own research and Czech Statistical                                         Office’s research) – descriptive statistics                                                                                         Source: Author’s processing according to Czech Statistical Office (2018). Use of information and                 communication technologies in households and by individuals – 2017 (in Czech). Czech Statistical                 Office. Retrieved from: https://www.czso.cz/csu/czso/vyuzivani-internetovych-ulozist-7h8xejoqlw                                                     and own research.                 If we compare answers of respondents of own research and 16 – 24 years old respondents                of the Czech Statistical Office’s research, the conclusions are same. All chosen types of                goods for everyday consumption, leisure time activities and households have been bought                                                            492","on the internet in general by more respondents of own research. The differences in these                two groups are considerable (everyday consumption goods: 17,2 % vs. 36,3 %, 3,8 % vs.                18,3 %; goods for leisure time activities: 28,5 % vs. 38,4 %, 4,1 % vs. 14,4 %, 35,0 %                vs. 56,2 %; household goods: 3,7 % vs. 23,4 %, 30,5 % vs. 60,4 %, 7,5 % vs. 19,5 %)                .                As we can see in Figure 5, the situation is similar by chosen types of bought services too.                In  general  we  can  state  more  respondents  of  own  research  than  the  Czech  Statistical                Office’s research (16+ years old respondents) who mentioned they have bought on the                internet in the last year have bought tickets on cultural and sport events (84,1 % vs. 33,2                %), accommodation (62,8 % vs. 37,4 %), travel tickets and rent a car (76,0 % vs. 18,4                %) on the internet in the last year. If we compare answers of respondents of own research                and 16 – 24 years old respondents of the Czech Statistical Office’s research, we can state                all  chosen  types  of  services  have  been  bought  on  the  internet  in  general  by  more                respondents of own research. The differences are considerable too (44,6 % vs. 84,1 %,                23,6 % vs. 76,0 %, 24,8 % vs. 62,8 %).                  Figure 5 Types of bought services on the internet (own research and Czech Statistical                                         Office’s research) – descriptive statistics                                                Source: Author’s processing according to Czech Statistical Office (2018). Use of information and                 communication technologies in households and by individuals – 2017 (in Czech). Czech Statistical                 Office. Retrieved from: https://www.czso.cz/csu/czso/vyuzivani-internetovych-ulozist-7h8xejoqlw                                                     and own research.                 4 Conclusions                In 2017 sales of internet shops in the Czech Republic were 115 billion CZK, it is 17 billion                CZK more than in 2016, which is the year-on-year increase of 17,35 %. The Czech internet                shops’ share in the total retail sales broke out the level of 10 % for the first time (the share                was 10,5 %). According to E-commerce Europe the Czech Republic belongs to countries                with  the  highest  share  of  on-line  shopping in  the  Europe. (Czech  News  Agency  –  CTK,                2018)                On  the  basis  of  data  gained  by  the  own  questionnaire  research  we  can  state,  99,4  %                respondents  have  bought  goods  or  services  on  the internet in the last  year. The most                respondents have bought clothes, shoes and dress accessories (69,7 % respondents who                mentioned  they  have  bought  on  the  internet  in  the  last  year),  books,  newspapers,                magazines, textbooks and e-books (54,4 % respondents who mentioned they have bought                on the internet in the last year) and other electronic equipments as mobiles, TVs, cameras                                                             493","etc. (45,9 % respondents who mentioned they have bought on the internet in the last                year) on the internet in the last year. If we focus on services, the most respondents have                bought tickets on cultural events (79,6 % respondents who mentioned they have bought                on the internet in the last year), travel tickets and rent a car (76,0 % respondents who                mentioned they have bought on the internet in the last year) and accommodation (62,8 %                respondents who mentioned they have bought on the internet in the last year) on the                internet in the last year.                If we compare these results and the results of the research of the Czech Statistical Office                (Czech Statistical Office, 2018), we can state all chosen types of everyday consumption                goods have been bought on the internet in general by more respondents of own research                (people studying at Faculty of Economics and Administration of Masaryk University) than                by respondents of the Czech Statistical Office’s research (16+ years old people from the                Czech Republic) in the last year. The similar situation we can observe by chosen types of                goods for leisure time activities and for households and by chosen types of bought services                too.  If  we  compare  answers  of  respondents  of  own  research  and  16  –  24  years  old                respondents of the Czech Statistical Office’s research, the conclusions are same. All chosen                types of goods for everyday consumption, leisure time activities and households and all                chosen types of bought services have been bought on the internet in general by more                respondents of own research. The differences are considerable.                 Acknowledgments                The paper was created with the support of the project MUNI/A/0753/2017 – Behavioral,                knowledge and economic aspects influencing the decision making of the subjects.                 References                Association of E-commerce (2018). Year 2017 was record for e-commerce, turnovers of                the  sale  of  goods  exceeded  115  billion  CZK  (in  Czech).  Retrieved  from:                https://www.apek.cz/clanky/rok-2017-byl-pro-e-commerce-rekordni-obraty-za-pr.                 Czech  News  Agency  –  CTK (2017).  This  year  e-shops  sales  will  break the  level  of  100                billion.   But   carriers   are   missing    drivers    (in   Czech).   Retrieved    from:                https://zpravy.aktualne.cz/ekonomika/trzby-e-shopu-letos-prolomi-hranici-100-miliard-                dopravcum-al/r~8a7570aed1b811e6a78c002590604f2e/?redirected=1495194613.                 Czech News Agency – CTK (2018). Czechia belongs to the countries where on-line shopping                is  most  popular.  Year  2017  underlined  it  more  (in  Czech).  Retrieved  from:                http://zpravy.e15.cz/byznys/technologie-a-media/cesko-patri-k-zemim-kde-jsou-                nakupy-on-line-nejoblibenejsi-rok-2017-to-jeste-podtrhl-1341730.                Czech  Statistical  Office  (2018).  Use  of  information  and  communication  technologies  in                households     and    by    individuals   –    2017    (in   Czech).    Retrieved    from:                https://www.czso.cz/csu/czso/vyuzivani-internetovych-ulozist-7h8xejoqlw.                 Financninoviny.cz (2011). APEK: Last year internet shops earned record 33 billion CZK (in                Czech). Retrieved from: http://www.finance.cz/zpravy/finance/293445-apek-internetove-                obchody-loni-utrzily-rekordnich-33-mld-kc/.                Kolář, V. (2018). E-shops participate in retail sales already by ten percents. Czechia is in                on-line   shopping    the   fifth   in   the   Europe   (in   Czech).    Retrieved   from:                https://byznys.ihned.cz/c1-66005950-e-shopy-se-na-maloobchodnich-trzbach-podileji-                jiz-deseti-procenty-cesko-je-v-online-nakupech-pate-v-evrope.                                                                       494","The Termination of Foreign Exchange Interventions and the                                       Balance Sheet of the Central bank                                                       Dalibor Pánek                                                     Masaryk University                                           Faculty of Economics and Administration                                                   Department of Finance                                           Lipová 41a, 602 00 Brno, Czech Republic                                                E-mail: panek@econ.muni.cz                 Abstract: The aim of the text is an analysis of the changes of selected items of the balance                sheet of the Czech national bank in the course of and after completion of foreign exchange                intervention as a monetary policy instruments and the context in the banking sector. The                analysis  of  the  manifestations  of  foreign  exchange  intervention  in  the  central  bank's                balance sheet was carried out on the basis of the data for the years 2012  – 2017. This                period includes the progress of the use of this tool of monetary policy and the state after                its completion. The text characterizes the effects of the active and passive sides of the                balance sheet of the central bank and assesses the achievement of the objectives set.                Analysis of selected items of the balance of the central bank was made on the basis of                theoretical knowledge of the resources listed and the practical knowledge of the author. In                conclusion,  the  author  presents  his  views  on  the  use  of  this  tool  of  monetary  policy,                especially in the period after his termination. For text processing were used methods of                description  of the  theoretical  and  practical  parts,  methods  of  comparison,  analysis  and                prediction in the part of the text following the development of the on-balance sheet items                in the course of and after completion of foreign exchange intervention.                Keywords:  Central  bank,  non-standard  monetary  policy  instrument,  exchange  rate                intervention,  the  balance  of  the  central  bank,  the  banking  sector,  the  reserve  banks,                foreign exchange reserves                 JEL code: G28                 1 Introduction                The starting point of the text is knowledge about the activities of the central bank acquired                in the course of historical development and on the basis of their current legal status in the                financial system. Attention to the text is focused on the features and tools of the central                bank, their use and impact on the balance sheet of the central bank. The basis for this text                is the constitutional position of the central bank of the Czech Republic, the law on the                central bank, the theoretical knowledge of the listed authors and the practical findings of                the author.                The main objective of the monetary policy of the Czech national bank is to maintain price                stability. The inflation target is set at 2 %. The basic standard instruments of monetary                policy of the national bank are interest rates, open market operations, automatic facilities                and reserve requirements. The main standard tool of monetary policy are open market                operations with a view to influence the development of the reserves of banks and interest                rates in the economy.                Foreign  exchange  intervention,  announced  in  November  2013  were  non-standard                measures the central bank. The bank board of the Czech national bank decided to use the                exchange rate of the domestic currency as the main tool for monetary easing. The aim was                the  weakening  of  the  domestic  currency  and  limiting  deflationary  pressures  in  the                economy. These tendencies already, it was not possible to suppress further reduction of                the interest rate of the central bank.                The aim of the text is an analysis of the manifestations of non-standard monetary-policy                measures in the balance sheet of the central bank in the course of and after his termination                in the years 2012 to 2017. To achieve the objectives of the text are used the methods of                description and comparison of the development of selected items of the balance of and                                                            495","relationships between selected items of the balance sheet and the termination of foreign                exchange intervention.                 2 The balance sheet of the central bank                 The balance of the central bank does not have the structure of the business balance of the                commercial banks. Assets balance sheet of the central bank containing the channels of                emission of money, the liabilities represent the location and form of money in the economy.                The balance of the central bank characterized by a high proportion of foreign exchange                reserves on the assets and the share of the reserves of the commercial banks and the                money in circulation in the liabilities of balance.                 The main focus of the text is devoted to the impact of exiting non-standard monetary policy                instruments on the balance sheet of the central bank in 2017. The development of the                balance sheet of the central bank, the assets of foreign exchange reserves, on the liabilities                of the development of the monetary base, reserves of commercial banks and the results                of  operations  of  the  central  bank.  The  period  of  the  course  and  termination of  foreign                exchange interventions changed the form of the balance of payments of the Czech national                bank. There has been a gradual increase in the balance sheet of the central bank more                than threefold                 Assets of balance sheet of the central bank                On the active side of the balance sheet of the central bank, we can track the movement of                the value of foreign exchange reserves in relation to currency interventions of the central                bank.  The  assets  of  the  Czech  national  bank  shall  contain  as  a  main  item  of  foreign                exchange reserves (over 90%). The structure of the reserve consists of deposits in foreign                banks, securities, gold and reserves with the IMF. The value of foreign exchange reserves                is recognised in the balance sheet of the central bank in domestic currency. Movement of                the exchange rate of the domestic currency fundamentally operates on the level of reserves                and affects the economic result of the central bank.                   Chart 1 The Sum of the Balance Sheet and Foreign Reserves 2012-2017 (bn. CZK)                                         Source: compiled by the author according to the Annual reports of the Czech National Bank                The decline in the exchange rate of the domestic currency increases the volume of foreign                exchange  reserves  and  the  reporting  of  the  economic  result  of  the  central  bank.  The                proceeds from the higher volume of invested foreign exchange reserves to further enhance                their value. The movements of the value of the foreign exchange reserves were mainly due                to foreign exchange interventions of the central bank. Foreign exchange intervention also                influenced the active item advances to banks, where they manifested long-term systemic                excess liquidity of commercial banks. Gradually increasing credit demand of the clients in                commercial  banks,  the  item  claims  to  commercial  banks  in  the  central  bank's  balance                sheets has a long-term zero value.                                                              496","Liabilities of balance sheet of the central bank                On  the  passive  side  of  the  balance  sheet  of  the  central  bank  in  the  foreign  exchange                interventions  showed  a  substantial  increase  in  the  monetary  base  involving  the  item                banknotes and coins in circulation, required and free reserves of commercial banks at the                central bank. These items form a significant part (90%) of the liabilities of the central bank.                  Chart 2 The Monetary Base, Reserve of Banks and Bank Loans 2012-2017 (bn. CZK)                                          Source: compiled by the author according to the Annual reports of the Czech National Bank                 Liabilities  of  the  central  bank  to  domestic  banks  grew  in  the  period  foreign  exchange                intervention  more  than  threefold.  The  increase  in  bank  reserves  caused  the  inflow  of                domestic currency into the banking sector the influence of foreign exchange intervention.                 Structurally  there has  been  an increase in  particular  voluntary  reserve  banks  influence                monetary easing. Increase voluntary reserves of the commercial banks, inadequate credit                demand and interest amount of reserves limit the potential returns of commercial banks.                A related influence on increasing the monetary base is the growth of banknotes and coins                in circulation, the value of the currency has reached 594 billion CZK in 2017.                 The  increase  in  the  liquidity  of  commercial  banks  in  the  course  of  foreign  exchange                interventions of the central bank has caused an increase in the credit activity of commercial                banks only partially. The volume of granted bank loans did not correspond to the growth                of bank reserves. He was influenced by the development of the domestic and the European                economy.                The  period  of  foreign  exchange  intervention  was  extended  several  times.  Prerequisite                completion in 2015, has been extended, the new governor of the central bank announced                in  2016  the  continuation  of  the  foreign  exchange  intervention.  The  termination  of  the                intervention announced by the board at the end of 2016 to the first half of 2017. Foreign                exchange intervention because of its nature a short-term instrument of monetary policy to                revive the economy, their use in the medium term, it causes some negative trends in the                economy.  It  is  an  advantage  of  certain  business  groups,  reduce  the  pressure  on  the                efficiency of firms and the impact of interventions is hampered by price increase import of                inputs and technology.                 3 The result of the management of the central bank                 Priority  activities  of  the  Czech  National  Bank  is  not  making  profit  but  the  appropriate                monetary policy for the domestic economy. According to economic theory, show the central                banks profit. Loss results created by the central bank is not an unusual situation and does                not have any substantial influence on activities of the central bank.                 During the years 2012 to 2017 recorded the results of operations of the central bank to                considerable  fluctuations.  The  result  of  the  management  of  the  Czech  national  bank                                                              497","fundamentally affect the foreign exchange differences of the domestic currency and the                related change in value of foreign exchange reserves.                         Chart 3 The Cumulative Economic Result of the CNB 2012-2017 (bn. CZK)                                          Source: compiled by the author according to the Annual reports of the Czech National Bank                 Turning point for the coverage of accumulated losses from previous years was the year                2014. This effect of exchange rate differences on the financial result of the central bank                will last for the entire period of non-participation of the Czech Republic in the euro area.                The development of the prices of the monetary reserves against the domestic currency and                their impact on the economic result of the central bank lasts even after the end of foreign                exchange intervention. You can assume that the different development of the price of gold                compared to the price of the currency reserves would reduce this dependency. Gold is                currently less than one percent of the share of the foreign exchange reserves of the central                bank. The increase of this share to five percent of the reserve would positively reduce the                dependence of the development of the prices of reserves on monetary reserves.                 4 Conclusions                Assets balance sheet of the central bank consist mainly of foreign exchange reserves, on                the liabilities is dominated by bank reserves and money in circulation. In the period 2012                –  2017  has  increased  the  balance  sheet  of  the  central  bank  three  times.  Significant                influence on the increase in the balance of payments should foreign exchange intervention                of  the  central  bank  with  the  aim  of  kick-starting  the  domestic  economy  and  the                achievement of the inflation target. Use the exchange rate of the domestic currency as an                instrument of monetary policy caused an increase in foreign exchange reserves on the                assets and the increase in bank reserves and money in circulation on the liabilities.                 A substantial increase in the share of bank reserves on the monetary base further affecting                the  liquidity  surplus  in  the  banking  sector.  Money  issued  into  circulation  in  the  period                foreign exchange interventions were not issued as a credit emissions relative to effective                business  plans  and  investment,  therefore,  you  cannot  assume  their  download  of  the                repayment of the loans. The money in circulation amounted to the fulfilment of the inflation                target, however, they may in the next period to create a cash imbalance.                During  the  years  2012  –  2017  affected  the  foreign  exchange  intervention  and  their                completion  twice  fundamentally  the  result  of  management  of  the  central  bank.  The                coverage of accumulated losses in 2014 was replaced by new loss after the termination of                the interventions in the year 2017. The direct influence of the development of the prices                of currency reserves on the financial result of the central bank could partially change the                change the structure of the reserves in the increase in the share of gold reserves. Adverse                influence on the management of the central bank will also increase interest rates associated                with interest bearing reserves of banks with the central bank. Borrowing by commercial                                                             498","banks from the central bank cannot be expected due to the permanent excess liquidity of                commercial banks.                 The precise calculation of the macroeconomic impact and the impact of foreign exchange                interventions cannot be determined. It can be noted that during the reporting period was                achieved by the recovery of the economy of the Czech Republic. Achieve the objectives of                inflation, GDP growth, reaching a value of around 5%, unemployment fell to the level of                around 3%. Except for the measures of the central bank positively affected the growth of                the improved condition of the euro area economy (increase GDP in 2017 by 2.5%), rising                household  consumption,  government  expenditure  including  the  increase  in  state                employees by 31 thousand and the stable performance of exporting companies.                 Managed  floating  exchange  rate  of  the  domestic  currency  gets  to  the  level  before  the                foreign  exchange  interventions,  supportable  estimates  for  its  development  cannot  be                accurately  determined.  The  movement  of  balance  sheet  items  of  the  central  bank,                especially foreign exchange reserves, will be further affected by its level. For the future it                is necessary to search for a solution in the final decision about the date of receipt of the                single European currency.                 References                 Czech National Bank (2018). The annual reports of the Czech National Bank. Retrieved                from: http://www.cnb.cz/cs/o_cnb/hospodareni/vyrocni_zpravy/.                Czech  National  Bank  (2018).  The  tools  of  monetary  policy.  Retrieved  from:                http://www.cnb.cz/cs/menova_politika/mp_nastroje/.                Czech     National     Bank     (2018).     Zpráva     o     inflaci.   Retrieved    from:                www.cnb.cz/miranda2/export/sites/www.cnb.cz/cs/menova_politika/zpravy_o_inflaci/201                8/2018_I/download/zoi_I_2018.pdf.                 Ministry of finance of the Czech Republic (2017). The state budget in a nutshell. Retrieved                from:          http://www.mfcr.cz/cs/o-ministerstvu/vzdelavani/rozpocet-v-kostce/statni-                rozpocet-v-kostce-2017/.                 REVENDA, Z. (2011). Central banking. Prague: Management Press. ISBN 9788072612307.                TŮMA, Z. (2017). Intervention and the loss of the Czech National Bank. Retrieved from:                http://www.roklen24.cz/a/iUpJe/zedek-tuma-intervence-a-ztrata-ceske-narodni-banky/.                                                                                             499","Investors’ Expectations, Price-Dividend Dependence and P/E Ratio                     for Common Stock: Specifics of Well-Developed Markets and                                                 Emerging Markets                                                           2                                                                              3                                   1                  Sergey Petrov , Nadezhda Yashina , Oksana Kashina , Nataliya Pronchatova-                                                        Rubtsova  4                                   1, 2, 3, 3, 4  Lobachevsky State University of Nizhny Novgorod                         Institute of Economics and Entrepreneurship, Department of Finance and Credit                          Bolshaya Pokrovskaya street 37, 603000 Nizhny Novgorod, Russian Federation                                             2                                                             3                    E-mail:  petrov_ss@list.ru,  sitnicof@mail.ru,  oksana_kashina@mail.ru,  pronat89@mail.ru                           1                                                                                      4                 Abstract:  It  is  well  known  that  a  role  of  dividend  payments  as  a  criterion  for  the                effectiveness of portfolio investments increases with the rise in their time horizon. The                comparison of share price performance and dividend payments in more than a hundred-                year retrospective had allowed to find out the phenomenon of anomalous volatility for stock                prices. The result had a powerful influence on the concept of risky asset evaluation. The                authors of the paper proposed to study time series of stock prices and dividend payments                in a somewhat different frame of reference. The aim of the paper is to examine investors’                perception  of  growth  prospects  for  equities  based  on  the  investigation  of  share  price                dynamics compared to annual dividend payments. The proposed theoretical model uses an                original procedure of continuously growing dividend discounting with arbitrary investment                period. The research deals, but not limited to, with the shares of American and Russian                corporations  from  1985  till  2016.  The  central  issue  of  the  research  is  to  compare  the                observed price-dividend dependence for the share with the one corresponding to classical                dividend discount model assuming that the growth of dividend is uniform and the period                of investment is infinite. The result of the analysis carried out is that both in well-developed                and in emerging markets authors' technique allows to gain an insight (for many securities)                into the primary motive for “representative shareholder” to evaluate a security: is it an                income  from  share  price  growth  or  a  willingness  to  receive  stock  dividends.  Similar                diagnostics  is  of  undoubted  interest  for  portfolio  management;  it  is  more  exact  for                developed markets that have longer history and are less volatile. We show in the paper                that the proposed method makes it possible to define the concept of earnings shares and                growth shares more concretely.                Keywords: Discount model for common stock pricing, price-dividends dependence for well-                developed  and  emerging  markets,  econometric  analysis  of  stock  price  and  dividends,                stockholders’ expectations evaluation, P/E ratio                 JEL codes: C-12, G-11, G-12, G-15, G-17                 1 Introduction                In  recent  decades  speculative  behavior  of  investors  has  an  impact  on  capital  market                developments in many respects. A lot of fundamental studies of capital asset pricing under                conditions  of  financial  market  turbulence  (see,  for  example,  Cochrane,  2011;                Shiller, 2014) show that the motives of investor’s behavior are materially more complicated                than classical asset pricing theory supposed (Cochrane, 2005). In the long run (getting off                a  subject  of  financial  market  variations)  price-dividend  dependence  is  still  a  general                criterion for verification of theoretical concepts of shareholders’ motives and theoretical                models of capital asset pricing (we do not also neglect other fundamental indicators such                as  “price-earnings  ratio”    and  “book-to-market  value”  for  share  (Graham,  2004)).  A                number  of  researchers  have  been  analyzing  this  dependence;  substantial  results  were                obtained  by  studying  price-dividend  dependence  trend  (Campbell  and  Shiller,  1987;                Campbell and Shiller, 1988; Engsted and Pedersen, 2010) as well as by exploring share                price  variations  about  the  trend  (Hussainey  et  al.,  2010);  the  last-mentioned  line  of                research  had  stimulated  the  development  of  behavioral  finance  (Fama,  1998;  Shiller,                2003; Shiller, 2014).                                                              500","The present paper being based on the analytical model proposed by Petrov et al. (2016)                aims, in  the first  place, to  develop  a technique  allowing  to  estimate  the importance  of                shareholders’  speculative  motive  (on  average  for  the  time  period  under  consideration)                using price-dividend dependence for the equities (we interpret shareholders’ speculative                motive as their focus on outrunning growth of the share price comparing to the growth of                the  corporation’s  financial  performance).  Such  investigations  (see  also  Greenwood  and                Shleifer, 2014) detail the momentary estimation of “the payment for expected growth” for                shares  obtained  by using  P/E  ratio  (Brealey  et  al.  (2010)); note    that  P/E  ratio  varies                significantly  year  after  year.  The  results  of  Petrov  et  al.  (2016)  show  that  although                shareholders’ speculative motive varies along with the change in market sentiment, it is                usually observed for one and the same securities; in contrast, the motive is not usually                observed for other shares (the market evaluates these shares as a source of dividend rent).                An insight into the speculative character of evaluating of any particular shares is important                for a portfolio investor: apparently, the price of speculative share can differ markedly from                its fundamental value; the fact makes a share price drawdown highly probable. On the                contrary, equities that are evaluated by capital market as a source of dividend rent, have                smaller chance for drawdown in case of a drop in the market.                 In the second place, we aims to test the approach developed in the paper for securities                trading in well-organized stock markets (we are placing greater focus on the American                shares) as well as for the equities of emerging markets (shares of Moscow Exchange are                considered as an example). An experience of some previous studies (Petrov et al (2016))                permits to expect that the joint analysis of share price dynamics and dividends becomes                less effective under conditions of emerging markets. Firstly, historical time series of key                variables are usually significantly shorter for emerging markets; at the same time, share                price is distinctly more volatile (Mirza and Afzal, 2012). Next, corporate management in                developing economies hasn’t formulated some standards of dividend policy yet; dividend                payments of corporations are relatively unstable in that context. Analyzing the original                data of Standard and Poor's Financial Services, one can conclude that dividend payments                                                                                                 th                of American corporations became more regular only roughly beginning in 1950 ; so the                hypothesis of continuously increasing dividends (Gordon, 1959) turned out to be urgent.                 2 Methodology                Consider an investor who intends to buy some share at the initial time  =  0 and to hold it                                                                                        t                 for  n   years  (time  t  is  a  discrete  variable);  let  d t  (t =  2 , 1  ,..,n )   are  the  expected  annual                dividends per share. Besides dividends, the investor may have some income at the end of                the period if he will sell the share; let V  is its appropriate expected price at the time n .                                                          n                The current price of the share  V  from the investor’s point of view in this case may be                                                  0                found as a sum of discounted dividends and discounted resale price with discount rate  r                (see, e.g., Brealey et al. (2010)):                          0                         V =  n   d t  t  +  V n  n  ,                                                      (1)                             t 1 =  1 ( +  r)  1 ( +  r)                The investor can’t know how dividends d  t  (t =  2 , 1  ,..,n )  will vary in the future. In this regard,                the hypothesis of the classical Gordon’s model (Gordon, 1959) about uniformly increasing                dividends looks quite plausible:                        d t+ 1  = d t  1 ( +  ) g ,                                                          (2)                 where  =t    , 1 , 0  ....( −n  ) 1 ,  g  - constant rate of growth (estimated by the investor). So the                present  value  of  dividends  in  the  relation  (1)  may  be  summarized  as  a  geometric                progression; so the general relation (1) of share evaluating is shaped into                             d 1( +  g)    1+  g  n   V                       V =   0 r −  g     1  1+  r     +  1 ( + n r) n   ,                       (3)                                      −                                                                         0                                                                                                                                                    501","where  d  - the previous dividend per share. If the investor does not plan to sell a share,                         0                we obtain the well-known Gordon’s relation (Gordon, 1959)                            d 1 (  +  g)                        V =  0 r −  g  .                                                                   (4)                         0                 We should stress now one substantial feature of Gordon’s model, which is the reference                point for the further empirical data analysis. As the future price V  at the moment  =  2 , 1  ,...                                                                                                   t                                                                                 t                is connected with dividend payment  d   + t  1  by relation similar to (4), the formula (2) gives                rise to the uniform growth of price with the same rate g :                         V t+ 1  = V t  1 ( +  ) g .                                                        (5)                So if we will apply the formula (3) for subsequent time  moments  =       2 , 1  ,..( −  ) 1  taking                                                                                     t                                                                                              n                them as datum, both summands in the right-hand side will annually rise proportionally by                a factor of  1 ( +  ) g . In other words, a peculiar kind of time dependence of the share price                resulting from the Gordon’s model (relation (5)) makes relations (3) and (4) equivalent to                each other at any  n  (i.e., the period of investment does not matter). These arguments                suggest how to verify the Gordon’s model for stock evaluating: dividend and share price                should increase proportionally.                The  formula  (3)  is  the  basis  for  the  further  reasoning  that  allows  to  reveal  investors’                expectations  of  securities’  investment  possibilities.  We  use  the  well-known  method                proposed  by  Fama  (1970).  This  method  (Fama,  1970;  Fama,  1998)  allows  to  verify                theoretical  “ex-ante”  models  using  historical  “ex-post”  data.  Following  Fama,  we  can                expect that if the stock market is efficient the relation (3) between the share price at the                beginning of each year and the previous annual dividend should be fulfilled upon the time-                average.                The procedure of revealing of investors’ expectations based on the relation (3) so requires                historical time  series  of:  actual  share price  V   at the  beginning  of  each  ( +  ) 1   year  for                                                                                             t                                                               t                exception  of  the  calendar  effect  “end  of  the  year”  (Brealey  et  al.,  2010);  we  have                considered averaged price for the first quarter as a price of the beginning of the year;                dividend per share  d  for the year “t ”.                                     t                In such a way we can represent equation (3) in an equivalent form:                            d 1 ( +  g)    1+  g  n   V                        V =  t r −  g     1   1+  r       +  1 ( + t+ r) n                                (6)                                      −                                                       n                         t                where the moment  t  is considered as initial.                 To verify the price-dividend dependence implying by formula (6) we have used some simple                econometric  technique.  The  Figure  1  illustrates  the  essence  of  our  method.  The  chart                represents price-dividend dependence for Microsoft Corporation from 2003 to 2014. The                vertical coordinate of every point shows the average share price V  (in dollars) for the first                                                                                   t                quarter of every year, the horizontal coordinate - annual dividend per share  d  (in dollars                                                                                               t                also) for the same year.                The Figure 1 demonstrates that historical share price and dividend per share are connected                by more or less stable dependence; full line in the chart marks its linear trend. Represent                the equation of the linear trend in general form                        V t  =  d t  +                                                               (7)                 where coefficients   and   are calculated with the help of econometric methods. The level                of their statistical significance can be measured by the criterion of t-statistics. However, it                is impossible to compare their values with parameters of the relation (6), because the                                                             502","second term in the right-hand side of the relation including the expected resale price V t+ n                is  obviously  non-stationary.  Another  problem  is  associated  with  hypothetical  period  of                investment; there is no reason to think of this “hidden” parameter as a constant.                     Figure 1 The Historical Dependence (2003-2014) between Microsoft Corporation’s                                     Share Price and the Previous Dividend Payments                                        Source: author’s calculations based on the data from website: http://ycharts.com                 Nevertheless, we draw a conclusion from the analysis of the Figure 1 that the Microsoft                Corporation’s shares are not evaluated by Gordon’s model. The fact is that coefficient                  of the linear trend is substantially positive (its average value  = 10 . 22 $, t-statistic value                t  =  . 6  77 ). This argument means that the second term in the relation (6), proportional to                the expected price V t+ n , have been increased more slowly than the first term, proportional                to the dividend  d . If the share evaluates in accordance with Gordon’s model, both terms                                  t                in the relation (6) should increase proportionally. In this case the trend line should pass                through the origin (the coefficient   should be equal to zero within the statistical error of                its calculation) as it follows from the relation (4).                 In other words, the “representative shareholder” of the Microsoft Corporation had little in                common with long-term investor who is present in Gordon’s model. His motives were not                speculative  (expected  growth  of  share  price  have  not  generated  its  current  price).                However, the extended analysis shows that evaluating of the shares was different in the                initial period of time (between 1986 and 2000): their price had increased faster than their                dividend. This fact was conditioned by the phenomenon of stock market “bubble” in the                USA in 1984 – 1999 (see, e.g., Schiller (2003)). The situation had drastically changed after                2000: the contribution of the expected share price to its current price had been steady                declined in comparison with increasing dividends.                 3 Results and Discussion                The  previous  arguments  reduce  to  a  simple  technique  of  revealing  of  investors’                expectations using the analysis of price and dividend time series. It needs to construct a                linear regression of the share price as a function of the dividend per share and to analyze                coefficients    and   of the equation (7). If the price-dividend dependence is statistically                significant one may conclude something about the investors’ motives.                1.  Coefficient   0 . This type of expectations was presented by the example of the shares                                               of  Microsoft  Corporation.  They  may  be  characterized  as  “income  shares”  because  the                supposed growth of the share price increment is relatively behind of changes in dividend.                Current share price increases (upon the average) more slow than dividend, and the trend                line on the chart similar to Figure 1 is displaced up from the origin.                                               2.  Coefficient    0 . In this case current share price increases relatively more rapid than                dividend. Price growth is generated by advancing expectations of the future share price                increment; these equities may be characterized as “growth shares”. The trend line on the                price-dividend chart is displaced down from the origin.                                                              503","3.  Coefficient   =  0 .  Current  share  price  (upon  the  average),  dividend  and,  as  a                consequence, supposed share price increment rise proportionally to each other. As we have                already noted, this situation is  characteristic for classical Gordon’s model; such type of                equities may be called “coherent shares”. The trend line on the price-dividend chart passes                through the origin.                 We  next  consider  the  most  typical  cases  of  revealing  of  investors’  expectations  for  a                number  of  American  common  stock  (between  1985  and  2016  predominantly)  and  for                Russian securities (between 2005 and 2016). We have examined the group of shares is                owned by corporations from diverse branches. In spite of the stock market boom in the                USA  in  1989-2000  and  its  subsequent  decline  price-dividend  dependence  for  American                securities is more or less distinct. However, only a few of them are pricing in accordance                with Gordon’s model assumptions.                “Coherent”  investors’  expectations,  for  example,  were  revealed  for  American  3M                Company’s  securities.  The  historical  dependence  (1986-2014)  between  the  share  price                (was obtained as before) and the previous dividend payment for the company is shown on                the Figure 2; we see that the changes in price were closely connected with changes in                dividend during the period under review. In contrast to Microsoft Corporation’s shares the                trend line in the Figure 2 displays the proportional dependence with    =  0  (see equation                (7)) within the statistical error of its calculation. It means that “representative stockholder”                of  the  3M  Company  is  essentially  close  to  a  long-term  investor  who  is  implied  in  the                Gordon’s hypothesis.                           Figure 2 The Historical Dependence (1986-2014) between 3M Company’s                                       Share Price and the Previous Dividend Payments                                          Source: author’s calculations based on the data from website: http://ycharts.com                 It is interesting to note that the “coherent” type of investors’ expectations makes possible                to analyze market evaluating of the share in more detail. If we compare the equation (7)                of the “ex-post” linear trend bearing in mind that   =  0  with “ex-ante” Gordon’s relation                (4), under hypothesis of market efficiency we obtain                         =  1 ( + g )( −  ) g  1 −                                                     (8)                                  r                        r =  −1  1 (  +  g +)  g                                                       (9)                 The coefficient    is calculated from the observed data on prices and dividends have been                used for the plotting Figure 2. To estimate the average rate of dividend growth  g  we note                that if dividend increases uniformly  according to the relation (2) its logarithm increases                linearly:                         lnd = lnd + t  ln( 1+  ) g                                                   (10)                                 t                           t                So, we should consider time series for the logarithm of dividend payments and study its                linear trend; the factor ln( 1+  ) g  may be then determined as a slope coefficient of the trend                                                              504","line.  Analyzing  time  series  of  dividend  per  share  and  share  price,  we  obtain  g  =  8 . 9  % ,                 r  =  7 . 9  % .                Our research has revealed a speculative type of investors’ expectations for a number of                American  “growth  shares”.  Consider,  for  example,  price-dividend  dependence  for  the                shares of Apple Corporation (see Figure 3). We note that the chart’s trend line is displaced                                                                                        0                down  from  the  origin  (  );  statistical  analysis  characterizes  the  result  as  quite                significant. Hence share price has tended to increase relatively faster than dividend; the                speculative motive have been the most probable reason of this.                      Figure 3 The Historical Dependence (1998-2014) between Apple Corporation’s                                     Share Price and the Previous Dividend Payments                                       Source: author’s calculations based on the data from website: http://ycharts.com                 In contrast with developed markets, stock market in Russia is highly volatile, so observable                price-dividend dependence is usually quite fuzzy. Most Russian corporations began to pay                relatively stable dividends only after the mid-2000s. Consequently, the available period for                revealing of shareholders’ expectations for Russian securities is not more than 9-10 years.                In general this amount of time is too short for econometric technique; however, particular                conclusions of the investigation are of some interest. The analysis of time series of share                price and dividend per share has disclosed that only two securities (The NOVATEK and The                Financial  Corporation  Sistema)  are  characterized  by  statistically  significant  dependence                between key variables. As for the 3M Company’s shares (that are also “coherent”) we have                estimated the average rate of dividend growth  g  and the discount rate r  for the NOVATEK                                                                                     r                Corporation’s shares. The result of our procedure gives  g  = 24 5 .  % ,  = 27  4 .  % ; apparently,                either  rate  of  the  company’s  growth  (note  that  the  expected  “ex-ante”  growth  is  in                agreement with the real “ex-post” growth for “coherent” securities) or the discount rate                for the shares are much greater for the emerging stock market in Russia. Probably, such                differences in the evaluating of shares in developed market and in emerging market reflects                substantial risk of “emerging” investments.                The revealing of investors’ expectations for most of Russian securities allows to classify                them as “income shares”. For example, Figure 4 illustrates the price-dividend dependence                for LUCOIL Corporation shares.                Next, objective estimation of the discount rate possible for the coherent shares expands                the  opportunities  of  P/ E -ratio  using  (see  Graham,  (2004)).  P/  E -ratio  may  be                represented as a sum of two components: “payment for the current company’s income” (it                             −                              1                is equal to  r ), and “payment for the expected growth” (see Brealey et al. (2010)):                        P  =  1  +  payment  for  the expected  growth                                (11)                        E   r                                                                  505","Figure 4 The Historical Dependence (2001-2016) between LUCOIL Corporation’s                                     Share Price and the Previous Dividend Payments                                    Source: author’s calculations based on the data from website: http://www.investfunds.ru                 For example, the Figure 5 illustrates dynamics of “payment for the expected growth” (grey                color), “payment for income” (black color) and P/E ratio (sum of the blue and green) for                the  coherent  shares  of  the  3M  Company.  According  to  the  research  conducted  for  the                shares the average P/E ratio is equal to 18.6. Using the estimation for the discount rate                (see above in this section), we find that average “payment for income” is equal to 10.4                (i.e. investors pay for the company’s income about 56% and for the expected growth about                44%).                Figure 5 Dynamics of “payment for expected growth” (grey color), “payment for income”                               (black color), P/E ratio (sum) for 3M Company (1986-2014)                                          Source: author’s calculations based on the data from website: http://ycharts.com                 We may conclude that payment for the income is a significant fraction of P/E-ratio for the                equities in developed market. Payment for the income is more stable than payment for the                expected  growth  because  investors’  expectations  can  vary  over  a  wide  range.  Similar                investigation for the NOVATEK Corporation’s equities shows that payment for the income                is not too large due to high risk of investment. If it is replaced by the payment for the                expected growth stock market becomes high volatile.                 4 Conclusions                Price-dividend dependence is quite steady for a lot of equities in developed markets. This                dependence allows to reveal investors’ expectations of the future share price growth. In                emerging markets clearly marked price-dividend dependence for the shares is not typical                as a result of large price fluctuations, so the procedure has been developed is correct only                for a few securities.                In developed markets (and for particular securities in emerging markets also) our method                allows to comprehend what is the primary motive for shareholders to evaluate a security:                is it the expected growth of its price or the dividend payments on it. As a result of investors’                                                             506","expectations  revealing  one  may  classify  equities  as  “income  shares”  or  “coherent”  or                “growth shares”. Only a few shares may be considered as “coherent”. The point of this                conclusion  is  that  shareholders’  plans  in  general  have  a  limited  period  of  investment.                “Coherent” shares are especially convenient for further investigation; analyzing their price-                dividend dependence one can determine the discount rate for them. Just as  P/   E -ratio for                common stock consists of two components – “payment for income” and “payment for the                expected growth” (Brealey et al. (2010)), so the discount rate of “coherent” shares may                be  represented  as  a  sum  of  the  “real”  and  the  “virtual”  parts.  This  virtual  part  is                considerably larger in all appearance for the securities in emerging markets; this assertion                is  in  agreement  with  the  idea  of  their  high  volatility.  The  procedure  of  price-dividend                dependence  analysis  permits to  define  a  concept  of  “shares  of income”  and “shares  of                growth” (Brealey et al. (2010)) more exactly.                 Acknowledgments                The  reported  study  was  funded  by  RFBR  according to  the  research  project №  18-010-                00909.                 References                Brealey, R., Myers, S., Marcus, A. (2010). 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Efficient Capital Markets: A Review of Theory and Empirical Work. The                Journal of Finance, vol. 25(2), pp. 383-417.                Fama, E. (1998). Market efficiency, long-term returns, and behavioral finance. Тhe Journal                of Financial Economics, vol. 49(3), pp. 283-306.                Gordon, M. (1959). Dividends, Earnings, and Stock Prices. The Review of Economics and                Statistics, vol. 41(2), pp. 99-105.                 Graham, B. (2004). The Intelligent Investor. New York: Harper Collins Publishers Inc.                Greenwood,  R.,  Shleifer,  A.  (2014).  Expectation  of Returns  and  Expected  Returns.  The                Review of Financial Studies, vol. 27(3), pp. 714-746.                Hussainey, K., Mgbame, C., Aruoriwo, M. (2010). Dividend Policy and Share Price Volatility:                UK Evidence. Journal of Risk Finance, vol. 12(1), pp. 57-68.                 Mirza,  N.,  Afzal,  A.  (2012).  Some  Preliminary  Evidence  on  Stock  Price  Bubbles  in  an                Emerging Market. The Romanian Economic Journal, vol. 15(44), pp. 55-86.                Petrov, S., Kashina, O., Murashkin, R. (2016). Examination of Stock Market \"Temperature\"                Using  Price-Dividend  Dependence  for  European  Shares.  In:  Proceedings  of  the  13th                International  Scientific  Conference  European  Financial  Systems  2016.  Brno:  Masaryk                University, pp. 552-559.                Shiller, R. (2003). From Efficient Markets Theory to Behavioral Finance.  The Journal of                Economic Perspectives, vol. 17(1), pp. 83-104.                                                              507","Shiller, R. (2014). Speculative Asset Prices. American Economic Review, vol. 104(6), pp.                1486-1517.                                                                                                                                                 508","Long-term Stock Returns of Companies Implementing Diverse                                    Dividend Policy: Evidence from Poland                                               Aleksandra Pieloch-Babiarz                                                     University of Lodz                        Faculty of Economics and Sociology, Department of Business Analysis and Strategy                                         41, Rewolucji 1905 St., 90-214 Lodz, Poland                                                 E-mail: apieloch@uni.lodz.pl                 Abstract: The aim of this article is to examine whether there is a statistically significant                relationship  between  the  type  of  dividend  policy  and  the  long-term  stock  returns.                Considering the diverse dividend policy of companies, I am particularly interested in the                role of constant dividend per share policy, constant payout ratio policy, extreme dividend                policy, residual dividend policy and hybrid dividend policy in shaping the market share price                of companies listed on the main market of the Warsaw Stock Exchange in the years 2001-                2016. I conduct the research employing the buy-and-hold abnormal return, Fisher’s LSD                test and OLS regression model. The main results of my empirical study indicate that the                buy-and-hold  returns  for  the  companies  implementing  a  specified  dividend  policy  are                higher than the buy-and-hold returns for the WIG index. Moreover, regardless of the type                of dividend policy, abnormal stock returns increase along with an increase in the number                of years of the regular dividend payouts. The main conclusion from my analysis is that                conducting the specified dividend policy has a significant impact on the long-term market                share price behavior. I observe that the diversity of dividend policy is accompanied by the                diverse  stock  returns  achieved  by  the  stock  market  investor.  The  scientific  research                presented in this paper seems to be valuable due to the lack of academic studies on the                impact of implementing the different types of dividend policy on achieving the long-term                abnormal stock returns.                 Keywords: dividend policy, stock returns, buy-and-hold abnormal returns, capital gain, the                Warsaw Stock Exchange                JEL codes: G10, G11, G14, G35, G41                 1 Introduction                 The dividend policy consists in making decisions regarding the distribution of net profit to                dividend and retained earnings. Those decisions are crucial for both the company and the                stock market investors, since adopting a specified dividend policy is a strategic decision                conditioning  not  only  the  company’s  development  opportunities  but  also  affecting  the                market value of company as well as the shareholder value (Lacina and Zhang, 2008). One                of  the  most important  aspects  of  dividend  policy is  to make  a  decision  about  dividend                initiation. An initial dividend is understood as the first dividend paid out after the company                has  gone  public  (Lipson  et  al.,  1998).  The  literature  emphasizes  that  the  decision  on                dividend  initiation  should  be  thought  out  because  the  stock  market  can  threat  it  as  a                declaration of further dividend payouts (Hobbs and Schneller, 2012). If expected dividend                is not paid out there may be a decrease in the market share price (Michaely et al., 1995;                Kumar, 2017). In turn, if the dividend occurs regularly in the years following the dividend                initiation  the  stock  market  usually  reacts  positively  (How  et  al.,  2011).  Such  dividend                payments are called the regular dividend payouts .                                                                  15                The company can pay out dividend implementing the diverse types of regular or irregular                dividend policy. Among the model forms of regular dividend policy are: constant dividend                per share (plus extra dividend) policy, constant payout ratio policy, extreme dividend policy                (100%) and residual dividend policy (Pieloch-Babiarz, 2017). In turn, among the model                    15  For the regular dividend payout the Warsaw Stock Exchange considers the dividend that                has been paid out for at least 5 following years.                                                            509","forms of irregular dividend policy are: extreme dividend policy (0%) and hybrid dividend                policy (Baker and Smith, 2006).                 The constant dividend per share policy provides shareholders with a fixed dividend per                share regardless of the net profit generated by the company. Therefore, this dividend policy                is based on regular but relatively low dividend payments. An increase in dividend per share                is observed only if the company has achieved higher and stable level of net profit. However,                if the net profit has increased unexpectedly and is not expected to be achieved at the same                level in the future, this policy ought to be adjusted, i.e. in the years of abnormal net profit                the ‘extra’ dividend should be paid out to avoid a decrease in the market value of company                in the years when abnormal net profits will not be achieved (Duraj, 2002). Moreover, the                companies  that  financial  standing  is  good  can  adjust  this  policy  by  paying  out  higher                dividend every year. In this case the dividend policy is called the growing dividend per                share policy (Zyguła and Oleksy, 2016). In turn, the essence of the constant payout ratio                policy consists in paying out a fixed part of the net profit. Therefore, the dividend per share                paid every year varies along with the change in net profit. The main disadvantage of this                policy is seen in a probable decrease in the market share price in the years of lower net                profits (Cwynar and Cwynar, 2007). By contrast, the residual dividend policy means paying                out dividend after financing the investment projects. Its implementation may result in the                variability of dividend per share, and may even lead to cessation of payouts in some years                affecting the market value of company (Baker and Smith, 2006). In turn, the extreme                dividend policy concerns two extremely different types of  dividend policy. The first one                consists in paying out 100% of net profit, which results in the necessity of raising external                capital to finance the investment. The second one, is the policy of not paying out dividend.                In this case the company uses the net profit mainly for development (Duraj, 2002). The                last type of dividend policy is the hybrid dividend policy that consists in combining at least                two different types of dividend policy.                The studies concerning the changes in the market share price of companies focus mainly                on the reaction of stock market investors to: the dividend initiation (Hobbs and Schneller,                2012; Lacina and Zhang, 2008), an increase or decrease in the dividend level (Michaely et                al., 1995; Kumar, 2017) and the length of time of the regular dividend payouts (How et                al.,  2011). However, there  are no  academic  research  regarding the impact  of  different                types of dividend policy on the market share price of company. I noticed, therefore, a                research gap that prompt me to conduct the scientific research in this area.                The main  aim  of  this  article is to investigate  whether  there is  a  statistically significant                relationship  between  the  type  of  dividend  policy  and the long-term  stock  returns.  This                research goal is important in terms of the impact of dividend policy on the market value of                company and the shareholder value. Therefore, it is crucial to posit the research hypothesis                stating that the shareholders of companies conducting the diverse dividend policy achieve                the diverse long-term stock returns.                The  remainder  of  this  paper  is  organized  as  follows.  Section  2  describes  the  research                sample  and  details  research  methodology.  Section  3  presents  the  results  of  empirical                research and discusses them. Section 4 highlights the main research findings.                 2 Methodology and Data                 Empirical research has been conducted among the companies listed on the main market of                the Warsaw Stock Exchange (the WSE) in the years 2001-2016. The condition for including                the company in the research sample is to initiate dividend in the analytical period and then                pay it out regularly for at least 5 years. The adopted selection criteria have been met by                87  companies.  However,  after  excluding  from  the  sample the  financial institutions  and                companies with missing data, the sample consists of 75 companies.                 The study is carried out in three stages. Firstly, the companies are categorized against the                types of dividend policy. Secondly, the long-term stock returns of regular dividend payers                are calculated. To do that, I employ the methodology of buy-and-hold return (BHR) and                assume that investor purchases shares at the closing price on the day of the resolution of                                                             510","the general meeting of shareholders concerning the dividend initiation (declaration date)                and holds them for 5 years. An implementation of such research assumptions eliminates                the impact of short-term market share price changes, particularly its increase in the last                days of listing before the record date, as well as its decrease on the ex-dividend date. The                long-term stock returns resulting from the adopted investment strategy are calculated as                the following (Michaely et al., 1995):                                  T                                          −                                             i 1                        BHR T DI  i ,  =  ( 1+ R  t i,  ) 1 =  2 ,  ,..., n                                            ,                                 =                                 t 2                          (1)                where  BHR  T DI  i ,   is the buy-and-hold return on shares of i-th company initiating and paying                out dividend regularly in the observation period T and  R  denotes the rate of return on                                                                           t i,                shares of the i-th company on the t-th day of trading.                The rates of return calculated on the basis of the above formula take into account not only                the factors specific to the company but also general characteristics of the stock market. To                eliminate the influence of the general changes in the stock market sentiment, I adjust the                obtained  results  by  the  rate  of  return on  the market  portfolio  using the  Warsaw  Stock                Exchange Index (the WIG index) on a given trading day as a benchmark. The buy-and-                hold return on the market portfolio is given as follows (Sosnowski, 2015):                                  T                                             −                        BHR WIG, i  =  ( 1+  R  ) 1 =  2 ,  ,..., n                                                                     (2)                                                i 1                                               ,                            T           WIG, t                                  =                                  t 2                where  BHR  WIG, i  is the buy-and-hold return on the WIG index corresponding to the  i-th                            T                company during the observation period T and  R  WIG, t  denotes the rate of return on the WIG                index on the t-th day of trading.                The buy-and-hold return on shares adjusted for the buy-and-hold return on the WIG index                refers to the abnormal changes in the market share price of companies paying out dividend.                The rates of return resulting from the buy-and-hold investment strategy are given by the                following formulas (Michaely et al., 1995):                         BHAR T DI  i ,  =  BHR T DI  i ,  −  BHR T WIG, i                                                                            (3)                                    T           T                                                  1                        BHAR T DI  i ,  =  ( + R  t i,  )−  ( + R WIG, t )                                                                (4)                                      1                                   = t 2       = t 2                where  BHAR  T DI  i ,   indicates the buy-and-hold abnormal return on shares of  i-th company                initiating and paying out dividend regularly in the observation period T.                 In  order  to  compare  the long-term  rates  of  return  on  shares  issued  by  the  companies                implementing diverse dividend policy, I use the average buy-and-hold abnormal return                given by the formula:                                    n                        BHAR DI  =  1   BHAR DI  i ,                                                                                    (5)                             T                                 n  = i 1  T                 where  BHAR  T DI  is the average buy-and-hold abnormal return on shares of the companies                initiating and paying out dividend regularly in the observation period T and n is the number                of companies.                The  statistical  significance  of  results  is  examined  using  the  conventional  statistics  (t)                described by the following formula (Lyon et al., 1999):                                                                511","BHAR  DI                        t =        T                                                                                               (6)                           σ (BHAR T DI )  n                                       where  (BHAR   T DI  ) stands for the standard deviation of buy-and-hold abnormal return.                 Conducting the study I compare the average buy-and-hold abnormal returns for both five                years of dividend payouts and five types of dividend policy implemented by the surveyed                companies. I use the descriptive statistics and Fisher’s Least Significant Difference (LSD)                test (Williams and Abdi, 2010) to explore all possible pair-wise comparisons of average                buy-and-hold abnormal returns observed for the companies conducting different dividend                policy.                In the third stage of the study, I investigate the existence of the relationship between                different  types  of  dividend  policy  and  the  level  of  long-term  stock  returns.  I  use  the                Ordinary Least Squares (OLS) regression model which is constructed in a way allowing to                avoid the perfect multicollinearity due to the so-called dummy variable trap. I drop from                the  model  the  dummy  variable  Const_DPSi  (which  is  equal  to  1  if  the  i-th  company                implements  the  constant  dividend  per  share  policy  and  0  otherwise)  and  estimate  the                structural parameters of the following model:                                                       )                        BHAR    =  β +  θ ( Growth _  DPS +  θ ( Resid _ DIV +  θ ( Const _  PAYOUT +                                                                        )                                                                                              )                             T  i ,  0  1             i   2            i    3                i           (7)                        +  β  PROFIT T  i ,  +  β  SIZE T  i ,  +  β  DEBT T  i ,  +  ε i                           1                                          2                                                      3                where BHART,i stands for the buy-and-hold abnormal return on shares of i-th company in                period T; Growth_DPSi denotes a dummy variable equal to 1 if the i-th company conducts                the growing dividend per share policy and 0 otherwise; Resid_DIVi is a dummy variable                equal to 1 if the i-th company implements the residual dividend policy and 0 otherwise and                Const_PAYOUTi stands for a dummy variable equal to 1 if the i-th company conducts the                constant  payout  ratio  policy  and  0  otherwise.  Moreover,  three  control  variables  are                considered as determinants of BHAR of dividend payers, i.e.: ΔPROFITT,i denotes the growth                rate of return on total assets of the i-th company in the period T; ΔSIZET,i stands for the                growth  rate  of  the  i-th  company’s  size  in  the  period  T  (it  is  measured  by  the  natural                logarithm of total capital) and ΔDEBTT,i is the growth rate of debt ratio of i-th company in                the period T.                The financial data used in this study are obtained from the Notoria Service database, the                market share prices are received from the stock market portal Infostrefa.com, while the                information about dividend policy is retrieved from the websites of surveyed companies.                The research is conducted using statistical package Statistica.                 3 Results and Discussion                The empirical research on the dividend policy shows that most of the companies listed on                the WSE do not pay out dividend regularly. Among the companies that pay dividend for                the first time in years 2001-2016, only 75 companies continue the dividend payouts for at                least 5 years after the dividend initiation. Analyzing the research sample in terms of the                type of dividend policy, it should be noted that the majority of companies implement the                stable  dividend  policy  that  is  manifested  in  a  fixed  or  increasing  payouts,  i.e.  growing                dividend  per  share  policy  (32%  of  companies)  and  constant  dividend  per  share  policy                (21.33%). Moreover, some companies conduct residual dividend policy (21.33%), hybrid                dividend  policy  (12%),  extreme  dividend  policy  (8%)  and  constant  payout  ratio  policy                (5.33%)  (see Table 1).                         16                    16  Since the extreme dividend policy (100%) can be considered as a type of the constant                payout  ratio  policy  in  this  paper  they  are  investigated  together.  Moreover,  the  hybrid                dividend policy is not analyzed in this study due to the lack of stable dividend payouts.                                                            512","Table 1 Research Sample Broken Down by the Type of Dividend Policy                                Specification                   No. of companies          Share (%)                 Constant dividend per share policy                      16                   21.33                 Growing dividend per share policy                       24                   32.00                 Residual dividend policy                                16                   21.33                 Constant payout ratio policy                              4                    5.33                 Extreme dividend policy (100%)                            6                    8.00                 Hybrid dividend policy                                    9                  12.00                 Sum                                                     75                 100.00                Source: own study on the basis of the data collected from Notoria Service and companies’ websites                 The  research  shows  that  in  each  of  five  periods  the  stock  market  values  the  regular                dividend payers higher than the companies included in the portfolio of the WIG index. This                is evidenced by the positive values of the average buy-and-hold abnormal returns. What                is more, the average buy-and-hold abnormal return increases along with the number of                regular dividend payments. At the end of the year of dividend initiation the average buy-                and-hold  abnormal  return  is  at  0.41%,  while  after  five  years  of  the  regular  dividend                payouts it is equal to 6.28%. The largest increase in the average buy-and-hold abnormal                return occurs after two, and then after five years of regular dividend payouts. The average                BHAR in two years after dividend initiation is over four times higher than the average BHAR                in the year of first dividend payment. In turn, the average BHAR after five years of payouts                exceeds more than twice the average buy-and-hold abnormal return for the previous period                (see Figure 1). The reason for this could be seen, among others, in a behavior of the long-                term investors for whom the fifth dividend payout is an important signal from the company                confirming  once  implemented  dividend  policy.  This  information  results  in  the  above-                average positive reaction of the capital market, which is reflected in the high values of the                abnormal BHAR after five years of regular dividend payouts.                    Figure 1 Average Buy-and-hold Abnormal Returns in Five-year Period after Dividend                                 Initiation (statistical significance at the level of α = 0.01)                           7                     Average buy-and-hold  abnormal returns (%)  4                          6                          5                           3                          2                          1                          0                                                              3 Years                                                2 Years                                 1 Year                                                              Period T       4 Years        5 Years                             Source: own study on the basis of the data collected from Infostrefa.com                The descriptive statistics presented in Table 2 provide some characteristics of companies                paying out dividend. The figures indicate the existence of diversity among the companies                in terms of the type of dividend policy and the period of payment. Regardless of the type                of dividend policy, it can be observed that after five years of regular dividends payouts,                the average buy-and-hold abnormal returns are higher than those in the year of dividend                initiation. At the end of the first year of payout, the highest average BHAR is noticed for                the companies implementing constant dividend per share policy (mean is at 0.86%). In                                                             513","this case, in half of companies BHAR is higher than 0.16% and in every fourth company it                is higher than 0.67%. In turn, after five years of dividend payout the companies conducting                constant dividend per share policy are valued relatively the lowest (mean is at 2.46% and                median is  at  0.17%).  The  reason  for  such market  share  price  behavior  could  be  seen,                among others, in relatively low dividend payouts. After five years of regular dividend the                highest average BHAR is observed for the companies paying out high dividend, i.e. the                companies  implementing  constant  payout  ratio  policy  and  growing  dividend  per  share                policy (36.21% and 26.74%, respectively). However, it should be noted that in those both                cases of dividend policy the average BHAR has decreased in relation to the average BHAR                received after four years of payout (52.46% and 30.84%, respectively). The reason for the                decrease in average BHAR could be explain, inter alia, by using the net profit to pay out                dividend rather than spend it on investment. Conversely, when analyzing the buy-and-hold                abnormal return for companies conducting residual dividend policy one should notice an                increase of mean and median. After five years of investment the average BHAR is at 3.69%,                while the median is at 2.54%. Given figures are higher than in the case of constant dividend                per share policy which indicates  higher market valuation of the companies investing the                net profit.                      Table 2 Descriptive Statistics of BHAR for Different Types of Dividend Policy (%)                 Specification     Des. Stat.     1 Year     2 Years     3 Years     4 Years     5 Years                                     Mean           0.856       1.837       0.994       1.245       2.460                                      S.D.          1.772       3.083       1.735       3.001       7.067                   Constant           Min          -0.237      -1.445      -1.802      -2.190      -1.584                 dividend per         .25           0.023      -0.173      -0.027      -0.209      -0.138                  share policy      Median          0.156       0.900       0.672       0.261       0.174                                      .75           0.670       3.245       1.552       2.060       1.816                                      Max           6.132       9.457       5.148       9.557      25.688                                     Mean           0.578       1.136      10.931      30.835      26.741                                      S.D.          0.803       1.933      20.352      53.140      75.969                    Growing           Min          -0.496      -0.905      -1.836      -1.669      -1.656                 dividend per         .25           0.034      -0.157       0.157      -0.220      -0.218                  share policy      Median          0.259       0.647       1.059       0.575       0.280                                      .75           1.097       1.735       5.214      83.988       2.258                                      Max           2.787       7.344      65.525    154.328     127.048                                     Mean           0.260       1.459       2.017       3.254       3.694                                      S.D.          0.514       2.411       4.021       4.429       4.138                    Residual          Min          -0.581      -0.655      -4.016      -1.335      -0.599                    dividend          .25          -0.053      -0.095       0.152       0.006       0.056                     policy         Median          0.091       0.253       0.738       1.865       2.537                                      .75           0.495       3.054       2.410       4,560       5.746                                      Max           1.333       7.156      11.101      11.437      11.440                                     Mean           0.503       5.264      26.673      52.458      36.207                                      S.D.          0.245       4.964      31.734      70.426      53.758                   Constant           Min           0.081      -0.382      -1.408       0.197      -0.080                  payout ratio        .25           0.375       0.723       0.885       1.121       0.676                     policy         Median          0.503       5.229       9.672      14.946      14.518                                      .75           0.650       9.458      62.386      96.041      61.962                                      Max           0.888      12.431      75.026    203.731     147.143                            Source: own study on the basis of the data collected from Infostrefa.com                The  results  of  Fisher’s  Least  Significant  Difference test  show  the  occurrence  of  some                statistically significant differences in the average buy-and-hold abnormal return depending                on the type of dividend policy. One can note that differences in the average BHAR given at                the end of the first year of payouts are not statistically significant which means that right                after the dividend initiation stock market investors do not differentiate between various                types of dividend policy. Some statistically significant differences start to be observed after                                                             514","two years of dividend payouts. These differences can be seen especially for constant payout                ratio policy. After five years of the regular dividend payments the average BHAR observed                for companies with constant payout ratio policy is statistically different with regard to the                others (see Table 3).                             Table 3 The p-value of Fisher’s Least Significant Difference Test                   Specification        1 Year        2 Years       3 Years       4 Years       5 Years                    GR1 vs. GR2          0.459         0.499         0.126          0.061         0.423                    GR1 vs. GR3          0.141         0.730         0.884          0.907         0.908                    GR1 vs. GR4          0.470         0.009         0.002          0.010         0.010                    GR2 vs. GR3          0.387         0.755         0.149          0.067         0.499                    GR2 vs. GR4          0.924         0.001         0.036          0.212         0.035                    GR3 vs. GR4          0.518         0.004         0.002          0.011         0.013                Symbols: GR 1 is a group of companies conducting constant dividend per share policy, GR 2  stands for                a  group  of  companies   implementing  growing  dividend  per  share  policy,  GR 3  means  a  group  of                companies  conducting  residual  dividend  policy  and  GR 4  is   a  group  of  companies   implementing                constant payout ratio policy.                            Source: own study on the basis of the data collected from Infostrefa.com                 Estimation results of the multiple regression model are presented in Table 4. Depending                on the duration of dividend policy the estimation results are diversified. The analysis of                coefficients for the model concerning all years of research allows to conclude that those                companies  that  increase  the  amount  of  dividend  every  year  are  valued  by  the  capital                market higher than those that pay out dividend per share at the same level. The BHAR of                companies conducting growing dividend per share policy is higher by 0.135 p.p. than that                of the companies with constant dividend per share policy. Moreover, the companies paying                out  a  constant  part  of  net  profit  every  year  are  valued  higher  than  those  paying  out                dividend at a fixed level. The BHAR of companies conducting constant payout ratio policy                is higher by 0.281 p.p. in relation to those with constant dividend per share policy. In                addition, the changes of return on total assets (ΔPROFIT) have a significant impact on the                long-term stock returns of companies (coefficient is at 0.180).                      Table 4 The Results of Estimation of Ordinary Least Squares Regression Model                   Specification      1 Year     2 Years      3 Years     4 Years     5 Years     All years                                     -0.128      -0.107       0.174       0.183       0.191        0.135                  Growth_DPS                                     (0.485)     (0.513)     (0.308)     (0.259)      (0.290)     (0.075)                                     -0.193      -0.040       -0.006      0.012       0.011        0.002                   Resid_DIV                                     (0.318)     (0.804)     (0.969)     (0.934)      (0.951)     (0.981)                                     -0.115       0.387       0.486       0.417       0.373        0.281                Const_PAYOUT                                     (0.509)     (0.013)     (0.002)     (0.005)      (0.029)     (0.000)                                      0.063       0.017       0.239       0.421       -0.011       0.180                    ΔPROFIT                                     (0.683)     (0.898)     (0.079)     (0.001)      (0.944)     (0.003)                                      0.005       0.063       0.154       0.004       -0.005       0.012                     ΔSIZE                                     (0.975)     (0.647)     (0.235)     (0.975)      (0.975)     (0.847)                                     -0.118       0.000       0.000       -0.013      0.007        0.022                     ΔDEBT                                     (0.447)     (0.998)     (0.997)     (0.917)      (0.967)     (0.714)                        R 2           0.049       0.194       0.268       0.319       0.125        0.101                  Adjusted R   2      0.002       0.095       0.174       0.235       0.005        0.080                   F-statistic        0.359       1.961       2.864       3.821       1.045        4.919                     p-value          0.901       0.089       0.018       0.003       0.409        0.000                        N              75          75           75          75          75          375                Note: The p-values for t test statistics are shown in the parentheses below coefficient estimates.                            Source: own study on the basis of the data collected from Infostrefa.com                                                                   515","4 Conclusions                The results of empirical research conducted on the Warsaw Stock Exchange show that most                of the companies paying out dividend regularly have implemented the fixed or increasing                dividend payouts. This finding allows to presume that one of the reasons for choosing such                dividend policy is signaling the good financial standing to the stock market investors which                can result in an increase of the market value of company. This conjecture seems to be                confirmed because regular dividend payers are usually valued higher than the companies                from the WIG index, and the average buy-and-hold abnormal return increases along with                the number of years of dividend payouts. What is more, the market value of company is                different due to the type of dividend policy. The companies conducting constant dividend                per share policy are valued relatively the lowest. In turn, the companies with constant                payout ratio policy, which in the conditions of the Polish market usually pay out a great                part of the net profit, are valued the highest. This indicates that surveyed companies are                valued by investors in relation to the amount of dividend that they pay out.                The  research  results  presented  in  this  paper  should  not  be  generalized  due  to  a  few                limitations. Therefore, there is a need to extend the study towards some other research                directions. First, the research sample should be extended to financial institutions because                they  usually  conduct  stable  policy  of  regular  dividend  payouts.  Second,  it  is  worth  to                conduct comparative analysis for companies listed on the global capital markets. Third, the                study should consider the other determinants of the market value of company.                 References                 Baker,  H.  K.,  Smith,  D.  M.  (2006).  In  Search  of  Residual  Dividend  Policy.  Review  of                Financial Economics, vol. 15, pp. 1-18.                 Cwynar, A., Cwynar, W. (2007).  Creating Company Value Through Long-term Financial                Decisions. Warsaw: Polish Accounting Academy.                Duraj, A. N. (2002). Factors for the Implementation of the Dividend Policy in Public Joint-                Stock Companies. Lodz: Publisher of University of Lodz.                Hobbs, J., Schneller, M. I. (2012). Dividend Signalling and Sustainability. Applied Financial                Economics, vol. 22(17), pp. 1395-1408.                How,  J.,  Ngo,  K.,  Verhoeven,  P.  (2011).  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Price  Reactions to Dividend                Initiations and  Omissions:  Overreaction  or  Drift?.  The  Journal  of  Finance,  vol.  50(2),                pp. 573-608.                Pieloch-Babiarz, A. (2017). Diversity of the Policy of Regular Dividend Payments and the                Financial Condition of Companies Listed on the Warsaw Stock Exchange, Entrepreneurship                and Management, vol. 18(7), pp. 273-291.                 Sosnowski, T. (2015). Motives and Effects of the Initial Public Offerings on the Warsaw                Stock Exchange. Equilibrium, vol. 10(2), pp. 207-222.                Williams,  L.  J.,  Abdi,  H.  (2010).  Fisher's  Least  Significant  Difference  (LSD)  Test.                Encyclopedia of Research Design, vol. 1, pp. 1-6.                                                            516","Zyguła, A., Oleksy, P. (2016). Dividend Policy of Companies Listed on the Warsaw Stock                Exchange on the Example of the Trading Sector. Research Papers of Wroclaw University of                Economics, vol. 439, pp. 405-416.                                                                                                                                               517","The Issue of Responsibility for Financial Security in Old Age and                                          Additional Retirement Saving                                            Sylwia Pieńkowska-Kamieniecka                                          University of Warmia nad Mazury in Olsztyn                                Department of Social Policy and Insurance, Faculty of Economics                                          Oczapowskiego 4, 10-719 Olsztyn, Poland                                           E-mail: sylwia.pienkowska@uwm.edu.pl                 Abstract:  In  connection  with  the  lowering  benefits  from  public  pension  systems                responsibility for financial security in old age should become more individualized. People                should take greater responsibility for future benefits and standard of living in old age. The                objective of the article is to assess how perception of who should take care of financial                security during old age impacts the decision regarding additional retirement saving. The                article presents the data of the research conducted by the Social Insurance Institution in                2016 on a representative for the whole population of Poland sample of 1,030 people. The                main conclusion which may be drawn from the research is that when individuals think that                it is mainly the State's responsibility to care of retirement security, they save less for old                age.                Key words: responsibility, saving, retirement, old age                 JEL codes: D14, D31,H31, H55, J32                1 Introduction                 In  the  face  of  reducing  the  generosity  of  the  pay-as-you-go  public  pension  systems                threatened  by  ageing  population  (Bohn,  2002;  Bӧrsch-Supan,  2004;  Chybalski  and                Marcinkiewicz, 2018), the issue of assuring economic safety for citizens in old age is broadly                debated in many countries in the world (Hershey et al., 2010). Introduction of multi-pillar                pension system at the end of the twentieth century in Poland which included mandatory                and voluntary elements of retirement provision stressed the role of voluntary saving for                old age (Rutecka-Góra, 2016).                The  most  significant  factor  which  determines  the  situation  of  pension  systems  is                demographic  structure  (Czapiński  and  Góra,  2016;  Bielawska  et  al.,  2015).  In  ageing                Europe, including Poland, the number of pensioners in relation to contribution payers has                increased, the scale of financial burden of professionally active individuals also increased                whereas the employment rate has decreased. As a result, with unchanged retirement age,                future ratio between the rate of pension and salary will be decreasing (Czapiński and Góra,                2016). Stiglitz (2000) claims that public pension systems should provide only minimum                benefits.                There  are  numerous  factors  which  determine  willingness  to  save,  including  additional                saving for old age. They include economic, social and legal factors as well as those which                refer to socio-demographic characteristics of a particular individual. The most significant                factors are gender, age, education, family size, annual income (Shinde and Zanvar, 2015;                Heckman  and  Hanna,  2015,  Walczak  and  Pieńkowska-Kamieniecka,  2018),  place  of                residence and marital status (Anioła-Mikołajczak and Gołaś, 2014). Women generally have                less retirement wealth (Brown et al., 2016). Munnell et al. (2014) point to the importance                of  starting  saving  early  (in  mid-30s)  and  retiring  late.  Meanwhile,  young  people  are                characterised  by  a  lower  retirement  awareness  which  results  from  lack  of  financial                knowledge, characteristic not only for this age group, but the whole society. Individuals                with lower level of education are less willing to save additionally for old age (Cole and                Shastry, 2007).                It is also important to stress job-related factors such as job characteristics and status of                employment  (Tamborini  and  Purcell,  2013;  Liberda  1999).  Mastrogiacomo  and  Alessie                (2014)  highlight  lower  pension  savings  among  self-employed.  While  they  are  more                financially knowledgeable than those who do not conduct business activity, they are less                                                            518","prepared for retirement (Sappleton and Lourenço, 2016). Of importance is also the degree                of trust in the pension system and financial institutions (Ben-Ner and Halldorsson, 2010).                The  relation  between  social  trust  (also in various institutions)  and  economic welfare  of                society members, also in old age, has been recognised and has become the subject of                numerous studies (Tonkiss, 2009).                Velkoff (2001) indicates that many elderly people expect financial help from adult children.                However, in most societies this support is in opposite direction, i.e. from elderly parents to                adult children. Yet, this trend is less visible in developing countries.                Above  all,  it  seems  crucial  to  asses  if  individuals  themselves  are  worried  about  their                financial  situation  in  the  future  and  if  they  undertake  any  actions  in  order  to  assure                adequate standard of living in old age. People must be also able to make well-informed                financial decisions (Lusardi and Mitchell, 2013) and need to realize that additional saving                for  old  age  (apart  from  the  mandatory  system)  is  becoming  a  necessity  (Oehler  and                Werner, 2008) as relying on old-age security provided by the State is not sufficient.                The aim of the article is to assess how perception of who should take care of financial                security during old age impacts the decision regarding additional saving for old-age.                Also,  it  analyses  other  demographic,  social  and  economic  factors  which  may  facilitate                additional saving for old age by individuals.                 2 Methodology and data                The  article  presents  data  of  representative  survey  study  regarding  the  knowledge  and                attitudes of Poles to social insurance, including additional saving for old age, which was                conducted  in  Poland  in  2016  at  the  order  of  the  Social  Insurance  Institution.  1,030                participants took part in the study. They were aged 15-75. To realize the research task,                the method of logistic regression was used. While constructing the logit model the following                dichotomous dependent variable was used:                Y – Do you save additionally for old age?                The aforementioned variable took on the following form:                                                      1, if the respondent saves                                            Y = {     0, if the respondent does not save                 Independent variables, both qualitative (with the reference groups in particular variables)                and quantitative, which were used in the model, are presented in Table 1. High p-value in                Hosmer-Lemeshow test (>.05) allows to conclude that the selection of variables to the                model was correct and the model is correct. Significance level to stay in the model was                0.05.                               Table 1 Descriptive Statistics for the Independent Variables                            Variable                   Variant of variable             N           %                                                  Qualitative variables                 Gender                           Women (ref. group)                  533         51.7                                                  Men                                 497         48.3                 Age                              Aged  60  and  more  (ref.          258         25.0                                                  group)                              144         14.0                                                  Aged 15-24                          300         29.1                                                  Aged 25-39                          328         31.8                                                  Aged 40-59                 Level of education               Higher (ref. group)                 149         14.5                                                  Primary                             170         16.5                                                  Vocational                          375         36.5                                                  Secondary                           334         32.5                 Place of residence               Rural areas (ref. group),           393         38.2                                                                                      336         32.6                                                            519","Cities with population <100         177         17.2                                                  k.                                  124         12.0                                                  Cities with population 100-                                                  499 k.                                                  Cities with population >500                                                  k.                 Marital status                   Single                              363         35.2                                                  Married                             482         46.8                                                  Divorced/separated                   70          6.8                                                  Widowed (ref. group)                115         11.2                 Status of employment             Full-time job (ref. group)          515         50.7                                                  Casual work                          50          4.9                                                  Unemployed                          114         11.2                                                  Pensioner/retiree                   264         26.0                                                  Student                              72          7.1                 Own enterprise                   No (ref. group)                    1.012        98.3                                                  Yes                                  18          1.7                 Trust  in  Social  Security  Yes (ref. group)                        449         43.6                 Institution                      No                                  454         44.1                                                  It's difficult to say               127         12.3                 Who  should  take  care  of  The State (ref. group)                  647         62.8                 financial  security  in  old  Citizens                               290         28.2                 age?                             It's difficult to say                93         90.0                 Old-age  security  should  I disagree (ref. group)                   328         31.9                 depend        mainly        on  I agree                              602         58.4                 individual          prudence  It's difficult to say                  100          9.7                 (individual prudence)                 Adult     children     should  I  strongly  disagree  (ref.           48          4.7                 support  financially  elderly  group)                 parents                          I rather disagree                   142         13.8                                                  I generally agree                   499         48.4                                                  I strongly agree                    242         23.5                                                  It's difficult to say                99          9.6                 Do  you  do  shopping  on  a  No (ref. group)                        568         55.1                 daily basis? (shopping)          Yes                                 462         44.9                                                  Quantitative variables                 Income of a household per  Continuous variable                 person     in   thous.    PLN                 (income)                        Source: own elaboration based on the research Social Insurance Institution, 2016                 3 Results and Discussion                The results of the research indicate that not all independent variables have a significant                impact on additional saving for old age. Table 2 presents only those factors which turned                to be statistically significant.                     Table 2 Estimates of Logistic Regression Model for Dependent Variable: Additional                                                    Saving for Old Age                                                                                      The                                                                       S.E.       significance     Exp(B)                                           Variant of variable                                                                                      level                  Place of residence      Rural areas (ref.                            .017                                         group)                                         Cities with population        .192           .511          1.134                                         <100 k.                                                             520","Cities with population        .241           .803           .941                                         100-499 k.                                         Cities with population        .249           .003          2.084                                         >500 k.                 Status              of  Full-time job (ref.                          .000                 employment              group)                                         Casual work                   .469           .001           .217                                         Unemployed                    .291           .004           .437                                         Disability                    .203           .000           .456                                         pensioner/pensioner                                         Student                       .368           .050           .486                 Own enterprise          No (ref. group)               .523           .028           .316                 Trust     in   Social  Yes (ref. group)                              .001                 Insurance               No                            .169           .001          1.744                 Institution             It's difficult to say         .308           .497           .811                 Financial  security  Country                                         .000                 in old age              (ref. group)                                         Citizens                      .171           .000          2.822                                         It's difficult to say         .350           .779           .907                 Individual              I disagree (ref.                             .010                 prudence                group)                                         I agree                       .184           .008          1.682                                         It's difficult to say         .355           .641           .847                 Shopping                No (ref. group)               .165           .004          1.601                 Const.                                                .241           .000           .132                 Cox–Snell's R-squared                                                .124                 Nagelkerke's R-squared                                               .185                 Hosmer-Lemeshow (p-value)                                            .249                 Log likelihood                                                     986.758                 N                                                                   1,013                        Source: own elaboration based on the research Social Insurance Institution, 2016                 Place of residence of the respondent has a significant impact on saving. Inhabitants of                cities with population over 500 k. are two times more likely to save additionally for old age                than those who live in the countryside or cities below 500 k. (no differences were observed                between these two groups). Moreover, greatest odds for saving for old age have individuals                with  a  full-time  job  and  stable  income  which  is  usually  subject  to  mandatory  social                insurance. As a result, they have greater knowledge regarding social insurance and greater                insurance awareness, including the one concerning the need to save for old age outside                the  public  pension  system.  The  remaining  categories  of  individuals  have  smaller                opportunities for saving and therefore also lower odds for pension savings. They include                mainly people with casual jobs or the unemployed (odds for additional old-age saving are                lower by approximately 78% and 56% as compared to the reference group, i.e. people                with full-time job). At this point it needs to be stressed that greater odds for savings than                individuals with causal jobs have only students or pensioners. This can be evidence that                learning about economy, i.e. gaining economic knowledge and experience connected with                social insurance such as drawing (low) pension, result in greater willingness to save.                Additionally, owning an enterprise does not foster additional saving for old age. In such                case,  odds  for  retirement  savings  decrease  by  68%  as  compared to  those  who  do  not                conduct business activity. This may result from previous experience connected with social                insurance, i.e. mandatory pension system and paid premiums for social insurance which in                the view of many entrepreneurs hinder business activity.                 Of importance is also the issue of trust to the institution which handles the public system                of pension security. Individuals who do not trust Social Insurance Institution have 1,7 times                higher odds for additional old-age saving than those who declare such trust. Lack of trust                                                             521","in this Institution may signal the perception that pension benefits offered within the public                pension system cannot guarantee decent life in old age. Therefore, individuals with little                trust in Social Insurance Institution save additionally more.                In the light of the article aim and the subject in question, of greatest significance is the                question  how  perception  of  who  should  take  care  of  financial  security  during  old  age                impacts  the  willingness  to  save  and  take  care  of  standard  of  living  during  retirement                individually.  It  can  be  observed  that  those  who  think  that  above  all  it  is  the  State's                responsibility to assure decent standard of living during retirement, have 2,8 times lower                odds for old-age savings than those who claim that it is an individual matter of each citizen.                Simultaneously,  the  results  of the  research indicate that  people  who  disagree  with  the                statement that old-age security should depend mainly on individual prudence have 70%                higher odds for additional retirement savings.                Moreover, the results obtained with the use of the model of logistic regression show that                doing daily shopping is a factor which determines additional saving for old age. People who                do shopping every day have approximately 60% higher odds for retirement savings outside                the public pension system. This may result from the fact that they are well-aware of prices                in the shops. As a result, they better understand and anticipate what financial resources                they will need for old age in order to maintain a similar standard of living as compared to                the one during their professional activity.                  4 Conclusions                The results of the research indicate that not all independent variables analysed in the model                of logistic regression have a significant impact on the decision to save for old age. The                greatest odds for additional retirement savings have individuals who live in big cities, have                a full-time job, do not own an enterprise, do shopping on a daily basis and have no trust                in the insurance institution which pays pension benefits from the public pension system.                Above all, old-age saving is mostly influenced by the individual's perception of who should                assure standard of living during retirement which would be adequate to one's needs. Only                those  who  claim  that  it  is  not  only  the  State's  role  but  one's  own  initiative,  will  save                additionally for old age outside the public pension system. Nonetheless, having in mind                this conclusion one needs to stress the generosity of the public pension system. According                to the study of F. Chybalski and E. Marcinkiewicz (2018), participation of working people                in  voluntary  pension  schemes  is  greater  in  countries  with  less  generous  basic  pension                systems.                Evidently it is crucial to highlight the issue of low awareness and knowledge of pension                system members observed not only in Poland, but also other countries worldwide (Lusardi                and  Mitchell,  2007;  Drexler  et  al.,  2013;  Potrich  et  al.,  2015;  OECD,  2016).  Shaping                insurance awareness by providing knowledge (Szumlicz, 2011) is key to understand the                need to save, make the decision to save and chose proper forms and methods of saving.                Proper  economic  education  should  result  in  greater  participation  in  voluntary  pension                systems and greater adequacy of benefits from the whole pension system (Cichowicz and                Rutecka-Góra, 2017). As a matter of fact, personal financial knowledge has a significant                impact on financial behaviours, including saving behaviours (Robb and Woodyard, 2011;                Pieńkowska-Kamieniecka and Walczak, 2016). It changes one's way of perceiving old age                and  raises  the  awareness  of  the  fact  that  financial  security  during  retirement  depends                mainly on one's own initiative, resulting in greater retirement prudence and the decision                to save additionally for old age.                 Therefore, it is  necessary  to  shape  the  perception  of individuals in  such  a  way to  fully                realize social and individual aims associated with financing consumption in old age, i.e. in                the period when we do not produce anything, but still want and need to consume (Czapiński                and Góra, 2016).                                                                522","References                Anioła-Mikołajczak,  P.,  Gołaś,  Z.  (2014).  The  Socioeconomic  Conditions  of  Saving                Behaviours in Polish Households. Oeconomia, vol 13(4), pp. 7-17.                Ben-Ner A., Halldorsson, F. (2010). Trusting and trustworthiness: What are they, how to                measure them, and what affects them. Journal of Economic Psychology, vol. 31(1), p. 64.                Bielawska K., Chłoń-Domińczak, A., Stańko, D. (2015). 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Does it                                                 Make a Difference?                                                                1                                         Anatoliy Pilyavskyy , Lucie Kopecká                                                                                 2                                           1  Lviv University of Trade and Economics                  Faculty of International Economic Relations and Information Technology, Department of Higher                                            Mathematics and Quantitative Methods                                         Tuhan-Baranovskogo 10, 79005 Lviv, Ukraine                                                E-mail: apiliavs@mail.lviv.ua                                                   2  University of Pardubice                  Faculty of Economics and Administration, Department of Mathematics and Quantitative Methods                                       Studentská 84, 532 10 Pardubice, Czech Republic                                           E-mail: lucie.kopecka1@student.upce.cz                 Abstract: The main aim of this article is to compare the Efficiency of Health Care Systems                in 35 OECD countries during period 2008 – 2015. The efficiency of health care system is                discussed  topic  in  many  articles  and  it  is  very  difficult  task  to  find  the  right  methods                according to which to measure health care efficiency. In our case for the comparing of the                efficiency of health care systems we use nonparametric method, namely Data Envelopment                Analysis (DEA). Two different output oriented models are used. The first one is with one                output: life expectancy at birth and three inputs: health expenditures, total health and                social employment and number of hospitals. The second one is with two outputs: all causes                of deaths and infant mortality and three inputs like in the first model. All indicators are                obtained  from  OECD  database.  Comparing  results  for  these  two  models,  that  are  very                similar, show that both developing and developed countries can be effective.                Keywords: Health care system, OECD countries, Efficiency, Data envelopment analysis.                JEL codes: I11, C60, C61                 1 Introduction                 Health state of population and health care belong to the most important questions which                the  individual  countries  must  solve.  Health  population  means  that  the  people  are  able                to work to earn money. This is the reason why the countries with a good health status                of population  have  a  highly  developed  economy.  However,  not  only  health  status                of population  influences  state  of  economy  but  on  the  other  hand  state  of  economy                influences health status of population as well as described in (Jindrová and Kopecká, 2017).                 Health care belongs to the most important area of the economy. The main results of health                care efforts high life expectancy and low mortality caused by critical illnesses should be.                Health care needs lots of financial funds which are used for health expenditure, health                employment, hospitals, hospital facilities etc. These health resources should be employed                effectively.  Many publications deal with issues of efficiency of health care systems, which                confirms the importance of this topic, for instance (Asandului et al., 2014; Bem et al.,                2014; Evans et al., 2000; Hollingsworth et al., 1999; Kujawska, 2015; Pilyavsky and Staat,                2006).                 The most of these papers use Data Envelopment Analysis (DEA) for measuring efficiency                not only of health care systems but also another firms. For details see (Charnes et la.,                1978; Charnes et al., 1995; Chlebounová, 2017; Dlouhý et al., 2007; Golubchikov, 2006;                Pilyavsky et al., 2006; Sherman and Zhu, 2006). This method evaluate efficiency of objects                according to values of input and output variables where it can be more of the both kinds                of variables. This is very difficult task how to determine input and output variables because                inputs should affect outputs but input variables should not be in association each other                as well  as output  variables.  Next  problem  can  be  observed  in  case  of  output  variables                of health care systems such as life expectancy and mortality because these outputs are                not  only  influence  by  health  care  but  they  are  influence  by another  factors  as  well.                For instance, gender, life style (alcohol, smoking), age structure of population but also                                                            525","political and economic conditions can be reasons for low life expectancy and high mortality                (AMIEHS, 2011; Kossarova et al., 2013; Mackenbach, 2013).                 OECD, 2016 compares life expectancy and mortality like outputs for measuring efficiency                of health care systems. Final result of this comparison is that mortality is more suitable                for measuring this efficiency because it is less affected by non-medial determinants than                life expectancy.                 2 Methodology and Data                 The main objective of this article is to compare the efficiency of health care systems in 35                OECD countries during period 2008-2015 by using nonparametric method, namely DEA.                DEA scores are computed based on inputs and outputs which are obtained from OECD                database. Following variables are determined as inputs: health expenditure per capita (all                financing schemes, US dollars), total health and social employment (per 1 000 population)                and hospitals (per 1 000 000 population). Next following variables are stated as outputs:                life expectancy at birth, all causes of death (per 100 000 population) and infant mortality                (per  1 000  live  birth).  However,  due  to strong  negative  association  between  live                expectancy at birth and all causes of death, two separate models are constructed.                Model Specification                It was Farrell, 1957 who first operationalized a frontier method to estimate the efficiency                of a decision-making unit (DMU) by using approach which compared an individual DMU’s                observed level of inputs and outputs with the best practice production frontier. The frontier                was derived by DMUs which could minimize inputs given output (input-oriented model) and                maximize  output  given  inputs  (output-oriented  model)  under  the  usual  assumption                of production such as homogeneity of degree 0, strong disposability of inputs and outputs                and ray homotheticity.                 CCR and BCC model (Charnes et al., 1995) belong to the basic DEA models which exist for                efficiency estimation of set of economic agents.                In this article output-oriented DEA model for measuring technical efficiency of health care                systems is used. The main aim of output-oriented model is to maximize the outputs with                the  same  level  of  inputs.  Formula  (1)  expresses  CCR  output-oriented  model  which                calculates technical efficiency proceedings from an assumption of constant return to scale                (CRS). For details see (Charnes et al., 1995).                                +   1   +   1   ;   .   .      +    =    ;      −    =      ;     ≥ 0;    ≥ 0;    ≥ 0           (1)                                                                +                                      ⃗  −                                                    −                                                                                      −                                                                               +                                ⃗  +                                                                      0                                                         0                where  X={xij} – matrix of inputs (i=1,2,…M) Y={yrj} – matrix of outputs (r=1,2,…N), ε >                0 is the  so  called  non-Archimedean  element  that is  determined  as  a  number,  which is                                                             -                                                         +                smaller than any positive real number; s , s – vectors of slacks.                Then technical efficiency is according to formula (2):                          =    .                                                                        (2)                            −1                Next  output-oriented  efficiency  can  be  estimated  proceedings  from  an  assumption                of variable  return  to  scale  (VRS).  This  assumption  is  ensured  by  the  introduction                of limitation into formula (1). Formula (3) expresses this limitation.                       1   = 1                                                                          (3)                       ⃗                VRS  means  that  increase  of  inputs  do  not  have  to  be  followed  by  the  same  increase                of outputs.                This model was developed from CCR and it is called BCC output-oriented model as describe                in (Charnes et al., 1995). Efficiency, calculated according to BCC model, is called pure                technical efficiency (PTE) which excludes scale effect. It means that technical efficiency                calculated according to CCR model can be decompose onto two components, namely pure                technical efficiency and scale efficiency (SE).                                                              526","3 Results and Discussion                As mentioned above, output-oriented models considering CRS and VRS were calculated                within estimation of efficiency of health care systems. However, for purposes of this article                the results of output-oriented DEA models considering VRS are displayed because both                CRS and VRS provide very similar results.                Both models employ the same three input variables, namely health expenditures per capita                (US  dollar),  total  health  and social  employment  per  1  000  population  and  number                of hospitals per 1 000 000 population. Health expenditure has increased on average from                3 101  US  dollars  per  capita  to 3 471  during  period  2008  –  2015.  Maximum  health                expenditures are 7 550 and on the other hand minimum expenditures are 891 in 2008.                In the  last  observed  year  2015  maximum  value  of  health  expenditures  is  8 749                and minimum value is 942. Total health and social employment has increased on average                as well from 45 to 49 per 1 000 population during this period. Maximum and minimum                values of this indicator are 105 and 8 in 2008 and 109 and 9 in 2015. Finally number                of hospitals per 1 000 000 population has decreasing trend. It has decreased from 31 to 29                on average. Maximum is 68 and minimum is 11 in 2008 and 72 and 10 in 2015.                 We also constructed two different models for the estimation of the efficiency of health care                system (according to the different outputs used), namely Model 1 and Model 2.                Model 1 has one output: life expectancy at birth. This indicator has grown from 79 to 81                years  on  average  during  period  2008  -  2015.  Maximum  and  minimum  values  have                increased as well. Maximum value of this indicator has increased from 83 in 2008 to 84                years in 2015 and minimum value has increased from 72 in 2008 to 75 in 2015.                The second model, Model 2 has two outputs: all causes of deaths per 100 000 population                and infant mortality per 1 000 live births. All causes of deaths have decreased from 877                in 2008  to  792  in  2015  on  average.  Maximum  value  of  this  indicator  has  decreased                from 1 375 to 1 163 during this period and minimum value has decreased from 634 to 583.                The  second  output  variable  called  infant  mortality  has  decreased  from  5  in 2008  to  4                in 2015 on average. Maximum value of this indicator has decreased from 16 to 13 during                this period and minimum value has decreased from 1.8 to 1.6. As mentioned above the                main  aim  of  output-oriented  model  is  to maximize  the  outputs  with  the  same  level                of inputs.  Thus,  for  measuring  efficiency  of  health  care  systems  reciprocal  values                of mortalities are employed.                Efficiency  scores  are  displayed  for  35  OECD  countries  and  for  Model  1  in  2008,  2011                and 2015 in figure 1.                                           Figure 1 Efficiency Scores (Model 1)                                                                                        527","Source: OECD database                 Figure 1 shows some countries which have effective health care systems in all three years,                namely Greece, Israel, Japan, Mexico, Slovenia, Spain and Turkey. Some of these countries                belong to the developing countries and some of them had to adopt austerity measures not                only in health care systems but also in other economy sectors during this period, namely                Greece and Spain. On the other hand countries such as Czech Republic, Denmark, Estonia,                Finland,  Hungary,  Latvia,  Slovak  Republic  and  United  States  show  very  low  efficiency                scores during these years. It means that these inefficient countries should have higher life                expectancy at birth with amount of resources which they use in comparing with another                effective countries.                Next efficiency scores for Model 2 in the same three years are displayed in Figure 2                                           Figure 2 Efficiency Scores (Model 2)                                                                                Source: OECD database                Figure 2 shows the same countries with effective health care systems in all three years but                Latvia is next effective country according to Model 2. However, the big growth in efficiency                scores is observed in case of many countries from 2008 to 2011 when countries increased                their efficiency scores or became effective. But on the other hand this increasing efficiency                of health care systems did not last long time. This situation occurred in case of these states,                namely  Australia,  Austria,  Canada,  France,  Germany,  Iceland,  Ireland,  New  Zealand,                Poland, Portugal, Slovak Republic, Switzerland, United Kingdom and United States. Finally                countries  such  as  Austria,  Belgium,  Denmark,  Finland,  Germany,  Hungary,  Ireland,                Norway, Portugal, Sweden, United Kingdom and United States show very low efficiency                scores  in  all  three  years.  It  means  that  these  inefficient  countries  should  have  lower                mortalities with amount of resources which they use in comparing with another effective                countries.                In table 1 the efficiency scores for 13 selected OECD countries in period 2008 – 2015 are                displayed. Countries of Western and Eastern part of Europe and non-European developing                and  developed  countries  are  selected.  Countries  which  are  effective  at  least  according                to one of two mentioned models from the beginning of the period or from the next year                after the first recording efficiency year are colored black. Next countries which became                effective at least according to one of models after the inefficient year are colored dark gray                and finally, countries which became inefficient according to both models after the efficient                year are colored light grey.                                                              528","Table 1 Efficiency Scores for Selected OECD Countries in Period 2008 - 2015                         Model      AUS  CZE  EST  FIN  FRA  DEU  ISR  JPN  MEX  POL  SVK  ESP  UK                    Model 1 2008  0.99  0.96  0.94  0.97  0.99  0.98  1.00  1.00  1.00  0.96  0.94  1.00  0.98                    Model 2 2008  0.89  1.00  0.87  0.89  0.94  0.83  1.00  1.00  1.00  1.00  0.67  1.00  0.86                    Model 1 2009  0.99  0.96  0.95  0.97  0.99  0.97  1.00  1.00  1.00  0.96  0.94  1.00  0.98                    Model 2 2009  0.93  1.00  1.00  0.82  0.94  0.82  1.00  1.00  0.91  0.89  0.71  1.00  0.86                    Model 1 2010  0.99  0.97  0.97  0.97  0.99  0.97  1.00  1.00  0.99  0.97  0.95  1.00  0.98                    Model 2 2010  0.93  1.00  1.00  0.96  0.94  0.82  1.00  1.00  0.81  0.88  0.66  1.00  0.86                    Model 1 2011  0.99  0.97  0.97  0.98  1.00  0.97  1.00  1.00  1.00  0.97  0.95  1.00  0.98                    Model 2 2011  1.00  1.00  1.00  0.83  1.00  0.86  1.00  1.00  1.00  1.00  1.00  1.00  0.94                    Model 1 2012  0.99  0.97  0.97  0.97  0.99  0.97  1.00  1.00  1.00  0.98  0.95  1.00  0.98                    Model 2 2012  1.00  0.78  0.90  0.87  0.98  0.91  1.00  1.00  1.00  0.83  1.00  1.00  1.00                    Model 1 2013  0.99  0.96  0.97  0.97  0.99  0.97  1.00  1.00  1.00  0.97  0.94  1.00  0.97                    Model 2 2013  1.00  1.00  0.81  0.82  1.00  0.82  1.00  1.00  1.00  1.00  0.72  1.00  0.96                    Model 1 2014  0.99  0.96  0.96  0.97  0.99  0.97  1.00  1.00  1.00  0.97  0.94  1.00  0.98                    Model 2 2014  0.91  0.84  1.00  0.88  0.91  0.80  1.00  1.00  1.00  0.89  0.71  1.00  0.82                    Model 1 2015  0.99  0.97  0.97  0.98  0.99  0.97  1.00  1.00  1.00  0.97  0.95  1.00  0.97                    Model 2 2015  0.91  0.81  1.00  0.98  0.91  0.80  1.00  1.00  1.00  0.87  0.72  1.00  0.81                                                   Source: OECD database                Countries such as Israel, Japan, Mexico and Spain have effective health care system nearly                in each year of this period. Despite the fact that some of these countries keep effective                health care systems they belong to the countries which have low health expectancy at birth                and high mortality. This is the reason for looking for another causes of these health results.                On the other hand most of Western European countries do not maintain effective health                care  system  and  use  health  care  resources  which  should  cause  higher  life  expectancy                at birth and lower mortality in comparison with effective countries.                 4 Conclusions                Based on OECD database our results showed that efficiency of health care systems does                not depend on state of health in individual countries because countries with the low life                expectancy and the high mortality can be effective as well as countries with the high life                expectancy and the low mortality. Especially developing countries belong to the countries                which  have  effective  health  care  system  during  all  period.  On the  other  hand,  the                developed  European  countries  such  as  Finland,  Germany  and  United  Kingdom  are  not                effective nearly all period 2008 – 2015. These countries employed too many health care                resources and they do not reach adequate life expectancy or mortality in comparison with                effective countries. This is the reason for the next research to determine how other factors                such  as environmental,  educational,  political  etc.  influence  on  the  health  care  system                efficiency in studied countries.                 Acknowledgments                 This research could be performed due to the support of the University of Pardubice student                project  grant  no.  SGS_2018_012,  Využití  rozsáhlých  souborů  dat  pro  management                vybraných    rizik  ve   veřejném     a   finančním   sektoru.   (Faculty   of   Economics                and Administration).                 References                 AMIEHS (2011). Avoidable mortality in the European Union: Towards better indicators for                the effectiveness of health systems. Retrieved from: http://amiehs.lshtm.ac.uk/.                                                              529","Asandului, L., Roman, M., Fatulescu, P. (2014). The efficiency of health care systems in                Europe:  a  Data  Envelopment  Analysis  Approach.  In:  7th  International  Conference  on                Applied  Statistics.  Procedia  Economics  and  Finance,  vol.  10,  pp.  261-268.  DOI:                10.1016/S2212-5671(14)00301-3.                Bem, A., Ucieklak-Jez, P., Prędkiewicz, P. (2014).  Measurement of Health Care System                Efficiency.  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A number of significant changes took place on the world markets                mainly the global economic crisis which had significant impact on the IPO activity. It is a                well-known fact that IPO numbers in Central and Eastern European capital markets are far                below the IPO volumes of their Western counterparts. This article´s objective is to look at                the most influential external IPO drivers such as development of interest rates, industrial                production  rates,  gross  domestic  product  growth  rates  and  the  local  stock  exchange                indices. In order to determine the impact of such IPO drivers the regression analysis was                used.                Key worlds: IPO, gross domestic product, interest rates, industrial production, CEE region                JEL codes: E440, G150, F210                 1 Introduction                 In  past  decades  Eastern  Europe  undergone  long  way  of  transformation.  From  planned                economy to market  orientated. A companies went from state owned to private owned.                Their  strategies  of  funding  changed  dramatically.  One  of  them  is  Initial  public  offering                hereinafter IPO. Corporate Finance Theory considers IPO as one of the most important                milestones in business life. (Helvege and Packer, 2003; Brau, Francis and Kohers, 2003;                Boehmer  and  Ljungqvist,  2004).  Ritter  (1991),  analysts  from  Ernst  and  Young  (2017)                report that IPO is the first sale of securities to the general public.                Main reason why companies go public lies in their need of extra capital. (Bancel and Mitoo,                2009, Kim and Weisbach, 2008, Draho, 2004, Zinecker and Meluzín, 2009) The timing of                entry to the capital market is a very difficult issue and is based on a whole range of motives.                It is reported that companies enter capital markets at favorable economic conditions that                support their continuous growth and development. (Loughran and Ritter, 1995; Ritter and                Welch,  2002).  Such  conditions  are  therefore  external  and  the  issuing  company  cannot                directly influence them.                Typically,  they  are  shaped  by  the  country's  economic  situation, legislative  frameworks,                maturity of the capital market, etc.  Those factors or rather drivers which make company                to go public are development of the gross domestic product, industrial production, interest                rates and inflation. They determines the state of economy where company operate and its                potential  success.  Relationships  between  IPO  and  macroeconomic  factors  have  been                investigated, for example, by Loughran et al. (1994). Their study analyzed the IPO timing                in  15  countries  in  relation  to  inflation-adjusted  stock  price  indices  and  gross  domestic                product rates. The results of the study indicate a positive relationship between IPO and                stock market prices, but no positive correlation with the business cycle was found. Ameer                (2012),  in  its  study,  presents  a  negative  relationship  between  interest  rate  and  IPO                numbers and a strong positive relationship between industrial output and IPO number in                the emerging market of Malaysia. Bilson et al (2002) also found only slight signs of a link                between local macroeconomic factors and returns on emerging capital markets. Breinliger                and  Glogova,  2002,  examined  selected  macroeconomic  factors  influencing  the  IPO  by                analyzing the sample of annual IPOs in six developed European countries for 18 years. The                hypothesis that  the  percentage  change  in  savings,  gross  domestic  product  growth  and                interest rates has a significant influence on IPO volumes was not supported by empirical                evidence. (Rees, 1997) has found in its research that the number and volume of IPO is                                                             531","influenced  by  the  development  of  the  stock  indexes  and  that  the  possibility  of  issuing                shares at high prices is an explanation of the motivation for the IPO. IPOs are positively                and significantly associated with stock market levels and, in the case of numbers, the IPO                is positively and significantly associated with the business cycle indicator. The causality                tests show that the stock index predicts both the value and number of IPOs (Rees, 2002)                Rydqvist and Hogholm (1995) compared data on a sample of family businesses in Sweden                and eleven European countries between 1970 and 1989. They found that most of the IPO                activities took place after a sharp increase in stock prices.                 Due to limited amount of studies focused on the central and eastern European countries                (CEE  countries)  in  the  post-crisis  period  this  article´s  objective  is  to  look  at  the  most                influential domestic external IPO drivers such as development of interest rates, industrial                production rates, gross domestic product rates and stock exchange indices returns. The                research question is as follows: How these factors might affect the numbers and volumes                of initial public offerings on the selected stock exchanges in Central and Eastern Europe                between 2010 -2016.                 2 Methodology and Data                In order to find out if and in what extent selected drivers influence IPO, quantitative data                from the period between 2010 and 2016 were analyzed. The sources of data were Eurostat,                World  Bank,  European  central  bank  and  national  statistical  offices  as  well  as  stock                exchanges websites. All the IPO data were recalculated in euro currency.                The Table 1 shows numbers and values of IPOs on selected capital markets. A significant                dominance of the Polish capital market is obvious. In total, 158 IPOs occurred where 88.6%                of all IPOs were conducted on the Warsaw Stock Exchange. In terms of value of capital                raised, only 8.9% of all IPOs are not from Poland.                  Table 1 Numbers and Values of IPOs on Selected Stock Exchanges Between 2010 and                                                           2016                                                  BCPP-Czech                             WPG-Poland             Republic       BÉT- Hungary        LJSE- Slovenia                                                                            Value                           No      Value ml.      No      Value      No      ml.      No      Value ml.                                      EUR                ml. EUR                                 EUR                                                                            EUR                  2010     34       4005,76        1      78,26      6       38       0           0                  2011     38       1931,26        1      10,00      2      0,49      2         23,6                  2012     16        818,60        0       0,00      0        0       0           0                  2013     13       1123,00        0       0,00      0        0       1           -                  2014     13        306,00        1      15,00      1        3       0           0                  2015     14        434,00        1      28,04      0        0       0           0                  2016     12        255,00        1     655,55      1        9       0           0                 Total   140        8873,62        5     786,85      10    50,49      3         23,6                         Source: Stock exchanges website, company prospectuses, FESE monthly reports                 Based on theoretical framework the following research question was outlined:                Are there any statistically significant links between IPO numbers (values) and external IPO                determinants?  External  determinants  include  development  of  long-term  interest  rates,                industrial  production  rates,  gross  domestic  product  growth  rates  and  the  local  stock                exchange indices. The following model was specified.                        IPOt=α+ β1GDPt + β2IPt + β3LTIRt + β4SEIRt + ut                                   (1)                                                                 532","Where β1GDPt represents GDP growth rates, β2IPt represents industrial production rates,                β3LTIRt represents long term interest rates, β4SEIRt represents stock exchange index                return developments.                The model estimation was performed with the regression model. The data was evaluated                at the significance level of α =5%. Statistica.CZ software was used.                 3 Results and Discussion                 Table  2  and  Table  3  show  the  results of  the  estimation  of the  model  described  by  the                formula  (1).  The  model  is  statistically  correct.  Based  on  the  F  test  no  significant                relationships were found between volumes of IPO and industrial production growth rates.                Similarly, stock indices growth rate have no explanatory power on the IPO activity on the                capital  markets  under  study.  Therefore,  the  only  statistically  influential  drivers  on  IPO                numbers are the GDP growth rates and the long term interest rates. As the GDP of selected                countries is increasing the interest of companies in implementing IPO is increasing as well.                The  positive  economic  outlook  is  proving  to  be  the  driver  of  IPO  on  selected  stock                exchanges. The interest rates represents the cost of the long term financing. Therefore                when a company is making a decision whenever to obtain large amount of capital through                borrowing  or  through  IPO,  the  unfavorable  interest  rates  may  be  the  reason  why  to                implement an IPO.                The GDP growth rates and the long-term interest rates were verified positively in terms of                IPO numbers, however, not in terms of IPO values (for details see Table 3). No significant                relationship between values of IPO and any external drivers was found.                    Table 2 Results of the Estimation Model Describing the IPO Numbers with External                               Determinants in Selected Countries between 2010 and 2016                      External                          Standard                  determinants       Coefficient         error          t-statistics         p-value                    Intercept          -7.7577           3.5365          -2.19360             0.3359                       GDPt             0.4745           0.2005           2.3655              0.0224                        IPt             0.0729           0.1767           0.4126              0.6819                      LTIRt             0.4551           0.1671           2.7230              0.0092                      SEIRt            -0.2695           0.1419           -1.8995             0.0640                  Observations                                       49                   Stand. error                   of residuals                                    7,0199                         R                                         0,5310                        R2                                         0,2820                     F(4,44)                                       4.3218                         p                                        <0,0049                                                  Source: Own calculations                     Table 3 Results of the Estimation Model Describing the IPO Values (ml. EUR) with                           External Determinants in Selected Countries between 2010 and 2016                    External                            Standard                 determinants        Coefficient          error         t-statistics          p-value                   Intercept          -471.503          307.5216          -1.5332             0.1323                      GDPt             0.2465            0.2118            1.1636             0.2508                       IPt             0.2409            0.1866            1.2910             0.2034                     LTIRt             0.2998            0.1765            1.6986             0.0964                     SEIRt             -0.1570           0.1498           -1.0408             0.3003                 Observations                                        49                  Stand. error                                     610.41                  of residuals                                                             533","R                                          0.4462                       R2                                          0.1991                   F(4,44)=                                        2.7349                        p                                         <0.0046                                                  Source: Own calculations                 The findings of this study are in contradiction with study of Loughran et al. (1994) and                Breinliger and Glogova (2002) who found no significant relationship between GDP growth                rates  and  IPO.  The findings  are  also  not in  accordance  with  study  of  Rees (1997)  and                Rydqvist and Hogholm (1995) who suggest that increase in stock prices and stock index                returns positively influence the IPO activity.                 4 Conclusions                 This article was focused on the development of IPOs on the capital market of the Czech                Republic, Poland, Slovenia and Hungary over the period between 2010 and 2016. In order                to find out if and in what extent selected external drivers influence IPO, quantitative data                were analyzed. Previous studies did not show consistency in terms of influence of external                drivers such as GDP growth rates, industrial production rates, long term interest rates and                stock exchange index return rates on IPO. For the purpose of this article only IPO from                main and secondary markets of selected stock exchanges were chosen. The outcomes of                regression  analyses  shows  that  business  cycle  represented  by  growth  rates  of  gross                domestic product has significant influence on IPO numbers activity as well as development                of long term interest rates as measure for long term cost of capital. However, there is no                significant  relationship  between  industrial  production  rates  and  stock  exchange  indices                returns rates. Nonetheless IPO values proved not to be influenced by any of the external                drivers tested by the described regression model.                 References                Ameer, R. (2012). Macroeconomic Factors and Initial Public Offerings in Malaysia.  Asian                Academy of Management Journal of Accounting and Finance, vol. 8(1), pp. 41-67.                Bancel,  F.,  Mittoo,  U.  R.  (2009).  Why  do  European  firms  go  public.  European  financial                management, vol. 15(4).                Boehmer, E., Ljungqvist A. (2004). On the Decision to Go Public: Evidence from Privately-                Held    Firms.   Retrieved    from:    https://papers.ssrn.com/sol3/papers.cfm?abstract_                id=1294437.                Brau, J. C., Francis, B., Kohers, N. (2003). The Choice of IPO versus Takeover: Empirical                Evidence. Journal of Business, vol. 76(4), pp. 583–612.                Breinlinger, L., Glogova, E. (2002). 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The Long-Run Performance of initial Public Offerings. The Journal of                Finance, vol. 46(1), pp. 3–27.                Rydqvist,  K.,  Hogholm,  K.  (1995).  Going  public  in  the  1980s-Evidence  from  Sweden.                European financial management, vol. 1, pp. 287-316.                 Warsaw Stock Exchange (2018). New listings. Retrieved from: https://www.gpw.pl/new-                listings.                World     Bank    (2010-2016).      World    Bank     Open     Data.    Retrieved    from:                https://data.worldbank.org.                                                                                               535","Capital Market Determinants of Going Public: The Evidence from                                                CEE Capital Markets                                                      Sylvia Plottová                                                    Newton College a.s.                                            Rašínova 103/2, Brno, Czech Republic                                          E-mail: sylvia.plottova@is.newtoncollege.cz                 Abstract: Capital markets within CEE region have shown that in case of new investment                opportunities  they  are  still  significantly  underdeveloped.  This  research  is  focused  to                examine the influence of the local capital market indicators on IPO emergence. The data                sets for the capital markets of Czech Republic, Poland, Hungary and Slovenia has been                analyzed over the period between 2010 and 2016. The following capital market indicators                and their impact on the IPO volumes were examined: market capitalization as percentage                of GDP, turnover as percentage of GDP, turnover velocity and number of listed companies.                To analyze the data the quantitative approach was applied as well as in depth comparison                of  capital  market  specifics.  The  regression  estimation  model  is  applied  to  identify  the                impact of the capital market determinants on the number of primary issues and the value                of capital raised by IPOs. The data is evaluated at the significance level of α=5%. Results                show that in Poland and Hungary the capital market determinants show certain degree of                influence on IPO activity.                 Keywords: capital markets, CEE region, market capitalization, initial public offerings                 1 Introduction                The IPO stands for primary offering of shares to public and is one of the most important                moments in company´s life. (Helvege and Packer, 2003; Brau, Francis and Kohers, 2003;                Boehmer and Ljungqvist, 2004, Meluzín and Zinecker 2009). Companies do not choose to                enter capital markets at any situation. Attractiveness and liquidity of the capital market                shows that companies enter the capital markets when other companies enter them too.                (Choe, Masulis and Nanda, 1993). De Albornoz and Pope (2004) found that the IPO is                influenced by the market valuation of companies in the same industry. So it occurs when                there is a positive development of the IPO in the sector in which the company operates or                in the sectors that are directly related. In cases where competing companies implement                the IPO successfully, other comparable companies will probably want to gain the same                benefits as emitting competitors. Another theory explains that IPO timing is derived from                the life cycle theory of an enterprise. This theory is based on the idea that an IPO occurs                when issuers find themselves at a particular stage in the business life cycle where they                need more capital for further growth. (Choe, Masulis and Nanda, 1993) The determinant                characteristics of the capital markets that influence the decisions of the securities issuers                in  emerging  public  capital  markets  in  Central  and  Eastern  Europe  were  subjected  to                empirical research of Roženský (2008), Peterle (2013) and Brzeszczynsky (2014). They                focused  on  the  attractiveness  of  primary  capital  markets  in  the  Central  and  Eastern                European region and used quantitative and qualitative indicators  for them. Quantitative                factors such as capital market size, capital market liquidity, and return on stock indices                have been tracked. Peterle (2013)  was examining the IPO in the CEE region between 2000                and  2009 found  that  capital market factors  such  as  market  size, liquidity,  and  market                capitalization to gross domestic product did not have a decisive impact on IPO in the mid-                Eastern  Europe.  However,  the  attractiveness  of  the  capital  markets,  measured  by  the                annual  yield  index  and  the  annual  market  growth  and  liquidity,  may  have  been  the                incentives for an increase in the number of IPOs over the reporting period. Brzeszczynski                (2014) explains that the decision of the Polish companies to issue securities on capital                markets is heavily influenced by capital market developments, and that IPO numbers are                rising  as  stock  prices  rise  and  decline  as  a  result  of  their  decline.  Groh  et  al  (2010)                concluded that investor protection, business management rules, size and liquidity of the                capital market are a determining factor for financial community expertise, business flows                                                             536","and exit opportunities. If companies know they want to diversify their asset portfolio and                there are favorable conditions on the market such as the growth of stock index values,                other companies entering the capital market, dynamic developments in related industries,                they will be more inclined to IPO implementation in their company in order to increase their                wealth further. This assumption is associated with IPO timing theories. (DeTienne et al.                2015)                 The  capital  market  indicators  were  widely  described  by  number  of  authors  however  in                terms of Central and Eastern Europe (CEE) there is a lack of knowledge how capital market                indicators might have taken influence on the IPO activity within the financial and economic                crisis. Hence, this article´s objective is to search for capital market determinants of going                public activity in selected countries of the CEE region. Capital market determinants such                as market capitalization as percentage of GDP, turnover as percentage of GDP, turnover                velocity and number of listed companies and their influence on going public activity are                subject of this research. From above stated literature review follows the main research                question: Do capital market indicators have an impact on IPO activity on the selected stock                exchanges in Central and Eastern Europe between 2010 and 2016?                 2 Methodology                 The data for this article was gathered from statistical reports published by individual stock                exchanges, namely by the Prague, Warsaw, Budapest and Ljubljana Stock Exchange. In                addition, the yearbooks of CEESEG were used. Due to different currencies, all IPO values                were recalculated while using historical EUR  – local currency exchange rates. Following                tables  show  the  data  sets  on  IPO  numbers  and  values,  numbers  of  domestic  listed                companies, market capitalization to GDP, number of transactions and turnovers.                Based on theoretical framework the following research question was outlined:                Are  there  any  statistically  significant  links  between  IPO  numbers  (values)  and  capital                market  determinants?  Capital  market  determinants  include  market  capitalization  as                percentage of GDP, turnover as percentage of GDP, turnover velocity and number of listed                companies. The following model was specified.                        IPOt=α+ β1MCPGDPt + β2TPGDPt + β3TVt + β4NLCt + ut                               (1)                Where MCPGDPt stands for market capitalization as % of GDP, TPGDPt stands for annual                turnover as % of GDP, TVt stands for turnover velocity, NLCt stands for number of listed                companies. The model estimation was performed with the regression model. The data was                evaluated at the significance level of α =5%. Statistica.CZ software was used.                Development of capital market indicators                Table  1  shows  the  IPO  numbers  and  values  in  the  investigated  capital  markets.  The                quantitative analysis suggests that the Polish capital market is significantly more important                than its counterparts in Czech Republic, Hungary and Slovenia. Remaining stock exchanges                recorded  in  total  only  11.3%  of  all  IPO.  The  value  of  capital  raised  by  IPO  in  Poland                represents 91.1% of total amount of capital raised in the whole region.                Table 1 Numbers and Values of IPOs on Selected CEE Capital Markets between 2010 and                                                           2016                                       WPG              BCPP            BÉT             LJSE                                Num.   Value ml.   Num.   Value   Num.    Value   Num.   Value ml.                                                            ml.                                                                           ml.                                          EUR                                              EUR                                                           EUR             EUR                         2010    34     4005.76     1     78.26      6     38      0         0                         2011    38     1931.26     1     10.00      2    0.49     2       23.6                         2012    16     818.60      0      0.00      0      0      0         0                         2013    13     1123.00     0      0.00      0      0      1         -                                                              537","2014    13     306.00      1     15.00      1      3      0         0                         2015    14     434.00      1     28.04      0      0      0         0                         2016    12     255.00      1     655.55     1      9      0         0                         Total   140  8873.62       5    786.85  10      50.49     3       23.6                    Source: WPG, BCPP, BÉT, LJSE, FESE monthly reports 2010-2016, prospectuses of selected                                                         companies                 Table 2 reports numbers of listed companies in investigated stock exchanges. It is obvious                that the Warsaw Stock Exchange is outnumbering all remaining exchanges based on this                criterion. Budapest Stock Exchange and Ljubljana Stock Exchange are similar in regard to                the number of listed companies. The Prague Stock Exchange is by far the smallest in terms                of amount of listed companies. The listed companies are divided by their domicil as „L“ for                local, „F“ for foreign and „T“ for total.                 Table 2 Numbers of Listed Companies in Selected CEE Stock Exchanges between 2010                                                         and 2016                                          GPW              BCPP             BÉT            LJSE                                      L    F    T        L   F   T       L  F  T         L  F  T                           XII-10   570  15  585     16  11  27     48  4  52     72  0  72                           XII-11   757  20  777        15  11  26       52  2  54      66  0  66                           XII-12   844  23  867        17  11  28       51  1  52      61  0  61                           XII-13   869  26  895    15  11  26    50  0  50    55  0  55                           XII-14   872  30  902        13  10  23       48  0  48      51  0  51                           XII-15   872  33  905        15  10  25       45  0  45      46  0  46                           XII-16   861  32  893     14  11  25     42  0  42     38  0  38                                Source: FESE monthly reports 2010-2016, BCPP, GPW, BÉT, LJSE                 The  capital  market  liquidity  is  stressed  by  the  following  set  of  indicators:  the  value  of                market capitalization as a percentage of GDP, turnover velocity and turnover as percentage                of GDP. For details see Table 3.                   Table 3 Capital Market Determinants per Stock Exchange in CEE between 2010-2016                  Year       Market cap % GDP          Turnover velocity in %  Turnover as % of GDP                 2010  WPG  BCPP  BÉT          LJSE  WPG  BCPP      BÉT    LJSE  WPG  BCCP  BÉT  LJSE                 2011  39.48  20.46  21.90  20.35  42.0  55.02  97.00  6.50  16.57  11.26  21.24  1.32                 2012  28.26  17.81  13.82  12.64  65.3  51.81  93.71  9.35  18.45  9.23  12.95  1.18                 2013  34.48  17.40  17.11  14.73  37.2  35.41  53.61  6.93  12.84  6.16  9.18  1.02                 2014  37.72  13.97  13.59  13.76  41.4  30.58  54.78  6.00  15.61  4.27  7.44  0.83                 2015  34.40  14.49  11.40  16.56  40.8  24.61  50.59  12.38  14.03  3.57  5.77  2.05                 2016  29.31  13.99  17.52  17.05  44.5  26.14  42.81  6.31  13.04  3.66  7.50  1.08                             Source: FESE monthly reports 2010-2016, World Bank, own calculation                 3 Research Results                The following Tables 4 and 5 show results of above outlined regression estimation model.                The model is statistically correct. Overall performance of the model is satisfying and R and                p values are calculated for each country.                As we can see in Table 4 Polish numbers of IPOs have a statistically significant link with all                the investigated capital market determinants. In terms of Polish IPO values the results                differ.  The  number  of  companies  listed  on  the  stock  exchange  is  the  only  statistically                significant determinant having impact on the value of capital raised by IPOs. It can be                concluded that Hypotheses H1, H2, H3 and H4 proved to be verified positively in regard to                                                             538","the  IPO  numbers.  The  hypotheses  that  market  capitalisation  (H1),  turnover  (H2),  and                turnover velocity (H3) have explanatory power for IPO values in the Polish capital market                could, however, not be supported by the model.                   Table 4 Results of the Estimation Model Describing the IPO Numbers and Values in                            Poland in the Years 2010-2016 with Capital Market- Determinants                    PL IPO                   numbers                 Explanatory     Coefficient    Standard        T-                   p-value                    drivers                       error     statistics                    Intercept      -139.24       11.8973     -11.7034                0.0072                   MCPGDPt          2.5246       0.1564      16.1372                 0.0038                    TPGDPt         -3.5663       0.2158      -16.5220                0.0036                      TVt           4.6123       0.2471      18.6617                 0.0028                      NLCt         -0.7429       0.0108      -68.6250                0.0002                 Observations 7, Standard error of res. 0.2882, R 0.9999, R2 0.9998, F(4, 2)= 4229.2,                                                    p. value <0.0002                    PL IPO                     values                 Explanatory     Coefficient    Standard        T-                   p-value                    drivers                       error     statistics                    Intercept      10743.1       14863.1      0.7228                 0.5448                   MCPGDPt         -0.3776       1.6108      -0.2344                 0.8364                    TPGDPt          0.8820       2.2225       0.3968                 0.7298                      TVt          -0.7966       2.5448      -0.3130                 0.7838                      NLCt         -0.8064       0.1114      -7.2346                 0.0185                       Observations 7, Stand. error of residuals 206.21, R 0.9874, F(4, 2)=39.397,                                                     p.value<0.0249                                                   Source: Own calculation                 When it comes to the Czech capital market going public is a rather rare strategy. Between                2010 and 2016 only few IPOs took place. Even though the numbers were low values of                those IPO were quite high. Nonetheless as we can see in table 5 there was no significant                statistical relationship found for both Czech IPO numbers and IPO values. Hypotheses H1,                H2, H3 and H4 were verified positively for neither Czech IPO numbers nor values.                    Table 5 Results of the Estimation Model Describing the IPO Numbers and Values in                        Czech Republic in the Years 2010-2016 with Capital Market- Determinants                      CZ IPO                    numbers                  Explanatory         Coefficient        Standard        T-statistics        p-value                     drivers                               error                    Intercept           10.8768            5.5997           1.9423            0.1915                    MCPGDPt             -1.7696            2.3521          -0.7523            0.5303                     TPGDPt             5.0013             6.4082           0.7804            0.5168                       TVt              -2.7390            4.5822           0.5977            0.6106                      NLCt              -0.7758            0.5266          -1.4731            0.2786                 Observations 7, Stand. error of residuals 0.4387, R 0.8547, R2 0.7305, F(4, 2)= 4.3218,                                                     p. value<0.4662                  CZ IPO values                   Explanatory         Coefficient        Standard        T-statistics        p-value                     drivers                               error                    Intercept           3595.3            3498.13           1.0277            1.0000                    MCPGDPt             -4.0134            2.9748          -1.3491            0.3097                                                            539","TPGDPt             7.8832             8.1046           0.9726            0.4333                       TVt              -4.6461            5.7952          -0.8017             0.568                      NLCt              0.4144             0.6660           0.6221            0.5973                 Observations 7, Stand. error of residuals 274.05, R 0.7543, R2 0.5690, F(4, 2)= 0.6601,                                                     p. value<0.6762                                                   Source: Own calculation                 As can be seen in Table 6 in case of Hungarian capital markets no significant statistical                relationship  between  IPO  numbers  and  capital  markets  determinants  were  found.  The                Hungarian IPO market is small and in this regard similar to the Czech IPO market; thus,                these results are not surprising. H1, H2, H3 and H4 weren´t verified positively when it                comes  to  IPO  numbers.  However  in  case  of  Hungarian  IPO  values  there  a  statistically                significant relationship exists between this indicator and market capitalization as % of GDP,                annual turnover as % of GDP and turnover velocity. Therefore, H1, H2, H3 proved to be                verified positively, H4 negatively in case of Hungarian IPO values.                   Table 6 Results of the Estimation Model Describing the IPO Numbers and Values in                           Hungary in the Years 2010-2016 with Capital Market- Determinants                     HU IPO                   numbers                 Explanatory       Coefficient      Standard error      T-statistics         p-value                    drivers                   Intercept         17.3938             7.4096            2.3474             0.1434                   MCPGDPt            -0.7778            0.4363            -1.7827            0.2165                    TPGDPt            2.2430             0.8753            2.5625             0.1244                      TVt             -0.5813            0.7228            -0.8042            0.5056                      NLCt            -0.5766            0.2473            -2.3318            0.1449                    Observations 7, Standard error of residuals 0.6622, R 0.9840, R2 0.9683, F(4, 2)=                                                 15.300, p. value<0.0622                    HU IPO                    values                 Explanatory       Coefficient      Standard error      T-statistics         p-value                    drivers                   Intercept          275.75            68.1746            4.0447             0.0560                   MCPGDPt            -1.0252            0.2298            -4.4596            0.0467                    TPGDPt            3.3244             0.4611            7.2083             0.0187                      TVt             1.8461             0.3808            -4.8470            0.0400                      NLCt            -0.3308            0.1303            -2.5391            0.1263                    Observations 7, Standard error of residuals 6.0927, R 0.9955, R2 0.9912, F(4, 2)=                                                 56.417, p. value<0.0174                                                   Source: Own calculation                 In case of Slovenian IPO numbers and values, no statistically significant relationship was                found.  All  the hypotheses  failed to  be verified.  Slovenia is the  smallest  economy  of  all                selected  countries  and  it´s  capital  market  is  very  limited  in  size,  liquidity  and                attractiveness. In 2015 Zagreb stock exchange took over 100% of shares of Ljubljana stock                exchange so it will be interesting to see whenever this new cooperation will bring anything                new for both investors and companies regarding future IPOs.                                                                   540","Table 7 Results of the Estimation Model Describing the IPO Numbers and Values in                           Slovenia in the Years 2010-2016 with Capital Market- Determinants                     SLV IPO                    numbers                  Explanatory        Coefficient     Standard error       T-statistics        p-value                     drivers                    Intercept           -8.679           10.3026            -0.8424            0.4882                    MCPGDPt            1.6729             2.6116            0.6405             0.5873                     TPGDPt            -5.0161            5.2404            -0.9572            0.4394                       TVt             4.5330             4.6351            0.9779             0.4312                      NLCt             0.3397             0.5074            0.6695             0.5720                    Observations 7, Standard error of residuals 0.6300, R 0.8866, R2 0.7862, F(4, 2)=                                                 1.8390, p. value<0.3818                    SLV IPO                     values                  Explanatory        Coefficient     Standard error       T-statistics        p-value                     drivers                    Intercept            7.00             924.79            0.0075             0.9946                    MCPGDPt            -0.3872            3.7058            -0.1045            0.9263                     TPGDPt            -0.7588            7.4361            -0.1020            0.9280                       TVt             0.6325             6.5772            0.0961             0.9321                      NLCt             0.5455             0.7200            0.7576             0.5277                    Observations 7, Standard error of residuals 56.557, R 0.7547, R2 0.5695, F(4, 2)=                                                 0.6616, p. value<0.67558                                                   Source: Own calculation                 4 Discussion and Conclusions                 This  article  was  focused  on  the  examining  the  influence  of  the  local  capital  market                indicators on IPO activity in selected capital markets from CEE region. The selected markets                include the Warsaw Stock Exchange, Prague Stock Exchange, Budapest Stock Exchange                and Lublania Stock Exchange. The IPO numbers and values of capital raised between 2010                and 2016 were analysed. To determine the influence of market capitalization as % of GDP,                annual  turnover  as  %  of  GDP,  turnover  velocity  and  number  of  listed  companies,  the                regression analysis model was used.                The results show that the influence of local capital market factors varies from market to                market and that a statistically significant influence of capital market determinants on IPO                numbers was found only for Polish capital market. The Czech, Hungarian and Slovenian                IPO  numbers  show  no  statistical  link  with  selected  capital  market  determinants.  While                looking at the results of dependence between IPO values and capital market determinants                two country´s results show a certain degree of influence. The IPO values were influenced                by number of listed companies in Poland and Hungarian IPO values were influenced by the                market capitalization as % of GDP, annual turnover as % of GDP and by turnover velocity.                Czech  and  Slovenian  IPO  values  were  not  proved  to  be  linked  to  the  capital  market                determinants.                The findings of this article are in contradiction to e.g. Peterle (2013). She reports that                capital  market  determinants  have  no  explanatory  power  for  her  IPO  sample.  However                findings of this article are in accordance with eg. Brzeszczynski (2014) who focused on the                Polish capital market and concluded that the IPO activity is strongly influenced by capital                market determinants. Groh et al (2010) also concluded that size and liquidity of the capital                market are among the determining factor for financial community expertise, business flows                and exit opportunities. And finally DeTienne et al. (2015) besides other factors see the                companies entering the capital market as determinant of IPO.                                                              541","References                Boehmer, E., Ljungqvist, A. (2004). On the Decision to Go Public: Evidence from Privately-                Held    Firms.   Retrieved    from:    https://papers.ssrn.com/sol3/papers.cfm?abstract_                id=1294437.                 Brau, J. C., Francis, B., Kohers, N. (2003). The Choice of IPO versus Takeover: Empirical                Evidence. 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Making sense of entrepreneurial exit                strategies: A typology and test. Journal of Business Venturing, vol. 30(2), pp. 255-272.                Eurostat (2016). Your key to European statistics-Database 2010-2016.  Retrieved from:                http://ec.europa.eu/eurostat/data/database.                Federation  of  European  Securities  Exchanges  (2016).  Monthly  reports  2010-2016:                Historical Data. Retrieved from: http://www.fese.eu/statistics-market-research/historical-                data/.                Groh, A. P., von Liechtenstein, H., Lieser, K. (2010). The European Venture Capital and                Private Equity Country Attractiveness Indices. Journal of Corporate Finance, vol. 16(2), pp.                205–224.                Gujarati, D. N. (2003).  Basic Econometrics. McGraw-Hill Higher Education. ISBN: 0-07-                112342-3.                Helwege, J., Packer, F. (2003). The Decision to Go Public: Evidence from Mandatory SEC                Filings   by    Private    Firms.    Retrieved    from:     http://www.cob.ohiostate.edu/                fin/dice/papers/2003/2003-18.pdf.                Meluzín, T., Zinecker, M., Łapińska, J. (2014). Determinants of Initial Public Offerings: The                Case  of  Poland.  Journal  of  Quantitative  Methods  for  Economics  and  Business                Administration, vol. 18, pp. 5-17. ISSN 1886-516X.                 Ljubljana    Stock    Exchange      (2010-2016).     For    Issuers.    Retrieved    from:                http://www.ljse.si/cgi-bin/jve.cgi?doc=1468.                 Peterle, P. (2013). Internal characteristics and external factors of IPO activities in Central                and  Eastern  Europe:  Empirical  analysis  and  comparison.  Faculty  of  Economics  of  the                University of Ljubljana, Working Paper.                Roženský, V. (2008). IPO na středoevropských akciových trzích (Initial Public Offerings on                Central European stock markets). Praha: Univerzita Karlova, Fakulta sociálních věd.                 Warsaw Stock Exchange (2018). New listings. Retrieved from: https://www.gpw.pl/new-                listings.                World     Bank    (2010-2016).      World    Bank     Open     Data.    Retrieved    from:                https://data.worldbank.org.                                                                542","Non-financial Information according to the Act on Accounting in                                                 the Czech Republic                                                   Veronika Popelková                                                     Masaryk University                                Faculty of Economics and Administration, Department of Finance                                            Lipová 41a, 602 00 Brno, Czech Republic                                                E-mail: 476057@econ.muni.cz                 Abstract:  The  Directive  of  the  European  Parliament  and  the  Council  of  the  EU  on  the                reporting  of  non-financial  information  of  some  large  enterprises  and  groups  was                implemented in the Czech accounting legislation. This Directive aims to achieve greater                transparency  and  comparability  of  social  and  environmental  information  between                undertakings from different sectors and the different Member States. For the first time in                the year 2017, entities and groups will be required to report non-financial information by                the wording of the Accounting Act. This contribution arises in response to current issues                and,  through  a  survey  of  publicly  available  information  in  ten  public  interest  entities,                presents  the  likely  future  development  of  a  non-financial  disclosure  solution  under  the                Accounting Act and the European Directive for the financial year 2017. This disclosure of                non-financial information will bring significant administrative burdens, but it can also help                reduce  costs  and  increase  efficiency.  The  paper  also  deals  with  the  importance  of  this                information for management and measuring of the corporate performance.                Keywords: accounting, non-financial reporting, Key Performance Indicator (KPI), corporate                performance management                JEL codes: M41, G30, Q01                 1 Introduction                With effect from 1 January 2017, the Accounting Act 568/1991 implemented the Directive                2014/95/EU of the European Parliament and of the Council amending Directive 2013/34/EU                as regards the disclosure of non-financial information and information on diversity by some                large  companies  and  groups.  Section  8  of  this  Act  -  The  disclosure  of  non-financial                information in § 32f introduces the obligation to provide non-financial information for:                     1.  Large entities that are both a company and a public interest entity provided that                       their average recalculate a number of employees exceed 500 at the balance sheet                       date.                    2.  Consolidating entity of a large group of entities that is also a public interest entity                       if, at the balance sheet date, it exceeds 500 employees on a consolidated basis                       during the accounting period.                Under § 32g (1), an entity discloses non-financial information at least for the following                items:                    •   the environment,                    •   social and employment,                    •   respect for human rights and                    •   anti-corruption and bribery.                The following table summarizes the possible information that an entity may disclose for                each issue.                                                                  543","Figure 1 Non-financial Information                 Environmental matters                          Human rights                 — material disclosures on pollution prevention   — occurrences of severe impacts on human                 and control                                    rights relating to its activities or decisions                 — environmental impact from energy use         — the process for receiving and addressing                                                                complaints, and mitigating and providing                                                                remedies to human rights violations                 — direct and indirect atmospheric emissions    — operations and suppliers at significant risk of                                                                human rights violations                 — use and protection of natural resources      — processes and measures for preventing                                                                trafficking in human beings for all forms of                                                                exploitation, forced or compulsory labor and                                                                child labor, precarious work, and unsafe working                                                                conditions, in particular as regards geographic                                                                areas at higher risk of exposure to abuse                 — waste management                             — how accessible their facilities, documents,                                                                and websites are to people with disabilities                 — environmental impacts from transportation or   — respect for freedom of association                 the use and disposal of products and services                 — development of green products and services   — engagement with relevant stakeholders                  Social and employee matters                    Anti-corruption and bribery                 — the implementation of fundamental            — anti-corruption policies, procedures, and                 conventions of the International Labour        standards                 Organisation                 — diversity issues, such as gender diversity and   — criteria used in corruption-related risk                 equal treatment in employment and occupation   assessments                 (including age, gender, sexual orientation,                 religion, disability, ethnic origin and other                 relevant aspects)                 — employment issues, including employee        — internal control processes and resources                 consultation and participation, employment and   allocated to preventing corruption and bribery                 working conditions                 — trade union relationships, including respect for  — use of whistleblowing mechanisms                 trade union rights                 — human capital management including           — employees having received appropriate                 management of restructuring, career            training                 management, and employability, remuneration                 system, training                 — health and safety at work                    — the number of pending or completed legal                                                                actions on anti-competitive behavior                 — consumer relations, including consumer                 satisfaction, accessibility, products with possible                 effects on consumers' health and safety                  — impacts on vulnerable consumers                  — responsible marketing and research                  — community relations, including the social and                 economic development of local communities                 Source: Own elaboration according to Commission Communication, Guidelines on the reporting of                         non-financial information (methodology for reporting non-financial information)                                                               544","2 The legislative framework for reporting non-financial information                Non-financial information under the Accounting Act must contain at least the following                structure (§ 32g (2)):                    •   a brief description of the business model,                    •   a description of the action taken by a company in the matter in question, if it fails                       to do so,                    •   a description of the results of these actions,                    •   a description of the principal risks associated with these issues and how these risks                       are managed,                    •   non-financial Key Performance Indicators (KPIs) relevant to the business activity                       concerned.                By  law,  an  entity  (group)  may  disclose  non-financial  information  in  an  annual  report                (consolidated annual report) or a separate report. When an entity discloses non-financial                information in a separate report, it publishes it on its website and provides a hyperlink to                this  report in its  annual  report.  A  separate  report may  also  be  published  by the entity                together with the annual report.                The law also literally states that for the disclosure of non-financial information, an entity                may use one of the frameworks that define the disclosure of corporate social responsibility                reports.  An  entity  shall  always  indicate  in  the  report  under  which  the  framework  was                compiled. On 5 July 2017, Official Journal of the European Union No. C 215/1 published a                methodology for reporting non-financial information. This methodology was based on the                following national, European and international reporting frameworks.                        Table 1 Reporting Frameworks for Disclosure of Non-financial Information                 CDP (formerly the Carbon Disclosure Project)   the International Integrated Reporting Framework                 the Climate Disclosure Standards Board        Model Guidance on reporting ESG information to                                                               investors of the UN Sustainable Stock Exchanges                                                               Initiative                 the OECD Due Diligence Guidance for Responsible   the Natural Capital Protocol                 Supply Chains from Conflict-Affected and High-Risk                 areas, and the supplements to it                 the Eco-Management and Audit Scheme (EMAS) and   Product and Organisation Environmental Footprint                 the related Sectoral Reference Documents      Guides                 the European Federation of Financial Analysts   the Sustainability Accounting Standards Board                 Societies' KPIs for Environmental, Social,                 Governance (ESG), a Guideline for the Integration of                 ESG into Financial Analysis and Corporate Valuation                 Global Reporting Initiative                   the Sustainability Code of the German Council for                                                               Sustainable Development                 Guidance for Responsible Agricultural Supply Chains   the Tripartite Declaration of principles concerning                 of FAO-OECD                                   multinational enterprises and social policy of the                                                               International Labour Organisation                 Guidance on the Strategic Report of the UK Financial   the United Nations (UN) Global Compact                 Reporting Council                 Guidelines for Multinational Enterprises of the   UN Sustainable Development Goals, Resolution of 25                 Organisation for Economic Cooperation and     September 2015 transforming our world: the 2030                 Development                                   Agenda for Sustainable Development                 Guiding Principles Reporting Framework on Business   UN Guiding Principles on Business and Human Rights                 and Human Rights                              implementing the UN ‘Protect, Respect and Remedy’                                                               Framework                  ISO 26000 of the International                 Organisation for Standardisation                   Source: Commission Communication, Guidelines on the reporting of non-financial information                                     (methodology for reporting non-financial information)                 These frameworks and methodologies can be a useful guide for corporations to report non-                financial information. The survey conducted in a selected sample of  entities/groups also                                                            545","focused  on  gaining  information  about  whether  or  not  an  entity  uses  any  of  the  above                frameworks to report this information for the year 2016.                 3 Methodology and Data                 This paper aims to define the concept of non-financial information according to the Act on                Accounting  and  their  importance  for  measuring  the  performance  of  commercial                corporations in the Czech Republic. The contribution in its theoretical level summarizes the                current legislative regulation of the given issue and also relies on the study and processing                of foreign and domestic resources using methods of analysis, synthesis and compilation.                The practical part analyzes the non-financial information of selected companies that meet                the criteria for mandatory disclosure according to the Act on Accounting from 2017. Based                on this detailed analysis are set to research questions aimed at finding the current state of                publication of non-financial information in the Czech Republic. Subsequently, the expected                future development in this area is defined.                The aim of the research part is to find out how the non-financial information in the Czech                Republic is presented in large accounting units and groups as defined by the Accounting                Act. This survey is based on publicly available data for the year 2016. Companies were                selected  by  criteria  for  mandatory  reporting  of  non-financial  information.  These  are                primarily  public  interest  entities  whose  securities  are  traded  on  stock  exchanges  and                banking  institutions.  For  this  purpose,  business  data  was  used  at  the  Prague  Stock                Exchange (www.pse.cz). All selected public interest entities meet the criteria for reporting                non-financial information - the number of employees and the annual total net turnover or                total assets. A detailed analysis of the annual reports, websites and separate reports on                the social responsibility of ten public interest entities is carried out. Based on this analysis,                the following research questions are identified:                1.  Does  the  entity/group  compose  a  separate  report  for  reporting  non-financial                information?                2.  Does  the  entity/group  use  any  frameworks  or  methodologies  for  disclosure  of  non-                financial                                                                     information?                3.  Does  the  entity/group  provide  non-financial  information  on  environmental  issues?                4.  Does  the  entity/group  provide  non-financial  information  on  social  and  employment                issues?                5.  Does  the  entity/group  provide  non-financial  information  on  human  rights  issues?                6.  Does  the  entity/group  provide  non-financial  information  on  corruption  and  bribery                issues?                7.  Does  the  entity/group  indicate  Key  Performance  Indicators  related  to  its  business                activity?                Research issues are assessed by the current version of the Accounting Act, the European                Directive  and  the  European  Commission's  methodology  for  reporting  non-financial                information.                 4 Results and Discussion                 The survey has shown that almost all companies publish some non-financial information                (some of the mandatory issues). Only one company does not publish any non-financial                information.  According  to  a  survey,  four  out  of  ten  surveyed  companies  compiled  a                separate report for disclosure, and three of these companies compiled this report according                to the Global Reporting Initiative (GRI) framework. Reports compiled by this framework                are entirely consistent with the Accounting Act and the European Non-financial Information                Directive. Five companies publish non-financial information in their annual report. Almost                all companies provide information on environmental and social and employment issues in                full or in part by statutory regulations. Four out of ten companies report wholly or partially                information  on  human  rights,  corruption  and  bribery  issues.  Six  out  of  ten  companies                publish Key Performance Indicators that relate to their business.                                                              546","Table 2 The Disclosure of Non-financial Information for the Financial Year 2016                      Entity/Group       Separate    Using    Environ   Social   Human      Anti-     Key                                         report    the CSR   mental     and      rights   corruptio  Performa                                                   framewo   matters   employe            n and      nce                                                   rk/meth               e                bribery   Indicator                                                   odology            matters                         s                  1   ČEZ Group         Report on   GRI     YES       YES      YES       YES      YES                                        Sustainable                                        Developme                                        nt                    2   ŠKODA AUTO a.s.   Report on   GRI     YES       YES      YES       YES      YES                                        Sustainable                                        Developme                                        nt                   3   ČD Group          NO, within   NO     YES,      YES,     NO        NO       YES,                                        the annual          partly    partly                      partly                                        report                                                    within the                                                                                                  quality                                                                                                  managem                                                                                                  ent                                                                                                  system                  4   Česká spořitelna a.s.   NO, within   NO   YES,   YES,    NO        NO       NO                                        the annual          partly    partly                                        report                    5   O2 Czech Republic   NO, within   NO   YES,      YES,     NO        NO       NO                     a.s.               the annual          partly    partly                                        report                    6   ČSOB Group        Social    GRI       YES       YES      YES       YES      YES                                        Responsibili                                        ty Report                    7   PEGAS NONWOVENS   NO        NO        NO        NO       NO        NO       NO                      SA                     8   Unipetrol, a.s.   Report on   NO      YES       NO       NO        NO       YES,                                        health,                                                   especially                                        safety and                                                environm                                        environmen                                                ental                                        tal                                                       matters                                        protection                  9   Kofola, a.s.      NO, within   NO     YES,      YES,     NO        NO       NO                                        the annual          partly    partly                                        report                     1  Komerční Banka,    NO, within   NO     YES,      YES,     YES,      YES,     YES                  0   a.s.              the annual          partly    partly   partly    partly                                        report                                                     Source: Own processing                 Based  on  the  survey,  the  question  arises  as  to  the  relationship  between  non-financial                information published by most of the surveyed companies and their performance. Aupperle                et  al.  (1985)  attempt  to  express  the  relationship  between  CSR  (Corporate  Social                Responsibility)  and  corporate  profitability  in  their  empirical  study.  Corporate  Social                Responsibility represents the integration of the social, environmental and economic aspects                of management into one functional unit to meet the needs of all Stakeholders. The authors                conclude that there is no clear relationship between Corporate Social Responsibility and                                                             547","corporate  profitability  and  the  assessment  of  this  relationship  is  highly  subjective.                However, since the beginning of the 1990s, the importance of intangible and intellectual                assets  (so-called  leading  indicators),  which  have  a  significant  impact  on  the  long-term                development  of  corporations,  is  increasing  with  the  criticism  of  traditional  financial                indicators (lagging indicators). These factors include the quality of products and internal                processes,  the  ability  and  motivation  of  employees,  and  relationships  with  other                Stakeholders. According to Kaplan and Norton (1996, p. 3), targeting these factors will                enable the organization:                    •   maintain  positive  relationships  with  existing  customers  and  gain  new  customer                       segments,                    •   provide innovative products and services as required by customers,                    •   produce highly personalized products at the lowest cost and in less time,                    •   develop skills and abilities of employees and motivate them to perform better,                    •   develop information technology, databases and systems.                In this context, some managerial approaches, tools and systems focus on intangible and                intellectual assets that are a prerequisite for the strategic development of society. Kaplan                and Norton (1992) formulated a comprehensive strategic management system – Balanced                Scorecard  in  Harvard  Business  Review.  This  system  serves  to  clarify  and  translate  the                vision  and  strategy  into  concrete  forms  of  measurable  goals.  The  measures  (Key                Performance  Indicators)  are  linked  to  strategic  goals  and  measure  the  degree  of                achievement  of  these  objectives.  So-called  strategic  actions  support  strategic  goals.                Strategic  actions  are  measures,  initiatives,  programs  or  activities  that  help  to  achieve                strategic goals. Strategic goals, measures (Key Performance Indicators)  and actions are                assigned  to  individual  points  of  view,  so-called  perspectives  -  learning  and  growth                (employee), internal processes, customer and financial.                Individual  perspectives  are  not  separated  but  are  interconnected  through  causal  links                between individual  strategic  goals.  Non-financial information  (indicators)  are monitored                within  the  perspectives  of  learning  and  growth  (employee),  internal  processes  and                customers.  Positive  development  of  these  indicators  is  reflected  in  the  results  of  the                financial perspective.                 Parmenter (2008) extends the four basic perspectives of the Balanced Scorecard system                to  the  environment/community  and  employee  satisfaction.  This  expanded  Balanced                Scorecard  corresponds  to  the  Stakeholder  Theory,  defined  by  R.  E.  Freeman  in  1984                (Freeman,  1984).  The  goal  of  this  theory  is  to  maximize  the  benefits  not  only  for  the                owners but also for all Stakeholders. Individual corporations may have different interest                groups (Stakeholders). It is usually possible to distinguish five major stakeholder groups                (Atkinson et al., 1997): customers, employees, suppliers (goods, services, debts), owners,                community. The expanded Balanced Scorecard system is also in line with Act on Accounting                568/1991  and  Directive  2014/95/EU  of  the  European  Parliament  and  the  Council.  The                individual  perspectives  include  environmental  issues,  social  and  employment  issues,                respect for human rights and the fight against corruption and bribery. This system thus                becomes  an  appropriate  tool  for  measuring  and  performance  management  of  public                interest entities that are required to report non-financial information.                 5 Conclusions                This  contribution  dealt  with  the  reporting  of  non-financial  information  according  to  the                amendment to the Act on Accounting and European Directive. This information is required                to be disclosed for the first time for the financial year 2017 by June 30, 2018. A survey of                publicly available information was carried out in ten public interest entities that met the                criteria  for  the  mandatory  disclosure  of  non-financial  information  for  the  financial  year                2016. The answer to the seven research questions was to estimate the expected future                development of disclosure of non-financial information for the year 2017. The analysis has                shown that a clear solution for the disclosure of non-financial information is to compile a                separate report containing that information. The survey confirmed that most companies                reporting non-financial information in a separate report compiled this report according to                                                            548","the Global Reporting Initiative (GRI) framework. Reports compiled under this framework                are fully in line with the amendment to the Act on Accounting. Therefore, we can expect                that  companies  will  disclose  non-financial  information  for  the  financial  year  2017  in                accordance with this framework or this framework will inspire them. This is also confirmed                by the KPMG survey (2017), which showed that 75% of the 250 largest companies in the                world use the GRI framework to publish non-financial information.                 The  paper  also  addressed  the  importance  of  non-financial  information  for  performance                measurement  and  management.  Existing  surveys  in  this  area  have  not  confirmed  the                precise  relationship  between  reporting  CSR  and  performance  (Aupperle  et  al.,  1985).                However, literature speaks of the significant influence of intangible and intellectual assets                on the long-term development of companies (Kaplan, Norton, 1996).  Also, Stakeholder                Theory  (Freeman,  1984)  assumes  a  synergy  effect  between  satisfying  interests  of                Stakeholders  and  maximizing  benefits  for  owners.  A  recommendation  for  companies                reporting non-financial information under the Accounting Act is the implementation of the                Balanced  Scorecard  extended  by  Parmenter  (2008).  This  system  extends  the  four                fundamental perspectives of the Balanced Scorecard (Kaplan, Norton, 1992). The extended                perspectives are the environment/community and employee satisfaction that encompass                legally required non-financial information. Orientation to this non-financial information, in                addition  to  administrative  burdens,  can  help  improve  performance  over  the  strategic                horizon.                  References                Act no. 563/1991 Coll., on Accounting, as amended.                Atkinson, A. A., Waterhouse, J. H., Wells, R. B. (1997). A stakeholder approach to strategic                performance measurement. Sloan management review, vol. 38(3), pp. 25-37.                 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(1992)  The  Balanced  Scorecard:  Measures  that  Drive                Performance. Harvard Business Review 70, no. 1, pp. 71-79.                 Kaplan,  R.  S.,  Norton,  D.  P.  (1996).  The  balanced  scorecard:  translating  strategy  into                action. Harvard Business Press.                 KPMG  (2017).  Průzkum  stavu  nefinančního  reportingu  2017.  Retrieved  from:                https://home.kpmg.com/cz/cs/home/pro-media/tiskove-zpravy/2017/10/nefinancni-                reporting-pruzkum.html                 Parmenter, D. (2008). Key Performance Indicators. Develop, Implement and Use Winning                Key Performance Indicators (KPI). Czech Society for Quality.                                                                 549","Evaluation of Expense Ratio of Insurance Portfolios Belonging to                    Selected Insurance Companies Operating on Czech Insurance                                                         Market                                              Lenka Přečková , Eva Vávrová                                                              1                                                                              2                                                1  Silesian University in Opava                       School of Business Administration in Karviná, Department of Finance and Accounting                                   Univerzitní nám. 1934/3, 733 40 Karviná, Czech Republic                                                 E-mail: preckova@opf.slu.cz                                                     2  Masaryk University                                Faculty of Economics and Administration, Department of Finance                                         Lipová 507/41a, 602 00 Brno, Czech Republic                                             E-mail: eva.vavrova@econ.muni.cz                 Abstract:  The  paper  focuses  on  evaluation  of  insurance  portfolios  belonging                to the insurance  companies  ČSOB  and  Komerční  pojišťovna  from  the  vantage  point                of growth rate of premium written and expense ratio of the insurance portfolio. The aim of                this  paper  is  to  ascertain  whether  the  selected  insurance  companies  experience  lower                expense  ratio  of  their  insurance  portfolio  than  what  is  the  average  expense  ratio                of insurance portfolios on the Czech insurance market. The reason for doing this research                is in the fact that the insurance companies ČSOB and Komeční pojišťovna are entities with                a  developed  bancassurance  model.  As the  authors  discovered in their earlier research,                insurance companies using this model experience lower expense ratio. This paper analyzes                the selected insurance companies on Czech insurance market considering the cooperation                between  the  bank  and  insurance  company.  It  analyzes  the volume  of premium  written                concluded through the distributional channel of bancassurance, and eventually compares                expense  ratios  of  the  selected  insurance  companies  with  the average  value  on  Czech                insurance market between 2012 and 2016. Expense ratio of life insurance in both insurance                companies shows lower values than the average value on the Czech insurance market. The                same applies to total insurance. The research confirms that Komerční pojišťovna shows                lower average value of expense ratio than ČSOB pojišťovna.                 Keywords: bancassurance, expense ratio, insurance market                JEL codes: G21, G22                 1 Introduction                 For the financial market, bancassurance is a topic of current importance. However, every                author defines bancassurance and other related terms differently. In order for a product                which  a bank  offers  to  be  considered  an  actual  bancassurance  product,  it  must  be                integrated. When selling a bancassurance product, it is automatically sold together with an                insurance one. According to Lím (2012), the more an insurance product is integrated with                the central product of a financial institution, the more likely it will become its organic part                and the better the sales will be.                There  is  a  variety  of  publications  which  compare  advantages  and  disadvantages                of bancassurance. What is more, these advantages and disadvantages can be assessed                from  different  viewpoints:  the  viewpoint  of  the  bank,  the  viewpoint  of  the  insurance                company  and  finally,  the  viewpoint  of  the  client.  The  main  advantage  of  using  this                distributional channel is that financial subjects can use it to reduce costs.                Bancassurance came into existence in the United States about 50 years ago. According                to Mesršmíd (2016), the development of bancassurance was supported by many different                economic  reasons.  The  target  was  to  take  advantage  of  the  synergy  to  reduce  costs                                                               550","and provide clients with a comprehensive offer and more services in what is called a one-                stop shop.                 According to the statistics provided by the European insurance and reinsurance federation                (2010), the results for bancassurance in Europe were as follows: Bancassurance was not                very well developed in non-life insurance and represented less than 10% in all countries of                Europe. Bancassurance was the main distribution channel for life insurance products in                many western European countries. It represented up to 84.5% of the total life business in                Portugal and reflected the growth of a model that was developed in the 1990s allowing                access to a large number of clients on a regular basis, combined with a good knowledge of                their financial resources. The high penetration rate of bancassurance in the distribution of                life insurance products was particularly true for unit-linked products. Bancassurance for                unit-linked products was notably strongly developed in Italy (91%), Belgium (74%), France                (65%) and Croatia (55%). In the Czech Republic, it is unfortunately not possible to assess                this distributional channel because there is no available data providing sales volume of                bancassurance products.                Daňhel  et  al. (2007)  claims  that in  consequence  of  a  bank merging  with  an  insurance                company,  operational  costs  can  decrease  by  10%  to  20%  for  one  of  the  integrating                partners.  According  to  Polouček  (1999),  providers  of  bancassurance  report  operational                costs lower by 40% compared with standard insurance companies. 82% bancassurance                groups operating in Europe have lower costs in providing life insurance than traditional                insurance companies providing life insurance.                A comparison of the expense ratios of life insurers in France in 1991 showed that insurers                relying on traditional agents had considerably higher expense ratios than banks’ in-house                companies (Genetay and Molyneux, 1998). The latter showed expense ratios varying from                3.1% to 9.1% while traditional insurers had expense ratios rating from 10.5% to 23.7%                (Leach, 1993).                Other studies performed in Ireland (10 life insurance companies from 1991 to 2000 were                examined) by Hwang and Gao (2005) suggest that the dummy indicating the distribution                of  insurance  policies  by  bank  branches  is  positively  related  to  cost  efficiency                and the coefficient  is  statistically  significant.  Research  done  by  Barros  et  al.  (2006)                in Portugal (14 life insurance companies from 1995 – 2003) confirmed that the dummy                indicating the ownership by a bank institution is positively related to cost efficiency, but                the coefficient is not statistically significant.                 According to Swiss Re (2007) bancassurance is often considered to be more cost effective                than  traditional  agency  and  broker  channels.  In  Italy,  the  total  expense  ratio,                as a percentage gross premiums from banks, was clearly lower than that of agents and                financial advisors. In France, bank channels had a clear cost advantage in 2005 compared                to companies which use intermediaries. In contrast, direct writing companies had a slightly                lower expense ratio than banks, due to lower administration cost. Similarly, in household                insurance in France, the bank channel enjoyed a lower expense ratio than other channels,                apart from “mutuals without intermediaries”.                 The  empirical  results  find  that  the  following  factors  have  played  significant  roles                in the expanding and consolidation of bancassurance: reduction in company risk, the size                of the company, reductions in company costs and increases in company revenues, the size                of the national banking industry, the level of financial deregulation within a country, and                the national inflation rate (Chen et al., 2009).                The article analyzes insurance portfolios of selected Czech insurance companies from 2012                to 2016. The analysis uses gross written premium (hereinafter referred to as GWP) and                gross  operating  expenses  (hereinafter  referred  to  as  GOE).  The  article  calculates  and                compares  growth  rate  of  GWP;  it  also  compares  selected  insurance  companies  from                the vantage point of market shares in the Czech Republic and the share of life insurance                in  total insurance.  The  article  then  compares  shares  of  bancassurance in total GWP; it                calculates and compares expense ratio indicator (hereinafter referred to as ER).                                                              551","The aim of this paper is to ascertain whether the selected insurance companies experience                lower expense ratio of their insurance portfolio than what is the average expense ratio of                insurance  portfolios  on  the  Czech  insurance  market.  For  this  research,  two  insurance                companies shall be selected: ČSOB pojišťovna, a.s. (hereinafter referred to as ČSOBP) and                Komerční pojišťovna, a.s. (hereinafter referred to as KP). These two offer their financial                products  using  bancassurance  as the  distributional  channel.  To achieve  the  target,  two                research  questions  shall  be  formulated.  Is  the  average  value  of  ER  in  selected                insurance companies lower than the average value of ER on the Czech insurance                market? Does the insurance company with a larger share of bancassurance in                GWP show lower value of ER? The average value shall be based on the average from                2012 to 2016.                 2 Methodology and Data                 The first part of this research shall introduce the property structures of ČSOBP and KP                using information  available in  annual  reports  (2017).  The  next  step  shall  be dedicated                to analyzing and comparing development of growth rate (GWP) for total, life and non-life                insurance in both of the insurance companies from 2012 to 2016. The average growth rate                for the monitored period shall be calculated together with the share of life insurance in                total insurance. The research uses data from the statistics of Czech Association of Insurers                (hereinafter referred to as ČAP).                The  following  part  shall  compare  development  of  the  market  share  of  ČSOBP  and  KP                on Czech insurance market. The share of bancassurance in total written premium shall be                compared for the period from 2012 to 2016. In case of KP, the research shall use data                from a questionnaire survey (2017) and in case of ČSOBP from Cach (2016).                The  final  part  of  the  research  shall  present  development  of  ER  indicator  for  both                of the insurance  companies  from  2012  to  2016.  It  was  calculated  the  ratio  indicator                “expense ratio” (ER), which is very often used in evaluating costs of insurance activities                (Pulchart, 2002; Gestel et al., 2007; Vávrová, 2014). It is the proportion of operating costs                and  premiums  written.  For  the  calculation,  it  was  taken  into  account  gross  operating                expenses (GOE) and gross premiums written (GWP). This indicator should reach an amount                lower than 30% (Vávrová, 2014). The formula for calculating is:                                                               =                                                                                                                                                                    (1)                                                   ER  is  a  calculation  for  total,  life  and  non-life  insurance  and  it  is  compared                with the development  of  the  insurance  market.  Subsequently,  average  values                for the selected period for both the insurance companies and the insurance market shall                be  calculated  and  compared.  The  data  necessary  for  calculation  of  ER  was  accessed                in ČAP’s statistics.                 3 Results and Discussion                ČSOB pojišťovna and Komerční pojišťovna                 ČSOB pojišťovna, a.s. (hereinafter referred to as ČSOBP), a member of the ČSOB holding                is a universal insurance company. It came into existence in 1992 and it has had its current                name since 2003. In 2003, ČSOBP merged with IPB Pojišťovna. The majority shareholder                of  ČSOBP  is  KBC  Verzekeringer  (Insurance)  NV  with  99.755%  share.  The remainder                (0.245%) is owned by Československá obchodní banka, a.s. (hereinafter referred to as                ČSOB). (Annual report 2017)                 ČSOB is a wholly-owned subsidiary of KBC Bank NV. KBC Bank is fully owned by KBC                Group NV. KBC Group is an integrated banking and insurance group that focuses                particularly on clients in the following areas: individuals, private banking, SMEs and mid-                sized corporations. In the same fashion, ČSOB focuses on linking banking and insurance                products also in the Czech Republic.                                                              552","Figure  1  shows  development  of  GWP  (total,  life  and  non-life)  using  growth  rate                in the period  between  2012  and  2016.  Growth  rate  of  life  insurance  shows  significant                fluctuations.  The  average  value  for  the  period  reaches  0.41%.  Growth  rate  in  non-life                insurance shows a rising tendency and reaches 4.6% on average.                                      Figure 1 Development of ČSOBP’ Growth Rate                                                              Source: Author’s own work based on ČAP’s statistics                  GWP of life insurance of ČSOBP in the period from 2012 to 2016 reaches average value of                6,559,675 thous. CZK and GWP of non-life insurance reaches average value of 4,740,918                thous. CZK. The average share of life insurance of ČSOBP in total insurance is 58%.                Since  the  acquisition  of  Komerční  banka,  a.s.  (KB)  by  Société  Générale  S.A.  in  2001,                Komerční pojišťovna, a.s. (KP) started implementing SG’s bank-insurance business model.                KP therefore cooperates primarily with companies that form part of the KB Group. The                majority owner of KP’s shares is insurance company SOGECAP S.A., SG’s wholly owned                subsidiary,  which  holds  a  51%  ownership  interest.  KB  is  a  49%  minority  shareholder.                (Annual Report, 2017)                 Figure 2 shows development of GWP (total, life and non-life) using growth rate in the time                period from 2012 to 2016. Growth rate of life insurance shows, just like in case of ČSOBP,                significant fluctuations. The average value for the period reaches 5.95%. Growth rate in                non-life insurance is stable and reaches 7.12% on average. It is clear that growth rates                life and total insurance are close to identical. The development is given by the fact that KP                is exclusively focused on arranging life insurance.                 GWP of life insurance of KP in the period between 2012 and 2016 reaches average value                of 7,253,944 thous. CZK and GWP of non-life insurance reaches average value of 331,906                thous. CZK. The average share of life insurance of KP in the total insurance is almost 96%.                                                                         553","Figure 2 Development of KP’ Growth Rate                                                               Source: Author’s own work based on ČAP’s statistics                 Based on the presented data, it can be concluded that both of the insurance companies                focus  on  developing  bancassurance  and  offer  integrated  bancassurance  products.  Both                of them are subsidiary companies of a financial group, of which they are members. In both                of these companies, insurance portfolio is dominated by life insurance. KP is exclusively                focused  on  life  insurance.  Growth  rate  in  the  period  2012-2016  shows  significant                fluctuations  in  both  of  the  insurance  companies.  Average  value  of  growth  rate  of  life                insurance is 0.41% in ČSOBP and 5.95% in case if KP. Development of non-life insurance                is more stable. There is a rising tendency in case of ČSOBP.                Comparison of market share and share of bancassurance                Figure 3 compares development of share on the insurance market in the period from 2012                to 2016. The average value of ČSOBP’s market share is 7.75% in the monitored period,                and 5.2% in case of KP. It is obvious that ČSOBP’s market share experienced diminution                in 2013 and 2014. In 2016, the value was again almost as high as in 2012. KP’s market                share rose from 2012 and in 2015 and 2016, it decreased to the value of 2012.                                Figure 3 Development of ČSOBP’s and KP’s Market Share                                                               Source: Author’s own work based on ČAP’s statistics                                                                 554","As already mentioned, both of the insurance companies prefer distribution of their financial                products  through  bancassurance.  The  following  table  shows  how  much  insurance  was                arranged using the distributional channel of bancassurance.                            Table 1 Share of Bancassurance in GWP in the Period 2012 - 2016                           Year                        Share of bancassurance, %                                                    ČSOBP                          KP                          2012                   Not specified                   93.82                          2013                        8.7                        95.68                          2014                        8.4                        95.80                          2015                        9.8                        92.96                          2016                       15.4                        93.39                           Source: Author’s own work based on Cach (2016) and questionnaire (2017)                 Volume  of  bancassurance  has  a  rising  tendency  in  case  of  ČSOBP.  It  reached  15.4%                in 2016.  The  growth  between  2015  and  2016  reached  57%.  KP  arranges  insurance                exclusively using bancassurance. Between 2012 and 2016, the share of bancassurance                exceeded 90%.                 Comparison and evaluation of expense ratio                Based on ascertained volumes of bancassurance, it can be assumed that in case of both of                the insurance companies, ER will reach lower values than what is the average value on the                Czech insurance market. It can also be predicted that ER will be lower in case of KP because                this insurance company arranges more contracts using bancassurance.                                          Figure 4 Development of Expense Ratio                                                                        Source: Author’s own work based on ČAP’s statistics                 Figure 4 shows values of ER of selected insurance companies and Czech insurance market                in the period from 2012 to 2016. In case of life insurance, the value of ER in both of the                insurance companies is lower than on the insurance market, this applies to all monitored                years. Total insurance shows the same results. In case of non-life insurance, values of the                two insurance companies are higher than on the market. Insurance companies reach higher                ER than what is the recommended value (30%). The market is below 30%.                                                             555","Figure 5 shows average values of ER of the selected insurance companies as well as the                market. The average values supplement the situation in Figure 4. In case of life and total                insurance,  both  ČSOBP  and  KP  reach  lower  average  values  than  the  average  value                of the market. In case of non-life insurance, both of them show higher average value than                the average value of the market. Table 2 compares average values of ER with the market.                                   Table 2 Comparison of Average Values of ER (in %)                                                             Average ER                 Average ER                                                               (Life)              (Life and Non-life)                 Market                                         23.38                     25.99                 ČSOBP                                          13.07                     21.62                 Difference between market and                   44                         17                 ČSOBP                 KP                                             5.87                       7.56                 Difference between market and                   75                         71                 KP                           Source: Author’s own work based on Cach (2016) and questionnaire (2017)                 Figure 5 confirms the assumption that the average value of ER of the insurance company                KP is lower than the average value of ER of the insurance company ČSOBP. This assumption                applies only to life and total insurance. In case of life insurance, the value of ER is 55%                lower and the value of ER in total insurance is 65% lower.                      Figure 5 Comparison of Development of ER Average Values from 2012 to 2016                                                              Source: Author’s own work based on ČAP’s statistics                 4 Conclusions                The  article  analyzes  insurance  portfolios  of  the  insurance  companies  ČSOBP  and  KP                in the Czech  Republic  from  2012  to  2016.  These  two  insurance  companies  focus  their                business  activities  on  offering  bancassurance  products.  Both  of  them  are  subsidiary                companies of financial groups to them they belong. ČSOBP’s share of bancassurance rose                and in 2016 exceeded 15%. In case of KP, the share of bancassurance is higher than 90%                over  the  whole  monitored  period.  In  both  of  the  insurance  companies,  their  product                portfolio is dominated by life insurance. ČSOBP’s average value of life insurance share is                58%. In case of KP, it is 96%. In the period from 2012 to 2016, ČSOBP shows average                market share of 7.75%. In case of KP, it is 5.2%.                 The aim of this paper is to ascertain whether the selected insurance companies experience                lower expense ratio of their insurance portfolio than what is the average expense ratio of                insurance portfolios on the Czech insurance market. To achieve the target, two research                                                             556","questions  were  formulated.  Is  the  average  value  of  ER  in selected  insurance                companies lower than the average value of ER on the Czech insurance market?                Does the insurance company with a larger share of bancassurance in GWP show                lower value of ER? The average value was based on the average from 2012 to 2016.                ER of non-life insurance in ČSOBP and KP does not reach lower values than those shown                by the Czech insurance market. This applies not only to the individual years in the period                from 2012 to 2016, but also to the average value over this period. In the same period, the                value of ER of non-life insurance is higher than the recommended value of 30%.                ER of life insurance in both ČSOBP and KP shows lower values than the average value                on the  Czech  insurance  market.  The  same  applies  to  total  insurance.  KP  reached                significantly lower value of ER. The average value of ER in life and total insurance is lower                by more than 70% as compared with the average value on the market.                The  research  also  confirms  that  KP  shows  lower  average  value  of  ER  than  ČSOBP.                The average value of ER of the insurance company KP is 55% lower in case of life insurance                and 65% lower in case of total insurance.                 Acknowledgments                 Research behind this paper was supported by the Czech Science Foundation within the                project 16-17796S ‘Affiliation with financial conglomerate as a determinant of performance                and risk of banks’.                 References                Barros, C. B, Barroso, N., Borges, M. R. (2006). Measuring Efficiency in the Life Insurance                Industry      with     a      Stochastic     Frontier      Model.     Retrieved      from:                http://www.ica2006.com/Papiers/3063/3063.pdf.                 Cach,  M.  (2016).  Bankopojištění  jako pohodlí  pro  klienta  a  výhoda  pro  banku.  Pojistný                obzor, vol. 2016(2), pp. 60-61.                CEA    (2010).    Insurance    Distribution   Channels   in   Europe.    Retrieved   from:                https://www.draudimas.com/allpics/distribution.pdf.                ČSOB       Pojišťovna     (2017).     Annual      report    2017.      Retrieved     from:                https://www.csobpoj.cz/documents/10332/32806/2017_vyrocni-zprava_CSOB-                Pojistovna.pdf/80b09439-e975-4027-b9fc-1cf08225f740.                 Daňhel, J., Ducháčková, E., Radová, J. (2007). Analýza globálních trendů ve světovém a                českém komerčním pojištění. Praha: VŠE.                 Genetay, N., Molyneux, P. (1998). Bancassurance. Hampshire: Palgrave Macmilan.                Gestel, T., Martens, D., Baesens, B., Feremans, D., Huysmans, J., Vanthienen, J. (2007).                Forecasting  and  analyzing  insurance  companies'  ratings.  International  Journal  of                Forecasting, vol. 23(3), pp. 513-529. DOI: 10.1016/j.ijforecast.2007.05.001.                Hwang T., Gao, S. (2005). An Empirical Study of Cost Efficiency in the Irish Life Insurance                Industry. International Journal of Accounting, Auditing and Performance Evaluation, vol.                2(3), pp. 264-280.                Chen, Z., Li, D., Liao, L., Moshirian, F., Szablocs, C. (2009). Expansion and consolidation                of  bancassurance  in  the  21st  century.  Journal  of  International  Financial  Markets,                Institutions and Money, vol. 2009(19), pp. 633-644.                Komerční  pojišťovna  (2017).  Annual  report  2017.  Retrieved  from:  https://www.kb-                pojistovna.cz/file/edee/cs/hospodarske-vysledky/vyrocni-zpravy/kp-vyrocni-zprava-                annual-report-2017.pdf?cddd38cf6fb6e5716ade2caf773d42c7                                                                557","Leach, A. (1993). European Bancassurance: Problems and Prospects to 2000, Financial                Times Management Reports. London: Financial Times Business Information.                Lím, P. (2012). Bankopoistenie je nielen o spolupráci s bankami. Pojistný obzor, vol. 89(2),                pp. 20-22.                Mesršmíd, J. (2016). Marketing v pojišťovnictví. Praha: Professional Publishing.                 Polouček, S. (1999). České bankovnictví na přelomu tisíciletí. Ostrava: Ethics.                Pulchart, V. (2002). K ziskovosti neživotního pojištění – pohled na dlouhodobé trendy ve                světě. Pojistný obzor, vol. 79(10), pp. 4-5.                Ricci,  O.  (2012).  Studying  the  Bancassurance  Phenomenon:  A  Literature  Review.  In:                Fiordelisi,  F.  and  Ricci,  O.,  eds.,  Bancassurance  in  Europe  -  Past,  Present  and  Future.                Hampshire: Palgrave Macmillan, pp. 139-178.                Swiss Re (2007). Bancassurance: Emerging Trends, Opportunities and challenges. Sigma,                vol. 2007(5), pp. 1-39.                 Vávrová, E. (2014). Finanční řízení komerčních pojišťoven. Praha: Grada Publishing.                                                                                                                       558","Comparison of Analysis Deviation Methods for the Multiplicative                                                      Operations                                        Barbora Ptáčková , Dagmar Richtarová        2                                                           1                                            1 VSB – Technical University of Ostrava                                         Faculty of Economics, Department of Finance                                     Sokolská třída 33, 702 00, Ostrava 1, Czech Republic                                              E-mail: barbora.ptackova@vsb.cz                                             2  VSB – Technical University of Ostrava                                         Faculty of Economics, Department of Finance                                     Sokolská třída 33, 702 00, Ostrava 1, Czech Republic                                              E-mail: dagmar.richtarova@vsb.cz                 Abstract: This paper is dedicated to comparison of analysis deviation methods for the                multiplicative operations. Evaluation of financial performance belongs to key activities of                each company. The importance of the performance concept is widely recognized, it is one                of the tools, knowing which helps to manage a company efficiently. Decomposition analysis                is  also  very  important.  Decomposition  analysis  helps  to  analyze  the  factors  affecting                selected key measures and quantify their impact on the key measures. In this paper five                basic  methods  of  analysis  of  deviations  will  be  compare  –  gradual  change  method,                decomposition method with a residue, logarithmic method, functional method and integral                method.  The  aim  of  this  paper  is  to  compare  analysis  deviation  methods  for  the                multiplicative  operations  used  for  quantification  of  the  influence  of  the  component                indicators to base indicator and verify if using of these five methods leads to the same                results. Methods of analysis of deviations will be applied to return on equity (ROE) indicator                of manufacturing industry of the Czech Republic and according these five methods main                factors will be find and influence of these factors will be quantified. Annual data will be                taken from Ministry of industry of the Czech Republic.                Keywords: financial performance, analysis of deviations, pyramidal decomposition, return                on equity                 JEL codes: C2, C5, C58, G3, G30                 1 Introduction                In today´s fast changing world and in the uncertain business environment, companies are                exposed to increasingly larger business risks which they have to manage often and with                new ways of doing so. Knowing the indicators representing financial performance related                to the dynamic environment, where companies operate is a key element by increasing the                competitiveness.  Financial  performance  of  companies  is  solved  by  many  authors,  see                Ehrbar (1998), Mařík (2005), Vernimmen (2005), Dluhošová (2010) or Abdoli et al. (2011).                Key financial indicators of financial performance of company can be found according to the                selected methods of financial performance.  Find key financial ratios for a company is first                step of evaluating of financial performance. It is also very important to quantify the impact                of key financial ratios on key measures. Method of pyramidal decomposition together with                the  analysis  of  deviation helps  to identify  and  quantify the impact  of  selected  financial                ratios to base ratio, Gurný, Richtarová, Čulík (2016). The impact of selected ratios to the                base  ratio  can  be  analyzed  according  to  static  and  dynamic  methods  of  analysis  of                deviation, Ptáčková (2015) or Richtarová (2016). Decomposition of variance is one of the                dynamic method used for quantification of impact of selected financial ratios to base ratio,                Ptáčková, Richtarová (2017). In this paper static methods of analysis of deviation will be                characterized and compared.                The  aim  of  this  paper  is  to  compare  analysis  deviation  methods  for  the  multiplicative                operations  used  for  quantification  of  the influence  of the  component indicators  to  base                indicator and verify if using of these five methods leads to the same results.                                                              559","2 Methodology and Data                Evaluation  of  financial  performance  belongs  to  key  activities  of  each  company.  The                importance  of  the  performance  concept  is  widely  recognized.  Financial  performance  of                company  can  be  analyzed  according  to  traditional  measures  or  modern  indicators,                Dluhošová, Ptáčková, Richtarová (2017). ROE, ROI or NOPAT are traditional indicators used                for financial performance of industry or company. These indicators are based on accounting                data and do not reflect costs of capital, Dluhošová (2010).                In  this  paper  financial  performance  of manufacturing industry  of the  Czech  Republic is                analyzed  according  to  return  on  equity  (ROE).  Main  influencing  factors  of  financial                performance are found according to pyramidal decomposition of this ratio. Return on equity                is one of the traditional measurement of financial performance. According to Vernimmen                (2005) or Dluhošová (2010) return on equity is generally expressed as                                EAT                        ROE =                                                                           (1)                                 E                where EAT is earnings after taxes and E is equity.                Method  of  pyramidal  decomposition  is  applied  to  return  on  equity  indicator  of                manufacturing industry of the Czech Republic. Pyramidal decomposition of return on equity                is shown in Figure 1.                                         Figure 1 Pyramidal Decomposition of ROE                                                                                       EAT/E                                                                             .                                                                EAT/R             R/A       .    A/E                              EAT/EBT           EBT/EBIT          EBIT/R                                                        Source: own calculation                 Pyramidal  decomposition  together  with  the  analysis  of  deviation  helps  to  identify  the                relationship between financial ratios and quantify the impact of component indicators to                base financial indicator. According to analysis of deviation it is possible to quantify the                impact of the changes in the component financial ratio on the base financial ratio, Zmeškal                (2013).                Generally, any base indicator x can be expressed as a function of component indicators                        x =  f a 1  2 ...a n )                                                           (2)                             ( ,a                The change of the base financial indicator can be determined as a sum of influences of                component financial indicators as                          x                          y =     x                                                                     (3)                                    i a                               i                                                               560","wherex  is the base financial indicator,  y    is the change in the base financial indicator,  a                                                          x                                               i                                                             is the i-th component ratio,  x  is the  impact of i-th component ratio on the change in                                                 i a                the base ratio.                Quantification  of  the  impact  of  component  financial  ratios  on  the  change  in  the  base                financial  ratio  in  pyramidal  decomposition  can  be  expressed  using  two  relationships  –                additive relationship and multiplicative relationship, Zmeškal (2013).                Deviation analysis for additive relationship                According to Zmeškal (2013) additive relationship between financial ratios is expressed as                         x =  a =  a a +  ... a                                                          (4)                                     +                                            +                                       2                                   1                                               n                               i                            i                Quantification of the impact under the additive relationship is generally applicable. The                total impact is divided in proportion to the changes in the component financial ratios as                                 a                         x =     i   y                                                                (5)                           i a    a i  x                               i                                       where  a =   a −  a is the value of the i-th component financial ratio at the beginning of                                    ,0                          i                               ,1                               i                                    i                the analyzed period and  a is the value of the i-th component financial ratio at the end of                                           i                analyzed period.                Deviation analysis for multiplicative relationship                Multiplicative relationship among financial ratios is expressed as                        x =  a =  a a   2   ... a   n                                                (6)                                    1                               1                            i                According the way in which the multiplicative relationship is handled, it can be distinguished                five basic methods: a method of gradual changes, a decomposition method with surplus,                a logarithmic method, functional method or the integral method, Zmeškal (2013).                If  method  of  gradual  changes  (MGCH)  is  used  for  quantification  of  an  influence  of                component  financial  ratios  to  base  financial  ratio  under  the  multiplicative  relationship,                influences are quantifies as follows                                        i                           ai                          x =    a  j ,0  a   a   y x  .                                            (7)                                               ,1                                               j                               j i        j i    x                According  Zmeškal  (2013)  calculation  simplicity  and  residue  free  decomposition  are                advantages  of  method  of  gradual  changes.  On  the  other  side,  there  are  some                disadvantages – strength of the influences depends on the ranking of financial ratios in the                calculation.                 Decomposition  method  with  a  residue  (DMR)  is  the  second  method  used  for                quantification of an influence of component financial ratios to base financial ratio. If this                method is applied influences are quantifies as follows                                    n      y    R                                 i                          x =       a     x  +  ,                                                     (8)                               a                           i a         j ,0   x  n                                   j i                 meanwhile, the residue is possible to quantify on the basis of the change of ratios as                                      n                        R =  −   a i a    y   x  .                             y                                         j                                          ,0                              x                                     j i     x                                                             561","The  advantage  of  the  decomposition  method  with  a  residue  is  that  results  are  not                influenced by the ranking of financial indicators. The existence of residue of component is                disadvantage of this method.                Logarithmic method (LM) is characterized by the decomposition of influences without a                residue. Generally, influences of component financial ratios is calculated as                              ln I                         x =     i a   y .                                                            (9)                           i a  ln I x  x                  Advantage of logarithmic method is that can reflect simultaneous changes of all component                ratios, there is no residue. One of disadvantage of this method is that all indexes must be                positive, because of index logarithm.                 The calculation of an influence according to functional method (FM) is expressed as                                                                                                                  1            1             1                    1                                                             +                                                              +                  x =     R     1+   R   j a   R   R   k a     R   R   R   + ...  y  .     (10)                    i a  R   i a      2           3   j a              4    j a  k a  a m      x                                                                                                                                                                                                                    x  j i     j i k i         j i k i m i                                                                                                                                  k j                                                                   k j m k                When  functional  method is  used for  quantification  of influences  of  component  financial                ratios, discrete returns are applied. Advantages are similar to logarithmic method and there                is no problem with negative values of financial indicators.                Integral  method  (IM)  is  last  method  used  for  quantification  of  influences  under  the                multiplicative relationship. Resulted influence quantification according integral method for                any component ratio is expressed as                              R                         x   =  i a    y   ,                                                        (11)                           i a  R    x                                ´ x                               a             N                                         x                 where   R =     i   and   R =   R  , R .                          i a  a i ,0        i= 1  i a  x                Advantages of integral method are same as for functional method, but it is also possible to                analyze non-linear operations and also interpretation can be easier, Zmeškal (2013).                Data                For the comparison of analysis of deviation methods under the multiplicative operations                annual data of manufacturing industry in period 2015 to 2016 were used. Input data were                taken from the website of Ministry of Industry of the Czech Republic. Financial ratios were                calculated on the base of input data according to chosen pyramidal decomposition of return                on equity. Values of financial ratios are calculated in Table 1.                                                 Table 1 Financial Ratios                                       RATIOS                T0              T1                                      ROE                 0,175            0,219                                      EAT/EBT              0,803           0,813                                      EBT/EBIT             0,870           0,941                                      EBIT/R               0,063           0,075                                      R/A                  1,768           1,823                                      A/E                  2,236           2,087                                               Source: mpo.cz, own calculation                                                                  562","3 Results and Discussion                First, MGCH is applied and absolute deviations of component ratios are calculated. Ranking                of financial ratios in the calculation is important when MGCH is used for quantification of                deviation  under  the  multiplicative  operations.  In  the  Table  2  there  are  three  possible                ranking of financial ratios, which are used for quantification of influence.                      Table 2 Order of Ratios for Analysis of Deviation – Method of Gradual Changes                           Order of ratios         MGCH_1           MGCH_2            MGCH_3                                  1                EAT/EBT             R/A               A/E                                  2                EBT/EBIT            A/E            EBT/EBIT                                  3                 EBIT/R          EBT/EBIT           EBIT/R                                  4                  R/A             EBIT/R           EAT/EBT                                  5                  A/E             EAT/EBT             R/A                                                   Source: own calculation                 Generally,  strength  of  the  influences  depends  on  the  ranking  of  financial  ratios  in  the                calculation. It is one of disadvantage of method of gradual changes. That´s why in the                calculation of influence according to MGCH ranking of component ratios were changed.                In the Figure 2 there are shown influences of component ratios in three scenarios of ranking                of component ratios.                            Figure 2 Absolute Value of Deviation – Method of Gradual Changes                                                 A/E                                               R/A                                             EBIT/T                           Ratios         EBT/EBIT                                              EAT/EBT                            -0,02      -0,01        0          0,01       0,02        0,03       0,04                                                       Absolut deviation                                                    MGCH_3   MGCH_2    MGCH_1                                                    Source: own calculation                 According to MGCH  it is possible to quantify the influence of component financial ratios to                base  financial  ratio.  This  analysis  has  confirmed  importance  of  ranking  of  financial                indicators. From the Figure 2 it is possible to see how the value of deviation is changed                when ranking of financial component ratios is changed. It is clear that the value of deviation                of each component ratio has different value when the ranking of ratios is various. According                to this analysis the value of influence is changing, but the strength (positive or negative)                is same.                All methods were applied for comparison. In the Table 3 there are calculated and quantified                values of deviations of each component ratios according to five chosen methods used for                quantification  of  deviation  under  the  multiplicative  operation.  Data  from  the  scenarios                MGCH_1 was used for analysis and comparing according to MGCH.                                                               563","Table 3 Absolute Value of Deviation – All Methods                  RATIOS            MGCH            DMR              LM             FM             IM                 EAT/EBT           0,215%         0,215%         0,240%         0,241%          0,218%                 EBT/EBIT          1,450%         1,432%         1,542%         1,546%          1,450%                 EBIT/R            3,577%         3,266%         3,356%         3,357%          3,307%                 R/A               0,698%         0,537%         0,593%         0,578%          0,510%                 A/E              -1,561%        -1,165%         -1,351%        -1,361%        -1,106%                  Σ                4,379%         4,285%          4,379%         4,361%         4,379%                 Residue                          0,094%                                                    Source: own calculation                From the Table 3 it is clear that the total value of influence of component ratios is same                for all methods. There are highlight values of deviations of component ratios, which have                the biggest influence according to used methods. The value of deviation of each component                financial ratio is different when different methods are used. One of the disadvantage of                DMR is creation of residue and attributability of this residue to any component ratio.  When                the residue is small this method can be used, otherwise the analysis can be distorted.                 Discussion                In the Figure 3 there are shown resulted values of absolute deviation of component ratios                to ROE indicator according to MGCH, LM, FM and IM. Results of DMR method were excluded                because of the existence of residue.                                            Figure 3 Absolute Value of Deviation                              0,04                               0,03                            Value of absolute deviation  0,01  EAT/EBT  EBT/EBIT  EBIT/T  R/A  A/E                             0,02                                    0                              -0,01                               -0,02                                                               Ratios                                                      MGCH   LM   FM   IM                                                    Source: own calculation                 For the analysis all methods used for quantification of influence under the multiplicative                operation were applied. According to analysis return on equity of manufacturing industry                of the Czech Republic between 2015 and 2016 is influenced mainly by the EBIT/R. This                indicator has highest positive influence and financial leverage has the highest negative                influence.                Strength (positive or negative) of the influence of component ratio calculated according to                different method is same. The value of deviation of each component ratio is different, only.                                                             564","When analysis of deviation is applied it is important to respect advantage and disadvantage                of  all  methods,  for  example  LM  is  not  possible  to  apply  when  index  of  logarithm  of                component ratio is negative. In such situation, in next level of decomposition it is suitable                to use different method for quantification of influence.                4 Conclusions                 This paper was dedicated to comparing of methods used for quantification of an influence                of component financial ratios to base financial ratio under the multiplicative relationship.                In this paper method of gradual changes, decomposition method with residue, logarithmic                method, functional method and integral method were compared. These five method were                applied to return on equity ratio of manufacturing industry of the Czech Republic.                The aim of this paper was to compare analysis deviation methods for the multiplicative                operations used for quantification of the influence of the component financial indicators to                base financial indicator and verify if using of these five methods leads to the same results.                Financial  performance  of  manufacturing  industry  of  the  Czech  Republic  was  analyzed                according to ROE indicator. Financial performance was analyzed between 2015 and 2016.                Method of pyramidal decomposition was applied to return on equity indicator and main                influencing  factors  of  financial  performance  of  manufacturing  industry  were  found.                Influences  were  calculated  according  selected  deviation  analysis  methods.  From  the                analysis  of  deviation  of  component  ratio  was  found  that  the  values  of  deviation  of                component ratios are different, otherwise the strength of the influence is same according                to all methods.                 Acknowledgments                 This paper was supported by the SGS Project VŠB  – TU Ostrava SP2018/154 \"Finanční                rozhodování podniků a finančních institucí za rizika”.                 References                 Abdoli,  M.,  Garkaz,  M.,  Golami,  Y.,  Pourkazemi,  A.  (2011).  The  comparative  study  of                ranking company's efficiency based on data envelopment analysis (DEA) and traditional                methods  (DuPont's  method).  In:  International  Conference  on  Economics  and  Finance                Research, IPEDR. Singapore: IACSIT Press, vol. 4, p. 34.                Dluhošová, D. (2010). Finanční řízení a rozhodování podniku. Praha: Ekopress.                 Dluhošová,  D.,  Ptáčková  B.,  Richtarová,  D.  (2017).  Application  of  selected  financial                performance methods to chosen industry in the Czech Republic. In: Proceedings of the 14th                international  scientific  conference  European  Financial  Systems  2017.  Brno:  Masaryk                University, pp. 67-75. ISBN 978-80-210-8609-8.                 Ehrbar, A. (1998). EVA the real key to creating wealth. New York: Wiley.                Gurný,  P.,  Richtarová,  D.,  Čulík,  M.  (2016).  Liquidity  analysis  and  prediction  in  the                processing industry by applying VG process: the case of the Czech Republic. Ekonomická                revue - Central European review of economic issues, vol. 20(1), pp. 17-28.                Maříková,  P.,  Mařík,  M.  (2005).  Moderní  metody  hodnocení  výkonnosti  a  oceňování                podniku. Praha: Ekopress.                Ptáčková, B. (2015). Financial performance variance analysis of non - linear decomposition                in  energy  sector.  In:  Financial  Management  of  Firms  and  Financial  Institutions:  10th                international  scientific  conference.    Ostrava:  VŠB  –  Technical  University  of  Ostrava,                pp. 1005-1010. ISBN 978-80-248-3865-6.                 Ptáčková, B., Richtarová, D. (2017). Analysis of the generators of the company´s financial                performance. In:  Finance and performance of firms in science, education and practice:                proceedings  of  the  8th  international  scientific  conference.  Zlín:  Tomas  Bata  University,                pp. 942-950. ISBN 978-80-7454-653-2.                                                             565","Richtarová,  D.  (2016).  Analysis  of  variance  application  for  manufacruting  industry                performance evaluation in the Czech Republic. In:  Managing and Modelling of Financial                Risks: proceedings of the 8th international scientific conference. Ostrava: VŠB –Technical                University of Ostrava, pp. 856-861. ISBN 978-80-248-3994-3.                Vernimmen, P. (2005). Corporate finance, theory and practice. England: Wiley.                Zmeškal, Z. (2013). Finanční modely. Praha: Ekopress.                Ministry of industry and trade of the Czech Republic (2015-2016). Analytical material and                statistics – Analysis of Czech economic development according to Ministry of industry and                trade. Retrieved from: http://www.mpo.cz/ministr-a-ministerstvo/analyticke-materialy.                                                                                                                                  566","Beliefs in Cryptocurrency: Exploring the Relationship between                                               Sentiment and Bitcoin                                                     Zuzana Rakovská                                                     Masaryk University                                Faculty of Economics and Administration, Department of Finance                                           Lipová 41a, 60200 Brno, Czech Republic                                            E-mail: zuzana.rakovska@mail.muni.cz                 Abstract: The rise of the Internet changed the way how financial markets work. Due to                user generated content, quite everybody might contribute to the large set of information                and the exchange of news happens faster than ever. An important variable in such new                era is sentiment, which causes market participants to act on noisy signals from Internet as                they were valid inputs. Importantly, sentiment is considered the crucial ingredient in price                formation  of  now  increasingly  attractive  digital  currencies.  Investors'  beliefs  affect                exchange rates of those currencies considerably simply because of lack of fundamentals                and regulation. In this paper we will examine the relationship between sentiment and price                of the most popular cryptocurrency, Bitcoin. We will employ three measures of sentiment                constructed  by  Sentix  survey  with  aim  to  explore  the  effect  of  both  institutional  and                individual investor's sentiment as well as overall sentiment of the community.                Keywords: Bitcoin, sentiment, Sentix, cryptocurrency, impulse-response                JEL codes: G40, G41                 1 Introduction                 Bitcoin is the most popular cryptocurrency which has been receiving increasing attention                since  its  inception  in  2008.  It  is  decentralized  and  is  not  controlled  by  any  respected                institution or government, it is connected to organized crime and money laundering, it                caused significant losses for its owners after repeated hacker attacks, but still, this digital                currency is being adopted by increasing number of retailers and the amount of investor                accounts in Bitcoin exchanges expands year-to-year.                It seems that public belief in Bitcoin is capable of overcoming any problems and what is                important,  Bitcoin  prices  seems  not  to  be  affected  by  performance  or  socio-political                changes in any country.  But what determines Bitcoin price? Since there are hardly any                fundamentals linked to it, answer to this question is becoming an emerging topic in the                current (not only academic) literature.                 Many authors argue, that Bitcoin is more asset than a currency (Yermack, 2014; Gomez-                Gonzales \& Parra-Polania, 2014). In fact, it is hard to assign any fundamental value to it,                similarly as it is hard to assign a fundamental value to stocks of young start-up companies                with highly uncertain future. Price of such assets is subject of speculation and trend chasing                driven by expected profits of holding it or selling it with a profit. Baker \& Wurgler (2007)                in  their  famous  study  showed  that  such  assets  are  prone  to  investors’  beliefs  and                preferences, and hence sentiment represents a crucial factor in their price dynamics.                Up until now, several papers have addressed the relation between sentiment and price of                Bitcoin or other cryptocurrencies. Those articles used various measures of sentiment, such                as  Google  search  query volume,  Twitter  sentiment  or  sentiment  mined from  dedicated                blogs.  None  of  those  articles,  however,  studied  effects  of  directly  measured  sentiment                towards Bitcoin. This paper uses the unique sentiment data provided by company Sentix                                                                                                         17                that  captures  investors’  sentiment  via  monthly  survey.  Sentix  distinguishes  between                 17  Sentix is a leading provider of services in the field of Behavioral Finance in Europe. It has been established in                2001 and since this year, sentix surveys on weekly basis around 5000 investors from more than 20 countries. One                of  the  important  characteristics  of  sentix  is  the  fact that  it  distinguishes  between  institutional  and  individual                investors (the ratio between individual and institutional participants is approx. 4 to 1) and hence provides an unique                opportunity for data analytics (www.sentix.com).                                                            567","individual and institutional investors and this fact offers the possibility to estimate effects                on Bitcoin for those two groups separately. This paper represents the first attempt in the                academic literature to model the relation between sentix Bitcoin Sentiment and price of                this popular cryptocurrency. Moreover, we are not aware of any other article which would                study the effects of individual and institutional sentiment separately.                The first study of effect of sentiment on Bitcoin exchange rate was conducted in Kristoufek                (2013). Author examined how investor attention measured by Google search volume and                frequency of visits on the Wikipedia page for term “Bitcoin” affect the currency. Results                showed bidirectional relationship: not only search queries drove prices but also prices were                shown  to  encourage  attention  about  the  term  on  the  internet.  In  his  second  study,                Kristoufek  (2014)  showed  that  along  the  speculative  nature  of  Bitcoin,  standard                fundamental factors such as money supply, play a role in Bitcoin price formation in the                long run. McAteer (2014) employed the standard financial proxy for sentiment – trading                volume,  and  analyzed  whether  sentiment  obtained  from  Twitter  via  sentiment  analysis                could serve as a proxy for Bitcoin sentiment as well. Author  showed strong correlation                between the two sentiment measures and showed that tweets are capable of predicting                Bitcoin exchange rates in the long run. Trading volume together with Google search volume                was also examined in Cretarola et al. (2017) who incorporated investors’ beliefs into newly                built valuation model for Bitcoin price. Relationship between Twitter sentiment and Bitcoin                prices was further explored in Georgoula et al. (2015) or Matta et al. (2015) who besides                social media sentiment also studied effects of Google search volume and Wikipedia search.                An interesting study was conducted in Bukovina \& Marticek (2016) who processed social                data sentiment from www.sentdex.com and studied its effect on Bitcoin price which was                decomposed into rational and less rational part. The most recent papers demonstrated that                sentiment gathered from Bitcoin forums such as www.bitcointalk.org might also serve as                a significant predictor of future Bitcoin prices (Mai et al., 2017; Kim et al., 2017).                The goal of this paper is to analyze how sentiment towards Bitcoin measured by unique                sentix-survey data affects price dynamics of this popular cryptocurrency. We will enrich                the analysis by investigating effects of individual and institutional sentiment separately.                Following  results  from  existing  literature  and  speculative  nature  of  Bitcoin  we  expect                significant effect of all three sentiment measures on currency exchange rate.                The present paper is organized as follows. Section 2 describes data we processed in our                analyses and proposes methodology to be employed. The central part of the present study                is formed in Section 3 in which we examine the results of our study and offer a general                discussion  of  the  analyzed  phenomenon.  Finally,  Section  4  delivers  conclusions  of  this                article.                 2 Data and Methodology                 We employed monthly time series data capturing dynamics of Bitcoin prices and investor                sentiment measured by Sentix survey in period from December 2013 to April 2018. We                decided to process monthly series because Bitcoin Sentiment measure is available only in                monthly frequency.                 Bitcoin prices, used in this study were downloaded from www.bitcoinity.org, an internet                page managed by Kacper Cieśla, which offers free analysis of various Bitcoin data such as                prices, volumes, market capitalization, etc.  We denoted it as BTCPrice and this variable                represents monthly weighted average of Bitcoin  - US dollar exchange rates (BTC-USD)                                                                                            18                published by the biggest and best established Bitcoin exchanges up to date .                Measures of sentiment towards Bitcoin were gathered from sentix database accessed via                Bloomberg terminal. Sentix (www.sentix.de) collects various sentiments on weekly basis                through  e-mail  survey  which  consists  of  three  parts:  Equity  Markets;  Bonds,  FX  and                  18   www.bitcoinity.org  page  summarizes  high  frequency  data  obtained  from  exchanges  such  as  bitfinex                (www.bitfinex.com), bitflyer (www.bitflyer.com), bitstamp (www.bitstamp.net), coinbase (www.coinbase.com),                kraken (www.kraken.com), hitbtc (www.hitbtc.com), itbit (www.itbit.com), and others.                                                            568","Commodities; and Theme of the Week. Bitcoin sentiment has been captured in the Asset                classes section of the last part, Theme of the Week since December 2013 with monthly                frequency. On each second Friday of a month, survey participants are asked to express                their willingness to invest in Bitcoin in medium term of 6 months by selecting a category                                                                                              19                from range of 1 to 5, with category 3 indicating neutrality. Resulting index  fluctuates                between -100 and 100. Respondents are divided into two groups: individual investors and                institutions based on their registration preferences and subsequent validation process in                case  of institutional investors.  As  of  May  2018,  there  were more  than  5  000 investors                registered for the survey and therefore called for their investment opinions.                Bitcoin sentiment measured by sentix could be viewed as a representation of investors’                rising  (falling)  preferences  or  beliefs  about  Bitcoin  which  is  then  translated  into  their                willingness to buy (willingness to sell). With a certain time lag, this willingness might result                in corresponding transaction on the market and subsequent price movements. In extreme                areas, this index might switch from direction indicator to contrarian and might represent                an alarm for increased probability of future trend reversals. In this paper we will study all                three sentiment measures: overall sentiment (SentixAll), sentiment of individual investors                (SentixInd) and institutional sentiment (SentixIns). The history of sentix Bitcoin sentiment                however, witnessed only negative values (with minimum of -63.75 for overall sentiment of                investors). In order to perform statistical inference, we decided to adjust the series by                adding 100 to each observation so as the range is shifted to 0 to 200 array. Having the                time series defined only in space of positive numbers is of great importance for possible                logarithmic transformation. Table 1 shows summary statistics for all four time series.                                               Table 1 Summary Statistics                                              BTCPrice        SentixAll      SentixIns       SentixInd                 Mean                          2068.8           53.99           50.82           57.15                 Minimum                       234.81           36.25             34             38.5                 Maximum                       14973             73               72             78.5                 Standard deviation            3388.1           9.86             8.34            12.2                                                 Source: Author’s calculation                              Figure 1 Dynamics between log Bitcoin prices and log sentiment                                                                  Source: Author’s calculation                 Dynamics  between  sentiment  measures  and  Bitcoin  prices  is  best  captured  by  their                logarithmic  transformations.  Figure  1  depicts  their  evolution in time.  In  “non-extreme”                areas such as period between February 2015 and July 2015, or January 2016 and April                2016,  Bitcoin  sentiment  (its  logarithmic  transformation)  represents  direction  indicator                 19  Bitcoin sentiment is calculated as:                                                                      ∑      =1   (  )                                                                                       = (3 −  ) ∗ 50                                                                                           where n represents number of survey participants and k(.) represents the given category of answer ranging from 1                to 5 (Source: www.sentix.de).                                                            569","which informs about rising beliefs in Bitcoin. In comparison, in extreme events  such as                those in April 2014 or August 2017 when Bitcoin price rose by 26 % and 43 %, respectively,                compared to previous month, our sentiment indicators demonstrate its contrarian nature.                In those periods, investors revealed falling preferences for buying Bitcoin.                The econometric analysis employed in this study consists of several steps that arise from                the nature of time series data. First of all, we tested stationarity of our variables and also                of  their  logarithmic  transformations  in  order  to  assess  preferred  bivariate  model                representation.  Stationarity  is  crucial  when  analyzing  time  series  as  inclusion  of  non-                stationary data in regression model might lead to incorrect inference of causality where no                relationship exists (Granger \& Newbold, 1974). The usual way how to address this problem                of spurious regression is differencing. Time series which became stationary after the first                differencing is integrated of order one (I(1)) and time series which become stationary after                d differencing is integrated of order d (I(d)). We used two test for stationarity: KPSS test                (Kwiatkowski  et  al.,  1992)  with  the  null  hypothesis  of  stationarity  (d=0)  against  the                alternative of unit root (d=1); and Augmented Dickey-Fuller (ADF) test (Dickey \& Fuller,                1979) for which the null hypothesis is on the contrary, that the series possesses a unit root                                              20                and hence is not stationarity . Table 2 summarizes. Results showed that both, original                series  and  their  logarithmic  transformations,  exhibit  non-stationarity  while  their  first                differences where proved to be stationary and hence available for valid statistical inference.                                             Table 2 Testing for Stationarity                                               ADF test [p-value]               KPSS test [p-value]                 BTCPrice                        0.206 [>0.10]                     0.838 [<0.01]                                   Log           -2.457 [>0.10]                    1.259 [<0.01]                                   Diff          -6.257 [<0.01]                    0.117 [>0.10]                               Log-Diff          -5.126 [<0.01]                    0.16 [>0.10]                 SentixAll                        -2.4 [>0.1]                      0.99 [<0.01]                                   Log           -2.632 [>0.1]                     1.018 [<001]                                   Diff          -5.864 [<0.01]                    0.111 [>0.1]                               Log-Diff          -6.022 [<0.01]                    0.113 [>0.1]                 SentixIns                        -1.2 [>0.1]                      0.71 [0.012]                                   Log           -1.227 [>0.1]                     0.723 [0.01]                                   Diff          -6.474 [<0.01]                    0.112 [>0.10]                               Log-Diff          -6.848 [<0.01]                    0.108 [>0.10]                 SentixInd                       -1.695 [>0.10]                    1.122 [<0.01]                                   Log           -3.019 [>0.10]                    1.148 [<0.01]                                   Diff          -10.95 [<0.01]                    0.121 [>0.10]                               Log-Diff          -10.42 [<0.01]                    0.133 [>0.10]                                                Source: Author's calculation                 However,  the  negative  feature  of  differencing  time  series  and  its  subsequent  use  in                regression is loss of possibility to infer long-run relationship between variables. Engle \&                Granger (1987) argues that level variables might still be used for valid inference if they                are represented by time series integrated by same non-zero order d (non-stationary) and                there exists their linear combination which is integrated of order 0 (stationary). In other                words,  valid  statistical  inference  might  be  assessed  if  non-stationary  series  are                cointegrated. While all our time series exhibited unit root, the next step of the analysis was                to  test  each  pair  of  BTCPrice  -  sentiment  measure  and  Log-BTCPrice  –  log-sentiment                measure  for  cointegration  using  Engle-Granger  two-step  method  (Engle  \&  Granger,                                                                                                    ⁄                20  Optimal lag p for ADF tests was determined according to Schwert (1989) as    = 12 ∗ (   100) 1 4 , where T                                                                                           ⁄                represents the number of observations.                                                            570","21                1987) . Cointegration was not detected for any of six studied pairs (BTCPrice – SentixAll,                BTCPrice – SentixIns, BTCPrice – SentixInd and their logarithmic counterparts), therefore                we opted for stationary Log-differences of our time series and the standard procedure for                analyzing multivariate series, Vector autoregression – VAR (Sims, 1980).                VAR model for two time series {   } and {   } could be written as:                                                                                                                                                1                                           t i                          x =  +  p    x +  p    y +                                              (1)                          t                                       i 1                                                  i 1                                                         t 1                                   =                                  i 1        i 1     t i                                              =                                          t i                                2                          y =  +  p    x +  p    x +                                              (2)                                                          t 2                          t                                       i 2                                                  i 2                                              =                                   =                                  i 1         i 1    t i                where p denotes the lag which is usually selected according to some information criteria,                such  AIC  (Akaike  Information  Criteria);  {   }  and  {   }  denotes  error  terms  that  are                                                                        2                                                               1                  possibly correlated; and {∆   } and {∆   } represent the first differences of non-stationary                                                                                                                         time series that are I(0) and hence stationary. Parameters    ,    ,     and     thus, could be                                                                                    1                                                                             1                                                                                 2                                                                                           2                estimated using ordinary least squares (OLS).                Since in such a model all variables depend on each other, individual parameter values only                provide  limited  information.  Following  Kristoufek  (2013)  we  will  use  impulse-response                analysis in order to get a better understanding of the model’s dynamic. Impulse responses                show how response variable reacts to a one-time impulse or shock in another variable and                this reaction could be observed in a plotted trajectory (Sims, 1980).                3 Results                In this part of the paper we will examine dynamic relationship between investor sentiment                measured by sentix survey which uniquely captures beliefs of both, individual investors                and institutions; and price of the most popular cryptocurrency – Bitcoin.                Based on the results from previous section (both original and logarithmic series are I(1)                while their differences are stationary together with rejecting cointegration hypothesis for                all  six  pairs)  we  decided  to  employ  the  first logarithmic  differences  of  our variables  of                interest in  Vector autoregression (VAR) framework. We performed three VAR regressions,                each  for  our  three  sentiment  measures:  overall  sentiment  of  investors,  sentiment  of                individual investors and institutional sentiment. Based on the Akaike Information Criteria                (AIC) we applied lag p=1 in VAR approach (VAR(1)) for the first two sentiment variables                and lag p=3 (VAR(3)) for institutional sentiment. To control for potential autocorrelation                and  heteroscedasticity  in  error  terms  we  used  heteroscedasticity  and  autocorrelation                robust  (HAC)  standard  errors  when  estimating  VAR.  We  tested  regression  results  for                autocorrelation and normality of residuals. We detected non-normality of residuals in the                first and the second regression (we rejected the normality hypothesis at 10 % confidence                level). Therefore, we conducted additional analysis to find out which equation residuals are                causing the problem and by plotting those problematic residuals we were able to identify                outliers . Subsequently, we added observation range dummies for those outlying points                        22                as exogenous variables to our problematic models.                We summarized results using impulse response functions (IRF). Since we used logarithmic                differences our IRFs show what is the impact of unanticipated 1 % change in one variable                (impulse) on another variable (response) over the next several periods. Figure 2 displays                    21  Engle-Granger cointegration test is based on testing residuals from OLS estimation of our variables of interest                (BTCPrice and respective sentiment measure) for stationarity using well known ADF test (Dickey \& Fuller, 1979).                If we reject the null then there is no unit root and the series are cointegrated (even though both series are integrated                of order 1, there is a linear relationship that is integrated of order 0).                22   In  both  studied  pair:  BTCPrice  –  SentixAll  and  BTCPrice  –  SentixInd  we  detected  the  same  outlying                observations: February 2014, September 2017, October 2017 and January 2018.                                                            571","the expected level of a shock surrounded by 90 % confidence interval (low estimate and                high estimate) in 12 month timeframe.                      Figure 2 Impulse Response Functions for Logarithmic BTCPrice and Logarithmic                                                   Sentiment Measures                                                                                                              Source: Author’s computation                 The first studied pair show significant results only in direction from SentixAll to BTCPrice                and up to the first period: 1 % shock in overall sentiment of investors causes immediate                2.2 % reaction of Bitcoin price and this reaction lowered to 0.4 % in the next period (the                first  period).  Causality  in  opposite  direction  was  not  proved  significant.  Similar  results                follow from our third model: 1 % shock in sentiment of individual investors propagated                immediately to  2.8  % increase  of  BTC  prices.  This  reaction  decreased to  0.7  % in  the                subsequent  month  and  the  reaction  vanished  completely  afterwards.  Response  of                SentixInd, similarly to SentixAll, to shocks in Bitcoin price was not significantly different                from zero at 10 % confidence interval (see response of SentixAllto BTCPrice and response                of SentixInd to BTCPrice in Figure 2).                                                             572","Lastly,  we  studied  dynamics  of  institutional  sentiment  and  Bitcoin  prices.  We  found                significant impulse response relationship in direction from sentiment to Bitcoin price in the                third period. Namely, 1 % shock in institutional sentiment caused decline in Bitcoin prices                by 4.8 % after three months. The effect of sentiment in the first two months as well as in                periods after third month were not significantly different from zero at 10 % confidence                interval (see response of BTCPrice to SentixIns in Figure 2). Causality in opposite direction,                from Bitcoin price to sentiment, was compared to previous two pairs found significant in                the first month after the change. This means that 1 % increase in Bitcoin price triggers                negative response of institutional investors by almost 3.6 % in the first period. Moreover,                graph which captures impulse response function for BTCPrice – SentixIns pair seems to                have longer memory than the other two examined pairs.                 4 Conclusions                 This  paper  studied  the  dynamic  relation  between  Bitcoin  price  and  investor  sentiment                towards this currency as measured by company sentix. We are the first who employed this                unique dataset in analysis of Bitcoin prices and we are the first authors who attempted to                estimate  the  effect  of  individual  and  institutional  sentiment  separately.  We  built  three                vector  autoregressive  (VAR)  models,  each  to  estimate  dynamics  between  one  of  our                sentiment measures and price of Bitcoin.                To  sum  up,  our  analysis  showed  that  investors’  beliefs  as  measured  by  sentix  Bitcoin                Sentiment do affect the price of cryptocurrency. Interestingly, there is a difference between                effect  of  individual  and  institutional  investors’  sentiment.  On  the  one  hand,  increased                sentiment  of  individual  investors  towards  Bitcoin  increases  price  of  this  popular                cryptocurrency immediately and the response of prices is weaker but still remains positive                also  in  subsequent  month.  This  result  is  intuitive  as  it  is  quite  easy  for  any  individual                investor to join the bandwagon – everybody can start trading at the dedicated exchanges                within a few days after registration and one does not need to have large capital to start                investing. Therefore, once there is increasing sentiment about Bitcoin, investors can easily                act on this sentiment as it was information and their increased preference towards Bitcoins                then shifts the prices up. In other words, individual investors might be considered as noise                traders (Kyle, 1985; Black, 1986) who when got overly optimistic, push prices above their                intrinsic values. The opposite causality, which would mean that increasing Bitcoin price                positively affects individual investors’ beliefs about the currency, was not shown.                On the other hand, when institutional sentiment rises, Bitcoin prices tend to response by                subsequent decrease three months after the change. This result is not straightforward. The                possible explanation might be the fact that institutions are more rigid when they form their                perceptions about new technology. As a result, their optimism might come too late – just                before the prices would start falling. Alternatively, individual investors might fear they lose                high  margins  once  big  institutions  join  the  Bitcoin  market.  Therefore,  they  lower  their                sentiment toward Bitcoins which would subsequently cause adverse price shifts. Reaction                of institutional preferences towards Bitcoin to changes in currency price were shown to be                negative in the first period after the change. This could be interpreted by firms’ skepticism                about cryptocurrencies and their fear that sharp increases would be followed by rapid loses                as it was observed in the past.                There are several recommendations for future research. First of all, a larger dataset should                be  used in  order  to  check  validity  of  results.  Secondly,  other  sentiment measures  and                possibly other fundamental variables could be added to improve the model fit.                 Acknowledgments                The  support  from  Masaryk  University  under  project  MUNI/A/0753/2017  is  gratefully                acknowledged.                                                                  573","References                Baker, M., Wurgler, J. (2007). Investor Sentiment in the Stock Market. Journal of Economic                Perspectives, vol. 21(2), pp. 129-151.                Bukovina, J., Marticek, M. (2016). Sentiment and Bitcoin Volatility.                Cretarola, A., Figa-Talamanca, G., Patacca, M. (2017). A Sentiment-Based Model for the                BitCoin. Theory, Estimation and Option Pricing, pp. 1-39.                Dickey, D. A., Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time                Series  With  a  Unit  Root.  Journal  of  the  American  Statistical  Association,  vol.  74(366),                pp. 427-431.                Engle,  R.  F.,  Granger,  C.  W.  J.  (1987).  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Retrieved from:                http://www.nber.org/papers/w19747.                                                              574","The Current Problems of the Eurozone and the Future                                    Transformation of the European Union                                                      Oldřich Rejnuš                                                     Masaryk University                                Faculty of Economics and Administration, Department of Finance                                           Lipová 41a, 601 00 Brno, Czech Republic                                                E-mail: rejnus@econ.muni.cz                 Abstract:  The  paper  deals  with  the  problems  of  contemporary  management  of  the                European  Union,  with  focus  on  economic  development  of  countries,  associated  in  the                European Economic and Monetary Union, using common currency. Its goal is to evaluate                the ways of fiscal management of the European Union and monetary regulation, carried                out by the European Central Bank, with focus on their efficiency and achievements. As                there are, apart from economic problems, a number of other problem zones, directly or                indirectly connected with analyzed economic problems, they are briefly specified in the                paper, too.  In conclusion, the influence of current way of management of the European                Union on the development of individual states, with special focus on European Economic                and Monetary Union states, so called “Eurozone” is evaluated. We are not going to evaluate                only  economic  situation  of  individual  countries;  we  want  to  assess  a  number  of  other                factors,  interconnected  with  economic  development  and  significantly  influencing                compactness of European Union countries.                Keywords: European Union, Eurozone, fiscal and monetary regulation, central banks                JEL codes: E58, F36, G15, G18, O11                 1 Introduction                 With regard to „Brexit“, which has already been under way, as well as with strengthening                expectations of EU disintegration, a number of serious topics on the future of Europe arose.                These are really serious topics, related to a number of troubled fields. In agreement with                an opinion of former US Secretary of State, Henry Kissinger, who said in 1974, that: „who                controls  money  can  control  the  world”  (Engdahl 2016)  it is  clear that  one  of  the  most                important topics, connected with the future of Europe, is, before all, its economic future.                This is tightly connected with the effort of individual countries, corporations or individuals                to gain strong control and acquire wealth. This is why the economic future of Europe is                tightly connected with issues of preservation of individual European countries, as well as                preservation or possible loss of national identity of traditional European population.                 2 Methodologies and Data                 When speaking about the development of the European Union, special focus must be given                to the problems of the currency union (monetary union), or Eurozone. The reason is that                the European Union is not a single entity; it is economic community of many countries,                setting up currency union, so called “Eurozone” little by little. Currently, the members of                the  currency  union  are  Belgium,  Estonia,  Finland,  France,  Ireland,  Italy,  Cyprus,                Luxembourg, Latvia, Lithuania, Malta, Germany, Netherlands, Portugal, Austria, Greece,                Slovenia, Slovakia and Spain. Functionality, or malfunction of this system, based on the                principle of single currency, has decisive influence on the development of the European                Union as a whole.                Current  problems  of  the  European  Union  have  grown  into  such  dimensions  that  the                founder, Euro creator and the very first senior economist of the European Central Bank                (ECB), Otmar Issing, said:  „the euro is a ticking time bomb which will soon blow up and                wipe out the whole EU project“. This is why we are going to analyze, before all, the reasons                of contemporary economic decay of the European Union, with main focus to the role of the                Eurozone.                                                              575","The methodology used represents a combination of qualitative and quantitative analysis.                Qualitative part of the research will be represented by economic theory findings; we are                going to analyze whether basic theoretical rules and principles are met in the process of                European Union management. Before all, it is important whether they are abode by the                European Union  authorities  and the  governments  of individual European  states in their                fiscal policies and the European Central Bank in its monetary policy. As far as interest rates                are concerned, principles „Classical theory of interest rates“, „The loanable funds theory“,                and „The rational expectations theory of interest rates“ will be taken into account. When                analysing  so  called  „printing  money“,  „Fischer´s  quantity  theory  of  money\"  will  be                considered. Quantitative component (segment, sector) will be based on economic data that                will  be  taken  from  databases  Tradingeconomics,  Eurostat,  European  Central  Bank  a                DebtClocks.eu.                 3 Results and Discussion                There  are lots  of  economic  problems in  contemporary  Europe,  mainly in the Eurozone.                Several  of  them  have  overgrown  into  dimensions  that  threaten  the  heart  of  Eurozone                existence, and consequently the whole European Union.                Extreme indebtedness of many EU countries, especially Eurozone members                 European Union was established in February 1992, when the Maastricht Treaty was signed.                At  the  same  time,  so  called  „convergence  criteria”  were  adopted;  meeting  them  was                assumed to be obligatory for the monetary union members and institutional form of the                Eurozone was conceived, with newly established European Central Bank (ECB); its primary                task was to care of price stability. Subsequently, on January 1 , 1999, cashless Euro was                                                                               st                introduced in the first 11 countries, while banknotes and coins were introduced three years                later, on January 1 , 2002.                                   st                As the European Union founders were aware of the fact that common currency adoption                would result in loss of possibility of the member states to perform their own monetary                policy,  they  were  concerned  about  the  possibility  of  high  deficit  of  public  budgets  in                economically  weaker  countries.  This  was  why  EU  member  states  adopted  mechanism                „Stability  and  growth  pact\"  (SGP)  in  1997;  this  pact  was  supposed  to  prevent  fiscal                indiscipline.  Beside  other  things,  it  stated  with  binding  effect  that  meeting  budgetary                targets  must  be  balanced  and  states  that  will  not  meet  this  requirement  will  have  to                undergo  „Excessive  deficit  procedure“ which  might  result in financial  sanctions.  On  the                other hand, the problems of financial crises solutions were not dealt with. It did not deal                with the possibility of helping countries that might get into insolvency or possibly decide                to leave the Eurozone.                 The  prerequisite  that  the  Eurozone  membership  would  lead  to  discipline  of  economic                policies  proved  to  be  wrong;  in  2002,  when  the  European  Commission  suggested  to                penalize France and Germany for their immoderate deficits, these two strongest European                countries not only persuaded the authorities that they would not pay any sanctions, but,                on top of that, they persuaded them to „soften” original principles. This led to gradual                indebting of a number of Eurozone countries and resulted in current catastrophic situation,                described in 2016 by the founder of EU and Euro creator Otmar Issing as follows: „The                Stability and Growth Pact has more or less failed. Market discipline is done away with by                ECB interventions. So there is no fiscal control mechanism from markets or politics. This                has all the elements to bring disaster for monetary union\" (Gutteridge 2016).   ◼             Danger for the functionality of European bank system  ◼             Nowadays, in connection with extreme indebtedness of many European countries, issues                connected  with  possible  Eurozone  bank  system  breakdown,  bank  systems  of  Italy  and                Greece  respectively,  are  discussed  more  and more  often,  where  there  are  problems in                many other countries, too. In view of this, we would like to know what the European Central                Bank has been doing all the time and how it responded to this situation.                                                               576","◼             After the outbreak of financial crisis in 2008, the bank released its monetary policy (which                was  correct)  and  provided  banks  with  very  cheap  and  almost  unrestricted  amount  of                money; it resulted, according to Sulík (2012), to non-systematic management of European                bank  system.  Concrete  examples  of  ECB  practice  can  be  seen  in  gradual  decrease  of                required warranties when purchasing bonds (debentures) from commercial banks up to                debentures without investment rating, permission to „print money” by Eurozone national                banks, legally unauthorized purchasing of treasury bonds of Eurozone countries, enabling                banks  to  create  Asset  Backed  Securities  (ABS)  and  subsequently  to  accept  them  as                warranties and later even to possibility to purchase them. And it also does „Quantitative                Easing“ (QE), based on intensive purchase of state and corporate bonds, the quality of                which is often substandard, from commercial banks. This means nothing less but extreme                „printing of money” (which definitely means breach of theoretic rules, namely Fischer´s                quantity theory of money). This fact is proved in following picture; the development of ECB                balance for the last ten years is shown, the value of which exceeded 4.55 trillion EURO in                May 2018.                           Figure 1 The Development of European Central Bank Balance Sheet in the Last Ten Years                                                                         Source: Tradingeconomics.com                In connection with the use of this nonstandard tool of monetary policy, topped up with                extreme decrease of interest rates, it is necessary to evaluate its efficiency, which seems                to be problematic; it can be seen in following Table 1.                         Table 1 The Current Development of GDP and Indebtedness within “Eurozone”                 Absolute nominal GDP p.a.                                            11 277 billion EURO                 Absolute amount of public debt                                       10 009 billion EURO                 Public debt to GDP ratio                                                         88.8 %                  Change of absolute nominal GDP p.a. (+/−) per second                      + 11 053 EURO                 Change of public debt (+/−) per second                                     + 4 195 EURO                                               Source: DebtClocks.eu (May 2018)                Above mentioned Table shows that the GDP of the Eurozone has been growing; at the                same time absolute amount of public debt has been increasing. As far as rather decreasing                relative measure of public debt towards GDP is concerned, it is rather unstable and in case                of possible decrease of GDP will grow fast. It follows that the realization of very intensive                QE and extremely low interest rates by ECB only keep economy of the Eurozone moving                and postpones solutions of growing problems towards the future.                Within  the  framework  of  the  analysis,  it  is  necessary  to  take  into  account  the  size  and                importance of individual countries.                                                             577","Figure 2 Ranking of EU States and Eurozone States According to Their Number of Inhabitants                                                                                                  Source: Eurostat and DebtClocks.eu                Above mentioned picture shows that after „Brexit”, the most influential countries, from the                point of view of the number of inhabitants, are Germany, France, Italy and Spain. These                four countries have 256 million inhabitants, which means almost three fourths or 75 %                from  the  total  number  of  340  million inhabitants in the Eurozone.  This  predetermines,                which states influence the Eurozone the most, both in political and economic field.                 What was the economic development of these four most important Eurozone countries in the                last years, is in the time of extremely expansive ECB policy, and how is the Eurozone economy,                or the economy of the whole EU influenced? Following table 2 shows the development of their                GDP and their indebtedness in the last three years, compared with the development of the                whole Eurozone or the EU, respectively. It is clear that three of these four most important                Eurozone countries – France, Italy and Spain, have been in long term economic problems. This                is proved by their very slow economic growth (given in common prices) accompanied (with                the exception of Germany) with constantly growing public debt.                                                                  578","Table 2 The Development of GDP and Indebtedness in the Eurozone, Germany, France,                                        Italy and Spain in 2008 and 2016 – 2017.                            GEO            INDICATOR        YEAR 2008    YEAR 2016           YEAR 2017                                               GDP /1          9 636 073      10 790 077       11 168 630                       Eurozone                     /2                     19 countries            DEBT            6 610 346        9 601 612        9 685 501                                             DEBT              68,6 %           89,0 %           86,7 %                                                 /3                                               GDP /1          2 561 740       3 144 050         3 263 350                                                 /2                       Germany               DEBT            1 669 035        2 145 473        2 092 643                                             DEBT /3           65,2 %           68,2 %           64,1 %                                             GDP /1          1 995 850        2 228 857        2 287 603                        France               DEBT            1 370 326        2 152 523        2 218 436                                                 /2                                             DEBT              68,8 %           96,6 %           97,0 %                                                 /3                                             GDP /1          1 632 151        1 680 948        1 716 935                         Italy               DEBT            1 671 401        2 219 546        2 263 056                                                 /2                                                 /3                                             DEBT             102,4 %          132,0 %          131,8 %                                             GDP /1          1 116 225        1 118 522        1 163 662                                                 /2                         Spain               DEBT              440 621        1 107 220        1 144 298                                             DEBT              39,5 %           99,0 %           98,3 %                                                 /3                                                      Source: Eurostat                GDP /1    – GDP at market (current) prices [millions EURO]                DEBT – Consolidated gross debt of general government [millions EURO]                     /2                     /3                DEBT – General government gross debt in percentage of GDP                 The  fact  that  economic  situation  of  France,  Italy  and  Spain  is  exceptionally  serious  is                evidenced by other macroeconomic data, especially the unemployment rate, surpluses or                deficits of their state budgets, and the yield of their state bonds (debentures)  – in this                particular case ten years obligations.                  Table 3 Selected Macroeconomic Indicators of the Most Important Eurozone Countries                                                         Budget             Budget                                      Jobless      surplus/deficit     surplus/deficit     Government                                        rate          in percent        in percent of       bond 10Y                   COUNTRIES                            of GDP          government             yield                                                                           revenue                                    [March 2018]          [2017]            [2017]        [May 30, 2018]                                         [1]              [2]                 [3]               [4]                     Eurozone           8,50 %            −0,9 %             −1,9 %         −0,36 % p.a.                     Germany            3,40 %            +1,3 %             +6,7 %         +0,37 % p.a.                      France           8,90 %            −2,6 %             −4,8 %         +0,69 % p.a.                       Italy         11,00 %             −2,3 %             −5,0 %         +2,92 % p.a.                      Spain           16,75 %            −3,1 %             −8,2 %         +1,53 % p.a.                                             Source: Tradingeconomics, Eurostat                 As we can see, the data from chart 3 make previous original conclusion even deeper. The first                indicator  shows  huge  problems  Spain,  Italy  and  France  have  been  experiencing  with                unemployment. In case of Spain the value is so extremely high that it clearly contradicts                frequent purpose built statements of politicians, who say that Spain has just bounced from the                bottom”. The second indicator evidences both surpluses and deficits of state budgets in 2017                towards GDP. Their size shows that Spain and France still do not follow valid EU rules. When                the deficits of their state budgets compare with the size of state revenues (the third indicator),                it is obvious that this is a difficult problem. The fourth indicator shows artificially decreased                return of state bonds (debentures) that is artificially decreased by ECB. As far as negative                                                             579","return  of  Eurozone  bonds,  emitted  by  the  European  Financial  Stability  Facility  (EFSF)  is                concerned, it can be considered to be absurdity, contradicting not only economic rules, but all                above mentioned interest theories.                EU system of rescue measures                There are two so called „EU rescue measures” - „European Stability Mechanism“ (ESM) and                „Banking Union“ (Rejnuš 2016). ESM is an institution, performing the function of rescue                fund of financial help for countries paying with Euro, where Eurozone member states are                obliged to pay any fund requirement that is raised towards them. As far as „Banking union”                is concerned, its goal is to solve important financial problems of the Eurozone „together”                and based on solidarity, while applying the rules of single bank regulation, single bank                uniform bank supervision and single system of deposit insurgence. Firstly, its goal is to                promote  the  principle  of  self-help  (bail-in)  towards  only  essential  involvement  of  public                resources into bank restructuring, while costs of their recovery should be preferably shared                by their shareholders and creditors. Secondly, responsibility for results of bank supervision                should be moved to the EU level; sufficient financial capacities for financial rehabilitation of                banks have to be created.                It is clear that this system denies fair solution of financial problems of individual Eurozone                countries  and  forces  responsible  countries  to  donate  or  even  rescue  countries  that  are                irresponsible; for example, in case of possible bankruptcy of Italy, the Eurozone would not                be rescued, anyway. We can expect that the inhabitants of those European countries at                whose expense the reallocation would be done (including Germany) would show massive                dissatisfaction; this would result in the termination of the Eurozone.                Export dominancy of Germany                As a result of single European currency, economically strongest Germany is more and more                competitive, when speaking about prices; as a result, the deficits of current accounts of                south European countries, are more and more balanced by surpluses of current accounts                of  Germany  (Singer  2012).  George  Friedmaman  says  that  in  the  last  fifteen  years,                Germany eliminated their competitors in other Eurozone countries and became an export                power with the highest surplus since unification of Germany. (Dubnová 2016). German                policy is focused on export and it creates growth of imbalance across Eurozone.  When it                entered  the  Eurozone,  Germany  managed  to  gain  markets  even  in  economically  risky                countries such as Portugal, Ireland, Italy, Greece, Spain etc. But it is necessary to pay for                products and services and when the economy is in crisis, it results into debts and the debt                wheels start to turn around. Jan Beňák describes it as follows: a car is made in Germany,                it is  bought  by  Spain;  Spain  does not have money  and this is  why they  make  a loan.                Germany exported a car and got money and as a result was able to provide a loan to Spain.                But it is not possible to suck from neighbouring countries over and over again. According                to Friedman, the markets where Germany exports are getting smaller. Unless Germany                has enough revenues from export, it will not be able to lend money to other countries;                subsequently, these countries will not be able to buy goods from them. It means that other                countries will not be able to buy German goods unless Germany voluntarily slows down                their  export.  Germany  will  not  slow  down  their  exports,  as  this  would  lead  to                unemployment. This shows a major problem in Europe, the victim of which is, for example,                Italy. Their banking system has started to collapse (Lišková 2017) due to the indebtedness                of the country and due to importunity of bank loans. (This is the case of other Eurozone                countries, too). And Germany will have to support Italy, as its bankruptcy would result in                breakdown of the whole Eurozone.  ◼             Subsidy mechanism of the EU                 All  EU  member  countries  are  obliged  to  pay  money  into  EU  budget;  the  money  is                subsequently administratively divided and sent back to individual states in the form of „EU                funding”. In fact, the citizens pay taxes, part of which is paid to the European Union. This                money is „redistributed” and individual member countries can „ask to receive them back”                with the condition the money will be used to support exactly given project approved by the                European Union. (It means that instead of a scooter, the parents would buy for their child,                                                             580","their town will have a waterpark and the parents involuntarily contributed to it.) This is the                way how things, sometimes various sorts of „rubbish” are funded; things that were decided                „on higher level”. Moreover, EU funds never pay the whole project, the countries always                have to pay the rest. The worst thing is the fact that the system of EU funding is practically                indestructible, as a huge number of officers depend on it. Eliminating this system would                probably result in the collapse of the whole European Union system.                 Existence of European tax paradises                According to non-governmental organization Oxfam International (2016) four of fifteen the                                                                                          rd                                                                                                        th                most harmful tax paradises are in the EU. These include Netherlands (3 ), Ireland (6 ),                                th                                                  th                Luxembourg (7 ) and Cyprus (10 ). This list is based on the measure to which individual                countries apply zero company tax, do not cooperate in the fight against tax evasion, are                non-transparent, how often unfair tax incentives that do not create new job opportunities                are  applied.  The  above  mentioned  status  evidences  hidden  activity  of  a  number  of                European politicians who spend money of „European tax payers” to covertly support their                home countries.                 4 Conclusions                It  has  been  commonly  proven  that  the  main  problem  of  the  Eurozone  is  based  in  its                heterogeneity, in forced connection of economically weak countries with the strong ones,                that can, as we have already mentioned, break European Union valid rules almost without                punishment, while small countries cannot do the same. Before all, common currency is in                crisis.  The  reason  is  that  single  currency  policy  resulted  in  distinctive  deepening  of                difference between the countries in the southern wing and the core EU countries, so that                the survival of the Eurozone depends solely on „crisis” measures, made by ECB; these                include non-standard loans, fiscal transfers and „political solidarity” of the European states.                The common Schengen Area has been collapsing, too. According to Janáček and Janáčková                (2017), these two pillars were to foreshadow a way from economic and monetary union to                political union; as they are currently weakening, they are becoming the “accelerator of the                European Union disintegration”.                The  European  Union,  originally  intended  to  be  an  expedient  alliance  of  cooperation  of                independent  European  countries,  turned  into  extraordinarily  complicated  bureaucratic                system, dominated by big countries. Meaningless bureaucratic decisions, incompetence,                enforcing  one’s  own  interests  and  arrogance  of  EU  leading  representatives,  disputes                between  individual  countries,  long-term  wrong  and  goal-directed  policy  of  ECB  and                enforced single currency resulted to contemporary crisis that might turn into collapse. The                problem is not  solely  based  on  stagnating  economy,  but,  before  all, in extremely high                indebtedness of the whole union, mainly of the most important Eurozone countries; it has                already reached such dimensions that it cannot be solved by standard economic tools. It                means that the persistence of the Eurozone is becoming more and more dependent crisis                measures of the European Central Bank and on enforced „political and economic solidarity”                among  European  countries.  There  are  further  risk  factors,  e.g.  extreme  growth  of                questionable  legislation  and  its  common  ignorance,  corruption  and  systematically                meaningless system of subsidies, further meaningless growth of the European Union and                non-regulated migration, bringing not only problems with the immigrants, but with some                ethnic groups, too (before all with Muslims). And last but not least, there is growth of                elitism and restricting of democracy for traditional Europeans, who are quite poor in some                countries.  In  2016,  a  study  was  performed  by  ECB  in  84  000  European  households;                according to it, ten per cent of the richest households in the Eurozone owned possessions                bigger than 496 000 EURO. Families in Luxemburg are the richest, with median of their                income  reaching  437  500  EURO,  while  in  Lithuania,  the  median  is  only  14  200  EURO                (Aktuálně.cz 2016).                In conclusion, we can say that due to unusual number of crisis factors, there are many                reasons that might initiate serious economic, social, political, ethnical, religious or other                shocks. The level of confidence in EU is historically the lowest and there is a possibility that                other countries might decide to leave it as well as threat of its decay (disintegration) and                                                            581","chaos,  which  may  lead  to  the  end  of  the  existence  of  national  states  and  the  end  of                democracy (Klaus 2012).                 References                 Aktuálně.cz (2016). Majetková nerovnost v eurozóně se zvyšuje, spočítali bankéři. Retrieved                from:   https://zpravy.aktualne.cz/ekonomika/majetkova-nerovnost-v-eurozone-se-zvysuje-                spocitali-bankeri/r~26885238c92411e6be7d002590604f2e/?redirected=1482514348.                Dubnová, H. (2016) Německo je novým „prasátkem“. Až jeho ekonomika padne, stáhne                celou Evropu. Retrieved from: http://zpravy.tiscali.cz/nemecko-je-novym-prasatkem-az-                jeho-ekonomika-padne-stahne-celou-evropu-272806.                Engdahl, F. W. (2016). From Golden Dollar to Petro Dollar to Narco Dollar. Retrieved from:                http://journal-neo.org/2016/05/09/from-golden-dollar-to-petro-dollar-to-narco-dollar/.                Gutteridge, N. (2016). The Euro is FINISHED' Currency's creator says it will soon COLLAPSE                and    destroy   the   EU.   Retrieved   from:    http://www.express.co.uk/news/politics/                721930/Euro-European-Union-collapse-single-currency-Otmar-Issing.                 Janáček, K., Janáčková, S. (2017).  Ekonomický úpadek Evropy. Praha: Institut Václava                Klause, 2017. ISBN 978-80-7542-035-0.                Kalabus, E. (2017). Vícerychlostní Evropa není partnerství rovných. Retrieved from: http://                www.czechfreepress.cz/emil-kalabus/vicerychlostni-evropa-neni-partnerstvi-rovnych.html.                Klaus, V. (2012). Blíží se konec demokracie a národního státu. Retrieved from: http://www.                parlamentnilisty.cz/arena/monitor/Klaus-Blizi-se-konec-demokracie-a-narodniho-statu-                247352.                Lišková,  A.  (2017)  Více  jak  pětina  italských  bank  čeká  na  vlastní  krach.  Retrieved  from:                https://roklen24.cz/a/SeQF2/vice-jak-petina-italskych-bank-ceka-na-vlastni-krach.                Oxfam International (2016). Four EU countries among world’s worst corporate tax havens,                new  report  revers.  Retrieved  from:  https://www.oxfam.org/en/pressroom/  pressreleases                /2016-12-12/four-eu-countries-among-worlds-worst-corporate-tax-havens-new.                 Rejnuš, O. (2015). Contemporary Development of the European Monetary Union and its                Risk  Factors.  In:  Proceedings  of  the  12th  international  scientific  conference  European                Financial Systems 2015. Brno: Masaryk university, s. 498-506. ISBN 978-80-210-7962-5.                Rejnuš,  O.  (2016).  Prediction  of  Future  Development  of  the  World  Economy  Under                Conditions of Negative Interest Rates. In: Proceedings of the 13th international scientific                conference European Financial Systems 2016. Brno: Masaryk university, s. 646-653. ISBN                978-80-210-7962-5.                 Singer, M. (2012). Krize eurozóny, 10. Mezinárodní konference European Governance-Corpora                te Governance „Perspektivy eurozóny“. Retrieved from: http://www.singer_2012_1026_cife.                 Sulík,  R.  (2012).  Euro  nejvíc  poškozuje  ECB.  Retrieved  from:  http://neviditelnypes.                lidovky.cz/evropa-euro-nejvic-poskozuje-ecb-d4l-/pekonomika.aspx?c=A120911_112902                _p_ekonomika_wag.                                                                              582","Comparative Analysis of Financing Sources in Domestic and                                          Foreign Enterprises in Poland                                                      Jerzy Różański                                                       University of Lodz                                                   Faculty of Management                                       Department of Finance and Strategic Management                                             Matejki 22/26, 90-237 Łódź, Poland                                              E-mail: jerzy.rozanski@uni.lodz.pl                 Abstract: Scope of activity of domestic enterprises and foreign enterprises is different.                The foreign enterprises act in more than only one country and that is why they have specific                capital  structure  and  specific  sources  of  financing.  It  confirms  polish  and  international                literature. The aim of this article is to compare sources of financing in domestic and foreign                small and medium enterprises in Poland. The point is whether the differences between the                structure of the financing sources of enterprises operating on an international scale  and                enterprises  operating  only  within  the  borders  of  one  country  give  the  first  group  of                enterprises a competitive advantage over the latter. The research was conducted in years                2008-2017 in  300  branches  of  international  enterprises  in  Poland  and in  300  domestic                enterprises, using documents prepared by enterprises and questionnaire of inquiry. The                research  procedure  was  as  follows.  After  a  random  selection  of  enterprises  (stratified                random sampling with division into small and medium enterprises) and collecting basic                data on the selected enterprises from the register of enterprises, a specialized unit dealing                with interviews and surveys of enterprises was engaged. Next, employees of this unit met                with the managers appointed by the management board of the selected companies and                helped  them  to  complete  the  questionnaire  prepared  by  the  author  in  each company's                headquarters. This resulted in 100% of responses in the selected companies that were                representatives of the larger group. The empirical findings suggest the differences between                sources of financing. The average, in this period, foreign enterprises used mainly two basic                sources of financing bank credit and funds created by mother enterprise. They took into                account  financing  by  retained  profit  and  venture-capital  funds.  Two  main  sources  of                financing in domestic enterprises, the most popular were bank credit and leasing. So we                noticed some typical differences. First of all, branches of foreign enterprises very seldom                use leasing as a source of financing, in contrast, in polish enterprises this form is very                popular.  Secondly,  additional  support  for  these  branches  is  financing  by  mother  –                enterprises – it gives possibilities of faster development and more flexible financing position                of their enterprises in host country. Finally the foreign enterprises prefer using alternative                sources of financing, rather than traditional ones.                Keywords: financing sources, domestic and foreign enterprises, parent company, daughter                company, criteria for financing sources                JEL code: F20, F21, F23, F30, F65                 1 Introduction                There  are  significant  differences  between  enterprises  operating  within  one  country  and                those  running  their  activity  internationally.  They  concern  not  only  the  organizational                structure, which in the case of enterprises  operating internationally must anticipate the                presence of the enterprise's headquarters in the home country (mother company) and the                functioning of company branches in the host countries. It also involves more multifactorial                systems of mutual financing of individual units included in such an organizationally complex                enterprise.  Admittedly, enterprises  operating  on  one  national  market  may  take  various                forms  as  well,  for  example,  a  multi-plant  enterprise,  and  the  flow  of  funds  between                individual plants, often located in different regions of the country, can be quite complicated,                but the scale of problems related to financial flows in an enterprise operating internationally                is much more profound.The aim of the article is to compare sources of financing in domestic                enterprises and branches of foreign small and medium enterprises in Poland. The point is                                                             583","whether the differences between the structure of financing sources of enterprises operating                on an international scale and enterprises operating only within the borders of one country                give the first group of enterprises a competitive advantage over the latter.                 2 Theoretical bases for financing enterprises operating nationally and                internationally                The differences between financing an enterprise operating on the domestic market and                enterprises functioning  on the international  market  are  noticed in  the literature  on  the                subject.  A.  Duliniec  states  that  “in  enterprises  operating  within  the  international                corporation, there is a much greater diversity of forms and places (markets) in terms of                obtaining financing compared to enterprises operating independently (without capital ties                with other companies in the country and abroad). Diversification of financing sources on                an international scale allows not only for the selection of the cheapest sources and forms                of  financing,  but  also  for  the  reduction  of  currency  risk,  which  inevitably  accompanies                financing with debt denominated in foreign currencies” (Duliniec, 2007, pp. 161-162).                 The international nature of the company's activity means that capital providers are more                willing to involve their resources in financing the current and development activities of such                an enterprise. While operating in different countries, such an enterprise has a better chance                of choosing sources of financing that are not only characterized by low costs of raising                capital, but are also more easily available, and the procedures related to obtaining them                are as simple as possible.                Naturally, an international company must also be vary of the possibility of occurrence of                certain dangers that may be associated with the use of financing by capital providers from                different countries, often with a different legal or tax system, or with high interest rate                changeability in such countries.                It also needs to consider the possibility of money transfer problems occurring between host                countries and the home country, and between individual host countries.                In the literature on the subject the greatest attention is paid to the financial flows occurring                on the line the parent company - the daughter company, and the specificity of financing                the daughter company as a subsidiary in relation to the parent company.                Two models of financing branches of international enterprises were presented in a similar                time by M. Perlitz (2000) and L. Kruschwitz (1999).                                                                                             584","Figure 1 The Perlitz’s Model                                                      Revenues from                                                  depreciation write-offs                          Internal financing (cash                                  flow)                                                      Profit retained                                                                            Financing by the                                                                            mother company                                                  Company’s own equity                                                                           Financing under an                                                                          incorporated holding                                                                               company                            External financing                             within the group                                                                          Financing by another                                                                           daughter company                                                     Foreign capital                                                                             Financing by the                                                                            mother company                                                                               Public sources                                                  Local equity (e.g. joint                                                        venture)                                                                             Private capital                                                                               providers                                                   Local foreign capital                          External financing from                             outside the group                           Country of origin of the                                                                            mother company                                                        Foreign capital         Third country                                                                             International financial                                                                               markets                    Source: M. Perlitz, “Internationales Management”, Lucius and Lucius, Stuttgart 2000, p. 509                 The Perlitz's Model shows that alongside the classic forms of financing such as:                    •   internal financing;                    •   external financing, using foreign capital,                there  is  also  financing,  which  M.  Perlitz  called  “external  financing  included  within  the                group.” It refers to the financing of branches, which is launched (from the company's own                or  foreign  funds)  by  the  parent  company  for  subsidiary  companies  grouped  in  an                international enterprise.                An  enterprise  operating  in  one  country  does  not  have  such  possibilities.  It  must  use                domestic  sources  of financing. There  are,  of  course,  possibilities  of transferring  money                between individual plants in a multi-plant enterprise, as well as financial transfers within                the  created  capital  groups.  However,  the  possibilities  of  controlling  capital  flows  are                incomparably smaller than in an international enterprise, which has the capability of using                mutual financing  of its units  within the  whole  enterprise  and  also  has  better access  to                international  capital  markets  and  the  international  banking  system.  Also  L.  Kruschwitz                (Kruschwitz 1999, p. 524) highlights the diversity of sources of financing at the disposal of                an enterprise operating internationally.                                                              585","Most often, the analyses of the advantages and disadvantages associated with financing                an enterprise operating internationally list the following benefits (Block and Hirt, 1992, pp.                644-646):                    •   more chances of raising capital, which is associated with a greater variety of ways                       to finance the company;                    •   opportunity to find cheaper sources of raising capital;                    •   possibility of flexible financing within an international enterprise (flow of shares and                       loans from one company to another);                    •   stronger bargaining position of the companies (the entire group is responsible for                       the financial and loan credibility of the companies).                 The disadvantages associated with financing enterprises operating internationally include:                the need, occurring sometimes, to finance the activity with foreign capital offered on local                money and capital markets, with high volatility of these markets …;                 occurrence of additional costs related to raising capital (transfer costs, currency exchange                costs, increased capital commissions);                the dangers connected with cooperation with a local enterprise with which a joint-venture                has been established.                Therefore,  for  enterprises  operating  internationally,  the  economic,  social  and  political                stabilization of the countries in which they invest is of fundamental importance. The lack                of  such  stabilization  makes it  very  difficult for  these  enterprises to  operate  effectively.                Unfortunately,  domestic  enterprises  do  not  have  the  choice  regarding  the  investment                location and they need to operate in their country, regardless of its economic and social                situation. A foreign investor, though, can always transfer the business to a country where                there are better operating conditions than in another host country.                 3 Results of research on the financing of foreign and domestic                enterprises in Poland                 The research was conducted in a group of 300 foreign enterprises that have their                branches in Poland and 300 enterprises, which are domestic companies operating only on                the Polish market.                In order to ensure comparability of these two groups of enterprises, stratified random                sampling was applied, taking into account two factors:                     •   size of the enterprise,                    •   sector of the economy in which the enterprise operates.                The random nature of the selection of companies for research ensured the                representativeness of the results of this study.                A questionnaire survey on the sources of financing of foreign enterprises operating in the                Łódź  region,  launched  in  2008,  included  300  foreign  enterprises  (their  branches).  The                method  of  random  sampling  was  used.  The  largest  group  were  micro-enterprises  -                employing up to 9 people (117 enterprises) - 38.9% of all companies surveyed, following                that were small enterprises - employing from 10 to 49 people which accounted for 32.2%                of all enterprises, next were medium enterprises (from 50 to 249 people employed) that                accounted  for  18.9%  of  the  respondents,  and  large  companies  with  250  and  more                employees - which made only 8% of the total sample. In the majority of cases these were                enterprises either with 100% of foreign capital (51%) or in which the greatest share of                capital was of foreign origin (29.9%). Only 17% of enterprises had a share of foreign capital                below 50%. This was related to the next characteristic of these enterprises. Namely, about                80% of the surveyed foreign enterprises in Poland were established as a new business                entity built from scratch (greenfield), and only 8.10% of the surveyed enterprises were                established as a result of taking over a local enterprise (brownfield). 9.1% of enterprises                were established as a joint-venture company of foreign capital with a local entrepreneur.                                                             586","Hence,  the  overwhelming  predominance  of  foreign  capital  in  the  companies  created  in                Poland was revealed. The research concerned the funding associated with the formation of                subsidiaries in the host country and the financing of the already existing foreign companies                in Poland.                The paper will discuss the second case. The research also included preferences related to                financing, which were explored in the surveys. The results of the research are presented                in Table 1.                          Table 1 Sources of Financing of a Foreign Enterprise (% of responses)                    SPECIFICATION               I PLACE              II PLACE             III  PLACE                 Bank loan - bank in the          24.9                  0.7                    -                 home country                 Bank loan - bank in the          10.0                  3.3                    -                 host country                 Bank  loan  -  third-            1.0                    -                    0.2                 country bank                 Investment        funds                 created  by  the  parent         32.6                  6.6                    -                 company                 Foreign     investment           3.3                   1.0                   0.7                 funds                 Profit retained                  16.6                  13.0                  3.4                 Depreciation write-offs          0.3                   3.3                   1.0                 Sale  of  unnecessary            1.7                   1.3                    -                 assets                 Issue of shares                  0.3                   0.3                    -                 Other                            7.6                    -                     -                                         Source: Author's own study based on surveys                 According to the survey results, the most common source of financing subsidiaries were                special  funds  created  by  the  parent  company  and  bank  loans.  It  is  noteworthy  that                regardless of when a branch was established (and the research showed that most of the                branches had already had several years of activity in Poland), the banks located in the                home country (mother companies) enjoyed the greatest trust, and the  banks operating in                the host country were used as the second option. Loans using the capital from third-country                banks were rare. On the other hand, retained profit, significantly lower depreciation write-                offs and the sale of unnecessary assets were of great importance.                The use of share issues as a source of capital was sporadic. However, foreign investment                funds were used as the source of external financing, usually in the form of venture capital                funds.                The  financial  support  of  the  subsidiary  by  the  parent  company  is  a  factor  that  is  very                important for the financing of branch development and its competitiveness. The scale of                this support poses a threat to domestic enterprises that are potential competitors for a                foreign enterprise.                Analogous surveys concerning sources of financing were conducted on a larger group of                domestic enterprises operating in the Łódź region. Of this group, 300 were selected for                comparison  -  namely  companies  of  similar  size  structure  to  the  structure  of  foreign                enterprises.                The following criteria were included in the list of the basic criteria for selecting the financing                sources of the company - in the order of importance of these criteria:                    •  costs of raising capital,                    •  credibility of financial institutions;                    •  availability of information about a given source of financing;                                                             587","•  extent of the proposed financing;                    •  burdensome procedures associated with financing;                    •  ability to obtain funds quickly.                These considerations resulted in the following sources of enterprise financing being used                in the studied group of enterprises, as presented below:                     •  loans and credits from financial institutions - 51.5% of cases;                    •  leasing - 50.6% of cases;                    •  depreciation write-offs - 44.3% of cases;                    •  retained profit - 37.8% of cases;                    •  aid programs (subsidies) - 36.4% of cases;                    •  trade credit - 31.5% of cases;                    •  additional payments of shareholders - 20.7% of cases;                    •  financial support of family, friends - 17.7% of cases.                 The research results indicate that most often domestic enterprises use credit and leasing                as the sources of financing, i.e. foreign sources of financing. A slightly smaller share was                that of the internal sources of financing, such as depreciation write-offs and retained profit.                Aid funds still play a large role in financing. Since the research involved mainly small and                medium enterprises, the participation of such financing sources, for instance additional                contributions from partners or loans from family and friends, is quite characteristic.                 A specific form of financing is trade credit, which is a hidden form of incentive to deal with                the supplier of materials, parts, but also finished products.                Comparing  the  sources  of  financing  of  domestic  and  foreign  enterprises,  we  see  both                similarities and differences. In both cases, the most popular source of financing is a bank                loan,  while  foreign  enterprises  do  not  use  leasing.  Internal  financing  sources  play  a                significant role in both groups of enterprises.                In the case of domestic enterprises, the equivalent of investment funds launched by the                parent company for the branch were aid funds, additional contributions of partners, or                family support.                The presented research results are not much different from the results of studies carried                out  in  Poland  in  the  years  1999-2002  as  part  of  a  project  of  the  Scientific  Research                Committee.                 The research carried out then (with the participation of the author of the article) covered                24 enterprises with foreign capital and 26 domestic enterprises. Statistical data of the State                Foreign Investment Agency, the Ministry of Economy and the Central Statistical Office were                used, as well as surveys conducted in randomly selected enterprises.                 The study showed that internal sources were the basic sources of financing in domestic                enterprises:                     •   retained profit,                    •   depreciation write-offs,                and among external sources:                     •   bank credit,                    •   leasing,                    •   trade credit.                In enterprises with foreign capital (branches located in Poland), fear of excessive use of                equity was evident - hence the vast majority of foreign resources (primarily bank credits).                This was connected with worse assessment of the prospects for the development of the                Polish economy and, in particular, its stability than in the studies conducted in 2008-2017,                when the Polish economy was assessed better.                                                              588","The  results  of  both  surveys  confirm  the  author's  opinion  that  in  national  enterprises                conservatism  still  prevails  in  the  selection  of  company's  own  and  foreign  sources  of                financing, while foreign enterprises are more bold in choosing their sources of financing.                The research also showed that small and medium enterprises, regardless of the form of                ownership, are characterized by a less diversified capital structure than large enterprises,                and this does not change over the years.                 4 Summary                 The results of the research conducted among foreign and domestic enterprises operating                in Poland (on the example of the Łódź region) showed that both kinds of companies use                traditional  sources  of  financing,  foreign  (credit),  and  their  own  (retained  profit).  The                domestic  companies  also  used  depreciation  write-offs.  Foreign  enterprises  used  foreign                investment funds (mainly venture capital). However, the share of less traditional sources                of financing is small.                All this causes that the capital structure of both groups of enterprises is quite limited, which                reduces their development opportunities. The research conducted in the group of domestic                enterprises indicated several reasons explaining why less traditional sources of financing                are not used. The reasons enumerated included:                     •   too high costs of raising capital;                    •   lack of knowledge of the given sources of financing;                    •   too  complicated  procedures  accompanying  the  acquisition  of  a  given  source  of                       financing.                The  reasons  why  such  sources  of  financing  as  private  equity  funds,  venture  capital,                business  angels,  and  issue  of  shares  or  bonds  were  not  used  in  foreign  enterprises  in                general or were used to a very small extent could be similar.                 References                 Ajami, R. A., Bear, M. M. (2006). The Global Enterprises. New York: International Business                Press.                Birkinshaw, J., Hood, N. (1998). Multinational Subsidiary Evolution Capability and Charter                Change in Foreign owned Subsidiary Companies.  The Academy of Management Review,                vol. 23(4), pp. 773-795.                Block, S. B., Hirt, G. A, Danielsen, B. R. (1992).  Foundation of Financial Management.                Boston: Irwin, Boston.                 Buckley, A. (1996). Multinational Finance. London: Prentice Hall.                Damodaran, A. (1997).  Corporate Finance-Theory and Practice. Ney York: J. Wiley and                Sons.                 Forte,  R.,  Brandao,  A.  (2008).  A  Signalling  Model  of  Firm’s  Foreign  Direct  Investment                Relocation Decision. International Journal of the Economics of Business, vol. 15(3), pp.                339-357.                Gost,  M.,  Hermann,  R.  (2008).  Determinants  of  Foreign  Direct  Investment  of  OECD                Countries. International Economic Journal, vol. 22(4), pp. 509-524.                 Głuchowski, J., Luterski, R., Jaaskelainen, V., Nielsen, N. (2001). Zarządzanie finansami w                korporacjach  międzynarodowych.  Toruń:  Wydawnictwo  Naukowe  Uniwersytetu  Mikołaja                Kopernika.                Kruschwitz, L. (1999). Finanzierung und Investition. München/Wien: R. Oldenbourg.                Perlitz, M. (2000). Internationales Management. Stuttgart: Lucius Lucius.                 Plawgo,  B.  (2004).  Zachowania  małych  i  średnich  przedsiębiorstw  w  procesie                internacjonalizacji. Warszawa: Orgmasz.                                                             589","Rymarczyk,  J.  (2004).  Internacjonalizacja  i  globalizacja  przedsiębiorstwa”.  Warszawa:                PWE.                 Wen,  M.  (2007).  Foreign  Direct  Investment,  Regional  Market  Conditions  and  Regional                Development.  Economics  of  Transition,  The  European  Bank  for  Reconstruction  and                Development, vol. 15(1), pp. 125-151.                Zorska, A. (2005). Efekty zagranicznych inwestycje bezpośrednich w gospodarkach krajów                goszczących. Master of Business Administration, vol. 5.                                                                                                                                     590","Profitability of Selected Technical Analysis Indicators                                                                               2                                                           1                                            Pavla Říhová , Milan Svoboda                                             1 University of West Bohemia in Pilsen                            Faculty of Economics, Department of Economics and Quantitative Methods                                         Univerzitní 22, 306 14 Plzeň, Czech Republic                                                    divisova@kem.zcu.cz                                              2 University of West Bohemia in Pilsen                            Faculty of Economics, Department of Economics and Quantitative Methods                                         Univerzitní 22, 306 14 Plzeň, Czech Republic                                                   svobodm@kem.zcu.cz                 Abstract: This empirical study is another part of our long-term research that deals with                stochastic  modelling  of  a  short-term  share  price  development.  We  use  Markov  Chain                Analysis  (MCA)  to  predict  the  share  price  development.  The  principal  of  this  method                consists in finding such states in which there is a high probability of changing the tertiary                trend. These states are then used to generate buy and sell signals. The aim of this study                is to compare the profitability of a business strategy using MCA with varying state space                with  the  selected  indicators  of  technical  analysis.  The  indicators  that  are  often                recommended in traders´ articles, i.e. MACD, RSI and OBV, have been selected for this                comparison. The basic success rate of individual methods is to overcome the profitability                of the Buy \& Hold (B\&H) passive strategy, profitability of which is calculated as well. The                study was performed using historical daily prices (open and close) of the ČEZ´s shares for                a period of 12 years; from the beginning of 2006 until the end of 2017 (3 010 trading                days). Based on the results of some selected technical indicators it can be stated that none                of them outperforms the B\&H strategy in the long term, while the models based on MCA                are able to outperform the B\&H strategy. If we include fees, the trading strategy using the                MCA is unprofitable. Only trading based on the RSI would bring a positive appreciation and                slightly outperform the B\&H strategy.                Keywords: Markov chain analysis, technical analysis, MACD, OBV, RSI                JEL codes: C53, G17                 1 Introduction                 A technical analysis (TA) may be one of the approaches used by traders for predicting the                future development of share prices, exchange rates or commodities. TA is an extensive set                of methods that predict future prices from past prices and trade volumes. Principles and                methods  of  TA  are  described,  for  example,  by  Murphy  (1999).  The  basis  of  TA  can  be                summarized by the following three theses:                    •   The  share  price is  determined  only  by a  mutual interaction  between  supply  and                       demand.  Supply  and  demand  are  influenced  by  fundamental  and  psychological                       factors.                    •   Share prices move in trends which are inertial. A trend change is caused by a change                       of  ratio  between  the  selling  and  buying  parties.  These  trend  changes  must  be                       identified in time by studying historical prices and trade volumes. Technical analysts                       distinguish three  types  of trends.  A  primary  trend  which lasts from  one  year to                       several years, a secondary trend which lasts for several months and a minor trend                       which lasts for several days to weeks.                    •   Development  cycles  and formations  are  repeated.  It is  caused  by  the  nature  of                       human  behaviour  that  tends  to  react  in  a  similar  way  under  the  same                       circumstances. This fact allows us to predict the future share price development.                TA is based on some scientific theories; however, it is, above all, based on a number of                empirical  findings.  Based  on  these  findings,  individual  methods  are  created  and  their                number is practically unlimited. There are many forms of technical analysis which can be                divided into 2 main groups:                                                            591","•   Graphical methods that rely on regularly repeated chart patterns. Chart patterns                       are a valuable part of technical analysis. Based on historical data, analytics have                       created standardized graphical patterns of share price development. They use them                       to identify signs of future share price movements.                    •   Methods based on technical indicators. Indicators represent a statistical approach                       to  technical  analysis.  These  indicators  are  calculated  on  the  basis  of  historical                       market data (price or volume). The role of an indicator is to provide a secondary                       measure to the actual price movements and reveal the trend change. There are                       many  types of technical indicators. One of the most popular group are methods                       based on moving averages.                 Methods  based  on  the  Markov  chain  analysis  (MCA)  belong  to  the  neglected  group  of                indicators. The MCA is used very little for a stochastic description of the behaviour of stock                markets. The infrequent application of MCA is probably also caused by some studies (Zhang                and  Zhang,  2009),  (Doubleday  and  Esunge,  2011)  and  (Vasanthi  at  al.,  2011).  The                common feature of these studies is the fact that they define the state space very simply.                The state space is defined on the basis of the intensity of daily changes and it does not                allow for suitable applications. Indicators based on this study were not able to beat the                Buy\&Hold  (B\&H)  passive  strategy.  Contrary  to  the  above,  the  study  (Svoboda,  2016)                defines the state space on the basis of the intensity of daily cumulative changes in share                prices. Indicators based on this approach have managed to overcome the B\&H strategy.                However, the empirical study (Říhová and Svoboda, 2017) has proved that trading models                with the varying state space, calculated as a moving standard deviation, on the average,                outperform trading models with the unvarying state space.                The aim of this study is to compare the profitability of a business strategy using MCA with                varying state space with the selected indicators of technical analysis. According to traders                the frequently recommended indicators, e. g. (Mitchell, 2018) have been selected for this                comparison. According to this author, MACD, RSI and OBV rank among the most common                indicators. The basic success rate of individual methods is to overcome the profitability of                the Buy \& Hold (B\&H) passive strategy, profitability of which is calculated as well.                 2 Methodology and Data                 MCA                The MCA indicator (Markov chain analysis) focuses on the estimate of tertiary trend change.                The MCA indicator is described for example in (Svoboda, 2016) or (Říhová and Svoboda,                2017). The design of the MCA indicator is based on the assumption that a share price                moves in short term trends and during the trend the share price accumulates a certain                profit or a loss in relation to the price at the beginning of the trend. The probability of this                trend change grows with an increasing accumulated loss or profit. One of eight states is                assigned to each trading day based on the accumulated profit or loss. The state when the                share price increases (decreases) is labelled Gi (Di). A general model of the state space is                defined by the following principle:                            D4: yt < -3Δt                    G1: 0 ≤ yt < 1Δt                            D3: -3Δt ≤ yt < -2Δt             G2: 1Δt ≤ yt < 2Δt                            D2: -2Δt ≤ yt < -1Δt             G3: 2Δt ≤ yt < 3Δt                             D1: -1Δt ≤ yt < 0                G4: 3Δt ≤ yt                 where Yt is a cumulative change of price and is interpreted as short basic indexes of daily                closing prices where the basic period is the day of the minor trend change, i.e. the transition                from a decrease into a growth or vice versa, yt is the percentage expression of Yt and Δt is                the width of the interval and is calculated as a moving standard deviation of length 20.                The above-mentioned parameters can be calculated according to the following relations:                                                               592","P                                                       P                                                            (                       Y =  Y    t    if   (P   P   P  )   or  P   P   P  )  else  Y =  t  ,        (1)                              1 −                         t                             t                               P t−  1   t− 2  t  1 −  t  1 −  t− 2  t  1 −  t  1 −  t  P t−  1                        y =  Y −  1,                                                                    (2)                         t   t                               1  19        2                         =        ( y −  y  ) ,                                                       (3)                         t            i   t                              20 i= 0                where Pt is the daily closing price in time t, Pt-1 is the daily closing price in time t-1 and Pt-                2 is the daily closing price in time t-2.                According to Říhová and Svoboda (2017), good results are achieved when trading signals                are generated by the following states:                                               Sell signal: in state G3 or G4                                               Buy signal: in state D3 or D4                 MACD                 Indicator  MACD  (Moving  average  convergence  divergence)  is  one  of  the  most  used                technical indicators. Traders give it the greatest attention. The professional studies, for                example (Anghel, 2015), have stated that traders using the MACD could sometimes obtain                abnormal  profit.  This  is  because  markets  are  mostly  inefficient.  It  is  a  trend-following                indicator that shows the relationship between two exponential moving averages of prices                (EMA). EMA is calculated according to the following formula:                         EMA  , t M  =   P + (1   −  ) EMA t− 1,M  ,                                   (4)                                    t                where EMAt,M represents exponential moving average in time t of the length M, Pt is the                daily  closing  price  in  time  t  and  λ is  a  weighting  multiplier  calculated  by  the  equation                  =  2  ( / M +  ) 1 . The initial EMA (EMAt=0) is usually a simple moving average of the last  M                share prices, thus from price Pt=-1 to Pt=-M.                The  MACD is  usually  calculated  by  subtracting the  26-day exponential  moving  average                from the 12-day EMA. MACD can subsequently be calculated according to the following                formula:                         MACD =  t  EMA t ,12  −  EMA t ,26                                              (5)                 The MACD can be interpreted using three different methods (crossovers, divergence and                dramatic  rise).  The first method  (crossovers) is  used in  this  study.  It  means  that it is                important when the MACD crosses the signal line, which is usually calculated as 9-day                exponential moving average of the MACD line. Signal line can be expressed as follows:                        SIGNAL =   EMA  (MACD  t t ,9                                                   (6)                                                )                                t                The buy and sell signal is generated when the MACD crosses the signal line. Trading signals                are subsequently generated by the following relations:                              Sell signal:    MACD t  1 −    SIGNAL t  1 −     and    MACD   t  SIGNAL t                               Buy signal:    MACD   t−  1    SIGNAL t−  1    and    MACD   t  SIGNAL t                 RSI                 The RSI indicator (Relative strength index) belongs to the types of indicators that are called                leading indicators. There are not many studies dealing with it. As stated for example in                (Halilbegovic at al., 2018), the reason for this may be the fact that it is not able to generate                a sustainable profit. The indicator moves between zero to 100, plotting recent price gains                                                             593","versus recent price losses. To calculate it, positive Ut and negative Dt share price changes                during the specified time period were used.  Ut (up period, gains) and  Dt (down period,                losses) can be calculated according to the following relations:                       U =  P P  t  1 −    if  P   P t  1 −    else  U = 0 ,                           (7)                               −                              t                                                       t                         t                                         t                        D =  P −  P t   if  P   P t  1 −    else  D =  0.                              (8)                               1 −                              t                                         t                                                       t                         t                And the RSI is formally calculated as follows:                                     100                        RSI =  100 −       ,                                                            (9)                                     +                           t                                    1 RS t                where RS is calculated according to the following relation:                                   t                              1      U                        RS =  n  i t n+ 1  i  .                                                        (10)                                 = −                          t                                   t                              1      D                              n  i t n+ 1  i                                 = −                The default time period for comparing up to down periods is 14 (n = 14) and we follow it                in this study. The important fact for traders is when the RSI reaches values of 70 and of                30. If the RSI exceeds the value of 70, it indicates that a market is becoming overbought                and it is expected that the share price will decrease in a short time. The RSI value below                30 indicates that a market is oversold and the share price will increase in a short time.                Trading signals are generated by the following relations:                                        Sell signal:    RSI t  1 −    70   and    RSI   t  70                                         Buy signal:    RSI t− 1    30   and    RSI   t  30                  OBV                Indicator OBV (On-balance volume) belongs to the indicators that measure positive and                negative volume flow. The volume may be represented as a daily total number of traded                shares  or  as  a  turnover  (a number  of traded  shares  multiplied  by  price).  According  to                (Tsang and Chong, 2009), there are not many empirical studies that are concerned with                the profitability of volume-based technical indicators, although business strategies using                the OBV can reward investors with notable yields. The OBV value is calculated according                to the following relations:                        OBV =  OBV +    V      if     P   P                                                        1 −                            t       t  1 −  t     t   t ,                        OBV =  OBV −   V t     if     P   P t ,                                       (11)                                     1 −                                                       1 −                                    t                                                 t                            t                        OBV =  t  OBV t−  1     if     P =  t  P t−  1 ,                where OBVt is the the value of OBV indicator in time t, V0 = 0, Vt is a trading volume in                time t (we use the number of traded shares in this study) and Pt is the daily closing price                in time t.                 The OBV is used to confirm the price trend and look for divergences between the OBV and                price to predict price movements. The rising OBV reflects a positive volume pressure that                can lead to higher prices, the falling OBV reflects a negative volume pressure that can lead                to  lower  prices.  Weaknesses  of  the  increasing  (decreasing)  trend  are  signalled  by  the                situation when the growth (decrease) of share price to the new high (low) is not confirmed                in the increasing (decreasing) value of the indicator in the same direction. In this situation                it is possible to expect a turn in the trend to decreasing (increasing). Literature does not                unambiguously  mention  when  the  buy  and  sell  signal  should  be  generated.  For  the                                                             594","purposes of this study, buy and sell signals are generated as follows. The sell (buy) signal                is generated in the situation when the new high (low) of price is not related to the new                high (low) point of the OBV indicator. It can be formally described as follows:                        P min,i  =  P t  1 −     if    P   P   P t           OBV min,i  = OBV t  1 −     if    OBV t− 2   OBV t  1 −    OBV                                              1 −                                       t−                                             t                                                                                                  t                                         2                        P max,i  = P t  1 −     if    P   P   P t            OBV max,i  = OBV t  1 −     if    OBV t− 2   OBV t  1 −    OBV    (12)                                       t−                                              1 −                                         2                                             t                                                                                                   t                Trading signals are therefore generated by the following relations:                                     Sell signal:    P max,i−  1    P max,i     and   OBV max,i−  1    OBV max,i                                     Buy signal:    P min,i−  1    P min,i     and   OBV min,i−  1    OBV min,i                Data and invested capital value                The profitability of the selected indicators of technical analysis has been verified on  the                ČEZ´s shares for a period of 12 years; from the beginning of 2006 until the end of 2017                (3 010  trading  days).  The  data  were  provided  by  Patria  Direct.  During  this  period,  the                company paid out dividends. Trading is executed according to the following rules:                    •   One trade (transaction) means the buying and subsequent selling of shares.                    •   If a buy or sell signal is generated on one day, the trade is implemented for the                       opening price from the following day.                    •   The whole capital is always invested (it means that it is theoretically possible to buy                       a part of a share).                    •   We do not take any transaction fees into consideration.                    •   We count and reinvest the paid out dividends after tax in case that we obtained the                       shares on the record day.                    •   A short selling is not taken into account.                    •   Two consecutive purchases are not possible.                 The invested capital value is calculated according to the following relation:                              n                        C n = C 0   S i  + D i  ,                                                    (13)                              = i 1  B i                where C0 = 1.000 is the initial value of the capital, Cn is the value of the capital after the                n  transaction, Si is the selling price in the i  transaction, Di are dividends after taxation                 th                                                             th                in case that during the i transaction there was ex dividend day, Bi is the purchasing price                                        th                       th                in the i  transaction.                3 Results and Discussion                 The profitability of each indicator is compared with the Buy \& Hold (B\&H) passive strategy.                This strategy is based on the fact that shares are purchased at the beginning of the period                and then they are no longer traded (regardless of fluctuations in the market). As for this                study, we only buy some more shares for paid dividends. If we invested one unit of capital                in ČEZ shares at the beginning of 2006 and reinvested the dividends paid, the value of                capital would be 1.079 the end of 2017.                 Figure 1 shows the development of the average value of invested capital over time if we                buy  and  sell  shares  according  to  trading  signals  of  all  the  selected  indicators  (and  in                comparison to the B\&H passive strategy).                                                                   595","Figure 1 Development of Capital Value                    2,5                       2                      1,5                       1                      0,5                     2006   2007  2008   2009   2010  2011   2012  2013   2014   2015  2016   2017  2018                               B\&H             MCA             MACD              RSI           OBV                                                Source: authors, own calculation                 The value of invested capital is recalculated when the share is sold (it is not calculated                continuously). Only at the end of 2017, if we had shares bought, we valued them at the                current price. This was the case of the OBV indicator. The Figure 1 shows the rapid rise in                the OBV value at the end of the period. As it is also evident from the Figure 1, except for                the OBV, all the other indicators have outperformed the B\&H strategy. The MCA indicator                is  the  most  successful  of  them.  The  indicator  detects  the  trend  change  best.  It  has                constantly outperformed the B\&H strategy since 2009. As we assumed, it has generated                the largest number of trades. Its ratio between profitable trade and a trade at a loss is also                the best one. The other indicators have not outperformed the B\&H strategy until 2016.                None of them was able to react to a rapid rise in a share price between 2006 and 2007.                 However,  the  period  of  12  years  is  too  long.  Investors  are  more  interested  in  the                performance of indicators in a shorter period. Therefore, we divided the defined period into                tree-years periods (the recommended investment horizon for investment in shares is 3                years). The profitability of the particular method was explored in these sub-periods and                therefore tree-year yields were calculated. The results of the calculations are given in Table                1.                                                 Table 1 Three-year Yields                             Date               B\&H         MCA        MACD         RSI         OBV                         2015 - 2017            -2.8%      15.8%       30.7%       15.7%       19.0%                         2014 - 2016            -6.2%      23.0%        7.4%       5.2%        -6.5%                         2013 - 2015           -27.2%       1.6%      -22.5%       3.8%        1.9%                         2012 - 2014           -13.7%      -8.3%      -21.2%       2.0%        2.6%                         2011 - 2013           -24.5%      -8.9%       -9.9%      -17.6%       8.0%                         2010 - 2012           -12.6%      15.0%       -5.2%      -18.1%       2.4%                         2009 - 2011           11.4%       53.2%       19.3%      -17.3%       54.9%                         2008 - 2010           -34.9%      21.0%      -26.2%       22.3%       -8.1%                         2007 - 2009            -2.4%      47.7%        7.5%       24.6%      -18.3%                         2006 - 2008            -2.8%      15.8%       30.7%       24.6%      -44.8%                         2006 – 2017            7.9%      111.3%      27.0%       21.6%        4.4%                                                   Source: own calculation                 We can conclude from the results in Table 1 that the MCA was the most successful indicator                also in the tree-year period. It was able to outperform the B\&H strategy for the whole                                                             596","period as the only one. Unfortunately, the excessive investors´ optimism of the MCA was                dampened down by the last analysis of selected indicators. The analysis focused on the                characteristics of selected indicators.                 For each indicator, these characteristics were calculated for the whole period: total yield,                average  annual  yield,  total  number  of  trades,  total  number  of  profitable  trades,  total                number of trades at a loss, average yield on a transaction, average yield on a profitable                trade  and  average yield  on  a  trade  at a loss.  All averages  are  calculated  as geometric                means and expressed as a percentage. The aforesaid characteristics are given in Table 2.                                      Table 2 Characteristics of Selected Indicators                              Characteristics              MCA        MACD         RSI         OBV                             Total yield (%)              111.3       27.0        21.6         4.4                       Average annual yield (%)           6.43        2.01        1.64        0.36                        Total number of trades            140         115          13          45                       Total number of profitable          92          48          7           25                                  trades                      Total number of trades at a          48          67          6           20                                   loss                           Average yield on a             0.54        0.21        1.52         0.1                            transaction (%)                           Average yield on a             2.62        5.26        8.36        5.64                          profitable trade (%)                      Average yield on a trade at        -3.35       -3.26       -5.92       -6.43                               a loss (%)                                                   Source: own calculation                As is obvious from the results, total yield of all indicators was positive. Nevertheless, the                most important characteristic for the practical trading is the average yield on a transaction.                Stockbrokers  charge  a  brokerage  fee  for  services  such  as  purchases,  sales  and  other                services connected to trading with shares. For example, the company Fio is the favourite                broker in the Czech Republic. Its trade transaction fee is 0.4 %.                  It means that the average yield on a transaction (buy and sell) must be 0.8 % or higher,                if we want to generate a profit on a transaction. At this point the dream of trading strategy                using  the  MCA  is  over.  If  we  include  fees,  the  trading  strategy  using  the  MCA  is                unprofitable. Only trading based on the RSI would bring a positive appreciation and slightly                outperform the B\&H strategy.                 4 Conclusions                This  study  shows  that  trading  using  the  technical  analysis  indicators  is  advantageous                mainly for brokers who charge brokerage fee for each transaction. This is also proved for                example in (Tharavanij at al., 2015). If we don´t include the brokerage fee, the trading                strategy using the MCA is the most successful one (compared to the other 3 strategies).                However, it is possible that the MCA indicator may outperform the B\&H strategy in case of                its different parameters (different length of moving standard deviation or different states                to generate trading signals) even after including transaction fees. The promising results of                this study are the reason why we are going to continue in the research in this field in the                future. We intend to concentrate on the following areas:                    •   to find the best length of the moving standard deviation;                    •   to confirm the results of this study by applying them to other shares;                    •   to perform a detailed analysis of successfulness of the trading strategies based on                       growth, digressive and sideway primary trends.                                                                597","Acknowledgements                The research project was supported by Grant SGS-2018-042 of the Faculty of Economics,                University of West Bohemia in Pilsen.                 References                Anghel,  G. D.  (2015).  Stock  market  efficiency  and  the  MACD. Evidence from  countries                around the world. Procedia Economics and Finance, vol. 32, pp. 1414-1431.                Doubleday, K. J., Esunge, J., N. (2011). Application of Markov Chains to Stock Trends.                Journal of Mathematics and Statistics, vol. 7(2), pp. 103-106.                Halilbegovic,  S.,  Celebic,  N.,  Kulovic,  D.  (2018).  Analysis  of  a  standalone  usage  and                limitations of relative strength index indicator in stock trading. Ecoforum, vol. 7(1).                 Mitchell,  C.  (2018).  Trend  trading:  The  4  most  common  indicators.  Retrieved  from:                https://www.investopedia.com/articles/active-trading/041814/four-most-commonlyused-                indicators-trend-trading.asp.                Murphy, J. (1999). Technical Analysis of the Financial Markets, 1st ed. New York: Penguin                Group.                 Říhová,  P.,  Svoboda,  M.  (2017).  Comparison  of  the  algorithmic  trading  using  the                homogeneous and non-homogeneous Markov chain analysis. In: Proceedings of the 14th                International Scientific Conference European Financial Systems. Brno: Masaryk University,                part 2, pp. 223-231.                 Svoboda,  M.  (2016).  Stochastic  Model of  Short-Term  Prediction  of  Stock  Prices  and its                Profitability  in  the  Czech  Stock  Market.  E\&M  Economics  and  Management,  vol.  16(2),                pp. 188-200.                Vasanthi, S., Subha M., V., Nambi S., T. (2011). An empirical study on stock index trend                prediction using Markov Chain Analysis. JBFSIR, vol. 1(1), pp. 72–90.                 Tharavanij, P., Siraprapasiri, V., Rajchamaha, K. (2015). Performance of technical trading                rules:  evidence  from  Southeast  Asian  stock  markets.  SpringerPlus,  vol.  552(4).  DOI:                https://doi.org/10.1186/s40064-015-1334-7.                Tsang, W., Chong, T. (2009). Profitability of the On-Balance Volume Indicator. Economics                Bulletin, vol. 29(3), pp. 2424-2431.                Zhang,  D.,  Zhang,  X.  (2009).  Study  on  Forecasting  the  Stock  Market  Trend  Based  on                Stochastic Analysis Method. International Journal of Business and Management, vol. 4(6),                pp. 163–170.                                                                                       598","The Influence of Accounting for Goodwill on the Bottom Line                                             of Business Corporations                                   Jaroslav Sedláček, Filip Hampl, Elena Láncošová                                                     Masaryk University                                Faculty of Economics and Administration, Department of Finance                                           Lipová 41a, 603 00 Brno, Czech Republic                        E-mail: sedl@econ.muni.cz; filip.hampl@mail.muni.cz; lancosovaelena@gmail.com                 Abstract:  The  paper  is  dedicated  to  methods  of  accounting  for  goodwill  and  their                relationship to the bottom line of business corporations. Methodically it is based on the                comparison of Czech and international accounting rules. Empirical research is conducted                on business companies operating in the Czech Republic in the period from 2007 to 2016.                Differences and connections between intangible assets including goodwill and performance                indicator represented by earnings before interests and taxes (EBIT) reported in accordance                with  the  Czech  Accounting  Standards  (CAS)  and  the  International  Financial  Reporting                Standards (IFRS) are examined. Particularly, Spearman’s correlation coefficient and F-test                are used for hypothesis testing. It was identified a lagged relationship between EBIT and                the  index  indicator  of  acquired  intangible/tangible  assets  and  concluded  that  approach                of CAS and IFRS regarding revaluation of goodwill is not comparable in terms of variability                and predictability.                Keywords:  correlation,  Czech  Accounting  Standards  (CAS),  goodwill,  intangible  assets,                International Financial Reporting Standards (IFRS)                JEL codes: M21, M41, G32                 1 Introduction                                                                           th                The  first  mentions  of  goodwill  can  be  found  in  the  18   century.  In  that  time  some                authorities recognized and evaluated the existence of business owner’s attributes who was                a respected and trusted member of the community. This personal value was considered                neither  transferable  by  selling  a  company  nor  declining  within  successful  operations  of                a business entity. In accordance with this opinion, goodwill could not be purchased – it had                to be obtained through proper actions and good moral character of the owner (Yang, 1927).                At the beginning of the twentieth century, corporate goodwill was recognized as an asset                of a business company in case of its selling for the price exceeding the sum of the market                values of entity’s tangible and intangible assets. This surplus was considered a payment to                the former owner for business goodwill. For purposes of the initial recognition of the total                amount paid, goodwill was entered as an asset into the new owner’s accounting books.                However,  it  was  a  common  practice  to  immediately  account  goodwill  against  owner’s                capital account(-s) – perhaps in agreement with the earlier belief that goodwill does not                represent a real business benefit due to its personal character.                The importance of goodwill consists in its capability of increase revenues. The price above                the value placed in other assets is paid for goodwill because there are  expected higher                profits  in  comparison  to  ordinary  investment  earnings.  The  company  is  purchased  not                primarily as a tool for securing a group of assets but as a tool for generating future cash                flows. Implicitly, this concept admits that underlying intangible attributes contribute to the                goodwill  of  the  company  which  cannot  be  measured  separately  –  thus,  its  verifiable                existence consists in the surplus between the value of the corporation as a whole and the                value of its net assets. According to Gynther (1969), the value of goodwill is the sum of                existing assets that are not included in the balance sheet of business corporations such as                special skill and knowledge, high managerial ability, social and business connections, good                name and reputation, monopolistic  situation, excellent staff, favourable situation, trade                names and established clientele.                For  decades  there has  been  a  discussion  among  accounting  theorists  about a  dilemma                whether goodwill should not be amortized in public companies because its value does not                                                             599","change by using (Catlett and Olson, 1968), or on the contrary should be amortized against                future  income,  paid-up  capital  or  undistributed  profits  (Esquere,  1914).  In  1970,  the                Accounting Principles Board in the United States of America accepted the recognition of                goodwill as an asset at acquisition cost with the requirement to its amortization against                future incomes from acquired assets. Therefore, it is not a primary goodwill,  which the                company  creates  by  its  own  business,  but  a  derived  (secondary)  goodwill,  that                the company  obtains  by  acquiring  (merging)  another  company.  Derived  goodwill  then                includes both the primary (internally generated) goodwill of acquired company and the                goodwill arising from the synergy obtained as a result of the combination of net assets of                acquirer and acquiree (or merging companies).                 Present accounting standards do not allow to account for primary goodwill because it is not                reliably measurable and therefore it does not meet one of the elemental assumptions for                its disclosure in the balance sheet. On the other hand, the secondary goodwill might be                recognized as an asset, even though it does not meet, in contrast to other assets, the                requirement of the separate existence. It cannot be traded itself for another asset or used                for meeting the liabilities. The measurement of the future economic benefits arising from                goodwill might be considered questionable because goodwill contributes to the creation of                certain  values  (the  most  frequently  measured  by  future  net  cash  inflows)  only  in                conjunction with other assets, not in itself. Goodwill itself is not separately appraised on                the market – it is another distinguishing factor from other assets. Therefore, goodwill is                the subject of exchange only within business combinations (Zelenka, 2006).                According to International Financial Reporting Standards (IFRS 3) and the United States                Generally Accepted Accounting Principles (SFAS 141) goodwill is measured as the excess                of the acquisition-date fair value of acquiree over the net of the acquisition-date amounts                of acquired identifiable assets and liabilities. A similar approach to determining goodwill is                embedded in the Czech Accounting Standards (CAS 13), except some differences affecting                financial position and performance of the accounting entity. In this context, some issues                need to be answered, in particular:                     •   Goodwill is a part of non-monetary and long-term intangible assets; according to                       Samuelson and Nordhaus (1992) for the first time, it appeared separately in the                       balance sheets of US capital companies in 1970. What relationship can be identified                       between  intangible  assets  (including  this  relatively  new  asset  category)  and                       performance of companies in the Czech Republic?                    •   Are impairment (IFRS) and amortization (CAS) comparable regarding randomness                       and variability?                    •   Is there a relationship between goodwill (expressed as index indicator) reported in                       accordance with CAS and IFRS?                    •   Is there a connection between goodwill and performance of companies reporting in                       accordance with CAS and IFRS?                    •   The equation of goodwill calculation suggests that determination of fair value of                       acquiree and its identifiable net assets is an important factor to goodwill calculation.                       Which valuation models can be used if the fair value cannot be applied?                 Positively formulated hypotheses represent answers to these questions. Their testing and                assessment are objects of our research and content of this paper.                                                                             600","2 Methodology and Data                Goodwill is probably the only kind of corporate asset that is individually measurable but                not  tradeable.  It  occurs  in  the  intangible  assets  category  which  is  considered  to  be  a                strategic  investment  and  mainly  lies  within  owners’  decision  authority.  Goodwill  brings                future economic benefits by contributing to the creation of new values for the  business                company (often measured by future net cash inflows) but only in conjunction with other                assets  (even  with  ones  that  are  not  reported  in  the  balance  sheet).  These  values  are                reflected in the company’s financial situation – they are related to the structure of strategic                assets  (Brealey,  Myers  and  Allen,  2006)  and  performance  measured  by  EBIT  or  EAT                (Horváthová and Mokrišová, 2017).                Our first null hypothesis supposes that the growth of EBIT in one year is not correlated to                the growth of the index indicator of acquired intangible assets against acquired tangible                assets in the next year. To test this hypothesis, mean values for each year of EBIT and                acquired  intangible/tangible  assets  were  computed  based  on  ten-year  data  of  Czech                industrial corporations (MIT, 2016). Testing of the hypothesis is performed by Spearman                correlation coefficient because the assumption of normality is not met.                     •   H0: the correlation of the growth of EBIT in one year and the growth of the index                       indicator of acquired intangible assets against acquired tangible assets in the next                       year is equal to zero.                    •   H1: the correlation of the growth of EBIT in one year and the growth of the index                       indicator of acquired intangible assets against acquired tangible assets in the next                       year is not equal to zero.                 The correlation coefficient rs of index indicators is tested at the significance level of α = 0.1                by the hypothesis H0: ρ = 0, against the alternative H1: ρ ≠ 0. The test criterion is as                follows:                           =        (        ,        )                                                   (1)                                                                                                                            where:                  rs            – Spearman correlation coefficient                  cov(rgX,rgY)   – covariance of the rank variables                  σrg           – standard deviations of the rank variables                The test statistics is computed by the following equation:                           =   √    −2                                                                   (2)                              1−   2                The test statistic is compared with the critical region which has the form:                           = −∞; −      (   − 2)) ∪ (      (   − 2); ∞)                                 (3)                                   1−             1−                                     2               2                Publications  of  accounting  and  auditing  multinationals  show  that  there  is  a  significant                amount of differences between IFRS/US GAAP and the Czech Accounting Standards. About                recognising and reporting of goodwill, there are important differences in the preparation of                the financial statements when goodwill  is revaluated in accordance with the accounting                principle of true and fair view. IFRS and US GAAP consider goodwill as an asset with an                unlimited useful life and require its testing for impairment at least once per accounting                period. On the other hand, the Czech regulation (Decree no. 500/2002 Coll.) requires a                straight-line amortization mostly over the period from 60 to 120 months, as decided by                the entity. Because the impairment and amortization affect cost and revenue accounts, the                different approach for the revaluation of goodwill can be reflected in the reported bottom                line.                The  second  hypothesis  tests  the  difference  in  variability  of  impairment  (IFRS)  versus                amortization  (CAS).  Different  methods  used  by  those  standards  would  suggest  that                amortization  will  have  linear  character  while  impairment  will  show  high  volatility  with                                                              601","unpredictable  development.  To  test  this  hypothesis,  means  of  amortization  values  and                impairment values (for each year from 2007 to 2016) were calculated based on data from                32  companies.  In  addition,  percental  differences  from  the  previous  year  were  created                because we are interested in their time development.                    •   H0: Variability of IFRS is lower or equal than the variability of CAS.                    •   H1: Variability of IFRS is greater than the variability of CAS.                 This hypothesis will be tested using the F-test, where                    •   H0: σCzech ≥ σIFRS                    •   H1: σCzech < σIFRS                 The F-test will be computed using the statistics:                          =              ℎ  ~   (           ℎ  − 1,              − 1)                   (4)                                                       where:                  S      – variance of each data sample                  n      – the number of observations in each data sample                The  third  and  the  last  hypothesis  will  be  test  the  existence  of  a  correlation  between                goodwill/EBIT  ratio  created  in  accordance  with  Czech  Accounting  Standards  and                goodwill/EBIT  ratio  based  on  IFRS.  To  compute  these  ratios,  sums  of  32  business                companies were used.                    •   H0: correlation of goodwill/EBIT ratio (CAS) and goodwill/EBIT ratio (IFRS) is equal                       to zero.                    •   H1: correlation of goodwill/EBIT ratio (CAS) and goodwill/EBIT ratio (IFRS) is not                       equal to zero.                To test the third hypothesis, the same methodology as in case of the first hypothesis is                used.                Goodwill calculation according to the definition incorporated in the accounting standards is                as follows:                        GT = FT – (A – L)I,F,D = (CT + SP + MS)F,DA – (A – L)I,F,D                       (5)                where:                  GT     – total goodwill                  FT     – acquiree’s fair value                  A       – acquiree’s assets                  L       – acquiree’s liabilities                  CT     – fair value of the consideration transferred by the acquirer                  SP     – fair value of the acquirer’s previously held equity interest in the acquiree                  MS     – non-controlling (minority) interest in the acquiree                  I      – acquiree’s amounts of the identifiable assets and the liabilities                  F      – fair value valuation                  D      – acquisition-date or merger-date                It is clear from the equation above that from the acquirer’s point of view, all acquiree’s                assets and liabilities have to be revaluated at fair value. For this purpose, an expert opinion                or one of the many valuation methods are applied to determine the value of intangible                assets. If the total price paid for the acquiree would not meet the criteria for setting the                fair  value  (for  example  there  is  an  evidence  that  the  market  participants  are  not                knowledgeable, independent of each other and willing to enter into a transaction, or they                are forced or compelled to do a transaction), then an expert opinion should be requested                again, or an appropriate valuation method should be used. Given the absence of an active                market,  measurement  of  fair  value  creates  a  problem  that  is  the  topic  of  professional                discussions (see for example Cochran, 2005, or Dieter and Ludenbach, 2013) which will                probably never end. Especially, the lack of reliable data and the number of estimates to be                used indicate a problem. Mielcarz and Wnuczak (2011) consider the revenue approach as                                                             602","a suitable valuation model if inefficiencies in the company operations will be eliminated.                Although, there are no simple or complex models, that would be able to determine the fair                value unequivocally and impartially, many theorists are inclined to revenue approach and                submit their own applications, for example Mařík (2007), Kislingerová et al. (2010) and                many others. For that reason, the quote analysis will be performed, and the principle and                the use of the models will be pointed out.                  3 Results and Discussion                To test the first hypothesis, Spearman's rank correlation coefficient was computed and                tested.  There  seems  to  be  a  relationship  between  lag  of  one  year  of  acquired                intangible/tangible assets and EBIT on a significance level of 0.1 (the null hypothesis was                rejected). Therefore, we may conclude that higher EBIT in one year causes higher acquired                intangible/tangible assets in the next year. Results are shown in Table 1.                          Table 1 Results of Spearman's Rank Correlation (the first hypothesis)                                       t-statistics                            28                                        p-value                            0.08309                                           r                              0.6666667                              Source: own calculation based on the data from financial statements                 The  time-series  showing  the  indicators  development  within  the  ten-year-period  are                graphically represented in Figure 1. They provide graphical confirmation of our findings                from testing of hypothesis one as they suggest that there is a lag in the reaction of acquired                intangible/tangible assets of one year.                 Figure 1 Development of Index Indicator (acquired intangible/tangible assets) and EBIT                                                 in the Period 2007-2016                                                          Source: own calculation based on the data from financial statements                 The second hypothesis is dealing with the difference in variability of  amortization (CAS)                and impairment (IFRS). The hypothesis is tested by F-test.                                     Table 2 Results of F-test (the second hypothesis)                                    F-statistics                             91.91                         Number of degrees of freedom                           8                                      p-value                              4.577e-07                              Source: own calculation based on the data from financial statements                                                              603","P-values are lower than our significance level of 0.1. Thus we reject the null hypothesis                and  accept  the  alternative  hypothesis.  Therefore,  we  conclude  that  variability  of                impairment  is  significantly  greater  than  the  variability  of  amortization.  Namely,  the                variability of impairment differences is 3.57 and variability of amortization is 0.37.                Figure  2  portrays  the  development  of  impairment  (IFRS)  and  amortization  (CAS).  It                graphically  confirms  our  findings  from  statistical  testing  as  it  is  obvious  that  while                amortization has rather regular character, impairment shows high variability with evidently                unpredictable development.                  Figure 2 Development of Means of Amortization (CAS) and Impairment (IFRS) in the                                                    Period 2007-2016                                                         Source: own calculation based on the data from financial statements                The last hypothesis suggests a correlation between goodwill/EBIT created in accordance                with CAS and goodwill/EBIT in accordance with IFRS. This hypothesis was again tested by                Spearman’s correlation coefficient. Table 3 shows our results.                          Table 3 Results of Spearman's Rank Correlation (the third hypothesis)                                      t-statistics                           216                                        p-value                            0.3871                                           r                             -0.3090909                              Source: own calculation based on the data from financial statements                 Spearman's rank correlation suggests that there is a  negative relationship between the                proportion of goodwill and EBIT by IFRS and Czech Accounting Standards, but this relation                is not statistically significant on a significance level of 0.1. Therefore, we are not able to                reject the null hypothesis.                 For better visualization, time series graph and scatterplot were created (Figure 3). Figure                3 suggests a negative relationship between goodwill/EBIT ratio created according to these                two approaches, but it is obvious that this relationship is very weak as data  are rather                scattered in space.                                                                    604","Figure 3 Scatterplot Expressing Correlation of Ratios Goodwill/EBIT (CAS) and                                                   Goodwill/EBIT (IFRS)                                                     Source: own calculation based on the data from financial statements                We’ve also wanted to find out which valuation models can be used if the fair value cannot                be applied. In cases when the fair value cannot be reliably measured, the income approach                represented by the discounted free cash flows (DCFF) method is used to determine the                market value of corporations according to following formula:                             n   CF  (1+  ) g  t  T  FCFF                FCFF                                           +                        V    =   0       t             t  t  +               1   +   T  T            (6)                            t= 1  1 ( +  WACC )  t  = n  1 (  +  WACC )  ( WACC− g T  1 ( )  +  WACC )                where:                   V       – the market value of the firm                   FCFFt   – free cash flow to the firm in year t                   FCFF0   – starting level of FCFF                   WACC  – weighted average cost of capital                   g       – constant expected growth rate iv FCFF                   n       – number of years planned                   T       – contemplated period length                 Alternatively, the equivalence method (Damodaran, 2000) based on economic value added                (EVA) can be used:                                    T                        V    =  OE 0   EVA  t t  +  EVA  1   +   T  T  A   +  0                       (7)                                +                                  t   1   =  1 (  r   +  e  )  r e  r   +   1 (  e  )                                   T                             =   V  OE 0   P /  L  t  r   -    e     OE  1   - t   +  P/L  1   +   T  r   -    e     T OE  t  A   +  0     (8)                                +                                                t                                  t   1   =  1 (  r   +  e )  r e  r   +   1 (  e )                       re = rf + rla + rpo + rfi                                                       (9)                where:                 V       = market value of the owners’ equity                 OEv     – value of the owners’ equity as at the valuation date                 EVAt    – economic value added in year t                 T       – number of years planned by EVA                 P/Lt    – net profit or loss of monitored enterprises in year t                 re      – owners’ equity expenses                 rf      – risk-free assets return                 rla     – risk surcharge for lower liquidity of a share at the market                 rpo     – risk surcharge for entrepreneurial risk                                                             605","rfi     – risk surcharge for financial risk                 A0      – other non-operating assets                Residual income models based on book value of company’s equity, expected net profit and                cost of capital (for more details see Feltham and Ohlson, 1995, or Begley et al., 2006) are                recommended as simpler models for measuring of goodwill. Figure 4 shows the results of                quote analysis.                   Figure 4 Quotes Frequency of Valuation Models of Business Corporations (thousands)                                              Source: own calculation based on the data from citation analysis                 From  the Figure  4, it is  obvious  that in  case  of  the value  determination  of  companies,                methods based on expected free cash flow are preferred to residual income models. That                is the answer to our last research question.                 4 Conclusions                 The  notion  of  goodwill  is  particularly  interesting  because  it  is  something  abstract,                unthinkable but today more and more significant. Its importance is essential primarily for                measuring company’s market value. Goodwill is perceived as an intangible asset in the                financial sector, but its precise and reliable quantification is relatively difficult and requires                appropriate professional attitude. Goodwill represents the amount that is the difference                between  the  company’s  accounting  valuation  and  its  market  valuation.  Goodwill  is                recognized  in  the  final  accounts  within  acquisitions  and  mergers  which  amount  has                increased steadily as a result of globalization in the recent years.                 We have identified the relationship between the ratio of acquired intangible/tangible assets                and EBIT representing the performance of companies in the Czech Republic. It seems that                acquired intangible/tangible assets react to changes in performance with a lag of one year,                which was confirmed using Spearman’s rank correlation. Based on our findings, we can                conclude that impairment and amortization are not comparable regarding variability and                predictability.  Impairment  has  significantly  higher  variability  than  amortization.                Amortization  shows  fluency  and  regularity  while  impairment  does  not  manifest  regular                pattern. It follows that users of IFRS financial statements cannot predict with sufficient                reliability  the  impact  of  impairments  on  the  bottom  line.  The  accounting  treatment  of                goodwill incorporated in IFRS and CAS differs, each set of regulation offers its own rules                for revaluation of goodwill which was shown by the low correlation between goodwill/EBIT                (CAS) and goodwill/EBIT (IFRS). In cases when fair value cannot be measured reliably, we                find  out  which  alternative  methods  are  used  the  most  frequently.  Based  on  the  quote                analysis we discovered that methods based on expected free cash flow  are preferred to                residual income models.                 Acknowledgement                The  paper  is  the  result  of  the  specific  research  no.  MUNI/A/0753/2017  –  Behavioral,                knowledge and economic aspects of the valuation and trading of financial and other assets.                                                                 606","References                Act no. 563/1991 Coll., on Accounting, as amended.                 Begley, J., Chabmerlmn, S. L., Li, Y. H. (2006): Modeling goodwill for banks: A residual                income  approach  with  empirical  tests.  Contemporary  Accounting  Research,  vol.  23(1),                pp. 31-68.                Brealey,  R.  A.,  Myers,  S.  C.,  Allen, F. (2006).  Principles  of  Corporate  Finance.  Boston:                McGraw-Hill.                 Catlett, G. R., Olson, N. O. (1968) Accounting for Goodwill. Accounting Research Bulletin                No. 24. New York: American Institute of Certified Public Accountants.                Cochran, H. J. (2005). Asset pricing. Princeton University Press.                 Damodaran A. (2000). Corporate finance: Theory and practice. New York: John Wiley \&                Sons.  Inc.                Decree no. 500/2002 Coll., which implements certain provisions of Act No. 563/1991 Coll.,                on  accounting  for  entities  that  are  businesses  maintaining  double-entry  accounting,  as                amended.                Dieter, CH., Ludenbach, N. (2013). IFRS Essentials. John Willey \& Sons, Inc.                Esquerre,  P.  J.  (1914).  The  Applied  Theory  of  Accounts.  New  York:  The  Ronald  Press                Company.                Feltham, G. A., Ohlson, J. A. (1995): Valuation and Clean Surplus Accounting for Operating                and Financial Activities. Contemporary Accounting Research, vol. 11(2), pp. 689-731.                 Gynther,  R.  S.  (1969).  Some  Conceptualizing  on  Goodwill.  Accounting  Review,  No.  4,                pp. 247-255.                 Horváthová, J., Mokrišová, M. (2017). Capital structure modelling and analysis of its impact                on business performance. Financial Assets and investing, vol. 8(2), pp. 19-36.                Kislingerová, E. et al. (2010). Manažerské finance. Praha: C. H. Beck.                 Mařík, M. (2007). Metody oceňování podniku. Ekopress, s.r.o., Praha.                Mielcarz, P., Wnuczak, P. (2011). DCF fair value valuation, excessive assets and hidden                inefficiencies. Contemporary Economics, vol. 5(4), pp. 44–57.                 MIT  -  Ministry  of  Industry  and  Trade. (2016).  Analytical  materials,  statistics.  Retrieved                from: http://www.mpo.cz/en/guidepost/analytical-materials-statistics/.                Samuelson P.A., Nordhaus W.D. (1992). Economics. 14th ed. New York: McGraw-Hill.                 Sedláček,  J.  (2016).  Comparison  of  valuation  of  financial  instruments  according  to  the                international  and  Czech  accounting  standards  in  the  context  of  performance  reporting.                Financial Assets and Investing, vol. 7(1), pp. 33-49.                Yang, J. M. (1927). Goodwill and Other Intangibles. New York: The Ronald Press Company.                Zelenka, V. (2006). Příslušnost goodwillu k prvkům vnitřních struktur účetních jednotek.                Český finanční a účetní časopis, vol. 1(1), pp. 107-117.                                                                         607","General Data Protection Regulation in the Context of Municipalities                                            Pavel Sedláček, Miroslav Svoboda                                                     Masaryk university                                 Faculty of Economics and Administration, Department of Finace                                           Lipová 41a, 602 00 Brno, Czech Republic                                     Corresponding authors’ email: 369947@mail.muni.cz                 Abstract:  The  aim  of  this  paper  is  to  analyse  the  impact  of  General  Data  Protection                Regulation  (GDPR)  and  implementation  costs  concerning  municipalities.  This  regulation                enforceable on 25th May 2018 aims primarily to provide control to EU citizens over their                personal data, unifying the regulation within all EU members. This regulation aggregates                new costs and duties for all subjects gathering personal data. This paper depicts the duties                and  changes newly  commencing  and  possibilities  of  their  implementation.  Changes  are                demonstrated on an example of one municipality, particular needs of change and total                costs of implementation are estimated for this subject. Comparing this subject to others,                we can make an assumption of total costs of GDPR implementation in Czech Republic in                the field of municipalities.                 Keywords: GDPR, data protection, EU, regulation, municipality                JEL codes: G30, K19, K24                 1 Introduction                GDPR (General Data Protection Regulation) has become recently a “bubble”, frequently                discussed in the media. There are hundreds of approval consent requests received in the                mail boxes crosswise EU. The intention of this regulation is to protect rights of its citizens                (all  over EU  +  EFTA (Iceland,  Norway,  Lichtenstein)  against unauthorized  treatment  of                their personal data. The reason for the introduction was the need for better data protection                for  citizens  in  today's  world,  where  marketing  databases  are  being  sold  for  marketing                purposes  on  daily  basis.  GDPR  protects  against  unauthorized  systematic  processing  of                personal data (Žúrek, 2017)                By personal data is understood any data about a person, that can be identified (with an                identifier).  Identifier  can  be  anything,  for  example  name,  date  of  birth  and  personal                identification number, student or employee number, phone number, bank account number,                location  data,  network  identifier,  dynamic  IP  address  or  some  information  concerning                physical, physiological, genetic, psychic, economic, cultural or social identity of a person                (Nulíček, 2017). Also if the person is identifiable by gathered information combined with                publicly accessible data, it is still considered as personal data. GDPR is not applicable to                artificial person, dead or not born natural person, and anonymized data. Special category                of personal data is sensitive data, such as racial or ethnical background, information about                religion,  political  and  philosophical  mindset,  health,  sexual  information,  labor  unions                membership,  genetic  or  biometric  information  or  children’  personal  data.  (Janečková,                2017)                Information  can  be  anonymized  (information  become  anonymous  and  lost  status  of                personal data) or pseudonymized (some information were split into parts and coded, it is                not possible to identify the person without merging and decoding the information, but it                might be still possible, thus it remains personal data).                This new regulation establishes easier access to data, new right on data transmissibility,                right to be forgotten and right to be informed in case of data theft or leakage (regulation                2016/679).                 2 Methodology and Data                By effective date of GDPR every personal data processing have to be based on a legal title.                As  mentioned  in  chapter  2  article  6  in  GDPR,  legal  title  can  be  contract  fulfillment,                                                             608","compliance with legal obligation, protection of vital interests, performance of a task carried                out in the public interest or in the exercise of official authority, legitimate interest (if not                overridden by exemption, such as fundamental rights and freedom) or consent (if none of                the mentioned apply). Consents have to be expressive, clear, under no pressure, informed,                revocable, demonstrable. (Nezmar, 2017)                As this is crucial for the text below, I will state some more facts concerning every legal                title. Contract fulfillment is quite common title, valid while the contract is valid. If there is                another usage of the data afterwards, e.g. recovery of debts, it is on basis of another title                – in this case legitimate interest. Compliance with legal obligation (e.g. as data transfer to                health insurance company) is another common legal title. It is legal duty (not option) to                fulfill this data processing. This title also covers right to fulfill duty of archiving data as                stated by the law (in Czech legal system it is 10 years for annual reports and final accounts,                5 years for accounts and records and 30 years for all documents concerning employee data                and  wages).  Protection  of  vital  interests  covers  for  example  the  rights  of  unconscious                person or proceedings taken while monitoring epidemic disease spreading. Performance of                a task carried out in the public interest or in the exercise of official authority covers the                right to process data by institutions such as vehicle technical inspection institutions, forest                guards etc. Legitimate interest can be almost anything, so it is the most questionable title.                It  can  be  camera  recording  of  street  for  property  protection  purposes,  employee                attendance software, creation of internal list of debtors etc. Legitimate interest is assessed                by proportionality test – is it legal and justified? Is it necessary? Is my gain above the                restriction of others’ rights? If yes, we can use this title without consent.                Concerning the municipalities, consent should be the least used title, as the position of a                municipality and a resident are not equal. Therefore, consent should be used only in cases                such  as  processing  of  data  whilst  public  property  ownership  transfers  or  collecting                employee or citizen contact details for some purposes. In other cases should be always                found other legal title.                Principle  of  necessary  data  minimization  should  be  always  taken  into  account  –  only                necessary  data  should  be  collected  and  used  only for  the  entitled  purpose  (if  data  are                collected for contract fulfillment, there is no possibility to use them for marketing purposes                or pass the data to other entity. Consent has to be separated from the contract.)                All entities in public sector and municipalities (as well as in private sector) have to be in                compliance with GDPR. I would like to demonstrate on an example, what has to be done                and figure out the total costs of implementation of GDPR within the municipality.                Initial point of GDPR compliance is data audit. Frequently it is not only audit of the bureau                itself, but also of institutions established by municipalities, such as schools or the municipal                police. Audit evaluates that only necessary data are collected, processed and used only for                purposes entitled and IT audit regarding data leakage, loss risks and data safety. GDPR                commands  the  duty  of  prevention  against  data loss  and leakage  caused  purposely,  by                neglectfully acting or inevitably by accident, such as natural disaster.                After this audit, necessary proceedings can be measured as well as estimation of total costs                of GDPR implementation for this particular case. There are many assumptions about total                cost of implementation, in range from millions to billions of Czech crowns. Even though                GDPR educational courses are currently the most demanded (source legal company Křížka                Kuckirová Legal s.r.o. and educational company 1.VOX s.r.o. management), most of the                municipalities rather select the option to order services of a legal company for this purpose.                Only the initial audit costs vary from 3 000 to 90 000 Czech crowns.                 Another obligation for municipalities is to name a DPO (Data Protection Officer). DPO is an                advisory and supervisory authority as well as contact point for the subjects. DPO must be                independent, so it is highly recommended to elect an external person; it must be assured,                that the person will have no conflict of interests.                It is required, that DPO must be familiar with information technologies and data security,                with national and EU law (especially GDPR and data protection), the organization itself,                                                             609","has empirical experience with application data protection measures and data processing                operations and must be able to promote data protection policy, precautions and measures                within the organization.                In the sample municipality, new guidance concerning response for events that may occur                regarding  to  GDPR  and  some  changes  in  unified  fields  –  new  employee  discretion                statement, consent with photocopy of ID, overall organization regulation with answer for                personal data access, change, limitation, transfer or deletion request, announcement of                prolonging the period to provide a response to the request.                Starting point for estimation of the total cost of GDPR implementation for municipalities is                ČSÚ (Czech statistic bureau). In the Czech republic there is 6258 municipalities, out of                which is 393 with delegated municipal offices (or so called municipalities of second level)                and out of those, there is 205 municipalities with extended field of activities (or so called                third level). The rest of municipalities (5865 are common municipalities).                                       Table 1 Municipalities in The Czech Republic                                                                         No. Of                                                      Number   Number                 No. of inhabitants                  Code of                               of       of     districts                  district,  Name of the district, of the region                  region                              munici-  cadastral  Total   Total  Municipal. Average/                                                      palities  districts                 average  mun. part                  CZ010    Capital City of Prague            1      112      112 1 280 508 1 280 508  11 433                  CZ020    Central Bohemian Region        1 144    2 074    2 803 1 338 982  1 170      478                  CZ031    South Bohemian Region           624     1 623    1 990  638 782   1 024      321                  CZ032    Pilsen Region                   501     1 396    1 537  578 629   1 155      376                  CZ041    Karlovy Vary Region             134      571      527   296 749   2 215      563                  CZ042    Usti Region                     354     1 061    1 153  821 377   2 320      712                  CZ051    Liberec region                  215      508      768   440 636   2 049      574                  CZ052    Hradec Kralove region           448      961     1 076  550 804   1 229      512                  CZ053    Pardubický kraj                 451      790      991   517 087   1 147      522                  CZ063    Highlands region                704     1 263    1 402  508 952     723      363                  CZ064    South-Moravian region           673      892      905 1 178 812   1 752    1 303                  CZ071    Olomouc region                  402      781      768   633 925   1 577      825                  CZ072    Zlín Region                     307      443      438   583 698   1 901    1 333                  CZ080    Moravian-Silesian Region        300      616      623 1 209 879   4 033    1 942                           Czech Republic                 6 258   13 091   15 093 10 578 820  1 690     701                           Source: ČSÚ (https://vdb.czso.cz/vdbvo2/faces/cs/index.jsf?page=statistiky)                In this calculation, we try to calculate the total cost of achieving the GDPR compliance                status  of  all  municipalities,  not  how  much  was  actually  spent.  This  means,  that  the                percentage of GDPR ready municipalities, which is accordingly to KKL estimation cca 40 %                is not counted with.                Another  trigger  for  GDPR  bubble  and  fear  from  the  majors  are  the  penalties  for                noncompliance. The amounts are enormous and Czech Republic did not manage to process                adaption regulative regarding fines and municipality prosecutions. Though, it is necessary                to realize, that the same people from ÚOOÚ (Czech bureau for protection of personal data)                who  are  responsible  for  reporting  and  punishing  the  municipalities.  In  addition,  it  was                announced by ÚOOÚ, that there will be only 30 revisal of GDPR compliance per year. If                they would split the controls to public and private sector half by half, the probability, that                that particular municipality will be controlled is 0,0024 %. This might be also the reason,                why only 40 % did meet the GDPR criteria.                To  sum  up,  new  duties  for  municipalities  are  assignment  of  DPO,  preparation  (written                regulative) for data leakage situation and all event that might happen regarding to GDPR                citizen  requests  and  have  IT  security  regulative.  Municipalities  have  to  go  through  all                                                             610","gathered data, think about the legal title of their possession and in case that no legal title                is applicable, a consent has to be gained and stored. They should also implement technical                and  organizational measures for  ensuring  highest  protection,  but  as  this is  not  further                specified, it is not any radical change.                3 Results and Discussion                 Total costs can be divided into several fraction: costs of analysis and initial audit (in Table                2 stated as initial methodic guidance and consulting), costs of implementation (stated as                Komplex GDPR services) and costs of DPO, process specification after GDPR  measures and                their changes – initial and monthly fees. Some municipalities did the implementation by                themselves – they studied the matter or bought some online tutorial or tool, that helped                them understand the matter and they appointed the DPO by themselves. This was usually                case of middle size municipalities – major competent to understand the matter, trying to                save some money from the budget and not avoiding work and responsibility. Huge helping                hand is from SMOCR (“svaz obcí a měst ČR” - Czech municipality alliance), offering the                municipalities help in this matter (most of the agenda is common for all municipalities) and                shared DPO. In other cases, a company was hired.                We have asked 5 companies offering GDPR solution for quote for every type of municipality                (first, second third level). The explicit list of chosen companies is Křížka Kuckirová Legal                s.r.o., CATANIA GROUP s.r.o. (project SPMO - služby pro města a obce), MCS Consulting,                s.r.o., 1. VOX a.s., Data Protection Services, s.r.o). Then we have done an average cost                for an average municipality in respective level.                                           Table 2 Average Cost of GDPR Agenda                Level           No.  Methodic guidance and consulting 2  Komplex GDPR services  2  DPO, process specification and actualization 1                1st level municipalities  5 865         4 955            9 000                        346 800                2nd level municipalities  188          29 670            75 000                       584 400                3rd level municipalities  205          89 890           191 900                       924 000                1  monthly fees, in this model we calculate expected costs in 10 years.                2  Average cost per unit                                                 Source: prepared by author                 4 Conclusions                With information gained in previous chapter, we can compare total costs from the model                with costs estimated in the media. As it is difficult to combine initial one-off costs with                monthly fees paid afterwards, we have decided to quantify the costs in ten years period.                If done so, total costs of GDPR regulation for municipalities in the Czech Republic is 2,493                billion Czech crowns. Even though, there is not many estimations of GDPR impact on                municipalities, we can compare this number with the few available. Mr. Jiránek (former                major of Kladno, now working as GDPR specialist for SMOČR) in Ekonomický deník assumes                at least 1 billion Czech Crowns for the third column of Table 2 (DPO and monthly fees for                management regarding GDPR). Study quantifies amount this as 1,855 billion, which proves                his statement.                 Acknowledgements                This article is one of the research outputs of the project MUNI/A/0753/2017 – Behavioural,                knowledge-based  and  economic  aspects  influencing  investment  decision  making  of                Masaryk  University  in  Brno,  Faculty  of  Economics  and  Administration,  Department  of                Finance.                 References                ČSÚ  (2018).  Statistiky.  Retrieved  from:    https://vdb.czso.cz/vdbvo2/faces/cs/index.jsf?                page=statistiky.                                                              611","Ekonomický  deník  (2018).  Miliardový  dopad  GDPR  na  obce  a  města.  Retrieved  from:                http://ekonomicky-denik.cz/miliardovy-dopad-gdpr-obce-mesta/.                 Janečková, E. (2017). GDPR – praktická příručka implementace. Wolters Kluwer.                Kučerová, A. (2012). Zákon o ochraně osobních údajů. Komentář. Praha: C.H. Beck.                Nezmar, L. (2017). GDPR: praktický průvodce implementací. Praha: Grada Publishing.                 Nulíček, M. (2017). GDPR / Obecné nařízení o ochraně osobních údajů, Praha: Wolters                Kluwer.                Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016                on the Protection of Natural Persons with Regard to the Processing of Personal Data and                on  the  Free  Movement  of  Such  Data,  and  repealing  Directive  95/46/EC  (General  Data                Protection Regulation).                Žůrek, J. (2017). Praktický průvodce GDPR, Olomouc: Anag.                                                                                                                            612","Syndicated Loan Market Germany: Deep Dive into Role Allocation                                                                                      3                                                               2                                                  1                                      Ilja Schaab , Eric Frère , Alexander Zureck                                   1  Masaryk University / FOM University of Applied Sciences                               Department of Finance at Faculty of Economics and Administration                                             Flakering 68, 45277 Essen, Germany                                                 E-mail: ilja.schaab@gmx.de                                     2  FOM University of Applied Sciences/Masaryk University                            isf Institute for Strategic Finance/Faculty of Economics and Administration                                          Leimkugelstraße 6, 45141 Essen, Germany                                              E-mail: alexander.zureck@fom.de                                              3  FOM University of Applied Sciences                               Department of Finance at Faculty of Economics and Administration                                          Leimkugelstraße 6, 45141 Essen, Germany                                              E-mail: alexander.zureck@fom.de                 Abstract: The selection and mandate of capable partners in a syndicate loan transaction                is essential to a successful realization. However, financial managers have not only to decide                which banks but also how many banks should be involved in the transaction at hand to                generate an optimal cost-benefit-ratio. While other studies have investigated the impact                of arranger reputation on the syndicate structure, this paper focuses on the identification                of  significant transaction-related  determinants  of  mandate  allocation  on  the syndicated                loan market in Germany. The analysis of the 2,161 data sets from transactions between                2001 and 2017 is conducted by the ordinary least squares method. The findings show that                the transaction volumes have a significant positive impact on the number of mandated                banks, whereas the complexity of the transaction, represented by the number and type of                facilities as well as the total allocation of arranger titles, has a significantly negative impact                on  the  mandate  allocation.  Furthermore,  the  study  reveals  a  structural  break  in  the                strategy of role allocation in the market which occurs in the year 2009 during the financial                crisis. Ever since, the allocation of MLA and Bookrunner titles increased significantly.                Keywords: Syndicated Loan Market, Mandated Lead Arranger, Bookrunner, Determinants                Role Allocation                JEL codes: G30, G32                 1 Introduction                With a record transaction value of 56.9 billion dollars, Bayer AG caused a sensation on the                German  syndicated  loan  market  during  the  acquisition  of  Monsanto  Inc.  Large,  listed                companies in Germany have already used the syndicated loan, which was utilized as a                bridging facility in the Bayer-Monsanto transaction, for decades as a stable, capital market                near funding source. The borrower receives funding from different lenders under a joint,                multilateral credit facility agreement. Despite the complex contractual documentation, this                form  of  financing  brings  many  advantages  for  borrower  (e.g.  eligibility  of  termination                rights) and lender (e.g. risk spreading), so that in addition to such large transactions, which                were  primarily  placed  on  the  bond  market  in  the  past,  more  and  more  medium-sized                enterprises  decided  to  use  this  source  of  funding  (e.g.  debut  transactions  in  2017  by                Kirchhoff Automotive or MANN+HUMMEL).                If a company chooses to pursue the syndicated loan as a funding option, it assigns the                management  of  the  transaction  to  one  or  more  Mandated  Lead  Arrangers  (MLA).  For                example, a total of 26 banks were mandated in this role in the above mentioned Bayer-                Monsanto transaction. Among the MLA, Bookrunner are designated to assume a central                task in the process by approaching potential lenders and controlling the credit book. The                roles of the MLA and the Bookrunner are particularly desirable in the bidding phase, as                they  are  not  only  endowed  with  additional  fees  in  the  internal  relationship,  but  also                generate  a  positive  external  outreach  for  the  respective  bank  (e.g.  Miller,  2008).  This                                                            613","external effect manifests itself within League Tables, which are provided by several data                service companies (especially Bloomberg and Thomson Reuters). The more of these titles                a market participant is able to achieve, the easier it is to stand out against the competition                with a good ranking in the League Tables.                Choosing several MLA and Bookrunner banks usually means a higher level of transaction                security for the borrower, which is especially important in case of high transaction volumes.                At  the  same  time,  however,  the  borrower’s  transaction  costs  also  increase,  since  lead                arranger expect higher fees due to their structuring efforts (e.g. Miller, 2008). Cost-benefit                balancing is therefore essential. League Tables are eligible to show financial managers,                which bank has received an MLA or Bookrunner title in how many transactions and which                volume has been disbursed. However, an indication of how many mandates were allocated                at  which  transaction  size is  only  available  quite  opaque  at  the  single transaction level.                Therefore, a conclusion, whether the volume or other criteria of transactions are decisive                for the number of mandated banks, would be linked with considerable research effort for                financial managers.                Based on this practice-oriented problem, the following study focusses on the identification                of  significant  transaction-related  determinants  of  mandate  allocation  on  the syndicated                loan market in Germany between 2001 and 2017.                 2 Literature Review and Hypotheses Development                 In order to concretize the research hypotheses, first, a review of empirical literature in this                field is necessary. Even though recent empirical studies primarily focus on the US market,                the main conclusions are outlined and projected on the available dataset.                Sufi  (2007)  found  in  his  empirical  study  that  information  asymmetries  could  have  a                significant effect on syndicate structures. The study shows that MLA banks sign higher                credit shares at transactions with opaque companies in order to signal careful data analysis,                which, however, scales down the total syndicate participants. Other lenders are primarily                determined by the geographic proximity to the borrower and, in particular, the duration of                their  credit  relationship.  The  reputation  of  the  MLA  banks  can  reduce  the  information                asymmetries and thus the syndicate consolidation. Chaudhry and Kleimeier (2015) found                similar results in their study. Banks with a particularly high reputation, measured by their                market share, can overcome the moral hazard and adverse selection problems, and reduce                the scaling down of the syndicate.                The results imply that the borrower can use League Tables at least to identify banks with                a high reputation, measured by their market share, to choose these for the transaction                and  thus  benefit from the  mentioned  advantages.  Gopalan  et  al.  (2011) found in their                study that insolvency of borrowers did not have a significant effect on the reputation of                MLA with particular sector dominance (market share greater than 25% in the respective                sector).  Cumulatively,  the  results  suggest  that  participating  banks  orient  themselves                towards  the  MLA’s  reputation,  although  it  is  not  a  reliable  indicator  of  the  borrower’s                solvency.                Bae et al. (2014) have shown in their multinational study that the number and the credit                share  of  the  arrangers  relative  to  the  participating  lenders  have  a  significant  negative                impact on credit risk premiums. The study confirms the results of Ivashina (2009), which                has also shown the effect based on the relative credit share of the arrangers. The authors                suggest  the  higher  competitiveness  and  the  diversified  credit  portfolio  of  arrangers  as                possible explanations for the lower credit risk premium.                 Ferreira  and  Matos  (2012)  investigate  the  effects  of  a  governance  structure  between                lenders and borrowers. The results show that banks that are able to exert influence on a                company (e.g. through a seat on the supervisory board) are selected more frequently for                an MLA title, and impose higher credit risk premiums in times of normal markets. Bushman                et al. (2016) found evidence that media reporting about a company can also have an effect                on the syndicate structure. Thus, positive reporting leads to a higher probability that banks                                                              614","with no previous credit connection will assume an MLA role or participate in the syndicate,                and furthermore that credit risk premiums will also be reduced.                 Summing up the empirical results, the higher transparency of a company or the reputation                of MLA banks can reduce ex ante information asymmetries within the syndicate, resulting                in a higher granularity of lenders in the transaction. Empirical studies have shown that                company size usually correlates positively with disclosure (e.g. Khlif and Souissi, 2010).                Thus, it is expected that the volume of a transaction has a positive effect on the number                of MLA and Bookrunner banks since it can be associated with company size (e.g. Byoun,                2008). Vice versa, it is expected that opaque companies or transactions are exposed to a                lower granularity of the syndicate.                 Taking the data availability into account, which is described in the following chapter, the                consistence of the described expectations will be tested with the German syndicated loan                market. The study focuses on the two following hypotheses (see table 1), which are tested                respectively with the MLA and Bookrunner roles.                                              Table 1 Research Hypotheses                  No.  Hypothesis                  H1A  Transaction volume has a significant positive impact on the number of MLA.                       Transaction volume has a significant positive impact on the number of                  H1B                       Bookrunner.                  H2A  The transaction complexity has a significant impact on the number of MLA.                       The transaction complexity has a significant impact on the number of                  H2B                       Bookrunner.                                                 Source: Own representation                3 Empirical Analysis                 Data and Variables                In order to test the defined hypotheses, information about the syndicated loan market                Germany is necessary. These are provided by the two data service companies Bloomberg                and  Thomson  Reuters  insofar  as  banks  report  syndicated  loan  transactions  to  receive                League Table scores. For the period between 2001 and 2017 data about 2,161 transactions                was taken from the Bloomberg database and verified in samples by the Thomson Reuters                database. One transaction from 2006 was removed from the data due to very low volume                and  missing  verification  records  in  the  second  database.  Furthermore,  there  are  17                transactions  with  missing  maturity  dates.  However,  these  were  not  removed  from  the                sample, since the transaction data was otherwise complete.                 According to Bloomberg (2017), when the issuer’s country of risk is Germany, all reported                and signed transactions are included into the German League Tables (four factors decision                model: management location,  country of  primary listing,  country  of  revenue,  reporting                currency  of  the issuer).  Hence,  the  data  sample is  presumed  as  representative  for the                German syndicated loan market.                The information about the individual transaction includes borrower, loan amount, number                and type of facilities, maturity as well as the distribution of roles within the syndicate.                Information about the entire composition of the syndicate, the signed loan amounts of each                bank in the syndicate and the agreed credit risk premiums are only available if the borrower                approves the publication. In 2017, for example, credit risk premiums are only available for                approx. 28.5% of the transactions, so the consideration of this variable does not appear                to be appropriate. For the majority of transactions only the mandatory data is available so                that the test variables for the study are specified as follows in table 2. The extraction of                the MLA and BR variable from the database is only possible with an intermediate step. The                information about role distribution in the syndicate is provided in text form. Based on this                text form, the variables are manually catalogued for each individual transaction.                                                               615","Table 2 Definition of Variables                  Variable     Definition                     MLA       Number of Mandated Lead Arrangers                      BR       Number of Bookrunners                     VOL       Transaction Volume                      GA       Total Number of Banks with a Reported Arranger Title                      TR       Number of Facilities                     LFZw      Volume-Weighted Transaction Term in Months                      FX       Facility in Foreign Currency (binary variable: available / not available)                                                 Source: Own representation                 The  low  degree  of  disclosure  within  the  German  syndicated  loan  market  leads  to  the                conclusion that many of the hypothesis tests conducted in US empirical research cannot                be applied. For example, the granularity of a syndicate cannot be ascertained, since not all                lenders and credit shares are disclosed. Further, it is also only indirectly possible to draw                conclusions on the type of transaction (club deal vs. syndication), in example when all MLA                banks sign the same share, and when the total sum of these shares then corresponds with                the total transaction value. Furthermore, the effects on the credit risk premium cannot be                tested,  since  the  agreed  spreads  remain  undisclosed  for  the  majority  of  transactions.                Therefore, the focus of the following analysis lies on the two defined hypotheses, which                can be tested from the given data set.                 Methodology                The descriptive statistics provided in table 3 indicate a market change over time. On the                one hand, it seems that since the financial crisis the market participants established a new                standard  regarding  the  role  allocation.  On  the  other  hand,  the  syndicated loan  market                became more granular than in the beginning of the noughties. In order to obtain relevant                results for theory and practice from the study, the first step is to test whether all available                samples should be included into the following testing.                                                Table 3 Descriptive Statistics                                                                               Mean   Median    Mean   Median                  Year    n         VOL         Mean VOL      Median VOL                                                                             MLA      MLA      BR       BR                  2001     40   EUR 46.129 bn   EUR 1.153 bn   EUR 0.428 bn    2.45     2.00    1.70     1.50                  2002     47   EUR 59.100 bn   EUR 1.257 bn   EUR 0.324 bn    2.13     2.00    1.79     1.00                  2003     55   EUR 75.977 bn   EUR 1.381 bn   EUR 0.500 bn    2.45     2.00    1.85     2.00                  2004     97   EUR 94.256 bn   EUR 0.972 bn   EUR 0.400 bn    3.22     3.00    2.11     2.00                  2005    120  EUR 126.740 bn   EUR 1.056 bn   EUR 0.488 bn    3.43     2.00    2.07     2.00                  2006    153  EUR 152.181 bn   EUR 0.995 bn   EUR 0.320 bn    2.67     2.00    1.92     2.00                  2007    114  EUR 119.160 bn   EUR 1.045 bn   EUR 0.400 bn    3.16     2.00    1.99     2.00                  2008     58   EUR 45.749 bn   EUR 0.789 bn   EUR 0.290 bn    3.05     2.00    2.07     2.00                  2009     42   EUR 59.113 bn   EUR 1.407 bn   EUR 0.699 bn    6.43     4.00    3.71     2.00                  2010    109   EUR 81.662 bn   EUR 0.749 bn   EUR 0.250 bn    5.57     3.00    3.84     2.00                  2011    160   EUR 80.053 bn   EUR 0.500 bn   EUR 0.203 bn    5.31     4.00    3.10     2.00                  2012    159   EUR 74.723 bn   EUR 0.470 bn   EUR 0.170 bn    4.30     3.00    3.07     2.00                  2013    190  EUR 106.780 bn   EUR 0.562 bn   EUR 0.250 bn    5.16     3.50    3.61     2.00                  2014    215  EUR 141.861 bn   EUR 0.660 bn   EUR 0.180 bn    5.05     3.00    3.33     2.00                  2015    217  EUR 125.520 bn   EUR 0.578 bn   EUR 0.167 bn    4.61     3.00    3.10     2.00                  2016    192  EUR 124.019 bn   EUR 0.646 bn   EUR 0.145 bn    3.85     2.50    2.44     2.00                  2017    193  EUR 121.384 bn   EUR 0.629 bn   EUR 0.201 bn    4.80     4.00    3.56     2.00                                                  Source: Own calculations                 The approach to identify a possible structural break in the data consists of multiple mean                value  comparisons  with  the  respective  year  as  factor  variable.  After  performing  the                Shapiro-Wilk test, the normal distribution assumption is rejected at a significance level of                0.1%  for  both  dependent  variables  MLA  and  BR.  Thus,  the  Kruskal-Wallis  test  for                                                              616","nonparametric independent samples is used in combination with the Benjamini-Hochberg                α correction. The Kruskal-Wallis test is in this case superior to the ANOVA model since the                normal distribution assumption is unnecessary and the α correction resolves the multiple                testing problem (Bühner and Ziegler, 2017).                            Table 4 Kruskal-Wallis Test with Benjamini-Hochberg α Correction                                            Dependent Variable: Mandated Lead Arranger                     Year                          ’01  ’02  ’03  ’04  ’05  ’06  ’07  ’08  ’09  ’10  ’11  ’12  ’13  ’14  ’15  ’16  ’17                    Year                     ’01                     ’02                  Dependent Variable: Bookrunner   ’05                     ’03                     ’04                      ’06                     ’07                     ’08                     ’09                     ’10                     ’11                     ’12                     ’13                     ’14                     ’15                     ’16                     ’17                 Coloration of respective cell corresponds to a p-value < 0.05                                                  Source: Own calculations                 Based on the statistical results shown in table 4, the dummy variable SB will be included                into the dataset starting with the year 2009 to model a possible structural break. It should                be noted at this point that the mean values for 2016 also differ significantly at the 5% level                from the mean values of most of the years following 2009. An explanatory approach could                be that the crisis, triggered in 2009, resulted in a higher need for transaction security,                which started to abate in 2016. Therefore, the year 2016 could rather be compared with                the years before the crisis and gets marked with the dummy variable as well.                For further analysis, the variable VOL is converted by a natural logarithm transformation                to logVOL and the variable FX is converted into a factor variable. For the first hypothesis,                only the influence of the transaction volume on the number of MLA and BR titles is tested.                For the second hypothesis, further operationalization is necessary to transform the abstract                construct of complexity of a syndicated loan transaction into a testable model. The achieved                data  from  Bloomberg  offers  several  variables  for  this  purpose.  On  the  one  hand,  the                number of facilities can be considered as an element of complexity. The more facilities are                agreed  upon,  the  higher  the  documentation  efforts  and  the  higher  the  transaction                complexity. This analogy is also applicable to foreign currency facilities. The term of the                transaction can also be described as an element of complexity since long-term transactions                usually require a stricter due diligence to document the borrower’s creditworthiness (e.g.                with strategic long-term planning documents). Finally, the status of competition within the                banking circle can also be included through the number of passive arranger titles (lead                arranger or arranger but not MLA) as one possible explanatory variable. The more banks                are competing for leading positions, the more attractive the transaction should be and the                greater  the  number  of  total  mandates.  For  the  second  hypothesis,  the  effect  of  the                explanatory variables logVOL (positive relationship), ΔGA (difference between GA and MLA;                positive  relationship),  TR  (negative  relationship),  LFZw  (negative  relationship)  and  FX                (negative relationship) as factor variable is tested on the dependent variables. The full                regression models are presented in the following equations (1) and (2).                                                               617","       =    +    ∗              +    ∗ Δ     +    ∗      +    ∗        +    ∗      +    ∗        (1)                                                            3                                                  2                                                                    4                                                                                                                                                           5                                                                                        6                                     1                                0                              =    +    ∗              +    ∗ Δ     +    ∗      +    ∗        +    ∗      +    ∗        (2)                                                                                                                           2                                   1                                                                   4                                                          3                               0                                                                                      6                                                                              5                The testing is performed with the Ordinary Least Squares method. Since heteroscedasticity                is present in the dataset, White’s heteroscedasticity-consistent covariance matrix estimator                (HC0) is used to optimize standard error estimation.                4 Results and Discussion                The estimated regression models are presented in table 5, where t-statistics are shown in                parentheses below the coefficient estimates, and 1% statistical significance is indicated in                bold. The results point out that most transaction parameters have a significant effect on                the number of MLA and Bookrunner mandates.                In  the  first  hypothesis test,  only the  transaction volume is modeled  as  an  explanatory                variable. Both tests showed that the transaction volume (logVOL) has a significant positive                effect on the number of allocated mandates so that the hypotheses H1A and H1B could be                confirmed.                                          Table 5 Results of Regression Analysis                                              MLA                                     BR                                  H1A          H2A       SB Model        H1B          H2B       SB Model                               -28.6401     -31.0269     -33.9022     -16.2437  -17.9077        -19.9275                 (Intercept)                                 (-18.79)     (-19.34)     (-20.97)     (-13.83)    (-14.03)     (-15.15)                                 1.6977       1.8862       1.9365       0.9856       1.0963       1.1316                   logVOL                                  (20.77)     (21.36)      (22.71)      (15.63)      (15.66)      (16.53)                                             -0.1161      -0.0618                   -0.0584       -0.0203                     ΔGA                                               (-6.06)      (-3.39)                   (-4.03)      (-1.49)                                              -0.0013       0.0023                    0.0031      0.0057                    LFZw                                               (-0.68)       (1.18)                    (2.19)       (3.70)                                             -0.3792      -0.2947                   -0.2155      -0.1561                     TR                                               (-7.54)      (-6.32)                   (-6.65)      (-5.32)                                             -1.1485      -1.0510                   -0.7631      -0.6945                     FX                                               (-4.09)      (-4.05)                   (-4.21)      (-4.16)                                                           2.3255                                 1.6335                     SB                                                           (14.47)                                (13.64)                      R²          0.3041       0.3421       0.4032       0.2129       0.2393       0.3022                   Adj. R²        0.3037       0.3405       0.4015       0.2126       0.2375       0.3002                                                  Source: Own calculations                 The  second  hypothesis  test,  however,  is  not  exactly  corresponding  with  the  predicted                relationships. While rising transaction volume still has a positive impact on the mandate                allocation, the ΔGA variable shows a significant negative influence. Thus, with more passive                arranger titles less MLA and Bookrunner mandates are granted. A possible explanation                could be that a company allocates passive arranger titles to important banks so that they                don’t leave the tender empty-handed although the important MLA and Bookrunner roles                were assigned to a small group of other applicants. Regarding the number of facilities (TR)                as well as the existence of a facility in foreign exchange (FX), the predicted significantly                negative relationships were confirmed. The volume-weighted transaction term (LFZw), on                the other hand, has no significant impact on the role allocation. Taking the coefficient of                determination into  account,  the  additional  explanatory  variables  cause  only  a  marginal                improvement. The goodness of fit is majorly impacted by the transaction volume. Although                the hypotheses H2A and H2B can be confirmed based on the given results, the model of                complexity should be reconsidered in further research.                The third analysis, including the structural break dummy (SB), provides an unexpectedly                proficient model improvement. The role allocation in the German syndicated loan market                has been significantly affected by a structural break in 2009 which led to higher numbers                                                             618","in MLA and Bookrunner mandates. A possible explanation could be the already discussed                necessity for transaction security caused by the financial crisis.                 The research results from the German market are partially consistent with the US market-                based studies of Sufi (2007) as well as Chaudhry and Kleimeier (2015). Usually, large,                listed companies have a higher degree of transparency due to disclosure obligations than                small-  or  medium-sized  enterprises.  According  to  the  authors,  a  lower  degree  of                information asymmetries should lead to a higher degree of granularity in the syndicate. On                the assumption that large-scale transactions are primarily initiated by larger companies,                this thesis is confirmed since the transaction volume has a significant positive impact on                the number of mandates. Furthermore, if it is assumed that large companies have more                professional investor relation and press offices, and thus are able to influence the media                reporting in their favor, the results of Bushman et al. (2016) are also confirmed.                 5 Conclusions                 The study shows that the syndicated loan market in Germany has developed dynamically                regarding the allocation of MLA and Bookrunner titles and the average transaction volume.                In particular, the effects of the financial crisis could have led to a sharp increase in the                allocation of mandates creating a structural break in the dataset. On the other hand, the                market has opened more and more for smaller transactions over the last few years, so                mid-sized enterprises can also increasingly use this market segment and thus the benefits                from a syndicated loan transaction.                The regression analysis reveals that transaction-related variables significantly affect the                allocation  of  MLA  and  Bookrunner mandates.  For  example, it  has  been  shown  that the                transaction volume has a significant positive effect on the number of mandated banks,                whereas the complexity of the transaction, represented by the number and type of facilities                as well as the total allocation of arranger titles, has a significantly negative impact on the                mandate  allocation.  The  weighted  transaction  term,  on  the  other  hand,  could  not  be                identified as significant determinant of role allocation. Projecting the results on practical                application, financial managers should not only focus on the transaction volume, but also                take  the  transaction  complexity  into  consideration  while  determining  the  optimal  cost-                benefit-ratio resulting from role allocation in the transaction at hand.                Nevertheless, further research is necessary to improve the explanatory power of the model.                In the next step, business-related indicators (e.g. leverage, EBITDA margin, and fixed-                assets-ratio) could also be added to the explanatory variables. In addition, the significant                difference of the year 2016 should also be further analyzed with data sets from the years                2018 et seq.                 References                 Bae, S. C., Chong, B., Kim, Y. (2014). Informational Frictions, Syndicate Structure, and                Loan Pricing: New Evidence from International Lending.  Asia-Pacific Journal of Financial                Studies, vol. 43(1), pp. 124–155.                Bloomberg  L.P.  (2017).  League  Table  Standards  and  Guidelines  (7th  ed.).  New  York:                Bloomberg L.P.                Bühner, M., Ziegler, M. (2017). Statistik für Psychologen und Sozialwissenschaftler. 2nd                ed. Hallbergmoos: Pearson.                 Bushman, R. M., Williams, C. D., Wittenberg-Moerman, R. (2016). The Informational Role                of the Media in Private Lending. Journal of Accounting Research, vol. 55(1), pp. 115–152.                Byoun,  S.  (2008).  How  and  When  Do  Firms  Adjust  Their  Capital  Structures  toward                Targets?. The Journal of Finance, vol. 63(6), pp. 3069–3096.                Chaudhry, S. M., Kleimeier, S. (2015). Lead arranger reputation and the structure of loan                syndicates. Journal of International Financial Markets, Institutions and Money, vol. 38(C),                pp. 116–126.                                                             619","Ferreira, M. A., Matos, P. (2012). Universal Banks and Corporate Control: Evidence from                the Global Syndicated Loan Market. The Review of Financial Studies, vol. 25(9), pp. 2703–                2744.                Gopalan,  R.,  Nanda,  V.,  Yerramilli,  V.  (2011).  Does  Poor  Performance  Damage  the                Reputation of Financial Intermediaries? Evidence from the Loan Syndication Market. The                Journal of Finance, vol. 66(6), pp. 2083–2120.                 Ivashina, V. (2009). Asymmetric information effects on loan spreads. Journal of Financial                Economics, vol. 92(2), pp. 300–319.                Khlif, H., Souissi, M. (2010). The determinants of corporate disclosure: a meta‐analysis.                International Journal of Accounting \& Information Management, vol. 18(3), pp. 198–219.                Miller, S. (2008). Handbook of Finance Volume 1. New Jersey: John Wiley \& Sons, Inc.                Sufi,  A.  (2007).  Information  Asymmetry  and  Financing  Arrangements:  Evidence  from                Syndicated Loans. The Journal of Finance, vol. 62(2), pp. 629–668.                                                                                                                           620","Backtesting Expected Shortfall – Is There a Method Suitable for                                                Middle-sized Banks?                                                     Eugenia Schmitt                                                       FinRiskConsult                                       Hammersbacher Str. 18, 81377 Munich, Germany                                          E-mail: eugenia.schmitt@finriskconsult.de                 Abstract:  The  financial  crisis  2008  revealed  weaknesses  in  the  valuation  and  capital                backing for market risks. The new regulatory requirements, the Fundamental Review of                the Trading Book (FRTB), is a reaction of the Basel Committee on Banking Supervision and                should create transparency based on a unification of market risk criteria to be met by all                banks. One of the novelties is the move from Value at Risk (VaR) to Expected Shortfall                (ES) as the standard for measuring risk and calculating capital. It is motivated by better                theoretical properties of ES as a risk measure. Especially, VaR doesn’t account for “tail                risk”. ES in contrast captures high-value low-probability events and has other advantages                too.  At  the  same  time,  ES  has  been  criticized  for  unavailability  of  simple  tools  for  its                validation,  for  possibilities  of  backtesting.  Because  backtesting  is  one  of  the  key                quantitative  tools  used  to  assess  the  conservativeness  of  the  risk  measurement                methodology, estimation techniques are being constantly improved. This article shows the                current, new methods for backtesting ES reviewed in literature and discuss which methods                are  appropriate  to  implement  in  middle-sized  banks.  ES  will  be  necessary  for  internal                validation and for regulatory control. Hence, practical methods for backtesting the ES are                of particular interest.                Keywords: risk management, regulation, expected shortfall, backtesting, tail risk                JEL codes: G18, G21, G32, C18                 1 Introduction                 Measurement of risk is the core task of the risk management in financial institutions. The                most popular method is the calculation of the Value at Risk (VaR). Even if it has many                advantages, as f. e. an intuitive interpretation because it expresses the loss in the amount                of money, or applicability to any financial instrument or to all risks in the portfolio, it has                been  criticized  for  some  shortcomings.  To  the  most  important  ones  count  its  lack  of                subadditivity and its inability to include the tail risk. (Artzner et al. (1999), Acerbi, Tasche                (2002)). It shows the amount of money a bank can lose with a pre-determined level of                confidence within a given time period if a tail event doesn’t occur.                These restrictions led to the application of an alternative risk measure. Expected Shortfall                (ES) is a risk measure, which has the important property of coherence and it counts for                the tail risk too. I. a. for this reason, in its consultative document on the Third Basel Accord,                dated May 3, 2012, the Basel Committee on Banking Supervision (BCBS) recommends to                replace VaR with ES in internal market risk models as a standard for measuring risk and                calculating  capital.  On  January  16th  2016,  the  BCBS  published  standards  for minimum                capital requirements for market risk where the VaR at level 1% is replaced with ES at level                2.5%. The implementation of the revised market risk standards is expected to be finalized                by January 2019, and banks will be required to report under the new standards by the end                of 2019. The ES calculation is calibrated on a stress period. Only sufficiently liquid risk                factors may be used.                In addition to the determination of an appropriate risk measure, its validation is one of the                central issues in market risk management. Statistical comparison of the predicted risk with                the actually realized profit and loss has been established as an essential building block of                backtesting procedure in banks. (Jorion, 2007) It is also one of the key quantitative tools                used by the regulators to judge, how conservative is the risk measurement methodology.                Beyond  the  regulatory  requirements,  backtesting  also  plays  an  important  role  in  the                                                             621","analysis of risk indicators. It provides useful information about the model and the input                data used too. Practical methods for backtesting the ES are therefore of particular interest.                 A debate about the unavailability of simple tools for ES-evaluation has been started very                soon. The study by Gneiting (2011) pointed out that ES is not elicitable. This finding led to                the belief, that ES could not be backtested efficiently at all. But, Acerbi and Szekely (2014)                show, that this condition is not necessary for backtesting, it is rather connected to model                comparision  than  to  model  testing.  Fissler  and  Ziegel  (2016)  show  that  ES  is  jointly                elicitable with VaR. Hence, it is possible to adopt the elicitability testing techniques for ES.                In recent years, a number of different backtesting ES frameworks have been proposed. For                example, the quantile-space Coverage Test of Costanzino \& Curran (2015, 2018) for the                family  of  spectral  risk  measures.  Backtest  for  ES  based  on  cumulative  violations  as                analogue to the  common  test  for  VaR by Du  and  Escanciano (2016).  Acerbi \&  Szekely                (2014)  develop  three  price-space  tests  and  show  that  elicitability  is  not  a  necessary                condition  for  backtesting ES. Emmer  et  al.  (2015)  approximate  ES  by  using VaRs  with                different  levels  and  applying  of  standard  backtesting  tools.  Righi  and  Ceretta  (2013)                propose the truncation-based ES backtests and use Monte Carlo simulation for significance.                The  approach  of  Wong  (2008)  who  introduce  a  parametric  saddle-point  method  and                focusses the average of the portfolio-losses realized magnitude that breach the VaR level                is close to the backtesting VaR. There are also a lot of works which use alternative methods                as f. e. Berkowitz (2001), Kerkhof and Melenberg (2004) among others.                 Many  methods  require  advanced  mathematical  modelling,  assumptions  or  large  data                samples. Middle-sized banks, which often have limited possibilities to establish and refine                sophisticated  procedures  are  interested  on  transparent,  easy  interpretable  and  easy                implementable backtesting framework. Therefore, as a part of the literature review work,                this  article  shows  the  new  statistical  methods  for  backtesting  ES  reviewed  in  current                literature focusing two questions:                     •   What  does  elicitability  mean  and  can  the  not-elicitabilty  of  ES  hinder  the                       backtesting?                    •   Is backtesting ES then more difficult as backtesting VaR?                    •   Are there methods suitable for implementing it in middle-sized banks, both in terms                       of performance and complexity?                The main finding is, that ES can be considered as a good risk measure and it is backtestable                by existing backtest frameworks which display superior power to the standard methodology                of Basel for VaR backtesting.                 2 Methodology and Data                This paper provides an explanatory framework for a link between the risk measure and the                procedure how to backtest the model results in the new light of the current regulatory                environment  based  on  a  literature  review.  The  aim  of  this  paper  is  to  offer  a  better                understanding  of  possibilities  for  backtesting  in  middle-sized  banks  and  the  role  which                certain characteristics of risk measures play in backtesting. For this reason, the methods                of description, analysis, synthesis and deduction are used.                Basic properties of Expected Shortfall                The limitation of VaR led to the implementation of an alternative, the coherent risk measure                ES.  The  axioms  of  monotonicity,  homogeneity,  translation  invariance  and  subadditivity                define the known class of coherent risk measures. (Artzner et al., 1999) It also counts for                the tail events of the loss distribution. Hence, ES is regarded as a suitable measure to                substitute VaR. ES is formally defined as follows: (Yamai, Yoshiba, 2002; Chen, 2014)                 Suppose L is a random variable denoting the loss of a given portfolio and VaRα(L) is the                VaR at the confidence level 100*(1 – α)%. Then ESα(L) is defined by the following equation:                                1                                  ∝                            (  ) = ∫        (  )                                                         (1)                                                                  ∝                                ∝ 0                                                            622","If L is a continuous loss distribution, then ES is the expected loss conditional on the loss                lying beyond the threshold α:                             (  ) =   (  |   ≥        (  ))                                               (2)                         ∝                                           ∝                As  shown  in  the  figure  1,  ES  can  be  interpreted  intuitively  as  the  mean  of  all  losses                exceeding the VaR quantile, so it accounts for the tail risk. (Acerbi, Tasche, 2002).                                      Figure 1 Value at Risk and Expected Shortfall                                                                           Source: Schmitt E. (2014)                 As can be seen in the figure 1, ES deals with extreme losses or losses of low probability                but a possible high impact. Therefore, the number of such losses observed in the available                data sample can be very small. Hence, it is necessary to design a backtest which deals                with this problem.                Backtesting and backtestability                Jorion (2007, p. 139) defines backtesting as “a formal statistical framework that consists                of verifying that actual losses are in line with projected losses. This involves systematically                comparing the history of VaR forecasts with their associated portfolio returns.”. He points                out, that this process should be an integral part of all VaR systems.                The  current  work  of  Acerbi  and  Szekely  (2017)  proposes  i.  a.  a  formal  definition  of                backtestable  statistics.  It is  motivated by  the  revisions in  regulatory  requirements, the                Fundamental Review of the Trading Book (FRTB) where the VaR1% will be replaced with                ES2,5% as well as by the discussion if ES is backtestable in general, based on the findings                by  Gneiting  (2011)  who  show  that  ES  is  not  elicitable.  The  problem  by  testing  of  the                accuracy of predictions  yt on a statistic  y(Ft) of the (“real”) distribution  Ft which is not                known, is that y(Ft) cannot be revealed ex post. The authors define the backtestability as                follows: (Acerbi, Szekely, 2017, p. 11)                A statistic y is F-backtestable, if there exists a backtest function      = (  ,   ) such that                                                                                                                [   (  ,   )] = 0,               =   (  ), ∀    ∈                               (3)                                                                        which is strictly increasing in the prediction     , ∀   ∈                              [   (   ,   )] <    [   (      )]                <                              (4)                                                              2                                                                                                    1                                             2                               1                                                                                                                 Backtestability allows model validation.                                                              623","Is elicitability a necessary backtesting criterium?                The aim of the evaluation of a forecast is to compare estimates with observed values.                Gneiting (2011) pointed out, that the mathematical property of elicitability is important for                the determination of optimal point forecasts and following for backtesting. He shows, that                VaR is elicitable, but generally ES does not fulfill this property. In general, a law invariant                risk  measure  transforms  a  probability  distribution  into  a  single-valued  point  forecast.                Hence, backtesting a risk measure denotes evaluating forecasting performance.                The concept of elicitability is originally introduced by Osband (1985) and formalized by                Lambert et al. (2008). The definition of elicitability is linked to that of scoring functions. A                scoring function assigns a numerical score to a single valued point forecast based on the                forecasted point and realization. (Emmer et al., 2015, Gneiting, 2011) A standard example                                                                             2                of a scoring function is f. e. the squared error s(x, y) = (x-y)  which is usually used for the                forecasting the mean or the absolute error s(x, y) =│x-y│.                A forecasting statistic which can be expressed in terms of a value of a scoring function has                the property of elicitability. For the property of elicitability to be valid, the scoring function                must be strict consistent. The scoring function   : ℝ  ℝ ⟶ ℝ  is consistent for the functional                                                                           +                f relative to the class P of probability measures on R if and only if for all P ∈ P, t ∈ f(P) and                x ∈ R:                           [  (  ,   )] ≤    [  (  ,   )]                                                 (5)                                                                                 where is L a real-valued random variable with distribution P. (Emmer et. al., 2015, p. 36,                Def. 2.5.) If the equality holds, the function s is strictly consistent, i.e. x ∈ f(P). In this case                it is possible to determine the optimal point forecast:                          =        min     [  (  ,    )]                                                                               (6)                                                  This means, that a functional can be seen as a minimization of a given scoring function.                Formally, a risk measure ρ is elicitable related to the set P of the probability measures P                given by L if a scoring function   : ℝ  ℝ ⟶ ℝ  exists for which holds true: (Gneiting, 2011,                                                            +                p. 749-751.) For all P ∈  P  and L P-distributed holds                            (  (  ),   ) <      (  ,   )                                                     (7)                                                                                   Gneiting shows, that for ES a scoring function does not exist. But this finding does not                mean,  that  ES  is  not  backtestable.  Even  before  2011,  Kerkhof  and  Melenberg  (2004)                published good performing tests using the functional delta method. The authors show that                backtests  for  ES  perform  better  than  tests  for  VaR.  In  2013  Emmer  et  al.  showed                conditional elicitability for ES consisting of two components which can be tested separately:                    1.  Calculate the quantile as VaRα(Y)                    2.  Calculate ESα(Y)                This property is an outcome of the elicitabilty of VaR because ES as conditional of VaR                takes a subset of the outcomes for which is ES a mean. A mean is elicitable. Emmer et al.                (2013) as well as Acerbi and Szekely (2014) concluded, that ES is the most appropriate                risk measure. Even if it cannot be backtested by using a single scoring function, there are                other suitable methods for backtesting. Some of them are presented in the next subpoint.                Common current backtesting methods                According to Emmer et al. (2015) there are three main groups of backtesting methods                depending on the type of forecast.                    •   The point forecast                Value of a variable is usually represented as the conditional expectation. Mean of an error                measure  or  a  scoring  function,  where  the  scores  are  averaged  over  forecast  cases,  is                compared  with  the  absolute  error  or  squared  error.  (Gneiting,  2011).  There  are  well                established methods, f. e. Elliot et al. (2013).                                                              624","•   The probability range forecast                It is an interval forecast for VaR and ES, where an interval is projected. Here, the value                lies in with the chosen probability. VaR backtesting is well developed. Campbell (2006)                shows a comprehensive review of backtesting methods.                    •   The complete probability distribution forecast or probability density function,                       Gneiting, Katzfuss (2014).                 Current methods for backtesting ES                In  the  following,  two  different  methods  of  backtesting  ES  published  after  2014  will  be                outlined.  They  are  parametric  as  well  as  non-parametric  and  with  as  well  as  without                simulation required. For implementing in middle-sized banks, these properties are of the                primary  interest.  There  are  more  methods  presented  in  the  literature  which  are  not                mentioned here. The methods chosen here do not need large data samples and so they                deal comfortably with the problem of few data availability.                     1.  Three unparametric methods, Acerbi and Szekely (2014)                Acerbi and Szekely (2014) presented three ES backtests which are model independent,                free  of  distributional  assumptions  other  than  continuity  and  are  non-parametric.  They                require  more  information  but  are  easy  to  implement.  The  authors  use  the  conditional                elicitability of ES, define a test statistic and use simulations for significance.                 The first method:                The ES is exploited as conditional on VaR and is written as:                           [         + 1 |       +          ,   < 0] = 0                                  (8)                                 ,                  X is a random variable, it represents returns.                The authors assume that VaR has been tested already and test separately the magnitude                of the realized exceptions against the model predictions. They determine the test statistic                using an indicator function of an α-exception and define the backtest function as:                                ∑      =1                                       (  ) =         ,    + 1                                                        (9)                        1                                ∑      =1                       The realized value Z1(x) is expected to be 0. If it is negative, ES is underestimated. The                test averages over exceptions and is insensitive to an excess of exceptions.                The second method:                This is a direct test for ES, without previously testing VaR, following from the unconditional                expectation                               ,    = −   [             ]                                               (10)                                                                   = ∑      =1               + 1                                             (11)                        2                                            ,                  If Z2 < 0 then ES is underestimated. The test averages over all days. It identifies an excess                of exceptions. It is possible to extend the test to non-continuous distributions by replacing                the indicator function. Frequency and magnitude of α-tail events are evaluated jointly.                The third method:                This method is a direct test for ES. It is inspired by Berkowitz (2001) and based on the                test  of  the  entire  return  distribution  –  the  test  for  uniformity  of  the  tail  of  empirical                distribution. To each quantile its value importance is assigned. It depends on the shape of                the tail of the distribution. A continuous predictive distribution Pt is assumed. It is to test                whether the observed ranks Ut = Pt(Xt) are i.i.d. and uniformly distributed U(0,1). ES is                estimated as:                                     1                       ̂                             ⃗                               (  ) (  ) = − [    ]  ∑ [    ]                                          (12)                                              :                                                                                                        625","and it is the average of Nα worst results, where N is the number of observed returns,                                                                                                              :                  is order statistics,    = {   } a vector of N i.i.d. draws. The test statistic the authors use is                                   ⃗                                                           defined as:                                             −1                                        ̂                                 1                           (  ) = − ∑             (  ) (      (   ⃗⃗ ))  + 1                             (13)                        3                                     =1                                         ̂                                            [     (  ) (   −1 ⃗⃗                                                (  ))]                                                                                                           ⃗                                                  ⃗⃗                where    =    (   ) are i.i.d.   (0,1);    = {   },    are i.i.d.   (0,1).                                                                                                                                                                    The hypothesis uses weak stochastic dominance. ES is underestimated if  Z3 < 0. ES is                backtested with other quantiles jointly. This test is less natural, but very general.                    2.  Backtesting distribution forecast, Emmer, Kratz, Tasche (2015)                 The proposed model is based on the representation of ES as an integrated VaR (Acerbi,                Tasche, 2002). The authors define four supporting points and calculate VaR amounts to                approximate ES linearly. In the practical use, the number of supporting points should be                determined individually depending on the sample size. The authors define ES as follows:                                  1  1                                   ∫        (  )                                (  ) =  1−                                                                                                                                           (14)                         1                       ≈ [       (  ) +        0,75  +0,25 (  ) +        0.5  +0.5 (  ) +        0.25  +0.75 (  )]                               ∝                         4                 The  idea  is  as  follows:  if  each  of  the  VaRs  can  be  backtested  and  is  appropriate,  the                estimate of ES is reliable. An examination of the tail of the loss data sample which is higher                than 2,5% should be manually reviewed whether in the observation data are some outliers.                Hence, for backtesting ES, the different VaR values are backtested jointly.                3 Results and Discussion                The  backtesting  of  VaR  tests  a  point  forecast,  the  methods  are  based  on  counting                exceptions. Hence, VaR is not difficult to backtest. As Acerbi and Szekely (2014) pointed                out, it is not because of the elicitability of VaR, but being a quantil it is a Bernoulli random                variable. ES is not directly backtestable and another approach has to be made. This issue                is not connected to the lack of elicitability.                In order to compare the predicted ES with the realized one, all methods outlined in the                present  paper  are  able  to  reject  the  predicted  ES  if  it  is  not  accurate.  The  strongest                performance  shows  Acerbi  and  Szekely’s  (2014)  first  method.  The  rejections  of  their                second and third method as well as the ones of the model proposed by Emmer et al. (2015)                are depending on the number of exceedances.                For middle-sized banks the three tests of Acerbi and Szekely (2014) seems to be the most                appropriate  ones,  because  they  are non-parametric  and  contain  no model  assumption.                Hence,  the  model  risk  can  be  omitted.  Anyhow,  for  the  statistical  test,  Monte  Carlo                simulation  is  necessary.  Additionally,  they  are  suited  for  regulatory  purposes.  As  the                authors show, they also have superior power to the standard Basel-VaR-backtesting.                 The backtesting method proposed by Emmer et al. (2015) is the less complex method                among the current approaches and therefore it can be implement easily by middle-sized                banks.  Compared  to  the  alternative  models,  it  is  a  comfortable  approach  because  it                approximates several VaR levels and combines statistical testing with manual inspections                of data in the upper tail of the distribution. It does not rely on any simulation. To use this                method, availability of sufficient amount of high quality data is necessary. In order to reach                the same certainty level, more data are required for the ES validation compared to the VaR                validation because for the calculation of ES at the level α, currently it is 2,5%, just α% of                the sample can be used. If only a few data are available, the statistical power of rejection                of an inaccurate model will be low. To choose an appropriate confidence level still can be                a challenge.                                                              626","For banks which are already using Monte Carlo method and parametric distribution, the                Acerbi and Szekely’s (2014) first model is recommended for their internal model. Banks                using historical simulation should choose the model of Emmer et al. (2015).                 4 Conclusions                Backtesting ES will be necessary for both, internal validation and fulfilling of regulatory                requirements.  To  find  an  appropriate  backtesting  method  of ES is therefore  of  a  great                interest. The main finding is, as Acerbi and Szekely (2014) show, that elicitability is not a                problem which would hinder backtesting ES. There are a number of practicable approaches                which can be implemented easily by middle-sized banks. All of the here proposed backtests                have a superior power to the standard VaR methodology used by Basel. To reach the same                level  of  certainty  more  validation  data  is  required  for  ES  than  for  VaR.  Full  predictive                distributions have to be stored. It should not be a technical problem any more but it is still                a challenge for an audit process.                Acknowledgments                 I thank Andreas Schmitt for the technical support.                 References                Acerbi C., Szekely, B. (2017). General properties of backtestable statistics. Retrieved from:                https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2905109.                Acerbi, C., Székely, B. (2014). Back-testing expected shortfall. Risk, vol. 27(11), pp. 1–6.                Acerbi, C., Tasche, D. (2002). On the coherence of expected shortfall. 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Backtesting  General  Spectral  Risk  Measures  with                Application to Expected Shortfall. Retrieved from: https://ssrn.com/abstract=2514403.                Costanzino, N., Curran, M. (2018). A Simple Traffic Light Approach to Backtesting Expected                Shortfall. Risk, vol. 6(2), pp. 1-7.                 Du, Z., Escanciano J. C. (2016). Backtesting Expected Shortfall: Accounting for Tail Risk.                Management Science. DOI: 10.1287/mnsc.2015.2342                Elliott G., Granger, C., Timmermann, A. (2013). Handbook of Economic Forecasting, vol. 2.                 Emmer, S.,  Kratz,  M.,  Tasche, D.  (2015).  What is the  best  risk measure in  practice?  A                comparison of standard measures. Journal of Risk, vol. 18(2), pp. 31-60.                 Fissler, T., Ziegel, J. F. (2016). Higher order elicitability and osband’s principle. The Annals                of Statistics, vol. 44(4), pp. 1680–1707.                Fissler, T., Ziegel, J. 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In: Proceedings of the 9th ACM Conference on Elecronic Commerce. Chicago,                IL.                Osband, K. H. (1985). Providing incentives for better cost forcasting. Berkley: University                of California. PhD thesis.                Righi,  M.,  Ceretta, P.  S. (2013).  Individual  and  Flexible  Expected  Shortfall  Backtesting.                Journal of Risk Model Validation. DOI: 10.21314/JRMV.2013.108                Schmitt, E. (2014). Stochastisches Stresstesting des Liquiditätstransformationsrisikos im                Rahmen  der  strukturellen  Liquiditätsrisikosteuerung.  München:    Ludwig-Maximilians-                Universität. Inaugural-Dissertation, head: Prof. Dr. Rudolph, B., Prof. Dr. Elsas, R.                Wong, W. K. (2008). Backtesting trading risk of commercial banks using expected shortfall.                Journal of Banking \& Finance, vol. 32(7), pp. 1404-1415.                Yamai, Y., Yoshiba, T. (2002). Comparative Analyses of Expected Shortfall and Value-at-                Risk:  Their  Estimation Error,  Decomposition,  and  Optimization.  Monetary  and Economic                Studies, pp. 87-122.                                                                                                         628","Profitability of Sector Mutual Funds and ETFs during Market                                                     Development                                                                                       3                                                                     2                                                    1                                    Martin Sirucek , Jan Vystoupil , Petr Strejček                                                 1  Mendel University in Brno                                   Faculty of Business and Economics, Department of finance                                         Zemědělská 1, 613 00 Brno, Czech Republic                                             E-mail: martin.sirucek@mendelu.cz                                                  2  Mendel University in Brno                                              Faculty of Business and Economics                                         Zemědělská 1, 613 00 Brno, Czech Republic                                             E-mail: martin.sirucek@mendelu.cz                                                  3  Mendel University in Brno                                   Faculty of Business and Economics, Department of Finance                                         Zemědělská 1, 613 00 Brno, Czech Republic                                              E-mail: petr.strejcek@mendelu.cz                 Abstract: Paper is focused on the profitability of investment into IT, finance, healthcare                and consumer goods oriented active and passive mutual funds and ETFs and its profit/loss                on growing, stagnant and decreasing market. Attention is also focused on purpose to set                a recommendation for investors what instrument bring higher return/lower loss on growing                or decreasing market and if investor can expect different results according to the sector                orientation, like what sector is more sensitive on bullish or bearish trend. Our result show                that ETF nor passive mutual funds wasn´t able to beat the market, where  sector index                brings in all situations better results than these investments. On bearish trend all sector                ETFs and passive mutual funds bring same results as sector index, better results bring only                active managed mutual funds. The lowest loss during this period brings healthcare active                managed mutual funds. On bullish/stagnant market was pretty the situation the same, but                on the growing market passive funds and ETF are more profitable than active mutual funds.                Keywords: ETF, mutual fund, Sharpe ratio, standard deviation, tracking error                 JEL codes: G11, G15                 1 Introduction                Active and passive portfolio management and/or differences in the returns of the active                and passive managed funds (incl. the ETFs) have long been a subject of discussion in the                investment world. Conclusions of the recently performed studies have tended to suggest                that the  fund  managers  are  not  able to  overcome  the  market in  the long  run  and  are                lagging behind performance of the passive mutual funds and/or the benchmark copying                ETFs.                Jensen  (1968)  examined  performance  of  115  mutual  funds  for  the  period  1945-1964.                Results  of  his  study  show  that the  active  fund  managers  were  on  average  not  able  to                overcome performance of the S\&P 500 benchmark, as evidenced by the negative alpha                coefficient.  Only  39  funds  had  the  alpha  coefficient  higher  than  0.  One  further  study,                dealing with comparison of performance of the passive and active investment strategy of                the mutual funds, was realized by Grinblatt, Titman (1989). Conclusion of their study, in                principle, corresponds with Jensen's conclusion; the latter states that aggressive growth                funds, growth funds and funds with a small volume of means may have the performance                above average, but it is interesting that these funds at the same time reach the highest                cost level and therefore their real performance, after deduction of the management costs,                does not reach the performance above average. This suggests that the investors cannot                benefit  from  capabilities  of  the  portfolio  managers  managing  these  funds.  A  similar                conclusion was reached by Malkiel (1995), who examined performance of the equity mutual                funds in the period of 1971 and 1991. Conclusion of his study, therefore, confirms that the                                                             629","analysed active funds – after deduction of the total expense ratio (TER)- lagged behind the                market performance, and adds that the investor pays to invest into the low-cost index                funds rather than to rely on portfolio manager capabilities of the active mutual funds. One                of the more recent studies concerning this issue was performed by Solin (2015). In his                study, the author presents similar conclusions like the previous authors, namely that in the                long-term investment horizon, most of the active managed mutual funds lag behind the                risk-weighted benchmark yield. The author also pinpoints that, when considering the taxes,                the chance that an active mutual fund will beat a comparable passive mutual fund is almost                zero.                Results of the Ippolito study (1989) came the conclusion opposite to that of the authors                mentioned above. The Ippolito study examined 143 funds in the period of 1965-1984 and                compared their performance with the S\&P 500 index. His results suggest that the risk-                weighted returns of the active mutual funds defeated the index even after deduction of all                charges and costs. Kon (1983) states in his study that managers of the active managed                funds have a certain ability of market timing. However, the other tests stress that the fund                managers  have  little  or  no  special  information  concerning  unexpected  market  returns.                Brown,  Goetzmann  (1995)  study  has  contributed  by  the  fact  that,  based  on  the  past                performance of specific funds, we can assume which funds to avoid in the future, but there                is no indication that if some funds defeated the benchmark in the past, they will beat it in                the future as well.                 The study by Harper, Madura, Schnusenberg (2006) deals with comparison of performance                of the international ETFs and the closed-end funds investing into the closed-end country                funds. The study was performed on the basis of the monthly data for the period of 1996-                2001.  The  authors  confirm  that  ETFs  have  a  low  tracking  error  compared  with  their                benchmark, i.e. that ETFs replicate their underlying index almost exactly. Conclusion of                their study confirms the hypothesis that the ETF funds have achieved on average higher                returns than the closed-end country funds. This claim is justified mainly by the fact that                the ETF funds have a lower expense ratio. They author also state that the ETF funds have                on average a higher Sharpe ratio than the corresponding closed-end country funds and                hence the ETFs earn higher returns than the funds utilizing the active investment strategy,                which  the  closed-end  country  funds  are  based  on.  Rompotis  (2009)  has  brought  an                interesting study; the author compared performance of the index mutual funds and the                ETFs within a single Vanguard \"family\". His analysis included in total 20 equity ETFs and                12 index mutual funds, and values of historical performance from the beginning of trading                of the relevant fund till the end of 2006 were taken for each individual fund. Results of its                study show that the ETFs and the index funds have similar average return and risk rate                values. Which means that Vanguard approaches these two alternative investments in a                similar way. Furthermore, ETFs and the index funds achieve lower return and risk rate than                their benchmarks.                 In his study, Sharifzadeh, Hojat (2011) was concerned with comparison of performance of                the ETFs and the index mutual funds in the period of 2002-2010. With respect to the fact                that  ETFs  and  the  index  funds  are  very  similar  by  their  nature  and  are  considered                substitutes, the author has tried to justify why popularity of the ETFs has been growing so                steeply since 2000. He included in total 34 ETFs, broken down into 12 different investment                styles (U.S.  Large  Cap, U.S.  REITS,...),  and  66 index  funds  with the  same investment                styles were affixed to these ETFs. The author de facto compared performance of in total                230 different pair combinations (58% of cases). Results of this study show that in terms                of the Sharpe ratio, the  ETF funds defeated the index funds in 134 cases out of in total                230  pair  combinations.  Conclusions  of  this  study  suggest  that  there  is  no  statistically                significant difference in returns between ETFs and the index funds at the level of individual                funds,  and  that  the  investors  choose  between  these  funds,  depending  on  specific                characteristics of individual products and the tax benefits. Agapova (2011), in her study,                has investigated whether or not the traditional index funds and the ETFs can be considered                substitutes. The study was performed with a sample of 171 index mutual funds and 11                ETFs in the period of 2000 and 2004. ETFs can be preferred by the investors with a higher                                                             630","liquidity, higher business needs and a higher tax sensitivity, whilst the traditional index                funds can be preferred by the investors with a lower liquidity, lower business needs and a                lower tax sensitivity. According to this study, the traditional index funds and the ETFs can                be considered substitutes, but not the perfect ones. Agapova also came to the conclusion                that ETFs have on average a lower tracking error and higher post-tax returns than the                index funds. Park, Jung and Choi (2014) have established that ETFs and the index funds                are substitutes, as the investors choose between these instruments on the basis of their                specific investment characteristics. Bello (2012) conducted a study comparing the equity                ETFs and the stock index funds over a long period and on a relatively large sample of funds.                In particular, he included in total 468 index funds and 337 ETFs into his study, and their                performance was tracked for the period of 2001 to 2012. Bello has established that ETFs                outperform the index funds, but with a higher volatility. Furthermore, ETFs have a lower                expense ratio, a lower portfolio turnover and fewer shares included in the portfolio, thus                being less diversified than the index funds. Bello came to the conclusion that the ETFs had                a  greater  tracking  error  and  tracking  error  volatility  than  the  mutual  funds,  which                contradicts the Kostovetsky study (2003) absolutely.                 2 Methodology and Data                To compare performance of the ETFs, index (passive) and active managed mutual funds,                funds from 4 economic sectors, having the highest representation - according to S\&P Dow                Jones Indices (2018) in the S\&P Composite 1500 index by the GICS sector categorization                - have been chosen. These sectors are as follows: Information Technology (24.4%, S\&P                1500  Information  Technology  benchmark),  Financials  (15.3%,  S\&P  1500  Financials                benchmark), Health Care (13.4%, S\&P 1500 Health Care benchmark) Cons Discretionary                sector (12.7 %, S\&P 1500 Cons Discretionary benchmark). From each sector, 1 ETF, 1                index mutual fund, 1 active managed mutual fund (a large cap fund) and 1 active managed                mutual fund (a small cap fund), were selected. Selection of the funds entering into the                analysis was performed objectively with strict adherence to the following criteria.                Common criteria for selection of ETF and passive/active managed mutual funds:                     •   Within the examined sector, 1 ETF, 1 passive and 1 active mutual fund with the                       highest / lowest NAV value is always selected.                    •   Establishment of the fund before 2007 (i.e. adequate fund history).                    •   It must be a share (investment) fund.                    •   At least 90% of all fund assets are invested in US companies. At least 80% of all                       fund assets are invested in sector shares.                    •   ETF funds are designed, based on physical replication.                                                                                                 23                    •   The fund must have at least 3 star ratings by Morningstar on 28.02.2018 .                    •   Option to choose automatic reinvestment of dividends or payment of dividends to                       the businessman account.                    •   Fund denominated in US$.                Performance and risk rate of the funds is assessed in context with market development                and its trend. Individual phases were monitored within the following time periods:                     •   Decline stage - period from 01.10.2007 till 31.01.2009.                    •   Growth stage – period from 01.09.2011 till 31.01.2015.                    •   Stagnation stage - period from 01.03.2015 till 30.04.2016.                These periods were chosen according to the Chow (1960) procedure (the same approach                used e.g. Rapach, Wohar (2004)), to test the null hypothesis of no structural change (H0:                δ = 0) against the alternative hypothesis of a  structural break at period k (H1: δ ≠ 0). The                Chow (1960) test is based on the Wald statistic:                   23 Morningstar  assesses  ETFs  and  mutual  funds  by  applying  a  1  to  5  star  scale,  based  on  the  achieved                performance (taking into account risks and charges) compared with similar mutual funds and the ETFs.                Morningstar (2018)                                                            631","2                             (  −2)∙   −(  −4)∙   2                          =                  2                                                          (1)                                           Where:                                  ′                      ′                 2                            2                                                                              =  (   ∙      ) ,    =  (   ∙      )  and     is the vector of least-squares residuals from:                                                                                                                              (  −2)                     (  −4)                          =      +       +                                                              (2)                             0                                  0                  and     is the vector of least squares residuals from equation (2) with the restriction    = 0.                                       Intuitively,we  reject  the  null  hypothesis  of  no  structural  break  if  the  sum  of  squared                residuals corresponding to the model with no breaks is significantly greater than the sum                of squared residuals corresponding to the model with a break at period k.                 The paper uses methodology of calculation of the returns applied by Sharifzadeh and Hojat                (2011) in his study. Returns of individual funds are calculated in the so-called Total Return                form.                                     1+                                                              1 ∗(     )−        0                            = {                                      } ∗ 100                            (3)                                                                           0                Where:                                     monthly value of the Total Return                                                          value of the assets per one investment certificate at the beginning                    0                                              of the period                                     value of the assets per one investment certificate by the end of the                    1                                              period                                                  price of the share on the day when all capital and dividend profits                                     are reinvested                                           amount  of  dividend  and  capital  gains  that  are  reinvested  within  a                                     given period of time                The standard deviation was used to quantify the risk rate of individual funds.                              1                                          ̅ 2                          = √ ∗ ∑     (   −   )    =    √12                                             (4)                                                                                                                                                                                                             =1                                                Where:                       monthly standard deviation                       number of months                       return on investment in month i                                          average monthly e Total Return value of the investment in question                 ̅                       annualized monthly standard deviation                                   The Sharpe ratio is used to assess performance and risk rate. According to the Morningstar                methodology (2018), the arithmetic Sharpe ratio is calculated, using the following formula.                                                                                                    [∏      =1 (1+      )]    −[∏      =1 (1+        )]                            ℎ                    =                                                         (5)                                                                                                                                    1                                                      ̅ 2                                  = √  ∗ ∑  (   −   )                                                   (6)                                                                                                                −1    =1                Where:                               annualized arithmetic standard deviation                                                     return on investment for the period i                                                 return on risk-free investment for the period i (risk-free interest rate)                                    m             number of periods (observations) per 1 year                              total number of periods                              average return on investment over the reference period                 ̅                                                            632","One of the components of the overall risk (standard deviation) can be reduced by a suitable                diversification. The second non-diversifiable component is the rate of the systematic risk                beta, measuring sensitivity of the investment to the movements of the selected market                benchmark.                                  (        ,      )                          =                                                                             (7)                             (      ) 2                Where:                       (   ,    )    covariance between the rate of return of the investment and the rate                                                                                         of return of the market index                (   )                spread of market index return                     2                                    The Excess Return is the last indicator used for comparison of performance of individual                funds. This indicator is calculated simply as the difference between the return of the fund                and return of the benchmark, which the fund in question is compared with.                The coefficient of determination measures the relationship (correlation) between the fund                and the selected benchmark and determines how many percent of the movements of the                monitored  fund  can  be  explained  by  benchmark  movements.  According  to  Morningstar                (2018), value of coefficient of determination 70-100% indicates a good correlation between                the fund and benchmark returns, 40-70% indicates an average correlation between the                fund and benchmark returns, and 1 - 40% indicates a low correlation. The coefficient of                determination can also be interpreted so that in case of high values of this coefficient, the                market risk represents a high share in the total market risk and the chosen benchmark is                suitable for comparison and vice versa.                 3 Results and Discussion                 Table 1 shows that at the decline stage, outbreak of the financial crisis, a large uncertainty                prevailed in the US stock market, with an average monthly standard deviation of around                5.74%. During this period, the US stock market recorded the average monthly losses of -                3.40%. At the growth stage, the US stock market recorded an average monthly return of                about 1.5%, with the lowest volatility compared to the decline and stagnation periods.                From  the  results  calculated  for  the  stagnation  phase, it  follows  clearly that  during  this                period  the  US  stock  market  recorded  only  negligible  returns,  but  with  a  high  average                monthly volatility of ca 3.89%.                   Table 1 Performance of the S\&P Composite 1500 Index in Individual Market Stages                                                             Bearish         Bullish         Stagnant                                                            market         market            market                 Total return (p.a.)                       -35.32%         17.95%            0.34%                 Average monthly profit                     -3.40%          1.44%            0.10%                 Standard deviation (p.a.)                  19.89%         11.60%            13.47%                 Average monthly st.dev.                     5.74%          3.35%            3.89%                 Sharpe ratio                                -1.87           1.54              0.02                                                   Source: own calculation                 Table 2 shows the return and risk indicators of the analyzed funds and their development                in the period, when the US stock market experienced a steep decline. We can say that the                ETFs  and  the index mutual funds  are developed  nearly identically  during  the  period  of                market decline, reaching similar monthly losses and monthly standard deviations. From                the overall view, during the decline stage of the US market, the active managed mutual                funds were doing better compared with the ETFs and the index mutual funds. On average,                the active managed funds had lower monthly losses and lower monthly standard deviation                values, which fact is also linked with a better Sharpe ratio. We can therefore say that active                managed mutual funds are more suitable for a declining market than ETFs and the index                                                            633","mutual funds, because they can better reallocate the assets and record lower losses. If we                compare individual sectors, we can claim that the funds focused on the Financials sector                were the worst. These funds reached the highest monthly losses with the highest volatility.                In this case, however, the result is not surprising, as the Financials sector was at the start                of the financial crisis. On the other hand, the funds from the Health Care sector reached                the best results in the period when the US stock market declined steeply. The average                monthly loss of the funds from this sector was only ca -1.80%, with the average monthly                volatility of 5.5%, and the annualized excess return reached 14.34% on average. The ETFs,                the index  mutual funds  as  well as the active  managed mutual  funds  developed  almost                identically within the Health Care sector. Interesting findings follow from the coefficient of                determination. All analysed funds from the IT sector and the Cons Discretionary sector                exceed 70%. Furthermore, based on the classic Beta indicator, which is higher than 1.0                for all these funds, we can state that in the declining market the funds from these sectors                develop in the direction of the market, but with a significantly higher volatility. Funds from                the Financials and Health Care sectors reach the absolutely opposite results; all funds from                these sectors have the coefficient of determination lower than 70%, which means that the                market explains less than 70% of their volatility, and hence the return on these funds is                influenced and affected by other factors.                                Table 2 Risk and Profit Profile of Funds on Bearish Market        Fund          Sector         Total        Av.      St. dev.      Av.      Sharpe     Excess     Beta     R 2                                    return     monthly      (p.a.)    monthly      ratio     return                                     (p.a.)       loss                 st. dev.   (p.a.)                  IT                -35.71 %    -3.36 %  25.08 %        7.24 %      -1.50     -0.38 %    1.17   85 %                  Finance           -61.19 %    -7.08 %  33.92 %        9.79 %      -1.86    -25.86 %    1.30   58 %                  Health Care       -18.77 %    -1.60 %  16.95 %        4.89 %      -1.22    16.55 %     0.68   63 %         ETF                  Cons. Discert.    -37.39 %    -3.58 %  24.34 %        7.03 %      -1.61     -2.07 %    1.13   85 %       Passive    IT                -36.25 %    -3.38 %  27.08 %        7.82 %      -1.41     -0.92 %    1.26   86 %       mutual     Finance           -55.63 %    -6.11 %  31.84 %        9.19 %      -1.80    -20.30 %    1.24   59 %        funds                  Health Care       -19.12 %    -1.62 %  17.94 %        5.18 %      -1.17    16.21 %     0.73   65 %                  Cons. Discert.    -40.49 %    -3.95 %  26.06 %        7.52 %      -1.62     -5.16 %    1.22   87 %       Active     IT                -28.19 %    -2.43 %  26.41 %        7.62 %      -1.14      7.13 %    1.13   72 %      large cap   Finance           -52.70 %    -5.76 %  26.00 %        7.50 %      -2.10    -17.38 %    1.07   67 %       mutual        funds     Health Care       -19.79 %    -1.61 %  22.80 %        6.58 %      -0.95    15.54 %     0.95   68 %                  Cons. Discert.    -32.23 %    -2.95 %  24.08 %        6.95 %      -1.41      3.09 %    1.09   80 %       Actives    IT                -35.74 %    -3.34 %  26.13 %        7.54 %      -1.44     -0.42 %    1.11   71 %      mall cap    Finance           -32.53 %    -3.10 %  17.59 %        5.08 %      -1.95      2.80 %    0.41   21 %       mutual        funds     Health Care       -26.28 %    -2.37 %  18.59 %        5.37 %      -1.51      9.05 %    0.70   56 %                  Cons. Discert.    -35.04 %    -3.28 %  24.40 %        7.04 %      -1.51      0.29 %    1.07   76 %                                                   Souce: own calculation                 From the calculated results in Table 3, we can state that the large cap active managed                mutual funds reached the highest monthly return from all monitored funds at the growth                market stage, in average and across all sectors. However, these large cap active managed                mutual funds also revealed the highest risk rate, as their average monthly volatility was                ca 4.35%. Taking in view the Sharpe ratio, we can say that on average and across all                sectors, the ETFs and the index mutual funds clearly outperformed the active managed                mutual funds. It can also be claimed that at the growth stage, the ETFs and the index                mutual  funds  were  less  volatile  and  hence  less  risky  than  the  active  managed  mutual                funds.The previous claim can be supported by the Excess Return indicator, according to                which the ETFs and the index mutual funds were in average and across all sectors (except                the funds investing into the Health Care sector) more successful in reaching higher excess                                                             634","returns than the active managed mutual funds. In this case the active mutual funds were                able to bring higher excess returns to the investors than ETFs and the index mutual funds.                When analysing individual sectors, the funds from the Health Care sector followed by the                funds from the Cons Discretionary sector again reached the best results. Both in the period                of decline and in the period of growth, the funds from the Health Care sector seem to be                the most advantageous investment, taking in view the Sharpe ratio. On the other hand,                funds from the Financials sector were once again the worst in this period, in terms of the                Sharpe ratio, mainly due to a high volatility of these funds. Funds from the IT sector are                relatively surprising, because at the growth stage they were unable - in the majority of                cases - to beat average returns of the US stock market. From the point of the coefficient                of determination, we can say that none of the analysed funds from the Health Care sector                reached  the  eligibility limit  of 70%,  and it  can  be  stated  that  the  returns  of the funds                focused  on  this  sector  cannot  be  explained  primarily  by  development  of  the  US  stock                market, it means that there are even other active variables ensuring such good results of                this  sector.  On  the  contrary,  movements  of  all  funds  from  the  Financials  sector  are                explained - from 70% as a minimum - by movements in the US stock market.                                 Table 3 Risk and Profit Profile of Funds on Bullish Market       Fund        Sector         Total         Av.         St. dev.     Av.      Sharpe     Excess     Beta      R 2                                 return    monthlyloss       (p.a.)    monthly     ratio     return                                 (p.a.)                                st. dev.    (p.a.)       ETF      IT               17.51 %          1.41 %  11.69 %       3.37 %       1.49    -0.44 %     0.88    76 %                Finance          19.31 %          1.59 %  16.33 %       4.71 %       1.18     1.36 %     1.29    84 %                Health Care      26.10 %          1.99 %  10.07 %       2.91 %       2.59     8.15 %     0.71    67 %                Cons. Discert.  21.82 %           1.73 %  13.08 %       3.78 %       1.66     3.86 %     1.06    88 %     Passive  IT                 18.32 %          1.48 %  13.47 %       3.89 %       1.36     0.36 %     1.03    79 %      Mutual    Finance          18.58 %          1.53 %  15.40 %       4.44 %       1.20     0.62 %     1.24    88 %      funds                Health Care      27.00 %          2.06 %  10.55 %       3.04 %       2.55     9.04 %     0.74    67 %                Cons. Discert.  22.03 %           1.75 %  13.76 %       3.97 %       1.60     4.07 %     1.12    89 %      Active    IT               17.13 %          1.42 %  14.97 %       4.32 %       1.14    -0.82 %     1.06    67 %      large     Finance          17.12 %          1.44 %  16.86 %       4.87 %       1.01    -0.84 %     1.32    82 %       cap     mutual     Health Care      35.79 %          2.66 %  13.90 %       4.01 %       2.57  17.83 %       0.83    48 %      funds     Cons. Discert.  22.14 %           1.77 %  14.60 %       4.22 %       1.51     4.18 %     1.03    66 %      Active    IT               14.93 %          1.25 %  14.48 %       4.18 %       1.03    -3.02 %     1.10    78 %      small     Finance          18.22 %          1.52 %  16.63 %       4.80 %       1.09     0.27 %     1.27    78 %       cap     mutual     Health Care      27.84 %          2.11 %  10.29 %       2.97 %       2.70     9.89 %     0.71    63 %      funds     Cons. Discert.  18.78 %           1.52 %  13.58 %       3.92 %       1.38     0.83 %     1.03    78 %                                                   Source: own calculation                 The stagnation phase was chosen for the period when a considerable uncertainty regarding                future development on the US stock market was prevailing, and the S\&P 1500 return index                for this period was finally only 0.40%. During this period of stagnation, 2 sharp drops were                recorded, but they only had a short duration of several tens of days. In particular, the first                drop in August 2015 was caused primarily by the fall of the Chinese stock and the US                market responded by a drop as well. The second drop was reported at the beginning of                2016,  when the  negative  mood lasting  from  summer  of 2015  really  hit;  reports  of  the                slowing Chinese economy and fall in oil prices did not have a positive impact on the stock                markets. Then there was a sharp rise in stocks till the end of 2017.                 It was again confirmed that both the ETFs and the index mutual funds are developed almost                identically,  and  there  are  only  slight  differences  in  their  performance.  If  we  compare                performance of the funds from the point of the Sharpe ratio, we can say that the ETFs and                                                             635","the index mutual funds achieve slightly better results than the active managed mutual                funds during the stagnation period across all sectors. Furthermore, it cannot be claimed                explicitly that the ETFs and the index mutual funds would be less volatile than the active                mutual funds, taking in view the standard deviation indicator. In  this period, the funds                from the Cons Discretionary sector were explicitly the best, when (with a single exception)                all  the  small  cap  active  managed  mutual  funds  reached  a  positive  Sharpe  ratio  value.                Further on, the funds focused on this sector showed the lowest volatility and the highest                excess return values. Quite surprising is performance of the funds from the Health Care                sector. The Health Care sector achieved the best results in the period of decline and even                in the period of growth. However, during the stagnation period, this sector, together with                the Financials sector, ranked among the worst ones.                               Table 4 Risk and Profit Profile of Funds on Stagnant Market        Fund         Sector         Total         Av.         St.dev.      Av.      Sharpe    Excess     Beta     R 2                                  return    monthlyloss      (p.a.)    monthly      ratio     return                                  (p.a.)                                st. dev.   (p.a.)        ETF     IT                 0.12 %         0.11 %  16.65 %        4.81 %       0.00   -0.22 %    1.12    82 %                Finance           -1.73 %        -0.05 %  15.68 %        4.53 %      -0.12   -2.07 %    1.07    84 %                Health Care       -1.47 %        -0.03 %  15.50 %        4.48 %      -0.10   -1.81 %    0.96    69 %                Cons. Discert.     5.29 %         0.51 %  14.35 %        4.14 %       0.36    4.95 %    1.02    91 %      Passive  IT                 -2.33 %        -0.09 %  17.13 %        4.95 %      -0.14   -2.66 %    1.19    87 %      Mutual    Finance           -0.97 %         0.01 %  15.13 %        4.37 %      -0.07   -1.31 %    1.03    84 %       funds                Health Care       -2.88 %        -0.13 %  16.62 %        4.80 %      -0.18   -3.22 %    1.00    66 %                Cons. Discert.     2.54 %         0.28 %  13.81 %        3.99 %       0.17    2.20 %    0.99    92 %       Active   IT                -1.24 %         0.03 %  19.00 %        5.49 %      -0.07   -1.58 %    1.10    60 %       large    Finance           -5.67 %        -0.40 %  14.94 %        4.31 %      -0.39   -6.01 %    1.04    87 %        cap      mutual    Health Care       -7.83 %        -0.52 %  19.76 %        5.70 %      -0.40   -8.17 %    0.97     43%       funds    Cons. Discert.     9.76 %         0.85 %  13.41 %        3.87 %       0.72    9.42 %    0.87    75 %       Active   IT                -0.91 %         0.02 %  16.25 %        4.69 %      -0.06   -1.25 %    1.11    85 %       small    Finance           -8.27 %        -0.62 %  15.85 %        4.58 %      -0.53   -8.61 %    0.95    65 %        cap      mutual    Health Care       -5.81 %        -0.37 %  17.62 %        5.09 %      -0.34   -6.15 %    0.93    50 %       funds    Cons. Discert.    -3.77 %        -0.26 %  12.73 %        3.68 %      -0.31   -4.11 %    0.81    73 %                                                   Source: own calculation                 The paper is focused on comparison of performance of individual funds at the stages of                decline / growth and stagnation of the US stock market. Based on the results achieved for                all monitored indicators, the active managed mutual funds prospered better than ETFs and                the index mutual funds. Compared with the passive funds, the active managed mutual                funds have, on average, lower monthly losses and even lower volatility values, resulting in                better  Sharpe  ratio  values.  Thus,  we  can  conclude  that  active  managed  mutual  funds                appears to be more suitable for a declining market than the ETFs and the index mutual                funds, as managers of these funds are able to reallocate assets better and record lower                losses.  Moreover,  the  assumption  and  conclusion  of  the  Sharifzadeh  and  Hojat  (2011)                study was also confirmed, as even in the declining market the ETFs and the index mutual                funds are developing very similarly and the differences in performance are only negligible.                From this finding, we can conclude that ETFs and the index mutual funds can be considered                substitutes.                If we consider the large cap and the small cap mutual funds one complex whole, we can                also conclude from the achieved results that in the analyzed period of growth, the active                funds reached almost identical returns as the passive funds. But the passive funds reached                this return with a lower risk rate compared with the active mutual funds, and therefore                                                             636","had better Sharpe ratio values. Based on this finding, we can also claim that ETFs and the                index mutual funds appear to be more suitable for the growth market. If we consider \"large                cap\" and \"small cap\" mutual funds separately, then it can be stated that the \"large cap\"                mutual  funds  achieved  on  average  the  highest  returns,  volatility  and  excess  return                (exceeding the benchmark) compared with all other analyzed funds. In any case, thanks                to their high volatility, the  Sharpe ratio values were low, i.e. the passive funds were a                better choice in this case as well.                During  the  stagnation  period,  both  the  ETFs  and  the  passive  mutual  funds  were,  on                average, slightly better than the active mutual funds, as they achieved better results in                terms of the Sharpe ratio.                 4 Conclusions                 Based  on  the  performed  analysis  and  the  obtained  results,  the  following  investment                recommendations can be defined.                The active managed mutual funds seem to be a more appropriate product for declining                markets, as at the stage of decline they were able to reallocate assets more flexibly and                to record lower losses than ETFs and the index funds. Passive funds lose  this ability due                to  their  specific  design.  On  the  contrary,  the funds  with  passive  portfolio  management                performed better on the bullish market. In the period of stagnation with a high uncertainty                about future market development, ETFs or the index mutual funds are also more suitable,                because during this period the portfolio managers can respond quickly to short-term steep                changes in the market by rapidly changing their asset portfolio and may not always have                a \"good luck\" when selecting the shares.                If the investor wishes to invest only and exclusively in any of the passive managed funds,                I recommend to select the ETFs or the index funds rather in terms of their availability,                brokerage  fees  and  tax  impacts.  Based  on  the  achieved  results,  the  difference  in                performance and risk rate between these funds is negligible only, and these funds can be                considered substitutes (see Sharifzadeh, Hojat (2011).                If the investor wishes to invest only and exclusively in the active managed mutual funds,                it is more advantageous to select the funds, which are on the market for a long time period                already,  which  have  low  TER  indicator  values  (up  to  1.0%),  high  ratings  from  various                agencies such as Morningstar, and which have a very high volume of the managed  assets                (in the order of billions of US$). I also recommend to select the funds from the sectors                having the long-term growth potential (we are not speaking about the Cons Discretionary                sector.                 The Health Care sector can be included among the sectors suitable for investing. Health is                the most valuable commodity and we may assume that this sector will maintain long-term                stable returns and low losses in the period of crises. Moreover, renewable resources, etc.                can be included among such sector(s). A small investor without a profound investment                experience should keep well clear of the volatile sector(s) such as the Financials sector,                which seems to be the most risky according to my findings, and we may expect that  -                despite certain regulations in this sector  – new or even exotic investment instruments,                which may cause further future financial crises, will appear.                 From  the  point  of  diversification,  ETFs  and  the  index  mutual  funds  are  explicitly  more                appropriate  than  the  active  managed  mutual  funds,  as  the  passive  funds  offer  a  wide                diversification with the minimum costs, precise reference index copying and a low market                volatility.                 References                 Agapova, A. (2011).  Conventional Mutual Index Funds Versus Exchange Traded Funds.                Journal of Financial Markets, vol. 14(2), pp. 323-343.                                                                637","Bello, Z. (2012). The relative performance of equity index ETFs and ekvity index mutual                funds. European Journal of Finance and Banking Research, vol. 5(5), pp. 35-46.                 Brown, S., Goetzmann, W. (1995). Performance Persistence. The Journal of Finance, vol.                50, pp. 679-698.                Chow, G., C. (1960). 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Retrieved    from:                http://www.morningstar.com/InvGlossary/morningstar_rating_for_funds.aspx.                                                                            638","Legal Background for a Different Expert Assessments of a Business                 Enterprise Belonging to a Commercial Company and of a Business                                        Share in a Commercial Company                                                       Roman Skalický                                                     Masaryk University                                           Faculty of Economics and Administration                                         Lipová 41a, 602 00 Brno, the Czech Republic                                                 E-mail: 8849@mail.muni.cz                 Abstract: The role of an expert witness is to provide the court with an independent and                neutral assessment (e. g. valuation) based on his expertise. The aim of this paper is to                define legal background of a business enterprise (belonging to a commercial company) and                a business share (in a commercial company) valuation within the legal system of the Czech                Republic. For this purpose, the paper examines the legal nature of a business enterprise                and  a  business  share  and  the  case  of  their  purchases  that  might  require  an  expert                valuation, compares them and defines the differences between them influencing expert                assessments so that it can subsequently result in a reliable valuation by an expert witness.                The  most  significant  differences  are  apparently  associated  with  public  claims  and  a                corporate name.                Keywords: expert witness valuation, business enterprise, business share, corporate name,                brand                JEL codes: M49, K22                 1 Introduction                The role of an expert is to provide the court with an independent and neutral assessment                based on his expertise. (Bělohlávek, 2011) The expert witness meets in his expert practice                both the requirement of expert valuation of a business enterprise or a part of it and the                requirement  of  expert  valuation  of  the  business  share  in  the  commercial  company,                especially  for  the  purposes  of  transactions.  Such  transactions  may  be  purchases,                respectively sale of a business enterprise or part thereof or transfer (up to 100%) of  a                business share in the commercial company.                According to Damodaran (2016), in its most common form, the intrinsic value of an asset                or business is estimated in a discounted cash flow (DCF) valuation, where the value can                be written as the present value of the expected cash flows on that asset, over its expected                life:                                       E(CF) 1  E(CF) 2  E(CF) 3       E(CF) n                       Value of asset =      +        +        + ⋯ +                                                                      (1)                                      (1 + r) 1  (1 + r) 2  (1 + r) 3  (1 + r) n                where E(CF)t is the expected cash flow in year t, r is the risk adjusted required rate of                return for investing in that asset and n is the life of the asset.                If we limit the viewpoint only to a commercial companies and focus only on a business                enterprise (belonging to the commercial company) as a whole and, on the other hand, to                a 100% business share in the commercial company, the valuation procedure in both cases                will be the same basis. The legal nature of these valuation items is, however, significantly                different.                 The  aim  of  this  paper  is  therefore  to  define  legal  background  of  a  business  enterprise                (belonging to a commercial company) and a business share (in a commercial company)                valuation. For this purpose, the paper examines the legal nature of a business enterprise                and a business share and the case of their purchases (and sales) that might require an                expert valuation, compares them and defines the differences between them influencing                expert assessments so that it can subsequently result in a reliable valuation. We assume                the legal system of the Czech Republic.                                                             639","2 Methodology and Data                The  paper  deals  exclusively  with  a  business  enterprise  as  a  whole  and  100%  business                share. It focuses only on cases of purchase, respectively sale as elementary dispositions                with a business enterprise and a business share. We assume the legal system of the Czech                Republic and this paper focuses on cases of commercial companies in the sense of the                Business Corporations Act (2012).                 To achieve the aim of this paper, the legal nature of a business enterprise and a business                share in the systematic of the Civil Code were detected.                This research assesses the legal nature of a business enterprise and a business share as                concepts of law and their possible purchases or sales and compares them to gain a legal                background for a reliable valuation provided by an expert witness. The analysis is founded                on  the  assumption  that  the  role  of  an  expert  witness  is  to  provide  the  court  with  an                independent and neutral assessment. (Bělohlávek, 2011)                The research approach was developed after an extensive review of academic literature and                research on brand and valuation (Aaker, 1996; Bharadwaj et al., 2011; Coopland, 2005;                Damodaran, 2008; Himme, Fischer, 2014; Chen, Zhang, 2013; Larkin, 2013; Liberman,                Herbden, 2012; Raggio, Leone, 2009; Rego et al., 2011; Salinas, Amber, 2009); Pinto et                al., 2010; Koller et al., 2010; Mařík et al., 2011.                As the phenomenon under study is complex and explanatory in nature, qualitative data                with content analysis proved to be the best way how to assess a legal background for a                reliable  expert  valuation.  As  essential  source  of  secondary  data,  represents  Acts  of                Collection  of  Laws  of  the  Czech  Republic  and  their  Explanatory  Memoranda  and                commentaries on law. Expert witnesses were interviewed to gain primary data.                To achieve the aim of this paper, material regulation of a business enterprise, a business                share  and  commercial  companies  in  the  Czech  Republic  current  legal  system  was                examined. Relevant laws were examined, especially the Civil Code (2012) and the Business                Corporations Act (2012), Explanatory memoranda, comments on laws (secondary data).                Considering the fact that the subject is an intersection of the law and the area of economics,                the  methods  used  for  the  study  were  mainly  content  analysis,  deduction,  induction,                compilation and comparison. The description and method of analogy were used for more                advanced considerations                 3 Results and Discussion                Business Corporations and Commercial Companies                 According to the Civil Code (2012), business corporations include commercial companies                and cooperatives. Commercial companies include an unlimited partnership and a limited                partnership  (“partnerships”),  a  limited-liability  company  and  a  joint-stock  company                (“capital companies”), as well as a European Company and a European Economic Interest                Grouping.                The Legal Nature of an Enterprise                According  to  Commission  Regulation  (EU)  No  651/2014  (2014),  “an  enterprise  is                considered to be any entity engaged in an economic activity, irrespective of its legal form.                This includes, in particular, self-employed persons and family businesses engaged in craft                or  other  activities,  and  partnerships  or  associations  regularly  engaged  in  an  economic                activity.”                The Legal Nature of a Business Enterprise                “Business  enterprise”  defined  by  the  Civil  Code  (2012)  is  the  concept  that  cannot  be                confused with “Enterprise” under the Commission Regulation (Skalická Dušátková, 2015).                A business enterprise  is  “an  organized  set  of  assets  and  liabilities  created  by  an                entrepreneur which, based on his will, are used to pursue his activities. An enterprise is                presumed to comprise everything that is typically used for its operation,” defined by the                                                             640","Civil  Code  (2012)  which  provides  the  refutable  presumption,  that  “an  enterprise  is                presumed  to  comprise  everything  that  is  typically  used  for  its  operation.”  A  business                enterprise is therefore definitely associated with the entrepreneur, whether legal person or                nature one.                Examining the legal nature of a business enterprise, we will come to a conclusion, that a                business enterprise is, within the Civil Code, a thing in a legal sense (legal abbreviation                and hereinafter a “thing”), meeting its definition “everything that is different from a person                and serves the needs of people”. It is a collective thing, although it is not explicitly stated                in the  Civil  Code. (Švestka  et  al,  2014)  A  collective  thing is  “a  set  of individual things                belonging to the same person, regarded as one object and bearing a single designation as                such, is regarded as a whole and constitutes a collective thing (universitas rerum).” (Civil                Code, 2012)                 A business enterprise comprises debts as well, because according to Civil Code (2012),                “assets  and  liabilities  of  a  person  consist  of  the totality of his property and debts,”                while “the  totality  of  what  belongs  to  a  person  constitutes  his  property.”                The difference between an enterprise and a business enterprise is shown in the following                table:                           Table 1 The Comparison of an Enterprise and a Business Enterprise                                                              Legal           Legal         Is willing                                                             nature       relationships                    Enterprise                                person       Legal entity        yes                    Business Enterprise                       thing          Subject-           no                                                                              matter                         Source: Own elaboration on the basis of the literature research (secondary data)                By purchasing a business enterprise, the buyer shall acquire everything that pertains to                the business enterprise as a whole. Even where the parties exclude an individual item from                the purchase whereby the whole does not lose its nature of a business enterprise, it is                considered as a purchase of a business enterprise. (Civil Code, 2012)                A purchase of a business enterprise is considered to constitute a transfer of the employer’s                activities.                 By purchasing a business enterprise, the buyer becomes a creditor in respect of claims and                a debtor in respect of debts which pertain to the business enterprise; however, the buyer                shall only assume those debts of the existence of which he was aware, or which he must                have  at  least  reasonably  expected.  If  the  creditor  has  not  given  his  consent  to  the                assumption of debt by the buyer, the seller is liable as a surety for the discharge of the                debt.                However, this private contract cannot transfer to the buyer public claims and debts of the                seller - for example, taxes, duties, public insurance, subsidies from public sources, etc.                These rights and obligations are linked to this unique legal entity. Under the Tax Code, the                tax transfer agreement is not effective against the tax administrator.                In the case of the sale of a business enterprise belonging to a commercial company, there                is a change in the structure of assets and liabilities in the balance sheet of that company,                where instead of individual assets and debts, the transaction has money and residual or                other  assets  and liabilities.  Then the  buyer is  the  sequel to  the  activity.  If  the  seller's                corporate name is not transferred in addition, the buyer continues to operate under his                own one. (Skalický, 2015).                Legislation does not require fundamentally expert assessment or expert participation when                buying  a  business  enterprise,  but  sometimes  negotiates  it  voluntarily  to  assess  the                suitability  of  the  purchase  price  and  to  limit  possible  future  disputes  regarding  any                objection to trade disadvantage or troublesome sales of the seller.                However, an expert valuation is required in the case of so-called related persons.                                                             641","The Legal Nature of a Business Share                “A business share represents a member’s interest (participation) in a business corporation                and  the  rights  and  duties  attached  thereto.”  (Business  Corporations  Act,  2012)  The                business share in a business corporation has a qualitative and quantitative aspect. The                quantitative portion of the share represents your own quantity and value of the share.                Qualitative aspects are rights and obligations that are property or non-property.                 For example, a property right, such as that a member shall be entitled to a profit share                and share of liquidation balance, a property obligation is for example a deposit obligation.                Non-property  rights  allow  the  member  to  participate  in  the  decision-making  and                management of the business corporation, non-property obligations stem from the founding                documents of business corporations.                According to the judgement of the Czech Supreme Court, the property aspect cannot be                separated from the set of rights as a separate right. (The Supreme Court Judgement, 2010)                 Each member may hold one business share only in the same business corporation. This                shall not apply to an interest in a capital company and to business shares of a limited                partner. A member’s business share in a business corporation may not consist in a security                or a book-entry security unless the business corporation is a capital company or unless                provided for by another legal regulation. (Business Corporations Act, 2012)                In the systematic of the Civil Code a business share means thing in a legal sense – movable                and intangible thing.                The transfer of a business share shall be effective vis-à-vis the commercial company upon                the delivery of an effective business share transfer agreement with certified signatures.                (Business Corporations Act, 2012)                As in the case of a business enterprise, the legislation does not require expert valuation or                the  participation  of  an  expert  in  buying  a  share  fundamentally.  However,  it  may  be                necessary for accounting or tax reasons.                The Comparison of a Business Enterprise and a Business Share                 Assuming  that  we  take  into  account  the  whole  business  enterprise  belonging  to  the                considered commercial company as well as the 100% share in the commercial company                under consideration, the comparison is shown in the following table:                 Table 2 The Comparison of a Business Enterprise (belonging to a commercial company)                                     and a Business Share (in a commercial company)                                                 Business Enterprise               Business Share                       Definition              an  organized  set  of  assets      a member’s interest                                             and  liabilities  created  by  an     (participation) in a                                             entrepreneur which, based on      business corporation and                                            his will, are used to pursue his      the rights and duties                                                       activities                   attached thereto                      Legal                   Collective thing (universitas          movable thing                      background                        rerum)                      intangible thing                      Subject of legal          entrepreneur – here the             a member of the                      relationships              commercial company              commercial company                                              (generally nature person or                                                     legal person)                      An individual item       can be excluded from the           cannot be excluded                                             purchase (whereby the whole                                              does not lose its nature of a                                                  business enterprise)                                                                642","Cannot be                      Public claims;                      transferred                 non-alienable assets                      Buyer                  shall acquire everything that        Becomes a member                                             pertains to the enterprise as a    (acquires the rights and                                                         whole,                obligations of a member)                                             becomes a creditor in respect                                               of claims and a debtor in                                                    respect of debts                      Accounting of the       significant modifications of                      commercial                  assets and liabilities                      company                      A corporate            Fundamentally is not a part of      Fundamentally is still                      name of the             the sold business enterprise     belonging to a commercial                      commercial                                                       company                      company                         Source: Own elaboration on the basis of the literature research (secondary data)                It may seem that the difference between the two is that in each case it is another entity of                legal  relationships.  However,  from  the  point  of  view  of  an  expert  witness  for  market                valuation purposes, this fact should not play a role.                The significant difference is mainly in the case of public liabilities etc. and, in the case of a                brand in the form of a corporate name (Skalický, 2017a). According to the definition of the                American Marketing Association, brand is “a name, term, design, symbol, or any other                feature that identifies one seller´s good or service as distinct from those of other sellers“                (American Marketing Association, 2017). A corporate name meets this definition as well.                (Skalický, 2016, 2017a) The corporate name as brand can have a significant impact on the                valuation (Skalický, 2017c)                 4 Conclusions                In the case of a business enterprise, even in the case of a business share, the valuation                will be the same basis. The expert witness can estimate the value of an asset as a function                of the cash flows generated by that asset, the life of the asset, the expected growth in cash                flows and the riskiness associated with the cash flows. That principle remains intact for                every business at every point in time, no matter how much uncertainty there is in the                process. (Damodaran, 2013)                While a business enterprise is a collective thing, a business share in the system of the Civil                Code represents a movable and intangible thing. Considering the case of buying a (whole)                business enterprise of the commercial company and purchasing (100%) a business share                in the (same) commercial company, the buyer, i.e. who joins the legal relationship, is a                different subject. While in the first case it will be the commercial company itself, in the                case of the latter it will be a member or members of that commercial company.                The  purchase  of  a  business  enterprise  will  bring  significant  changes  in  the  assets  and                liabilities of the commercial company's accounting, the purchase of the business share in                the accounting of the company itself will not affect.                In the case of buying a business enterprise, even if we consider its whole and not just a                part, we can separate an individual item, whereby the whole does not lose its nature of a                business enterprise. At the same time, in the case of a business enterprise, we need to                allocate some cases - we cannot, for example, transfer public liabilities, which in some                cases can cause significant differences in valuation.                 Other significant differences in expert valuation can be caused by a brand in the form of a                corporate name. If the corporate name is not transferred at the same time as the business                                                                643","enterprise,  the  brand  remains  for  the  seller  and  must  be  taken  into  account  for  the                valuation.                 In conclusion, although the expert valuation will be based on a similar basis, account should                be taken of the legal nature of the business enterprise or a business share.                 Acknowledgments                 This paper was written with support from the specific project Behavioral, Knowledge and                Economic  Aspects  of  Trading  and  Valuation  of  Financial  and  Other  Assets                (MUNI/A/0753/2017).                 References                Act no. 89/2012 Coll., the Civil Code, as amended.                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ISBN 978-80-7509-499-5.                                                                                                      645","The Financialization and Operating Performance of Non-financial                            Companies Listed on the Warsaw Stock Exchange                                              Błażej Socha , Dariusz Urban                                                                             2                                                           1                                                     1  University of Lodz                           Faculty of Management, Department of Finance and Strategic Management                                          Matejki Street 22/26, 90-237 Łódź, Poland                                               E-mail: blazej.socha@uni.lodz.pl                                                      2  University of Lodz                           Faculty of Management, Department of Finance and Strategic Management                                          Matejki Street 22/26, 90-237 Łódź, Poland                                              E-mail: dariusz.urban@uni.lodz.pl                 Abstract:  This  paper  deals  with  the  issue  of  financialization  of  companies.  The                financialization  reflects  the  growing  asymmetry  between  production  and  exchange                processes, in particular regarding the financial component of the exchange process. This                asymmetry  was  revealed  along  with  gradual  changes  in  the  financial  behavior  of  non-                financial enterprises, banks and households. The indication of this transformation process                has been increasing the profit generated through financial transactions instead of operating                activities. The main goal of this article was to answer the question about the coefficient                between the level of financialization and the operating performance of companies from                Poland.  The  research  sample  consists  of  the  listed  non-financial  companies  and  the                research period covers 2011-2017. The authors used calculated on their own synthetic                indicators  of  company  financialization  reflecting  their  financial  as  well  as  investment                performance and financial indicators measuring operating activity performance. In order                to achieve the research goal, a standard statistical analysis of results was performed and                correlation coefficients were used to measure the strength and the direction of association                that  exists  between  the  level  of  financialization  and  the  operating  performance  of                companies.  The  empirical  findings  suggest  the  existence  of  differences  in  terms  of                operating  activity  performance  (e.g.  EBITDA  Margin,  EBITDA/Total  Assets,  Assets                Turnover,  Revenue/Working  Capital)  among  companies  with  different  level  of                financialization.  It  also  suggests that financialization  reflecting  investment  performance                interact  with  operating  activity  performance  differently  to  financialization  reflecting                financial performance.                Keywords: financialization, ratios of financialization, operating activity performance                JEL codes: D22, F65, G30                 1 Introduction                 Over the last two decades, the economies all around the world have undergone profound                transformations;  the  role  of  government  has  diminished  while  the  one  of  markets  has                increased,  economic transactions among countries have substantially risen and domestic                as well as international financial transactions have grown. This changing landscape has                been characterized by the rise of neoliberalism, globalization and financialization. While                many  studies  have  been  devoted  to  neoliberalism  and  globalization,  research  on  the                phenomenon  of  financialization  is  relatively  new.  In  fact,  there  is  no  even  common                agreement in literature about the definition of the term, and even less about its significance                (Epstein, 2005).                Financialization  reflects  the  growing  asymmetry  between  production  and  exchange                processes, in particular regarding the financial component of the exchange process. This                asymmetry was revealed along with the gradual changes in the financial behavior of non-                financial enterprises, banks and households. The indication of this transformation process                has been increasing the profit generated through financial transactions instead of operating                activities (Lapavitsas, 2013). By financialization scholars also mean the shift in competitive                capitalism  from  manufacturing  and  distribution  of  physical  commodities  as  being  the                                                             646","principal economic activity to the economy which is essentially structured on the basis of                abstract  principles  and  the  accumulation  of  wealth  on  the  basis  of  non-tangible  assets                (Styhre,  2014).  Literature identifies   three  different  approaches  to  the  phenomenon  of                financialization (van der Zwan, 2014). The first approach examines financialization as a                new regime of capital accumulation. Financialization is a pattern of accumulation in which                profits  accrue  primarily  through  financial  channels  rather  than  through  trade  and                commodity production. In the second approach, financialization is being analyzed in the                context of the shareholder value orientation as a guiding principle of corporate behavior.                The third approach to financialization points out the issue of financialization of everyday                life, suggesting that the transition of finance into daily life has been made possible by the                democratization of finance, whereby the financial product and services have become more                available to large parts of the population.                Marek Ratajczak suggests that in the context of companies the process of financialization                leads  to  fundamental  changes  in  management  and  ownership,  especially  in  large                corporations (Ratajczak, 2014). Firstly, financialization increases the role of institutional                investors from the financial sector, seeking possibilities to gain profits in the short run.                Secondly,  financialization  shifts  the  perspective  in  management  of  companies  from                stakeholders to shareholders. Thirdly, due to financialization in the economic and financial                analysis of companies excessive pressure is being exerted on indicators referring to short-                term  profitability  rather  than  on  the  long-term  perspective  of  stable  growth  of  the                company. According to William Lazonick (2013) companies are increasingly evaluated by                measures like earnings per share rather than by the goods and services they produce.                Financialization changes the relationship between the non-financial corporate sector and                financial markets (Orhangazi, 2008).                The empirical findings of research from developed markets suggest a link between different                aspects  of  financialization  and  investment  activity  of  non-financial  corporations  (Davis,                2017; Gutiérrez and Philippon, 2016; Orhangazi 2008; Crotty, 2005). The authors point                out  the  shift  in  the  portfolio  composition  towards  financial  assets,  suggesting  that  the                financial investment increasingly replaces, or even crowds out the fixed investment. They                also present the evidence of changes in the structure of external finance, including growth                in both indebtedness and own-stock repurchases, especially among large firms. Moreover,                there is an expansion in the financial profits earned by non-financial firms as well as an                increase in payments of non-financial corporations to financial markets. Some large non-                financial  corporations increasingly  resemble financial  firms,  being  viewed  as bundles  of                assets rather than capital accumulating enterprises. These changes in company behavior                are accompanied with a slowdown in capital accumulation despite the rising profitability of                the firm.                Summing up, financialization of the economy, households as well as companies over the                last  couple  of  years  has  attracted  significant  attention  from  both  academics  and                practitioners. This attention was especially focused on western economies with developed                markets.  The  publications  of  authors  from  emerging  markets  were  filling  the  gap  in                knowledge of the theoretical aspect of this process rather than addressing the empirical                research questions. Meanwhile, given the scale and scope of financialization of the listed                companies  from  developed  stock  exchanges,  there  is  a  need  for  understanding  and                analyzing  the  issue  of  financialization  of  non-financial  companies  from  emerging                economies. It has become essential to answer the question whether financialization occurs                in these companies and to what extent the path of financialization observed in developed                markets is imitated by the companies from emerging markets.                The main goal of this research is to answer the question about the correlation coefficient                between the level of financialization and the operating performance of companies listed on                the Warsaw Stock Exchange. The paper is organized as follows: Section 2 describes the                methodology of the research as well as the data, Section 3 presents and discusses the                empirical findings, Section 4 concludes.                                                               647","2 Methodology and Data                The  research  sample  consisted  of  public  companies  included  in  the  WIG  index  on  the                                                 th                Warsaw  Stock  Exchange  on  30   March  2018.  We  excluded  from  our  research  sample                companies  from  financial  industry  and  companies  that  reported  negative  equity  in  the                research  period.  For  companies  included  in  the  research  we  calculated  financialization                ratios based on the methodology proposed by Szczepankowski (2017) for the assessment                of the level of financialization in the area of investment and financial activity (see Table 1).                The  research  period  covers  2011-2017.  The  required  data  comes  from  the  financial                statement published in the Eikon database.                                            Table 1 The Financialization Ratios                 Area            Indicator  Formula                   Investment                   [Long-term Investments – Affiliate Companies + Long-                 Area (INV)      INV1         term Investments – Other + Note Receivable – Long-term                                              + Other Long-term Assets] / Fixed Assets                                              [Total Current Assets – Total Inventory – Accounts                                 INV2         Receivable – Trade Net – Prepaid Expenses] / Total Current                                              Assets                                              [Interest Income (Non-Operating) + Investment Income                                 INV3                                              (Non-Operating) - Foreign Currency Gains] / Total Revenue                                              [Purchase of Investments + Acquisition of Business] /                                 INV4         [[Purchase of Investments + Acquisition of Business +                                              Capital Expenditures]                                              [Sale of Business + Sale/Maturity of Investment] / [Sale of                                 INV5         Business + Sale/Maturity of Investment + Sale of Fixed                                              Assets]                                 INV          [INV1 + INV2 + INV3 + INV4 + INV5] / 5                 Financial                    [Long-Term Debt + Other Long-Term Liabilities + Notes                 Area            FIN1         Payable/Short-Term Debt + Other Current Liabilities] /                 (FIN)                        [Liabilities + Equity]                                              [Long-Term Debt + Notes Payable/Short-Term Debt] /                                 FIN2                                              Liabilities                                              [Interest Expense (Non-Operating) + Investment Expense                                 FIN3                                              (Non-Operating)] / Total Expense                                              [Debt Reduction + Cash Interest] / Financing Cash Flow                                 FIN4                                              Out                                 FIN5         Debt Issued / Financing Cash Flow In                                  FIN          [FIN1 + FIN2 + FIN3 + FIN4 + FIN5] / 5                                    Source: own elaboration based on Szczepankowski (2017)                 After  calculating  partial  financialization  ratios  (INV1-INV5  and  FIN1-FIN5),  the  level  of                financialization in the investement (INV) and financial area (FIN) were calculated and the                value  of  all  ratios  ranges  from  0  to  1.  In  the  next  step  the level  of  financialization for                companies  from  different  NAICS  sector  was  shown.  In  the  last  step  we  calculated                correlation  coefficient  between  the  level  of  financialization  and  operating  performance                (revenue/working  capital,  EBITDA  Margin,  EBITDA/Total  Assets,  Total  Assets  Turnover,                Fixed Assets Turnover) and pretax return on assets.                 3 Results and Discussion                The highest level of financialization in the investment area (see Table 2) was observed in                professional, scientific and technical services and the construction sector, especially due to                                                             648","a  relatively  high  level  of  short-term  financial  assets  in  current  assets  and  investment                inflows  from  financial  assets  in  total  investment  inflows.  What  is  more,  the  level  of                financialization in these two sectors  grew faster than in the other sectors. Manufacturing                and wholesale trade were the sectors with the lowest and stable level of financialization.                In general, Polish listed companies seemed to behave in a secure way and the main pillars                of its investment area were cash and short-term securities. A very low level of the financial                income in total revenues confirmed the previous assumption.                            Table 2 The Level of Financialization in the Investment Area (INV)                  NAICS Sector      Statistic  2011     2012     2013     2014     2015    2016     2017                 Information       Min        0,0282   0,0075   0,0184   0,0170   0,0167   0,0172   0,0032                                   Max        0,6559   0,7838   0,6226   0,7916   0,6538   0,7808   0,5909                                   Mean       0,2491   0,2290   0,2436   0,2529   0,2140   0,2260   0,1969                 Manufacturing  Min           0,0042   0,0020   0,0031   0,0013   0,0000   0,0031   0,0125                                   Max        0,5069   0,7761   0,6453   0,6350   0,8432   0,6161   0,4704                                   Mean       0,1344   0,1427   0,1329   0,1348   0,1350   0,1263   0,1376                 Professional,     Min        0,0240   0,0407   0,0408   0,0089   0,0078   0,0161   0,0276                 Scientific, and   Max        0,6309   0,7928   0,8880   0,7946   0,6677   0,7457   0,7863                 Technical         Mean       0,2357   0,2747   0,2590   0,2684   0,2275   0,2645   0,3007                 Services                 Wholesale         Min        0,0063   0,0058   0,0074   0,0076   0,0073   0,0063   0,0087                 Trade             Max        0,5867   0,7321   0,7550   0,5930   0,5946   0,7050   0,7005                                   Mean       0,1988   0,1386   0,1286   0,1833   0,1554   0,1675   0,2138                 Construction      Min        0,0000   0,0078   0,0000   0,0011   0,0038   0,0116   0,0062                                   Max        0,5898   0,6744   0,7293   0,6293   0,6182   0,5949   0,8692                                   Mean       0,2416   0,2065   0,2627   0,2644   0,2561   0,2056   0,2858                 Others            Min        0,0041   0,0065   0,0064   0,0053   0,0047   0,0011   0,0234                                   Max        0,7690   0,9798   0,7481   0,6769   0,6119   0,9267   0,5864                                   Mean       0,2318   0,2204   0,2156   0,2136   0,2099   0,2094   0,2071                                                  Source: own elaboration                 The  level  of  financialization  in  the  financial  area  was,  in  general,  higher  than  in  the                investment area. Construction and manufacturing were the sectors with the highest level                of this ratio whilst companies from professional, scientific and technical services sectors                had the lowest level of financialization. The median value for the FIN5 ratio suggested that                more than a half of the companies in 6 out of 7 research periods had received financial                inflows only from the issued debt. Moreover, a relatively low level of liabilities to equity                caused that the level of financial expenses in total expenses were also low. The obtained                results and their development in the examined period proved that Polish listed companies                treated financial activity in a subordinate manner to their operating activities.                             Table 3 The Level of Financialization in the Financial Area (FIN)                  NAICS Sector      Statistic  2011     2012     2013     2014     2015     2016    2017                 Information       Min         0,0001   0,0038  0,0016  0,0051  0,0054  0,0031  0,0028                                   Max         0,6294   0,5648  0,6333  0,6258  0,6109  0,6119  0,6572                                   Mean        0,3067   0,2777  0,2982  0,3486  0,3210  0,3197  0,2474                 Manufacturing  Min            0,0054   0,0025  0,0000  0,0013  0,0021  0,0007  0,0006                                   Max         0,7400   0,8210  0,8054  0,7625  0,7678  0,6818  0,6706                                   Mean        0,4414   0,4244  0,4136  0,4065  0,4110  0,4049  0,3746                                   Min         0,0001   0,0020  0,0001  0,0001  0,0001  0,0006  0,0082                                                              649","Professional,     Max         0,6150   0,6567  0,5854  0,5637  0,6302  0,6922  0,4391                 Scientific, and   Mean        0,2443   0,3075  0,2791  0,2872  0,2921  0,3096  0,2067                 Technical                 Services                 Wholesale         Min         0,1792   0,0027  0,0104  0,0194  0,0172  0,1925  0,1105                 Trade             Max         0,5941   0,6170  0,6171  0,6203  0,6035  0,6514  0,5295                                   Mean        0,3830   0,4038  0,3691  0,3738  0,3917  0,4173  0,3056                 Construction      Min         0,0487   0,0147  0,0014  0,0123  0,0049  0,0006  0,0032                                   Max         0,6733   0,6294  0,6826  0,6706  0,7127  0,6533  0,7509                                   Mean        0,4436   0,4334  0,4328  0,4174  0,4062  0,4081  0,3434                 Others            Min         0,0051   0,0030  0,0019  0,0073  0,0027  0,0053  0,0022                                   Max         0,7133   0,6999  0,7366  0,7160  0,6732  0,7046  0,6714                                   Mean        0,3782   0,3944  0,4206  0,4109  0,4126  0,4245  0,3699                                                  Source: own elaboration                 In the next step of the research authors checked the correlation coefficient between the                level  of  financialization  and  the  operating  performance  of  companies  supplemented  by                pretax ROA ratio. As shown in Table 4, companies with higher level of financialization in                the investment area generated less revenue from their assets. A negative and statistically                significant  linear  correlation  in  3  out  of  7  periods  was  observed  for  the  level  of                financialization in the investment area and EBITDA margin. Another important fact is the                one that the direction of correlation coefficient is constant for all periods.                 Table 4 Pearson Correlations Between The Level of Financialization in Investment Area                                          (INV) and Standarized Financial Ratios                  Ratio/Year             2011      2012      2013      2014       2015      2016      2017                                                                                               *                 Revenue/              -0,089    -0,102     -0,076    -0,083    -0,078   -0,107     -0,122                 Working Capital                                                                **                                            **                                                      **                 EBITDA Margin,       -0,160    -0,143    -0,188      -0,092    -0,055     0,008    -0,045                 %                 EBITDA/Total          -0,080    -0,104     -0,082    -0,077    -0,055    -0,067   -0,123                                                       *                                                                                                         *                 Assets, %                                                                           *                                                                                    **                                                                                                        **                                                                                              **                                                      **                 Asset Turnover       -0,147    -0,160      -0,094   -0,108    -0,162    -0,166    -0,173                                            **                 Fixed Asset            0,016     0,043     0,005    0,232      0,060      0,081     0,081                                                                          **                 Turnover                 Pretax ROA, %          0,000    -0,099     -0,007    -0,043    -0,091    -0,027    -0,048                ** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level.                                                  Source: own elaboration                Results in Table 5 present that the level of financialization in the financial area was also                correlated with operating performance ratios and pretax ROA. In 2015-2017 companies                with the highest level  of  financialization  generated higher  revenues  from their  working                capital,  but  it  required  more  fixed  assets.  Moreover,  the  level  of  financialization  was                negatively correlated with EBITDA Margin and pretax ROA.                   Table 5 Pearson Correlations Between The Level of Financialization in Financial Area                                          (FIN) and Standarized Financial Ratios                 Ratio/Year             2011      2012      2013      2014       2015      2016      2017                                                                                     *                                                                                              **                                                                                                        **                 Revenue/              -0,003     0,095     0,026     0,098     0,135    0,156     0,184                 Working Capital                 EBITDA Margin,        -0,062    -0,055     -0,007    -0,060    0,026     -0,015     0,113                 %                                                             650","*                                                                                              **                                             *                                                                          **                                                      **                 EBITDA/Total          -0,120    -0,155     -0,038   -0,138    -0,119    -0,174     -0,118                 Assets, %                                            **                 Asset Turnover       -0,152     -0,019     -0,034    -0,069    -0,070    -0,068    -0,023                 Fixed Asset          -0,198    -0,159      0,032     -0,075   -0,165    -0,146    -0,142                                            **                                                      **                                                                                                         *                                                                                    **                                                                                              **                 Turnover                                            **                 Pretax ROA,%         -0,160    -0,153      -0,064   -0,140    -0,108    -0,203    -0,129                                                                                     *                                                                          **                                                                                              **                                                      **                                                                                                         *                ** Correlation is significant at the 0.01 level; * Correlation is significant at the 0.05 level.                                                  Source: own elaboration                The obtained results may suggest that companies with different levels of financialization                can generate different levels of operating performance and pretax return on assets. All                statistically  significant  correlation  coefficients,  excluding  the  revenue  to  working  capital                ratios, showed that the higher level of financialization coexisted with the lower level of                operating  performance  ratios.  It  is  interesting  and  important  to  point  out  that                financialization in the investment area and the financial area are correlated with different                operating performance ratios.                4 Conclusions                The  main  goal  of  this  research  was  to  answer  the  question  concerning  the  correlation                coefficient  between  the  level  of  financialization  and  the  operating  performance  of                companies listed on the Warsaw Stock Exchange. Our findings suggest that the level of                financialization  and  the  area  of  financialization  can  justify  the  level  of  operating                performance and profitability. Furthermore, the level of financialization is different in the                sectoral section. The provided data also showed us that the level of financialization of non-                financial public Polish companies is relatively low. Empirical findings also prove that we                should analyse separately the investment and financial area of financialization because of                its correlation with different operating performance ratios.                Our paper contributes to the better understanding of the role of the financialization process                in the financial management of companies, and according to the authors’ best knowledge                it is the first attempt to investigate the role of finanancialization in financial performance                of  Polish  listed  companies  as  an  example  from  emerging  markets.  For  the  better                understanding of the financialization effect on companies perfomance further research is                required, especially in the sectoral section and with the use of the cause and effect analysis.                A comparative analysis of factors affecting financialization on the emerging and developed                markets would be valuable from the scientific point of view.                References                 Crotty,  J.  (2005).  The  neoliberal  paradox:  the  impact  of  destructive  product  market                competition and ‘modern’ financial markets on nonfinancial corporation performance in the                neoliberal era. In: Epstein, G., ed., Financialization and the World Economy. Northampton,                MA: Edward Elgar. pp. 77–110.                Davis, L. (2017). Financialization and Investment: A Survey of the Empirical Literature.                Journal of Economic Surveys, vol. 31(5), pp. 1-27.                 Epstein, G. (2005). Financialization and the World Economy. Cheltenham: Edward Elgar.                Gutiérrez, G., Philippon, T. (2016). Investment-less growth: an empirical investigation.                NBER Working Paper 22897.                 Lapavitsas C. (2013). Profiting Without Producing. How Finance Exploits Us All. London,                New York: Verso.                Lazonick, W. (2013) From innovation to financialization: how shareholder value ideology                is destroying the US economy. In: Wolfson, M., Epstein, G., eds., The Handbook of Political                Economy of Financial Crises. New York, NY: Oxford University Press. pp. 491–511.                                                               651","Orhangazi,  O.  (2008).  Financialisation  and  capital  accumulation  in  the  non-financial                corporate sector. Cambridge Journal of Economics, vol. 32(6), pp. 863–886.                 Ratajczak,  M.  (2014).  Ekonomia  w  dobie  finansyzacji  gospodarki.  Ruch  Prawny,                Ekonomiczny i Socjologiczny, vol. LXXVI(2), pp. 259-271.                Styhre, A. (2015).  The Financialization of the Firm. Managerial and Social Implications.                Cheltenham: Edward Elgar Publishing Limited.                Szczepankowski, P. (2017). Koncepcja i zastosowanie syntetycznego wskaźnika stopnia                finansyzacji  przedsiębiorstw.  Finanse,  Rynki  Finansowe,  Ubezpieczenia,  vol.  2(86),                pp. 287–302.                 Van  der  Zwan,  N.  (2014).  Making  sense  of  financialization.  Socio-Economic  Review,                vol. 12, pp. 99-129.                                                                                                                               652","The Effect of the Accounting Hocus–Pocus on the Management                                    Earnings Forecast Precision in the IPO                                                                                        2                                                        1                                   Tomasz Sosnowski , Anna Wawryszuk–Misztal                                                     1  University of Lodz                    Faculty of Economics and Sociology, Department of International Investments and Finance                                        Rewolucji 1905 Street 41, 90–255 Lodz, Poland                                            E–mail: tomasz.sosnowski@uni.lodz.pl                                              2  Maria Curie Sklodowska University                                    Faculty of Economics, Department of Corporate Finance                                    Maria Curie-Sklodowska Square 5, 20-031 Lublin, Poland                                              E–mail: anna.wawryszuk@umcs.pl                 Abstract:  Issuers  of  initial  public  offerings  can  make  a  voluntary  decision  to  post  the                earnings forecast in the prospectus. This paper explores whether managers of IPO firms                take intentional action in the form of earnings management to reduce the forecast error                and thereby improve the quality of such forward looking financial information revealed in                the going public process. Using a sample of 87 IPOs from Warsaw Stock Exchange between                2006  and  2015  we  find  that,  generally,  managers  of  the  new  listed  companies  use                discretionary accruals and abnormal cash flows from operations in the periods around the                IPO to improve the accuracy of the earnings forecast disclosed in the IPO prospectus. Real                earnings management is likely to take place in the year preceding IPO and accruals-based                earnings management is used in the IPO and following year.                Keywords: IPO, forecast error, real earnings management, discretionary accruals                JEL codes: G11, G17, G32                  1 Introduction                The issue of earnings management is the topic of many empirical studies. According to                Healy and Wahlen “Earnings management occurs when managers use judgment in financial                reporting and in structuring transactions to alter financial reports to either mislead some                stakeholders about the underlying economic performance of the company or to influence                contractual  outcomes  that  depend  on  reported  accounting  numbers”  (1999,  p.  368).                Earnings  management  results  from  the  existence  of  the  asymmetry  information                phenomenon, which is clearly observed during the IPO process. The IPO prospectus issued                by  a  company  seeking  to  be  public  becomes  the  main  source  of  information  for  IPO                investors and a tool for the mitigation of the information gap. In particular, the pre-IPO                financial  statements  included  in  the  prospectus  are  the  subject  of  investor’s  interest.                Bearing  in  mind  that  profit  is  one  the  most  important  accounting  numbers  taken  into                consideration  by  investors,  the  managerial  engagement  into  earnings  management                process is expected to take place around the period of IPO (Teoh et al., 1998; Gao et al.,                2017).  This  behavior  is  motivated  by  aiming  at  portraying  a  more  favorable  earnings                picture,  and thus  boosting  the  IPO  offer  price  (Gao  et  al.  2017,  p.  90).  The  high  IPO                proceeds  are  possible  due  to  the  fact  that  investors  do  not  understand  the  process  of                earnings  management,  and  thus  they  are  not  aware  that  the  reported  earnings  are                borrowed from the future or the past (Teoh et al. 1998, p. 1938). There are some empirical                studies that provide evidence on earnings management practice around the time of  IPO:                Teoh et al. (1998), DuCharme et al. (2001), Miloud (2014). Nevertheless, Rosenboom et                al. (2003), Ball and Shivakumar (2008), document the conservative reporting before IPO.                 Earnings management in the IPO-year or before IPO is mainly motivated by the increase                in the IPO proceeds, but another incentive to earnings manipulations occurs in the year                following IPO if a company decides to include earnings forecasts in the IPO prospectus.                Revealing earnings projections in the IPO prospectus is a voluntary decision which may be                used  as  a  signal  that  reduces  the  level  of  asymmetry  information.  It  may  affect  the                                                              653","investor’s decision on capital involvement in the company and subsequently increase initial                valuation (Hartnet, 2010).                 The usefulness of voluntary issued profit projections depends on its credibility. Moreover,                as  Kasznik (1999)  suggests, inaccurate  earnings forecasts  can  result in litigation  costs                associated with actions taken by shareholders and the loss of management reputation for                accuracy.  Inaccuracy  may  also  lead  to  the  negative  share  price  revision  which  is  the                consequence of the phenomenon known as negative earnings surprise (Matsumoto, 2002).                In  order  to  avoid  the  cost  of  inaccurate  forecasts,  managers  tend  to  manage  actual                earnings towards the forecasted level.                Prior  research  on  the  above  area  usually  answers  the  question  whether  the  earnings                management occurs in the year following the IPO in order to achieve the projected value                of  earning  included  in  prospectus.  For  example,  Gramlich  and  Sørensen  (2004)  find                evidence that accrual management is used by the managers of Danish IPO companies to                meet  the  earnings  forecast  target  voluntary  disclosed  in  the  IPO  prospectus.  More                specifically,  they  point  out  that  earnings  management  reduces  (increases)  reported                earnings  if  pre-managed  earnings  and  actual  sales  are  higher  (lower)  than  forecasted                values (Gramlich and Sørensen, p. 256 ). What is more, their results give evidence that                forecasts errors are reduced by implementing discretionary accruals. Cormier and Martinez                (2006) document that French IPOs issuing forecasts are more likely to manage earnings                one year after IPO compared with companies which do not reveal such forecasts. What is                more, the level of earnings management is higher for these forecasting companies whose                actual earnings are lower than expected. The earnings management practice to meet the                forecasted earnings, i.e. the use of discretionary accruals in the year following IPO, is also                documented by Cormier et al. (2014) for Canadian IPO companies. The study by Ismail                and  Weetman  (2007)  for  Malaysian  IPOs,  which  are  obliged  to  apply  the  regime  of                mandatory disclosure of the profit forecast in the IPO process, also shows that companies                manage earnings to meet the forecasts, both in case the pre-managed earnings are lower                and higher than their expected value revealed in the prospectus.                 Nevertheless, some studies provide contrary results. Hutagaol et al. (2012) find that in                case of 165 Indonesian IPOs there is not a significant difference in earnings management                practices between forecasters and non-forecasters. Buchner et al. (2017) document that                companies going public on the London Stock Exchange revealing earnings forecasts are                less likely  to  manage  earnings  than those  which  do  not include  such  projections.  They                explain that due to the costs associated with inaccurate forecasts (i.e. the negative impact                on firms value) a company decides to include earnings forecasts only if this information is                credible (Teoh et al., 1998; Buchner et al., 2017, p. 94).                 In this study we investigate the links between managerial activities aiming at reporting                certain  value  of  earnings  around  IPO  and  the  precision  of  forward-looking  financial                information revealed in the going public process. More specifically, we empirically examine                the effect of earnings management in a form of discretionary accruals and real activities                manipulation  on  the  accuracy  of  the  earnings  forecast  voluntarily  disclosed  in  the  IPO                prospectus and ask a question whether managers of IPO firms take intentional action to                reduce  the  forecast  error  and  thereby  improve  the  quality  of  management  earnings                forecast in the IPO prospectus. Thus, we contribute to the literature concerning motives                behind earnings management activity of IPO companies.                As our study includes three years around the IPO, we state three hypotheses. Firstly, we                assume that before the IPO managers manipulate earnings to show the most favorable                picture of the company and e.g. inflate the issue price. Due to this fact, borrowing earnings                from  the  future  makes  achieving    better  financial  results  in  the  following  year  more                challenging and preparing optimistic as well as accurate earnings forecasts in the year of                IPO becomes quite difficult, our first hypothesis states as follow:                H1:  Pre-IPO  upward  earnings  management  reduces  the  accuracy  of  earnings  forecast                revealed in the IPO prospectus.                                                               654","The  second  hypothesis  stems  from  the  assumption  that  voluntary  decision  to  include                looking forward information in the prospectus leads to earnings manipulation to meet their                forecasted  value.  Therefore,  the  accurate  earnings  forecast  increases  the  credibility  of                managers to the public, we posit:                H2:  The  upward  earnings  management  in  the  year  the  firm  goes  public  increases  the                accuracy of the earnings forecast disclosed in the IPO prospectus.                 In the year following the IPO we expect that increasing reported earnings through earnings                manipulation is very limited due to the fact that similar actions could have been taken                before  and  in  the  year  of  IPO,  which  makes  it  more  difficult  to  continue  the  earnings                management process to upward earnings. If the management board decides in advance to                apply an aggressive income-increasing policy of reporting in the future, it will be prone to                limit  the  profit  transfer  to  the  current  period.  Such  a  strategy,  undoubtedly,  makes  it                difficult  to  meet  a  specific  level  of  profit  that  is  expressed  as  expected  in  the  IPO                prospectus. Thus, we formulate our third hypothesis:                H3: The increase in the reported earnings through earnings management in the post-IPO                year diminishes the accuracy of earnings forecast published in the IPO prospectus.                 2 Data and Methodology                In  order  to  test  our  hypotheses,  we  construct  a  sample  of  IPOs  as follows. Firstly,  we                identify all new companies on the main market of Warsaw Stock Exchange (WSE) between                2006-2015. This sample period reflects the current legal status in Poland regarding the                disclosure  of  the  forward-looking  financial  reporting  in  the  IPO  prospectus.  Since  the                beginning  of  2006  Polish  stock  companies  have  been  obliged  to  apply  uniform  legal                regulations concerning the going public process and the content of the prospectus. Then,                in  order  to  ensure  the  sample  homogeneity,  form  the  number  of  337  companies  we                exclude: (1) IPO of foreign companies as they may apply different accounting regulations,                (2) banks and insurance companies due to the dissimilarity of their financial reporting, (3)                companies previously traded in the alternative public trading systems (e.g. NewConnect in                Poland) because of the significant difference in the pre-IPO public data availability, (4)                IPOs without the sale of primary or secondary shares for different reasons of going public,                (5)  companies  with  missing  accounting  data  necessary  for  earnings  management                detection. Thus, the final sample consists of 159 firms (earnings management sample), of                which  87  disclosed  the  earnings  forecast  for  the  IPO  year  in  the  prospectus  (forecast                accuracy sample). The data on the earnings forecast and some company’s characteristics                are hand-collected from prospectuses. The financial statement data are obtained either                from annual reports available in the Notoria Service database or are supplemented from                prospectuses and WSE official statistics.                By and large, our research refers to the influence of earnings management activities on                the  accuracy  of  the  profit  forecast  in  the  IPO  prospectus.  We  investigate  if  managers                transfer earnings between the IPO year and periods around this corporate event to meet                optimistic earnings projections made and revealed to the public before IPO, as Teoh et al.                (1998) suggest. Thus, our study requires the use of appropriate measures of the forecast                accuracy, earnings management, and other factors that may generally affect the quality of                financial projections. Therefore, we employ the OLS regression using the forecast accuracy                sample, with the general form of equation as follows:                                     0                                                                    j                         Accuracy =   +     j Earnings management +      k Control +                 (1)                                                                                   k                Our main measure of the forecast accuracy is an absolute forecast error (AFER), which                shows how much of the actual value of a given financial category differs from its prediction,                regardless  of  the  fact  whether  the  error  is  due  to  over-  or  underestimation.  AFER  is                calculated as the absolute value of the difference between the reported value of profit for                the IPO year and its forecast value as given in the prospectus scaled to the absolute value                of the forecast. Furthermore, we use the forecast error (FER), i.e. the difference between                the actual value of profit for the IPO year and its forecast form in the prospectus divided                                                             655","by the absolute value of forecast, to investigate the bias of the missprediction. FER takes                negative value if the forecast prepared and disclosed to the public was over optimistic and                its  positive  value  shows  that  the  forward-looking  information  in  the  prospectus  was                conservative and too pessimistic (Yau and Chun, 1999).                Next,  as  the  direct  observation  of  the  earnings  management  activities  is  practically                impossible,  we  adopt  a  set  of  proxies  commonly  used  in  literature  to  detect  earnings                management. We employ both real and accrual-based earnings management. Firstly, we                follow Roychowdhury (2006), Cohen and Zarowin (2010) and use the abnormal level of                production costs (REM_P), the abnormal level of discretionary expenditures (REM_D) as                well  as abnormal  cash  flows from  operations  (REM_CFO)  to investigate  managers’  real                economic  actions  to  maintain  accounting  appearances.  Then,  so  as  to  detect  earnings                management through discretionary accruals (DACC) we employ Larcker and Richardson                (2004) model,  which is  a  modification that is  widely  applied in  the literature model  of                Dechow et al. (1995) and is more appropriate for the assessment of accruals in growing                companies. Abnormal values of the production costs (PROD), discretionary expenditures                (DISX), cash flows from operations (CFO) and total accruals (TACC) are computed as their                actual value minus the normal level of each of them are calculated using the estimate                coefficients from the following equations:                        PROD  t  =  +   1   +   SALES t  +     SALES t  +     SALES t− 1  +      (2)                         TA t− 1   0   1 TA t− 1  2  TA t− 1  3  TA t− 1    4   TA t− 1    t                         DISX t  =  +    1  +   SALES  t− 1  +                                       (3)                         TA t− 1  0    1  TA t− 1  2  TA t− 1  t                         CFO t  =   1   +   SALES t  +    SALES t  +                                (4)                        TA t− 1  1  TA t− 1  2  TA t− 1  3  TA t− 1  t                         TACC  t  =   1  +    SALES −  AR t  +   PPE t  +   BV t  +   CFO t  +      (5)                                                      t                         TA t− 1  1 TA t− 1  2     TA t− 1       3  TA t− 1  4  MV t  5  TA t− 1  t                 where TA refers to total assets, SALES is the net sales, ∆SALES is the change in net sales                from the preceding year, ∆AR is the difference in accounts receivable in a given year, PPE                is gross property, plant, and equipment, BV and MV are the book and market value of                common equity, respectively.                Referring to our hypothesis we can expect both positive and negative coefficients between                our depended value AFER and proxies employed as earnings management detectors in the                periods around IPO. In detail, the expected sign on coefficients presents Figure 1.                                                Figure 1 Research Design                                                                    Source: authors’ own work                Prior studies on earnings management usually estimate the above regressions for each                year and every industry separately. However, as at least 15 observations in each year a                                                             656","sub-sample is required (Zang, 2012; Lo et al., 2017), the use of industry classification in                the IPO research with the limited number of quite divers companies is problematic and                eliminates most cases. For that reason we follow Ecker et al. (2013), who recommend                grouping companies in a relatively homogeneous sets according to the size of the firm,                measured  by lagged total  assets.  Therefore,  in  order  to  identify earnings manipulation                activity in the earnings management sample, we divide all analyzed IPOs into 10 separate                groups of a similar size (+/- 1 company), and then estimate each model separately for                each decile group for pre-IPO, IPO and post-IPO year.                Because the analysis of correlations between particular forms of earnings management                shows  a  strong  and  statistically  significant  collinearity  between  REM_P  and  REM_D,  we                follow Zang (2012) and aggregate these two forms of real earnings management into one                proxy, REM_P\&D, by taking their sum after multiplying the residuals form DISX equation                by –1 so that its higher values denote a greater increase in the reported earnings.                In addition, regarding the prior research on factors affecting the accuracy of the profit                forecast  disclosed in  IPO  prospectuses we include  several  control  variables.  As interval                between the earnings forecast and the realization of earnings make long-term predictions                more difficult (Bédard et al., 2016), we employ HORIZON, i.e. the number of days from                the release of the prospectus to the end of the IPO year. Moreover, larger and older IPOs                have been shown to have a greater forecasting precision (Yau and Chun 1999; Jelic et al.,                1998;). Hence, we also employ SIZE, i.e. the total value of sales in the year prior to the                IPO and AGE, i.e. the number of years between the IPO date and the date of the company’s                incorporation. For the estimation of regression coefficients, we convert HORIZON, SIZE                and AGE into a logarithm.                3 Results and Discussion                 Summary statistics of the variables applied in  our study are presented in Table 1. The                results show that, on average, actual earnings differ from their projected value at 34%,                which means that the forecasts disclosed in prospectus are inaccurate. What is more, the                forecasts prepared by managers are overoptimistic (the mean FER at -16.83%).                                        Table 1 Summary Statistics of All Variables                 Specifi-cation   Mean     Std. Dev.   Median      Min         Q1        Q3         Max                 Panel A: Depend Variable                 AFER             0.3400     0.5125     0.2059     0.0011     0.0570    0.3724     3.2360                 FER             –0.1683     0.5924  –0.0308  –3.2360  –0.2989          0.1304     0.7436                 Panel B: Pre-IPO Year Earnings Management Activity                 REM_CFO         –0.0043     0.2294  –0.0160  –0.9468  –0.1339          0.0894     0.9528                 REM_P\&D         –0.0114     0.9322     0.0344  –7.5337  –0.1855        0.2467     1.6503                 DACC            –0.0026     0.1148  –0.0220  –0.2843  –0.0732          0.0430     0.4428                 Panel C: IPO Year Earnings Management Activity                 REM_CFO          0.0020     0.4694  –0.0094  –3.0910  –0.1240          0.1423     1.7510                 REM_P\&D          0.0028     0.7499     0.0817  –5.0340  –0.2438        0.2663     2.4166                 DACC             0.0119     0.2849     0.0049  –1.4952  –0.0524        0.0812     0.9108                 Panel D: Post-IPO Year Earnings Management Activity                 REM_CFO         –0.0037     0.1702  –0.0375  –0.4892  –0.0977          0.0617     0.9725                 REM_P\&D         –0.0128     0.3438     0.0311  –1.3046  –0.1305        0.2129     0.8656                 DACC            –0.0074     0.1570     0.0081  –0.9400  –0.0406        0.0485     0.3411                 Panel E: Control Variables                 HORIZON              172          2       201         26        140        278       487                 SALES             46035           4     45622        292     19844     128234  1645672                 AGE                   15          2        14           3         9         20       149                                                  Source: own calculations                                                             657","Regarding the earnings management process one can see that such activities are used in                the years around IPO. Although the mean values of our proxies amount to only about 1%                of the total assets or less, it does not mean that Polish new stock companies have not put                into practice earnings management in periods around the going public event. The level of                these variables significantly varies across companies, investigated years and forms of the                earnings manipulation. Moreover, the data in Table 1 reveal some differences in the use of                earnings management tools between the years around IPO.                Table  2  presents  the  Pearson  correlations  coefficients  between  the  applied  variables.                Significant  correlations  between  some  variables  used  as  proxies  of  the  earnings                management process are not the random ones. The negative correlation indicates that                some  of  the  earnings  managements  methods  are  used    interchangeably  whereas  the                positive sign for REM_P\&D and DACC suggests a simultaneous application of this methods.                However, the observed links between variables are not constant over time.                               Table 2 Correlation Matrix - Pearson Correlation Coefficients                   Specifi-     REM_CFO  REM_P\&D           DACC       HORIZON         AGE        SALES                    cation                 Panel A: Pre-IPO Year                 REM_CFO            1.0000                 REM_P\&D            0.0481       1.0000                 DACC               0.0054     –0.0673       1.0000                 HORIZON           –0.0432     –0.0528      –0.0128       1.0000                 AGE                0.0474       0.0501     0.2335        0.0124       1.0000                                                                   **                 SALES              0.0657       0.0236      0.0168      –0.0990    0.3358 ***      1.0000                 Panel B: IPO Year                 REM_CFO            1.0000                 REM_P\&D        –0.4664  ***     1.0000                                         **                 DACC            –0.2393      0.3049 ***     1.0000                                                                   **                                          *                 HORIZON          –0.1791        0.0680   –0.2403         1.0000                 AGE               –0.0443       0.1063      0.0937       0.0124       1.0000                 SALES             –0.0272       0.0617      0.0334      –0.0990    0.3358 ***      1.0000                 Panel C: Post-IPO Year                 REM_CFO            1.0000                 REM_P\&D        –0.3372  ***     1.0000                 DACC               0.0199     –0.0526       1.0000                                                                    *                 HORIZON            0.0767       0.0728    –0.2040        1.0000                 AGE                0.0792       0.0522      0.0326       0.0124       1.0000                 SALES             –0.0273       0.0329      0.0287      –0.0990    0.3358 ***      1.0000                  **                * ,  , and  ***  indicate significance at the 10%, 5%, and 1% levels, respectively.                                                  Source: own calculations                Table 3 provides some insight into the results of our multiple regressions models. In the                year preceding the IPO only two detectors of earnings management are associated with                the  earnings  forecasts  accuracy:  REM_CFO  and  REM_P\&D.  Nevertheless,  the  signs  of                coefficients for these variables does not allow us to strictly state if applying real earnings                management methods is associated with the lower earnings accuracy in the year before                IPO  and, thus, confirms our first hypothesis. Although the coefficient on REM_CFO is in                line  with  our  expectation  form  H1,  the  coefficient  on  REM_P\&D  points  to  a  different                dependence.                                                               658","Table 3 Earnings Forecast Accuracy and Earnings Management -OLS Regression Results                   Specifi-        Pre-IPO Year                IPO Year               Post-IPO Year                    cation       AFER         FER         AFER          FER          AFER          FER                 Intercept        0.3430      –0.2637        0.8103     –0.8995        0.6344      –0.5615                                                                   *                                (0.6669)    (–0.4308)     (1.7162)     (–1.6475)     (1.3023)    (–0.9576)                 REM_CFO         –0.4076       0.4243      –0.0998        0.1897       0.2977      –0.0333                                        *                                                      *                              (–1.9460)      (1.7019)     (–0.9513)     (1.5645)     (1.0547)    (–0.0981)                 REM_P\&D         –0.0870       0.1216      –0.0349        0.0638      –0.0287       0.1099                                                      *                                        *                              (–1.6830)      (1.9754)     (–0.5303)     (0.8387)    (–0.2062)     (0.6552)                 DACC             0.5171      –1.0352      –0.8984        1.1928      –1.3269       1.6769                                                     **                                (1.1990)  (–2.0166)  (–5.4444)   ***   (6.2516) ***  (–4.5570) ***   (4.7851) ***                 HORIZON          0.2194      –0.1978        0.1286     –0.0692        0.1629      –0.1334                                                     **                                        **                                                                   *                                                                                            **                               (3.1360)   (–2.3765)       (1.9542)     (–0.9089)  (2.4310)       (–1.6542)                 AGE             –0.1188       0.1314      –0.0723        0.0552      –0.1041       0.0897                                        *                                                                                             *                              (–1.7680)      (1.6428)     (–1.2490)     (0.8251)  (–1.7019)       (1.2184)                 SALES           –0.0739       0.0690      –0.0842        0.0840      –0.0783       0.0774                                                      *                                        **                                                                               **                                                                                                          **                                                                                            **                              (–2.3112)      (1.8145)  (–2.9883) ***   (2.5772)   (–2.6309)   (2.1618)                 Adj. R2          0.2499       0.2048        0.4146       0.4142       0.3466       0.2917                 F–statistic      5.7741       4.6904        11.151     11.1343        8.6028       6.9025                 p–value          0.0001       0.0004        0.0000       0.0000       0.0000       0.0000                                                * **                The t-statistics are in parentheses.  ,  , and  ***  indicate significance at the 10%, 5%, and 1% levels,                respectively.                                                  Source: own calculations                The  data  presented  in  Table  3  provide  evidence  that  in  the  year  of  IPO  discretionary                accruals are used by managers to meet the forecasted value of earnings. This result is                consistent with our second hypothesis. However, the estimation results for real earnings                manipulation  activities  lack  the  statistical  significance  and  seem  to  be  unimportant  in                explaining the level of the forecast error. Similar results are observed for the year following                the IPO, but in this case these findings are not in line with our third hypothesis assuming                that the earnings forecasts accuracy is decreased due to the earnings manipulation. Our                findings  suggest  that  after  IPO  the  incentive  to  manage  earnings  is  still  valid  and                discretionary accruals are constantly applied to the reporting policy. Managers look ahead                and continue to apply the income-increasing discretionary accrual management.                4 Conclusions                 This paper empirically examines the use of real earnings management and discretionary                accruals around IPO and the real activities manipulation in order to increase the accuracy                of the earnings forecast voluntarily published in the IPO prospectus.                Our  findings  provide  supportive  evidence  for  the  conclusion  that  managers  use  some                methods of earnings management in the years close to going public so as to reduce the                earnings forecasts errors. The data show that especially in the year of IPO discretionary                accruals  are  used  to increase  the  reported  earnings in  order  to  achieve  the forecasted                earnings disclosed in prospectus. Moreover, prior to the IPO we observe applying the real                activities  manipulation,  in  the  year  of  IPO  and  after  the  discretionary  accruals  are                associated with the level of earnings forecasts. This result may be explained by the fact                that before IPO the accrual-based earnings manipulation is more constrained by auditors                (Zang, 2012) and the extensive scrutiny of IPO prospectuses makes companies report less                aggressively prior to IPO (Ball and Shivakumar 2008; Venkataraman et al., 2008).                  References                Ball,  R.,  Shivakumar,  L.,  (2008).  Earnings  quality  at  initial  public  offerings.  Journal  of                Accounting and Economics, vol. 45, pp. 324‒349.                                                               659","Bédard, J., Coulombe, D., Courteau, L. (2016). The Credibility of Earnings Forecasts in IPO                Prospectuses and Underpricing. 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The Accounting Review, vol. 87(2), pp. 675-703.                                                                                                                           661","Preliminary Results of Non-bank Consumer Credit Providers in the                 Professional Qualifications Test by the Act No. 257/2016 Coll., on                                                  Consumer Credit                                                                               2                                                           1                                             Ivan Soukal , Eva Hamplová                                                1,  University of Hradec Králové                               Faculty of Informatics and Management, Department of Economics                                   Rokitanského 62, 500 03, Hradec Králové, Czech Republic                                                 E-mail: ivan.soukal@uhk.cz                                                 2  University of Hradec Králové                               Faculty of Informatics and Management, Department of Economics                                   Rokitanského 62, 500 03, Hradec Králové, Czech Republic                                                E-mail: eva.hamplova@uhk.cz                 Abstract:  The new Act No. 257/2016 Coll., on consumer credit states obligatory duty for                consumer  credit  providers  and  intermediaries  to  ensure  and  prove  the  professional                qualification  level  of  all  employees  involved  in  the  provision  and  intermediation  of  a                consumer  credit.  Qualification  can  be  proven  by  the  Act  No.  257/2016  Coll  only  at                accredited institutions. Such institutions provide a testing based on questions prepared by                the  market  regulator  and  supervisor  –  the  Czech  national  bank.  No  one  is  allowed  to                provide or intermediate a consumer credit without a successful test pass at an accredited                institution. We analyzed the test results of 287 employees of two medium-sized non-bank                financial institutions who took the final qualification test at an accredited institution. The                goal is to present the test results so far and to find whether gender and age are factors of                the test score. We set four main and three auxiliary hypotheses regarding an effect of                gender and age variable on the test performance. We find that neither of the variables is                a factor. Although some level of variance is present in a more detailed analysis, it is not                statistically significant. Therefore, we claim that regarding the test by the act no. 257/2016                Coll. on Consumer Credit the results are independent of age or gender of an employee.                Our result was unexpected concerning the age variable. In a discussion, we explain that                results might be biased by “early-takers” employees. Therefore, we expect an increase in                test results variance and change in the test result distribution skewness in a future as more                regular employees will participate.                Keywords: consumer credit, act no. 257/2016 Coll., qualification test, non-bank financial                institution                 JEL codes: G23, G28                1 Introduction                 The latest legal development concerning the provision and intermediation of a consumer                credit  brought  many  changes  when  the  Act  No.  257/2016  Coll.,  on  Consumer  Credit                became effective. All providers have to obtain the authorization to pursue the business of                a non-bank consumer credit provider or the authorization to pursue the business of an                independent intermediary from the Czech National Bank (thereinafter as CNB). Otherwise,                they won’t be allowed to provide or intermediate a consumer credit anymore. Everyone,                who provided loans on the basis of a trade license, had to now apply for the license of the                CNB till February 28, 2018 at the latest. Any person that provided such services and did                not apply for it in time lost the license for a provision and intermediation of a consumer                credit. The provision or intermediation of consumer credit is no more considered as a trade                (Part II of Act No. 258/2016 Coll). Now it is defined as a special regulated activity on the                financial market. CNB is the only granting authority regarding an authorization for such                business.  Article  18(1)(c)  of  Act  No.  257/2016  Coll.,  on  Consumer  credit  states  an                obligatory  duty  to  ensure  and  demonstrate  qualified  performance  of  all  persons  that                participate  in  the  process  of  the  provision  and  intermediation  of  a  consumer  credit.                Qualification can be proved only by passing a qualification test organized by accredited                                                             662","professional examiners, (Decree No. 381/2016 Coll.). The Act No. 257/2016 Coll. is in force                since the December 1, 2016 and the latest date to prove a qualification is December 1,                2018. Until then it is possible for a person to temporarily continue in the provision and                intermediation of a consumer credit but there is a duty to send the statutory declaration                that a qualification was achieved by studying, training etc. CNB can verify this claim any                time.  Nevertheless,  the  only  option  how  to  continue  provision  and  intermediation  of  a                consumer credit (tied consumer credit, consumer credit for another purpose than housing                and mortgages) is to obtain a certificate from an accredited professional examiner and to                be included in the CNB’s registry.                The aim of the paper is to examine whether the gender or the age are the factors of the                employee’s  test  performance.  We  examined  this  possible  relation  on  the  test  results                provided  to  us  by  accredited  professional  examiner.  The  examiner  wished  to  remain                anonymous  and  we  are  as  well  bound to  respect  an  anonymity  of non-bank consumer                credit  providers  that  used  his  services.  This  accredited  professional  examiner  provides                qualification testing to non-bank financial institutions. We analyzed results of employee’s                test results from the first two months of testing. The goal is to present the test results so                far and to find whether gender and age are factors of the  test score. We perform this                analysis to find how much are the employees successful and to identify which variable                should be analyzed in a greater detail in a future analysis of a significantly larger sample.                 2 Methodology and Data                Out test sample consisted of 288 records of individual test performance records. All persons                are employees of non-bank financial institutions. We analyzed the final test only; study or                training test results are beyond the scope of the analysis. Analyzed results were gathered                from the qualification test for provision and intermediation of a consumer credit for other                purpose than housing, see (Article 3(1)(i) of Act No. 257/2016 Coll., on Consumer Credit).                The qualification test took place during January and February 2018. Every test contained                60  test  questions  (professional  expertise  testing)  and  two  case  studies  (professional                competence testing). Each test was consisted of both multiple question types: one correct                answer (75% of the test) and multiple correct answers (25% of the test). Any wrong or                missing answer in multiple correct answers question resulted in incorrect result concerning                the whole question. Professional skills were tested by two case studies with five questions                each (one correct answer questions). Qualification testing test can take two hours at most                (Article  9  –  11  of  Decree  No.  384/2016  Coll.).  To  successfully  pass  a  professional                examination, an examinee must obtain at least                    •   three-quarters of the points attainable in all the examination questions,                    •   three-fifths  of  the  points  attainable  in  the  examination  questions  concerning                       professional expertise, and                    •   three-fifths  of  the  points  attainable  in  the  examination  questions  concerning                       professional  competence,  which  shall  be  considered  an  independent  area  for                       calculation purposes (Article 12 of Decree No. 384/2016 Coll.)                All statistics were performed in IBM SPSS 20, charts were constructed in IBM SPSS 20 and                MS Excel 2013. Although standard statistic tests are described in many monographs and                books  dealing  with  general  statistics,  we  chose  to  source  our  methodology  from  more                focused  sources.  We  needed  to  assess  relationship  between  variables  in  professional                qualification tests which is an area of educational research although an application area is                qualification testing in financial intermediation area. Therefore, we selected books focused                on quantitative research in education and especially test evaluation.                 Chi-Square test of independence measures the difference between a statistically generated                expected result and an actual result to see if there is a statistically significant difference                between them, i.e. to see if the frequencies observed are significant. This test is used to                in contingency table to investigate an independence between categorical variables, see                e.g. (Best and Kahn, 2006; Corty, 2007; Cohen et al., 2008, Hoy, 2010, Wellington, 2015).                The hypothesis states that there is no statistically significant difference between observed                                                              663","samples  or  specifically  in  in  case  of  an  contingency  table  that  tested  variables  are                independent. We tested two hypotheses in contingency table independence test:                     •   H_gender_indepence: the test result is independent on gender of a participant.                    •   H_age_independence: The test result is independent on age of a participant.                This  widely  used  test  is  suitable  for  gender  and  age  effect  on  other  variables.  It  was                employed  e.g.  by  Sulaiman  (2012)  who  tested  the  relationship  between  financial  risk                tolerance and each of the demographic features including gender and age. Hedvičáková et                al. (2017) tested financial knowledge and year of study dependence. Pahl, J. (2008) tested                an independence of gender and credit usage and also employment categories. Aesaert and                Van Braak (2015) used chi-square test of independence to identify gender as a factor of                measured competence.                T-test for independent samples is a suitable tool for means comparison of a dependent                variable  between  two  samples  or  to  be  more  specific  to  discover  whether  there  are                statistically significant differences between the means of two samples, using parametric                data drawn from random samples with a normal distribution (Best and Kahn, 2006; Corty,                2007;  Cohen  et  al.  2008;  Hoy,  2010).  The  t-test  has  two  variants:  the  t-test  for                independent samples and the t-test for related samples often called paired samples and                therefore paired t-test. The former assumes that the two samples are unrelated to each                other  and  that  is  the  variant  we  employed.  The  hypothesis  states  that  there  is  no                statistically  significant  difference  between  the  means  of  two  samples.  We  tested  one                hypothesis:                    •   H_gender_scores:  There  is  no  difference  in  the  test  score  of  male  and  female                       employees.                 Due to  an  assumption  that the  sets  of  scores  have  approximately  equal variances,  we                performed a Levene's test for equality of variances, see e.g. (Cohen et al., 2008; Muijs,                2008). Although we possessed not just means, but the whole samples where variance can                be calculated on, SPSS does this test automatically and a researcher then chooses a correct                type of the t-test result. Also, Levene's test for equality of variances was employed before                ANOVA. Homogeneity of variance is tested in a hypothesis that variance is equal in both                samples. Our auxiliary hypotheses were then:                    •   H_gender_scores_aux:  The  test  scores  variance  is  the  same  for  both  male  and                       female employees.                    •   H_age_scores _aux: The test scores variance is the same in all age groups.                ANOVA  or  One-way  analysis  of  variance  is  an  effective  way  to  determine  whether  the                means of more than two samples are too different to attribute to sampling error. In other                words, if there is no statistically significant difference between samples’ means. The ANOVA                consists  of  these  phases:  within-groups  variance  calculation,  between-groups  variance                calculation, F-ratio calculation by between-groups and within-groups variance fraction and                F-ratio assessment (Best and Kahn, 2006; Corty, 2007; Cohen et al. (2008). Aesaert and                Van Braak (2015) investigated gender influence on the total test scores using a t-test and                a one-way between-groups ANOVA. We tested a hypothesis:                    •   H_age_scores: The test score means in age groups are the same.                Bonferroni all-pair-wise multiple comparison test is a part of a post-hoc analysis in ANOVA                or MANOVA. Its purpose is to isolate where the differences among samples are. It replaces                a series of t-tests which might be eventually misleading due to an inability to reflect a total                number of samples. Therefore, e.g. Smalheiser (2017) or Cohen et al.(2008) recommends                employing Bonferroni all-pair-wise multiple comparison test or other similar tool such as                Turkey HSD test. Bonferroni test calculation multiplies each of the significance levels from                the least square difference t-test (in SPSS as LSD) by the number of tests performed. The                post-hoc analysis presents results of hypotheses for each sample stating that the means                in two samples are the same.                    •   H_age_scores_aux: The test score means in each pair of age groups are the same.                                                             664","All tests were performed at the significance level of 5%.                 3 Results and Discussion                The share of successful participant among male employees was 85,9% and 85,1% among                female employees. Such figures are very close to each other, nevertheless as the number                of participants grows, the statistical test sensitivity grows as well. Therefore, a hypothesis                test  was  performed.  We  tested  the  hypothesis  H_gender_indepence:  the  test  result  is                independent on the gender of a participant. We failed to reject the hypothesis a p-value                0,87. We claim that there is no statistical evidence that test result (passed/failed) depends                on  gender.  Both  the  male  and  female  employees  got  the  same  rate  of  success  in  the                qualification test. The shares of participants that passed or failed are presented below.                We constructed a box-plots of the test score in points to provide a closer look at the data,                see fig. 1.                                       Figure 1 Box-plot of Exam Points by Gender                                                                                      Source: own research                 We can see some difference in a level of variance and also some outliers. The test score                95%  confidence  intervals  for  mean  was  <81;84>  points  for  female  employees  and                <81;86>  for  male  employees.  Such  values  are  equal  in  real-world  point  of  view.                Nevertheless, T-test for independent samples was performed to assess whether there is a                statistically significant difference in the test score between male and female sample. In                order  to  perform  the  T-test  the  Levene's  test  for  equality  of  variances  tested  auxiliary                hypothesis H_gender_scores_aux: The test scores variance is the same in both male and                female employees. We failed to reject a hypothesis with p-value 0,78 and therefore we                accepted the results as suitable for the T-test with the same sample variance. Independent                samples T-test tested hypothesis H_gender_scores: There is no difference in the test score                of male and female employees. We failed to reject the hypothesis with p-value 0,48. We                claim that the difference in the test score means is not statistically significant and therefore                the test scores means are the same for both genders.                 The sample was divided into groups by the age, to be more specific we decimalized the                age and created groups of young employees below the of thirty, then employees in their                                                               665","thirties, forties, fifties etc. The box-plots on figure 2 presents the test scores achieved in                different age groups.                                     Figure 2 Box-plot of Exam Points by Age Decimals                                                                                          Source: own research                 A different level of variance  can be observed, nevertheless, the means are of a similar                value. Therefore, it was performed a chi-square contingency table test of the hypothesis                H_age_independence: The test result is independent on the age of a participant. To be                more specific the age groups were tested against the test results (passed/failed). We failed                to reject the hypothesis with a p-value 0,21. We claim that the test result (passed/failed)                is independent of the age of an employee.                After the hypothesis testing we calculated 95% confidence interval for mean for each group                to provide greater detail, see table 1.                                     Table 1 The Test Score in Different Age Groups                                                                                 95% Confidence                                                                                Interval for Mean                                                                      Std.        Lower       Upper                           Age_group              N       Mean    Deviation       Bound       Bound                      Less than 30                27      78,59      12,65         73,59       83,60                      30-39                       67      82,27       9,92         79,85       84,69                      40-49                       138     83,50       9,83         81,85       85,15                      50 and over                 56      82,75      11,98         79,54       85,96                                                    Source: own research                There  can  be  identified  some  difference  in  the  youngest  group  to  the  40-49  years  old                employees group. Therefore, we performed ANOVA on the test score to test the age groups                in  greater  detail.  However,  before  that  the  Levene's  test  for  equality  of  variances  was                performed. We failed to reject the hypothesis with p-value 0,28. Therefore, we can assume                that variance is the same in all age groups and so the ANOVA can be employed. The tested                                                            666","hypothesis was H_age_scores: The test score means in age groups are the same. We failed                to reject the hypothesis with p-value 0,18. We claim that the between-group difference is                not statistically significant and therefore, the test score does not differ in the age groups.                This result was a bit of a surprise because we expected that experienced employees would                perform statistically significantly better than the youngest workers or the oldest. The lowest                mean result of the youngest group compared to employees in the age of 40-49 partially                supports  this  expectation  but  so far,  the  hypothesis testing  was inconclusive.  To  more                closely  evaluate  actual  intergroup  difference,  we  performed  a  post-hoc  analysis  by                Bonferroni all-pair-wise multiple comparison test, see a result in table 2 below.                                        Table 2 The Test Score Post-hoc Analysis                     Age_group         Age_group        Mean Difference       Std. Error        Sig.                                        compare                   Less than 30      30-39                     -3,67              2,41           ,77                                     40-49                     -4,90              2,22           ,17                                     50 and more               -4,15              2,48           ,56                                                    Source: own research                Not even the post-hoc analysis found a statistically significant difference in the test scores.                Generally, we can sum the results up by a claim that neither of the variable gender or the                age are factors of the performance in the qualification test by the Act No. 257/2016 coll.,                on consumer credit.                Such results might offer a managerial implication that neither of these variables should                play role in managerial decisions regarding the quality of the employee. However, we do                not support such claim at least not yet. We argue that pilot study data might have been                biased. If so, then a source of the bias would be connected to an “early-taker” or in more                colloquial  manner  an  “eager-beaver”  issue.  This  issue  is  known  phenomenon  from                educational sciences field but it is present also in marketing research because Albert et al.                (2010) describe it also as a problem of a survey design. Quota or time-limited surveys can                be  biased  by “eager-beaver”  participants.  The  problem lies in  a fact  that these  survey                participants complete their task as the first group. They may have common characteristics,                such as being incentive driven, having hidden agendas, or just having a certain personality                type. Any of these factors may lead them to dominate and influence the results. Bleiklie                (2009)  uses  the  term  and  describes  this  term  as  someone  who  is  “eager  and  rapid                implementer”.  The  “early-taker”  group  Rajbanshi  et  al.(2015)  connect  with  a  higher                productivity in their model. In our case, we might face a similar problem.                The  data  comes  from  the  beginning  of  the  testing  period.  Therefore,  we  expect  that                employees in our data are likely to be these “early-takers” or “eager-beavers”. Accordingly                mentioned sources we expect such persons to be more motivated, more hard-working and                overall more prepared for the test. Otherwise, they would take later exam term to gain                more time for studying. Albert et al. (2010) recommend employing a phased launch survey                in order to avoid such bias. Since this is an exploratory study of the first data, it is not                possible. Nevertheless, the testing will be performed at least for half a year and maybe till                winter  2018.  Therefore,  we  may  observe  different  results  as  more  and  more  regular                employees will take the test. We expect an increase in variability and also a decrease of                the test result distribution negative skewness. As the number of regular employees will be                growing  the  share  of  motivated,  hard-working  and  well-prepared  employees  will  be                shrinking. Therefore, we expect a higher frequency of low scores (variance increase) and                shift of the mean to lower values (negative skewness decrease).                 4 Conclusions                 The new Act No. 257/2016 Coll., on consumer credit states obligatory duty for consumer                credit providers and intermediaries to ensure and prove the professional qualification level                of all employees involved in the provision and intermediation of a consumer credit. the Act                                                             667","No.  257/2016  Coll.,  on  Consumer  Credit.  We  performed  the  analysis  of  288  individual                records of the qualification tests organized by an accredited professional examiner in the                first months of the year 2018.                The goal of the paper was to present the test results so far and to find whether gender and                age are factors of the test score. The test score 95% confidence intervals for mean was                <81;84> points for female employees and <81;86> for male employees. We tested the                hypothesis that the test result is independent on the gender of a participant and failed to                reject  it.  We  employed  the  Chi-Square  test  of  independence.  Additional  hypothesis                concerning the group scores confirmed the results of the first hypothesis that a gender is                not a factor of the test score. We employed the T-test for independent samples with the                same variance assumed. The second variable to be tested was the age of participants. We                tested the hypothesis that the test result is independent on age of a participant. We failed                to  reject  this hypothesis.  We  employed  the  Chi-Square  test  of independence.  Then  we                tested the hypothesis that the test score means in all age groups are the same. We failed                to  reject  this  hypothesis.  We  employed  ANOVA  and  Bonferroni  all-pair-wise  multiple                comparison  test  is  a  part  of  a  post-hoc  analysis.  The  post-hoc  analysis  showed  some                difference  between  the  youngest  group  compared  to  employees  in  the  age  of  40-49.                Nevertheless, it was not statistically significant. Therefore, we claim that neither of the                variable gender and age are factors of the qualification tests result or score.                Finally,  a  managerial implication  of  our  analysis  Is that that neither  of  these variables                should  play  role  in  managerial  decisions  regarding  the  quality  of  the  employee.                Nevertheless, we assume that our pilot study data might have been biased. If so, then a                source of the bias would be connected to an “early-taker” or in more colloquial manner an                “eager-beaver” issue. Therefore, we suggest repeating the testing on the full sample at                the  end  of  testing  in  winter  to  verify  whether  an  increase  of  regular  employees  share                changed the group parameters or not.                 Acknowledgments                 This  study  is  supported  by  internal  project  IGA  2018  “Support  of  pedagogical  work,                technical equipment, and communication with experts”, both at Faculty of Informatics and                Management, University of Hradec Kralove, Czech Republic.                 References                Act no. 257/2016 Coll., on Consumer Credit.                Act no. 258/2016 Coll., Act No 258/2016  amending certain laws in connection with the                enactment of the Consumer Credit Act.                Aesaert,  K.,  Van  Braak,  J.  (2015).  Gender  and  socioeconomic  related  differences  in                performance-based ICT competences.  Computers \& Education, vol. 84, pp. 8–25. DOI:                10.1016/j.compedu.2014.12.017.                 Albert,  B.,  Tullis  T.,  Tedesco,  D.  (2010).  Beyond  the  Usability  Lab.  Boston:  Morgan                Kaufmann.                Best, J. W., Kahn, J. W. (2006). Research in education. 10th ed. Boston: Pearson.                 Bleiklie, I. (2009). Norway: From Tortoise to Eager Beaver?. Higher Education Dynamics:                University Governance, vol. 25, pp. 127-152. DOI: 10.1007/978-1-4020-9515-3                 Cohen,  L.,  Manion,  L.,  Morrison,  K.  (2007).  Research  Methods  in  Education.  6th  ed.                Abington: Routlege.                Corty,  E.  (2007).  Using  and  interpreting  statistics:  a  practical  text  for  the  health,                behavioral, and social sciences. St. Louis: Mosby Elsevier.                Decree no. 381/2016 Coll., on applications, notifications and the submitting of statements                pursuant to the Consumer Credit Act.                                                              668","Decree no. 384/2016 Coll., on professional qualifications for the distribution of consumer                credit.                 Hedvičáková M., Svobodová L., Dittrichová J., Král M. (2017). Financial literacy and money                management  of  the  pupils in the  Czech  education  system.  In:  Novák,  P., Jurigová,  Z.,                Kozubíková,  L.,  Zlámalová,  J.,  eds.,  Proceedings  of  the  8th  International  Scientific                Conference Finance and Performance of Firms in Science, Education and Practice. Zlín:                Tomas Bata University, pp. 244-259.                Hoy, W. K. (2010). Quantitative research in education: primer. London: Sage.                Muijs,  D.  (2004).  Doing  quantitative  research  in  education  with  SPSS,  reprint  2008.                London: Sage.                Pahl, J. (2008). Family finances, individualisation, spending patterns and access to credit.                The     Journal    of    Socio-Economics,      vol.    37(2),    pp.     577-591.     DOI:                10.1016/j.socec.2006.12.041.                Rajbanshi,  R.,  Huang,  M.,  Wydick,  B.  (2015).  Measuring  Microfinance:  Assessing  the                Conflict  between  Practitioners  and  Researchers  with  Evidence  from  Nepal.  World                Development, vol. 68(April 2015), pp. 30-47. DOI: 10.1016/j.worlddev.2014.11.011.                 Smalheiser,  N.  L.  (2017).  Data  Literacy:  How  to  Make  Your  Experiments  Robust  and                Reproducible. London: Academic Press.                 Sulaiman, E. K. (2012). An Empirical Analysis of Financial Risk Tolerance and Demographic                Features of Individual Investors. Procedia Economics and Finance, vol. 2(October 2012),                pp. 109-115. DOI: 10.1016/S2212-5671(12)00070-6.                 Wellington,  J.  (2015).  Educational  Research:  contemporary  issues  and  practical                approaches. 2nd ed. London: Bloosbury Academic.                                                                                                         669","The Role of Nonfinancial Indicators in the Process of Credit Risk                                                     Assessment                              Martina Sponerová , Miroslav Svoboda , Miroslav Sponer                                                                        1                                                                                             2                                                  1                                                    1  Masaryk University                                Faculty of Economics and Administration, Department of Finance                                           Lipová 41a, 603 00 Brno, Czech Republic                                            E-mail: sponerova.martina@seznam.cz                                                   254801@mail.muni.cz                                                     2  Karel Englis College                                                    Management Institute                                         Mezírka 755/1, 602 00 Brno, Czech Republic                                               E-mail: miroslav.sponer@vske.cz                 Abstract: Credit risk management is closely related to the bankruptcy prediction and even                it  was  intensively  investigated  by  the  banking  industry  it  is  still  at  the  forefront  of                researchers. There is a pressure on financial institutions to still improve their credit risk                management system to eliminate credit risk which is one of the most significant financial                risks in the banking industry. Many research studies, scientific articles and publications are                still  trying  to  find  the  best  method  to  predict  bankruptcy  through  different  bankruptcy                models. The reviews from the last years show that using only financial data is not sufficient                to  predict  bankruptcy  precisely  and  some  of  the  researchers  also  include  nonfinancial                indicators  to  their  models.  The  aim  of  this  article  is  to  find  which  of  the  nonfinancial                indicators have the most significant prediction power to predict failure of Czech small and                middle enterprises (SMEs) based on the exclusive nonfinancial data from a bank. We have                investigated around 1000 companies from the SMEs segment, of which 100 failed during                the period 2010 – 2016. The most important nonfinancial indicators from the credit risk                point of view were found by using logistic regression.                Keywords: credit risk, bankruptcy prediction, SME, nonfinancial indicator                JEL codes: G32, G33, C53                 1 Introduction                 Bankruptcy  prediction  has  long  attracted  the  interest  of  researches.  Academics  and                practitioners  have  focused  their  research  to  improve  the  performance  of  existing                bankruptcy models and they are still developing new models and methods to precisely                predict business failure.                The business failure is closely connected with credit risk which is one of the biggest risk of                financial institutions. Hence, there is a pressure to financial institutions to still improve                their credit risk management systems to predict bankruptcy as accurately as possible.                Very interesting ways of research from the last years is the development of bankruptcy                models specifically designed for each company feature, such as size (Altman and Sabato,                2007), industry (Chava and Jarrow, 2004) or age (Wilson and Altanlar, 2014). Tascon and                Castano (2012) suggest that the more homogeneus the characteristics of the companies                used for the construction of a prediction model are, the better their predictive capacity will                be. In the same line, Internal Ratings Based systems, under Basel recommendations, also                suggest to lenders to build models geared towards the specific characteristics of corporate                sub-populations, tuned to changes in the macro environment and tailored to the available                data.                The  actual trend in the  default  prediction  area  is  to incorporate  also  non-financial  and                macroeconomic information as predictor variables which was examined by e.g. Grunert et                al. in 2005,  Altman  et  al. in  2010,  Moon  and  Sohn in  2010.  They found  that  the  non-                financial and macroeconomics variables greatly improve the accuracy performance of the                proposed bankruptcy models.                                                             670","The main problems of using only financial indicators as failure predictors are that they are                not  able  to  express  long-term  targets  of  the  company,  they  are  not  connected  with  a                company´s strategy, they are based on historical data, they are lagging indicators. If the                financial indicators should express the company´s targets, there could exist a danger of                handling with profit. (Synek, 2016)                The aim of this article is to find which of the available nonfinancial indicators have the most                significant prediction power to predict failure of Czech SMEs based on the nonfinancial data                from a bank.                We focus on SMEs because they are reasonably considered as the most important segment                of the economy many countries. For OECD members, the percentage of SMEs out of the                total  number  of  firms  is  higher  than  97%.    SMEs  employ  approximately  two  third  of                employees and create more than half of added value in EU-28 (Eurostat, 2017). Thanks to                their simple structure, they can respond quickly to changing economic conditions and meet                local customers´ needs, growing sometimes into large and powerful corporations or failing                within a short time of the firm´s inception.                 2 Literature review                The default prediction literature for corporates is well known. Many authors during the last                fifty years have examined several possibilities of predicting default or business failure. The                seminal works in this field were Beaver (1967) and Altman (1968). The researcher William                Beaver was the first to apply a number of ratios which could discriminate between failed                and non-failed companies up to five years prior to bankruptcy. Altman improved Beaver´s                method and assessed a more complete financial profile of firms. Variables in Altman´s                model were classified into five standard ratio categories - liquidity, profitability, financial                leverage, activity, and solvency. Being the first person to successfully develop multiple                discriminate analysis (MDA) prediction model with a degree of 95.0% rate of accuracy, he                is  considered  the  pioneer  of  insolvency  predictors.  Altman´s  model  has  been  applied                successfully in many studies worldwide concerning the subjects of capital structure and                strategic management, investment decisions, asset and credit risk estimation and financial                failure of publicly traded companies (Lifschutz and Jacobi, 2010).                 For many years thereafter, MDA was the prevalent method applied to the default prediction                models. It was used by many authors, for example very often cited in research literature                is Taffler model developed in Great Britain in 1977 (Taffler, Tishaw, 1977). Another MDA                model was developed by Inka and Ivan Neumaier in 1995 and is known as IN95. This                model was constructed especially for the Czech market and was updated in next years.                Considering  these  MDAs´  problems,  Ohlson  (1980),  for  the  first  time,  applied  the                conditional  logit  model  to  the  default  prediction  study.  The  practical  benefits  of  logit                methodology are that they do not require the restrictive assumptions of MDA and allow                working with disproportional samples. After Ohlson, most of the academic literature used                logit models to predict default. Another, very often cited model, which uses conditional                probability, is the model by Mark E. Zmijewski (1984). He was the pioneer in applying                probit analysis to predict default but, until now, logit analysis has given better results in                this field. A probit approach is the same as the logit approach, only the distribution of                random variables is different.                From a statistical point of view, logit regression seems to fit well with the characteristics                of the default prediction problem, where the dependent variable is binary and where the                groups  are  discrete,  non-overlapping  and  identifiable.  The  logit  model  yields  a  score                between 0 and 1, which conveniently gives the client´s probability of default. Despite the                theoretical  differences  between  MDA  and  logit  analysis,  academic  studies  show  that                empirical results are quite similar in terms of classification accuracy (Altman et al., 2010).                We  witness  a  substantial  increase  in  the  number  and  complexity  of  default  prediction                studies due to the rapid advancement in technology and methodology. Above all, we can                mention  artificial  neuron  networks  used  e.g.  by  Angelini  et  al.  (2007),  decision  trees                                                              671","method used e.g. by Gulnur and Fikret (2011) and hazard models used e.g. by Shumway                (2001).                 Empirical  literature  also  highlights  the  significance  of  qualitative  information  such  as                business type, industrial sector, location, age, etc. in understanding of firms´ credit risk                behavior (Grunert et al., 2005). Altman et al. (2010) took account of this issue and studied                about 5.8 million SMEs, of which 66 000 failed during the observed period 2000–2007.                They  reported  that  the  prediction  performance  of  Altman  and  Sabato  (2007)  model                improved by about 13% when qualitative information was added. They found that data                relating  to  legal  action  by  creditors  to  recover  unpaid  debts,  company  filing  histories,                comprehensive  audit  report/opinion  data  and  firm-specific  characteristics  make  a                significant  contribution  to  increasing  the  default  prediction  power  of  risk  models  built                specifically for SMEs (Altman et al., 2010).                 Grunert et al. (2005) created several bankruptcy models using both financial and non-                financial  variables  (age  and  type  of  business  sector  etc.).  They  concluded  that  the                combination of financial and non-financial variables improves the accuracy performance of                the developed models. Peel et al. (1986) and Whittred and Zimmer (1984) show, using a                sample of SMEs from the UK, that the timing of the submission of annual accounts is an                indicator of financial failure. Other studies also suggest that unfavorable audit reports (Peel                and Peel, 1989) and the existence of payment problems (Wilson and Altanlar, 2014) are                relevant variables for predicting the failure. However, whereas the importance of financial                factors  is  widely  accepted  because  their  impact  is  measurable,  the  relevance  of  non-                financial variables is mainly considered in a holistic manner.                 3 Methodology and Data                 Our dataset consist a sample of non-financial indicators for 1000 SMEs that survived in the                period 2010 – 2016 out of which around 200 companies that failed in this time period. This                data  was  exclusively  gained  from  a  bank  database  by  random  selection  of  SMEs  that                survived and all of SMEs that failed during above mentioned period.                We have at disposal non-financial indicators used in a bank as supplementary indicators                when  assessing  creditworthiness  of  a  client.  There  are  eight  groups  of  non-financial                indicators  containing  economic  environment,  competitive  position,  relationship  with                suppliers  and  customers,  country  risk,  management  of  a  company,  ownership  of  a                company, relationship with a bank, relationship with other creditors.  The complete list of                these indicators is mentioned in the Tables 1 - 8.                             Table 1 Economic Environment - List of Non-financial Indicators                         Coding                                Description                        QS_1_2        Main  market  competition  –  relation  of  aggregate                                      demand and aggregate supply (production capacity)                        QS_1_2_1  Low (demand exceeds production capacity)                        QS_1_2_2  Average (stable demand is in line with production capacity)                        QS_1_2_3  Strong (demand is lower than supply)                                                   Source: own processing                               Table 2 Competitive Position - List of Non-financial Indicators                         Coding                                Description                        QS_2_1        Year-on-year client growth in comparison to industry                                      growth                        QS_2_1_1  Above-average (2% more than sector performance)                        QS_2_1_2  Average (sector performance +/- 2%)                        QS_2_1_3  Under-average (more than 2% below industry performance                                      or no comparison)                        QS_2_2        Position  on  the  main  market  -  according  to  the                                      proportion of client revenues                                                             672","QS_2_2_1  Dominant - the ability to influence pricing                        QS_2_2_2  Significant - price recipient                        QS_2_2_3  Average - comparable to other subjects                        QS_2_2_4  Marginal or market entry                        QS_2_3        The flexibility and ability of the company's innovation                        QS_2_3_1  High flexibility: the company is able to innovate products or                                      change  the  scope  of  business  within  months  without  high                                      costs                        QS_2_3_2  Low  flexibility:  product innovation  or  eventually  completely                                      changing of business scope is possible within the years with                                      additional costs                        QS_2_3_3  No flexibility: the company is not able to innovate products,                                      or change the business scope                        QS_2_3_4  Impossible to assess: product innovation / change of business                                      scope is not relevant                        QS_2_4        Degree of diversification of client activities                        QS_2_4_1  A diverse business scope in different sectors                        QS_2_4_2  A diverse business scope in the same sectors                        QS_2_4_3  One main business scope and other activities                        QS_2_4_4  One main business scope                                                   Source: own processing                    Table 3 Relationship with Suppliers and Customers - List of Non-financial Indicators                         Coding                                Description                        QS_3_1        Customers  -  share  of  individual  customers  on  client                                      revenues                        QS_3_1_1  Very low dependence (below 10% incl.)                        QS_3_1_2  Low dependence (between 10% -20% incl.)                        QS_3_1_3  Strong dependence (between 20% - 50% inc.)                        QS_3_1_4  Very strong dependence (over 50%)                        QS_3_2        Customers – share of overdue receivables                        QS_3_2_1  Excellent (overdue receivables share usually does not exceed                                      5% incl.)                        QS_3_2_2  Good (overdue receivables share is usually 5% - 15% incl.)                        QS_3_2_3  Bad (overdue receivables share is usually 15% - 30% incl.)                        QS_3_2_4  Very  bad  (overdue  receivables  share  usually  exceeds  30%                                      incl.)                        QS_3_3        Demand      stability   (demand      for    products/client                                      services)                        QS_3_3_1  Stable demand                        QS_3_3_2  Long-term        steady   demand     but   short-term    negative                                      fluctuation was identified (i.e. from several weeks to about 3                                      months)                        QS_3_3_3  Unstable demand                        QS_3_4        Suppliers - client dependency on individual suppliers                        QS_3_4_1  Dependence on any of suppliers does not exceed 20%                        QS_3_4_2  The  share  of  any  of  the  suppliers  exceeds  20%  and  its                                      substitution is possible anytime                        QS_3_4_3  The  share  of  any  of  the  suppliers  exceeds  20%  and  its                                      substitution is not possible or it would be very difficult to                                                   Source: own processing                                                                 673","Table 4 Country Risk - list of Non-financial Indicators                        Coding                                Description                        QS_4_1        Export, import – territory risk                        QS_4_1_1  The  client  has  direct  export/import  only  with  industrialised                                      countries  or  has  no  direct  export/import  or  the  risk  of  the                                      country is insured                        QS_4_1_2  The  client  has  direct  export/import  only  with  industrialised                                      countries  and  export/import  with  risk  countries  is  less                                      significant                        QS_4_1_3  Client's export/import is mainly directed to risk countries and                                      the risk associated with it is not always adequately treated                                      (payment terms, insurance, etc.)                        QS_4_2        Currency risk                        QS_4_2_1  Direct export/import in currency of a similar volume (natural                                      hedging) or exchange risk is adequately secured on financial                                      markets                        QS_4_2_2  Direct  export/import  in  individual  currencies  with  max.                                      deviation  up  to  20%  and  currency  risk  is  not  adequately                                      secured on the financial markets                        QS_4_2_3  Direct  export/import  by  individual  currencies  is  realized  in                                      different volumes and currency risk is not adequately secured                                      on the financial markets                        QS_4_2_4  No or insignificant direct export/import                                                   Source: own processing                            Table 5 Management of a Company - List of Non-financial Indicators                         Coding                                Description                        QS_5_1        Credibility and stability of management – knowledge of                                      history and moral management profile                        QS_5_1_1  Majority of management is unchanged for more than 2 years                                      and  there  is  no  information  about  immoral  behaviour  of                                      management members in this or other companies for at least                                      6 years                        QS_5_1_2  Majority of management is unchanged for less than 2 years                                      and  there  is  no  information  about  immoral  behavior  of                                      management members in this or other companies for at least                                      6 years                        QS_5_1_3  Majority of management is unchanged for less than 2 years                                      and there is insufficient information about the behavior of the                                      management members in this company or other companies                        QS_5_1_4  There  is  information  about  the  immoral  behavior  of                                      management members in this or other companies over the                                      last 6 years                        QS_5_2        Management capabilities                        QS_5_2_1  Management ability to control the company is above average                        QS_5_2_2  Management ability to control the company is the average                        QS_5_2_3  Management ability to control the company is under average                                                   Source: own processing                             Table 6 Ownership of a Company - List of Non-financial Indicators                        Coding                                Description                        QS_6_1        Clear and transparent ownership relation                        QS_6_1_1  The owner is known with transparent links                        QS_6_1_2  The owner is known with unclear or non-transparent links                        QS_6_1_3  The owner is not known                                                            674","QS_6_2        Owner´s credibility and stability                        QS_6_2_1  There is a decisive share enabling the strategic objectives to                                      be held without change for at least 6 years and there is no                                      information  about  the  negative impact  of the  owner to  the                                      client                        QS_6_2_2  There is a decisive share enabling the strategic objectives to                                      be held without change for at least 2 years and there is no                                      information  about  the  negative impact  of the  owner to  the                                      client                        QS_6_2_3  There is no decisive share enabling the strategic objectives to                                      be held or the history of the owner is shorter than 2 years and                                      there  is  no  information  about  the  negative  impact  of  the                                      owner to the client                        QS_6_2_4  There is negative information about the activity of the owner                                      to the client                                                   Source: own processing                             Table 7 Relationship with a Bank - List of Non-financial Indicators                        Coding                                Description                        QS_7_1        Liabilities repayment to a bank                        QS_7_1_1  The credit history of the client is longer than 6 years and for                                      this period the delay has not exceeded 5 days                        QS_7_1_2  The credit history of the client is from 2 to 6 years and for this                                      period the delay has not exceeded 5 days                        QS_7_1_3  The credit history of the client is shorter than 2 years and for                                      this period the delay has not exceeded 5 days                        QS_7_1_4  Instalment delays over the last 6 years exceeded 5 days                        QS_7_2        Fulfilment of terms and conditions                        QS_7_2_1  Regular fulfilment of terms and conditions                        QS_7_2_2  In the past, the client was in a situation where he did not fulfil                                      the contractual conditions (financial or non-financial), but it                                      was a unique situation and/or acceptable level of default.                        QS_7_2_3  The  client  has  repeatedly  failed  to  fulfil  the  terms  and                                      conditions                        QS_7_2_4  Not to be evaluated (new client or client without credit history                                      in a bank)                        QS_7_3        Turnover development on bank accounts                        QS_7_3_1  Client keeps most (all) payments via accounts in a bank                        QS_7_3_2  The  share  of  clients  payments  corresponds  to  the  bank's                                      share of financing                        QS_7_3_3  The bank is not the main bank of a client with a very small                                      share of the client's payment or the share of clients payments                                      does not correspond to the bank's share of financing                        QS_7_3_4  Not to be evaluated (new client or client without relationship                                      history in a bank)                        QS_7_4        Information quality from a client                        QS_7_4_1  High quality                        QS_7_4_2  Good/standard quality                        QS_7_4_3  Insufficient quality                        QS_7_4_4  Client without credit history in a bank                                                   Source: own processing                                                                 675","Table 8 Relationship with Other Creditors - List of Non-financial Indicators                        Coding                                Description                        QS_8_1        Obligations repayment towards the state                        QS_8_1_1  There  are  no  overdue  payables  to  the  tax  office,  social                                      security administration, health insurance company during the                                      last 4 years                        QS_8_1_2  There  are  or  have  been  overdue  payables  to  the  above                                      mentioned institutions during the last 4 years                        QS_8_1_3  As a result of the overdue payables to the state, health and                                      social  insurance,  the  execution  was  set,  with  a  significant                                      negative impact on the continuity of the client's cash flow and                                      its financial situation over the last 4 years                        QS_8_2        Liabilities repayment to suppliers                        QS_8_2_1  Excellent - the share of overdue payables generally does not                                      exceed 5% incl.                        QS_8_2_2  Good – the share of overdue payables is usually 5% - 20%                                      inc.                        QS_8_2_3  Bad - the share of overdue payables is usually 20% - 30%                                      inc.                        QS_8_2_3  Very bad - the share of overdue payables is usually more than                                      30% inc.                                                   Source: own processing                 To find which of non-financial variable in each group has the most predictive power logistic                regression through IBM SPSS software was used. We have tested each group separately                to find the most significant variables in each group. The most significant variables were                used to develop a bankruptcy prediction model. The reliability of the model was tested by                ROC curve.                 ROC (Receiver Operating Characteristic) curve is a graphical method, which is based on a                square showing the relationship between true positive rate (TPR – also called sensitivity)                and false positive rate (FPR – also called fall-out). TPR measures the proportion of positives                that are correctly identified as such. FPR is also known as probability of false alarm, and it                is calculated as the ratio between the numbers of negatives that are wrongly identified as                positives. TPR is applied to the y-axis and FPR on the x-axis. ROC curve combines the                values of TPR and FPR.                There are two possible extreme cases. The first case occurs when the predicted values are                absolutely similar as real values. In this case the curve copies the border of the graph                beginning in down left corner through upper left corner and ends in upper right corner. The                second case is the exact opposite and describes the model with no predictive power. The                curve in this case is a diagonal from down left corner to upper right corner of the graph.                Thus, the closer the curve is to the upper left corner, the better predictive power of the                model.                ROC  curve  is  closely  related  to  AUC  (Area  Under  Curve)  indicator  that  numerically                represents the graph and helps with comparison of two or more models. This indicator                quantifies  the  area  under  the  curve  and  is  useful  for  comparing  two  or  more  curves,                because they are transformed into one measure and easily comparable. AUC ranges from                0.5 to 1, where a higher value indicates a better prediction model.                 Model Specification                Logistic regression is the appropriate regression analysis to conduct when the dependent                variable is dichotomous (binary).  Like all regression analyses, the logistic regression is a                predictive  analysis.   Logistic  regression  is  used  to  describe  data  and  to  explain  the                relationship between one dependent binary variable and one or more nominal, ordinal,                interval  or  ratio-level  independent  variables.  The  dependent  variable  should  be                dichotomous  in  nature  (e.g.  in  our  case  bankrupt  or  non-bankrupt  companies).  There                                                             676","should  be  no  outliers  in  the  data,  no  high  correlations  (multicollinearity)  among  the                predictors.  Tabachnick  and  Fidell  (2013)  suggest  that  as  long  correlation  coefficients                among independent variables are less than 0.90 the assumption is met. Mathematically,                logistic regression estimates a multiple linear regression function defined as:                                          2 2                                     1 1                                               3 3                                                                                             (  +       +       +       +⋯          )                         (  ) =                                                                                                      (1)                                                3 3                                      1 1                                                                                                   2 2                              1+       (  +       +       +       +⋯          )                4 Results and Discussion                We have tested each group of non-financial variables to find which variable has the most                predictive power and we found the most predictive variables based on logistic regression                analysis  of  each  group.  The  list  of  selected  variables  is  mentioned  below.  We  do  not                mention all results and coding of respective variables because there is not enough space.                                            Table 9 List of Selected Variables                        Coding            Coeficient B                    p-value                        QS_2_1        Year-on-year client growth in comparison to industry                                      growth                        QS_2_1_1              -0.285                        0.044                        QS_2_1_2             -0.676                         0.000                        QS_2_1_3                                            0.000                        QS_2_2        Position  on  the  main  market  -  according  to  the                                      proportion of client revenues                        QS_2_2_1             -0.895                         0.035                        QS_2_2_2             -1.622                         0.000                        QS_2_2_3             -0.503                         0.002                        QS_2_2_4                                            0.000                        QS_3_3        Demand      stability   (demand      for    products/client                                      services)                        QS_3_3_1             -0.812                         0.000                        QS_3_3_2             -0.527                         0.000                        QS_3_3_3                                            0.000                        QS_4_1        Export, import – territory risk                        QS_4_1_1              0.050                         0.908                        QS_4_1_2              0.640                         0.156                        QS_4_1_3                                            0.001                        QS_5_2        Management capabilities                        QS_5_2_1             -2.265                         0.000                        QS_5_2_2             -1.995                         0.000                        QS_5_2_3                                            0.000                        QS_6_1        Clear and transparent ownership relation                        QS_6_1_1              0.027                         0.968                        QS_6_1_2              0.559                         0.417                        QS_6_1_3                                            0.004                        QS_6_2        Owner´s credibility and stability                        QS_6_2_1             -0.543                         0.211                        QS_6_2_2             -0.060                         0.892                        QS_6_2_3             -0.691                         0.119                        QS_6_2_4                                            0.000                        QS_7_1        Liabilities repayment to a bank                        QS_7_1_1             -1.746                         0.000                        QS_7_1_2             -0.819                         0.014                        QS_7_1_3             -0.628                         0.075                        QS_7_1_4                                            0.000                        QS_7_2        Fulfilment of terms and conditions                                                              677","QS_7_2_1              0.502                         0.075                        QS_7_2_2              0.950                         0.001                        QS_7_2_3              1.211                         0.000                        QS_7_2_4                                            0.000                        QS_7_3        Turnover development on bank accounts                        QS_7_3_1             -1.079                         0.000                        QS_7_3_2             -0.427                         0.172                        QS_7_3_3             -0.714                         0.028                        QS_7_3_4                                            0.000                        QS_8_1        Obligations repayment towards the state                        QS_8_1_1             -2.401                         0.000                        QS_8_1_2             -1.117                         0.065                        QS_8_1_3                                            0.000                        QS_8_2        Liabilities repayment to suppliers                        QS_8_2_1             -1.298                         0.000                        QS_8_2_2             -0.527                         0.000                        QS_8_2_3             -0.216                         0.164                        QS_8_2_4                                            0.000                                                   Source: own processing                Above mentioned variables entered into logistic regression again with below mentioned                result.                                              Table 10 Final List of Variables                        Coding            Coeficient B                    p-value                        QS_2_1_3                                            0.000                        QS_2_2_2             -1.188                         0.000                        QS_4_1_3                                            0.006                        QS_7_1_4                                            0.000                        QS_7_3_4                                            0.000                        QS_8_1_1             -1.786                         0.004                        QS_8_1_3                                            0.000                        QS_8_2_1             -1.103                         0.000                        QS_8_2_2             -0.416                         0.002                        QS_8_2_4                                            0.000                                                   Source: own processing                 The model was tested by ROC curve. The result is quite good when we know that there are                only non-financial indicators in the model. AUC reaches 75.8% as you can see in Figure 1.                                                                            678","Figure 1 Results                                                                     Source: Output from IBM SPSS Statistics                The best predictive indicators are indicators year-on-year client growth in comparison to                industry  growth,  position  on  the  main  market  -  according  to  the  proportion  of  client                revenues,  demand  stability  (demand  for  products/client  services),  export,  import  –                territory  risk, liabilities  repayment  to  a  bank,  turnover  development  on  bank accounts,                obligations repayment towards the state, liabilities repayment to suppliers. The creditors                should especially pay attention if:                    •   the client´s year-on-year growth is more than 2% below industry performance,                    •   the client's export/import is mainly directed to risk countries and the risk associated                       with it is not always adequately treated (payment terms, insurance, etc.),                    •   client instalments delays over the last 6 years exceeded 5 days,                    •   the client is new,                    •   the execution was set as a result of the overdue payables to the state, health and                       social insurance with a significant negative impact on the continuity of the client´s                       cash flow and its financial situation over the last 4 years,                    •   the share of overdue payables to suppliers is more than 20%.                 5 Conclusions                Empirical  literature  emphasizes  the  significance  to  add  qualitative  information  to                bankruptcy prediction models. Researches applying to qualitative information importance                as failure predictors, recommends to add qualitative information such as business type,                industrial  sector,  location,  age,  etc.  This  study  analyzed  the  exclusive  dataset  of  non-                financial  indicators  used  in  a  bank  as  supplementary  indicators  when  assessing                creditworthiness of a client.                Our  findings  confirm  that  non-financial indicators  could  be  very  useful when predicting                failure. We found that the most important groups of non-financial indicators are year-on-                year  client  growth  in  comparison  to  industry  growth,  position  on  the  main  market  -                according  to  the  proportion  of  client  revenues,  demand  stability  (demand  for                products/client services), export, import – territory risk, liabilities repayment to a bank,                turnover  development  on  bank  accounts,  obligations  repayment  towards  the  state  and                liabilities repayment to suppliers.                We developed a bankruptcy prediction model based only on non-financial indicators with                75,8% predictive power. These findings confirm that non-financial indicators could be very                helpful to predict bankruptcy together with traditional financial indicators.                                                               679","Acknowledgments                The  support  of  the  Masaryk  University  internal  grant  MUNI/A/1092/2017  is  gratefully                acknowledged.                 References                Altman, E. I., Sabato, G. (2007). Modelling Credit Risk for SMEs: Evidence from the U.S.                Market. ABACUS, vol. 43(3), pp. 332–357.                Altman, E. I., Sabato, G., Wilson, N. (2010). The value of non-financial information in small                and  medium-sized  enterprise  risk  management.  The  Journal  of  Credit  Risk,  Vol.  6(2),                pp. 1-33.                Angelini,  E.,  Tollo  DI.,  G.,  Roli,  A.  (2007).  A  neutral  network  approach  for  credit  risk                evaluation. 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ISSN 0021-8456.                                                                    680","The Recent hHstory of E-commerce in Poland and the Perspectives                                       for Online Sale Growth in Future.                                                 Anna Sroczyńska-Baron                            1  University of Economics in Katowice, Department of Applied Mathematics                                            ul. 1 Maja 50, 40-287 Katowice, Poland                                               E-mail: kozak@caprisoft.com.pl                 Abstract: We have just entered an epochal period of change in the word of traditional                markets  -  the  period  that  is  characterized  by  the  great  development  of  e-commerce.                Thanks to the great development of IT technologies, the concept of e-commerce got a new                importance. Online shops and online auctions are the significant part of e-commerce. They                are more and more popular all over the world. There are a lot of advantages of this form                of shopping for both clients - for example time saving or just cheaper price, and sellers -                for example cost reductions or bigger number of potential clients.  Of course there are also                some important disadvantages but it is visible that the problems are being solved slowly.                The main aim of this work is to examine the position of e-commerce  in Poland, to observe                the changes in its structure in a few last years and to understand the influence of some                factors  on  the  dynamics  of  online  sale  growth  with  the  use  of  statistical  tools.  The                examination shows that Poland is not even in respect of opportunities for e-commerce but                the differences between leaders and the rest of the country are decreasing. Money, access                to Internet and computer are not decisive for the development of online shopping.                Keywords: e-commerce, online shopping, TOPSIS                JEL codes: L81, C38, D19                 1 Introduction                 Nowadays, e-commerce becomes one of the most important part of people’ life all over the                world. It is more and more popular way of shopping because of great development of IT                technology and wider and wider range of Internet. Not only business sector but household                also make use of the online shopping. Clothes and sport foods are the most popular objects                people buy online. One should also take into consideration that the number of Internet                users and potential buyers is still increasing. This situation is an effect of great advantages                of this form of shopping (Franco and Regi, 2016). Time and price are the most important                factors which influence the increase of online sale. The potential client can save much time                when he uses an Internet search engine to analyze the great amount of different goods in a                short time and to compare the price. He does not also waste his time by visiting a shop                physically and can buy things he needs at any time, even at night when he has more free                time. It is also obvious that prices in Internet are much lower than in a traditional shop.                There are not typical shopping area and salesmen so the cost of running this kind of shop                is less  expensive.  Of  course  one  cannot  forget  about  some  obvious  disadvantages,  for                example the client is not able to touch the product and examine it thoroughly and he must                take  into  the  consideration  the  potential  risk  that  the  product  would  not  fulfil  the                requirements or, even worse, he will pay for non-existent product. However, it is generally                observed that the online sale is still increasing thanks to updating new technologies and                coming  up  with  new  solutions,  for  example  the  possibility  of  free  return  or  system  of                feedbacks, which help us conquer some problems connecting with online-sale. Nowadays,                it is estimated that nearly 85% households have got access to the Internet at home, nearly                80% regularly use it and 55% order or purchase goods or services over the Internet for                private use in the EU countries (Digital Society in Poland, 2016). What is more, statistical                European is used to spend 970 Euro per year on online shopping. The upward trend is                generally visible in Europe but one should notice that not every country follows it. Most of                the  countries  from  the  north  of  Europe  does  not  agree  with  this  feature,  for  example                Sweden, Finland or Norwegian. On the other hand, online shopping is the most popular                way of purchasing goods in England, where 83% people use Internet to do it, what is a                very  high  result in  comparison  with  Romanian  where  only  12%.  So  the form of  online                                                             681","shopping  is  more  and  more  popular  in  EU  but  one  must  consider  that  there  are  still                enormous differences among countries. The level of popularity of online shopping in EU is                diverse  and  still  depends  on  particular  countries  in  spite  of  political  and  economical                changes. Poland belongs to the countries where the upward trend is observed but online                sale is not still heady. The result of 42% of people who use Internet to purchase goods is                much lower than the mean for UE (13 percentage points) and two times lower than in                England.                There  are  a  lot  of  works  considering  special  part  of  e-commerce  like  online  auctions                (Sroczynska-Baron, 2017) but in this work e-commerce will be analyzed wider  – as all                transactions conducted thanks to Internet. The only limit is the part of online sale where                clients are just private people. The companies purchasing products by Internet are not the                object of the examination.                 The  analysis  of  digital  situation  used  to  show  huge  differences  between  particular                voivodeships in Poland, but nowadays it seems to be more equalized. It is estimated that                80,1% of households have got the access to computers. The worst situation is still in the                eastern Poland but there was observed great change at the level of 13% in the last 5 years.                When the access to the Internet at home is examined, even bigger changes are observed.                It is estimated that 80,4% of households have got the access to Internet and the growth                of it in the eastern Poland is more than 23% (Digital society in Poland, 2016). Of course,                these are only two factors and the question is if they are enough to make online shopping                more popular? In this work the results for two years will be compared. There will be done                the analysis of main factors which influence the level of online shopping in both periods.                Next the ranking of voivodeships will be presented and compare with real data. The main                aim of this work is to examine if commonly known factors, for example technical factors,                as access to computers and Internet, are crucial for online shopping and influence directly                the level of online shopping by private people? There is also presented a short answer for                the question how e-commerce in Poland is being changed?                 2 Methodology and Data                The factors influencing online shopping                  The most important factor that influences the level of online sale is price without any doubt.                Freebaim said that clients would be main beneficiaries of the development of e-commerce                thanks to lower prices (Freebairn, 2001). Therefore, one can consider that the lower income,                the bigger interest in lower prices and economical necessity to find occasional prices offered                by online shopping system. On the other hand, the total lack of the source of income make                households impossible to use computers and Internet, so the factors as unemployment rate                or  poverty  rate  should  influence  negatively  the  level  of  online  shopping.  Next,  the                environment of life of a potential user of e-commerce should be analyzed. It is said, that the                presence of big shopping centers do not foster online sale (Weltevreden and Rietbergen, 2007).                Clients are able to spend only little time to go and see the product what is invaluable and                usually have got great choice of different kinds of them. So the closeness of big cities also                influences negatively the level of online shopping. There are also some social factors worth                the examination. It is commonly known that people between 25 and 34 years old are the                group, which do online shopping most often. What is more, families with children more often                buy online (Newsweek, 2015), maybe because of bigger necessity of saving or less free time                or just bigger need (for example constantly new clothes or toys). On the other hand older                people prefer traditional form of shopping. Sometimes the barrier is lack of skill of using                computer or Internet. So not only the age but the level of education may influence the desire                of online shopping (Šimičevic, Jaković and Ježovita, 2013).                In this work a few main factors, both social and economical will be considered, to classify                the voivodeships in Poland into similar groups where  the conditions for the increase of online                sale are the most favorable and the least favorable.                                                                682","Methodology                One of the methods, which allows to construct the ranking is multi-criteria decision analysis                method TOPSIS. It is a  technique for order of preference by similarity to ideal solution                (Hwang and Yoon, 1981). It compares a set of alternatives by identifying weights for each                criterion,  normalizing  scores  for  each  criterion  and  calculating  the  geometric  distance                between each alternative and the ideal alternative. In this method a poor result in one                criterion can be negated by a good result in another criterion, what allows to obtain a                realistic ranking.                The first step is the normalization process because the parameters or criteria are often of                incongruous dimensions in multi-criteria problems. It could be conducted according to the                formula:                                                               =                                                                             (1)                                                        √∑      =1         2                where xij - the observation of criterion j for alternative i. The best alternative is described                as:                                      }           ℎ                  ℎ                                                                                  {                               +                          0    = {                                                                      (2)                                     {   }           ℎ                    ℎ                                                                                                                                     The worst alternative is described as:                                     {   }           ℎ                    ℎ                                                                                                                                             −                          0    = {                                                                      (3)                                       {        }          ℎ                    ℎ                                                           The distance between the best alternative and the rest of them is the following:                                           2                        +                                        +                          = √∑     (   −    )                                                           (4)                                                                         =1                                        0                            0                The distance between the worst alternative and the rest of them is the following:                                           2                        −                                        −                          = √∑     (   −    )                                                           (5)                          0                                                                                0                                   =1                The values of aggregate variable are calculated with the use of the following formula:                                −                          =     0                                                                       (6)                                                      +                               +   −                              0    0                where    ∈ [0,1]. The highest value of aggregate variable means the best alternative and                                          the lowest value shows the worst one.                    After the linear arrangement, the alternatives are divided into groups on the grounds                of the values of aggregate variable. In this work four groups will be arbitrarily considered:                I group characterized by high level of aggregate variable:                    ∈ [          −   ,            ]                                                                                                           II group characterized by average level of aggregate variable:                    ∈ [          − 2  ,           −    ]                                                                                                            III group characterized by low level of aggregate variable:                    ∈ [          − 3  ,           − 2   ]                                                                                                            IV group characterized by minimum level of aggregate variable:                   ∈ [          ,           − 3   ]                                                                                                    where    =                  −                   i=1,2,…,n .                                4                In this work  TOPSIS will be used to classify voivodeships in Poland and divide into the                groups characterized by the similar environment for online shopping.                                                            683","Data                In  this  work  the  following  factors  were  chosen  to  the  examination  of  classification  of  the                voivodeships in Poland for online shopping environment with the use of TOPSIS method:                    a)  social factors                       x1 – number of graduated per 10000 residents                       x2 – number of children                       x3 – percentage of residents between 25 – 34 years old                       x4 – percentage of retired residents                    b)  economical factors                       x5 – the average income per resident per month                       x6 – percentage of household with the computer                       x7 – percentage of household with the access to Internet                       x8 – poverty rate                    c)  geographical factor                       x9 – the number of big cities – more than 40000 residents                In this work the data for 16 voivodeships in Poland in 2015 and 2016 (data for 2017 are still                not present at GUS) were gathered and analyzed with the use of TOPSIS method. Variables                x1, x2, x3, x6, x7 are the criteria having a positive impact and  x4, x5, x8, x9 are the criteria                having a negative impact.                 3 Results and Discussion                Data for 16 voivodeships in 2015 are presented in Table 1. The order of the voivodeships                is the following: Dolnośląskie (1), Kujawsko-Pomorskie (2), Lubelskie (3), Lubuskie (4),                Łódzkie (5), Małopolskie (6), Mazowieckie (7), Opolskie (8), Podkarpackie (9), Podlaskie                (10),  Pomorskie  (11),  Śląskie  (12),  Świętokrzyskie  (13),  Warminsko-Mazurskie  (14),                Wielkopolskie (15), Zachodniopomorskie (16).                  Table 1 The Factors Influencing Online Shopping for 16 Voivodeships in Poland in 2015                  No     x1         x2          x3          x4          x5       x6     x7     x8     x9                   1      120     486 540  0,161124  0,204587        1 471,8  77,3  73,8        9,5    7                   2       84     378 825  0,156845  0,190918        1 245,7  66,2      66     12,5    5                   3      106     382 720  0,156577  0,200489        1 226,7  79,9  78,6       14,9    5                   4       47     184 524  0,159483  0,187524        1 419,6  77,8  74,7       11,4    2                   5       88     418 642  0,148345  0,218355        1 362,0  68,9  65,8       10,3  11                   6      149     638 551  0,163008  0,186202        1 305,5  78,5      76     14,3    3                   7      131     994 402  0,157277       0,20041    1 756,2  82,9  80,6        8,3  12                   8       85     159 393     0,15745  0,201872      1 288,7  82,3  78,7       10,6    3                   9       89     394 644  0,162274  0,182177        1 081,6  79,1  76,1       17,1    7                  10       91     207 394  0,159335  0,194352        1 257,9  77,2  76,5       13,4    3                  11      106     447 792  0,161797  0,183604        1 380,9  80,5  78,1       13,6    8                  12       85     767 533  0,154713  0,206672        1 420,5  75,8  73,3        7,8  25                  13       78     211 695  0,151911  0,210475        1 202,8  78,7  77,3       18,6    4                  14       71     266 937  0,160268  0,177356        1 280,5  78,7  76,9       21,1    3                  15      110     665 584  0,162569  0,182439        1 287,8  80,6  80,1       16,7    7                  16       77     296 619  0,155322  0,195472        1 426,9  78,6  77,6        8,1    6                                  Source: own computation based on Central Statistical Office                 Data for voivodeships in the same order for 2016 are presented in Table 2.                 Table 2 The Factors Influencing Online Shopping for 16 Voivodeships in Poland in 2016                   No     x1        x2          x3          x4         x5       x6     x7      x8    x9                    1    115     487 606    0,157184  0,212069  1 586,0  78,8  80,7           9,2     7                    2      78    376 479    0,154602     0,19754    1 308,6  78,2  77,2      13,8     5                    3      97    378 515    0,154851     0,20629    1 299,1     75    75,7   17,2     5                                                              684","4      42    183 640    0,155957  0,195203  1 498,5  81,3  79,5           8,8     2                    5      77    417 570    0,146016  0,224844  1 458,2  78,1  76,9           9,1    11                    6    144     639 225    0,161352  0,190956  1 423,5  82,5  82,1          16,1     3                    7    124  1 004 436  0,153801  0,205529  1 780,9  84,1  84,9              8,5    12                    8      74    158 146    0,155318  0,208516  1 389,9         79    82,1   10,3     3                    9      85    390 816    0,161278  0,187583  1 134,1         82    83,2   21,3     7                   10      79    206 087     0,15834    0,199124  1 419,8  74,9        74    14,7     3                   11    101     450 189    0,159516     0,18963    1 560,4  85,7  83,9      11,5     8                   12      74    767 290    0,151347  0,213515  1 513,2  78,8  78,8           9,0    25                   13      64    209 086    0,150454  0,216974  1 305,9  76,8  77,3          18,7     4                   14      64    264 364    0,158042     0,18451    1 371,8  74,6  78,3      18,6     3                   15      98    667 280    0,159907  0,188339  1 400,0         82    81,9   16,6     7                   16      70    295 149     0,15178    0,203862  1 482,9  78,5        80     9,7     6                                  Source: own computation based on Central Statistical Office                The values of aggregate variables were calculated and presented in Table 3.                             Table 3 The Aggregate Variables qi for 16 Voivodeships in Poland                              1         2         3         4         5         6         7         8                   2015    0,6573  0,6148       0,6264    0,5771    0,5250  0,7661  0,6748  0,5901                   2016  0,6554  0,6066         0,6073    0,5753    0,5220  0,7556  0,6783  0,5803                              9        10        11         12        13       14        15        16                   2015    0,5771  0,5967       0,6018    0,3768    0,5563  0,5656  0,6750  0,5878                   2016    0,5538  0,5810       0,6081    0,3625    0,5453  0,5707  0,6695  0,5734                                                  Source: own computation                 The ranking of the voivodeships is the following:                    Table 4 The Ranking of the Voivodeships in Poland in Respect of Examined Factors                                                 2015                       2016                                  1    MAŁOPOLSKIE                MAŁOPOLSKIE                                  2    WIELKOPOLSKIE              MAZOWIECKIE                                  3    MAZOWIECKIE                WIELKOPOLSKIE                                  4    DOLNOŚLĄSKIE               DOLNOŚLĄSKIE                                  5    LUBELSKIE                  POMORSKIE                                  6    KUJAWSKO-POMORSKIE  LUBELSKIE                                  7    POMORSKIE                  KUJAWSKO-POMORSKIE                                  8    PODLASKIE                  PODLASKIE                                  9    OPOLSKIE                   OPOLSKIE                                 10    ZACHODNIOPOMORSKIE  LUBUSKIE                                 11    PODKARPACKIE               ZACHODNIOPOMORSKIE                                                                  WARMIŃSKO-                                 12                                       LUBUSKIE                   MAZURSKIE                                       WARMIŃSKO-                                 13                                       MAZURSKIE                  PODKARPACKIE                                 14    ŚWIĘTOKRZYSKIE             ŚWIĘTOKRZYSKIE                                 15    ŁÓDZKIE                    ŁÓDZKIE                                 16    ŚLĄSKIE                    ŚLĄSKIE                                                  Source: own computation                One can observe 4 groups of voivodeships that are different in respect of factors influencing                the level of online shopping in 2015:                    a)  first group:  małopolskie, wielkopolskie and mazowieckie                    b)  second group: dolnośląskie, lubelskie, kujawsko – pomorskie, pomorskie, podlaskie,                       opolskie, zachodnipomorskie, podkaprackie and lubuskie                    c)  third group: warmińsko – mazurskie, świętokrzyskie and łódzkie                                                            685","d)  forth group: śląskie                Similar division can be conducted for data for 2016:                    a)  first group:  małopolskie, mazowieckie and wielkopolskie                    b)  second group: dolnośląskie, pomorskie, lubelskie, kujawsko – pomorskie, podlaskie,                       opolskie, lubuskie, zachodnipomorskie and warmińsko - mazurskie                    c)  third group: podkarpackie, świętokrzyskie and łódzkie                    d)  forth group: śląskie                Three voivodeships, which favour the most the increase of online sale in respect of examined                factors are the same in both years (the group “a”). There is a better environment for e-                commerce in pomorskie, lubuskie and warmińsko – mazurskie in 2016 in comparison with                2015. A little bit worse situation can be observed in podkarpackie. Theoretically, the worst                situation is in śląskie in both 2015 and 2016. The changes of the values of the factors in two                years in percentages for these four voivodeships, where the situation is observed to be a                little bit different, are presented in Table 5.                       Table 5 The Changes in [%] the Values of the Examined Factors in Pomorskie,                                   Lubuskie, Warmińsko – Mazurskie and Podkapackie.                   No     Δx1       Δx2       Δx3      Δx4      Δx5      Δx6      Δx7     Δx8      Δx9                    4    -0,106    -0,005    -0,022    0,041   0,056     0,045  0,064  -0,228  0,000                    9    -0,045    -0,010    -0,006    0,030   0,049     0,037  0,093     0,246  0,000                   11    -0,047     0,005    -0,014    0,033   0,130     0,065  0,074  -0,154  0,000                   14    -0,099    -0,010    -0,014    0,040   0,071    -0,052  0,018  -0,118  0,000                                                  Source: own computation                 It is visible, that the factor poverty rate has got determinative participation in increase or                decrease of these four voivodeships. For example in spite of nearly 10% increase of the                access  to  Internet,  much  bigger  poverty  rate  has  caused  the  decline  of  podkarpackie.                Generally, none of radical changes are observed and the groups are similar in respect of the                chance for online shopping increase in 2015 and 2016.                 There is one more problem – how close or far from real situation is that one described by                examined factors? There are presented real data from 2015 and 2016 as the involvement of                individuals purchasing goods or services in the last 12 months for private use in Table 6.                   Table 6 The Involvement of Individuals Purchasing Goods or Services in the Last 12                                Months for Private Use in Particular Voivodeships in Poland                               1         2         3         4         5         6         7         8                   2015       40,9      30,1      34,2       40,4     31,2      35,3      44,5      36,4                   2016         41      37,4      38,4       41,9     36,7      42,7      47,1      39,9                              9        10        11         12        13       14        15        16                   2015       28,3      44,7      36,3       39,4     31,2      33,1      34,3        36                   2016       36,5      41,4      44,5       47,1     35,5      40,6      34,3      43,6                                  Source: own computation based on Central Statistical Office                         Table 7 The Ranking of the Voivodeships in Poland in Respect of Real Data                                                  2015                       2016                                  1    PODLASKIE                  MAZOWIECKIE                                  2    MAZOWIECKIE                ŚLĄSKIE                                  3    DOLNOŚLĄSKIE               POMORSKIE                                  4    LUBUSKIE                   ZACHODNIOPOMORSKIE                                  5    ŚLĄSKIE                    MAŁOPOLSKIE                                  6    OPOLSKIE                   LUBUSKIE                                  7    POMORSKIE                  PODLASKIE                                  8    ZACHODNIOPOMORSKIE  DOLNOŚLĄSKIE                                                               686","WARMIŃSKO-                                  9                                       MAŁOPOLSKIE                MAZURSKIE                                 10    WIELKOPOLSKIE              OPOLSKIE                                 11    LUBELSKIE                  LUBELSKIE                                       WARMIŃSKO-                                 12                                       MAZURSKIE                  KUJAWSKO-POMORSKIE                                 13    ŁÓDZKIE                    ŁÓDZKIE                                 14    ŚWIĘTOKRZYSKIE             PODKARPACKIE                                 15    KUJAWSKO-POMORSKIE  ŚWIĘTOKRZYSKIE                                 16    PODKARPACKIE               WIELKOPOLSKIE                                                  Source: own computation                 One can observe 4 groups of voivodeships that are different in respect of the involvement of                individuals purchasing goods or services in the last 12 months for private use in 2015:                     a)  first group:  podlaskie, mazowieckie, dolnośląskie, lubuskie  and śląskie                    b)  second group: opolskie, pomorskie                    c)  third group: zachodnipomorskie, małopolskie, wielkopolskie, lubelskie and warmińsko                       – mazurkie                    d)  forth group: łódzkie, świętokrzyskie, kujawsko – pomorskie and podkarpackie                Similar division can be conducted for data for 2016:                     a)  first group:  mazowieckie, śląskie, pomorskie                    b)  second  group:  zachodniopomorskie,  małopolskie,  lubuskie,  podlaskie  and                       dolnośląskie                    c)  third group: warmińsko – mazurkie, opolskie and lubelskie                    d)  forth  group:  kujawsko  –  pomorskie,  łódzkie,  podkarpackie,  świętokrzyskie  and                       wielkopolskie                The biggest increase (more than 23%) is observed for kujawsko – pomorskie, podkarpackie                and warmińsko – mazurskie voivodeships in the involvement of individuals purchasing goods                or services in the last 12 months but this did not improve the situation of these districts of                Poland in comparison with the rest of the land. They are still one of the worst, but – what is                important for future -  the difference is a little bit less between them in 2016 than in 2015.                The interesting thing is a huge decrease of podlaskie – it was the leader in 2015 and one                year later it is located in the middle of the second group. The second voivodeship with the                similar history is dolnośląskie and wielkopolskie, which is located six positions lower than                one year earlier. But the uniqueness of podlaskie is caused by negative increase of online                sale in this region.                When one want to compare the results obtained by the analysis of the potential factors with                real history some interesting facts are visible. Model TOPSIS indicated the worst voivodeships                in respect of the environment for online sale in a proper way omitting śląskie, which is the                biggest surprise. All factors show that online sale should not develop in this region but it                belongs to the first group of voivodeships for real data. And the second surprise comes from                wielkopolskie. There is an opposite situation – all factors favor online sale but in 2016 it has                got the worst involvement of individuals purchasing goods or services in the last 12 months                for private use in Poland. Mazowieckie belongs to the leader group for TOPSIS model and                real data.                 4 Conclusions                 In this work the environment for online shopping in different voivodeships in Poland where                examined and compared in 2015 and 2016. There was a huge change in technical factors                such access to computer and Internet at home at this time, that is why these two years                were compared. The examinations show that Poland is not even in respect of opportunities                for e-commerce. There are some parts of Poland that favour this kind of shopping the least.                                                            687","What is more important, they are not identified with the poorest region of Poland – the                voivodeships belong rather to diameter of Poland from south – east to north – west, not                to  the  east  part  of  Poland.  Therefore,  money  seems  not  to  be  the  main  factor.  The                distribution of the voivodeships in Poland in both years are similar in spite of changes in                access to Internet and computer. These factors are not crucial as well. One should consider                all factors together to examine the possibilities of the development of online shopping in a                particular district. What is more, the factors mentioned in the literature and reports most                often are not enough. They allowed to find the worst regions and one of the best (the                capital city) in a proper way but the rest of them were more or less different in comparison                to real data. The most interesting situation is in śląskie, where online shopping is very                popular and wielkopolskie, where online shopping is very poor contrarily all factors. Maybe                it is  not  possible  to  separate  e-shopping  conducted  by  private  people  from e-shopping                conducted by companies, which was ignored in this work. It seems to be natural that if a                potential customer works with computer at his work and uses Internet to purchase products                or services by Internet for companies, it is more natural for him to do it at home. So maybe                including this kind of factor would improve the results. On the other hand, one cannot                forget about mentality, which is hard to measure but influences the level of online shopping                of particular countries for sure. Maybe it also influences inner division of one country.                When  the  real  data  for  2015  and  2016  are  considered  one  can  also  observe  that  the                diversification  of  particular  voivodeships  are  still  present  in  spite  of  better  access  to                computer and Internet for households in 2016. The leaders are śląskie and mazowieckie,                but  the  differences  between  them  and  the  rest  of  the  country  are  decreasing.  The                involvement of individuals purchasing goods or services in the last 12 months for private                use  in  Poland  has  increased  by  4%  in  2016  and  concurrently  the  coefficient  of                diversification  has  decreased  by  4%.  Maybe  in  a  few  next  years  the  differences  will                completely disappear as Internet is considered as the future of shopping.                 References                Franco,  C.,  Regi,  B.  (2016).  Advantages  and  challenges  of  e-commerce  customers  and                businesses:  in  Indian  perspective.  International  Journal  of  Research  Grantaalayah,                vol. 4(3).                Freebairn, J. (2001). Some Market Effects of E-Commerce. Singapore Economic Review,                vol. 46(1).                GUS (2016). Digital Society in Poland 2012-2016. Retrieved from: https://stat.gov.pl/.                Hwang, C., Yoon, K. (1981). Multiple Attribute Decision Making – Methods and Applications.                A State-of-the-Art Survey. Berlin, Heidelberg, New York.                Newsweek (2015). Polak na zakupach. Jak wygląda e – handel nad Wisłą. Retrieved from:                http://www.newsweek.pl.                Šimičevic, V., Jaković, B., Ježovita, J. (2013). Perceived Barriers to e-commerce: Empirical                Evidence from EU Countries. Interdisciplinary Description of Complex Systems, vol. 11(1).                Sroczyńska-Baron, A. (2017). The analysis of the efficiency of on-line auctions in Poland                based on data coming from service Allegro.pl. In: Proceedings of the 14th International                Scientific Conference European Financial Systems 2017. Brno: Masaryk University, p. 320-                328.                 Weltevreden, J., Rietbergen, T. (2007). E-Shopping Versus City Centre Shopping: The Role                of Perceived City Centre Attractiveness. Tijdschrift voor economische en sociale geografie,                vol. 98(1).                                                                    688","The Financial and Institutional Environment for Sustainable                   Economic Growth of Developing Countries: the Case of Western                                                         Balkan                                                                    2                                                1                           Jelena Z. Stanković , Evica Petrović , Ksenija Denčić-Mihajlov                                                                                                3                                                      1  University of Niš                                                    Faculty of Economics                                    Trg kralja Aleksandra Ujedinitelja 11, 18000 Niš, Serbia                                               E-mail: jelenas@eknfak.ni.ac.rs                                                       2  University of Niš                                                    Faculty of Economics                                    Trg kralja Aleksandra Ujedinitelja 11, 18000 Niš, Serbia                                            E-mail: evica.petrovic@eknfak.ni.ac.rs                                                       3  University of Niš                                                    Faculty of Economics                                    Trg kralja Aleksandra Ujedinitelja 11, 18000 Niš, Serbia                                        E-mail: ksenija.dencic-mihajlov@eknfak.ni.ac.rs                 Abstract: The economic growth and its sustainability have attracted significant interest of                academics  and  policy  makers  in  recent  decades.  Faster  economic  growth  observed  in                developing countries has been associated with capital mobility. Therefore, the first aim of                this study is to examine the finance-growth link and effectiveness of financial systems in                promoting  sustainable  economic  growth  in  developing  countries.  On  the  other  hand,                developing countries have experienced vast changes in their economic and institutional                environment in order to integrate with the world economy. Thus, the second aim of this                study is to examine the quality of the institutional environment and its role in providing                conditions  for  sustainable  growth  in  developing  countries.  The  proposed  analyses  are                conducted on the sample of Western Balkan countries using indicators provided by the                World Bank database. According to the results of panel data analysis the level of financial                development, especially the level of banking sector development, significantly influences                the level of economic growth. It can be concluded that international capital flows are not                the main determinant of the growth rate of GDP in observed developing countries as well                as that financial development leads to economic growth.                Keywords:  financial  development,  institutional  development,  sustainability,  economic                growth, developing countries                JEL codes: F43, G28, O16                 1 Introduction                 The relationship between economic growth and its sustainability, on one side, and financial                and  institutional  development,  on  the  other,  have  attracted  significant  interest  of                academics and practitioners in recent years. The economic theory supports the argument                that  a  developed  financial  and  institutional  environment  increases  the  efficiency  of                intermediation by reducing asymmetric information, transaction and monitoring costs and                supports investment by providing a set of instruments with different risk-return trade-offs.                Through the offer of a mixture of instruments and information to companies to deal with                adverse shocks, it can reduce volatility of the economy. Furthermore, for a given level of                saving, more developed financial sector increases the productivity of investment. All these                functions  should  produce  a  more  efficient  allocation  of  limited  human,  financial  and                material  resources  and  accelerated  technological  progress  which leads to  the  economic                growth. Efficient financial system together with developed institutional infrastructure is the                prerequisite  for  a  sustainable  economic  growth,  which,  according  to  United  Nations                Sustainable Development Goals, “will require societies to create the conditions that allow                people to have quality jobs that stimulate the economy while not harming the environment”                (United Nations, 2015).                                                             689","The concept of sustainability and sustainable growth at the company, national and global                level has attracted a lot of attention during the last decades. Numerous challenges, such                as  global  warming,  climate  change  and  energy  regulation,  data  protection,  resource                scarcity, social conflicts and migrations, force companies to respect the requirements for                sustainability management (Denčić-Mihajlov and Zeranski, 2017). On the other hand, the                problems  concerning  economic  stagnation,  poverty,  illness,  endangering  the  living                environment  and  influencing  fast  globalization  show  the  need  for  national  and  global                sustainable growth (Islam et al., 2003).                The revival of interest in growth theory and empirical economics is initiated by the global                economic crisis and its consequences. The drastic declines in economic activity during the                crisis caused by the adverse implications of financial disturbance, underlined the need to                choose  the  optimal  size  of  financial  systems  for  sustainable  economic  growth.  Prior                research is mainly concentrated on “endogenous growth” theories that consider the long–                term growth rate as the outcome of government policies, including institutional frameworks                and financial system development (Barro, 1991).                In the neoclassical growth theory, a steady economic growth rate can be achieved with the                proper amounts of the three driving forces: labour, capital and technology. Subsequent                theories and models argue that other factors, such as, for example, technological progress,                innovations, governmental policies and interventions, culture and religion, trade policy,                economic openness, currency undervaluation, education or institutions, have also a role in                promoting  economic  growth.  The  empirical  studies,  which  show  that  financial  sector                development has an economically important impact on growth, mostly relay on the broad                samples of countries at different development levels (Levine, 2003). However, economic                growth in developing and transition economies is strongly influenced by “transition” factors                such  as  initial  conditions,  scope  of  structural  reforms  together  with  market-oriented                infrastructure  and  sound  economic  policies  made  to  influence  the  development  of                macroeconomic stability (Mervar, 2004). The progress in structural reforms allows a more                efficient use of available resources and realization of technological progress, promoted by                increased trade openness and foreign investment. Theoretically, technological changes can                be  improved  by  foreign  direct  investment  (FDI)  through  the  effects  of  spillover  of                knowledge and new capital goods, but what determines the size of FDI’s contribution is                overall business environment in recipient countries (Chamarbagwala et al., 2000). On the                other hand, the level of development of the domestic financial sector has an internal impact                on the relationship between FDI and economic growth (Lee and Chang, 2009). Considering                the  fact  that  FDI  and  domestic  financial  markets  are  important  sources  of  capital                investment funds, the focus of this paper is the analysis of their relation to economic growth                in transition countries of the Western Balkan. The aim of this study is twofold: to examine                the  finance-growth link  and  effectiveness  of  financial  systems in  promoting  sustainable                economic growth and to analyse the quality of the institutional environment and its role in                providing conditions for sustainable growth in developing countries. The proposed analyses                are conducted on the sample of the Western Balkan countries using relevant indicators                provided by the World Bank database.                This paper is organized as follows: section 2 sets the context by providing evidence how                political  instability  and  financial  development  contributed  to  the  economic  growth  of                selected  countries.  Section  3  describes  the  data  and  details  econometric methodology.                Finally, section 4 discusses the main results, while the summary of conclusions is presented                in section 5.                 2 The role of finance and political instability in the economic growth of the                Western Balkan countries                Globalization typically impacts transition economies by connecting their emerging capital                markets, labour markets and tradable goods markets to the international economy. The                path  of  transition  in  the  Western  Balkan  countries  was  rather  different.  Although  SFR                Yugoslavia had specific international relations, especially with the European Community,                and  experience  with  market-oriented  economic  reforms,  its  successor  states  were                                                             690","progressing diversely. Except Slovenia that had smoother transition, the extreme political                instability in the region during the 1990s negatively influenced economic performance and                postponed many of the economic reforms of the other Western Balkan countries. Moreover,                the  attainments  to  achieve  transition-related  economic  objectives  were  significantly                influenced  by  delay  in  support  and  integration  with  European  Union  (EU)  as  well  as                inappropriate economic policies (Uvalic, 2012). After the initial negative economic effects                of the transition measures, such as a significant decline in production, high inflation, an                increase in unemployment, social inequality and poverty (Uvalic, 2003), convergence with                average EU macroeconomic standards in certain aspects has already occurred (Sanfey and                Milatovic, 2018). Considering economic development it can be concluded that the Western                Balkan  countries  have  succeeded  in  decreasing  the  gap  with  EU  in  the  previous  two                decades. In the period from 2001 to 2016 the average annual GDP growth rate of this                region was 3.2%, while GDP of EU had been increased by the average rate of 1.4%. Under                the impact of global economic crisis in 2008 the average growth rate of this region was                decreased to 1.2% (0.7% in EU) due to two recessions as a late response on the global                economic crisis in 2009 and 2012 (graph 1 left). Also, all Western Balkan countries have                reached high degree of price stability and convergence on inflation rates with advanced EU                countries.                 Figure 1 The Grow Rate of GDP per Capita (left) and FDI (right) of the Western Balkan                                            Countries in the Period 2006-2016                                                                   Source: authors’ calculation                 However, a major constraint on economic development in the Western Balkan countries                was  insufficient  level  of  savings  and  low  investment  rates.  As  a  result  of  several                interconnected  factors,  among  which  there  were  low  levels  of  income,  underdeveloped                capital and financial markets and delays in banking reforms, was the deficiency of financial                resources. Therefore, financial liberalization was the main step in economic transition that                should  open  borders  for  foreign  capital  inflows  and  attract  new  investments.  Financial                systems of all Western Balkan countries have certain country specifics, but in general all                financial systems are bank-oriented with financial markets of insufficient depth and minor                influence  of  other  institutional  investors  (insurance,  pension  and  mutual  funds)  on  the                financial flows (Kozarevic et al., 2017). Under such terms of financial development these                countries became highly dependent on foreign capital inflows. However, FDI to the Western                Balkan countries during the first decade of transition was very low. Due to unstable political                environment in the period 1989-1996 the cumulative FDI inflows in the Western Balkan                countries (except Bosnia and Herzegovina) amounted only 2% of the total FDI invested                into 27 transition economies in Europe and remained at the low level until 2000s (Uvalic,                2003). Driven by geographical and institutional factors, FDI inflows have increased after                2000s  (graph  1  right),  but  their  limited  volume  and  inadequate  structure,  mainly                concentrated in services (banking sector, telecommunications, retail trade and real estate),                produce insufficient restructuring of basic industries (Estrin and Uvalic, 2014). Moreover,                the Western Balkan countries still lag behind EU standards in the terms of governance                quality, especially in the level of corruption followed by the government effectiveness and                political  stability  (Sanfey  and  Milatovic,  2018).  Considering  the  fact  that  quality  of                institutional  environment  and  business  environment  free  of  corruption  are  main                                                            691","determinants  of  FDI  inflows  (Zeneli,  2014),  further  development  will  be  significantly                influenced  by  the  undertaken  governance  reforms  and  the  level  of  convergence  of                government development with EU norms.                 3 Methodology and Data                In  this  study  a  panel-data  approach  is  used  covering  5  middle  income  countries,  as                classified by the World Bank in 2018, the successor states of the SFR Yugoslavia - Serbia,                FYR Macedonia, Bosnia and Herzegovina and Montenegro, and Albania. These countries                are  selected  for  its  combination  of  underlying  structural  and  systemic  transformation                problems. However, these countries have strong economic potential and encounter with                the same challenges related to convergence in macroeconomic fundamentals due to the                prospect of their accession to the European Union.                 The relationship between financial and institutional development on one side and economic                growth on the other is examined using regression analysis and causality test for panel                data. Dependent variable in analysis is economic growth measured by the growth rate of                GDP per capita, while explanatory variables are selected among the World Bank indicators                of  financial  and institutional  development.  In  order  to  cover various  aspect  of  financial                development we use following indicators from the Global Financial Development database:                liquid liabilities (LL) over GDP as a traditional proxy of financial development indicating size                of the financial system; domestic credit to private sector (DCPS) over GDP as a proxy for                the degree of the financial intermediary activities provided to the private sector; broad                money (BM) over GDP as a measure of a given country’s money supply; and deposit money                banks' assets (DMBA) to GDP (%) as an indicator of the size of banking sector of a given                country. Indicators of capital market development as well as indicators of size and activities                of  other institutional investors  are  not considered in  this  research  since  data for  some                countries are not available.                 Indicators  that  are  used  to  observe  institutional  development  in  the  Western  Balkan                countries are selected among Worldwide Governance Indicators of the World Bank. For the                purposes of this study we use following indicators: government effectiveness (GE) as a                measure of the quality of public services, their independence from political pressure and                the quality of policy formulation; regulatory quality (RQ) as an indicator of government                ability to provide sound policies and regulations that promote private sector development;                and control of corruption (CC) that indicates the extent to which public power is exercised                for private gain. For the purpose of proposed analysis we use the countries’ rank among                all countries covered by the aggregate indicator.                Foreign capital flows in the countries of the Western Balkans are approximated by FDI net                inflows over GDP obtained from the World Development Indicators database. In order to                balance  panel-data  set  we  use  annual  data  from  the  period  2006-2016.  Descriptive                statistics for variables used in analysis is shown in table 1.                                Table 1 Descriptive Statistics for Used Variables (sample)                  Variable      Number of         Mean          Standard       Minimum        Maximum                              observations                      deviation                  Δ GDP             55           0.02791         0.02859        -0.05997       0.10505                  Δ FDI             55           0.19548         0.84782        -0.85598       4.50720                  Δ LL              55           0.05823         0.13712        -0.08176       0.74769                  Δ DCPS            55           0.07776         0.23318        -0.16815       1.21087                  Δ BM              55           0.05941         0.14928        -0.22360       0.85095                  Δ DMBA            55           0.07925         0.16292        -0.14800       0.93556                  Δ RQ              55           0.03902         0.07347        -0.15238       0.29288                  Δ GE              55           0.03351         0.15207        -0.36672       0.64286                  Δ CC              55           0.01583         0.09784        -0.26665       0.28138                                                 Source: authors’ calculation                                                               692","Since economic time series are generally non-stationary, series of selected variables are                transformed using differentiation, which in practical analyses provides the stationarity of                the time series. Testing for stationarity is conducted using test of panel unit root of Im,                Pesaran and Shin (2003) and Maddala and Wu (1999) and obtained results are presented                in table 2.                                  Table 2 Results of the Panel Unit Root Tests (sample)                           Test                                      Statistic         p-value                          Im, Pesaran and Shin (2003)                -17.76           0.00000                          Maddala and Wu (1999)                      334.49           0.00000                                                    Source: authors’ calculation                 Based on the results of tests it can be concluded that the series of variables are stationary.                The  relationship  between  the  stationary  time  series  can  be  determined  using  a  linear                regression  model.  Therefore,  we  test  the  impact  of  institutional  quality  and  financial                development on economic growth through a general standard model in the following form:                        ΔGDP =   β  i 0  +  β  i 1  ΔFDI +  β  i 2  ΔLL +  β  i 3  ΔDCPS +  β  i 4  ΔBM +  β  i 5  ΔDMBA +      (1)                             it                                                                                it                                                                                              it                                                                     it                                              it                                                        it                        +  β  i 6  ΔRQ +  β  i 7  ΔGE +  β  i 8  ΔCC +  ε it                                                      it                                 it                                           it                in which we incorporate all selected variables for country i at time t, β0 is a constant term                indicating the start level of country growth, coefficients β1,2,…,8  are the rates at which GDP                grow rate changes with respect to the rate of change of concrete variable, ɛit is the error                term.                Considering  observed  countries’  specifics  we  use  fixed  effect  model  to  estimate  the                proposed panel regression model, which allows intercept to vary for each cross-section and                thus account for the individual effects. The slope coefficient β is estimated using ordinary                least square (OLS) method. After statistically significant variables are determined, we use                the  Dumitrescu-Hurlin  test  (2012)  in  order  to  test  the  causal  relationship  between                variables. This test follows the linear heterogeneous regression model shown in equation                (2):                                   K            K                                              +                        y i, t  =   +   ( i  k) y i, t − k    i ( k) x i, t− k  +   i, t          (2)                               i                                                =                                  k 1          k 1                                   =                                      *                           +                where KN  and KN  and   =     ( i  ) 1 (  ,..., i (K ) ) and i,  (k )  and   i (k ) indicate constant term,                                             i                                                                       i                lag parameter and slope coefficient, respectively. The null hypothesis that supports the                absence of homogenous Granger causality for all cross-section units in the panel is defined                as:                        H :  i1  =  ... =  iK  =  0   i = 1 ,..., N                                  (3)                         0                Assuming that Granger causal relationship may exist between some units in the panel, but                not necessarily for all, the alternative hypothesis thus writes:                              = 0     i  = ,...,1  N                        H :   i                   1                                                    (4)                         1                                                          i   0    i  = N 1  + ,...,1  N                where N1[0, N-1] is unknown and N1<N. If N1=0, there is causality for all units in the                panel and if N1=N, there is no causality for all units in the panel and H1 reduces to H0.                 4 Results and Discussion                 The application of proposed regression models resulted in statistically significant results                given  in  the  table  3.  Variations  of  selected  independent  variables  determine  31.17%                            2                (adjusted R ) of the variation of the GDP per capita growth rate.                                                               693","Table 3 Regression Results                                 Explanatory variables            Estimation  Standard                                                                                  error                                 Δ FDI                              0.00123      0.00397                                 Δ LL                              0.21976  *    0.08177                                 Δ DCPS                            0.11887  *    0.04776                                 Δ BM                              -0.14986 *    0.06494                                 Δ DMBA                            -0.14297 *    0.05874                                 Δ RQ                               0.06037      0.04718                                 Δ GE                               0.00688      0.02275                                 Δ CC                              -0.00580      0.03824                                 R 2                                      0.45190                                 Adjusted R  2                            0.31169                                 F-statistics                            5.06468  *                                 * indicates significance at the 0.01 level.                                                        Source: authors’ calculation                 However,  statistically  significant  relationship  is  observed  only  between  GDP  per  capita                growth rate and indicators of financial development. If we consider relationship between                growth rate of liquid liabilities and growth rate of GDP per capita, it can be concluded that                this  relationship  is  positive  and  statistically  significant  verifying  that  financial  system                growth  positively  influence  the  economic  growth  of  the  Western  Balkan  countries.  A                positive  and  statistically  significant  relationship  also  exists  between  the  growth  rate  of                domestic credit to private sector and the growth rate of GDP per capita. Considering the                dominant role of banking sector in the financial systems of the analysed countries due to                insufficiently  developed  capital  market,  it  can  be  stated  that  credit  financing  have                significant  influence  on  economic  growth.  On  the  other  side,  negative  and  statistically                significant relationship is observed between the growth rate of GDP per capita and growth                rate of broad money and domestic money banks’ asset. These results indicates that in the                Western Balkan countries there is still excess supply of money that negatively influence                economic growth, as well as issue of necessarily oversized banking sector. Namely, since                alternative sources of long-term financing in observed countries are scarce, economies are                enforced  to  use  the  expensive  bank  credits,  which  negatively  influence  the  economic                growth.                 According to the obtained results there is no statistically significant relationship between                economic growth and the change of institutional environment in the analysed sample of                countries. Contrary to the expectation and results of other similar studies, we could not                verify the relationship between economic growth and growth rate of FDI inflows either.                                   Table 4 Results of the Dumitrescu-Hurlin Test (2012)                           →                       Δ GDP                          ←    Δ GDP                           Δ LL                    0.37227    Δ LL                    -0.00099                           Δ DCPS                  2.2146 *     Δ DCPS                -0.59653                           Δ BM                    14.174 *     Δ BM                  -1.08120                           Δ DMBA                 -0.99084      Δ DMBA                 5.4235 *                           * indicates significance at the 0.05 level.                                                 Source: authors’ calculation                The results of causality test presented in table 4 indicate that there is causal relationship                between the growth of domestic credit to private sector and broad money, on one side,                and growth rate of GDP per capita on the other. However, the causality between these                variables  is  not  bidirectional,  but  we  can  conclude  that  growth  of  GDP  per  capita  has                promoted the growth of banking sector in the Western Balkan countries.                                                                 694","5 Conclusions                The main objectives of this study were to examine the link between financial development                and economic growth, on one side, and institutional development and economic growth,                on the other, in the Western Balkan countries. In order to cover the most important aspects                of  financial  and  institutional  development  analyses  included  four  indicators  of  the                development  of  financial  intermediation  and  three  indicators  of  governance  and                institutional quality, as well as indicator of foreign capital inflows. The annual growth rate                of GDP per capita was used as a proxy for economic growth. All indicators were obtained                from the relevant World Bank databases. The relationship between selected indicators was                examined using panel data analysis – regression analysis and causality test.                The conducted empiric research showed that process of financial deepening contributed to                process of economic growth. Namely, a unidirectional causal relation between the growth                rate of money supply and economic growth as well as the growth rate of credits approved                to  private  sector  and  economic  growth  was  confirmed.  On  the  other  hand,  empirical                findings  suggested  that  the  economic  growth  promoted  development  of  financial                intermediation. Due to the specific “transition” factors the dominant role in the financial                intermediation  was  taken  over  by  banking  sector.  However,  in  the  previous  years  this                aspect  of  financial  market  became  oversized  at  the  expense  of  other  aspects,  such  as                capital market, insurance sector, pension and mutual funds. In order to provide sustainable                economic growth the countries of the Western Balkan should adopt the adequate reforms                in order to promote development of all aspects of financial system that would enhance the                foreign capital flows and achievement of transition goals.                 In  the  existing  state  of  the  Western  Balkan  countries institutional  environment  did  not                prove to have significant influence on the economic growth. Considering the specifics of                each  country  in  the  sample,  further  country  focused  analysis  could  provide  important                insight into governance and institutional quality issues and its relation to economic growth.                 References                 Barro, R. J. (1991). Economic growth in a cross section of countries. The quarterly journal                of economics, vol. 106(2), pp. 407-443.                Chamarbagwala, R., Ramaswamy, S., Wunnava, P. V. (2000). The role of foreign capital                in domestic manufacturing productivity: empirical evidence from Asian economies. Applied                Economics, vol. 32(4), pp. 393-398.                Denčić-Mihajlov,  K.,  Zeranski,  S.  (2018).  Development  of  sustainability  indicators:                approaches,  challenges  and  opportunities. Facta  Universitatis,  Series:  Economics  and                Organization, vol. 14(4), pp. 291-306.                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Berlin: The Aspen Institute, pp. 46-51.                                                                                                             696","A Call to Theorize Digital Information Technology Due Diligence:                       Analysis of Research Gaps for the Subject of Information                         Technology Due Diligence in Mergers and Acquisitions                                                                                       3                                     Dirk Stein , Alexander Zureck , Tina Jäger                                                 1                                                                       2                                             1  FOM University of Applied Sciences                          Lecturer for Entrepreneurship / Turn Around Management at Chair of Finance,                                       Lecturer for Information Technology Management                                          Leimkugelstraße 6, 45141 Essen, Germany                                                E-mail: dirk.stein@fom-net.de                                     2  FOM University of Applied Sciences/Masaryk University                            isf Institute for Strategic Finance/Faculty of Economics and Administration                                          Leimkugelstraße 6, 45141 Essen, Germany                                              E-mail: alexander.zureck@fom.de                             3  FOM University of Applied Sciences/Fontys University of Applied Sciences                    Lecturer for Scientific Research Methods/Lecturer for Accounting and Marketing Controlling                                          Leimkugelstraße 6, 45141 Essen, Germany                                                 E-mail: tina.jaeger@fom.de                 Abstract:  The  role  of  information  technology  in  mergers  and  acquisitions  has  gained                increased importance in recent times due to digitalization. Beside its importance for value                creating processes within an organization, information technology can be more and more                regarded as a core asset, for which sound valuation is necessary in order to determine any                risks  and  support  of  assessing  a  adequate  acquisition  price  for  information  or  digital                technology in merger and acquisition transactions. This paper identifies five dimensions of                a  due  diligence  for  information  technology.  These  dimensions  for  assessment  for  the                research gap are the technological scope setting and the respective planning and execution                process for a information technology due diligence, the dimension of the interrelation to                other due diligence audit fields as well as the assessment of previous research approaches                in  the  information  technology  due  diligence  audit  field  and  the  potential  impact  of                digitalization  on  planning  and  execution  of  information  technology  due  diligence.  The                before mentioned five dimensions represents the research analysis grid of this article and                elaborates issues in this regards the current state of research on the topic as the main                result of this article.                Keywords:  information  technology,  due  diligence,  Merger  \&  Acquisition,  corporate                valuation, risk management, digital due diligence                 JEL codes: D23, G32, G34, M15                 1 Introduction                The concept of due diligence has been established in mergers and acquisitions processes.                From a buyer’s perspective, in a mergers and acquisitions process, the importance of the                due diligence to reduce risk potential and acquisition price is obvious (Berens et al., 2010b).                This buyer expectation is empirically confirmed (Berens et al., 2010c). Therefore this paper                takes the buyers perspective for the following investigations.                It is empirically confirmed, that often unplanned integration activities due to a merger and                acquisition transaction are necessary with the effect that the initial expectation regarding                acquired  value  and  communicated  objectives  have  a  significant  negative  deviation                (McKiernan/Merali, 1995; Pack, 2005; Strasser, 2000). Especially synergy potentials are                not  realized  in  many  cases,  which  can  explain  ex  post  most  failures  of  mergers  and                acquisition (Pack, 2005; Weber, 1991) Desired synergies, as a strategic objective for an                acquisition  need  a  detailed  plausibility  check  before  the  acquisition  decision  is  taken                (Cullinan et. al., 2004). Due to this situation due diligence is widely seen as an appropriate                instrument in mergers and acquisition.                                                             697","The  significant  strategic  importance  of  IT  for  strategic  business  success  is  stated  in                literature (Cullinan et. al., 2004). IT is as well a critical success factor to create business                flexibility and effective performance improvements in company’s organization in order to                achieve strategic objectives of the acquisitions.                The analysis of impacts for the subject of information technology due diligence in merger                and acquisitions should take the buyers perspective. It is supposed, furthermore, that the                buyer  has  the  intention  to  achieve  a  competitive  advantage  through  acquiring  and                internalizing the acquisition target, which means explicitly no divestiture, no not using,                shut down or closing the acquisition object after deal closing.                 2 Theoretical framework                Asymmetric information distribution regarding the acquisition object between buyer and                seller  can  be  assumed for  merger  and  acquisition  projects.  Normally the  seller  will not                provide too much information to the buyer on a voluntary basis because the risk in terms                of a negative impact on his sale price is probable.                This  phenomenon  exists  between  contracting  parties  with  regards  to  distribution  of                relevant due diligence information in mergers and acquisitions (Spremann, 1990).                The asymmetric information distribution between buyer and seller in terms of incomplete                information in  the  process  of  contractual  design is  one  of the  core  assumptions  of  the                Principal Agent Theory (Laux, 1990; Eisenhardt, 1989; Fama/Jensen, 1983).  This theory                addresses  the  handling  of  the  typical  problem  of  asymmetric  information  distribution                between the principal and the agent. The principal in this context is the buyer and  the                agent is the seller. The instrument to solve the information problem of the buyer before                the final acquisition decision is made, is the concept of due diligence (Berens et al., 2011b).                Therefor the Principal Agent Theory is suitable and chosen as a theoretical foundation for                the research.                The problem of insufficient information during acquisition processes before the acquisition                decision is made is confirmed in literature (Rigall/Hornke, 2007). It is empirically confirmed                that the problem of asymmetric information distribution has in numerous cases a negative                impact on the buyer’s transaction cost.                Transaction cost are defined as all cost with relation or caused by the contractual process                of a mergers and acquisition project. Main transaction cost drivers in theory are costs for                gathering  information  regarding  the  acquisition  object  to  support  the  mergers  and                acquisition decision process of the buyer. In this study these costs are defined as cost for                an digital IT due diligence test from an ex-ante perspective, i.e. before signing a contract                between buyer and seller.                The ex-ante cost of an IT due diligence could avoid unplanned ex-post cost, in terms of                post-merger integration cost, due to better information about the object for the buyer. This                is the main rational and value added in context of this doctoral thesis for an IT due diligence                test  in  mergers  and  acquisitions.  Therefor  the  transaction  cost  theory  (Coase,  1937;                Williamson, 1979) is suitable as well and set as theoretical foundation in addition to the                Principal Agent Theory to theorize digital information technology due diligence.                 This  research  underlies  the  assumption  that  the  buyer  has  the  intention  to  acquire                information technology to improve his own business in terms of technology, processes or                efficiencies.                The Principal Agent Theory and the Transaction Cost Theory are chosen as the theoretical                foundation for this research. These theories allows the definition of the research questions                and the methodology to theorize digital technology due diligence between seller and buyer.                 3 Current State of Research                 There are several studies regarding IT due diligence. Sayana published in 2002 an audit                process  on  information  technology  (Sayana,  2002)  Instead  of  IT  the  synonym  IS                                                             698","(information  systems)  is  used.  The  publication  has  no  explicit  research  question  and                focuses on an audit process as a facilitator of good corporate governance. Sayana describes                an  IT  audit  process.  There  is  no  obvious  consideration  of  mergers  and  acquisitions                purposes.  The  audit  process  includes  the  physical  and  environmental  review,  systems                administration  review,  application  software  review,  network  security  review,  business                continuity  review  and  data  integrity  review.  No  scientific  approach  underlies  the                development of the IT audit process, or the data source and the sample of investigation.                Sayanas  publication  is  rather  a  report  of  individual  professional  experience  in  IT  audit                processes.                A framework for conducting IT due diligence in mergers and acquisitions is the publication                by Sundberg et al. (Sundberg et al., 2006). It examines the IT due diligence in terms of                what needs to be done in the pre-merger and post-merger phases. The examined factors                for  IT  due  diligence  are  strategy  alignment,  business  continuity,  human  capital,  IT                operations and IT assets in the used IT due diligence framework. This framework focuses                mainly on the fulfillment of regulatory requirements regarding IT due diligence in mergers                and acquisitions. The publication has no interrelation to an overall mergers and acquisitions                strategy. The framework is based on the methodologies of COBIT, COSO and ITIL from a                theoretical perspective. It is enhanced with practical knowledge of IT professionals and                companies from past acquisition projects under confidentiality. A sample description of the                used practical knowledge for this framework is not provided and the necessary transparent                development process of the results is not given either.                Larsen et.al. published the investigation of 17 IT-Governance tools on the basis of a case                study. (Larsen et al., 2006). The research question is: How is IT-Governance adapted in                the selected case study firm? The selection criteria for using IT-Governance tools are the                investigated factors of the publication.  The study makes a positive distinction between IT                audit and IT due diligence. In this context Sisco postulated that IT due diligence objectives                need  to  be  defined  first.  The  following  scope  of  an  IT  due  diligence  is  recommended:                Current  IT  operations,  risk  and  risk  avoidance  plans,  financial  plan,  capital  investment                requirements,  leverage  opportunities  and  recommend  plans,  transition  plan,  IT  due                diligence report (Sisco, 2002).                 A dedicated blueprint for an IT merger due diligence was has been published by Bathia                (Bathia, 2007). A precise research question is not mentioned in this so called blueprint                concept for IT due diligence. The investigated factors have a clear relation to a given IT                merger strategy. Bathia recommends linking the factors customer products and services                and IT support as determinants to the IT merger strategy. These factors will consequently                determine  the  scope  of  an  IT  due  diligence.  IT  due  diligence  according  to  the  Bathia                blueprint includes technological considerations of IT organization, network, application and                information architecture, data center, facilities and contracts with staff, vendors, partners,                customers  and  business  process  providers.  The  study  does  not  detail  a  transparent                scientific  development  process  of  the  recommended  IT  due  diligence  blueprint.  A                description of the sample for primary research results for this blueprint is missing as well                as the applied methodologies and models.                 IT audit as a critical business process was analyzed by Carlin et al. (Carlin/Gallegos, 2007).                This article formulates no specific research question. It describes an IT audit process. The                theoretical foundation is the COBIT IT-Governance reference model. The fulfillment of legal                requirements and regulations is the main objective of the IT audit process. The described                IT  audit  process  includes  the  following  activities:  define  audit  scope,  define  audit                objectives,  perform  a  preliminary  evaluation,  conduct  compliance  testing,  conduct                substantive  testing  in  warranty  area,  complete  evaluation  in  internal  controls  and                communication of weaknesses. No relation of the IT audit to a consideration of merger and                acquisitions  needs  is  observable.  The  included  primary  research  data  is  mostly  not                transparent.  A  sample  description is  missing  as  well.  The focus is  on  the  fulfillment  of                regulatory  requirements  and  of  overall  strategic  business  requirements in  mergers  and                acquisitions.                                                               699","Andriole publishes a recommendation of selection criteria for decision support of acquiring                and deployment of technologies (Andriole, 2007). Fifteen selection criteria are discussed.                The defined selection criteria are obviously not scientifically derived, but traceable. The                importance  of  an  IT  due  diligence  process  is  underlined  but  not  further  specified.  The                benefits of an IT due diligence process are to reduce risk and to increase the return of                technology.                 For  an  initial  IT  due  diligence  Delak  describes  an  approach  with  emphasis  on  risk                identification before capital investment in finance industry (Delak, 2008).  The research                goal is to identify the most appropriate concept for IT due diligence. The study relies on                case studies in independent financial institutions outside bank industry. A model for IT due                diligence of the National Bank of Slovenia underlies the publication.                Scope  of  the  publication  are  frameworks,  models,  best  practices  and  standards  for                conducting IT due diligence in different types of companies. The scope is similar and nearly                a subset to Larsen et.al.  (Larsen et al., 2006) and Sundberg et. al. (Sundberg et al.,                2006). A combination of the comparison of existing IT governance models with research                methodologies is the methodology of this publication. Primary research is not mentioned                nor obvious. A sample description is not given. The authors state that an IT due diligence                tool is under development. So far no progress is published. From a scientific point of view                the used proprietary model of the National Bank of Slowenia is not transparent and not                available for scientific evaluation purposes.                 A framework for delivering an information system due diligence with scientific focus on                rapid information system due diligence is published in 2013 (Delak/Bajec, 2013a). The                approach  is  a  mix  of  methodologies  of  literature  review  with  objective  of  potential  IT                governance  model  comparison  regarding  strength  and  weaknesses,  expert  opinion                involvement and a case study. The expert opinion involvement is not transparent in terms                of documentation of results. The sample of compared models comprises nineteen analyzed                IT  governance  frameworks.  There  are  three  case  studies,  focusing  on  the  European                transition economies Bulgaria, Kosovo and the Russian Federation. The result is a synthesis                of  information  systems  due  diligence  with  a  simple  decision  process  model  and  a                questionnaire with validation according the three selected three case studies.                 Information  system  due  diligence  data  as  an  input  for  knowledge  management  was                published 2013 by Delak and Bajec as well (Delak/Bajec, 2013b). It is based on the 2013                publication by Delak and Bajec (Delak/Bajec, 2013a). The scientific research question is:                How  to  use  an  information  system  due  diligence  framework  to  facilitate  information                systems knowledge management? The result of this analysis is the recommendation of a                re-use  of the  developed  process  (Delak/Bajec,  2013a)  from  the first  publication  of  the                authors. The recommendations are based on two case studies and the personal expertise                of the authors.                Wright  and  Altimas  published  a  process  of  a  due  diligence  in  information  technology                (Wright/Altimas, 2015). The key question in this publication is defined as “Are you buying                an IT asset or liability?”  An IT due diligence process model is the result of this publication.                The process contains the following steps: Understand the deal, scope and risk assessment,                perform reviews, report findings and post deal activities. The investigated technological                scope of consideration for IT due diligence are risk analysis, identification key systems,                reliability of key systems, upgrade and patching strategy of systems, systems resilience                strategy, IT security and regulatory requirements (Wright/Altimas, 2015). The paper relies                on  four  case  studies  which  are not  available  for  assessment  purposes  and  professional                consultancy experience of the authors.                Bloch and Zerfass examined, why IT due diligence can make or break a deal (Bloch/Zerfass,                2010). The deep knowledge regarding all aspects about IT environment of the acquisition                target  is  stated  as  critical  to  analyze  strategic  goals  of  the  intended  transaction.  The                recommended  factors  to  be  considered  are  date  center,  network  strategy,  hard  and                software architecture, hard and software currently in use, hard and software maintenance,                hard and software development strategy, software licensing models, desktop systems and                                                             700","their maintenance, human resources with skills to run current IT environment, headcount                for IT operations, capacity utilizations for hard and software and their limits, current IT                projects, IT procurement, usage of outsourcing and freelancers.                In addition to these lists of investigation factors for IT environment, Bloch and Zerfass                recommend their IT due diligence approach, which can be seen as an IT due diligence                process.  The  process  steps  are  preparation,  information  screening,  analysis  /  Q\&A`s,                evaluation, result, integration of results in offer. They see the preparation step of the IT                Due Diligence approach as part of the overall mergers and acquisition process, in which                the intended deal strategically should be understood in terms of relations to IT of the deal.                Howson considers IT due diligence in two forms (Howson, 2003). Information Technology                (IT)  is  understood  as  a  set  of  information  systems,  process  and  hardware  to  run  the                business. Production technology (PT) is defined as processes, equipment and know-how to                make products or deliver service. Due to an overlapping of the content of both technology                types the due diligence of both should be considered together.  Howson categorizes the                IT/PT due diligence into three levels.                The audit level answers the key question of what equipment and software is being used                with  regards  to  its  quality.    Howson  recommends  on  the  audit  level  considerations  of                vulnerability, trust and complexity. Vulnerability is defined in the context of accessibility                and  number  of  users,  trust  is  defined  in  considerations  of  employee  vetting  and                management  concerns  and  complexity  is  defined  in  considerations  of  technology  risk,                organizational risk and system design.                The management level should answer the key question of how well the IT/PT supports the                business. The strategic level should answer the key question about the sufficiency of the                used technology and organization for the future.                 4 Research Gaps in Digital IT Due Diligence                 Based on the current state of research, research gaps for IT due diligence are identified. It                includes a synthesis approach of five assessment perspectives:                                              Figure 1 Impacts of Digitalization                                                                      Source: own processing                 First of all, the observed IT due diligence processes and it`s process steps are collected                and  analyzed.  The  process  perspective  is  chosen  because  it  is  a  common  approach  to                conduct a Due Diligence. Berens et al. mentions finish-to-start dependencies during a Due                Diligence  which  requires  a  process  perspective  (Berens  et  al.,  2011a)  to  approach  the                nature of an IT Due Diligence.                 The  objective  of  the  research  to  theorize  information  technology  due  diligence  is  the                development of an IT due diligence framework based on scientifically good practices and                                                             701","norms. The investigation and assessment of existing IT due diligence process models is a                baseline or the starting point in this research.                 As  second  perspective,  the  recommended  technological  scope  of  consideration  in  the                current state-of-research is evaluated. The technical and managerial criteria are generally                based on IT-Governance Management Models like COBIT or ITIL. The approaches to IT Due                Diligence  in  the  current  literature  are  inconsistent,  heterogeneous  and  often  not  well                scientifically founded. The development of an improved IT due diligence process requires                the consideration of proper scientific standards, the usage of a generally accepted process                improvement methodology and the involvement of particular knowledge from experts.                Due to the cross functional nature of digital information technology the IT due diligence is                consequently cross functional as well. IT due diligence with focus on pure technological                considerations may not sufficient for the decision process in the context of mergers and                acquisitions. Partial due diligence tests like legal and financial due diligence test may be                necessary in order to have a holistic view on the target acquisition object for the decision                process.  These partial tests are identified in the literature review.                The fourth evaluation perspective is the evaluation of the literature review sources from a                scientific  perspective  in  order  to  identify  approaches  and  limitations  of  the  reviewed                publications regarding applied scientific standards.                A. IT Due Diligence from a Process Perspective                 Most  authors  focus  on  risk  assessment  and  fulfillment  of  regulatory  requirements  of                information  technology  with  regard  to  a  merger  and  acquisition.  They  neglect  aspects                which may be important for internalization in buyers organization. This is in general not                sufficient for a sound decision about information technology as acquisition object as part                of  a  merger  and  acquisitions  decision.  Especially  a  joint  consideration  of  an  IT  due                diligence, with the intended business strategy from a buyers perspective, is missing in                reviewed  publications  as  well.  The  IT  due  diligence  results  are  mostly  not  sufficiently                integrated into the overall mergers and acquisition transaction process before deal closing.                A comprehensive and integrated IT due diligence process model is missing or rudimentary                outlined in literature.                A few authors provide simple IT due diligence processes and approaches (cf. Table I in                Appendix). Their process approaches are different in terms of defined process steps and                it`s granularity. The necessity and importance of a standardized IT due diligence process                model  for  mergers  and  acquisitions is discussed in the  reviewed literature for  a  better                support of mergers and acquisitions decisions. This standardized IT due diligence process                model must be integrated into an overall mergers and acquisition process.                The evaluation of the existing concepts of IT due diligence is summarized as follows. Most                of  the  publications  focus  on  regulatory  issues  and  advocate  a  heterogeneous  process                perspective.  Authors  are  using  IT  audit  processes  in  order  to  evaluate  the  regulatory                alignment of information technology management processes.  The majority of the observed                IT due diligence processes are not designed from the strategic mergers and acquisition                perspective of an internalization of the acquisition object into buyer’s organization.                B. Scope of Technological Consideration of Digital IT Due Diligence                The technical object of investigation for an IT due diligence in the analyzed literature is                heterogeneous  pronounced.    The  publications  give  on  a  high  level  assistance  which                technological areas should be considered and analyzed. In the analyzed literature of IT,                concept is undifferentiated defined and used as a general term for an IT due diligence from                a technical perspective.                This  undifferentiated  approach  leads,  due  to  the  cross-functional  nature  of  IT,  to  the                problem that, the subject of examination is diffuse and the result, in terms of the benefits                of  IT  due  diligence  for  merger  and  acquisition  transaction  success,  is  problematic  or                questionable. A merger and acquisitions transaction intends to buy at a target acquisition                target which includes more and more IT today and in future.                                                             702","Orlikowksi and Iacono (Orlikowski/Iacono, 2002) have identified the problem of insufficient                definition of the IT concept in their research commentary and called to theorize the IT                artifacts more precisely and to include them into research and studies. The undifferentiated                use of the IT concept in analyzed literature can be seen in that way that IT is used as a                general term and definition on a high level.                Table 2 in the appendix shows, that the analyzed literature has overall no homogeneous                view of what needs to be considered and analyzed from a technological perspective.                The  selection  assessment  attributes  IT-Architecture,  Data  Center  /  Network,  Human                Resources,  Risk  and  IT  Security,  Operations  of  IT  and  Sourcing  were  identified  in  the                literature  review  and  are  as  well  the  most  important  evaluation  attributes  for  an                information  technology  artefact  from  an  IT  Governance  perspective  and  are  widely                accepted by IT Governance Models (Weill/Ross, 2004; Harmer, 2013; Bernard, 2012).                The IT Governance Models use the term domain or management domain as synonym for                the term evaluation attributes (Harmer, 2013).                C. Interrelations of Digital IT Due Diligence with other partial Due Diligences                The analysis of the IT due diligence publications gives numerous indications that IT due                diligence is linked to  other  partial  due  diligence tests.  Touchpoints  can  be  observed  to                financial  due  diligence,  legal  due  diligence  HR  due  diligence,  cultural  due  diligence,                operational  due  diligence,  tax  due  diligence  and  intellectual  property  due  diligence                (Koch/Menke, 2011).                The  literature  review  identified  additional  publications,  that  also  mention  touchpoints                between  an  IT  DD  and  other  partial  due  diligence  tests  as  depicted  in  table  3  of  the                appendix.                The  result  of  this  analysis  in  terms  of  touchpoints  from  IT  due  diligence  to  other  due                diligence tests is, as well, heterogeneous pronounced. Especially the scope of the identified                touchpoint in terms of scope and considerations varies between the reviewed publications.                From  a  research  perspective  there  is  a  need  to  harmonize  the  touchpoints  of  IT  due                diligence to the other due diligence tests for merger and acquisitions purposes to assure a                consistent scope and quality in context of an IT due diligence project.                D. Research Approach of Current State of Research                From a research perspective it must be criticized, that the methodology of most analyzed                publications does not meet proper scientific standards and practice. The results are often                based  on  practical  experiences  of  the  authors  which  are  neither  comprehensible  nor                replicable from a research perspective. The cited case studies are often not transparent                and not accessible to analysis by other scientists. In cases of expert interviews, as primary                research data, interview minutes are neither cited nor available for research verification                purposes.                 The  applied  case  studies  in  the  analyzed  publications  have  further  deficits.  They  are                reflecting in many cases only specific industries. Their transferability in terms to generate                general implications  to  a  standard  methodology  for  IT  due  diligence is  doubtful.  Some                authors confirm this issue implicitly. Some case studies and expert opinions are marked as                confidential, which is another disadvantage for scientific analysis.                The reviewed publications provide in summary only rudimentary information for conducting                an IT due diligence.  The search for scientific publications about the implementation of an                IT due diligence in the context of mergers and acquisitions rendered only a few studies. In                contrast,  near-scientific  publications  based  upon  the  authors  own  experiences  or  case                studies have been far more prevalent. They are taken into consideration in this literature                review, since their authors can be considered as experts in the field. Their findings might                have some value for the analysis of this thesis at a later stage. They will however be treated                most critically.                                                               703","Ideas for the process dimensions and considerations for the development of an IT due                diligence process model lead to an explorative research approach. The application of expert                interviews for the development of a standardized IT due diligence process model could not                be observed in the available literature.                The knowledge of experts in this field however is expected to provide valuable insights for                the development of a standardized IT due diligence process model that is capable of being                applied  in  practice  for  IT  Due  Diligence  internalization  scenario.  Moreover,  expert                interviews are a data collection method that provides more multifaceted insights for the                scientific research than personal experiences and the study of a single ITDD case.                The need for research about a proper scientifically developed model for an IT due diligence                process is evident. Table 4 in the appendix provides an overview of the evaluation.                E. Consideration of Digitalization                In addition to the reviewed aspects of IT due diligence one aspect is not considered by                most researchers in this field. Many companies find themselves exposed to an aggressive                market environment, which is often driven by technological advance and digitalization. The                role of IT is also regarded as being enabler of the digitalization itself (Châlons/Dufft, 2016).                Scheer mentions, that digitalization often affects the business models of companies and                thus  effects  the  requirements  of  IT  systems  that  need  to  be  accessible  from  multiple                devices and channels and concludes, that due to digitalization, the world becomes more                flat  and  more  complex  (Scheer,  2016).  Urban/Ahlemann  point  out  that  the  effects  of                digitalization  may  be  a  treat  to  business  processes  and  business  continuity  itself                (Urban/Ahlemann, 2016). This development has tremendous effects on the valuation of IT                systems, that need to be considered in the course of an digital IT due diligence.                The literature review indicates that this important aspect has not been sufficiently analyzed                by researchers (See Table IV in Appendix). In this regard the literature was reviewed by                searching for digitalization-related keywords such as Big Data, RFID, Internet of Things or                Industry 4.0. Only Andriole has considered digitalization as being an influencing factor for                IT due diligence (Andirole, 2007).                 Thus, the impact of digitalization on IT due diligence can be regarded as an overarching                research gap that has implications for the process-related research dimensions as outlined                before. For this reason the effects of digitalization on the process of IT due diligence will                be part of the research.                 5 Conclusions                 Based upon the foregoing analysis of the subject of IT due diligence in the current state of                research, it can be concluded that there are multifaceted research gaps.  Three research                gaps are related to the process of IT due diligence itself. These are referring to the overall                process of IT you diligences, the technological scope of its consideration and interrelations                with other partial due diligence tests.                One methodological research gap has been identified in the way that academic literature                has approached the topic. Research in this field is mostly based upon personal experiences                and case studies, which offers only limited perspective on the multifaceted aspects of an                IT due diligence. Moreover, the research process itself might profit from a more generally-                oriented approach, rather than relying on personal experiences and specific cases.                In  addition,  the  topic  of  IT  due  diligence  is  gaining  additional  importance  due  to  the                technological advancements that are labelled under the term of “digitalization”. Impacts of                digitalization on the due diligence of IT in the context of mergers and acquisitions have not                sufficiently been researched by the academics in this field, so that there impacts can be                regarded as an additional and overarching research gap. The below figure illustrates the                five research gaps that have been derived from literature.                                                               704","Figure 2 Digitalization                                                                      Source: own processing                 The managerial implications of this analysis are, that managers need to consider multiple                aspects when assessing the IT value when preparing a merger or acquisition in order to                match the requirements of their IT due diligence.                Specific research dedicated to gain insights into these five research gaps might increase                the degree of standardization in IT due diligence, which can contribute to a more timely                and efficient conduction of IT due diligences. Thus, researchers might put and focus on                assessing the topic with research methods that provide more in-depth data sets. These                data  sets  might  be  acquired  by  conducting  quantitative  research  and  by  assessing  the                expert opinion of multiple experts on the topic.                  References                Andriole,  S.  J.  (2007).  Mining  for  Digital  Gold:  Technology  Due  Diligence  for  CIOs.                Communications of the Association for Information Systems, vol. 20, pp. 371-381.                 Berens, W., Hoffian, A., Strauch, J. (2011a). Planung und Durchführung der Due Diligence.                In:   Berens,    W.,   Brauner,   H.   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Ely: ITGP.                                                                                              707","Influence of Capital Market on the US Unemployment and GDP                                                                             2                                                          1                                               Erik Suchý , Michal Šoltés                                               1  Technical University of Košice                                 Faculty of Economics, Department of Banking and Investment                                            Němcovej 32, 040 01 Košice, Slovakia                                                 E-mail: erik.suchy@tuke.sk                                                2  Technical University of Košice                                 Faculty of Economics, Department of Banking and Investment                                            Němcovej 32, 040 01 Košice, Slovakia                                                E-mail: michal.soltes@tuke.sk                 Abstract: In this paper we analyze the size of the US capital market and its attributes.                Following the 2008 economic crisis, many countries’ economies or even whole European                Union and US were in recession. Unemployment rate increased, GDP declined and some                businesses bankrupted, mainly banks. In order to support economic growth, countries have                taken various steps such as quantitative easing, interest rate cuts, investment incentives                etc.  The  US  market  has  recovered  from  the  crisis  sooner  than  rest  of the  world,  even                though the trigger for the crisis was in the US. The US economy is considered to be the                most  developed,  as  it  has  a  huge  banking  and  capital  market.  The  banking  sector  or                mortgages market to be concrete was the trigger for the crisis, but the capital sector can                be considered as a support system in the rebuilding of the US economy. In our paper we                deal with the US capital market, which is the largest in the world and supports the US                economy and therefore employment, GDP, economic growth, capital inflows and more. The                size and usability of the US capital market is unparalleled and therefore its aspects need                to be further explored. The European Union has one of the main objectives of building a                capital markets union on the US image where capital markets operate at a much better                level. For this reason, our research addresses various indicators of the US capital market                and their impact on unemployment and GDP in the US.                Keywords: capital market, stock market, stocks, unemployment, GDP                JEL codes: F21, G15, O16, E22, E44                 1 Introduction                 For the proper functioning of the economy, healthy economic growth and development,                the effective functioning of the overall financial system and the financial market is crucial.                Capital market, as part of the financial market, significantly affects the size of the real                economy of the country. In this paper, we will focus on analyzing the United States (US)                capital market as the most developed market in the world. We will analyze the capital                market  with  respect to  the macroeconomic indicators,  namely the impact  on  economic                growth and unemployment. The aim is to find out what impact the US capital market has                on the underlying macroeconomic indicators.                 The  bulk  of the literature  provides  extensive  evidence  of  the importance  of  the  capital                market in the country's economy. Authors and scientists focus on the correlation between                capital market indicators and two or more macroeconomic indicators (e.g. Zhou (2011),                Coskun et al. (2017)). As a result, there are various analyses of the causal relationship                between the financial market, economic growth and economic performance. Schumpeter                (1911)  first  came  up  with  the  idea  of  exploring  the  financial  market  and  the  pace  of                economic  growth,  focusing  on  investment,  credit  mechanism  and  the  creation  of  new                capital from savings. Investments which make a surplus on the capital market, stimulates                the overall financial market and consequently support economic growth.                Due to the size of the financial system, Coskun et al. (2017) deal with the issue in more                detail  and  analyze  the  financial  market  in  relation  to  the  specific  economic  sector  and                subsector. According to the conclusions of a large number of research papers, a strong                correlation between the financial market and economic performance has been confirmed.                                                            708","On the other hand, some studies (e.g. Kaserer, Ch., Steffen-Rapp, M. (2014)) point to the                fact that economic growth can improve and stimulate financial market, but the impact of                economic  growth  on  the  capital  market  is  questionable  in  view  of  the  government's                economic policy that affects the effective allocation of funds. In terms of economic policy,                the capital and financial markets also have an impact on the financing of public debt and                government  deficits  (e.g.  Fincke,  Greiner  (2015),  Nordin,  Nordin  (2016),  Coskun,  Y.,                Seven, U., Ertugrul, HM, Ulussever, T. (2017). The public debt financing is based on the                relationship between the development of government bonds on the capital market and real                economic  growth,  with  the  above-mentioned  studies  agreeing  that  there  is  a  positive                correlation between government bonds´ performance and economic growth.                 Further scientific papers point to the relation between the capital market and economic                growth in terms of market liquidity and rates of return. Beck, Levine (2004) \& Boubari, Jin                (2010)  concludes  that  the  purchase  and  sale  of  stock  market  shares  as  a  long-term                appreciation of financial assets will foster economic growth in the country but, on the other                hand, by distributing their savings into long-term investments, they push away current                consumption, which eliminates real money in the economy and has a negative impact on                current economic growth. The market liquidity and the relationship between savings and                investments are also addressed in a study Coskun, Y., Seven, U., Ertugrul, HM, Ulussever,                T. (2017) in which states that more liquid markets eliminate market risk in the longer term                and investment costs, which favorably affect economic growth.                 Another way to determine the correlation between economic growth and the capital market                is through exploring the diversification of risk and the development of the international                integration of financial markets. Ologunwa, O.P., Sadibo, O.V. (2016) describes the risk                shift to higher yielding projects that can significantly boost economic growth as compared                to low risk investments that can, on the contrary, cause a slowdown in economic growth                or even a reduction in economic welfare. On the other hand, we can meet with the literature                examining the integration of financial markets and the impact of globalization on the level                of real output in emerging countries, for example study by Yepes-Rios, B., Gonzalez-Tapia,                K., Gonzalez-Perez, M.A. (2015) which applies capital market research in the countries of                Latin America.                 Pan L., Mishra, V. (2018) characterizes the capital market in terms of economic theories                and economic models. As stated, the relationship between the capital market and economic                growth can be analyzed according to the five basic economic models:                       •  Keynes  model:  analyzes  the  relationship  between  investment  and  savings,                       examines and defines the liquidity trap;                       •  Neoclassical  model:  According  to  this  model,  all  capital  market  operations  are                       cost-free because they consider money and capital as substitutes;                       •  McKinnon  model:  assesses  the  relationship  of  deposit  interest  rates  and                       investments;                       • IS-LM: provides a detailed analysis of interest rates and savings in relation to real                       macroeconomic output and the money market;                       • Endogenous Economic Growth Model: Explains the relationship to the financial                       market on the basis of technological progress and innovation.                Following the global financial crisis, studies focusing on the development of the financial                and capital market in terms of an optimal capital structure in relation to public finances                and the accumulation of capital by banking institutions or analyze the impact of foreign                capital flows on macroeconomic performance (Carp, L. (2012)).                 Choi, S., Lounani, P. (2015) studies the development of the unemployment rate in the US                through the structural autoregressive model and the impact of economic shocks on the                financial market during the economic crisis. Based on data for the US stock index S\&P500,                the results show two main conclusions: overall market uncertainty has a short-term impact                on the unemployment rate, while sectoral uncertainty related to the crisis period had a                steady impact on unemployment.                                                               709","Pan L., Mishra, V. (2018) argues that there are also critics among the economists who                question  the  relationship  between  economic  growth,  economic  performance,  and  the                capital market. The main causes of skepticism are the asymmetry of information (market                conditions  as  well  as  insufficient  information  about  companies)  and  the  reluctance  of                investors to invest in new shares. Another issue that these economists argue is that authors                analyzes the relationship between economic growth and the financial sector and recognizes                that there may be strong correlation between the variables. However, this correlation is                not explored deeply, so it remains questionable whether there is a significant link between                the real economy and the financial and capital markets.                In  terms  of  empirical  exploration  of  capital  markets,  we  can  find  among  the  literature                several  approaches  such  as  regression  models,  causal  relationships,  impulse-response                functions, or autoregressive VAR models. Within the models, they analyze relationships                using primarily monthly or quarterly data of developed countries (especially US market                data), or examine the development of financial markets in emerging markets (such as the                Chinese, Indian, Turkish and Latin American markets).                This  paper  consists  of  four  parts:  the  introductory  part  describes  the  theoretical  and                empirical overview of the expert studies covering interdependence of the capital market                and  the  real  economy;  secondly  we  describe  methodology  used  in  the  model  and  the                acquisition of input data; in the next part the research results are discussed and the final                part summarizes the main results and sets out recommendations for potential research.                The main purpose of this work is to investigate the impact of the United States capital                market on the level of macroeconomic indicators such as unemployment rates and the rate                of economic growth.                 2 Methodology and Data                In  analyzing  the  capital  market  and  economic indicators,  the  choice  of  methodology is                important.  This  work  will  be  based  on  dependence  on  economic  growth  (GDP  and                unemployment) and on the characteristics factors of the capital market. We assume that                certain  characteristics  affect  the  US  capital  market  and  ultimately  affect  the  country's                unemployment  and  GDP  growth.  We  have  data  available  from  1975  to  2015  that  we                obtained from the World Bank database.                We  used  annual  data  for  all  variables  and  apply  two-stage  least  squares  regression                analysis. The general linear model is defined as:                        GDP =   i  +  X  + u      (1)                                             it                                       it                            it                       UNEM =       +  X  +  u    (2)                              it   i    it    it                For t=1,…T and i=1,…N, where GDPit (Gross domestic product) is a dependent variable,                used  as  a  representative  figure  for  the  economic  growth  of  the  country.  UNEMit                (Unemployment) is a dependent variable in our second model. Xit is the time variant matrix                of explanatory variables – characteristics of capital market in our case, I is the unobserved                time-invariant individual effect and uit is the error term, Bai (2009).                 For  both  models,  we used  as  explanatory  variables:  Market  capitalization (%  of  GDP),                Stocks traded turnover ratio (%), Stocks traded total value (% Y2Y), Stock market total                value traded to GDP (%) and Stock market turnover ratio (%). Our goal is to determine                the statistical significance of the model as a whole and whether individual indicators affect                GDP growth and unemployment in the US economy.                 3 Results and Discussion                Both models for GDP growth and unemployment are statistically significant. However, not                all coefficients affect dependent variables, so they were removed from our model. In the                following model, we derived formula from variables that were not correlated with each                                                               710","other - eliminating highly correlated variables. For the GDP model, the resulting formula is                as follows:                               = 0,0420571 ∗      − 0,047284 ∗          + 0,0483991 ∗        +           (3)                                                                                                                    Table 1 Estimated Coefficients from US GDP Growth Model                                Coefficients                        Estimate                p-value                  Market capitalization (MC)                        0,0420571              0,0002 ***                  Stocks traded total value (STTV)                  -0,047284              0,0140 **                  Stock market return (SMR)                         0,0483991               0,0759 *                Note: ***, **, * denote significance levels on 1, 5 and 10 per cent respectively. R-Squared is 0,687079. According to Pesaran-                Taylor test, there is not heteroscedasticity presented in the model, z = 1,16769. Residuals are normally distributed, Chi-                square=3,9378.                                 Source: own elaboration using data from World bank database                 Table 1 shows that GDP is positively influenced by two factors: Market capitalization and                Stock market return negatively influenced by Stock traded total value. Growth of market                capitalization has a positive impact on GDP, as market capitalization reflects the size of the                capital market; we can say that larger and stronger capital markets have impact on GDP                growth. Market capitalization is, in essence, determined by the size of companies, and it is                obvious that companies in the economy are larger; they increase production volume and                thus contributing to GDP growth in the country. Stock market return is the second factor                that positively influences GDP growth. This variable is a return on the capital market, which                means  that if the  capital market is in positive  numbers,  companies listed  on the  stock                market are doing well and ultimately supporting GDP growth. Our results of GDP analysis                are similar to some studies mentioned above, such as Coskun, Y., Seven, U., Ertugrul,                H.M.,  Ulussever,  T.  (2017),  or  Kaserer,  Ch.,  Steffen-Rapp,  M.  (2014),  and  proved  a                relationship between capital market and real economy. Moreover, as Pan, L., Mishra, V.                (2018)  claimed  in  conclusion,  our  analysis  also  proved  a  positive  relationship  between                capital market and the size of companies in the economy. There is an evidence that the                stronger capital market, the higher GDP in the economy is and therefore also the lower                unemployment rate.                On the other hand, we can see that stocks traded total value has no positive effect on GDP                growth, which can be justified by the small importance of the volume of trades on the                capital market. We argue that the factors that have a positive influence on GDP are more                based on quality and Stocks traded total value more on quantity, because it expresses the                volume or the value of trades, which does not have to influence GDP, according to our                results.                Figure 1 shows our results graphically. It can be seen the relationship between GDP and                capital market in this figure. As it has been already stated above, we can also see that the                influence of GDP on market capitalization and stock market return is positive, meanwhile                stock traded total value has a small negative effect on GDP growth.                                                                       711","Figure 1 GDP Growth Compared with MC, STTV and SMR                               350                                                            10                                       Market capitalization  (left)                                     Stocks traded total value (left)                                     Stock market return Y2Y (left)                                        GDPgrowthannual (right)                              300                                                                                             8                              250                                                                                             6                              200                       % of GDP   150                                                        4                                  100                                                            2                                 50                                                                                             0                                0                                                                                            -2                              -50                                 1975   1980   1985   1990   1995   2000   2005   2010   2015                                                   Source: own ellaboration                 Secondly, we analyzed an influence of the US capital market and unemployment rate in                our  unemployment  model.  For  this  model,  we  used  standardized  data.  The  resulting                formula can be described as follows:                                 = 10,3869 − 0,0265562 ∗          + 0,0540119 ∗        − 0,0685065 ∗      + 0,0415102 ∗                                                     +                                                  (4)                                                                          Table 2 Estimated Coefficients from US Unemployment Model                                Coefficients                        Estimate                p-value                  Const                                              10,3869              6,99 e -16  ***                  Stocks traded turnover ratio (STTR)              -0,0265562             0,0,0032 ***                 Stock market return Y2Y (SMR)                     0,0540119              0,0063 ***                                                                                                 -6                 Market capitalization (MC)                       -0,0685065             3,42 e ***                 Stock market total value traded (SMTVT)           0,0415102              0,0003 ***                Note: ***, **, * denote significance levels on 1, 5 and 10 per cent respectively. R-Squared is 0,56185. According to Pesaran-                Taylor test, there is not heteroscedasticity presented in the model, z = 1,40411. Residuals are normally distributed, Chi-                square=0,68997.                                 Source: own elaboration using data from World bank database                 Based on the results that is presented in the Tab. 2, we see that Stock traded turnover                and Market capitalization has a positive impact on unemployment (lower unemployment).                These  factors  influence unemployment,  as  the  growth  of  market  capitalization  and  the                capital  market  turnover  support  the  capital  market,  ultimately  the  companies  and                institutions that create jobs.                On the other hand, the Stock Market Return Y2Y (year-to-year) and Stock market total                value  traded  does  not  affect  the  reduction  of  unemployment.  We  are  based  on  the                assumption that capital market gains and the volume of trades on the capital market do                                                              712","not have a direct (positive) impact on unemployment as these factors apply to the business                and profit of non-job-creating investors.                 The similar results is stated in study Choi, S., Lounani, P. (2015) where authors analyzed                unemployment  rate  and  economic  shocks.  Also,  our  findings  about  an  impact  of                unemployment  on  economic  activity  is  comparable  to  one  of  conclusion  of  Choi,  S.,                Lounani,  P.  (2015)  who  claims  that  unemployment  is  affected  by  indicators  of  capital                market only in the short-term period of time.                The results of our unemployment model are presented in Fig. 2.                        Figure 2 Unemployment Compared with Statistically Significant Coefficient                      450                                                                             12                                       Market capitalization (left)                                 Stocks traded turnover ratio (left)                                    Stock market return Y2Y (left)                     400       Stock market total value traded (left)                                         Unemployment (right)                                                                                                     10                     350                      300                                                                                                     8              % of GDP   250                                                                          6                      200                      150                                                                                                     4                     100                       50                                                                                                     2                       0                       -50                                                                             0                         1975     1980     1985     1990     1995     2000     2005    2010     2015                                                   Source: own ellaboration                4 Conclusions                 The  aim  of  this  work  was  to  analyze  relationship  between  the  capital  market  and  the                underlying macroeconomic variables that we applied to the US data. The literature provides                evidence of a correlation between the capital market and the country's economy, either at                the  microeconomic  level  (market  liquidity  analysis,  efficient  allocation  of  financial                resources) and at the macroeconomic level (analysis of various macroeconomic indicators                in relation to the financial market as a whole).                 In our paper, we have come to the conclusion that the capital market or capital market                factors significantly affect GDP growth and unemployment. The Stock market total value                traded negatively affects GDP growth and even unemployment, so we can consider this                factor to be significant in a negative sense. 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International Journal of                Economics and Finance, vol. 3(2), pp. 111-115.                                                                                714","Identification of Financial Strategy in E-commerce                                                                         2                                                   1                             Veronika Svatošová , Sylva Chalupová , Petra Dvořáková          3                                                1,2,3  Mendel University in Brno                                  Faculty of Regional Development and International Studies,                                       Department of Regional and Business Economics                                   Street and Building Number, 613 00 Brno, Czech Republic                                           1 E-mail: veronika.svatosova@mendelu.cz                                             2 E-mail: xchalup8@node.mendelu.cz                                             3 Email: xdvora89@node.mendelu.cz                 Abstract:  The  researches  and  practice  declare  the  strategic  key  factor  of  long-term                business success is considered to be strategic approach to financial management along                with proposing and implementing financial strategy. However, this area explored is often                among businesses deeply underestimated. The current researches focus on the modern                aspects  of  e-commerce,  nevertheless,  no  impact  is  given  to  financial  strategy  in                e-commerce.  Therefore,  this  paper  deals  with  determining  the  financial  strategy  in                e-commerce. The main objective of paper is to identify the financial strategy of selected                enterprises primarily focused on e-commerce. The research sample of enterprises involved                to  the  survey  is  selected  based  on  the  following  restrictive  criteria:  headquarter  of                enterprise in the Czech Republic, legal form of business joint stock company, existence of                enterprise in e-commerce minimum 5 years, main business area B2C market, enterprises                trading  mostly  electronically  selling  goods.  The  main  research  methods  are  following:                analysis of financial statements of selected enterprises, selected methods of situational                analysis and financial analysis and selected methods of descriptive statistics and financial                strategy  model  simulating  in  Vensim  program.  Input  data  is  used  for  the  year  2016                compared to the year 2011, i.e. observing the development in 5 years. Findings identified                the selected enterprises apply in most cases a conservative financial strategy (i.e. strategy                of maximum liquidity). The conclusion of paper is based also on proposing the series of                recommendations for effective creating and implementing financial strategy in e-commerce                with  a  help  of  dynamic  financial  strategy  model  that  could  support  the  long-term                competitive  development  of  business in  e-commerce.  This  model  simulates  changes  of                conservative financial strategy into aggressive financial strategy focusing on maximizing                profitability during next 5 years, especially during the period of rapid economic growth and                massive e-commerce development.                Keywords:  e-commerce,  financial  strategy,  situational  and  financial  analysis,  Vensim                program, financial strategy model                 JEL codes: M20, M21, M29, G30, G39                 1 Introduction                Strategic approach to managing business activities is considered to be a baseline for a                long-term business success and its development or strategic market position. However,                creating  and  implementing  appropriate  corporate  and  functional  strategies  among                businesses  in  practice  is  obviously  deeply  underestimated.  When  managing  finance  in                business is used, it is obviously limited on functioning accounting and strategic approach                to financial aspects of business is missing. The same prerequisite is given to the area of e-                commerce  even  though  this  business  field  is  gaining  more  and  more  popularity  and                importance in global economy. Therefore, the paper focuses on determining the financial                strategy in e-commerce. The research deals with the identification of financial strategy of                of  enterprises  primarily  focused  on  e-commerce  with  a  help  of  modelling  of  financial                strategy for e-commerce in Vensim program. Finally, the dynamic financial strategy model                for  e-commerce  is  proposed  that  suggests  the  optimal  e-commerce  financial  strategy                development and that could support the long-term competitive development of business                in e-commerce.                                                               715","2 Theoretical Background – Financial strategy and E-commerce                E-commerce  represents  just  a  part  of  e-business.  E-business  includes  expect  of  e-                commerce also e-administration, e-logistics, e-government, e-learning etc. E-commerce is                defined as a purchase of goods or services carried out by computer networks. The term                includes  service  and  support  after  the  purchase  too.  Definitions  state  that  providing                business information and maintaining business relationships are also important part of e-                commerce. Business-to-consumer (B2C) e-commerce is highly broadened owing to offering                a  quick  response  to  request  or  wide  offer  of  products  (Kunešová  et  Mičík,  2015).  EE-                commerce is internationalized and people are generally used to buy things online across                national borders. It means a lot of new business opportunities for small domestic firms or                international firms (Hallikainen  et  Laukkanen, 2018).  The  objective  of  the e-commerce                model is to give consumers together, on one side, and the sellers on the other and to                determine  the  way in which  they  communicate.  The  seller  created  business strategies,                which aim to increase sales and build consumer trust (Marković at al., 2016). Electronic                commerce can be also used as a tool to reduce the economic space between countries                (Villa et al., 2018) Because the collaboration with foreign associates is very important in                today’s digital world for better organizational performance and co-creating value (Nadeem                et al., 2018).                The financial strategy is defined as a relatively coherent and interconnected set of strategic                financial objectives, criteria and rules that underlie such planning (Landa and Polák, 2008).                Three steps to set up a successful financial strategy are following (Mallete, 2006): Step 1                – Establish appropriate financial capital structure, following which a determination would                be made of the magnitude of its cash surplus; Step 2 – Understand whether a company is                undervalued or overvalued in the market, by examining investors’ expectations; Step 3 –                Develop  a  financial  strategy,  to  be  proposed  to  the  Board  for  approval,  ensuring  the                company’s operations are sufficiently funded, that financial balance is achieved. According                net working capital, three basic financing strategies are then distinguished to (Režňáková,                2012, p. 107-108): “aggressive financial strategy – net working capital was is negative.                The part of long-term assets is financed by short-term resources. These situations occur                in  a  period  of  rapid  business  growth,  extensive  investment  or  withhold  payments  to                suppliers; conservative financial strategy –the long-term sources of financing are used                to  finance  seasonal  fluctuations  in  current  assets.  Here,  it  is  typical  lax  approach  to                inventory management and collection of its receivables or prompt payment of liabilities to                suppliers; balanced financial strategy – consistency between the maturity of financial                sources with a lifetime of assets in the company is ensured.”                Considering  the  expansion  of  information  technology  and  new  business  models,  e-                commerce is more ordinary in many sectors (learning, shopping, tourism etc.) in these                days.  The  difference  between  e-commerce  enterprises  and  traditional  enterprises  is                obvious. That is the reason why also financial management differs. Financial management                in e-commerce companies has wider possibilities. The study on financial management is                very important, especially for the sustainable development of e-commerce companies. E-                commerce  often  follows  a  new  business  model,  which  works  on  browser  or  server                applications, offering several commercial trade activities for people from all over the world                (Li, 2017). Financial management along with financial strategy in e-commerce companies                is centralized. The optimal mode of centralized financial management is a network financial                system. It helps to allocate funds collectively in all branches in one big company. Another                important mode is to coordinate finance and business, which is easier with today´s speed                of internet (Tu, 2016). We can see two kinds of financial strategies and goals which e-                commerce  brings.  The  first  one  is,  the  enterprise  want  to  realize  a  maximum  profit.                Enterprises should reduce their costs and improve their income to increase their profit,                which  they  can invest  to  their  development.  The  second  financial  strategy is to  realize                maximum  values  of  enterprises  (Tu,  2016).  Global  firms  have  important  advantage  in                influencing online pricing for consumer products thanks to the achievement of economies                of scale. Global firms in developed countries, for example the Amazon, can utilize their                resources and network scale to easily emasculate the local markets of developing countries                (Broome, 2016). Critical success factors for electronic commerce include: adding value,                                                            716","focusing  on  a  niche,  expanding,  maintaining  flexibility  and  segmenting  geographically,                adopting the right technology to handle a critical perception, providing exceptional service,                creating an effective connectivity, and understanding the culture of the local markets (Villa                et al., 2018). According to the abilities of firms these changes may create new opportunities                or offer new challenges such as a shift from being a producer depending on wholesalers                which  provide  access  to  markets,  to  dealing  directly  with  the  end-customers  (Broome,                2016).  “With  the  deepening  of  the  research  of  electronic  commerce,  expanding  the                research angle of ecommerce enterprises, the traditional financial management theories of                the  enterprises  need  to  be  updated,  the  traditional  pattern  of  enterprise  financial                management has been difficult to adapt to the needs of the times, therefore explore the                E-commerce  Context  for  enterprise  management,  the  survival  and  development,  is                especially suitable for the financial management mode of enterprise electronic commerce                not only has the theoretical necessity, and the realistic necessity” (Lili, 2016).                “The concept of network finance and the implementation of the corresponding strategy is                a major innovation of enterprise financial management theory. The efficiency of capital                management  directly  determines  whether  the  financial  management  of  e-commerce                enterprises is effective or not, and to a large extent affect the success or failure of e-                commerce business. Decentralization is a major feature of the e-commerce enterprise, as                the premise for the construction of e-commerce enterprise financial organization mode it                is  necessary to  set  up  Financial  Shared  Service  Center  as  a  financial  organization  core                connection points” (Jia, 2016). We know the traditional models of financial management:                Centralized Type, Decentralizing and Type Fusion of Centralization and Decentralization.                E-commerce cannot accept these traditional models anymore. New financial processes in                these days created a new financial management model with huge theoretical foundation                and accounting practices: Network of Financial Services (Cui, 2016).                 3 Methodology and Data                 The paper is based on the research that aims to identify the financial strategy of selected                enterprises  primarily  focused  on  e-commerce.  The  identification  of  financial  strategy  is                realized with a help of financial strategy model proposed in Vensim program as a result of                previous research Svatošová, Svobodová, 2016; Svatošová, 2017). For fulfilling the main                aim of the research, the selected methods of financial analysis and situational analysis are                used  (details  see  in  Model  specification)  complemented  with  analysis  of  financial                statements  of  selected  enterprises.  The  modelling  has  been  provided  for  median  and                arithmetic mean of selected financial analysis indicators for the year 2016 compared to the                year 2011 to observe the development of financial situation of e-commerce in 5 years.                Data for the year 2017 is not available yet.                 Research Sample                Research Sample is consisted of enterprises primarily focused on e-commerce selected on                following restrictive criteria: headquarter of enterprise in the Czech Republic, minimum                existence of the enterprise for 5 years, focused on B2C market solely selling goods, legal                form  joint  stock  company  (their  financial  statements  are  regularly  published  in  Trade                Register)  and  member  of  Association  for  Electronic  Commerce  (as  a  reliability  of                seriousness and trustworthiness of their business activities). Studying the environment of                Czech e-commerce has shown that there is no comprehensive database of businesses that                trade mainly electronically. Therefore, for the purpose of the research, the database was                created on the basis of the restrictive criteria defined above. Each individual company was                searched     based    on     the   following    sources:    https://www.apek.cz/seznam-                členu,http://wwwinfo.mfcr.cz/ares/ares_es.html.cz,       https://portal.justice.cz/Justice2/                Uvod/uvod.aspx.                 Based  on  the  restrictive  criteria,  in  summary  26  enterprises  primarily  focused  on  e-                commerce  (having  e-shops  or  e-malls)  were  involved  to  the  research  sample.  11                enterprises  have  up  to  50  employees,  6  enterprises  have  up  to  250  enterprises,  1                enterprise has up to 500 employees and remaining 8 enterprises have over 500 employees.                3 enterprises belong to the 10 biggest Czech enterprises trading mostly electronically (i.e.                                                            717","Alza.cz, Mall.cz, Datart). The oldest enterprise in research sample was set up in 1991 (i.e.                originally enterprise selling only “offline”), the youngest enterprise was set up in 2011.                Nowadays,  it  is  almost  impossible  to  make  difference  among  enterprises  that  were                originally  selling  solely  traditionally  and  later  were  connected  into  e-commerce  and                enterprises that were selling earlier only online and later started selling also traditionally.                Both groups of enterprises are involved into this research sample. Individual e-retailers                are not subject of the research because it is focused on the biggest enterprises primarily                oriented on e-commerce on the Czech market; furthermore individual e-retailers in most                cases have not complemented data for financial analysis in financial statements (there are                not published in Trade Register)and are not willing to be participated this research. The                research sample is consisted of relatively homogeneous group of selected enterprises.                Model Specification                 This dynamic financial strategy model derives from the basic principles of financial analysis                that explores the profitability, liquidity and the cost and capital efficiency. For creating the                model,  the  selected  variables  of  financial  analysis  were  used  (see  Table  1)  –  i.e.  ROE                (Return on Equity) and ROA (Return on Assets) as a basic variables of profitability, Total                (Current)  Liquidity  as  a  complex  liquidity  variable,  Long-term  Coverage  (Level  of                Capitalization) and WACC (Weighted Average Cost of Capital) as a complex variable for                cost  and  capital  efficiency  evaluation  (details  see  Table  1  and  Svatošová,  Svobodová,                2016; Svatošová, 2017). Cost of Equity is calculated based on INFA methodology (MPO                ČR, 2016; MPO ČR, 2011).                                       Table 1 Variables in Financial Strategy Model                   Return on Equity          =                          (ROE)                                                  Return on Assets          =                          (ROA)                                                                                                                                       Total Liquidity                                  =                                                     ℎ      −                                                                                                                           +        −                                                     +                                  Long-term Coverage                                                =                                                                                                                                                                                                                                                              =       ∗  +       ∗ (1 −   ) ∗                                                                    WACC (Weighted           =       +       +         +                                Average Cost of                  Capital)                                                                                                =                         ∗ (1 −   )                                           ℎ                                                                           Financial Strategy                            +        +                             +                                              +                           Model                                                        =  5                Legend: EAT – Earnings after Taxation, RE – Cost of Equity, RD – Cost of Debts, t – Tax rate, E – Equity, D – Debts, C - Total                Capital, RF – risk-free rate,  RB – business risk, RFS – risk premium of financial stability, RLA – risk premium of company size                                        Source: own work based on (Svatošová, 2017)                Afterwards, the scoring evaluation for individual variables in model was determined (see                Table 2). The set points on interval 1 – 5 are determined, where 5 means the excellent                result  and  1  means  very  bad  result.  The  selected  values  and  set  points  of  individual                variables are inspired by Kralicek Quick test.                     Table 2 Evaluation of Variables in Financial Strategy According to Points (1 – 5)                                         Excellent    Very good (4)    Good          Bad       Very bad  (1)                                            (5)                         (3)          (2)                  ROE                      > 0.50        > 0.30       > 0.10        > 0.00        < 0.00                  ROA                      > 0.15        > 0.12       > 0.08        > 0.00        < 0.00                  Total liquidity          > 1.80        > 1.50       > 1.00        > 0.80        < 0.80                  Long-term coverage       > 1.1          > 1         > 0.98        > 0.95        < 0.95                  WACC                     < 0.05        > 0.05       > 0.15        > 0.25        > 0.30                                        Source: own work based on (Svatošová, 2017)                 Final  results  of  the  dynamic  strategy  model  are  pointed  as  an  arithmetic  average  of                received points of set variables (see Table 1) that determine current financial strategy. A                detail  description  of  individual  strategies  is  given  in  Table  3.  The  concrete  financial                strategies are inspired by these sources (Režňáková, 2012, p. 107-108; Živělová, 2014).                                                             718","Table 3 Received Points and Final of Results to Concrete Financial Strategy                   Evaluation                                   Type of                   According to   Financial                           Description of                    Received      Strategy                           Financial Strategy                     Points                                 Strategy of   Aggressive strategy: maximizing the profitability, low or negative value of working                     4 – 5        maximum     capital,  possibilities  of  high  volume  to  long-term  investments,  potential  of  the                                 profitability   company to be expanded and be progressive, the opportunity for absolute innovations                                 Strategy of   Balanced strategy: reaching the reasonable value of working capital and acceptable                                 proportional   profitability,  the  short-term  investments  or  long-term  investments  with  lower                     3 – 3.9                                 profitability   volumes could be realised, the expansion of company is possible, but only moderate,                                 and liquidity   not progressive                                 Strategy of   Conservative  strategy:  high  volume  of  working  capital,  low  profitability,                     2 – 2.9      maximum     conservative  approach  to  the  managing  the  long-term  investments  (no  long-term                                   liquidity   expanding the company, focusing on operational issues of the business)                                              Rescue strategy: the effort to be rescued from bankruptcy, bad values of financial                                  Crisis and   analysis (liquidity, profitability,  indebtedness, etc., i.e.  no comprehensive  financial                     1 – 1.9     remediation   strategy is in the company realised, change of corporate and business strategy, the                                   strategy   change of company conception, production and business, looking for new sources and                                              opportunities for rescue and redevelopment of the company                                        Source: own work based on (Svatošová, 2017)                 Based on the information above, the financial strategy model without dynamics in Vensim                program was created (see Figure 1), in which the direct links of dependent and independent                selected variables are observed that have direct impact on final results.                                   Figure 1 Financial Strategy Model without Dynamics                                                         Source: own in Vensim program based on (Svatošová, 2017)                4 Results and Discussion                 The financial strategy model has been then simulated for identifying the current financial                strategy  among  26  selected  enterprises.  For  final  evaluation,  the  arithmetic  mean  and                median values  among  26  enterprises  have  been  used  (see  Table  4;  for  the year  2016                compared  to  the year  2011).  The  results  are influenced  by imperfections  due  to  using                arithmetic mean and mean for each variable and also by the fact that accruals and deferrals                have been not calculated in this financial analysis. The median and mean values have been                selected to transform the data on one value as the form of preliminary research (the other                research will focus on financial strategy of individual selected enterprises in e-commerce                and their comparison).                       Table 4 Input Data for Creating the Financial Strategy Model in E-commerce                                  (n = 26 enterprises primarily focused on e-commerce)                                                       MEDIAN VALUES             ARITHMETIC MEAN VALUES                           Issue/Year               2011           2016           2011           2016                             Assets                 60489          107037        339317.8       600487.4                           Fixed Assets              8288          9375          78088.5        110572.8                          Current Assets            50451         100299.5       248708.7       478460.1                           Inventories             26004.5         51687          130544        298966.4                                                            719","Receivables             13131.5         35917         79142.92       145145.4                     Short-term financial assets    3560.5         9182.5        38635.85       38960.5                             Equity                 10584         12598.5        148111.1       102605.6                         Registered capital          2000          2000          73347.69       70796.62                             Funds                   200            200          6484.038        21955                         Retained earnings           3410           -32          52961.96       -11912.3                       Economic result (EAT)        765.5          1493          15197.5        26289.23                             Debts                  28878          136245        183477.6       474015.3                            Reserves                  0              0           1070.692        5537.5                        Long-term liabilities         70           259.5         18318.42       40098.35                      Long-term bank credits          0              0           1070.692       14854.69                       Short-term liabilities       16099          56435         138318.42      335451.4                      Short-term bank credits         0              0           30988.62       50953.92                         Cost of interests            33            635          1281.154       6534.115                           t – tax rate              19%            19%            19%            19%                         rf – risk-free rate*       3.79%          0.58%          3.79%          0.58%                         rB – business risk*        2.68%          2.63%          2.68%          2.63%                  rfs – risk premium of financial stability*   3.65%   6.10%      0.03%          0.20%                   rLA – risk premium of company size*   5%        5.35%          5.35%          5.35%                         rE – cost of equity        15.12%        14.66%          11.85%         8.76%                         rD – cost of debts          0%             0%            2.10%          5.81%                              ROE                0.120069 (3)   0.407812 (4)    0.10692 (3)   0.245399 (3)                              ROA                0.0150144 (2)   0.0140635 (2)   0.0465594 (2)   0.0442845 (2)                          Total liquidity         2.25187 (5)   1.45644 (3)     1.51425 (4)    1.25017 (3)                        Long-term coverage       0.127911 (1)   0.0405938 (1)   0.513102 (1)   0.282356 (1)                             WACC                0.0378297 (5)   0.00758445 (5)   0.0623218 (4)   0.0548998 (4)                         Financial strategy          3.2             3             2.8            2.6                                                  Strategy of    Strategy of                    Type of the financial strategy    proportional   proportional   Strategy of   Strategy of                                                                                                maximum                                                                                maximum                          in current year         profitability   profitability and   liquidity   liquidity                                                  and liquidity   liquidity                 *calculated according to INFA methodology (details see MPO ČR, 2016; MPO ČR, 2011), changes in cost of equity and cost of                                   debts have been adopted according to INFA methodology in 2011 and 2016                 Source: own work with help of Vensim program (Note: the amounts are given in thousands CZK)                                  Figure 2 Financial Strategy Modelling for E-commerce                                      (example, year 2016, arithmetic mean values)                                                                   Source: own processing in Vensim program                 We have to highlight there are differences and gaps in financial indicators among observed                enterprises, especially in the volume of assets, portion of equity and debts or final economic                results.  These differences may be also caused by the size and origin of enterprise (also                due to making difference among enterprises trading traditionally and electronically and                vice versa). Despite these facts we can define financial strategy for the observed group of                enterprises.  Based  on  the  financial  strategy  model  simulating  in  Vensim  program,  the                selected enterprises focused primarily on e-commerce apply in most cases a conservative                financial strategy (i.e. strategy of maximum liquidity; final value of the model is 2.6 for                arithmetic mean values in the year 2016, details as an example simulating see in Figure                2). The same financial strategy is used in the year 2011 when applying arithmetic mean                                                             720","variables. When the median variables is set into this model, the financial strategy for both                observed years is balanced financial strategy (i.e. strategy if proportional profitability and                liquidity). We can conclude enterprises focused primarily on e-commerce focus on higher                liquidity when having lower profitability (in both cases when involving arithmetic mean and                median  variables).  The  main  reason  of  using  the  conservative  financial  strategy  in  e-                commerce is missing strategic thinking on investment and long-term development                of the business (this statement has been proven in previous research, i.e. Svatošová,                2018,  the  similar  results  are  observed  in  e-commerce).  The  other  economic  indicators                confirmed these results. Based on financial analysis in 2016 compared to 2011, the                economic  results  (EAT)  reached  lower  values  also  due  to  the  fact  many  enterprises  in                research sample reached economic loses. The higher liquidity is caused by higher values                of  short-term  receivables  and  inventories.  On  average  the  current  ratio  of  observed                enterprises is 2.29 (the recommended value 1.5 – 2.5), quick ratio 1.45 (the recommended                value 1 – 1.5) and cash ratio is 0.77 (the recommended value is 0.3 – 0.5). The similar                values of liquidity were reached in 2011 too. On average, receivable turnover period is 334                days in 2016 and 62 days in 2011, inventory turnover period is 55 days in 2016 and 51                days in 2011 and suspension of payments period is 110 days in 2016 and 95 days in 2011.                Money turnover cycle is 277 days. On average in 2016, ROA is 8.62 % and ROE is 25.6 %,                in 2011 ROA is 4.45 % and ROE 10.26 %. Equity ratio is 32 % and debt ratio 68 % in both                observed years. Short-term indebtedness is 56 % in 2016 and 49 % in 2011. Long-term                indebtedness  is  9.61  %  in  2016  and  16  %  in  2011.  On  average  in  2016,  the  level  of                capitalization is 0.27 (the recommended value is around 1), in 2011 this value was on                average 0.48.                 Recommendation                Especially, during the area of massive e-commerce development due to rapid economic                upturn, it is recommended to be focused on higher profitability that could be used in other                investment activities in e-commerce, i.e. expansion or innovation along with technological                progress.  Therefore,  figure  3  proposes  the  Dynamic  Financial  Strategy  Model  for  e-                commerce for the next 5 years (the starting point year 2016 on example of arithmetic                mean values). This model demonstrates changes in fixed assets (growth by 50 million CZK                each year), growth in financial means (by 50 million CZK each year), growth in economic                result (by 50 million CZK each year) and growth in long-term indebtedness (by 50 million                CZK each year), growth in cost of equity (by 1 % each year) and growth in cost of debts                (by 1% each year). The simulation of these changes has caused a change of conservative                financial  strategy  into  aggressive  financial  strategy  focusing  on  improving                profitability (in model with final value 4 in 5 years).                               Figure 3 Dynamic Financial Strategy Model for E-commerce                                           (year 2016, arithmetic mean values)                                                      Source: own processing in Vensim program, prerequisite for next 5 years                                                            721","Figure  4  illustrates  changes  in  the  proposed  Dynamic  Financial  Strategy  Model  for  e-                commerce during the next 5 years, i.e. variables of ROE, ROA, current ratio (total liquidity),                level  of  capitalization  and  WACC.  The  final  average  evaluations  of  model  have  been                changed  from  2.6  into  4  that  reflect  an  aggressive  financial  strategy.  These  changes                suppose  the  economic  growth  and  e-commerce  expansion  for  the  next  5  years  with                prerequisite of investment into online business (generating growth in assets and long-term                indebtedness while respecting acceptable value cost on total capital in the form of WACC).                The suggestions should lead into economic development in e-commerce and improving                long-term competitiveness of observed enterprises.                   Figure 4 Changes and Its Impact on Variables in Dynamic Financial Strategy Model                                            (in 2016, arithmetic mean values)                                                   Source: own processing in Vensim program, prerequisite for next 5 years                 5 Conclusions                The theoretical framework and results of the research has confirmed only few researches                are dedicated to the issue of financial strategy and financial management in e-commerce.                With  the  development  of  e-commerce,  new  approaches in  business  have  to be  applied                including  the  financial  management.  This  paper  focused  on  identification  of  financial                strategy of enterprises primarily focused on e-commerce. The observed research sample                of enterprises primarily focused on e-commerce apply conservative financial strategy (i.e.                strategy of maximum liquidity) caused be lower level of profitability. The identified financial                strategy  in  e-commerce  is  based  on  higher  receivable  turnover  period  and  inventories                turnover period. Especially, during the period of rapid economic growth and massive e-                commerce  development,  it  was  recommended  to  change  the  financial  strategy  into                aggressive strategy focusing on improving profitability that could support the long-term                competitive development of business in e-commerce. Dynamic Financial Strategy Model in                Vensim program simulated the changes of observed variables within 5 years. The paper                was focused on small group of enterprises primarily focused on e-commerce; nevertheless,                it  could  provide  a  baseline for  the  strategic  approach in  managing  finance in  Czech  e-                commerce.  The  further  research  activities  will  focus  on  greater  research  sample  of                enterprises primarily oriented on e-commerce based on comparison of individual groups of                e-retailers and other enterprises trading mostly electronically.                 Acknowledgments                This paper was prepared in the framework of the project IGA of Mendel University in Brno                entitled “The importance of strategy and strategic management of small and medium-sized                enterprises primarily focused on e-commerce” with the registration number FRRMS - IGA                - 2018/003.                                                                722","References                Bender, R., Ward, K. (2012). Corporate Financial Strategy. 2nd ed. Oxford: Routledge.                 Broome, P. A. (2016). Conceptualizing the foundations of a regional e-commerce strategy:                Open  networks  or  closed  regimes?  The  case  of  CARICOM. Cogent  Business  \&                Management, vol.  3(1),  Retrieved  from:  https://www.cogentoa.com/article/10.1080/                23311975.2016.1139441.                Cui,  J.  (2016).  Study  on  the  Dynamic  Financial  Management  Mode  under  E-commerce                Era. 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Finanční analýza podnikové sféry se zaměřením na konkurenceschopnost                sledovaných  odvětví  za  rok  2011.  Retrieved  from:  https://www.mpo.cz/assets/                dokumenty/46478/52522/591162/priloha002.pdf                 MPO ČR (2016). Finanční analýza podnikové sféry se zaměřením na konkurenceschopnost                sledovaných         odvětví        za       rok        2016.        Retrieved        from:                https://www.mpo.cz/assets/cz/rozcestnik/analyticke-materialy-a-statistiky/analyticke-                materialy/2017/5/FA4Q16.pdf                Nadeem, A., Abedin, B., Cerpa, N., Chew, E. (2018). Editorial: Digital Transformation \&                Digital Business Strategy in Electronic Commerce - The Role of Organizational Capabilities.                Journal of Theoretical and Applied Electronic Commerce Research, vol. 13(2), pp. 1-8.                 Režňáková, M. (2012). Efektivní financování rozvoje podnikání, 1st ed. Praha: Grada.                                                               723","Svatošová,  V.,  Svobodová,  Z.  (2016).  Determination  of  Financial  Strategy  of  Selected                Company  and  its  Importance  for  Other  Business  Development  -  Case  Study.  In:                Proceedings of the 13th International Scientific Conference European Financial Systems                2016. Brno: Masarykova univerzita, pp. 776-786.                Svatošová.  V.  (2017).  Identification  of  Financial  Strategy  in  Small  and  Medium-sized                Entrepreneurship. Acta Universitatis agriculturae et silviculturae Mendelianae Brunensis =                Acta  of  Mendel  University  of  agriculture  and  forestry  Brno,  vol.  65(4),  pp.  1435-1453.                Retrieved from: https://acta.mendelu.cz/media/pdf/actaun_2017065041435.pdf.                Svatošová. V. (2018). Importance of Strategy and Aspects of Strategic Development in                Small and Medium-Sized Entrepreneurship. Ekonomický časopis, vol. 66(4), pp. 329-349.                Tu,  Y.  (2016).  Study  on  the  Strategies  of  Financial  Management  in  E-Commerce                Enterprises.  In:  Proceedings  of  the  2016  International  Conference  on  Education,                Management, Computer and Society. Shenyang, China, vol. 37, pp. 1350-1352.                 Villa, E., Ruiz, L., Valencia, A., Picón, E. (2018). Electronic Commerce: Factors Involved in                its  Adoption  from  a  Bibliometric  Analysis.  Journal  of  Theoretical  and  Applied  Electronic                Commerce  Research,  vol.  13(1),  pp.  39-70,  Retrieved from:  https://www.researchgate                .net/publication/322190489_Electronic_Commerce_Factors_Involved_in_its_Adoption_fro                m_a_Bibliometric_Analysis/overview.                Živělová, I. (2014). Financial Management. 2nd ed. Brno: Mendel University in Brno.                                                                                                                   724","Qualitative Indicators (Soft-facts) in Internal Rating Systems                                                        Martin Svítil                                         Soukromá vysoká škola ekonomická Znojmo                                               Loucká 656/21, 66902 Znojmo                                                E-mail: svitil.martin@svse.cz                 Abstract: The article builds on last year's work, which compared internal rating systems                of three banks from the German-speaking region. This year's paper deals in detail with the                analysis of qualitative indicators (soft - facts), which is one of the two main components                of rating systems of banking clients. The qualitative indicators (soft - facts) have different                wage in the considered rating systems, although in all of them the wage is lower than 50%                and / or the ability of soft-facts to change the overall rating score is limited. On the other                hand, in all cases the importance of qualitative factors is significant enough to pay attention                to them. The research is focused on the rating of business entities, more precisely the                corporate, (especially limited liability companies or joint-stock companies). It does not deal                with  the  rating  of  natural  persons  or  non-profit  organizations,  municipalities  etc.  The                procedure  of  collecting  empirical  data  as  well  as  data  from  relevant  literature,  their                assessment according to the criteria of verifiability and relevance and the application of                the  induction  method  was  used  and  a  generalization  of  conclusions  was  subsequently                made. The result of the research shows that two categories of qualitative indicators (soft -                facts) are present in all considered rating systems: (i) quality of company’s management                and  /  or  strategy  and  (ii)  market,  where  the  evaluated  company  is  present.  Another                regularly  present  qualitative  indicator  is  (iii)  accounting  or  related  indicators  like                information  system  or  audit  quality.  On  the  other  hand,  the  use  of  the  factor  (iv)                relationship with the bank (or similar) is quite different across the surveyed rating systems.                The roles of members of staff in making of the rating are also mentioned.                Keywords: Rating, Risk Management, Soft-Facts, Qualitative factors                 JEL code: G24                 1 Introduction                My last year's works (Svítil, 2017a, Svítil, 2017b) have dealt with the comparison of three                internal  rating  systems,  compatible  with  IRB  Approach  for  Basel  II.The  result  of  the                research showed, that the structures of compared bank’s internal rating systems are very                similar, based mainly on integration of \"hard-facts\" rating based on quantitative indicators                and \"soft-facts\" rating based on qualitative indicators.                To continue my research, I decided to focus on the qualitative factors (soft - facts), which                seems to be much less detailed in the recent literature (see below). In just a few of works,                I have managed to find comparable data like those I have worked with, and in all cases to                a single rating system only. If it was worthwhile, I added these data to my comparison                (Belás,Cipovová  (2011),  Kavan  (2017)  and Radojevic,  Suknovic  (2008),  for details  see                below). My own sources are three internal rating systems used currently and / or in the                recent  past  by  three  banks  or  banking  groups  originating  from  the  German-speaking                economic  environment  and  regions.  All  the  mentioned  banks  use  their  rating  tools  for                estimate the credit risk of their clients, expressed in the PD (probability of Default).                My goal was to find out how much the structure of used qualitative factors (soft- facts) is                similar  or  even  the  same  across  the  particular  rating  systems  and  what  weights  of                individual factors are used for calculations.                Belás and Cipovová (2011) analyze the requirements for rating models of banks in relation                to  financial  performance  and  compare  them  to  selected  credit  scoring  and  bankruptcy                models. In their work, there is just one real model (used by existing bank) mentioned. The                conclusion of the research showed, that \"Bankruptcy and credit scoring models produce                results that aren’t quality sufficient for credit decisions. The final decision about proposal                to  qualified loans  should  be  based  on expert  estimates  of  credit  analyst,  which  should                                                            725","assess whether the company is able to withstand the temperature rise or it’s threatened                by insolvency on the basis of detailed financial analysis.” (Belás, Cipovová (2011), p.119).                This conclusion confirms that it is meaningful and important to examine the qualitative                elements of the rating systems, which is the essence of this article.                Grunert, Nordenand and Weber (2005) in their work came from a similar environment as                the data in my text: they surveyed data from four major German banks. Their conclusion                is  unambiguous:  the  combined  use  of  financial  and  non-financial  factors  leads  to  a                significantly  more  accurate  default  prediction  than  the  single  use  of  financial  or  non-                financial factors. But they have been limited by the availability of data.                The importance of qualitative factors for rating is also mentioned by other authors, like                Guserl  (2014),  or  Morrow  (1998),  or  Crouhy,  Galai,  Mark  (2001):  “The  rating  process                includes  quantitative,  qualitative  and  legal  analyses.  (...)  The  qualitative  analysis  is                concerned with the quality of management, and includes a thorough review of the firm’s                competitiveness within its industry as well as the expected growth of the industry and its                vulnerability to technological changes, regulatory changes and labor relations.” (Crouhy,                Galai, Mark (2001), p. 51).                The research conducted by Fracassi, Petry and Tate (2016) examined the influence of the                analyst's subjective judgment on the outcome of the rating, and although the article deals                more with credit rating agencies (CRA) than banks, its outcome is worth mentioning: “We                find  that  significant  variation  in  credit  ratings  can  be  explained  by  differences  in  the                dispositional optimism of the analysts covering the firm. (...) Our results suggest that some                firms can face more frictions in raising capital simply because they are covered by less able                credit  analysts.  Perhaps  of  more  significance,  our  results  suggest  that  long-term                relationships between firms and the analysts who rate their debt issues can lead to inflated                ratings and costs of capital that are too low.” (Fracassi, Petry, Tate (2016), p. 535). My                article  briefly  focuses  on  attempts  to  minimize  possible  negative  impacts  of  subjective                assessment by one person in Part 3.3.                 2 Data and methodology                For  comparison,  documentation  for  three  rating  systems  used  by  three  different  banks                (banking groups) from the German-speaking environment was available. Since some of                these documents are confidential and have not been granted full disclosure, the results of                the investigations are limited in some respects. It is also not possible to publish the names                of the mentioned banks (banking groups). The data are anonymized accordingly, however,                in order not to affect their value for the research. These three banking institutions and                their rating systems are listed under A, B and C.                 Data  from  available literature  were  also  used  for  comparison:  Belás,  Cipovová  (2011),                Kavan (2017) and Radojevic, Suknovic (2008). The first mentioned source concerns a real                (anonymous) bank acting in Central Europe, the second one is a leasing company owned                by an Austrian bank. The last mentioned source is just a proposal (not yet used in practice),                but in my view it is interesting enough to be included in the comparison. These three rating                systems are listed under D, E and F, where F is the unused proposal.                The  research  is  focused  on  the  rating  of  business  entities,  more  precisely  corporate,                (especially limited liability companies or joint-stock companies). It does not deal with rating                of natural persons or non-profit organizations, municipalities etc. Excluded are also the                banks, insurance companies, leasing companies etc. For all these entities the examined                banks (A, B, C) have special different modules in their rating systems.                 The procedure of collecting empirical data, their assessment according to the criteria of                verifiability and relevance and the application of the induction method was used and a                generalization of conclusions was subsequently made.                                                                726","3 Results and Discussion                The weight of the qualitative factors (soft- facts) in the rating systems                 For better understanding of the rating system as a whole, it is needed to shortly describe                its basic structure. This structure (as shown in the Figure 1) is quite similar for all of the                mentioned rating systems (except rating system E in some respects), just the names used                for particular steps are different. At the same time we pay attention to the weight of the                qualitative criteria (soft - fact) on the rating result.                                     Figure 1 The Basic Structure of the Rating Systems                                               Quantitative /         Qualitative /                                            Hard-facts rating      Soft-facts rating                                                           Weighting formula                                                                Basic rating                                Manual down- / upgrade                  Warning signals /   Risk factors                                                           Recommended                                                            rating                                        Guarantor’s rating            External rating                                                           Final customer’s                                                            rating                                   Source: Author by using banks “A”, “B” and “C” rating manuals                 All  three  compared  rating  tools  first  produce  the  part  of  the  rating,  based  on  clients’                financial statements (financial data). After possible other inputs like sector data, budgets                etc., the financial statement rating becomes quantitative or hard-facts (HF) rating, as                one of the two main sources for the final result. The second source is the qualitative or                soft-facts (SF) rating, based on qualitative criteria (see below).                In the next stage, the hard-facts rating and the soft-facts rating are integrated together,                according  to  a  given  weighting  formula.  This  formula  gives  the  weight  (meaning)  of                quantitative and qualitative factors on the rating result. In all the compared rating tools,                the quantitative factors (hard-facts) are more important (they enter the calculation with                higher  weight).  Please  do  not  confuse  this  high-level  formula  (HF  to  SF)  with  the                importance of individual qualitative factors for the soft - facts rating, as discussed below.                At bank A, the hard - facts and soft - facts ratings are weighted at fixed ratio of 2:1. In                addition,  soft  -  facts  cannot  improve  the  financial  statement  rating  by  more  than  two                grades (there are 8 grades total), whereas downgrading is unlimited.                 At bank B the weight is fixed too, at ratio 60% (HF) to 40% (SF). In the contrary of the                bank A system, the up- or downgrading by soft-facts is not limited in any way.                                                             727","The bank's C rating system uses more advanced approach, where the weight is dependent                on operating performance (revenues), as the Table 1 shows:                     Table 1 Bank C Weight of Quantitative (hard - facts) and Qualitative (soft - facts)                                                        Indicators                                                                             Weight                     Revenues as in Income statement                                                               Hard Facts               Soft Facts                    < EUR 1,1 Mio.                                 50 %                    50 %                    EUR 1,1 – 7,3 Mio.                             60 %                    40 %                    EUR 7,3 – 21,8 Mio.                            62 %                    38 %                    EUR 21,8 – 75 Mio.                             65 %                    35 %                    >EUR 75 Mio.                                   67 %                    33 %                                                Source: Bank “C” rating manual                 As the operating performance increases, the weighting of the qualitative factors (soft-facts)                for the final rating result decreases. On the one hand, because the quality of the balance                sheets usually increases with the size of the company and, on the other hand, because the                creditworthiness  of  small  businesses  is  often  very  much  related  to  the  person  of  the                entrepreneur. Smaller companies are more endangered by one person’s mistakes or loss                (like death of the owner / CEO in one person), their processes (like accounting) are usually                less intensively checked by internal and external audits etc.                 It is also worth mentioning that for small companies with revenues of up to EUR 1,1 million,                the Bank C system is the only one from all compared systems, that gives the same weight                to both quantitative and qualitative factors.                The bank D (as mentioned in Belás, Cipovová, 2011) used the weight 43,5% for soft facts,                later changed to 30%. As (Belás, Cipovová, 2011, p. 111) say: \"(...) our cooperating bank                has changed the ratio of qualitative and quantitative criteria in rating model used by RFB                from the original ratio of 43,5: 56,5% to 30:70%, as the impact of the current crisis. There                are frequent cases, where the client with good performance is granted with higher risk                level just because of changes in aggregate indicators of the industry, in which the company                operates. This phenomenon is quite common issue in World’s banking practice. The clear                disadvantage of this approach is sweeping behavior towards the clients and pro-cyclical                approach of credit risk management (low rate of individualization of loan products).\"                The  rating  system  E  mentioned  by  Kavan  (2017)  uses  a  different  approach:  the                qualitative factors themselves do not create a soft - facts rating, they only can change (up-                or downgrade) the hard - facts component of the rating. The system uses six groups of                qualitative factors (see below for details), every group can change the final rating at most                by 0,5 degrees up or down, the total possible upgrade by soft - facts is limited to 1 degree,                total  downgrade  is  not  limited  (the  final  rating  has  the  range  1  to  5  degrees).  Note:                Originally, the ownership structure and management criteria are formally grouped into one                category,  but  for  better  comparability  with  other  systems,  I've  divided  them  into  two                categories. Off course, I have retained the weights and therefore the influence to the final                rating.                Radojevic and Suknovic (2008) use in their paper a rating model (my designation rating                system F) with fixed weights 60% (HF) to 40% (SF), with no limits for up- or downgrade.                Using the weights (as mentioned above), the hard-facts rating and the soft-facts rating                result  together  in  a  basic  or  automated  rating.  Such  credit  rating  is  then  manually                reviewed for possible manual down- or (in some cases) upgrading.                Next to the possible manual downgrade, all the banks use a fixed list of warning signals or                risk factors. If one or more of them appear at the client, the rating must be downgraded                in an appropriate manner.                 After the down- or upgrade or after non-use of this possibility (which is the most common                case), the basic rating becomes the client’s rating or recommended rating, which can                                                             728","be (or even must be under some circumstances) modified by other guarantor’s rating or                external rating, if existing. The banks use exact and similar-to-each-other rules, at what                circumstances the rating of the guarantor or the external rating (usually made from one of                the big rating agencies like Moody’s or S\&P) must be used.                When the last step (guarantor’s rating or external rating) is made or skipped, the rating                becomes overruled rating or final customer’s rating.                 Qualitative indicators (soft - facts)                Each of the monitored banks or financial institutions uses their own range of indicators.                Whilst there are some generally accepted methods that can be used as a starting point for                assessing quantitative indicators (hard - facts, like e.g. bankruptcy models), for soft-facts                there are not. That's why I find it interesting to compare what indicators the individual                banks have chosen and how their methods of calculating soft-facts differs.                Technically, in all cases, the process contains a filling in a prepared form (questionnaire)                in the rating software, either in the form of a direct selection on a given scale for individual                factors, or by answering questions from which the system itself calculates the result.                In  all  the  banks,  the  qualitative  indicators  are  grouped  into  several  aggregated                categories (topics). In some cases, these aggregated high - level categories are further                broken down into more detailed questions, otherwise they are not (for details see below).                 The following Tables 2 and 3 show the categories (topics), as used by particular banks.                Banks A, B and C were the primary objectives of my research, data on D, E and F rating                systems from other sources (see above) serves to compare.                    Figure 2 Qualitative Indicators (soft - facts) Part 1. Please note that the numbers of                                                   weights can be rounded.                                     Rating A                Rating B                Rating C                           Management          15% Strategy                 Management                                                                        17%                     30%                           Internal organisation  15%                       Accounting                           (Accounting,                           Technology)                                                    -                                          20%                                                                            Qualifications of                                                                            employees /                                                                            Technology         10%                           Market / Industry   15% Influence / Market       Market incl. supplier                                                    position                and customer                                                                       27% structure           30%                           Relationship with the  40%                       Relationship with                           bank                     -                       bank               10%                           Miscellaneous (Age of  15% Economic situation                           the       company,       (general, influence on                           Location...)             the company)       27%                                                     Divergence from                                                    planning in last 3 yrs.  15%                                                     Business expectations                                                    incl. bank's view  15%                                  Source: Author by using banks “A”, “B” and “C” rating manuals                                                               729","In rating system of the bank A, the criteria are structured along the following categories:                    •   Management:  Successor,  Qualification  of  Management,  Personal  sphere  of                       management                    •   Internal  Organisation  of  Company:  Accounting,  Organisational  structure,                       Technological standard, External information policy                    •   Market / Industry: Dependency on suppliers, Dependency on customers, Sensitivity                       to economic cycles, Rank in industry, Position within the regional context, Product                       development / Range of products, Other dependencies                    •   Relationship with the bank                    •   Miscellaneous:  Age  of  company,  Credit  standing  of  entrepreneur  /  shareholders,                       External credit rating / credit reports, Location / region                For every category, the list of detailed questions is prepared for the analyst.                The bank B do not use further structuring of the categories, but similarly to bank A, there                are the questions (one or two for every category), which should help the analyst to assess                the company properly. For example, the question in the category Market influence is: Is                the company able to co-determine / decisively influence the market development due to                its position in its main markets?                The bank C also uses predefined sets of questions, with between five and ten questions                on each topic. In this case, the analyst answers the questions directly to the software. The                system  than  evaluates  the  answers  himself  and  at  the  end  it  shows  the  result  of  the                individual categories as well as the whole soft-rating rating.                    Figure 3 Qualitative Indicators (soft - facts) Part 2. Please note that the numbers of                                                   weights can be rounded.                                  Rating D                   Rating E                  Rating F                         Management +               Management                Management                        Business Strategy /                        Planning            23,0%                     12,5%                      24,0%                        Audit quality              Accounting                Accounting, Planning                                                                             and Information                                                                             systems                                            18,5%                     25,0%                      38,0%                                                   Qualifications of                                                   employees /                                                   Technology         12,5% Equipment + Systems  17,0%                        Market Position /          Market and                Market and market                        Share + Industry Risk      Competition               position                                            31,0%                     25,0%                      21,0%                          -                          -                         -                         Ownership structure        Ownership structure                                             27,5%                     12,5%                                                   Economic situation +                                                   Divergence from                                                   planning           12,5%                    Source: Author by using dates from \"D\": Belás, Cipovová (2011), \"E\": Kavan (2017) and  \"F\":                                                  Radojevic, Suknovic (2008)                 If  we  compare  all  the  mentioned  rating  systems  and  their  main  soft-facts  indicators                (topics),  we  can  identify  some  similarities:  all  systems  take  (i)  Management  (or  the                                                             730","closely related topic Strategy in case of bank B) and (ii) Market position of the company                as  important  criterions,  although  with  different  weights  (from  12,5%  to  30%  for                Management and from 15% to 31% for Market).                As the next most common criterion, three systems use (iii) Accounting, although in bank                F together with Planning and Information system (with the high weight of 38%). The bank                A uses a more general category Internal organisation, where the Accounting is included.                The rating system D includes the category Audit quality. Unfortunately, the data source                (Belás, Cipovová (2011))  does not specify the extent to which this category deals with                accounting, however, due to the structure of the other categories, this can be assumed                with a high probability.                 Among other soft - facts indicators, there are already substantially more variations, similar                to the weights that each category has on the overall rating result. An interesting difference                to mention is surely the category Relationship with the bank, which includes the period                as well as the quality of the collaboration between the bank and the customer (thus the                rated company). The rating system A take this indicator as the most important one with                the weight of 40% (rounded), the system C makes do with 10% weight and all the others                do  not  use  this  indicator  at  all  (they  use  only  problems  in  the  relationship  /  previous                collaboration with the customer as a warning signal, if existing). Even the later approach                can  make  a  good  sense,  considering  the  result  of  the  rating  process  as  a  universal                transferable number. The possibility to transfer the rating result can, but does not need to                be relevant for the bank.                Another  noteworthy  difference  between  the  rating  systems  is  the  use  of  the  unique                indicators in the rating system B, concerning the divergence from company’s planning in                last three years and the company’s business expectations including the bank’s own view                and opinion to these expectations.                The roles of members of staff in making of the rating                In the context of soft - rating creation, it is worthwhile to briefly mention the collection and                processing of the data in terms of competencies. These details are usually not mentioned                directly in the manuals of the particular rating systems, their settings are rather governed by                internal regulations of particular banks, or even just by habit.                Typically, two or three people participate in the process of making of the soft - facts rating                (four- or six-eyes principle). The first one is a (i) representative of the bank (sometimes                called account manager, relationship manager or similarly) who is in direct contact with the                client. He / she acquires from the client both the data for the creation of hard - facts rating,                as well as he / she also gets an image about the functioning of the client's business during                the negotiations. The information from the representative is therefore very valuable, on the                other hand the representative is materially involved in the deal and thus wishes the client's                rating to be as good as possible. There is a possible risk of conflicts of interest existing.                Therefore, in most cases, the credit rating itself is the task of a second person, (ii) the analyst                of  the  Risk  management  department.  The  analyst  gets  the  information  from  the                representative, makes his own research (on web, in publicly accessible databases, in news...)                and then fills in the appropriate form (questionnaire) in the rating system. In some cases this                role is entrusted to the (i) representative of the bank and the four-eyes principle is respected                by the (iii) manager.                Finally,  there  is  always  (iii)  a  person  with  the  authorization  power  (manager)  who                approves the final rating result. Mostly, this person is the chief of Risk management. Banks                thus try to minimize potential conflicts of interest and also they prefer to make an \"average\"                from the subjective views of more persons (members of staff) in making of the rating. The                very fact of using soft - facts itself logically involves a subjective view, which the creators of                the rating systems count on. But the inclusion of more people's views and multiple sources of                information is definitely desirable.                                                               731","4 Conclusions                The result of the research shows that in all the compared rating tools, the qualitative factors                (soft-facts) are have lower weight (are less important) than the quantitative (hard - facts),                but with the weight from 33% to 50% they surely are not negligible.                 Two  categories  of  qualitative  indicators  (soft  -  facts)  are  present  in  all  considered  rating                systems: (i) quality of company’s management and / or strategy and (ii) market, where the                evaluated company operates. Another regularly present qualitative indicator is (iii) accounting                or related indicators like information system or audit quality. On the other hand, the use of                the factor (iv) relationship with the bank (or similar) is quite different across the surveyed                rating systems.                The possible risk of conflicts of interest in data collection and making of the rating are solved                by four - eyes principle (two persons involved in make of the rating) or mostly even three                persons involved.                References                 Bank “A”, “B” and “C” Rating Manuals (2011), (2004), (2008).                Belás, J., Cipovová, E. (2011). Internal model of commercial bank as an instrument for                measuring credit risk of the borrower in relation to financial performance (credit scoring                and bankruptcy models). Journal of Competitiveness, vol. 3(4), pp. 104 - 120. Retrieved                from: https://search.proquest.com/docview/1315219115?accountid=16531                Crouhy, M., Galai, D., Mark, R. (2001). Prototype risk rating system. Journal of Banking                and  Finance,  vol.  25(1),  pp.  47-95.  Retrieved  from:  https://search.proquest.com/                docview/17874989?accountid=16531                Fracassi, C., Petry, S., Tate, G. (2016). Does rating analyst subjectivity affect corporate                debt pricing? Journal of Financial Economics, vol. 120, pp. 514-538.                Grunert, J., Norden, L., Weber, M. (2002). The role of non-financial factors in internal credit                ratings.   St.   Louis:   Federal   Reserve   Bank    of   St   Louis.   Retrieved   from:                https://search.proquest.com/docview/1698225667?accountid=16531.                Guserl, R. (2014). Mit hard facts und guter kommunikation zu besseren ratings. Controlling                \& Management Review, vol. 58, pp. 78-83. DOI:  http://dx.doi.org/10.1365/s12176-014-                0968-6                Kavan (2017). Rizikové faktory ve výkazech pro rating. Praha: Bankovní institut vysoká                škola Praha, Katedra financí a ekonomie. Master Thesis.                 Morrow, B. E. (1998). The growing importance of qualitative factors in assigning financial                ratings  to  insurers.  The  Interpreter,  vol.  70(5),  pp.  1,  26-29.  Retrieved  from:                https://search.proquest.com/docview/226070047?accountid=16531                Radojevic, G., Suknovic, M. (2008). Fuzzy sets in financial analysis.  Journal of Decision                Systems,  vol.  17(2),  pp.  211-223.  Retrieved  from:  https://search.proquest.com/                docview/207659159?accountid=16531                Svítil, Martin (2017a). Banking rating systems for IRB: Comparison of three systems from                German-speaking environment. Financial Assets and Investing, vol. 2/2017.                Svítil, Martin (2017b). Comparison of banking rating systems. In: Proceedings of the 14th                International  Scientific  Conference  European  Financial  systems  2017.  Brno:  Masaryk                University.                                                                    732","Interest Rate Risk in the Banking Book (IRRBB) – Comparing                     Variance/Covariance with Historical Simulation and Several                      Copula Functions with Focus on the Actual EBA Guidelines                                                                                  3                                                                                              4                                                               2                            Martin Svoboda , Svend Reuse , Annika Rüder , Noel Boka                                              1                                                    1  Masaryk University                                Faculty of Economics and Administration, Department of Finance                                           Lipová 41a, 60200 Brno, Czech Republic                                            E-mail: Martin.Svoboda@econ.muni.cz                                       2  FOM Hochschule für Oekonomie und Management                                               isf Institute for Strategic Finance                                 Luxemburger Allee 121, 45481 Mülheim an der Ruhr, Germany                                                E-mail: svend.reuse@fom.de                                       3  FOM Hochschule für Oekonomie und Management                                                    Finance Department                                         Friedenstraße 39, 22089 Hamburg, Germany                                               E-mail: Annika.Rueder@web.de                                       4  FOM Hochschule für Oekonomie und Management                                                    Finance Department                                  Gutenbergstraße 47, 45473 Mülheim an der Ruhr, Germany                                                 E-mail: noel.boka@gmx.de                 Abstract:  Interest  rate  risk  and  its  measurement  are  important  for  banks  worldwide.                Strategic maturity transformation positions in combination with the historical low level of                yields  leads  to  the  question,  whether  the  standard  risk  measurement  models  as                variance/covariance or historical simulation lead do adequate results. This article answers                this question and offers an empirical analysis in which several alternative Copula functions                are used to quantify interest rate risk. The results are compared to the EBA guidelines on                IRRBB. The aim is to show if the six interest rate risk scenarios that are defined by the EBA                are an adequate measurement method.                 Keywords: Value at Risk, Copula, EBA, IRRBB                JEL codes: G21, G24, G32                 1 Introduction                Interest  rate  risk is  one  of  the  main  risks in  the  banking  sector  (see  Reuse 2012a for                definitions). This is strengthened by the actual low levels of yields in Europe, especially in                Germany. In addition, German banks are nearly the only ones that offer long maturities to                their  customers  and  follow  a  long-term  oriented  maturity  transformation  strategy                (discussed in detail in Reuse and Svoboda, 2014a, pp. 37; Reuse and Svoboda 2014b, pp.                377).                While  the  efficiency  of  several  interest  rate  risk  benchmarks  were  analyzed  in  several                articles (see Frère, Svoboda and Reuse 2008; Reuse 2012b; Reuse and Svoboda 2014b),                the methods of risk quantification in IRRBB have not been analyzed or changed since the                last 15 years. This article focusses on the German market. Two academic research aspects                should be analyzed/answered:                 a)  Which VaR approach fits best to quantify IRRBB?                    This paper compares the existing quantification methods as historical simulation and                    variance/covariance approach with several Copula functions in order to show, which                    approach fits best to quantify IRRBB.                b)  Are the six EBA interest risk scenarios an adequate measurement approach?                    In addition, the newest consultation paper of the EBA with its six interest risk scenarios                    is analyzed and compared to the Copula results.                                                             733","Therefore,  this  article  is  structured  as  follows.  Section  2  describes  the  relevant                measurement methods of interest rate risk measurement and derives the six scenarios of                the EBA Paper. Section 3 offers data and methodology of the empirical analysis. In section                4, the results are presented. The article ends with a final conclusion and an outlook.                2 Measurement Methods of IRRBB                 Existing methods in Theory                Measuring interest rate risk in the banking book can split up in economic value (EV) and                earnings measures. Therefore, earnings measures expect the future profitability based on                different yield curve scenarios (Steinwachs 2012, pp. 93 f.). Economic value measures                display changes in the net present value. Hence, the changes of the net present value are                defined as a continuous or a discrete distribution (Rolfes 2008, pp. 92) with typical two                outcomes (Value at Risk and Expected Shortfall) (Daníelsson 2012).                While  historical  simulation,  variance/covariance  and  sometimes  the  Monte-Carlo                Simulation are very common for IRRBB, Copula models get common in the recent past.                Relating  to  these  quantitative  risk  measures  a  regular  cycle  of  the  yield  curve  lead  to                adequate  results  (Sievi  et  al.  2011,  pp.  333  f.).  Although  the  length  of  the  data  base                provides a slow adaption of the risk forecast. Even if there is a shorter and smaller data                base a low volatility causes a systematic underestimation of risk (Duckheim 2014, pp. 63).                Table 1 sums up the main aspects of these four methods of risk quantification.                                Table 1 Comparison of Different Interest Rate Risk Measures                                     Historical        Variance/         Monte-Carlo           Copula                                    simulation         covariance                distribution      empirical         normal             no restrictions    no restrictions                assumption                non-linear        fully considered   approximately     fully considered   fully considered                Cashflows                data              regarding an adequate   Variance/covariance -  parameters of the   parameters of the                requirements      historical data set no   matrix      distribution       marginal distribution                                  special requirements                                                                                          and the dependence                                                                                          structure                effort            low/medium        for linear CFs low   high             high                goodness of       depending on      depending on the   depending on several   depending on several                fit               stationarity property   normal distribution of   adjustments good   adjustments good                                                    the empirical data                                                                       goodness of fit                                                                                          goodness of fit                                  and the length of the                                  empirical data                extrem            depending on the   low fitting on extreme   depending on the   depending on the                events            empirical data    events             selected distribution   selected distribution                                                                                          good fit on extreme                                                                       good fit on extreme                                                                       events             events                                  -  easy to implement   -  quick risk measure   -  besides empirical   -  besides empirical                                  -  well comprehensible   -  easy to implement   also other possible   also other possible                                  -  no assumption about                 interest rate changes   interest rate changes                                   correlations or                     -  freedom of      -  no distribution                                   distributions                         distribution       required distribution                                                                         assumption         assumption                                                                       -  consideration of fat   -  consideration of fat                                                                         tails e.g.         tails, asymmetric                                                                       -  best fit for non-linear   distributions e.g.                                                                         cashflows        -  additional mapping of                advantages                                                                  the dependence                                                                                            structure                                                                                          -  especially for non-                                                                                            linear dependence                                                                                            structures                                                                                          -  best fit for non-linear                                                                                            cashflows                                                                                          -  consideration of                                                                                            remaining term                                                                                            effects, volatility                                                                                            clusters                                                              734","Historical        Variance/         Monte-Carlo           Copula                                    simulation         covariance                                  -  possible negative   -  assumption of normal   -  mathematical   -  mathematical                                   effects of         distribution       sophisticated      sophisticated                                   autocorrelation   -  linear assumption for   -  model risk   -  model risk                                  -  ex post and ex ante   risk factors and the                                   stationarity of the   portfolio                                   data set           (approximatively for                                  -  no consideration of   non-linear portfolios                disadvantage       implied volatility   with a Taylor series)                s                 -  possibility of non-  -  systematic                                   adequacy           underestimation of                                   consideration of   fat tails or high                                   leptokurtic flanks or   kurtosis ex post and                                   fat tails          ex ante stationarity                                  -  especially for   of the data set                                   simple/linear                                   portfolios                   Source: Reuse 2006, pp. 366; Steinwachs 2012, pp. 118; Rolfes 2008, pp. 104; Steiner et al.                 2002, pp. 69; Jorion 2007, pp. 270; Drosdzol 2004, pp. 107; Eddari 2007, pp. 28; Hlawatsch and                                                    Reiching 2010, pp. 5                 Starting with the historical simulation, ex post observed changes of interest rates were                extrapolated  to  the  current  yield  curve.  These  yield  curve  changes  could  computed  as                absolute,  relative  and  logarithmic  changes  (Wiedemann  2013,  pp.  92).  It  has  to  be                mentioned  that  the  ex  post  changes  repeat  itself  whereby  stationarity  is  a  necessary                assumption of yield curve changes (Schierenbeck et al. 2008, pp. 96 f.). Self-Repeating                yield curve changes predict varying economic values over time. The computed empirical                distribution of EV achieves a VaR quantile (Choudhry 2014, pp. 487). At least the freedom                of model assumptions is a great advantage of the historical simulation (Drosdzol 2004, pp.                107).                The normal distribution is a key element of the variance/covariance approach (Steinwachs                2012, pp. 118). If the risk factors follow the normal distribution, the linear changes of the                portfolio follow the normal distribution as well. The standard deviation as a measure of risk                leads to the change of the net present value of the economic value of the non-trading-                book portfolio (Eddari 2007, pp. 10).                 The  Monte  Carlo  method  expands  the  historical  simulation  by  randomized  numbers  to                achieve hypothetical variations of interest rate risk changes in the risk forecast. Especially                ex  post  not  observable interest  rate  changes  that  could  take  place in  the  future  could                improve the risk forecast (Eddari 2007, pp. 28). Hence, an increasing yield curve is not                observable  in  the  past,  these  uncertainty  of  economic  value  changes  get  enhanced.                Creating new yield curve scenarios by the Monte Carlo method assumes a model for the                evolution.  The  evolution  of  interest  rate  is  typical  driven  by  Wiener  processes.  The                correlation of the random sampling is generated by Cholesky-decomposition (Wiedemann                2013, pp. 67). Driven by historical and hypothetical yield curve scenarios the economic                values  of  the  non-trading-book  portfolio  are  simulated.  The  computed  distribution                procedure  of  economic  value  changes  is  identical  to  the  above  mentioned  historical                simulation.                 The  Copula  approach  removes  the  restriction  that  dependence  structure  and  marginal                distributions must originate from the same distribution family. The dependence structure                can be modeled independently of the marginal distributions (Hlawatsch and Reiching 2010,                pp. 5). Furthermore, it is possible to model the marginal distribution in different ways.                (Hahn 2012 pp. 181).                Interest Rate Risk Scenarios                Even though the six EBA scenarios cannot be defined as professional risk measurement                methods, they may lead to additional capital buffers in the EU (see Reuse 2018, pp. 148-                153). As a consequence, they have to be analyzed and compared to the above-mentioned                methods.                                                               735","In Annex III (EBA 2017, pp. 51), the EBA provides data to generate these six interest rate                risk scenarios. Referring to the Basel II interest rate shocks (plus and minus 200 basis                points  on  current  the  yield  curve)  the  EBA  Guideline  extends  these  two  yield  curve                scenarios up to six scenarios. The IRRBB scenarios for measuring the Economic Value of                Equity measures (EVE) are a parallel shock up and down, a steepener shock, which means                that the short rates decrease while the long rate increases, a flattener shock (short rate                increases and long rate decreases) and a short rate shock up and down. The parameters                of the scenarios are specified by the EBA for the main currencies. Data basis for estimating                these parameters are time series of the “riskfree” interest rate of sixteen years (2000 –                2015).  In  detail the EBA  defines the following  shock  parameters:  short  rate  250  basis                points, long rates 150 basis points and parallel shift 200 basis points whereby these shock                parameters get relevant for 19 defined maturity bands.                The  EBA  does  not  specifies  the  valuation  curve,  so  using  a  bank-specific  risk-free                calculation curve is possible. Furthermore, the guideline does not provide a floor for low                interest rate environments. Hence, negative interest rates are possible. Figure 3 visualizes                these 6 scenarios.                               Figure 1 Yield Curves of the EBA Interest Rate Risk Scenarios                     3.00                    2.00                    1.00                  yield (in %)  -1.00  1  2  3      4       5       6       7       8        9      10                    0.00                     -2.00                     -3.00                    -4.00                                                          time  bucket                         current yield curve  parallel up  parallel down  steepner  flattener  short up  short Down                                 1 yr.  2 yr.   3 yr.   4 yr.  5 yr.   6 yr.   7 yr.  8 yr.   9 yr.  10 yr.                 current yield curve  -77.0 bps.  -71.0 bps.  -59.1 bps.  -44.1 bps.  -29.2 bps.  -13.1 bps.  2.0 bps.  17.3 bps.  30.6 bps.  41.9 bps.                 parallel up    200.0 bps.  200.0 bps.  200.0 bps.  200.0 bps.  200.0 bps.  200.0 bps.  200.0 bps.  200.0 bps.  200.0 bps.  200.0 bps.                 parallel down  -200.0 bps.  -200.0 bps.  -200.0 bps.  -200.0 bps.  -200.0 bps.  -200.0 bps.  -200.0 bps.  -200.0 bps.  -200.0 bps.  -200.0 bps.                 steepner      -112.9 bps.  -73.0 bps.  -45.2 bps.  -15.3 bps.  8.0 bps.  26.2 bps.  40.3 bps.  51.3 bps.  59.8 bps.  66.5 bps.                 flattener      148.9 bps.  107.9 bps.  79.2 bps.  48.4 bps.  24.4 bps.  5.7 bps.  -8.8 bps.  -20.1 bps.  -28.9 bps.  -35.8 bps.                 short up       160.7 bps.  129.1 bps.  107.1 bps.  83.4 bps.  64.9 bps.  50.6 bps.  39.4 bps.  30.7 bps.  23.9 bps.  18.6 bps.                 short Down    -130.6 bps.  -104.9 bps.  -87.0 bps.  -67.7 bps.  -52.8 bps.  -41.1 bps.  -32.0 bps.  -24.9 bps.  -19.4 bps.  -15.1 bps.                                        Source: Deutsche Bundesbank 2017; EBA 2017                 3 Data and Methodology                Used Data                This analysis focusses on the German market and wants to quantify the risk of maturities                up to ten years, modelled by a gliding 10-year strategic position (see Reuse and Svoboda                2014b, p. 378). Therefore, German sovereign bond yields with maturities from 1 to 10                years  are  used  (see  Deutsche  Bundesbank  2017,  BBK01.WZ3400  to  BBK01.WZ3409).                These yields are chosen, because they offer the longest history and reflect a nearly riskfree                ratio.  The  theoretical  optimal  credit  risk  free  OIS  (overnight  index  swap)  curve  is  not                available for such a long time. The data is available for 46 years, from 1972-09 to 2017-                11. Figure 2 shows the development of the 1, 5 and 10-year history.                                                                  736","Figure 2 Development of the 1, 5 and 10 Year German Sovereign Bond Yields                           14.00                             12.00                            10.00                             8.00                          yield in %  6.00                             4.00                             2.00                            0.00                               1972-09  1973-08  1974-07  1975-06  1976-05  1977-04  1978-03  1979-02  1980-01  1980-12  1981-11  1982-10  1983-09  1984-08  1985-07  1986-06  1987-05  1988-04  1989-03  1990-02  1991-01  1991-12  1992-11  1993-10  1994-09  1995-08  1996-07  1997-06  1998-05  1999-04  2000-03  2001-02  2002-01  2002-12  2003-11  2004-10  2005-09  2006-08  2007-07  2008-06  2009-05  2010-04  2011-03  2012-02  2013-01  2013-12  2014-11  2015-10  2016-09  2017-                           -2.00                                                       1 Year  5 Years  10 Years                                             Source: Deutsche Bundesbank 2017                 The figure shows that the yields follow a decreasing path and become negative in 2012.                The main assumption for e.g. the historical analysis, a history free of a trend, cannot be                stated.  This  special  situation  increases  the  necessity  to  improve  IRRBB  measurement                methods.                Description of Methodology                The  paper  uses  an  idealized  portfolio  representing  the  gliding  10-year  position.  This                portfolio eliminates effects of reinvestments and reduction of the residual maturity. The                forecast is computed with a disposition horizon of one year and a confidence interval of                99.0 percent. In Accordance with the EBA scenarios, absolute changes of the yield curve                are used as risk factors for calculation. The forecast quality is determined by comparing                the ex-ante risk forecast with the ex-post performance.                Thus, the data history and also the number of risk factors for calculating the VaR increases                over  time.  Essential  parameters  like  mean  and  standard  deviation  for  the                variance/covarianceapproach is estimated for each calculation date.                Copulas  can  be  constructed  with  the  empirical  dependence  structure  and  empiricial                marginal  distribution  on  the  one  hand  and  by  using  a  parametric  distribution  for  the                dependence structure and marginal distribution on the other hand. The empiricial Copula                is based on discrete data. As a result, the Copula is not clearly mathematically defined.                However, the distributions are fitted by analyzing the QQ-plots of the empirical data and                the  parametric  distributions.  This  is  done  via  visual  inspection  by  comparing  the  plots                (Jorion  2007,  pp.  208  and  Daníelsson  2012,  pp.  28).  In  accordance  with  the                variance/covariance-method,  the  parameters  for  the  Copulas  are  estimated  for  each                calculation.                 The VaR of the 10-year position is determined by variance/covariance, historical simulation                and the following Copulas:                                                                   737","Table 2 Used Copulas                                               dependence structure          marginal distribution                   Normal Copula                normal distribution           normal distribution                                               student-t-distribution,       student-t-distribution,                  t-Copula                                               degrees of freedom= 6         degrees of freedom= 6                  Normal Empiric               normal distribution           empirical distribution                  Empiric Normal               empirical distribution        normal distribution                                                     Source: own figure                 Besides the empirical distribution, the normal distribution and the t-student distribution                are implemented. The main advantage of the student-t-distribution is that fat tails can be                modelled better. These fat tails are able to include macroeconomic shocks or asymmetric                market movements (Jorion 2007, pp. 208).                Computing a Copula-construction every time bucket of the gliding 10-year-position is set                as  a  marginal  distribution.  Thus,  it  is  necessary  to  construct  the  Copulas  with  10                dimensions. Basis for the parametric distributions are 10.000 uniformly distributed random                numbers, which are transformed into the desired distribution using the inversion method.                The random numbers are generated by using a Monte Carlo simulation. Afterwards the                uncorrelated  random  numbers  are  transferred  to  correlated  random  numbers  by  the                Cholesky  decomposition.  In  order  to  isolate  the  dependence  structure,  the  correlated                random numbers or empirical data are converted to a unit-square. Dependence structure                and the marginal distribution are linked by a quantile-mapping. At least the VaR estimation                of the gliding 10-year position is provided as a difference of a quantile of the confidence                interval and the expected value. The analysis of the results and the comparison of the                estimated VaR with the EBA scenarios are presented in section 4.                 4 Discussing the Results                Comparing Copulas to Variance/Covariance and Historical Simulation                 Figure 3 offers the results of all six evaluation methods including the ex post analysis. At                the beginning of the analysis, we observe a period of high volatility in the first third of the                time period, which implicate an increasing risk forecast. The risk figures decrease over                time.  Decreasing  risk  leads  to  the  low  interest  rate  environments,  which  states  lower                changes of interest rates. Furthermore, we observe a decreasing variance. Due to the high-                risk factor changes in the past, the risk values are systematically overestimated in phases                of low interest rates (Wegner/Sievi 2017, pp. 4 f.). Thus, absolute changes are generally                not  independent  of  the  interest  rate  level.  In  addition,  we  can  observe  an  increasing                present value in periods of low interest rates. The time series has two structural breaks:                German reunification and introduction of the euro.                Basically,  there  are  only  a  few  VaR-outliers  (comparing  risk  forecast  and  ex  post                performance). The small number of outliers can be observed in volatile market phases,                which derives the general volatility for the risk estimation. The last outlier can be observed                1980. The student-t-Copula predicts the highest risk figures. The variance/covariance VaR                and the student-t Copula VaR have no backtesting-outliers. The tail dependence of the                student-t-Copula overestimates the decreasing risk figures. This  attribute is particularly                interesting in periods of rising interest rates. The distribution of the gliding 10-year position                seems to be leptokurtic. The normal distribution overestimates the risk. In addition, the                high  volatility  at  the  beginning  of  the  time  series  influences  the  risk.  The  Copula  with                normal distributed marginal distributions and empirical dependence structure forecasts the                lowest risk figures.                                                               738","Figure 3 Ex post Performance and Risk Forecast, 1-year-holding-period, 99.0%                                                        Confidence                      10.00%                         5.00%                          0.00%                   yield delta (in %)  -5.00%  1973-09  1974-11  1976-01  1977-03  1978-05  1979-07  1980-09  1981-11  1983-01  1984-03  1985-05  1986-07  1987-09  1988-11  1990-01  1991-03  1992-05  1993-07  1994-09  1995-11  1997-01  1998-03  1999-05  2000-07  2001-09  2002-11  2004-01  2005-03  2006-05  2007-07  2008-09  2009-11  2011-01  2012-03  2013-05  2014-07  2015-09  2016-11                           -10.00%                        -15.00%                            ex post performance         variance-covariance         historical simulation                           normal copula               t copula                    empirical normal copula                           normal empirical copula                                                  Source: own calculations                 Table 3 visualizes the backtesting of the risk values.                                          Table 3 Relative Forecast Goodness-of-fit                                                                                       VaR excesses (in %)                 variance-covariance                                                           0.000%                 historical simulation                                                         1.186%                 copula: normal-distributed marginals, normal-distributed dependency structure  0.000%                 copula: t-distributed marginals, t-distributed dependency structure           0.000%                 copula: empirical-distributed marginals, normal-distributed dependency structure  1.186%                 copula: normal-distributed marginals, empirical-distributed dependency structure  1.186%                                                  Source: own calculations                 According to the Basel model risk traffic light approach relative VaR excesses above 1%                are not “green”. Hence, for these excesses there is a detailed analysis necessary.                 In  fact,  the  consideration  of  empirical  distribution  components  (marginal  distribution,                dependence structure or both (historical simulation)) leads to the rejection of the prognosis                quality. Only the parametric distribution methods have a sufficient forecast-quality over                the whole time. For that risk measures we could not observe any outlier for about 35 years.                Although, all outliers could observe until 1980 and may draw the conclusion, that these                outliers should not be over-interpreted. Hence, the stationarity assumption respectively                the representativeness of ex post changes for a risk forecast could questioned.                The variance/covariance-method shows good results and is a simple model. The Copula                models cannot generate a better forecast quality than the variance/covariance-approach                (using a long data history). In addition, Copulas have a higher model risk and require more                computational  effort.  The  reduction  of  the  risk  figures  suggests  a  misallocation  of  risk                taking capacity.                At least, all VaR-methods have a good forecasting quality, but overestimate the risk. The                overestimation of the risk figures is caused by the high volatility at the beginning of the                time series. Particularly this overestimation can be observed in the current low-interest-                                                             739","rate phase. The higher level of risk forecasting results can be explained by high absolute                risk factor changes in the first third of the time series.                 Comparing Copulas to the EBA Scenarios                Comparing the VaR-methods with EBA scenarios is illustrated in figure 4.                 Figure 4 Risk Forecast of the Quantitative Risk Methods and the EBA Interest Rate Risk                                                         Scenarios                      0.15                                            parallel down                                 variance-covariance  historical simulation  empirical copula                      0.10                                     empirical                       0.05                   normal copula  t-copula  normal         flattener  short down                   yield delta (in %)  -0.05                                 steepner    short up                     0.00                            -0.10                                                                   parallel up                      -0.15                                                        measurement methode                    variance-covariance    historical simulation  normal copula         t copula                   empirical normal copula  normal empirical copula  parallel up       parallel down                   steepner               flattener             short up               short down                                                   Source: own calculations                 The parallel up shock (+200 basis points) forecasts the highest risk. This scenario is already                calculated  today  and  estimates  regularly  higher  risk  figures  than  the  institute’s  own                measurement methods. All other scenarios show significant lower risks. In expectation of                rising interest rates in the future and in accordance with decreasing present values, this                low risk forecasting has to be discussed. A further flattening of the yield curve shows no                further risk potential for the gliding 10-year position. The scenario short up generates the                second highest risk figures of the EBA scenarios. In line with the low interest rate survey,                the  expectation  of  rising  interest  rates  shows  the  highest  risk  potential  for  most                institutions.                 5 Conclusions                 Summing up the main results leads the following aspects:                •   All VaR-risk measurement methods lead to a high risks that bind too much economic                    capital.                •   Copulas are a flexible method o quantify and aggregate risk.                •   The t-student Copula seems to generate the best backtesting results.                •   The historical simulation and the variance/covariance approach lead to better results                    than expected.                •   The EBA scenarios do not offer additional perception. It is surprising that the parallel                    up shift, often discussed as a bad scenario, leads to a risk value similar to the VaR                    approaches. The other five scenarios underestimate risk.                In the next step, the two academic research questions have to be answered. Table 4 sums                up the results.                                                            740","Table 4 Research Questions                   Research Question            Argumentation                                  Result                                               The existing VaR approaches lead to better         ✓                  a) Which  VaR  approach  results than expected, but the Copulas,                     fits  best  to  quantify  especially the t-Copula lead to reliable                     IRRBB?                    results as well. Nearly all VaR methods                                               overestimate risk in an ex post analysis.                  b) Are the six EBA interest  No, only the 200 BP shock leads to                 ✓                     risk    scenarios    an  comparable results. The other five scenarios                     adequate                  do not offer additional findings.                     measurement                     approach?                                                     Source: own figure                 Last it hast to be stated that Copulas can enrich the risk management of IRRBB and should                be implemented at least in big European banks.                Acknowledgement                 Support  of  Masaryk  University  within  the  project  MUNI/A/0753/2017  is  gratefully                acknowledged.                 References                 Choudhry,  M.,  Moskovic,  D.,  Wong,  M.  (2014).  Fixed  Income  Markets  –  Management,                Trading and Hedging. 2nd ed. Singapur: Wiley Finance Series 2014.                 Daníelsson, J. (2012). Financial risk forecasting – The Theory and Practice of Forecasting                Market Risk, with Implementation in R and MATLAB®. 2nd ed. Singapur: Wiley Finance                Series 2012.                Deutsche Bundesbank (2017).  Aus der Zinsstruktur abgeleitete Renditen für börsennot.                Bundeswertpapiere      mit   jährlicher   Kuponzahlung     (Monats-    und    Tageswerte).                BBK01.WZ3400-BBK01.WZ3409.            Retrieved    from:     https://www.bundesbank.de/                Navigation/DE/Statistiken/Zeitreihen_Datenbanken/Geld_und_Kapitalmaerkte/geld_und_                kapitalmaerkte_list_node.html?listId=www_skms_it03b..                Drosdzol, A. (2004). Zinsmanagement mit Zinsstrukturmodellen. Frankfurt: Bankakademie                Verlag 2004.                Duckheim,     S.   (2014).   Nationale   Regulierung    der   Gesamtbanksteuerung      und                Risikotragfähigkeit. In: Zeranski, S., ed., Gesamtbanksteuerung in der Praxis – im Kontext                verschärfter  regulatorischer  Neuregelungen.  Heidelberg:  Finanz  Colloquium  Heidelberg                2014, pp. 42–73.                EBA (2017). Consultation Paper. Draft Guidelines on the management of interest rate risk                arising   from   non-trading    book    activities.   EBA/CP/2017/19.    Retrieved   from:                https://www.eba.europa.eu/documents/10180/2006934/Consultation+Paper+on+Guideli                nes+on+technical+aspects+of+the+management+of+interest+rate+risk+%28EBA-CP-                2017-19%29.pdf.                Eddari,   A.   (2007).    Risikomaße    in   der   Finanzmathematik.     Retrieved   from:                https://publications.ub.uni-mainz.de/theses/volltexte/2008/1667/pdf/1667.pdf.                Frère, E., Svoboda, M., Reuse, S. (2008). Der gleitende 15-Jahresatz im Kontext etablierter                Benchmarks— sind diese zu schlagen?. Bankpraktiker, vol. 2(5), pp. 232–236.                Hahn,  G.  (2012).  Portfoliobildung  mit  Copulas.  In:  Hockmann,  H.  J.,  Thießen,  F.,  ed.,                Investmentbanking. Stuttgart: Schäffer-Poeschel Verlag, 3rd ed., pp. 181-186.                                                                741","Hlawatsch,  S.,  Reiching,  P.  (2010).  Konstruktion  und  Anwendung  von  Copulas  in  der                Finanzwirtschaft.  Otto-von-Guericke-Universität  Magdeburg,  Working  Paper  Series,  no.                16/2010.      Retrieved     from:      http://www.fww.ovgu.de/fww_media/femm/femm_                2010/2010_16.pdf.                Jorion, P. (2007). Value at Risk – The New Benchmark for Managing Financial Risk. 3rd ed.                New York: McGraw Hill Education.                 Reuse,  S.  (2006).  Berechnung  des  Value-at-Risk  mit  der  Monte-Carlo-Simulation  –                Vorstellung bestehender Modelle und Würdigung der Ergebnisse. Bankpraktiker, vol. 1(07-                08), pp. 366–371.                 Reuse, S. (2012a). Definition und Ausprägung des Zinsänderungsrisikos. In: Reuse, S.,                ed., Zinsrisikomanagement. Heidelberg: Finanz Colloquium 2012, 2nd ed., pp. 7–20.                 Reuse, S. (2012b). Definition effizienter Benchmarks für die passive Steuerung. In: Reuse,                S., ed., Zinsrisikomanagement. Heidelberg: Finanz Colloquium, 2nd ed., pp. 159–167.                Reuse, S. (2018). Nachhaltigkeit  der Geschäftsmodelle von Banken und Sparkassen im                Kontext  von  Niedrigzinsen  und  SREP.  In:  Michalke,  A.,  Rambke,  M.,  Zeranski,  S.,  ed.,                Vernetztes  Risiko-  und  Nachhaltigkeitsmanagement  – Erfolgreiche  Navigation  durch  die                Komplexität und Dynamik des Risikos. Wiesbaden: Springer Gabler, pp. 147–164.                Reuse,  S.,  Svoboda,  M.  (2014a).  Empirical  Analysis  of  the  Efficiency  of  Maturity                Transformation in the Czech Republic. Intercontinental Journal of Finance Research Review,                vol. 2(5), pp. 36–51.                Reuse, S., Svoboda, M. (2014b). Comparison of the Efficiency of Maturity Transformation                between German and the Czech Banks. In: Proceedings of the 8th International Conference                on Currency, Banking and International Finance, The Role of Financial Sector in Supporting                the  Economic  Recovery  of  CEE  Countries.  Bratislava:  Ekonóm,  University  of  Economics                Bratislava, 1. vyd., pp. 376–388.                Rolfes,  B.  (2008).  Gesamtbanksteuerung  –  Risiken  ertragsorientiert  steuern,  2nd  ed.                Stuttgart: Schäffer-Poeschel.                 Schierenbeck, H., Lister, M., Kirmße, S. (2008).  Ertragsorientiertes Bankmanagement  –                Band 2: Risiko-Controlling und integrierte Rendite-/Risikosteuerung. 9th ed. Wiesbaden:                Gabler Verlag.                Sievi,  C.  R,  Wegner,  O.,  Freundorfer,  E.  (2011).  Integration  von  Marktpreisrisiken,                Stuttgart: Deutscher Sparkassenverlag.                Steiner,  M.,  Bruns,  C.,  Stöckl,  S.  (2012).  Wertpapiermanagement  –  Professionelle                Wertpapieranalyse und Portfoliostrukturierung. 10th ed. Stuttgart: Schäffer-Poeschel.                 Steinwachs, P. (2012). Abbildung und Messung von Zinsänderungsrisiken. In: Reuse, S.,                ed., Zinsrisikomanagement. Heidelberg: Finanz Colloquium, 2nd ed., pp. 89–140.                Wegner,  O.,  Sievi,  C.  R.  (2017).  Lösungswege  der  Modernden  Historischen  Simulation.                Betriebswirtschaftliche  Blätter.  Retrieved  from:  https://www.dr-sievi.de/fileadmin/                downloads/aufsaetze/BBL0317_Wegner_HistorischeSimulation_Onlineausgabe.pdf.                 Wiedemann,  A.  (2013).  Risikotriade  –  Teil  1  –  Messung  von  Zins-,  Kredit-  und                operationellen Risiken. 3rd ed. Frankfurt: Frankfurt School 2013.                                                                          742","Random Strategy Versus Technical Analysis Strategy: The Case of                                           GBP/USD Intraday Trading                                                      1                                                                     2                                                                                          3                                 Miroslav Svoboda , Tomáš Plíhal , Pavel Sedláček                                                    1  Masaryk University                                Faculty of Economics and Administration, Department of Finance                                           Lipová 41a, 603 00 Brno, Czech Republic                                                E-mail: 254801@mail.muni.cz                                                     2  Masaryk University                                            Institute of Financial Complex Systems                                           Lipová 41a, 603 00 Brno, Czech Republic                                                E-mail: 370621@mail.muni.cz                                                     3  Masaryk University                                Faculty of Economics and Administration, Department of Finance                                           Lipová 41a, 603 00 Brno, Czech Republic                                                E-mail: 369947@mail.muni.cz                 Abstract:  This  paper  provides  a  comparison  between  the  strategy  based  on  technical                analysis and the strategy based on random trading on a highly liquid GBP/USD foreign                exchange market. We analyze six months of data, and in every intraday trading session,                only a single position will be opened. Technical analysis strategy uses essential indicator                relative strength index (RSI). Every trading position will have the risk-reward ratio (RRR)                3 to 1. In addition, another trading positions on the GBP/USD currency pair will be opened                at the same time each day, without technical analysis. The time of entry into position will                be indicated by past high liquidity on a given currency pair at a given time with a similar                risk-reward-ratio (RRR) 3 to 1. We want to answer the question whether it is preferable to                use the technical analysis indicators or to open a trading position randomly in intraday                trading. In other words, this article aims to compare the strategy of technical analysis and                the random strategy in intraday trading concerning the profitability of these trades. On the                basis of our preliminary results, we expect that the random strategy will show us higher                profitability  or  lower  loss  ability  than  the  strategies  based  on  the  technical  analysis                indicators in the observed period.                Keywords: technical analysis, investment decisions, foreign exchange markets, currency                markets, random strategy, backtesting                JEL codes: G11, G12, G15, G17                 1 Introduction                 Technical analysis deals with making decisions of investors based on the historical price                and other readily available market data. Use of the technical analysis in FX markets has                been confirmed by many surveys like Taylor and Allen (1992), Menkhoff (1997), Lui and                Mole (1998), Oberlechner (2001), Gehrig and Menkhoff (2004), and Menkhoff and Taylor                (2007).                A trading strategy based on technical trading rules that is profitable in the long term is                inconsistent with the weak form of the efficient market hypothesis. Some earlier studies                supporting the profitability of technical analysis in the foreign exchange markets (Sweeney                1986;  Levich  and  Thomas,  1993;  Neely,  1997;  LeBaron,  1999,  2002).  In  theory,  the                foreign exchange market should be efficient because of very high turnover and domination                of professional traders that should not be influenced by the sentiment of retail investors                (Sager and Taylor, 2006; Menkhoff and Taylor, 2007)                Some  of  the  recent  studies  like  Hsu  and  Taylor  (2013)  and  Coakley,  Marzano,  and                Nankervis (2016) analyze technical trading rules of large samples in the foreign exchange                market applying stepwise-SPA test. They also include transaction costs and control for data                snooping bias, and they found little or no evidence of profitability of these rules. On the                                                             743","other  hand,  Coakley,  Marzano,  and  Nankervis  (2016) found  that  relatively  new  trading                indicators  like  Bollinger  bands,  RSI  and  MACD  remains  robustly  profitable  even  after                controlling for data snooping bias. Zarrabi, Snaith and Coakley (2017) used FDR analysis                and  concluded take  over  20 years  up to  75% from  7650  trading  rules  have predictive                ability. However, it is necessary to update portfolio at least monthly because no set of                trading rules holds for a long time.                 Our  paper  aims to  test  selected  strategy  based  on  technical  analysis  and  compare  the                results with simple random strategy. We try to show that on the foreign exchange market                the  most  straightforward  strategy  is  usually  able  to  provide  better  results  than  more                sophisticated ones, at least in the long term. Moreover, we want to motivate our future                research in this field that will use much bigger dataset and more currency pairs.                The rest of the paper is organized as follows. Section 2 contains methodology and data.                Section 3 presents results and discussion, and the section 4 concludes.                 2 Methodology and Data                For  our  research,  we  use  two  investing  strategies  on  the  forex  market  (FX).  The  first                strategy (RSI strategy) is based on one of the most popular indicators of technical analysis                – Relative strength index (RSI) developed by Wilder (1978). The second strategy (random                strategy) does not use any indicator of technical analysis.                 For backtesting, we choose currency pair GBP/USD from 1/1/2017 to 5/30/2017. Risk-                reward-ratio (RRR) has been set to 3 to 1. It means that stop-loss was set to 20 pips under                the opening price and target was set to 60 pips above the opening price. We executed both                types of orders, such as buy orders and sell orders. We compare two investment strategies,                so there is no need to include commissions and spreads. In real trading, our strategies                would be less profitable. The fees would be around 1-2 pips per position according to the                rules of a particular broker. The following Table 1 shows the basic characteristics of our                analysis.                                                Table 1 Basic Characteristics                        Currency pair                                               GBP/USD                        Chart time frame                                                 H1                        Type of chart                                               Candlestick                        Time period                                                 1/1/2017 –                                                                                    5/30/2017                        RRR                                                            3 to 1                        Profit                                                        60 pips                        Loss                                                          20 pips                        Time zone                                                     UTC +1                                                Source: Author's calculations                 RSI strategy                Relative  strength  index  is  one  of  the  most  popular  momentum  indicators.  RSI  informs                investors  about  price  changing  in  advance,  which  is  the  main  difference  from  other                indicators of technical analysis. Therefore we have chosen RSI indicator for our strategy.                RSI indicator oscillates between 0 and 100, above 70, the market is overbought, and below                30, the market is oversold. Our RSI strategy is summarized in the Table 2.                                                                         744","Table 2 Characteristics of RSI Strategy                        Period of RSI                                          9                        Limits of overbought                                   70                        Limits of oversold                                     30                        Signal to buy                        First closed candle after RSI rise up                                                                           from 30                        Signal to sell                        First closed candle after RSI drop                                                                        down below 70                        Close position                        Break through stop loss or target                                                Source: Author's calculations                 The following Figures 1 and 2 demonstrate the signal to open the position (buy position                in this example) and signal to close the position for our RSI strategy.                                                    Figure 1 Signal to Buy                                                            Source: Author's calculations, tradingview.com                                       Figure 2 Close Position (break through target)                                                           Source: Author's calculations, tradingview.com                 Random strategy                Our random strategy is based on trading without technical and fundamental analysis. We                choose random parameters to enter trading positions. Trading positions are executed at 9                a.m. (UTC +1) every trading day (without weekends). The hour was selected for opening                trading hours in Britain. That is why we expected a higher rate of currency pair deals. We                have entered the sell position each odd day, and we have entered the buy position each                even day. The reason for the alternation of positions was the removal of imbalances in the                                                             745","case of long-term currency pair trend. The Table 3 sums up the rules for opening and                closing random strategy.                                        Table 3 Characteristics of Random Strategy                        Period of trading                      Every day, without weekend                        Signal to buy                              9:00, UTC + 1, even day                        Signal to sell                             9:00, UTC + 1, odd day                        Close position                        Break through stop loss or target                                                Source: Author's calculations                                             Figure 3 Even Day, 9:00, UTC +1                                                           Source: Author's calculations, tradingview.com                 The Figure 3 shows an example of opening and closing position according to our random                strategy. It is an even day, therefore we open a buying position at the time 9:00 UTC +1.                After reaching our target 60 pips the position is closed.                 3 Results and Discussion                We use six months of data and manually backtest our strategies presented in the previous                Chapter 2. In the Table 4 are our result for both strategies. The random strategy opened                one position every trading day, and it resulted in 129 positions in total. For RSI strategy,                we  also  tried  to  open  one  position  a  day,  but  there  are  some  days  without  a  signal.                Therefore, no position was opened, and we have only 109 observations.                We can see that approximately half of all positions was opened as long and the other half                in short position. For random strategy, it is evident due to our rules for entering the position                (even days buy, odd days sell), but this also holds for RSI strategy.                                                                                     746","Table 4 Results                                                        RSI strategy              Random strategy                  Number of open positions                    109                         129                  Number of long positions                    51                          63                  Number of short positions                   56                          66                  Number of profitable                        34                          44                  positions (60 pips profit)                  Number of loss positions                    75                          85                  (20 pips loss)                  % of success                             31.19 %                     34.11 %                  Total profit                             540 pips                    940 pips                  Average profit per position               5 pips                      7,3 pips                  Time period                              6 months                    6 months                                                Source: Author's calculations                 Further, we should notice the accuracy (the number of successful positions). With RRR 3                to 1, we have to correctly open at least 25 % of positions to be break even. Both positions                end in green numbers with more than 30 % of profitable positions. The random strategy                did a little bit better job with a success rate of 34.11 % in comparison with 31.19 % from                the RSI strategy. Total profit was 540 pips (5 pips per position on average) for RSI strategy                and 940 pips (7.3 pips per position on average) for random strategy.                 4 Conclusions                In this paper, we compared two trading strategies. The first one uses RSI indicator and the                second one was randomly opened every day at the same time. We tested both strategies                on GBP/USD currency pair for six months from 1/1/2017 to 5/30/2017 and set risk-reward-                ration to 3 to 1.                According to our results, both strategies were profitable even when considering fees, but                random strategy performs better. To be able to decide which of these strategies is better                we naturally need much longer timeframe and also try different currency pairs. However,                it will be the objective of our future research, when we want to focus more on a comparison                of simple random trading strategies with more sophisticated trading rules. Our results in                this paper only suggest, that there is some potential for random strategy and it could be                interesting to investigate this issue more deeply.                 Acknowledgments                 The  support  of  the  Masaryk  University  internal  grant  MUNI/A/1092/2017  is  gratefully                acknowledged.                 References                Coakley, J., Marzano, M., Nankervis, J. (2016). How profitable are FX technical trading                rules?. International Review of Financial Analysis, vol. 45, pp. 273-282.                Gehrig, T., Menkhoff, L. (2004). The use of flow analysis in foreign exchange: Exploratory                evidence. Journal of International Money and Finance, vol. 23, pp. 573–594.                Hsu, P. H., Taylor, M. P. (2013). Forty years, thirty currencies and 21,000 trading rules: A                large-scale,  data-snooping  robust  analysis  of  technical  trading  in  the  foreign  exchange                market. Working paper, no. 21.                Laïdi, A. (2008). Currency Trading And Intermarket Analysis. 1nd ed. Wiley Trading.                 LeBaron, B. (1999). Technical trading rule profitability and foreign exchange intervention.                Journal of International Economics, vol. 49, pp. 125-143.                                                                747","LeBaron,  B.  (2002).  Technical  trading  profitability  in  foreign  exchange  markets  in  the                1990s. Brandeis University Working paper.                 Levich, R. M., Thomas, L. R. (1993). The significance of technical trading-rule profits in the                foreign  exchange  market:  A  bootstrap  approach.  Journal  of  International  Money  and                Finance, vol. 12(5), pp. 451-474.                Lui,  Y.  H.,  Mole,  D.  (1998).  The  use  of  fundamental  and  technical  analysis  by  foreign                exchange dealers: Hong Kong evidence. Journal of International Money and Finance, vol.                17, pp. 535–545.                Menkhoff,  L.  (1997).  Examining  the  use  of  technical  currency  analysis.  International                Journal of Finance and Economics, vol. 2(4), pp. 307–318.                Menkhoff, L., Taylor, M. P. (2007). The obstinate passion of foreign exchange professionals.                Technical analysis. Journal of Economic Literature, vol. 45, pp. 936–972.                 Neely, C. J. (1997). Technical analysis in the foreign exchange market: A layman's guide.                Federal Reserve Bank of St. Louis Review, vol. 79(5), pp. 23-38.                 Oberlechner, T. (2001). Importance of technical and fundamental analysis in the European                foreign exchange market. International Journal of Finance and Economics, vol. 6(1), pp.                81–93.                Taylor,  M.  P.,  Allen,  H.  (1992).  The  use  of  technical  analysis  in  the  foreign  exchange                market. Journal of International Money and Finance, vol. 11, pp. 304–314.                 Sager, M. J., Taylor, M. P. (2006). Under the microscope: The structure of the foreign                exchange market. International Journal of Finance and Economics, vol. 11, pp. 81-95.                 Sweeney,  R.  J.  (1986).  Beating  the  foreign  exchange  market.  Journal  of  Finance,  vol.                41(1), pp. 163-182.                Wilder,  J.  W.  (1978).  New  concepts  in  technical  trading  systems.  Trend  Research,                McLeansville, NC.                Zarabi,  N.,  Snaith,  S.,  Coakley,  J.  (2017).  FX  technical  trading  rules  can  be  profitable                sometimes!. International Review of Financial Analysis, vol. 49, pp. 113-127.                                                                                                  748","Business Entities and Loans in the Czech Republic                                                           1                                                                                    2                                      Libuše Svobodová , Jaroslava Dittrichová                                                1  University of Hradec Králové                               Faculty of Informatics and Management, Department of Economics                                    Rokitanského 62, 500 03 Hradec Králové, Czech Republic                                              E-mail: libuse.svobodova@uhk.cz                                                 2  University of Hradec Králové                               Faculty of Informatics and Management, Department of Economics                                    Rokitanského 62, 500 03 Hradec Králové, Czech Republic                                             E-mail: jaroslava.dittrichova@uhk.cz                 Abstract: The aim of the article is to monitor and to analyze value and types of loans in                the Czech Republic as a source of financing for business entities. The first section focused                on statement of problem briefly describes the situation on the loan market. Main part is                focused  on  results  that  analyzes  the  development  of  provided  loans  to  entrepreneurial                subjects  in  the  Czech  Republic.  Interest  is focused  on  the value  of  loans  in the  Czech                currency  and  in  foreign  currency.  Values  and  ratios  of  both  sources  of  financing  are                compared in the article. Subsequently, the value of loans according to type of loan are                analyzed. Data from the Czech National Bank were used to process the article. The results                are clearly presented in processed graphs. Results show that entrepreneurs have used in                the biggest value long-term loans instead short-term and medium-term loans to finance                liabilities in recent years. Even in the context of increased foreign trade, the share of foreign                currency  financing  is  growing.  Medium  term  loans  have  from  2010  the  biggest  ratio                between Czech and medium currencies.                Keywords: comparison, development, loan, statistics, value                JEL codes: G21, M21                 1 Introduction and problem statement                 The objective of financing a business entity is to efficiently secure the necessary amount                of capital, emphasizing the minimum cost of capital acquisition and avoiding the financial                risk  of  the  enterprise.  Loans  are  the  basic  sources  of  financing  consumer  demand  for                businesses.  There  are  several  classifications  of  bank  loans.  We  will  use  classification                according to time and type of currency. According time consumer loans are divided on                short, medium and long term maturities. Financing of long-term property can be done with                long-term resources. Long-term assets change into money over a longer period of time.                Long-term funding sources should therefore be used as a short-term loan could put the                company  in  financial  distress.  Circulating  of  non-fixed  assets  states  that  those  assets                change their form from material to money and vice versa. The resources in short-term                assets  tied  for  a  short  period  of  time,  the  structure  should  be  flexible,  mainly  due  to                changes in time, scale and structure. Loans for short-term cash needs provided by financial                institutions may be debit balances on current accounts and overdraft accounts. Consumer                loan,  credit  card,  overdraft  account,  bill  of  exchange,  revolving  loan,  lombard  credit,                business loan or bank guarantees are the short or medium types of financing.                Regulation of consumer credits includes the rules of negotiations of subjects on financial                market with consumers. The Consumer Credit Act was extended with a range of entities                subject  to  the  Czech  National  Bank  supervision  with  effect  from  1  December  2016.  It                applies  to  all  providers  of  consumer  credit  and  consumer  credit  agents.  The regulation                concerns persons referred in Section 44 (1) of the Act on the Czech National Bank in the                performance of activities performed by such persons on the basis of a authorization, license                or registration of the Czech National Bank (CNB) or a similar permission to operate in the                Czech Republic by a foreign authority in the field of consumer protection.                A well-functioning internal market in consumer credit also requires a harmonized approach                within  the  European  Community  that  will  adapt  the  laws  to  future  forms  and  the                                                            749","development  of  consumer  credit in  the  context  of the  mobility  of  European  consumers                according to Directive 2008/48/EC of the European Parliament and of the Council of 23                April 2008 on credit agreements for consumers.                The  credit  conditions  survey  and  the  development  of  consumer  loans  serve  for  users                (central banks, banks, financial agents) to provide valuable information on developments                in the consumer credit market. The qualitative information is obtained, for example, by the                CNB in the form of a structured questionnaire survey directly from a representative sample                of banks operating on the Czech credit market participating in this survey (CNB, Lending                survey, 2018a) or European Central Bank for all European countries (ECB, 2018).                The CNB started to do a survey in summer 2012. The questionnaire focuses on both sides,                the credit supply side (credit standards and terms and conditions for approving loans as                stipulated by banks) and the credit demand side (demand for loans among households and                corporations as perceived by banks) and assesses the most significant factors underlying                changes in supply or demand. The CNB survey covers not only the observed evolution of                credit  standards  and  credit  demand  in  the  previous  quarter,  but  also  the  expected                developments  in  the  three  next  months.  The  CNB  publishes  the  results  of  the  regular                quarterly  rounds  of  the  survey  as  an  electronic  text  publication  containing  graphic                illustrations and as data files containing aggregate results in late January, April, July and                October. (CNB, Lending Survey, 2018a) European Central Bank also presents results fourth                in a year.                 According to news reports, indebtedness is still growing in the Czech Republic. The Czech                Economy is rising, unemployment is declining and financial institutions motive with low                interest rates to borrow money. Due to market developments and previously published                articles  that  have  been  devoted  by  the  author  of  the  paper  earlier  (Hedvičáková,                Svobodová, 2015, 2016, 2017), we know that mortgage loans were almost still growing.                Question  is  whether  it  is  in  total  for  business  entities.  Business  entities  in  the  Czech                Republic use leasing, long-term loan, mortgage loan or bonds as a source of long-term                finance. The World Bank Group (2018) evaluate the ease of obtaining credit for businesses                in relation to \"doing business\", which was dealt in Svobodová, Hedvičáková (2015). For                managing of long term finances it is substantially to be as stable as possible (Černohorská,                2015).  The  calculator  of  bank  service  calculations  and  mortgage  loans  was  dealt  by                Hedvičáková,  Soukal (2012).  Allen  and  Paligorova  (2015)  dealt  with lending in  Canada                during the financial crisis, when lending was restricted to public companies, but not to                private  companies.  They  lent  to  private  businesses  because  they  could  get  excessive                revenue.  Godlewski,  Sanditov  (2018)  investigate  the  effect  of  the  network  of  financial                institutions  on  the  certification  value  of  bank  loans  using  data  on  syndicated  loans  to                European companies. They find that the presence of more central leaders in a syndicate                substantially  increases  the  stock  market's  reaction  to  loan  announcements. Kim et. al.                (2018) founded that firms with a reputation for IT capability tend to have more favorable                price and non-price terms for loan contracts and are less likely to have their credit rating                downgraded  or  to  report internal  control  weaknesses  than  firms  with  no  IT  reputation.                Isynuwardgana, Muslih, (2018) presented that through the implementation of governance                will decrease the cost of debt the company. Research Wlodarczyk, Szturo (2018) analyse                the factors affecting the credit availability and their influence on development of Polish                small and medium companies, such as company's size and age, financial results or the                length of relationship with the banking institution, as well as the features characterizing                the banking sector. The results demonstrate that in Poland, similarly to other European                countries, small and medium companies have a more limited access to credit availability                than large companies. Moreover, a significant dependence of bank credit availability from                the size of the company, liquidity, profitability and the situation in the banking sector was                demonstrated.                                                                 750","2 Methodology and Data                Goal of the paper is to analyse the situation on the loan market in the Czech Republic that                were provided to non-financial business entities in 1993 – 31.3. 2018 in their values and                according to type.                 As for applied methodology, primary and secondary sources were used. Data illustrating                the issue on the loan scene were gained from the Czech National Bank and the European                Central Bank. Data from the Czech National Bank were always used in period as they are                presented by the Czech National Bank. There were used the oldest and the newest data                that are available. Results are presented in graphs that were done by the authors of the                article.  They  are  accompanied  by  the  comments.  Secondary  sources  include  websites,                technical  literature,  information  gathered  from  professional  journals,  discussions  and                participation at professional seminars and conferences. Then it was necessary to select,                categorize and update available relevant information from the collected published material                so that basic knowledge about the selected topic could be provided.                 3 Results and Discussion                 Credit standards remained broadly unchanged for loans to non-financial corporations in                2018  Q1.  Most  factors  had  a  neutral  impact.  An  easing  of  standards  was  fostered  by                competitive pressure and a good liquidity situation of banks. The terms and conditions for                approving corporate loans were eased. Average interest margins declined, but much less                broadly than in the past. The recent broad decrease in interest margins on corporate loans                thus halted. The easing of terms and conditions was more marked for large corporations,                occurring  mainly  through  lower  collateral  and  maturity  requirements.  Corporations’                demand for loans rose in 2018 Q1 (net percentage of 17%). Financing of fixed investment                and working capital was an important factor. Part of the banking market perceived a rise                in demand for financing of mergers and acquisitions and corporate and debt restructuring.                The still low interest rates also had a favourable effect. By contrast, demand for loans was                reduced by the use of alternative forms of financing by corporations, i.e. generation of                internal funds and issuance of debt securities. Banks expect no change in credit standards                and an increase in demand for loans (an NP of 18%) in 2018 Q2. Credit standards for loans                to sole traders were unchanged and demand for such loans fell further (net percentage of                6%). (CNB, Bank lending survey, 2018a)                 Loans to nonfinancial corporations                Chart in Fig. 1 presents the results of loans and receivables from non-financial corporations                in the Czech Republic in Czech crowns. The data were available from January 1, 1993 to                March 31, 2018 in the Czech National Bank's statistical data. The chart shows the data for                each year. The most stable trend in values was recorded by loans and receivables over the                medium term. At the same time, until 1995-1996, it is the least used product of comparable                solutions. Short-term loans up to one year were by 2009 the most used instrument. The                largest amount of money was lent to this instrument in 1996-1997, when entrepreneurs                lent  CZK  350  billion.  Then,  their  volume  began  to  decline  until  2004.  Since  2005,  the                volume  has  begun  to  increase  again  until  2009  and  has  fluctuated  since  2010  with  a                declining trend. In terms of long-term loans, they recorded the largest fluctuations and                changes. Their popularity grew until 2001, and then dropped significantly. By 2005, they                remained at a similar level. However, the change occurred in 2006, when lending of this                type began to grow again. Between 2011 and 2015, they were rather stagnant (reason                may be financial crisis) and then continued to grow until 1.1.2018.                                                                   751","Figure 1 Non-financial Corporations - Loans by Term (CZK)                      450000                     400000                     350000                     300000                     250000                     200000                     150000                     100000                      50000                          0                             1/1/1993  1/1/1994  1/1/1995  1/1/1996  1/1/1997  1/1/1998  1/1/1999  1/1/2000  1/1/2001  1/1/2002  1/1/2003  1/1/2004  1/1/2005  1/1/2006  1/1/2007  1/1/2008  1/1/2009  1/1/2010  1/1/2011  1/1/2012  1/1/2013  1/1/2014  1/1/2015  1/1/2016  1/1/2017  1/1/2018                                         Short term loans    Medium term loans     Long term loans                               Source: own elaboration based on Czech National Bank (CNB, 2018b)                 Until December 31, 1996, foreign currency loans were not recorded in the tables of the                Czech National Bank (CNB, 2018b). It is the reason why differences were not recorded until                1997 and increase in values is evident from the graph in 1997. The graph has similar axis                shapes and trends as in the previous graph (fig. 1). Long term loans again recorded higher                values since 2009 before short term loans and medium term loans. It is not surprising                because long-term loans are usually associated with a higher loan amount than short or                medium term loans.                             Figure 2 Non-financial Corporations - Loans by Term (CZK+FC)                      600000                      500000                      400000                      300000                      200000                      100000                           0                             1/1/1993  1/1/1994  1/1/1995  1/1/1996  1/1/1997  1/1/1998  1/1/1999  1/1/2000  1/1/2001  1/1/2002  1/1/2003  1/1/2004  1/1/2005  1/1/2006  1/1/2007  1/1/2008  1/1/2009  1/1/2010  1/1/2011  1/1/2012  1/1/2013  1/1/2014  1/1/2015  1/1/2016  1/1/2017  1/1/2018                                        Short term loans    Medium term loans     Long term loans                               Source: own elaboration based on Czech National Bank, (CNB, 2018b)                The next section will compare the amount of loans in foreign currencies and the percentage                distribution of credits in the Czech crowns and foreign currencies. Foreign currency data is                reported  in  the  CNB's  statistical  data  from  1.1.1997.  For  this  reason,  the  data  will  be                compared from 1.1.1997 to the latest data.                                                               752","Figure 3 Non-financial Corporations – Comparison of Loans by Term According to Value                                               of CZK and Other Currencies                            180000                          160000                          140000                          120000                          100000                           80000                           60000                           40000                           20000                               0                                  1/1/1997  1/1/1998  1/1/1999  1/1/2000  1/1/2001  1/1/2002  1/1/2003  1/1/2004  1/1/2005  1/1/2006  1/1/2007  1/1/2008  1/1/2009  1/1/2010  1/1/2011  1/1/2012  1/1/2013  1/1/2014  1/1/2015  1/1/2016  1/1/2017  1/1/2018                                        Short term loans    Medium term loans     Long term loans                               Source: own elaboration based on Czech National Bank, (CNB, 2018b)                In 2018, long-term resources were funded by a foreign currency of 160 billion. Since 2002,                they have been used at the highest value for foreign  currency financing. However, the                record was recorded as at 30.4. 2017 when foreign currency was lent more than CZK 170                billion. The breakthrough occurred in 2002 when short-term and medium-term loans were                higher than long-term loans. Interesting are the trends. While long-term and medium term                loans provided in a foreign currency have a growing linear trend, short-term loans have                declined in the monitored period.                If we focus on the share of financing in CZK and in foreign currencies, it is evident from                the graph in Figure 4 that the share of foreign currency financing to CZK increased from                                   th                                                          st                14% to 30% in 31  of March 2018 from 1  of January1997. In some monitored periods in                2017, the proportion was up to 32%. Despite the recorded fluctuations, the linear function                of total loans and their ratios of financing in CZK are decreasing.                 Figure 4 Non-financial Corporations – Comparison of Loans by Term According to Ratio                                               of CZK and Other Currencies                             95%                           90%                           85%                           80%                           75%                           70%                           65%                           60%                                 1/1/1997  1/1/1998  1/1/1999  1/1/2000  1/1/2001  1/1/2002  1/1/2003  1/1/2004  1/1/2005  1/1/2006  1/1/2007  1/1/2008  1/1/2009  1/1/2010  1/1/2011  1/1/2012  1/1/2013  1/1/2014  1/1/2015  1/1/2016  1/1/2017  1/1/2018                                            Short term loans                   Medium term loans                                       Long term loans                    Loans - total                                       Lineární (Loans - total)                               Source: own elaboration based on Czech National Bank, (CNB, 2018b)                                                            753","Even though medium term loans were the least used in the year 2017 compared to CZK                and  foreign  currency,  since  31  July  2010  their  percentage  share  recorded  the  biggest                difference. Medium term loans were in 2017 used in foreign currency 40% of values.                Loans to nonfinancial corporations according to value                If we focus on loans according to the amount, debts for loans over CZK 30 million and                loans up to CZK 30 million were most borrowed in value, see fig. 5. Other lending also                agree with the loan amount. The smallest total amount was borrowed for loans with a value                of up to CZK 7.5 million. For the two lowest categories, the data were released only from                1.1.2010. If we add a linear trend line, in all categories it is decreasing, to the lowest loans                up to CZK 7.5 million, which shows a small increase.                    Figure 5 Non-financial Corporations – Comparison of Loans by Value of Loan, New                                                        Businesses                  80000                 70000                 60000                 50000                 40000                 30000                 20000                 10000                     0                         Jan-04  Jul-04  Jan-05  Jul-05  Jan-06  Jul-06  Jan-07  Jul-07  Jan-08  Jul-08  Jan-09  Jul-09  Jan-10  Jul-10  Jan-11  Jul-11  Jan-12  Jul-12  Jan-13  Jul-13  Jan-14  Jul-14  Jan-15  Jul-15  Jan-16  Jul-16  Jan-17  Jul-17  Jan-18                                  Loans up to 7,5 mil. CZK                 Loans 7,5 - 30 mil. CZK                               Loans up to 30 mil. CZK                  Loans over 30 mil. CZK                               Lineární (Loans over 30 mil. CZK)                               Source: own elaboration based on Czech National Bank, (CNB, 2018c)                 The latest  graph,  fig.  6  shows that  the  most  used  tool for  new  business is  overdrafts,                revolving loans and credit cards.                      Figure 6 Non-financial Corporations – Comparison of Loans by Value of Loan,                              Overdrafts, Revolving Loans and Credit Cards, New Businesses                    250000                    200000                     150000                    100000                      50000                        0                           Jan-04  Jul-04  Jan-05  Jul-05  Jan-06  Jul-06  Jan-07  Jul-07  Jan-08  Jul-08  Jan-09  Jul-09  Jan-10  Jul-10  Jan-11  Jul-11  Jan-12  Jul-12  Jan-13  Jul-13  Jan-14  Jul-14  Jan-15  Jul-15  Jan-16  Jul-16  Jan-17  Jul-17  Jan-18                                      Overdrafts, revolving loans and credit cards    Overdrafts                               Source: own elaboration based on Czech National Bank, (CNB, 2018c)                                                            754","The CNB has monitored separately overdrafts that show a linear upward trend. By contrast,                overdrafts, revolving loans and credit cards together show a declining linear trend. The                chart  shows  that  there have  not  been significant  fluctuations in  overdrafts  since  2010.                Overdrafts,  revolving  loans  and  credit  cards  are  subject  to  greater  fluctuations  in  the                reference periods.                 4 Conclusions                According to the data from the Czech National Bank (2018a) entrepreneurs have used in                the highest value long-term loans instead short-term and medium-term loans to finance                liabilities in recent years. It is interesting to monitor market developments. Financing of                business  entities  significantly  affects  development  and  innovation  in  companies,  as                consumer and mortgage loans or other types of long loans usually represent investments                in fixed assets. Long term loans have lower interest rates than the consumer credits or                other products used for short or medium-term of financing. The disadvantage of mortgage                loan is real estate collateral and greater administrative burden. Real estate may not need                to  be  secured  in  some  cases,  for  example,  when  providing  an  American  mortgage.                However, this is usually linked to a higher interest rate and subsequently an APR (annual                percentage rate). Other long-term loans or medium-term loans depends on the negotiation                between companies and bank institutions. The most often used products by companies in                the financial institutions how to borrow the money are the overdrafts. They are classified                as the short-term of medium-term funding. New businesses of loans by value of loan were                from January 2004 recorded the highest value of loans more than 30 mil. CZK and loans                from 7,5 mil. CZK to 30 mil. CZK. In the context of increased foreign trade, the share of                foreign currency financing is growing in the Czech Republic and also values of those type                of financing are rising.                 Acknowledgments                 The  paper  was  elaborated  in  the  framework  of  the  specific  research  \"Evaluation  of                investments within the Industry 4.0 concept\" and the internal grant project \"Support of                pedagogical work, technical equipment and communication with experts\" at the University                of Hradec Králové, Faculty of Informatics and Management.                 References                 Act no. 6/1993 Coll., on the Czech National Bank.                Allen, J., Paligorova, T. (2015). Bank loans for private and public firms in a liquidity crunch,                Journal of Financial Stability, vol. 18, pp. 106–116.                Černohorská, L. (2015). Impact of Financial Crisis on the Stability Banking Sectors in the                Czech Republic and Great Britain. Procedia Economics and Finance, vol. 26. pp. 234-241.                 Directive 2008/48/EC of the European Parliament and of the Council of 23 April 2008 on                Credit Agreements for Consumers and Repealing Council Directive 87/102/EEC.                Godlewski, Ch. J., Sanditov, B. (2018). Financial Institutions Network and the Certification                Value of Bank Loans. Financial management, vol. 47(2), pp. 253-283.                Hedvičáková,  M.,  Svobodová,  L.  (2017).  Mortgages  for  individuals,  businesses  and                municipalities in the Czech Republic. In: 15th International Scientific Conference on Hradec                Economic Days 2017. Hradec Králové: Univerzita Hradec Králové, pp. 279-286.                Hedvičáková,  M.,  Soukal,  I.  (2012).  Retail  core  banking  services  costs  optimization.                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Corporate Governance and Cost of Debt. Advanced                science letters, vol. 24(4), pp. 2690-2692.                 Kim, J. B., Song, B. Y., Stratopoulos, T. C. (2018). Does Information Technology Reputation                Affect Bank Loan Terms?. Accounting Review, vol. 93(3), pp. 185-211.                Wlodarczyk, B., Szturo, M. (2018). The impact of credit availability on small and medium                companies. Entrepreneurship and sustainability issues, vol. 5(3), pp. 565-580.                World      Group      Bank,      (2018).      Doing     business.      Retrieved     from:                http://www.doingbusiness.org/                                                                                                        756","Recent Changes in the Polish Deposit Guarantee Scheme                                                     Łukasz Szewczyk                                             University of Economics in Katowice                                         Department of Banking and Financial Markets                                           1 Maja St. 50, 40-287 Katowice, Poland                                           E-mail: lukasz.szewczyk@ue.katowice.pl                 Abstract: Deposit guarantee scheme is an important pillar of every modern financial safety                net. During the global financial crisis, those schemes have undergone significant changes,                which were the result of the need to cope with challenges caused by the recent crisis. In                Poland, deposit guarantee scheme, which was established in 1994, plays an important role                in providing financial stability in the financial system. In the new financial rules, introduced                by the EU Deposit Guarantee Scheme Directive (2014/49/EU), some important changes                have  been  introduced  into  its  functioning,  to  provide  greater  level  of  harmonization  of                solutions between systems within EU. The main objective of this study is to present the                most  important  changes  introduced  into  Polish  deposit  guarantee  scheme  and  to  show                some of the possible consequences of these changes for the Polish banking sector. A main                research method used in this paper will be critical analysis of the literature connected with                this subject and reports issued by the Polish deposit guarantee scheme. Results of this                research  will  help  to  understand  current  problems  of  deposit  protection  in  the  Polish                banking system.                 Keywords: deposit guarantee scheme, global financial crisis, resolution, financial stability                JEL codes: G21, G28, G01                 1 Introduction                Deposit  guarantee  schemes  play  an  important  role  in  modern  safety  nets.  They  are                responsible for protecting depositors in case of bank bankruptcy and therefore they have                an important influence on financial stability. During the global financial crisis most of the                systems have undergone some important changes, adapting them to the new conditions.                Some systems introduced new, unconventional measures as a response to the crisis and                some of these changes have been made permanent. Bank Guarantee Fund has also gone                through  some  adjustment  process,  although  global  financial  crisis  didn’t  have  very  big                impact on Polish financial system. This adjustment has been connected with the new rules                introduced in EU, when adopting new Directive on Deposit Guarantee Schemes in 2014. It                resulted  in  the  adoption  of  the  new  Act  on  Deposit  Guarantee  Fund  in  2016.  This  act                introduced some changes in the construction of the system. On the other side recent years                have brought some important challenges for the System. The year 2014 have brought                bankruptcy in credit unions. In 2015 and 2016 two cooperative banks collapsed, bringing                necessity to reimburse the depositors.                The paper presents some of the most important changes in the Polish deposit guarantee                scheme and it focuses on some important aspects of its functioning in the recent period of                time. Results of this research will help to understand current issues of deposit protection                in the Polish banking system and its meaning to financial stability                 2 Deposit protection- theoretical studies                Deposit  insurance  has  been  the  subject  of  a  number  of  studies,  both  theoretical  and                empirical. The history of research can be divided into four periods (from the creation of                first systems in the 1930s, through their increasing popularity in the 1970s and 1980s,                1990s which are connected with attempts to regulate them on international level, especially                in EU, up to post 2008 time, which saw the great change in their constructions as a result                of  global  financial  crisis).  The  main  problems  covered  in  these  studies  are  shown  on                figure 1.                                                              757","Figure 1 Deposit Guarantee Schemes Research Fields                       governance                    and relations                                 Public policy            Premium,                                                                    objectives                      to other             membership                                     funding and                     safety net            and coverage                and                    cost                     institutions                                   mandates                                                 Source: Pruski and Kerlin, 2015                 The major sources of the research of DGS are not only the works of academics but also                reports published by international organizations, e.g. International Monetary Fund, OECD,                Financial Stability Board (Financial Stability Board, 2012) or International Association of                Deposit  Insurers.  The  last  institution  has  begun  doing  such  research  to  support  policy                makers responsible for the functioning of deposit guarantee schemes in their jurisdictions.                The dynamics of global financial crisis has made some extra space for research to fill. It is                important to say that the post- crisis reality is completely different from the pre-crisis state                of  affairs.  The  scale  of  the  crisis  and  the  number  of  bank  failures  means  that  deposit                guarantee  schemes  have  faced  the  big  challenge  and  it  should  be  pointed  out  in  the                research  (Pruski  and  Kerlin,  2015).  During  the  global  financial  crisis  the  expansion  of                financial safety net was substantial, especially for crisis countries and extended beyond                traditional deposit insurance. The main activities in this area consisted of (Demirguc- Kunt                et al., 2014):                     •   increasing statutory coverage,                    •   abolishing co-insurance,                    •   introducing a government guarantee on deposits,                    •   introducing a government guarantee on non- deposit liabilities,                    •   introducing a government guarantee on bank assets,                    •   undertaking a significant nationalizations of banks.                 It is important to say that during global financial crisis policy makers pointed out many                problems,  which  deposit  guarantee  schemes  have  to  overcome.  The  main  problems                observed in the area of DGS were mainly (Szewczyk, 2015):                    •   unsustainable funding, which was mainly connected with the lack of risk sensitive                       funding arrangements,                    •   limited possibility to use DGS in a crisis situation, especially towards systematically                       important financial institutions,                    •   possible  negative  impact  on  financial  stability,  especially  through  weakening  of                       market discipline,                    •   incompatibility  of  individual  systems  which  may  lead  to  unefficiency  in  crisis                       management.                3 Deposit insurance in Poland- selected issues                Bank Guarantee Fund (BGF) in Poland was established in 1994 by adoption of the Act of                BGF. The Fund is an independent legal person with its office in Warsaw and was established                to  maintain  stability  in  the  Polish  banking  sector.  The  operations  of  the  BGF  focus  on                guaranteeing, aiding, controlling and analysing. It is intended that the Fund reimburse, in                the  event  of  the  bankruptcy  of  a  bank    and  credit  union  which  is  a  participant  in  the                guarantee scheme, funds accumulated in a bank up to the amount specified by the Act on                the BGF. This is the so-called “pay-box function”. The second basic task of the Fund is to                make available financial assistance to its participants which have found themselves faced                with  a loss  of  solvency  and  are  engaging in independent  reforms,  and  also  to  support                processes concerning the merger of endangered banks with strong banks. This is known                as  the  “risk-minimizer”  function  in  that  the  BGF  may  play  an  important  role  in  crisis                                                             758","management by providing additional liquidity to credit institutions, mitigating the risk of                instability of the banking sector (Zawadzka, 2011).                 To sum up, the most important functions of the Fund are (Bankowy Fundusz Gwarancyjny,                2016):                     •   guaranteeing each depositor a payout of its deposits held in bank or credit union up                       to the statutory limit (100 000 EUR),                    •   preparing and updating resolution plans and assessing their feasibility,                    •   participating in resolution colleges,                    •   conducting resolution,                    •   writing down and converting capital instruments,                    •   executing the tasks in the scope of restructuring of credit unions,                    •   collecting and analyzing the information,                    •   developing  analysis  and  forecasts  for  early  identification  of  the  threats  in  the                       banking sector and credit union sector,                    •   participating in the works of the Financial Stability Committee,                    •   participating in the works of the EBA permanent committee for resolution,                    •   providing financial  assistance  to  banks  from  the  cooperative  banks  restructuring                       fund.                 The process of reimbursement of depositors is shown on figure 2.                                        Figure 2 Depositors Reimbursement Process                                 •suspending the activities of a bank or credit union                               •appointing the judge commissioner                     Financial                    Supervision   •submitting a bankruptcy petition                     Authority                                   •transferring the list of depositors to BGF                  The receivership of                    a bank/credit                      union                                  •transferring the data and funds to the agent bank                               •strating the disbursments                       BGF                                            Source: Bankowy Fundusz Gwarancyjny, 2016                 The main objective of this process is that the depositors will be disbursed in as short time                as  possible.  Before  the  global  financial  crisis,  the  time  that  BGF  had  to  conduct  the                procedure of disbursement was limited to three months. In the opinion of EU policy makers,                this time was too long, and when adopting the new Directive it has been shortened and                now it is limited to seven working days from the day of bankruptcy of credit institution. It                is important to say that in recent years some of the credit institutions in Poland collapsed                and it resulted in payments of guaranteed funds to depositors of failed institutions. Recent                disbursements are shown in table 1.                                                                759","Table 1 Disbursements Done by BGF in 2014-2016                     Name of            Date of           Number of     Liabilitis to      Total                     entity          fulfilment of        depositors    depositors     disbursements                                          the                              (PLN       until the end of                                      guarantee                           million)       2016 (PLN                                      conditions                                          million)                     SKOK             18.07.2014             74 042        817,5           810,6                     Wspólnota                     SKOK w           12.12.2014            45 449        2 246,6         2 240,3                     Wołominie                     SBRiR w          23.11.2015            33 144         2 037          2 028,3                     Wołominie                     SKOK             08.01.2016            17 200         183,9           182,7                     Kujawiak                     SKOK             25.02.2016             8 343         170,2           167,0                     Polska                     SKOK             25.04.2016             7 380         81,9             79,8                     Jowisz                     SKOK Arka        11.05.2016            13 937         93,1             91,4                     SKOK             22.07.2016             8 348          88              85,0                     Skarbiec                     BS w             24.10.2016             5 666         145,7           141,6                     Nadarzynie                                        Source: Bankowy Fundusz Gwarancyjny, 2016                 Among failed institutions were seven credit unions and two cooperative banks. The number                of  bankruptcies  in  the  credit  unions  sector  shows  that  they  are  in  a  difficult  financial                situation and it is necessary that their depositors are protected by BGF. It’s also in line                with the EU guidelines, according to which depositors of every type of credit institution                should be protected in case of its bankruptcy. BGF has become responsible for deposit                guaranteeing in credit unions in 2013.                In this particular area of BGF activity it is important to say that Polish banking sector is                stable nowadays. When looking at the number of reimbursements in the period of 1995-                2016 it can be seen that most of bankruptcies took place between 1995-2001 (so at the                first period of the function of BGF). It can be explained with the fact that Polish banking                sector after transition to the market economy was very weak and it resulted in some fails                among banks (especially cooperative banks), as presented in table 2.                                    Table 2 Disbursements Done by BGF in 1995-2016                               Year              Commercial         Cooperative      Credit                                                  banks               banks         unions                              1995                   2                  48             -                              1996                   1                  30             -                              1997                   -                  6              -                              1998                   -                  4              -                              1999                   1                   -             -                              2000                   1                  1              -                              2001                   -                   -             -                              2002-2013              -                   -             -                              2014                   -                   -             2                              2015                   -                  1              -                              2016                   -                  1              5                                                 Source: Capiga et al., 2017                 When looking at the problem of funds used to disburse depositors, it can be seen that in                terms of value  the biggest financial burden occurred in 2015 when SBRiR (cooperative                                                             760","bank) collapsed and 2 037 million PLN had to be reimbursed. Second biggest bankruptcy                happened in 2000, when one of the biggest Polish banks (Bank Staropolski) collapsed and                about 147 thousands of depositors had to be reimbursed (figure 3).                      Figure 3 Funds Allocated to Guarantee Disbursements in Banks (PLN million)                   2500                                                                                                2037                  2000                    1500                   1000                                                626                   500                         105  50,8  6,4  8,2  4,7    12,5 0,1  0,1  0,4  0,1  0,1  00,004    0      145,7                     0                                             Source: Bankowy Fundusz Gwarancyjny, 2016                 The Fund has also got new powers in providing financial help in the process of restructuring                of credit unions. Help can be transferred not only to the insolvent credit union but also to                the bank, which participates in the process of restructuring. In the years 2014-2016 BGF                granted support in the form of subsidies for the total amount of 458,5 million PLN (table                3).                      Table 3 Financial Support Granted in the Forms of Subsidiaries in 2014-2016                                           List                               Year of       Subsidy                                                                             granting        (PLN                                                                                          thousand)                        Alior Bank SA (SKOK Św. Jana z Kęt)                    2014        15 895,8                      Pekao SA (SKOK im. Mikołaja Kopernika)                   2015        101 926,3                              PKO BP SA (SKOK Wesoła)                          2015        278 858,4                            Alior Bank SA (SKOK im. S.K.                       2016        52 533,74                                    Wyszyńskiego)                         Alior Bank SA (Powszechna SKOK)                       2016        9 310,12                                        Source: Bankowy Fundusz Gwarancyjny, 2016                 All actions taken by BGF which involve its funds lead to the problem of funding model                adopted by deposit guarantee scheme in Poland. This problem is highly complex, but it is                worth mentioning that the funding model has changed significantly in recent years. It is a                consequence of the adoption of the new Directive on deposit guarantee schemes, followed                by the adoption of new Deposit Guarantee Scheme Act in 2016 in Poland. The main aspect                of this new funding model is to adopt risk premiums in place of flat premiums paid by the                credit institutions. According to this model credit institutions that cause higher risk in the                system, pay more than less risky one. The risk is evaluated upon the set of indicators                covering such aspects as capital, liquidity, assets quality, business model and management                and  possible  losses  of  the  fund.  There  is  a  set  of  indicators  in  every  aspect,  that  are                evaluated and on this basis a premium is set. The main objective of this new model is to                build a stable fund, which will enable to cover the costs of reimbursements in case of large                bank failure and through this, it will have positive impact on financial stability.                                                               761","4 Resolution process- BGF as a resolution authority                The single resolution mechanism is an important mechanism introduced after the global                financial crisis. The last financial crisis revealed that the Member States of the European                Union  did  not have tools  appropriate  for  liquidation  of large  financial institutions  being                under the bankruptcy threat which were not able to continue their business independently                due to their weak financial situation. At the same time, the systemic risk occured. There                were fears that uncontrolled bankruptcy of „too big to fail” financial institutions would pose                a threat to their clients and to the economy as a whole. Having no appropriate tools at                their disposal, the Member States granted aid to threatened entities from their national                budgets. The European Commission came to the conclusion that taxpayers should not bear                the costs of rescuing the „too big to fail” financial institutions. The decision was taken that                the responsibility should be borne in the first place by owners and then by creditors of                entities under the bankruptcy threat (www1).                BGF has been chosen to act as a resolution authority in Poland and it is authorised to (BFG,                2016a):                    •   sell shares of a threatened entity,                    •   sell the business of a threatened entity or its part,                    •   establish  a  bridge  institution  –  a  special  institution  to  which  good  assets  and                       liabilities (deposits) of a threatened entity will be transferred,                    •   burden owners and certain creditors of a threatened entity with its losses, so as to                       rebuild its capital and enable it to continue its operation (bail-in) – burdening with                       the  losses  takes  place  via  write  down  of  shares  of  the  owners  and  possibly  via                       conversion or write down of liabilities of certain creditors,                    •   transfer low-quality assets to a special institution,                    •   file to a court for bankruptcy or liquidation of a residual entity remaining after a                       partial or total sale of its business,                    •   conduct liquidation of the discontinued business of the entity.                 Main resolution tools adopted in the Deposit Guarantee Act from 2016  are presented in                table 4.                                                 Table 4 Resolution Tools                        Sale of         This  instrument  enables  the  Bank  Guarantee  Fund  to                       business         transfer  in  whole  or  in  part  the  business  of  an  entity                                        being under the bankruptcy threat to another financial                                        institution of good financial standing or to sell its shares                                        to  a  new  investor,  which  will  ensure  stability  and                                        development of the said entity                        Bridge           In  the  case  of  the  bridge  institution  tool  a  special  purpose                      institution       vehicle  established  by  the  BGF,  in  the  form  of  a  bank  or  a                                        brokerage house is the acquirer of property rights of the entity                                        under resolution and acquires them in exchange of assumption                                        of liabilities of the said entity. The whole process of „the sale”                                        of  property  rights  of  the  entity  under  resolution  takes  place                                        under the terms analogous to those applied in the case of the                                        sale of business tool.                   Write down or    In       some    cases,   due    to   scale,   profile   or   internal                    conversion of       interconnectedness of an entity, it may be impossible to find an                      liabilities       acquirer willing to acquire the business in its entirety. In such a                                        case, the BGF may write down a part of liabilities of an entity                                        under  the  bankruptcy  threat  or  convert  them  into  shares  in                                        order to cover losses and rebuild own funds                        Asset           The  asset  separation  tool  involves  a  transfer  of  selected                      separation        property  rights  of  an  entity  under  resolution  to  a  special                                        purpose vehicle established by the Fund                                                    Source: BFG, 2016b                                                             762","It is important to add that this resolution mechanism is new and it hasn’t been used in                Polish banking system so far. None of the credit institution that failed in recent years has                been considered systemically important. The only resolution in EU that has been carried                out so far involved Banco popular Espanol S.A. in 2017 (European Parliament, 2017).                5 Conclusions                 Global  financial  crisis  proved  that  although  deposit  guarantee  schemes  are  important                institutions in the  financial  safety nets,  some  changes in their  construction have  to  be                made. The construction of these systems differ among individual countries, except EU,                where a specific Directive unifies most of the aspects of their construction.                 In Poland, Bank Guarantee Fund has gone some changes as a result of the crisis and new                rules within EU. It is seen that crisis didn’t affect Polish banking system very much but in                recent years some problems in credit institutions have arisen and some credit institutions                collapsed.  It  led  to  the  necessity  to  reimburse  their  depositors  and  started  a  broad                discussion  whether  the  Fund  will  be  able  to  handle  a  situation  in  which  a  large  bank                collapse. The solution in this regard may be an introduction of a new funding model, which                makes the contribution paid by credit institutions risk-dependent.                Another important change that happened lately was connected with expanding the scope                of participants of the system. Nowadays not only banks but also credit unions are members                of  deposit  guarantee  scheme,  which  follows  the  rule  that  depositors  of  every  credit                institution should be protected.                Finally, deposit guarantee scheme in Poland got an important power to act as a resolution                entity, being responsible for conducting the resolution process, if needed.                 All of these changes aim to make deposit guarantee scheme stronger and better prepared                for the challenges that are ahead of it.                 References                 Bankowy      Fundusz    Gwarancyjny     (2016).     Annual    Report.   Retrieved    from:                https://www.bfg.pl/wp-content/uploads/2017/09/BFG_raport_2016_eng.pdf.                Capiga, M., Szustak, G., Szewczyk, Ł. (2017). Banki spółdzielcze na rynku finansowym w                Polsce. Warszawa: CeDeWu.                Demirguc- Kunt, A., Kane, E., Laeven, L. (2014). Deposit Insurance Database. IMF Working                Paper, WP/14/118, p. 3-19.                European  Parliament  (2017).  The  resolution  of  Banco  Popular.  Retrieved  from:                http://www.europarl.europa.eu/RegData/etudes/BRIE/2017/602093/IPOL_BRI(2017)602                602_EN.pdf                Financial  Stability  Board  (2012).  Thematic  Review  on  Deposit  Insurance  Systems.                Retrieved from: http://www.fsb.org/wp-content/uploads/r_120208.pdf?page_moved=1                Pruski,  J.,  Kerlin,  J.  (2015).  Characteristics  of  Deposit  Insurance  Research  and  the                Challenges Ahead. Bezpieczny Bank, vol. 2(59), pp. 113-125.                Szewczyk, Ł. (2015). The funding model of deposit guarantee schemes. Lessons from the                crisis. Studia Ekonomiczne. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowiach,                vol. 238, pp. 144-152.                Zawadzka, P. (2011). Current rules of the deposit guarantee system in Poland. University                of Wroclaw, pp. 67-68.                BFG     (2016a).    Resolution    in   questions     and    answers.    Retrieved    from:                https://www.bfg.pl/en/resolution/resolution-in-questions-and-answers/.                BFG (2016b). Resolution. Retrieved from: https://www.bfg.pl/en/resolution/.                                                              763","Profitability Determinants of Polish Outsourcing Service                 Enterprises from the Point of View of Top and Middle Management                                                                                             3                                                                         2                              Rafał Szmajser , Mariusz Andrzejewski , Marcin Kędzior                                              1                                                   1  Capgemini Poland S.A.                                        Al. Jerozolimskie 96, 00-807 Warszawa, Poland                                               rafal.szmajser@capgemini.com                                                2  Cracow University of Economics                                  Faculty of Finance and Law, Financial Accounting Department                                           ul. Rakowicka 27, 31-510 Cracow, Poland                                         E-mail: mariusz.andrzejewski@uek.krakow.pl                                               3  Cracow University of Economics                                   Faculty of Finance and Law, Corporate Finance Department                                           ul. Rakowicka 27, 31-510 Cracow, Poland                                              E-mail: kedziorm@uek.krakow.pl                 Abstract:  The  aim  of  the  article  was  empirical  verification  of  the  most  important                operational, human and technological factors affecting profitability  of Polish outsourcing                service  enterprises  from  the  point  of  view  of  top  and  middle  management.  The  most                important  factors  of  their  profitability  were  discussed  and  characterized.  Among  the                statistically verified factors of profitability the following ones were differentiated: providing                services  from  different  geographical  locations  in  the  world,    supporting    business  by                technological solutions in a scope of automation and analytical solutions, a proper level of                employee  abilities  and  competences    and  their  position  classification  determining                profitability of outsourcing centers in the financial service sectors, programs of  continuous                process improvement (e.g. lean, six sigma),  developing and procuring talents inside an                organization,  a  greater  possibility  of  scalability  of  the  operating  activity,  supporting                organizations to make mergers and takeovers, an appropriate price model, quickness of                implementing changes, standardization and  transformation of processes. The analysis was                made for the outsourcing businesses in the financial service sector conducting their activity                on the territory of Poland. According to the respondents  the most important determinants                of  profitability  influencing  profitability  of  operating  of  the  outsourcing  centers  are:                standardization  and  automation,    providing  services  from  many  locations,  and  an                appropriate level of  employee abilities and competences. The data were collected on the                basis of specially prepared questionnaire. The empirical results were verified using ANOVA                variance analysis.                Keywords: corporate profitability, outsourcing service enterprises, technological factors,                human factors, process factors, Poland                 JEL codes: M41, M42, M21, L6, L2                 1 Introduction                In the literature there are lots of analyses, empirical studies on profitability and its factors                analyzed in the sector of the listed companies, frequently manufacturing companies. In the                literature there were mainly verified factors of a financial character, such as: indebtedness,                liquidity,  capital  intensity,  risk,  assets  turnover,  and  working  capital  (Bhayani,  2010;                Babalola, 2013; Nunes et al., 2010; Demir, 2009; Lazaridis and Tryfonidis, 2006). The                analysis  also  included  factors  of  a  non-financial  character,  such  as:    expenditures  on                research  and  development,  advertising  expenditures,  market  share,  market  share                concentration,  development  opportunities,  age,  a  size  of  an  enterprise,  corporate                governance systems etc. (Anastassopoulos, 2004; Grinyer and McKiernan, 1991; Feeny et                al., 2005; Bernotas, 2005). There were verified many times the factors of a macroeconomic                character  such  as inflation,  economic growth,    a level  of  per  capita,  economic  growth,                interest rates, a type and a character of budgetary expenditures (Pervan and Kramaric,                2010;  Isshaq  and  Bokpin,  2009;  Asimakopoulos  et  al.,  2009).  Some  institutional                                                             764","determinants such as  a development of stock exchanges and bank markets, legal systems,                a level of economic freedom etc. were also subjected to the analysis (Lee and Hong, 2012;                Knight and Shi, 2005; Gugler et al., 2013). There is no more comprehensive verification                of the determinants of an operational, technological or human character. Therefore, in the                article  the  following  variables  having  a  potential  influence  on  profitability  of  business                entities  such  as:  providing  service  from  lots  of  geographical    locations  in  the  world,                supporting  business  by  technological  solutions  in  a  scope  of  automation  and  analytical                solutions, an appropriate level of abilities and competences of employees and a level of                their position classification determining profitability of outsourcing center in a sector of                financial services, programs of continuous process improvement  (e.g.  lean, six sigma),                developing and procuring talents inside an organization, a greater possibility of scalability                of the operating activity, supporting  organizations to make  mergers and takeovers, an                appropriate  price  model,  quickness  of  implementing  changes,  standardization  and                transformation  of  processes  will  be  subjected  to  empirical  verification.  To  the  best                knowledge of the authors a great majority of them did not undergo an empirical verification                in the subject literature.                In the article the outsourcing enterprises in the financial service sector will be analyzed.                So far the profitability determinants have been verified empirically only to a limited extent                for this group of enterprises. The production sector enterprises were the most frequent                subject  of  analysis  (Gill  et  al.,  2011;  Love  et  al.,  2009;  Czarnitzki  and    Kraft,  2010;                Gedajlovic and Shapiro, 2002),  definitely less frequently trading (Asimakopoulos et al.,                2009) or financial companies (Aburime, 2008; Beck et al., 2006). The authors do not know                either about studies on the determinants of profitability of the outsourcing enterprises in                the financial sector services conducting their activity on the territory of Poland.                The objective of this article is to verify empirical factors  influencing profitability of the                outsourcing centers operating in Poland. The following outsourcing centers, among other                things, such as: Accenture Services, Alexander Mann Solutions, Amway, Capgemini, Cisco                Systems,  Electrolux,  Hewlett-Packard,  Hitachi  Data  Systems,  HSBC  Service  Delivery                Polska, Lufthansa Global Business Services, MARS Financial Services Europe, PWC, Shell,                Sony  Pictures  Global  Business  Services,  State  Street,  Tata  Consultancy  Services,  UBS,                Zurich  Insurance  Company,  and  firms  being  recipients  of  the  outsourcing  services  or                consulting firms operating in the outsourcing branch  - Delphi, EDF, ING, mBank, Staco                Polska, Van Gansewinkel, EY and WNS Global Services were subjected to the analysis. The                data  were  collected  on  basis  of  a  developed  questionnaire.  The  empirical  results  were                verified on the basis of ANOVA variance analysis.                 2 Factors influencing profitability of the outsourcing centers                 In  the  world  literature  on  this  subject  there  are  few  studies  determining  factors  of                profitability  of  the  outsourcing  center.  In  the  studies  performed  by  KPMG  and  HfS                representatives  of  middle  and  higher management   from  the  outsourcing  branch,  their                clients and analysts and consultants were subjected to the survey [The Global Business                Services  Industry  Study].  The  representatives  of  the  outsourcing  service  providers                constituted  26% of the respondents,  the consultants, advisors and the branch analysts                – 33% of the respondents, and the mangers responsible for running shared service centers                and their internal clients – 41%. The questions concerning priorities of the activity of the                particular  firms, determinants having an impact on competitiveness and profitability of the                activity, including the plans for the future were directed to different groups performing the                key functions in the organizations. There were recognized the following  significant factors                for an organization as having a positive impact on profitability: aiming at reducing  costs                of the operating activity, improving a quality of operating data used to make management                decisions,  better  use  of  automation  to  decrease  an  influence  of  human  labor,  greater                scalability  of the operating activity, use of information technologies to provide business                solutions, increased efficiency of operation management, better co-operation between the                particular organization units inside the firm, greater standardization of processes, greater                responsibility of operating departments for providing the added value, greater end-to-end                                                             765","control  over  the management  processes,  an integrated  purchase  strategy in  the  entire                organization.                 On  the  basis  of the  results  of the  studies  made  by the KPMG  and HfS  firms it  can  be                assumed that the verification of the influence of the profitability  determinants having an                impact on efficiency  of operating of the outsourcing centers should be performed on the                following groups of the factors:                   •  human factors, including: the optimal organization and operating structure, structured                    ranges of the activity and levels of provided services, employee competences, a proper,                    selection of location of the provided services, structured career paths, access to  talent                    resources  in  the  organization,  continuous  trainings  and  employee  development                    (Sridharan, 2013; Thomas, 1994);                  •  process factors, including: global standards of processes, active supervision and control                    over  effectiveness  of  processes,  continuous  modeling  of  the  finance-accounting                    processes by use of  improving methods, such as: reengineering, lean, six sigma and                    benchmarking  (Bevilacqua  and  Thornhill,  1990;  Du  and  Liao,  2010;  Ohno,  1988;                    Pyzdek, 2001; Wuellenweber et al., 2009);                  •  technological factors, including: an optimized set of available technologies based on a                    set of standard operating systems of the ERP type, use of automation and robotics of                    processes  (Gorla  and  Chiravuri,  2010;  Hirschheim  and  Dibbern,  2009;  Martin  and                    Beimborn, 2009);                  •  factors of  pricing the provided services, including:  price strategies adapted to a type                    of a service and client expectations.                Finally the following factors will be verified empirically: providing service from a number of                geographical locations around the world, supporting business by technological solutions,                especially  via  automation  and  analytical  solutions,  an  appropriate  level  of  abilities  and                competences of employees and the level of their seniority in the organization, programs                of continuous process improvement (e.g. lean, six sigma), developing and procuring talents                inside  an  organization,  a  greater  possibility  of  scalability  of  the  operating  activity,                supporting  organizations  in  mergers  and  takeovers,  an  appropriate  price  model,                promptness of implementing changes, standardization and  transformation of processes                (compare: Gorla and Chiravuri, 2010; Baraka et al., 2013; Behara, 2011; Feuerlicht et al.,                2003; Waema et al., 2009).                 3 Results and Discussion                 The  survey  was  conducted from  January  1  to 31, 2016.  It  was  addressed  to  over 150                respondents representing suppliers and recipients of outsourcing services. The research                used  a  specially  constructed  questionnaire  built  using the  Survey  Monkey  program.  97                responses  were  obtained,  64  responses  were  complete  (66%).  Such  sample  size                guarantees at the confidence level 0.9 obtaining the  assessment of structure indicators                with a maximum estimation error of no more than approx. 10%.                Firstly, the participants of  the questionnaire surveys evaluated significance of particular                determinants  of profitability of the outsourcing organizations/shared service center in a                scope of providing  the financial-accounting services at present and for 2-3 years. The                answers were classified in 5 groups: „no impact”, „slight  impact”, „moderate impact”, „big                impact” and „very big impact” (table 1).                For the respondents the greatest importance has standardization and automation, next                providing  services  from  many  locations,  and  on  the  third  place,  the  most  important                determinant  is  an  appropriate    level  of  employee  abilities  and  competence  and  their                position classification.                                                                   766","Table 1 Classification of Answers of Respondents to the Question on an Impact of the                 Selected Determinants on Profitability of the Outsourcing Organizations/ Shared Service                                                     Center at Present                                                                                                  Number                                                                                          Very                                                       No     Slight   Moderate   Big                of                            Question no.                                                   big                                                     impact  impact    impact    impact            answer                                                                                         impact                                                                                                     s                 1. Providing services from many                2        15        32      10        59                 geographical locations in the world                  2. Supporting business by                      3        20        27       9        59                 technological solutions in a scope of                 automation and analytical solutions                  3. Appropriate level of employee               1        16        35       7        59                 abilities and competences  and their                 position classification                  4. Continuous process improvement              2        22        28       7        59                 programs (e.g. lean, six sigma)                  5. Developing and procuring  talents           2        22        28       7        59                 inside organization according to the                 adopted strategy                   6. A greater possibility of scalability       3        25        28       3        59                 of operating activity                  7. Supporting organization to realize   3     14        26        14       2        59                 mergers and acquisitions                  8. Appropriate price model                     6        15        29       9        59                 depending on a type of a service                 (e.g. the transaction fixed price                 model)                  9. Quickness of implementing                   4        18        27      10        59                 changes                  10. Standardization and                        3        11        36       9        59                 transformation                           Source: the author’s own elaboration on basis of a developed questionnaire                 The ANOVA one-way analysis of variance was used in order to investigate if a position held                at  work  can  differentiate  statistically  significant  factors  influencing  profitability  of  the                outsourcing organizations or the center.  The results of the one-way analysis of variance                include: sums of squares of determinant deviations (SS Effect), degrees of freedom (DF),                sums of squares of error deviations (SS Error), an average sum of squares of determinant                deviations  (MS Effect),  an average sum of squares of error deviations  (MS Error), statistic                values of test F and p-value. The p-value    results lower than 0,05  indicate statistically                significant results.                On  basis  of  the  data  from  table  2  it  should  be  noticed  that  a  position  held  at  work                differentiates significantly two determining factors  influencing  profitability  of outsourcing                organizations/shared  service  center:  an  appropriate  level  of  employee  abilities  and                competences and their position, and continuous process improvement programs (e.g. lean,                six sigma) . An influence of an appropriate level of employee abilities and competences                           24                and  their  position  now  on  profitability  of  the  outsourcing  organizations/shared  service                center was evaluated the highest by presidents, and the lowest – managers  (table 3).  An                influence  of  continuous  process  improvement  programs  (e.g.  lean,  six  sigma)  on                     24  In these studies a significance level equal to 0,05 was taken.                                                            767","profitability  of  the  outsourcing  organizations/shared  service  center  was  evaluated  the                highest by presidents, and the lowest – a director.                   Table 2 The Results of One-way Analysis of Variance for the Determinants Influencing                  Profitability of the Outsourcing Organizations/shared Service Center Now Differentiated                                           by a Type of a Position Held at Work                 Question no   SS Effect   DF  MS Effect  SS Error   Df   MS Error      F        p-value                      1        0,05304     2    0,02652   31,5740    56   0,56382    0,04704     0,95407                      2        0,24753     2    0,12376   35,8541    56   0,64025    0,19331     0,82478                       3        4,45841     2    2,22920   20,4907    56   0,36590    6,09229    0,00407**                      4        3,81423     2    1,90711   27,0671    56   0,48334    3,94568    0,02439*                      5        2,70312     2    1,35155   28,1782    56   0,50318    2,68602     0,07929                      6        1,97807     2    0,98903   24,7338    56   0,44167    2,23928     0,11991                      7        2,53174     2    1,26587   45,4004    56   0,81072    1,56141     0,21837                      8        3,78162     2    1,89081   38,7268    56   0,69155    2,73416     0,07625                      9        1,72352     2    0,86175   37,9375    56   0,67745    1,27205     0,28228                      10       2,65368     2    1,32684   28,2615    56   0,50467    2,62912     0,08032                Significant differences at the level of : *** p<0,001; ** p<0,01; * p<0,05.                           Source: the author’s own elaboration on basis of a developed questionnaire                 On basis of the data from table 3 it can be stated that  the factor  influencing profitability                at  present  which  was  evaluated  the  highest  by  the  presidents  is  standardization  and                transformation, by the mangers - quickness of implementing changes, and by the directors                -  an  appropriate  level  of  employee    abilities  and  competences    and  their  position                classification.                    Table 3 The Averages and Standard Deviations of Evaluation of the Determinants                    Influencing Profitability of the Outsourcing Organizations/shared Service at Present                                      According to a Type of a Position Held at Work                  Question no     Characteristic      President        Manager        Director      Total                      1             Average            3,875          3,814815        3,875      3,847458                               Standard deviation    0,806226         0,786278       0,619139    0,738441                      2             Average            3,6875         3,777778        3,625      3,711864                               Standard deviation    0,946485         0,640513       0,885061    0,788951                      3             Average            4,125          3,518519        4,000      3,813559                               Standard deviation    0,619139         0,70002        0,365148    0,655864                      4             Average            4,000          3,703704        3,3125     3,677966                               Standard deviation    0,632456         0,724028       0,704154    0,729683                      5             Average            4,000          3,481481        3,6875     3,677966                               Standard deviation    0,730297         0,70002        0,704154    0,729683                      6             Average            3,8125         3,37037         3,500      3,525424                               Standard deviation    0,655108         0,687702       0,632456    0,678638                      7             Average            3,2500         2,962963        2,6875     2,966102                               Standard deviation    0,930949         0,758616       1,078193    0,909075                      8             Average            3,6875         3,925926        3,3125     3,694915                               Standard deviation    1,014479         0,828619       0,60208     0,856099                      9             Average            4,000          3,666667        3,5625     3,728814                                                              768","Standard deviation    0,894427         0,877058       0,629153    0,826928                      10            Average            4,1875         3,814815        3,625      3,864407                               Standard deviation      0,7500         0,735738       0,619139    0,730083                           Source: the author’s own elaboration on basis of a developed questionnaire                   Table 4 The Results of One-way Analysis of Variance for the Determinants Influencing                   Profitability of the Outsourcing Organizations/shared Service Now Differentiated by a                                       Type of a Position Held at Work in 2-3 years                  Question no    SS Effect   DF  MS Effect  SS Error   Df   MS Error     F        p-value                      1         2,15980     2   1,07990  54,7893    56   0,978381    1,10376     0,33826                      2         0,04520     2   0,02259  28,6666    56   0,511905    0,04415     0,95647                      3         7,74329     2   3,87164  21,1041    56   0,376860   10,27343    0,0058***                      4         2,70092     2   1,35045  33,6041    56   0,600074    2,25049     0,11479                      5         3,21536     2   1,60767  29,7338    56   0,530961    3,02787     0,05641                      6         1,00220     2   0,50109  31,5740    56   0,563823    0,88875     0,41688                      7         3,78457     2   1,89228  56,3171    56   1,005663    1,88163     0,16184                      8         7,53566     2   3,76783  47,8541    56   0,854539    4,40920    0,01661*                      9         0,59153     2   0,29576  36,5949    56   0,653480    0,45260     0,63827                      10        2,83777     2   1,41888  32,7893    56   0,585524    2,42327     0,09787                Significant differences at the level of: *** p<0,001; ** p<0,01; * p<0,05.                           Source: the author’s own elaboration on basis of a developed questionnaire                 A position held at work differentiates significantly two determinants influencing profitability                of the outsourcing organizations/shared service center at present (table 4): an appropriate                level of employee abilities and competences and their position classification– an impact in                2–3 years,  and an appropriate price model depending on a type of a service (e.g. the                transaction fixed price model) – an impact in  2–3 years.                    Table 5 The Averages and Standard Deviations of Evaluation of the Determinants                  Influencing Profitability of the Outsourcing Organizations/shared Service Center in 2-3                                   Years According to a Type of a Position Held at Work                  Question no        Characteristic      President    Manager      Director       Total                       1               Average              3,500      3,925926      3,9375      3,813559                                  Standard deviation      1,032796     1,071517    0,771902       0,9909                       2               Average              4,500      4,444444      4,500       4,474576                                  Standard deviation      0,730297     0,751068    0,632456      0,703585                       3               Average             4,3125      3,555556      4,2500      3,949153                                  Standard deviation      0,478714     0,697982     0,57735      0,705245                       4               Average             4,0625      3,888889      3,500       3,830508                                  Standard deviation      0,680074     0,800641    0,816497      0,79117                       5               Average             4,1875      3,62963       3,750       3,813559                                  Standard deviation      0,655108     0,791695     0,68313      0,753717                       6               Average             4,1250      3,814815      3,875       3,915254                                  Standard deviation      0,619139     0,735738    0,885061      0,74944                       7               Average              3,625      3,296296      2,9375      3,288136                                  Standard deviation      0,957427     0,775332    1,340087      1,017957                       8               Average             4,1250      4,111111      3,3125      3,898305                                                             769","Standard deviation      0,885061     1,012739      0,7932      0,97724                       9               Average              4,375      4,148148      4,3125      4,254237                                  Standard deviation      0,619139     0,988538     0,60208      0,800716                      10               Average              4,500      4,074074      3,9375      4,152542                                  Standard deviation      0,632456     0,828619    0,771902      0,783748                           Source: the author’s own elaboration on basis of a developed questionnaire                 The perspective influence of an appropriate level of employee abilities and competences                and  their  position  classification  on  profitability  of  the  outsourcing  organizations/shared                service  center  were  evaluated  the  highest  by  the  presidents,  and  the  lowest  –  by  the                managers (table 5). The perspective influence of an appropriate price model depending on                a type of a service on profitability of the outsourcing organizations/shared service center                was  evaluated  the  highest  by  the  president,  and  the  lowest  –  by  the  managers.  The                determinants influencing profitability in the perspective of 2-3 years which was  evaluated                the highest by the presidents are supporting business by technological solutions in a scope                of automation and analytical solutions and standardization and transformation, and by the                managers  and  directors  –  supporting  business  by  technological  solutions  in  a  scope  of                automation (table 5).                 4 Conclusions                In  the  new  global  economy  enterprises  are  forced  to  undertake  initiatives  adjusted  to                changing conditions of the environment and to look for innovatory solutions anticipating                the  market  needs  and  giving  an  opportunity  to  increase  competiveness  and  brand                profitability. Such thinking imposes flexibility, a necessity of system reconstruction and                structural changes in a firm, what will allow it to implement new procedures and  measures                in compliance with  methods of modern management and the global market  principles.                The firms which do not have any skills of adaptation to the changing situation on the market                and new business challenges, stop developing gradually, and with time they lose an ability                to meet environment needs, what results in a decrease of their profitability, or limiting a                scale of their activity. It is connected with the fact that operating of enterprises in the age                of the market globalization is determined equally by what goes on in its environment and                by what  goes on in it itself, but external conditions often influence more a possibility of                conducting an activity than  internal conditions of an enterprise.                To sum up the results of these studies it can be assumed that according to the respondents                the most important determinants of profitability influencing profitability of operating of the                outsourcing centers are: standardization and automation, next –  providing services from                many locations, and then –an appropriate level of  employee abilities and competences                and position classification.  In the perspective of 2-3 years the greatest importance for the                respondents  will  have  supporting  business  by  technological  solutions  in  a  scope  of                automation and analytical solutions. The second most important factor will be quickness of                implementing changes, whereas the third one– standardization and transformation. The                results are similar to research prepared by KPMG and HfS. They also paid attention to                technological impact, implementing global services, following process and industry trends,                operating  maturity  and  readiness.  Surprisingly  they  haven’t  mention human  factor  and                abilities and competences.                It seems that in the nearest years new technologies and innovations will determine success                of the outsourcing enterprises to the greatest extent. In a scope of future trends of studies                they should be, undoubtedly, enlarged  by the outsourcing centers located also in other                states, also including profitability determinants  of the outsourcing centers not verified so                far.                                                                 770","References                Aburime, T. U. (2008). Determinants of bank profitability: Macroeconomic evidence from                Nigeria. Retrieved from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1231064.                Anastassopoulos,  G.  (2004).  Profitability  differences  between  MNE  subsidiaries  and                domestic firms: the case of the food industry in Greece. Agribusiness, vol. 20(1), pp. 45-                60.                Asimakopoulos, I., Samitas, A., Papadogonas, T. (2009). Firm-specific and Economy Wide                Determinants of firm Profitability. Greek Evidence Using Panel Data. 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We choose countries from the group named V4 - the Czech                Republic, The Slovak Republic, Hungary and Poland. From the organisations, that evaluate                the  competitiveness  of  the  countries,  we  choose  the  World  Economic  Forum.  This                organisation published the Global Competitiveness Index. The fifth pillar of that index is                dedicated to higher education and training. So we analyse the position of V4 countries in                this  5th  pillar  during  the  5  years  period  and  try  to  show,  which  country  is  the  most                competitive in this pillar. In the next part of the article, we analyse other factors, which                may  affect  the  competitiveness  in  higher  education.  The  dependence  between  selected                indicators, the position in the 5th pillar of GCI is analysed according to position of the                countries in world known ranking from the area of education.                Keywords: V4 countries, higher education, Global Competitiveness Index                JEL codes: O11, I25                  1 Introduction                The  first  definition  of  competitiveness  was  given  by  Porter  (1990).  He  identifies  this                phenomenon  as  the  ability  of  the  country  to  create  innovations  in  order  to  achieve                advantages over other nations. (Despotovic et al., 2016) Competitiveness is regarded in                the contribution as the set of institutions, policies, and factors that determine the level of                productivity of a country. The total factor productivity is a key factor for economic growth                in  macroeconomics  theory.  (Soukup,  2017)  Competitiveness  is  a  factor  of  economic                growth,  that  forces  innovation  (Mokrišová,  2016)  and  increases  productivity  (Vavrek,                Adamišin, Kotulič, 2017), (Kowal, Paliwoda-Pękosz, 2017). The basic prerequisite for the                growing  living  standard  of  the  country's  population  is  long-term  sustainable                competitiveness of the domestic economy, which is conditioned by the competitiveness of                its businesses. (Hečková, Chapčáková, 2011)                The role of education in the highlight of competitiveness can be described as follows. The                U.S. government reported that high personal education levels equated to greater lifetime                monetary earnings. On average, personal income has risen approximately 10 percent for                every additional year of schooling. In impoverished nations, the personal income gains can                be as large as 20 percent for each additional year of schooling. Research has not shown a                linear relationship between the general level of education attainment and a nation’s gross                domestic product (GDP). (Krueger, Lindhal, 2001 In Tienken, 2008)                 The relationship between education and national economic growth slopes upward at lower                levels of education (primary grades K–4), then plateaus at middle school levels (grades 5–                8), then falls downward at secondary and postsecondary levels (Krueger, 1999). Increases                in the general population’s education level may have a greater influence on the economy                in nations (Bednarova et al., 2014) with nonexistent or lower-performing economies than                in highly developed economies because economically low-performing countries lack the                critical mass of human capital necessary to build a high-functioning economy. Nations with                strong  economies  (e.g.,  the  top  22  nations  on  the  Growth  Competitiveness  Index)                                                              773","demonstrate  a  weaker  relationship  between  increases  in  education  attainment  and                economic growth. (Tienken, 2008)                 To conclude it, we can say, that labour productivity and economic growth are key factors                to maintain and improve the competiveness of nations in the global market. It is related                to  the  fact  that  labour  productivity  depends  on  the  availability  and  quality  of  labour                resources and applied technologies. As a result, labour productivity heavily influences the                production process and production costs. And production costs affect the competiveness                of nations in the global market. (Auzina-Emsina, 2014)                 2 Methodology and Data                 In this part, we try to set the aim and the methods, which we have used in this study. The                aim of the paper is to analyse the competitiveness of selected countries in the field of                higher  education.  From  the  organisations,  that  evaluate  the  competitiveness  of  the                countries, we choose the World Economic Forum. This organisation published the Global                Competitiveness Index every year. They are the most famous world institutions dealing                with the competitiveness of the national economy. (Gavurova et al., 2016)                 The Global Competitiveness Report has long been a globally recognized ranking of country                competitiveness and a tool for benchmarking country strengths and weaknesses (Porter,                Delgado, Keters, Stern, 2008) The global competitiveness is defined in terms of the set of                institutions, policies, and factors that determine the level of productivity of an economy                which  in  turn  results  in  the  prosperity  of  the  country.  (John,  Duangekanog,                Wichayachakorn, Vikitset, 2017) This index consists of 114 indicators, which are grouped                into 12 pillars. The fifth pillar of that index is dedicated to higher education and training.                We  try  to  show,  which  country  is  the  most  competitive  in  this  pillar.  To  obtain  the                comparable results, we calculate the average values in case of GCI and the 5th pillar score.                In  the  next  part  of  this  study,  we  analysed  other  factors,  which  may  affect  the                competitiveness in higher education. We have chosen indicators typical for the mentioned                area. We analysed:                    •   Employment rates of young people from 15 - 34 years,                    •   Young people (15 - 34 years) neither in employment nor in education and training                       (%),                    •   Population by educational attainment level (%).                Finally, we have compared our results from analysis with ranking. We have chosen Human                Development Index to compare the countries in this world known ranking.                For our study, we have chosen countries from the group named V4 - the Czech Republic,                The Slovak Republic, Hungary and Poland. The period for analyses was set to 5 years, from                2013 to 2017. The data sources for our comparison were Eurostat, WE Forum and statistical                offices of V4 countries.                  3 Results and Discussion                Firstly, we have investigated the competitiveness of the selected countries. According to                overall Global Competitiveness Index (GCI) in selected period, we can say, that the best                competitive country is the Czech Republic. This economy obtained the best competitive                position from V4 countries in the last 3 years. The score of this country has rising tendency.                On the second place was Poland. Its score rises every year. (Sawicz, 2017) Slovakia is                actually on the third place from V4 countries. Its position rapidly rose up in the last years                (Širá et al., 2017).                                    Table 1 The position of Selected Countries in GCI                                        GCI          GCI           GCI           GCI          GCI                                   2013/2014  2014/2015  2015/2016  2016/2017  2017/2018               ø GCI                                      Score        Score         Score         Score        Score      score                  Country           (position)   (position)    (position)   (position)    (position)                                                             774","4,63                  Czech Republic    4,43 (46)    4,53 (37)     4,69 (31)     4,72 (31)    4,77 (31)                                                                                                        4,26                  Hungary           4,25 (63)    4,28 (60)     4,25 (63)     4,2 (69)     4,33 (60)                                                                                                        4,52                  Poland            4,46 (42)    4,48 (43)     4,49 (41)     4,56 (36)    4,59 (39)                                                                                                        4,22                  Slovakia          4,10 (78)    4,15 (75)     4,22 (67)     4,28 (65)    4,33 (59)                              Source: own processing according to Global competitiveness reports                 When we have compared the scores among V4 countries and have found, that there are                little differences among them. We can see this in the case of average scores. But in their                positions, there are big differences. It is caused by the large number of analysed countries                with similar scores.                                           Figure 1 GCI Score of V4 Countries                             5                            4,8                           4,6                            4,4                           4,2                             4                            3,8                           3,6                               GCI 2013/2014 GCI 2014/2015 GCI 2015/2016 GCI 2016/2017 GCI 2017/2018                                      Czech republic     Hungary       Poland      Slovakia                                    Source: own processing according to data from WE Forum                 In the Table 2, we have analysed the position and the score of V4 countries in the 5th pillar                of GCI. We can see, that the best position has almost had the Czech Republic. The worst                position has Hungary, with the lowest score of all analysed countries. The position of Poland                and Slovakia has worsen among the analysed period. When we look at the year 2013, we                can see, that Poland, the Czech Republic and Hungary have similar scores. But 4 years                later  the  situation  has  changed  and  there  are  bigger  differences  among  mentioned                countries.                             Table 2 The Position of Selected Countries in the 5th pillar of GCI                                    5th pillar    5th pillar   5th pillar   5th pillar   5th pillar   ø 5th                                  2013/2014    2014/2015  2015/2016  2016/2017  2017/2018            pillar                                     Score        Score         Score        Score        Score      score                  Country          (position)   (position)    (position)   (position)   (position)                                                                                                      5,08                  Czech republic   4,85 (39)    5,02 (35)     5,1 (29)      5,2 (27)    5,25 (27)                                                                                                      4,53                  Hungary          4,72 (44)    4,68 (52)     4,56 (57)    4,36 (72)    4,33 (73)                                                                                                       5                  Poland           4,88 (37)    5,04 (34)     5,05 (31)    5,03 (37)    4,98 (40)                                                                                                      4,56                  Slovakia         4,44 (58)    4,65 (56)     4,62 (53)    4,54 (61)    4,54 (62)                              Source: own processing according to Global competitiveness reports                                                              775","When  we  calculate  the  average  score  of  5th  pillar,  we  can  see,  that  there  are  bigger                differences among V4 countries, that average score of GCI. Poland and the Czech Republic                have similar average score value, about 5. Hungary and Slovakia have similar average of                5th pillar values, about 4,5, too.                                         Figure 2 5th Pillar Score of V4 Countries                             5,4                             5,2                              5                            4,8                             4,6                            4,4                             4,2                              4                                   5th pillar  5th pillar  5th pillar   5th pillar  5th pillar                                  2013/2014   2014/2015    2015/2016   2016/2017   2017/2018                                         Czech republic     Hungary       Poland      Slovakia                                    Source: own processing according to data from WE Forum                 Now, we have studied development of other indicators typical for the area of education.                These indicators will help us to complete our image of education. This is due to the better                complexity of analysed area.                 Employment  of  young  people  is  very  important.  This  factor  gives  us  the  first  view  on                educational system of the country, because the correct educational system prepares the                young people for the labour market. So they do not have any problems to be employed.                But, of course, we must take into account the current unemployment rate and from this                perspective to analyse the employment of young people.                           Table 3 Employment Rates of Young People from 15 - 34 Years (%)                                                                                         Overal employment                                   2013       2014       2015       2016       2017          rate 2017                 Czech                 Republic           77,9       78,8       80,2      84,5       87,6            78,5                 Hungary            69,3       72,8       76,3      80,0       79,5            73,5                 Poland             71,2       73,3       75,5      78,2       80,4            70,9                 Slovakia           67,9       69,7       71,5      76,9       77,4            71,1                                    Source: own processing according to data from Eurostat                 The best possibilities for young people employment are in the Czech Republic. In Poland                and Hungary the situation is similar. In all V4 countries situation has improved during the                analysed period. When we compare it with the overall employment rate in the last analysed                year, the situation in employment of young people is in all V4 countries better.                                                                    776","Table 4 Young People (15 - 34 years) Neither in Employment Not in Education and                                                       Training (%)                                               2013       2014       2015      2016       2017                          Czech Republic         15,6       15,0       14,9      13,6       12,4                          Hungary                20,4       18,1       16,9      15,8       15,0                          Poland                 17,4       16,7       15,8      15,1       14,3                          Slovakia               21,5       21,4       19,9      18,2       18,6                                    Source: own processing according to data from Eurostat                 The lowest values of young people neither in employment nor in education and training                were in the Czech Republic. But the other analysed countries have these values on similar                level. But the positive of this indicator is, that during the analysed period in all V4 countries,                these values have decreased.                     Table 5 Population by Educational Attainment Level, Upper Secondary and Post-                                          secondary Non-tertiary Education (%)                                               2013       2014       2015       2016       2017                          Czech Republic       69,0       68,5      67,8       67,1       66,5                          Hungary              57,8       57,7      57,2       57,6       58,0                          Poland               61,3       60,7      60,3       60,2       59,8                          Slovakia             67,5       66,7      66,5       66,0       64,5                                    Source: own processing according to data from Eurostat                 The last indicator selected from the area of education is educational attainment level. Upper                secondary and post secondary educational attainment level was in Hungary and Poland at                an unchanging level and in the Czech Republic and Slovakia it was slightly declining. But                overall, the level of this indicator is similar in all countries, where the highest values were                in the Czech Republic and Slovakia.                Finally, we compare the position of V4 countries in ranking dedicated to educational level                of the country.                The Human Development Index (HDI), is a composite statistic index. This index takes into                account human health, literacy levels, education and per capita income. (Meričková et al.,                2017) These indicators are used to rank countries. When the country score is higher, then                the education, lifespan and GDP per capita is higher.                                                 Table 6 HDI index 2015                                                             Score           Rank                                     Czech Republic          0,878            28                                     Hungary                 0,836            43                                     Poland                  0,855            36                                     Slovakia                0,845            40                                                   Source: own processing                 When we compare the score of this index in V4 countries in 2015, we can see similar values                among the countries. The better position in HDI ranking has the Czech Republic, the worse                has Hungary.                 4 Conclusions                 We have analysed V4 countries according to their competitiveness, especially in the field                of  education.  The  best  position in  competitiveness  ranking  of  the 5th  pillar  of  GCI  has                                                               777","obtained the Czech Republic. This country was the most successful in all analysed indicators                from the area of education, too.                 The  competitiveness  of  Slovakia  was  according  to  average  GCI  score  during  analysed                period  2013  -  2017  the  worst  from  V4  countries.  But  in  the  5th  pillar,  the  position  of                Slovakia was higher than in Hungary. When we analysed indicator typical for the area of                education, only in one indicator (educational attainment level) was Slovakia on the second                place. The development of all indicators show that, Slovakia did not even rank in HDI in                the top positions among the analysed countries.                If the country wants to become competitive, it must have, according to our meaning, good                results in education area. Because good education system can prepare the young people                to be employable, and to best perform their work, and so, we can expect the grow in other                macroeconomy indicators and finally the grow in the competitive level of the country.                 Acknowledgments                This  work  was  supported  by  the  Slovak  Research  and  Development  Agency  under  the                contract No. APVV-15-0322.                 References                 Auzina-Emsina,  A.  (2014).  Labour  productivity,  economic  growth  and  global                competitiveness in post-crisis period. Procedia-Social and Behavioral Sciences, vol. 156,                pp. 317-321.                 Bednarova, L., Vavrek, R., Vozarova, I. K., Kotulic, R., Adamisin, P., Kubak, M., Sira, E.                (2014).  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Polish Journal of Management Studies, vol. 16(2),                pp. 290-301.                                                                                                              779","Copulas and Credit Risk Models                                                         Yuan Tian                                            VŠB – Technical University of Ostrava                                         Faculty of Economics, Department of Finance                                      Sokolská třída 33, 702 00 Ostrava 1, Czech Republic                                                  E-mail: yuan.tian@vsb.cz                 Abstract: The topic of this paper is copulas and credit risk models. Generally, there is a                core  implicit  assumption  of  credit  risk  models  that  the  critical  variables  are  normally                distributed, which is too simplified in the reality. There is no compelling reason for choosing                the normal distribution. Therefore, the goal of this paper is to find out the real distributions                based on the concept of copulas and then better quantify the credit risk. There is a portfolio                that consists of ten bonds issued by quoted companies in the Frankfurt Stock Exchange                (FSE) with a 10-million-euro total nominal value over one year, from January 9 , 2017 to                                                                                                th                January 8 , 2018. The credit risk of the portfolio is quantified under the framework of the                          th                CreditMetrics™ model, a typical industry example of the threshold models. Two main types                of  copulas  include  elliptical  copulas  and  Archimedean  copulas.  The  parameters  of  a                parametric copula are estimated by MLE and then the copula is selected by computing AIC                and BIC. Compared with the original CreditMetrics™ model with an assumption of normal                distribution, the probability density curve obtained based on copulas are more right-tailed                and the credit risk of the portfolio is better quantified.                 Keywords credit risk, copula, CreditMetrics™, distribution function, Value-at-Risk                JEL codes: G11, G21, G24, G31                 1 Introduction                Bank regulators encourage banks to devote many resources to better quantify the financial                risks they are faced with and credit risk is typically considered the most important financial                risk.  There  is  a  basic  assumption  that  the  critical  variables  are  normally  distributed.                However, this assumption is too simplified in the reality and there is no compelling reason                for choosing the normal distribution, so the goal of this paper is to remove this assumption                and try to find out the real distributions based on the concept of copulas. One of typical                industry  model,  the  CreditMetrics™  model  (Cupton,  1997),  is  applied  to  a  portfolio                consisting  of  10  bonds  traded  on  the  Frankfurt  Stock  Exchange  (FSE).  Each  bond  is                represented  equally  in  a  nominal  value  of  1  million  euro.  Under  the  framework  of  the                CreditMetrics™ model, the normal distribution is replaced by the real distributions based                on copulas to quantify the credit risk of the portfolio more precisely.                  2 Credit risk models and copula functions                Credit risk models can typically be classified into the structural and reduced-form models.                The structural models assume that defaults occur when the value of the firm falls below a                certain default point and a certain recovery is paid; while the reduced-form models assume                that defaults occur exogenously and are unpredictable, and a separately specified recovery                is paid. Frey and McNeil (2001) points out that the asset correlations are not enough to                describe  dependence  between  defaults  and it is  the  copula  of  the latent  variables  that                determines the higher order joint default probabilities for obligors, and thus determines                the extreme risk in the portfolio. McNeil et al. (2015) proves that the loss distributions                implied  by  threshold  models  are  very  sensitive  to  the  copula  of  the  critical  variables.                Therefore, it is reasonable to calibrate the dependence models for the critical variables                based on the concept of copulas.                Threshold models                 The threshold models are the multivariate versions of the Merton model for portfolio credit                risk over a predetermined time horizon. The threshold models assume that the default                occurs when the latent variable (   ) lie below the critical deterministic thresholds (   ) at                                                                                                                                                                                                                          780","the maturity (T). In the threshold models, we write    = (   , ⋯ ,      ,   ) and    = (   , ⋯ ,    )′ in                                                                                                                                                                                                        1                                                                                                                                                     ,1                a  m-dimensional  space,  a  threshold  model  therefore  can  be  given  by  (   ,   ).  Let     =                                                                                                               (   , ⋯ ,    )′  be  the  m-dimensional  random  vector  of  the  latent  variables,  the  model  is                                            1                equivalent to (  ,   ) with:                                   ,                                       1 2                            ln(     )−(      −    )                                       2                          =    0,        , ∀   = 1, ⋯ ,   ,                                             (1)                                                                  √                                       1 2                            ln(     )−(      −    )                                       2                          =    0,          √    , ∀   = 1, ⋯ ,   .                                      (2)                                           Mixture models                The  mixture  models  assume  defaults  of  individual  companies  to  be  conditionally                independent  given  a  set  of  common  factors,  which  are  usually  interpreted  as                macroeconomics variables and stochastically modelled.                The  Bernoulli  mixture  models  are  special  cases  of  general  mixture  models,  where  the                dependence is modelled by a vector of random variables    = (   , ⋯ ,    ), which is called a                                                                                 1                                                                                                        Bernoulli loss statistic. If all marginal distributions of L are Bernoulli, namely    ~  (1,    ),                                                                                                                                                                                                                               the total loss is defined as    (  )  = ∑        . Given some    <    and a p-dimensional random                                                                                                                =1                vector    = (   , ⋯ ,    )′, the random vector    = (   , ⋯ ,    )′ follows a Bernoulli mixture model                                                                                                                                           1                                                                1                with factor vector ψ if there are functions    : ℝ → [0,1], 1 ≤    ≤   , such that, conditional on                                                                                                                                               ψ,  the  components  of  Y  are  independent  Bernoulli  random  variables  satisfying                  (   = 1|   =   ) =    (  ).                                                                         Let    = (   , ⋯ ,    )′ in {0,1}  be a random vector that represents which obligors defaulted                                                                      1                                                 and which survived. Since the default indicator Y are conditionally independent, so:                                                                                                  (   =   |   =   ) = ∏       (   =    |   =   ) = ∏        (  )    (1 −    (  )) 1−      ,   (3)                                                                                                                                                                                                                                                  =1                                                                                                                        =1                where the default probability of the company i is given by    =   (   = 1) =   (   (  )) and the                                                                                                                                                                                                                                                                                   unconditional distribution of the default indicator vector Y is obtained by integrating over                the distribution of the factor vector ψ.                Copula functions                A copula uses a marginally standard uniform representation of a multivariate distribution                function.  In  other  words,  an  m-dimensional  copula  is  a  fixed  distribution  function  C:                                      [0,1] → [0,1]  of  a  vector  of  random  variables  (   , ⋯ ,    ),  namely    (   , ⋯ ,    ) =                                                                                                 1                                                                                                                                                                                                                                                                    1                  (   ≤    , ⋯ ,    ≤    ), where   (   ≤    ) =     and    ∈ [0,1] for all i. There are three properties                                                                                                                                                                                              1                                                   1                                                                                                                        that a copula must hold:                    •     (   , ⋯ ,    ) = 0 if    = 0 for any i,                                                                                                         1                    •     (1, ⋯ ,1,    , 1, ⋯ ,1) =     for all    ∈ {1, ⋯ ,   },    ∈ [0,1],                                                                                                                                                                         •   for     all    (   , ⋯ ,    ), (   , ⋯ ,    ) ∈ [0,1]    with      ≤       we   have                                                                                                                                                                                                       1                                                                                                                                                                                                                                                     1                                                                 ∑ 2    1 =1  ⋯ ∑ 2       =1 (−1) 1 +⋯+         (   1   1  , ⋯ ,            ) ≥ 0,  where       1 =         and       2 =         for  all     ∈                       {1, ⋯ ,   }.                The central theorem is called Sklar’s Theorem, which provides a valuable result on the                uniqueness of copulas for continuous random variables. Let F be a joint distribution function                with margins    , ⋯ ,    . Then there exists a copula C: [0,1] → [0,1] such that, for all    , ⋯ ,                                                                                                                                                                                                                         1                                                                                                    1                                                      in ℝ = [−∞, ∞],                   ̅                         (   , ⋯ ,    ) =   (   (   ), ⋯ ,    (   )).                                   (4)                           1                                                                                                                                                                                   1                                           1                If the margins are continuous, then C is unique; otherwise C is uniquely determined on                                                              ̅                          ×           × ⋯ ×           , where           =    (ℝ) denotes the range of    . Conversely, if C is                                                                                                                                                         1                             2                                                                                                                                        a copula and    , ⋯ ,     are univariate distribution functions, then the function F defined in                               1                                                      (4) is a joint function with margins    , ⋯ ,    .                                                     1                                                                            The Gaussian copula             with the correlation matrix Σ is defined as:                                                       −1                                            −1                                  (   , ⋯ ,    ) ≔    (   (   ), ⋯ ,    (   );  ),                      (5)                                                                                    1                             1                                                                                                                                                                    781","where     is the standard m-dimensional normal distribution function,     is the inverse of                                                                                        −1                                                                                                                      −1                                                                                     −1                the standard univariate normal distribution function, and    =    (   (   )   (   )) for   ,    ∈                                                                                                                                                                                                                                                                                              {1, ⋯ ,   }.                                                 The t copula      ,    with the correlation matrix Σ and and υ degrees of freedom is:                                                       ,   (   , ⋯ ,    ) ≔    (      −1 (   ), ⋯ ,       −1 (   );   ,   ),       (6)                                               1                             1                                                                                                                                                         where     is the m-dimensional t-distribution with zero mean and     is the inverse of the                                                                                                                                univariate t-distribution with υ degrees of freedom.                We  show  1000  simulated  random  variates  from  the  bivariate Gaussian  copula  and  the                bivariate  t  copula  in  Figure  1.  The  first  row  presents  the  Gaussian  copula  with  the                correlation coefficient equals to 0.7, 0.8, and 0.9, and the second row presents the t copula                with the degree of freedom equals to 3 and the correlation coefficient equals to 0.7, 0.8,                and 0.9.                  Figure 1 Generation of 1000 Random Variates from the Gaussian Copula and t copula                                                                 Source: own processing                 Archimedean copulas usually have simple closed forms and are important for modelling                the portfolio credit risk. Let the function   : [0,1] → [0,∞) be a continuous, convex, and strictly                decreasing function, which is subject to    (0) = ∞ and   (1) = 0. The Archimedean copula                based on the generator    is defined as:                                       −1                         (   , ⋯ ,    ) =    (  (   ) + ⋯ +   (   )) =    −1 (∑       (   )),           (7)                                             1                                                                                                                                                                                                          =1                                                             1                where     is the pseudo-inverse function of   .                        −1                Table  1  below  summarizes  the  three  well-known  Archimedean  copulas  include  Gumbel                copula (Gumbel, 1960), Clayton copula (Clayton, 1978), and Frank copula (Frank, 1979).                                      Table 1 Three Important Archimedean Copulas                 Name       Generator  Generator inverse                Function             Parameter                                                                                     1                                                                                                                                  1                                                 Gumbel       (− ln   )       exp [(−  )   ]             {− [∑(− ln   ) ] }       ∈ [1, ∞)                                                                                                                                                                                                                                                                                           =1                                                                                      1                                                                                     −                               1                          1                 Clayton      (   −    − 1)    (1 +     )       max [(∑       −    −    + 1)  , 0]     ∈ [−1, ∞)\\{0}                                                      −                                                                                                      =1                                                                   1                                           1                                                                              =1                 Frank      − ln    −      − 1   − ln[1 +    (   −    − 1)]   − ln [1 +  ∏     (    −         − 1) ]      ∈ ℝ\\{0}                                                                                        −    − 1                                (   −    − 1)   −1                                                   Source: own processing                                                            782","We show 1000 simulated random variates from the Gumbel copula, Clayton copula, and                Frank  copula  in  Figure  2.  The  first  row  presents  the  Gumbel  copula,  the  second  row                presents  the  Clayton  copula,  and  the  third  row  presents  the  Frank  copula  with  the                parameter alpha equals to 2, 4, and 6 in three different columns respectively.                Figure 2 Generation of 1000 Random Variates from the Gumbel Copula, Clayton Copula,                                                     and Frank Copula                                                                                Source: own processing                 Fitting a copula to data                The main method of estimating the parameters θ of a parametric copula     is maximum                                                                                                               likelihood estimation (MLE). We will estimate margins and construct a pseudo-sample of                observations  from  the  copula.  Then  we  will  estimate  the  copula  parameters  from  the                pseudo-sample by MLE.                We firstly assume that an individual data vector is    = (   , ⋯ ,    )′,    ∈ {1, ⋯ ,   }, then the                                                                                                                                                    ,1                                                                                   ,                                                                                             (     ,   )          (     ,   )                pseudo-sample  observations  are  plotted  at  the  coordinates  (        ,       ),  where                                                                                      +1       +1                                                                ̂                         (   ) is the  rank  of       in    , ⋯ ,    .  Let     , ⋯ ,      denote  estimates  of  the marginal                                                                      ̂                                                          ,                                                    1,                                               ,                                                                                                   ,                                                                   1                distribution functions, and the pseudo-sample from the copula     consists of    , ⋯ ,    :                                                                                              ̂                                                                                ̂                                                                                                    ̂                                                                                                                                                                                                                                                                                         1                                        ′                                            ̂                        ̂                                                      ̂                          = (   , ⋯ ,    ) = (   (   ), ⋯ ,    (   )) ′.                                (8)                             ̂                                    ̂                                                                                                           ,1                                             1                                                            ,                                  ,1                                                                  ,                  The MLE is obtained by maximizing:                                                  ̂                              ̂                                   ̂                       ln   (  ;    , ⋯ ,    ) = ∑     ln    (   ),                                     (9)                                            =1                                                                                                                       1                                                                                                                                                 (   1 ,⋯,      )                with respect to θ, where     is the copula density given by   (   , ⋯ ,    ) =  .                                                                              1                                                                                                                                                                                                                               1 ⋯                       Then we select the copulas by computing Akaike and Bayesian information Criteria (AIC                and BIC, respectively) for all available copulas and the copula with the minimum value is                chosen.  For  observations     ,     ∈ {1, ⋯ ,   },     ∈ {1, 2},  the  AIC  of  a  bivariate  copula  c  with                                            ,                  parameter(s) θ is given by:                              ≔ −2 ∑     [  (   ,    |  )] + 2  ,                                      (10)                                              ,2                                          ,1                                    =1                where    = 1 for one-parameter copulas and    = 2 for two-parameter copulas. Similarly, the                BIC is given by:                              ≔ −2 ∑     [  (   ,    |  )] + ln(  )   .                                (11)                                              ,2                                          ,1                                    =1                Note that if the BIC is chosen, the penalty for two-parameter copulas is stronger.                                                            783","3 CreditMetrics™ based on copulas                There is a portfolio that consists of ten debt assets traded on the Frankfurt Stock Exchange                (FSE) with a total nominal value of 10 million euro. Each bond is represented equally in a                nominal value of 1 million euro in order to avoid bias caused by high nominal values of                bonds. The basic information about these bonds are presented in Table 2.                                         Table 2 Basic Information of Ten Bonds                                                              Nominal                  Market                   Name                Ratings  Coupon                   Maturity                 Pcs.                                                               value                   price                   Deutsche Post          A-        2,75%       1 000 €   10/2023  113,67%        1 000                   E. ON                  A-        5,80%       1 000 €    4/2018  106,69%        1 000                   Metro                 BBB-       1,50%       1 000 €    3/2025  100,01%        1 000                   Volkswagen             A+        2,37%  100 000 €       9/2020  105,80%            10                   NIKE                  AA-        2,25%       2 000 €    5/2023     99,98%        500                   Commerzbank          BBB+        0,08%       1 000 €    6/2023     97,34%      1 000                   Bayer                  A-        1,87%       1 000 €    1/2021  107,25%        1 000                   Nestle Holdings        AA        4,25%       2 000 €    3/2020  104,26%          500                   Danone               BBB+        3,00%  200 000 €       6/2022  101,54%             5                                           Source: Frankfurt Stock Exchange (FSE).                 The recovery rate of the senior unsecured class is shown in Table 3.                                     Table 3 Recovery Rate of the Senior Unsecured                           Seniority Class              Mean              Standard Deviation                           Senior                      51.13%                   25.45%                           Unsecured                                              Source: Carty \& Lieberman [96a].                 The values of the risk-free rate from 2017 to 2026 are derived from the interest rate swaps                (IRS), which can be found on the official website of the Deutsche Bundesbank. The forward                rates are shown in Table 4.                         Table 4 Spot Rates (IRS) and Forward Rates (   ) from 2017 to 2026 (%)                                                                                                                                                                    Year     2017    2018     2019     2020    2021     2022     2023    2024     2025     2026               IRS      -0.16  -0.18     -0.12    -0.03    0.08     0.20    0.32     0.45     0.56    0.68                        -0.16  -0.19     -0.01    0.23     0.51     0.82    1.02     1.38     1.49    1.69                                                                                     Source: Deutsche Bundesbank.                We  can use the  VineCopula  package in  R  to  select  an  appropriate  bivariate copula  by                maximum  likelihood  estimation  according  to  AIC  and  BIC  through  the  function                BiCopSelect. Note that we transfer the selection algorithm into the pseudo-observations                in the interval of [0,1] using the function pobs. Let us take the calculation of the correlation                coefficient between Deutsche Post and E.ON as an example. Note that we narrow the range                of the possible families to select from for the simplification, which means we select the                appropriate copula only from the basic elliptical and Archimedean copulas we introduced,                namely Gaussian, t, Gumbel, Clayton, and Frank copula.                 library(\"readxl\")                DP <- read_excel(\"Credit Risk_Copula.xlsx\")[,1]                EON <- read_excel(\"Credit Risk_Copula.xlsx\")[,2]                library(VineCopula)                u1 <- pobs(as.matrix(cbind(DP,EON)))[,1]                u2 <- pobs(as.matrix(cbind(DP,EON)))[,2]                selectedCopula <- BiCopSelect(u1,u2,familyset=1:5)                selectedCopula                                                             784","Bivariate copula: t (par = 0.58, par2 = 3.44, tau = 0.39)                The fitting algorithm select a t copula for the input data.  It estimates that the rho is 0.58,                degree of freedom is 3.44, and Kendall’s tau is 0.39. Then we can use the copula package                to double check the parameters by the suggested copula.                 library(copula)                t.cop <- tCopula(dim=2)                set.seed(500)                m <- pobs(as.matrix(cbind(DP,EON)))                fit <- fitCopula(t.cop,m,method='ml')                coef(fit)                    rho.1          df                0.5772656   3.4360974                 It is good to see that the parameters of the fitted copula are nearly the same as those                estimated  by  the  function  BiCopSelect.  We  then  can  build  the  copula  and  obtain  the                correlation coefficient. The function rCopula generates the random variables.                u <- rCopula(254,tCopula(dim=2,rho,df=df))                cor(u,method='spearman')                         [,1]      [,2]                [1,] 1.000000  0.552392                [2,] 0.552392  1.000000                We can obtain all correlation coefficients in the similar way and establish the correlation                matrix of the portfolio based on the copulas.                Under the framework of the CreditMetrics™ model, we can obtain the probability density                curve of the portfolio value as presented in Figure 3. It is obvious that the results based                on copulas are more right-tailed and the peak point is greater compared with the original                results, because the real data in the portfolio follow the student’s t-distribution, which is                better at reflecting the extreme values than the normal distribution. For the original results,                the maximal probability is 68.76% when the portfolio value is 10,796,195 €; while for the                results based on copulas, the maximal probability is 74.59% when the portfolio value is                10,838,269 €.                                Figure 3 Probability Density Curve of the Portfolio Values                                  80%                                 70%                               Probability (%)  50%                                 60%                                   40%                                 30%                                 20%                                 10%                                  0%                                      10283219,928  10303000,440  10322780,951  10342561,462  10362341,974  10382122,485  10401902,996  10421683,508  10441464,019  10461244,530  10481025,042  10500805,553  10520586,064  10540366,576  10560147,087  10579927,598  10599708,110  10619488,621  10639269,132  10659049,644  10678830,155  10698610,666  10718391,178  10738171,689  10757952,20  10777732,712  10797513,223  10817293,734  10837074,246  10856854,757                                                               Portfolio values (€)                                                         Copula    Original                                                    Source: own processing                Besides, the probability distribution of the portfolio values are shown in Figure 4, which                also illustrates that the results obtained from copulas are more right-tailed than the original                results. When the cumulative probability is equal to 100%, the maximal portfolio value                                                              785","based on copulas is 10,880,342 €; while the original maximal portfolio value is 10,856,855                €, which is slightly lower.                                   Figure 4 Probability Distribution of the Portfolio Values                                 120%                              Cumulative probability (%)  80%                                100%                                   60%                                 40%                                  20%                                  0%                                      10283219,928  10303000,440  10322780,951  10342561,462  10362341,974  10382122,485  10401902,996  10421683,508  10441464,019  10461244,530  10481025,042  10500805,553  10520586,064  10540366,576  10560147,087  10579927,598  10599708,110  10619488,621  10639269,132  10659049,644  10678830,155  10698610,666  10718391,178  10738171,689  10757952,20  10777732,712  10797513,223  10817293,734  10837074,246  10856854,757                                                               Portfolio values (€)                                                        Copula    Originial                                                   Source: own processing                 Furthermore, we can adjust the scale of the probability distribution of the portfolio values                to focus on the portfolio values with low probabilities as shown in Figure 5. It is now more                visual to find the portfolio value with one certain selected probability, for example, when                the probability is equal to 0.5%, the portfolio value based on copulas is around 10,579,928                € while the original portfolio value is approximately 10,560,147 €.                          Figure 5 Probability Distribution of the Portfolio Values – Adjusted Scale                                 0,025                               Cumulative probability (%)  0,015                                  0,02                                       0,01                                  0,005                                     0                                        10283219,928  10303000,440  10322780,951  10342561,462  10362341,974  10382122,485  10401902,996  10421683,508  10441464,019  10461244,530  10481025,042  10500805,553  10520586,064  10540366,576  10560147,087  10579927,598  10599708,110  10619488,621  10639269,132  10659049,644  10678830,155  10698610,666  10718391,178                                                              Portfolio values (€)                                                        Copula     Originial                                                   Source: own processing                 4 Conclusions                The main assumption that the critical variables are normally distributed is applied to most                credit risk models. However, there is no compelling reason for using the normal distribution                for the critical variables, so we can remove this assumption and obtain the real distributions                based  on  the  concept  of  copulas  from  the  pseudo-sample  by  maximum  likelihood                estimation (MLE). The CreditMetrics™ model is applied to a portfolio consisting of 10 bonds                                                             786","traded on the Frankfurt Stock Exchange (FSE). After comparing the original results and the                results based on copulas obtained under the framework of the CreditMetrics™ model, it is                able  to  find  that  the  credit  risk  is  usually  underestimated  if  the  normal  distribution  is                assumed, and the extreme values cannot be reflected precisely. Therefore, it is reasonable                to  combine  the  copulas  and  credit  risk  models  to  better  quantify  the  credit  risk.                Furthermore, copulas are widely applied to the pricing of the credit portfolio instruments,                such as CDO tranches and basket credit derivatives.                 Acknowledgments                The research is due to the support provided by the project SGS VŠB-TUO SP2017/148. All                support is greatly acknowledged.                 References                Clayton, D. (1978). A model for association in bivariate life tables and its applications in                epidemiological studies of familial tendency in chronic disease incidence. Biometrika, vol.                65, pp. 141-151.                 Cupton, G. M., Finger, C. C., Bhatia, M. (1997). CreditMetrics Technical Document. New                York: J. P. Morgan.                 Frank, M. (1979). On the simultaneous associativity of F(x,y) and x+y-F(x,y). Aequationes                Mathematicae, vol. 19, pp. 194-226.                Frey, R., McNeil, A. J., Nyfeler, M. A. (2001). Copulas and credit models. Retrieved from:                http://statmath.wu.ac.at/~frey/publications/copula-credit.pdf.                Gumbel, E. (1960). Distribution de valeurs extremes en plusiers dimensions. Publications                de l’Institut de Statistique de l’Université de Paris, vol. 9, pp. 171-173.                McNeil, A. J., Frey, R., Embrechts, P. (2015). Quantitative Risk Management: Concepts,                Tehniques and Tools. Princeton: Princeton University Press.                                                                                                      787","Impact of Mergers on the Company Performance                                               Petr Valouch, Josef Nesleha                                Faculty of Economics and Administration, Department of Finance                                           Lipova 41a, 602 00 Brno, Czech Republic                                             E-mail: petr.valouch@econ.muni.cz                                                     josef.nesleha@econ.muni.cz                 Abstract:  The  paper  deals  with  the  impact  of  a  merger  on  the  company  financial                profitability. A merger usually leads to a stronger position on the market as this is the most                frequent reason why companies decide to venture into such a form of synergy. For the                purposes of this research, the following criterion has been taken into account when defining                the company performance: ROE (Return On Equity). The test is based on a comparison of                the indicators at the moment of the merger and five years after the moment of the merger.                To enhance the validity of the test, companies that experienced a merger and companies                that have not experienced it are used. The usage of both of the files should help to confirm                that the merger itself stands for the indicators improvement. In total, the data file consists                of 277 merged companies and 232 companies that have never merged.                 Keywords: Merger, impact, profitability, ROE, statistical test                JEL codes: G34, M41                 1 Introduction                Mergers are usually based on several economic motives. The main motives of mergers in                the Czech Republic are administration simplification, tax optimization, operational synergy,                unification  of  control  structures,  stronger  market  position,  simplification  of  ownership                relations and better access to loans (Sedláček, Hýblová and Valouch, 2015). The main goal                of  the  merger  is  usually  to  increase  the  economic  efficiency  of  the  merged  company                compared to the overall efficiency of all merging companies. From this reason the mergers                are  the  object  of  many  financial  and  economic  analysis  trying  to  identify  the  main                opportunities  or  barriers  of  mergers  in  the  area  of  economic  and  financial  efficiency,                especially in the US (Brouwer, 2008; Hensel, 2010), or China (Yan, Tian, 2014).  Other                papers analysed new approaches to efficiencies in mergers and merger analysis (Kwoka,                2015).  Many  empirical  studies  suggest  that  the  general  view  is  that  mergers  tend  to                increase shareholder wealth but decreases efficiency for the target firm (Li, 2016). The                Czech  Republic has  only  a  few  basic  analyses  of merger  efficiencies  (Režňáková,  Pěta,                2016; Valouch, Sedláček, Králová, 2016). Recent studies have also researched the factors                affecting efficiency of mergers and their economic impacts as well as the relations between                merger  development  trends  and  particular  microeconomic  and  macroeconomic  factors                (Dorata, 2012; Komlenovic, Mamun and Mishra, 2011; Marks, 1997; Vu, Shi and Hanby,                2009). Unanswered question remains whether the mergers in the Czech Republic influence                positively the efficiency of merged companies or the changing efficiency is caused by other                reasons  like  economic  cycle,  GDP  growth,  inflation  rate,  unemployment  rate,  etc.  This                paper should bring the answer to this question.                 2 Methodology and Data                The main aim of the paper is to identify whether the mergers realized in the Czech Republic                influence the economic results and efficiency of merged companies in the period five years                after the merger. For the purposes of this paper, the company performance is defined by                indicator  ROE  (Return  on  Equity).  The  sample  of  277  merged  companies  has  been                compared to the sample of 232 unmerged companies and their economic performance has                been analysed. As mentioned, we have analysed one basic indicator in both samples  –                Return on Equity (ROE). We have used five years period to compare the indicator across                both  samples.  The  data  required  for  the  analysis  of  both  the  merged  companies  and                companies that have never merged were obtained from the Commercial Bulletin and the                Commercial Register of the Czech Republic.                                                            788","The  test  is  based  comparison  of  two  samples  (for  each  of  the  groups  –  merged  and                unmerged companies). As we talk about dependent variables, pair tests are supposed to                be employed. The first variable (level of Return on Equity at the first moment) and the                second  variable  (level  of  Return  on  Equity  at  the  second  moment  (in  five  years))  are                dependent each other. Therefore, we do not use two-sample independent tests.                The test is based on the following null hypothesis (H0) and alternative hypothesis (H1):                     •   H0:  The  mean  of  Return  on  Equity  (ROE)  is  the  same  for  both  of  the  analyzed                       moments                    •   H1: The mean of Return on Equity (ROE) is not the same for both of the analyzed                       moments                Originally, we intended to apply parametric tests (pair t-test). Yet this procedure has failed                as a consequence of non-compliance with the normality. For any t-test, the data needs to                be derived from the normal statistical distribution. Taking into account the significance                level 5% (alfa=0.05), the following tables (Table 1 and Table 2) confirms that the normality                has been rejected for all of the variables (for the first variable, for the second one and also                for the variable computed as a difference between the first two variables).                     Table 1 Results of Normality Test of Merging Companies (for Return on Equity)                     Variable/Statistics             Computed value of                   p-value                                                          statistics                          ROE(0)/D                          0.4281                         <0.01                       ROE(0)/SW-W                          0.1417                        0.0000                          ROE(5)/D                          0.4119                         <0.01                       ROE(5)/SW-W                          0.1175                        0.0000                     ROE(difference)/D                      0.3905                         <0.01                  ROE(difference)/SW-W                      0.1958                        0.0000                                      Source: Own computation, processed in STATISTICA                 The same conclusion has been revealed in case of the second group of companies. Again,                all  of  the  variables  have  been  proven  as  samples  not  fulfilling  the  normal  statistical                distribution.                  Table 2 Results of Normality Test of Companies Without Merger (for Return on Equity)                      Variable/Statistics             Computed value of                   p-value                                                          statistics                          ROE(0)/D                           0.419                         <0.01                       ROE(0)/SW-W                          0.1047                        0.0000                          ROE(5)/D                          0.4266                         <0.01                       ROE(5)/SW-W                          0.1568                        0.0000                     ROE(difference)/D                       0.393                         <0.01                  ROE(difference)/SW-W                      0.2135                        0.0000                                      Source: Own computation, processed in STATISTICA                 As a result of the non-compliance with normality tests, non-parametric statistics have been                run. To support the validity of results, the following two tests have been conducted for                each data group:                    •   Wilcoxon Matched Pairs Test                    •   Sign Test                Although t-test is a stronger and more validate statistical tool, such procedure cannot be                correctly used as long as the dataset is not derived from the statistical normal distribution.                Therefore,  these  non-parametric  tests  have  been  employed  in  order  to  verify  the  null                hypothesis.                                                                789","3 Results and Discussion                The results are divided into 2 parts. In the first, only the companies that merged between                2001 and 2010 are analyzed. As the normality tests failed (see the results in the chapter                2 Methodology, particularly Table 1 and Table 2), non-parametric tests are used. Prior to                the tests themselves, there is a table summarizing basic descriptive statistics of both of                the variables (ROE at the moment of the merger and ROE at the moment of five years                after the merger).                  Table 3: Descriptive Statistics for ROE at the Moment of the Merger and the ROE after                                                        Five Years                     Variable             Valid N         Mean          Median        Standard Dev.                     ROE(0)                 277        -0.185339       0.051301          3.454844                     ROE(5)                 276        -0.051702       0.069916          2.619096                                      Source: Own computation, processed in STATISTICA                 Even the table containing only descriptive statistics indicates that the indicator Return on                Equity has improved. While at the moment of the merger, the average value reaches  -                0.185, five years later it (on average) reaches the value around -0.05.                                   Table 4: Results of the Wilcoxon Matched Pairs Test                      Pair of Variables     Valid N          T              Z             p-value                      ROE(0) \& ROE(5)          276       18772.00       0.256923         0.797238                                      Source: Own computation, processed in STATISTICA                The results of Wilcoxon Matched Pairs Test do not confirm that there is an impact of a                merger on the indicator Return on Equity. The computed p-value is not small enough to                reject the null hypothesis of the test. In other words, we have not proven that there is not                an impact of the merger, yet we also failed to confirm that there is an impact.                The second applied test – Sign Test – reaches the same conclusion. The p-value value in                the  amount  of  approximately  0.3  fails  to  reject  the  null  hypothesis,  meaning  that  the                impact of the merger on the company performance (measured by the indicator Return on                Equity) has not been confirmed.                                             Table 5: Results of the Sign Test                      Pair of Variables     Valid N      Percent            Z             p-value                                                           v<V                      ROE(0) \& ROE(5)          276       53.26087        1.02328         0.306176                                      Source: Own computation, processed in STATISTICA                 Although neither of the tests rejects the null hypothesis, looking back at the data of the                table 3 (Descriptive statistics for ROE at the moment of the merger and the ROE after five                years), there is a visible improvement in Return on Equity between the moment of the                merger and five years after the merger.                The same procedure has been conducted for the data file consisting of companies that did                not merge (the data is for the same period of time so that other conditions and market                conditions are similar for all the tested companies). As well as for merging companies, the                first table (Table 6) summarizes the basic descriptive statistics. In case of companies that                not merged, it is clear that the average value of Return on Equity has dropped between                the first and the second moment. The same period of time has been used – particularly                five years.                        Table 6: Descriptive statistics for ROE at the first and the second moment                     Variable             Valid N         Mean          Median        Standard Dev.                     ROE(0)                 232         0.568843        0.12144          5.118506                     ROE(5)                 232         0.428147        0.07477          5.900436                                      Source: Own computation, processed in STATISTICA                                                            790","The table 7 summarizes results of the first employed test (Wilcoxon Matched Pairs Test).                The law p-value rejects the null hypothesis of the test, which also indicates the high value                of the test statistics.                                   Table 7: Results of the Wilcoxon Matched Pairs Test                       Pair of Variables     Valid N          T              Z             p-value                      ROE(0) \& ROE(5)          232       10189.00       3.248993         0.001158                                      Source: Own computation, processed in STATISTICA                 The same conclusion has been verified in the second test, Sign Test. The p-value almost                hits zero claiming that there has been a significant gap between Return on Equity at the                first  moment  and  at  the  second  moment.  As  well  as  in  the  previous  test,  the  p-value                confirms that there is a statistically significant discrepancy between the first and the second                measured moment when it comes to the indicator Return on Equity. However, looking back                at the table 6 (Descriptive statistics for ROE at the first and the second moment), it is                obvious that Return on Equity has dropped between the first and the second moment. This                leads us to an important difference between both of the groups. While the movement of                Return  on  Equity  of  the  first  group  (merged  companies)  was  not  rendered  statistically                significant,  the  basic  descriptive  statistics  show  that  there  might  be  some  tendency                between the first and the second moment in the favour of the second moment (five years                after the run merger).                 The means of the second group of companies (unmerged companies) has been proven to                be different, yet higher values are measured in case of the first moment. In other words,                while merger might have some positive effects on the company performance (defined by                the indicator Return on Equity), companies that not merged and have been a part of this                study have suffered from a decrease in the same indicator.                                             Table 8: Results of the Sign Test                      Pair of Variables     Valid N      Percent            Z             p-value                                                           v<V                      ROE(0) \& ROE(5)          232        37.500        3.742233         0.000182                                      Source: Own computation, processed in STATISTICA                 From the results of the descriptive statistics, therefore, it is quite interesting to conclude                that, in the case of the merged companies, the average results of return on equity and the                increase  in  the  median  of  this  indicator  were  improving  in  the  period  under  review.                However,  the  average  return  on  equity  of  the  merging  companies  in  a  given  sample                remained negative for five years after the merger. This conclusion is not very optimistic.                On the other hand, there is a positive trend to improve (increase) this indicator. Therefore,                it seems that it is true that a relatively long time after the merger is needed to achieve the                significant positive results of the merger. However, the results indicate that a merger can                contribute to improving the financial performance of the merging companies and should                become a legitimate tool considered by the management of companies and their owners,                in particular, to maximize the efficiency of corporate governance and possibly solve their                financial problems.                This  fact  has  to  be  mentioned  even  more  so  that  in  the  control  sample  of  unmerged                companies even a statistically significant decrease in the return on equity was statistically                proven in the period under review. From the point of view of the trend of improving the                return on equity in the period under review, it can be stated that the merged companies                have performed significantly better than the unmerged companies.                On  the  other  hand,  however, it  cannot  be forgotten that  the  starting  conditions  of  the                merging companies were significantly worse than for unmerged companies, and despite                the  positive  development  of  the  return  on  equity  of  the  merged  companies  and  the                deteriorating  tendency  of  the  unmerged  companies,  unmerged  companies  and  their                medians  of  return  on  equity  are  still  significantly  better  than  those  of  companies  that                merged.  However,  the  difference  in  these  values  between  the  two  analyzed  groups  of                                                            791","companies 5 years after the merger has significantly decreased in relation to the initial                difference between these two figures. Thus, another potential question arises as to how                companies that were not merged at that time would have gone if they joined the merger.                It is possible that even in their case the results of their profitability could be better than it                actually was.                 4 Conclusions                The goal of the contribution has been to identify whether the mergers realized in the Czech                Republic influence the economic results and efficiency of merged companies in the period                of five years after the merger. To verify this hypothesis, two non-parametric tests have                been run - Sign Test and Wilcoxon Matched Pairs Test after what we rejected the normality                of the analysed samples.                 The results have shown that merged companies have increased their Return on Equity                between the moment of the merger and five years after the merger. However, it needs to                be mentioned that this difference has not rendered to be statistically significant. Roughly                said, the utilized non-parametric tests have failed to reject the null hypothesis, saying that                average values for both of the moment are the same. On contrary, in case of unmerged                companies, it has been confirmed that the average of Return on Equity at the first moment                is  different  from  the  average  measured  at  the  second  moment.  Nevertheless,  the                movement has  been  negative.  The  group  of  unmerged  companies has  suffered  from  a                decrease in the value of Return on Equity (ROE).                Based  on the  results  of  the  analysis, it  appears that it  can  be  concluded  that  the  ROE                indicator was improved from the point of view of the merged companies as compared to                the companies that have not merged in the analysed period. It, therefore, seems that the                merger contributes to the improvement of this indicator (despite the fact that this influence                was  not  proved  statistically).  On  the  other  hand, it is  also necessary to  state  that  the                average  value  of  the  ROE  of  the  merged  companies  is  still  lower  than  in  the  case  of                unmerged companies. Therefore, it seems that under the terms of the analysed set of                companies, mergers of companies in the Czech Republic occur mainly in situations when                these companies solve already serious financial problems, often associated with a negative                return on equity. The merger is likely to serve as one of the last possibilities for the financial                stabilization  of  the  merging  companies.  However,  the  return  on  equity  of  the  merged                companies  is  getting  better  after  the  mergers,  but  the  question  remains  whether  this                improvement is sufficient to prevent companies experiencing financial difficulties before                the  merger  from  being  able  to  definitively  eliminate  these  problems  after  the  merger.                Research results suggest that this is probably not the case for all companies. On the other                hand,  if  the  merger  did  not  take  place,  the  results  of  these  companies  could  still  be                significantly worse, as the results for a control sample of unmerged companies indicate.                However,  although  the  statistical  effect  of  mergers  on  the improvement  of ROE  of  the                merged  companies  has  not  been  proven,  the  results  of  descriptive  statistics  show  an                improvement in this indicator for the merging companies. It can be said that the merger                can contribute to the improvement of the return on equity and its realization should be                considered as one of the possibilities for potential improvement of the profitability of the                companies in the Czech Republic as well as for possible solution of the financial problems                of these companies.                At  the  same  time, it  must  be  borne in  mind  that  the  manifestation  of  positive  merger                results  takes  some  time.  Five  years  after  the  merger, it is  possible  to  observe  already                improving results in the values of the equity of the merged companies represented by the                descriptive statistics of these indicators, but even during this period, the impact of the                merger on this improvement was not statistically demonstrated. However, a comparison                with the sample of unmerged companies shows quite convincingly that a merger can make                a significant contribution to this improvement as it is able to effectively solve some of the                financial inefficiencies of the merging companies and thereby can contribute to improving                their profitability for the owners measured by the ROE indicator.                                                              792","Acknowledgements                This article is one of the research outputs of the project MUNI/A/0753/2017 – Behavioural,                knowledge-based  and  economic  aspects  influencing  investment  decision  making  of                Masaryk  University  in  Brno,  Faculty  of  Economics  and  Administration,  Department  of                Finance.                 References                Brouwer, M. T. (2008). Horizontal mergers and efficiencies; theory and anti trust practice.                European Journal of Law and Economics, vol. 26(1), pp. 11-26.                Dorata, N. T. (2012): Determinants of the Strengths and Weaknesses of Acquiring Firms                in  Mergers  and  Acquisitions:  A  Stakeholder  Perspective.  International  Journal  of                Management, vol 29(2), pp. 578-590.                Hensel, N. (2010). Can Industry Consolidation Lead to Greater Efficiencies? Evidence from                the U.S. Defense Industry. Business Economics, vol 45(3), p. 187-203.                Komlenovic, S., Mamun, A., Mishra, D. (2011).  Business Cycle and Aggregate Industry                Mergers. Journal of Economics and Finance, vol. 35(3), pp. 239-259.                Kwoka, K. (2015). The Changing Nature of Efficiencies in Mergers and in Merger Analysis.                The Antitrust Bulletin, vol.60(3), pp. 231-249.                Li, T. (2016): A study on the impact of mergers \& acquisitions on shareholders' wealth and                efficiency. 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Information, vol. 17(12B), pp. 6371-                6378.                                                                                    793","Technical Provisions As One of the Risk Management Modes of                      Commercial Insurance Companies Operating on the Czech                                                  Insurance Market                                                            1                                                                              2                                            Eva Vávrová , Lenka Přečková                                                    1  Masaryk University                                Faculty of Economics and Administration, Department of Finance                                         Lipová 507/41a, 602 00 Brno, Czech Republic                                             E-mail: eva.vavrova@econ.muni.cz                                                 2  Silesian University in Opava                       School of Business Administration in Karviná, Department of Finance and Accounting                                   Univerzitní nám. 1934/3, 733 40 Karviná, Czech Republic                                                 E-mail: preckova@opf.slu.cz                 Abstract: The aim of this paper is to identify differences between classical approaches to                the creation and investment of technical provisions and the approaches resulting from the                implementation  of  the  Solvency  II  Regulatory  Directive  into  insurance  markets  in  the                European Union member states. With regard to the interpreting the results of the analyses,                the subsequent aim of this paper is to draw specific conclusions to evaluate the adequacy                of the creation and investment of technical provisions in selected commercial insurance                companies operating on the Czech insurance market and to propose recommendations in                the case of identified deficiencies.                Keywords:  commercial  insurance  company,  creation  and  investment  of  technical                provisions, insurance market, risk management, Solvency II Regulatory Directive                 JEL codes: G11, G22                 1 Introduction                Technical provisions must be set up by commercial insurance companies and reinsurers to                settle all insurance/reinsurance obligations that are probable or certain, but their amount                and the time at which they arise are uncertain. Technical provisions  represent the very                important  and  the  largest  part  of  the  liabilities  of  an  insurance  company.  Creation  of                technical provisions depends on the types of insurance (Burling, Lazarus, 2011). Other                volume of technical provisions is created in the case of risk-based types of insurance and                another volume is created in the case of reserve-based types of insurance. For the reserve-                based  types  of  insurance,  technical  provisions  are  created  from  the  whole  amount  of                received  premiums  written,  while  for  the  risk-based  types  of  insurance  the  technical                provisions  are  created  from  a  certain  part  of  the  received  premiums  written,  and  the                technical provisions compensate for the time, local and material inconsistency between the                received premiums written and the indemnity payments. The technical provisions for risk-                based types of insurance must prove liquidity in the short term, so it is suitable for short-                term investment on the financial market, unlike reserve-based types of insurance which is                used for long-term investment. Different technical provisions can therefore be observed in                life insurance companies and in non-life insurance companies (Ducháčková, 2015). In the                year 2015, the amount of the technical provisions of non-life insurance companies was                CZK 55 654 162 thousand for Czech Insurance Association (ČAP) members. The amount                of  the  technical  provisions  created  by  life  insurance  companies  was  CZK  274  323 310                thousand in 2015 (ČAP, 2017). Thus, the size of the technical provisions for life insurance                exceeded more than five times the size of the technical provisions for non-life insurance.                The view of technical provisions differs within the classical approach and within Solvency                II  Regulatory  Directive.  The  classical  approach  can  be  characterized  as  an  accounting-                based approach, while Solvency II Directive uses a market-consistent approach. Within the                legislation,  Solvency  II  distinguishes  only  three  types  of  technical  provisions:  for  life                insurance liabilities, for non-life insurance premiums and for indemnities/insurance benefits                in non-life insurance. Within Solvency II Directive, another difference is that the technical                                                             794","provisions are created with help of an optimal estimate of future cash flows that are related                to the reserved liability. Additionally, these estimated cash flows should be discounted at                the time of the estimate and then increased by the risk premium that is related to the risk                of the estimated procedure and the risk-free interest rate used for discounting the cash                flows (Cipra, 2015).                       TP = discounted BE + RM,                                                         (1)                 where TP ... technical provisions; BE ... best estimate; RM ... risk margin.                The amount of technical provisions is calculated as the sum of the best estimate and the                risk premium. It is, in fact, the amount that an insurance or reinsurance company would                have to pay for the transfer of all insurance/reinsurance liabilities to another insurer or                reinsurer (Hull, 2015). BE corresponds to the expected size of future cash flows related to                a specific liability for technical provisions. Discounted BE uses a risk-free interest rate to                get the current value of future cash flows (Cipra, 2015). The risk premium is determined                as  the  present value  of  the  cost  of  holding  the  solvency  capital  requirement (SCR) for                unsecured risk throughout the portfolio period using the risk-free interest rate (Courchene                et al., 2008). For the calculation of technical provisions, financial market information and                other available data of underwriting risks are used. Technical provisions are calculated in                a prudent, reliable and objective manner.                Under  Solvency  II,  information  must  be  reliable,  consistent,  realistic  assumptions,                appropriate actuarial/statistical methods (Mesršmíd, 2015). Solvency II focuses primarily                on  coordinating  the  calculation  of  technical  provisions.  For  example,  methods  and                procedures need to be developed and used to assess the accuracy with which reserves                reflect reality. The calculation shall comply with the requirements of the Directive and the                degree  of  uncertainty  in  the  technical  provisions  shall  be  assessed  and  explained.  In                addition,  Solvency  II  emphasizes  ensuring  the  suitability  of  methods,  models  and                assumptions, evaluating data quality in calculations, best estimate analysis, evaluation of                underwriting  policies,  assessing  adequacy  of  collateral,  reporting  to  managing  and                supervisory bodies (Brosemer, Lepschi and Lord, 2011).                The Solvency II Directive requires insurance and reinsurance companies to have internal                processes and procedures to ensure the suitability, completeness and accuracy of the data                used to calculate the technical provisions. In order for data to be considered as suitable,                they must meet at least the following criteria: there is no undue estimation error resulting                from  the  scale  or  nature  of  the  data,  the  data  are  consistent  with  the  methods  and                assumptions used to calculate the provisions, and the data appropriately reflect potential                risks. For the completeness of the data, the following assumptions must be met: the data                include sufficient information to identify trends and assess the potential risk signs, data                are available for each homogeneous risk group in the calculation, and no relevant data are                excluded from the calculation without justification. Data accuracy requires following: data                do  not  contain  significant  errors,  data  from  different  time  periods  used  for  the  same                estimate are consistent, and recording data is consistent over time and done in a timely                manner (Dreksler, 2013).                 There  are  different  methods  to  determine  the  best  estimate  for  both  life  insurance                companies and non-life insurance companies. According to Brosemer, Lepschi and Lord                (2011), the best methods for life insurance companies are following: one method is to                derive DAV tables. The portfolio is divided into risk groups that are as homogeneous as                possible.  However,  individual  sub-portfolios  are  steadily  decreasing  with  gradual                differentiation according to risk characteristics and will soon become too small for the right                conclusions.  The  rates  derived  vary  considerably  and  subject  to  a  high  degree  of                uncertainty. Another method is using generalized linear models (GLMs) that offer solutions                to this problem. These are, in fact, statistical models in which risk factors are analyzed at                the same time. Rates serve as a starting point and are tailored for individual groups to                reflect risk, allowing valid conclusions even for smaller groups. GLMs allow for a better                understanding of portfolios, revealing correlations, interactions in data, and thus bringing                more  than  traditional  methods.  For  the  best  estimate  of  non-life  insurance  company                                                             795","provisions, the following items must be calculated (Brosemer, Lepschi and Lord, 2011):                loss allowance for losses already incurred, technical provisions for liabilities that have not                yet been incurred. All cash inflows and outflows, such as premiums, various payments,                etc., have to be included. The best estimate methods for non-life insurance companies are                the Chain-ladder method, the Additive method, and the Bornhuetter-Ferguson method.                Also,  a  number  of  studies  have  been  launched  to  examine  how  the  size  of  technical                provisions has changed under the new Solvency II regulatory framework. Cipra (2015)                states that according to various quantitative impact studies, the provisions created under                Solvency II are significantly lower than within Solvency I, up to 10-15%. It is mainly for                the following reasons: discounted cash flow, no implicit risk margins, account shall be taken                of investment income from unearned premiums, and the absence of equalization provision.                Also, the authors Peleckiené, Peleckis (2014) confirm that the technical provisions for life                insurance companies calculated using the best estimate method plus the risk margin tend                to be less than the provisions calculated according to previous methods because the implicit                prudence that existed  under the former regime is eliminated and thereby the available                solvency  capital  is  increasing.  The  level  of  technical  provisions  for  non-life  insurance                companies decreased by 10-15% because of discounting. The average ratio of technical                provisions created under Solvency II and under Solvency I varies more for the non-life                branch (70-100%) than for the life branch (90-102%). Courchene et al. (2008) states that                according  to  the  fourth  quantitative  impact  study  (QIS4),  technical  provisions  created                under Solvency II are typically lower by about 17% than created under Solvency I.                Under the Solvency II methodology, neither investment instruments nor asset allocation                are clearly identified, according to Kriele and Wolf (2014). Regulation is included in the                internal control system of the insurance company and becomes a part of the market risk                and  credit  risk  assessment  within  the  solvency  capital  requirement  (SCR).  However,                Ducháčková (2015) identifies the following approaches to investing:                     •   Investments only in assets that are identifiable for the insurance company, can be                       measured, monitored, managed, controlled and reported. Therefore, assets need                       to be invested so as to ensure the liquidity and security of the entire portfolio.                    •   If  some  assets  are  intended  to  settle  liabilities,  it  is  necessary  to  invest  them                       proportionately  to  the  nature  and  duration  of  those  obligations  and  in  the  best                       interests of all participants and beneficiaries.                    •   Diversification  should  be  done in  such a  way  that there is no  dependence  on  a                       particular asset, issuer, group of persons or geographical area. There should be no                       large accumulation of risk in the portfolio as a whole.                    •   Derivatives are only negotiated if their liabilities lead to risk mitigation.                 Höring (2013) addresses whether the new capital requirements for market risk arising from                the Solvency II regulatory framework will significantly affect the investment strategy of                European insurance companies. He compares the standard Solvency II market risk capital                model with the S \& P evaluation model for a fictitious but representative European life                insurance company. The standard Solvency II model remains less capital intensive than                the  S  \&  P  rating  model.  For  insurance  companies  with  a  good  rating  and  permanent                regulatory solvency, a significant change in the asset allocation due to the introduction of                the Solvency II regulatory requirements is not expected. Indeed, surveys by European                insurance  companies  show  that  the  reduction  of  shares  and  their  alternatives  is  not                expected. Similar results are expected in typical investment portfolios of non-life insurance                companies dealing with property and accidents (Zweifel and Eisen, 2012).                Braun,  Schmeiser  and  Schreiber  (2017)  examine in  their  study  the influence of  capital                requirements on the selection of life insurance portfolio under the Solvency II Directive.                The  authors  argue  that,  as  a  result,  it  can  lead  to  the  allocation  of  assets  that  is  not                beneficial to policyholders or shareholders. There is a risk assessment of portfolios and the                role  of  expected  returns  is  totally  ignored.  The  effects  of  diversification  are  taken  into                account  only marginally.  This  approach  is not  able  to  distinguish  between investments                based on risk/yield profiles and correlation. Capital requirements strongly prevail. There                are some serious consequences. First, portfolios that include stocks, corporate bonds, real                                                            796","estate, and hedge funds are penalized inadequately under Solvency II, so that balanced                asset allocation is only available with a large volume of equity. Especially in life insurance                companies  it  can  cause  problems.  Expected  portfolio  returns,  which  consist  mainly  of                money market instruments and government bonds, may be too small to cover the interest                rate guaranteed over a long period of time. Second, insurers may  be led to choose the                asset allocation that will increase instead of reducing the volatility of their equity. Third,                effective  portfolios  are  not  systematically  preferred  over  inefficient  portfolios,  as  some                companies may need to expose themselves to a higher than the required level of market                risk to cover capital costs.                 2 Methodology and Data                The aim of this paper is to identify differences between classical approaches to the creation                and  investment  of  technical  provisions  and  the  approaches  resulting  from  the                implementation  of  the  Solvency  II  Regulatory  Directive  into  insurance  markets  in  the                European Union member states. With regard to the interpreting the results of the analyses,                the subsequent aim of this paper is to draw specific conclusions to evaluate the adequacy                of the creation and investment of technical provisions in selected commercial insurance                companies  operating  on  the  insurance  market  in  the  Czech  Republic  and  to  propose                recommendations in the case of identified deficiencies.                Two  commercial  insurance  companies  were  analyzed,  namely  DIRECT  pojišťovna  and                Česká pojišťovna. These two commercial insurers were chosen for comparing creation of                technical  provisions  and  their  investments  in  the  case  of  both,  non-life  and  universal                insurance  companies.  The  data  and  other  information  were  received  from  the  annual                reports,  reports  of  solvency  and  financial  situation  (SFCR)  of  the  individual  insurance                companies  and  from  the  individual  results  of  the  members  of  the  Czech  Insurance                Association  (ČAP).  Subsequently,  adjusted  ratios  indicate  the  adequacy  of  creating                technical provisions. According to Vávrová (2014), the following indicators were used:                technical provisions / net premiums written; investment / technical provisions; technical                provisions / equity; technical provisions / net premiums earned.                In the next part, the investments of individual insurance companies were analyzed, and a                comparison  of  investments  with  other  insurance  companies  was  performed  taking                predefined criteria in account. The data of the individual insurance companies was obtained                from the annual reports and from individual results of the members of the Czech Insurance                Association (ČAP). Further, a discussion of the information obtained in the previous part                follows and recommendations are suggested. Then, the authors summarize the results.                 3 Results and Discussion                Thus,  technical  provisions  with  other  balance  sheet items  are  compared. Calculation  of                ratios helps to show the adequacy of technical provisions.                         Table 1 Adjusted Ratios Calculated for DIRECT pojišťovna (2012-2016 )                                                   2012         2013         2014       2015        2016                 technical provisions / net       1.431        1.363        1.553      1.493      1.149                 premiums written                 investment / technical           115.7        103.5        99.9        87.5       82.6                 provisions (%)                 technical provisions /           348.7        614.0       378.3       227.6      189.1                 equity (%)                 technical provisions / net       143.4        140.9        146.4      138.4      118.4                 premiums earned (%)                             Source: calculation based on annual reports of the insurer (2013-2017)                                                                797","The first indicator (see Tab. 1) measures technical provisions and net premiums written.                The recommended value is not given as it is necessary to monitor the development. In                2016, when there was the first obligation to create technical provisions under the Solvency                II regulation, technical provisions compared to net premiums written declined by 23.04%.                According to various studies, an average decrease of 15% is expected. As a result, the                DIRECT  pojišťovna  dropped  more.  The  indicator  measuring  investment  and  technical                provisions has been declining. The recommended value is higher than 100%. In 2016, the                share of investment to technical provisions was less than 83%. In the last years, therefore,                all  technical  provisions  have  not  been  invested.  DIRECT  pojišťovna  should  therefore                increase the volume of investment at least to the extent corresponding to the size of the                technical  provisions.  Another  indicator  measures  technical  provisions  and  equity.  The                recommended value is less than 350%. In 2015 and 2016, the indicator was already below                this threshold. In 2013 and 2014, the DIRECT pojišťovna could have difficulties in fulfilling                its obligations. The last indicator measures technical provisions and net premiums earned.                The recommended value is between 100% and 150%. However, this indicator never went                beyond the recommended value.                          Table 2 Adjusted Ratios Calculated for Česká pojišťovna (2012-2016)                                                    2012       2013        2014        2015       2016                 technical provisions / net         1.407      1.457       1.584       1.259      1.206                 premiums written                 investment / technical             125.1      126.2       137.5       147.8      160.7                 provisions (%)                 technical provisions /             363.4      353.1       289.6       230.6      207.8                 equity (%)                 technical provisions / net         140.6      132.6       158.4       125.8      120.6                 premiums earned (%)                             Source: calculation based on annual reports of the insurer (2013-2017)                 Again, modified ratios were used. These adjusted ratios should optimally be used for non-                life insurance. According to Tab. 2, the indicator of technical provisions in relation to net                premiums written declined by 4.21% in 2016 and in 2015 by 20.52%. The second indicator                measures investment with technical provisions. The recommended value is higher than                100%. This indicator has been above this threshold. Another ratio is the ratio of technical                provisions to equity. The default value is less than 350%. According to this indicator, the                insurance company could have difficulties in fulfilling its obligations in analyzed years 2012                and 2013. The last indicator measures technical provisions and net premiums earned. The                recommended value is between 100% and 150%. This indicator was calculated for non-                life insurance only. Only in 2014 was the value slightly outside limits, namely 158.4%.                For comparison of individual investment items with DIRECT pojišťovna, three insurance                companies were selected: AXA pojišťovna, Slavia pojišťovna and ERV Evropská pojišťovna.                All  of  them,  they  are  members  of  the  ČAP  and  their  premiums  written  in  the  non-life                insurance branch is comparable, according to the methodology of the ČAP. All insurers,                except for Slavia pojišťovna, invest the largest amount of funds in debt securities  – see                Fig. 1, 2. Slavia pojišťovna, on the other hand, prefers deposits with financial institutions.                                                                       798","Figure 1 Share of Debt Securities on Investments of Insurance Companies (2012-2016)                                  1                                0,9                                0,8                                             AXA                                0,7                                0,6                                             Slavia                                0,5                                0,4                                             ERV Evropská                                0,3                                             pojišťovna                                0,2                                                                                Direct                                0,1                                 0                                      2012    2013   2014    2015    2016                                Source: graph based on annual reports of the insurers (2013-2017)                   Figure 2 Proportion of Shares in Investments of Insurance Companies (2012-2016)                                 0,16                                 0,14                                                                                AXA                                0,12                                 0,1                                            Slavia                                0,08                                0,06                                            ERV Evropská                                                                                pojišťovna                                0,04                                0,02                                            Direct                                  0                                       2012   2013    2014    2015   2016                                Source: graph based on annual reports of the insurers (2013-2017)                 To  compare  the  investment  items  with  Česká  pojišťovna  (ČP),  the  following  insurance                companies were chosen: Kooperativa pojišťovna (KOOP), Allianz pojišťovna and Generali                Pojišťovna (GP). All these insurance companies are members of the ČAP, they offer both                life and non-life insurance and they are among the insurance companies with the highest                premiums written, according to the methodology of the ČAP. All four selected insurance                companies invest the most of their finances in debt securities, as seen in Fig. 3. In this                respect, therefore, they do not differ. They differ by investing in other financial instruments                (see Fig. 4, 5).                                                                        799","Figure 3 Share of Debt Securities on Investments of Insurance Companies (2012-2016)                                 0,8                                0,7                                0,6                                0,5                                                 ČP                                0,4                                                 KOOP                                0,3                                                 ALLIANZ                                0,2                                                 GP                                 0,1                                 0                                      2012     2013     2014    2015     2016                                Source: graph based on annual reports of the insurers (2013-2017)                   Figure 4 Proportion of Shares in Investments of Insurance Companies (2012-2016)                                 0,12                                  0,1                                 0,08                                                ČP                                 0,06                                                KOOP                                                                                    ALLIANZ                                0,04                                                                                    GP                                0,02                                   0                                       2012     2013    2014     2015    2016                                Source: graph based on annual reports of the insurers (2013-2017)                      Figure 5 Share of Investment in Business Groupings in Financial Placements of                                            Insurance Companies (2012-2016)                                 0,16                                0,14                                0,12                                  0,1                                                ČP                                0,08                                                KOOP                                0,06                                                ALLIANZ                                0,04                                                GP                                0,02                                  0                                       2012     2013    2014     2015    2016                                Source: graph based on annual reports of the insurers (2013-2017)                 Compared to the other three largest insurance companies, Česká pojišťovna almost does                not use another interesting investment, i.e., investment in land and buildings. The problem                                                             800","may be lower liquidity. On the other hand, some land and real estate are appreciated in                last years.                 4 Conclusions                 The aim of this paper was to identify differences in the existing approaches to the creation                and investment of technical provisions resulting from the new Solvency II regulation, to                interpret the results of the analyses and draw specific conclusions to evaluate the adequacy                of  the  creation  and  investment  of  technical  provisions  in  selected  Czech  commercial                insurance companies. Technical provisions in the insurance companies DIRECT pojišťovna                and Česká pojišťovna were analyzed, the adequacy of technical provisions was determined                using  adjusted  ratios,  and  investments  were  compared  with  financial  placement  of                technical provisions in similar insurance companies.                 For  DIRECT  pojišťovna,  the  calculated  ratios  have  revealed  insufficient  volume  of                investment considering technical provisions since 2014 and in the years 2013-2014, the                insurance company could have problems meeting commitments caused by too low equity                to  technical  provisions.  Even  Česká  pojišťovna  could  have  difficulties  in  fulfilling  its                commitments in some years, as the volume of equity to technical provisions was too low.                In  2016,  when  the  Solvency  II  Regulatory  Directive  became  applicable,  investment  in                bonds fell sharply in DIRECT pojišťovna, and its investment grew to a large extent in other                lending and loans and deposits with financial institutions. Also, investment has increased                to a certain extent in shares and other land and buildings. Investment of Česká pojišťovna                in bonds, investment funds and shares declined moderately, investment in loans increased.                 Acknowledgments                 Research behind this paper was supported by the Czech Science Foundation within the                project 16-17796S ‘Affiliation with financial conglomerate as a determinant of performance                and risk of banks’.                 References                Braun, A., Schmeiser, H., Schreiber, F. (2017). 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Berlin: Springer-Verlag.                                                                                                                             802","Activities Classification for Controlling Purposes in the State                                                Administration Unit                                                                                2                                                            1                                           Jana Vodáková , Nela Sglundová                                                   1  Masarykova univerzita                                           Faculty of Economics and Administration                                        Lipová 507/41a, 602 00, Brno, Czech Republic                                            E-mail: Jana.Vodakova@econ.muni.cz                                                      2  Univerzita obrany                                                Faculty of Military Leadership                                          Kounicova 65, 662 10 Brno, Czech Republic                                               E-mail: nela.sglundova@unob.cz                 Abstract: This paper deals with activities proceeding within the Ministry of Defence of the                Czech  Republic  (MoD  CR)  included  into  the  Czech  state  administration  and  their                classifications for planning, budgeting and accounting purposes. The main aim of the paper                is to analyze existing planning, budgeting and accounting classifications of activities and                evaluate their mutual linkage and applicability for controlling purposes. Authors come to                the  conclusion  that  though  planning,  budgeting  and  accounting  classifications  are  not                identical at present they may be relatively easily modified and harmonized and so used for                cost controlling purposes. Main data sources represent scientific articles, legal directives                and regulations, internal directions and MoD CR internal databases. In the first part of the                paper  the  authors  analyze  and  compare  planning,  budgeting  and  accounting                documentation, in the second part of the paper they select one representative planned                activity and link it with its real costs utilizing MoD CR controlling software RON and Data                cube.                 Keywords:  controlling,  management  accounting,  state  administration,  activity,  public                sector accounting                JEL codes: H83, H72, H11, M41                  1 Introduction                Implementation of the accrual basis to the Czech public sector accounting has enabled to                record full range of assets, liabilities, expenses and revenues, and communicate financial                statements in similar structure as businesses from 2011. This step has extended not only                information capability of accounting data towards external users, but also internal ones.                Across  the  public  sector  units,  i.e.  also  the  state  administration,  it  was  consequently                possible  to  continue  in  a deeper  internal  classification  and  analysis  of  main  accounting                categories through any proper analytical (managerial) tool. Similar drift is known of course                longer time from other developed countries when an effort called new public management                (Hood, 1991) was launched.                Nowadays a relatively vast evidence of business-like methods and tools utilization from                various  units  included  to  the  public  sector  all  over  the  world  is  available.  Quite  often                managerial tools are used for evaluation of health-care services (for example from recent                years Silva and Macedo, 2015 or Macuda, Baran and Kludacz-Alessandri, 2016), social or                educational services – controlling utilization (Pavlik, 2017). But an evidence of the provable                cost  savings  or  even  efficiency  increase  seems  still  rare.  Some  authors  suggest  that                gathering various analytical data remain still rather administrative procedure without any                wider managerial utilization (Sanger and Bryna, 2013, Monro, 2003 or Poulsen, 2009).                Probable reasons of this situation differ among experts. Some of them ascribe it to business                tools unsuitability for the area of the public sector, but majority of them attributes it to                improper or inconsistent implementation of business-like tools (Monro, 2003, Reichborn-                Kjennerud, 2013 or deVries, 2010) and some point out of course political reasons too.                Some studies then deals with a role of management accounting in new public management                                                              803","or a linkage between (management) accounting and management framework in the public                sector (for example Silva and Macedo, 2015 or Hirsch, Nitzl and Schauss, 2015).                 The  Ministry  of  Defence  of  the  Czech  Republic  (MoD  CR)  included  to  the  Czech  state                administration similarly as other public sector units has decided to implement any suitable                managerial  tool  to  support  economical  and  efficient  drawing  of  financial  sources  too.                Therefore in 2013 it published new economic conception (MoD CR, 2013) and introduced                an intention to implement controlling mainly as an official system of cost control. From                2014  when  the  economic  conception  came  into  force  till  2018  relatively  economically                separated cost centres were founded within MoD CR and controlling on the level of these                cost  centres  were  implemented.  Though  there  still  exist  any  methodical  and  technical                issues to solve it can be concluded that relevant costs are allocated to every relating cost                centre at present.                 2 Methodology and Data                 This  paper  deals  with  controlling  implementation  process  in  the  terms  of  the  state                administration units. From a broader view controlling can be understood as a managerial                tool contributing to achievement of unit´s aims. For purposes of the paper we concentrate                mainly on controlling of costs, i.e. monitoring planned and real costs using mainly financial                and management accounting data. Such controlling concept was introduced by the Ministry                of  Defence  of  the  Czech  Republic  (MoD  CR,  2013)  and  it  is  gradually  implemented  at                present. In our former research we have analysed implementation process development                with its strengths and shortcomings (Vodáková and Sglundová, 2017) and its actual state.                It can be summarized MoD CR is able to allocate majority of real costs directly to partial                cost units (or cost centres) and their sub-units at present which is definitely an important                step forward. However the problem is that existing monitoring is not able to record costs                of  activities  which  would  be  evidently more  beneficial for  managerial  purposes  such  as                planning, budgeting or performance measuring.                The main aim of the paper is to examine whether it would be possible to allocate planned,                budgeted and real costs to partial activities of a selected cost centre and whether there                exist  any  suitable  attribute  linking  planning,  budgeting  and  accounting  classification  of                activities.  Examining  sample  represents  one  cost  unit,  i.e.  a  rapid  deployment  brigade                including 6 sub-units, command and staff. Command and staff provide the whole cost unit                with management and administrative activities and so record mostly administrative costs.                4 cost sub-units ensure combat (operating) activities as firing, tactic, topographic or driving                training, 2 supporting sub-units carry out activities as maintenance of the long-term assets                logistic support and so on. We monitored activities performed by above described cost unit                and its sub-units within one year time period.                As for research procedure, firstly we have analysed planning, budgeting and accounting                documentation, mostly relating internal directives of MoD CR. We have considered either                to record partial real activities proceeding at a selected cost centre and their frequency for                a certain period of time, or to use any existing classification of activities. Because of lesser                difficulty and an effort to utilize any existing and practical tool we have chosen the second                variant. So we have analysed planning documentation, i.e. methodical direction RMO No                66/2012 (MoD CR 2012) and yearly prepared Activity plan of MoD CR elaborated for our                selected cost unit. Further we have examined any possible linkage between classification                of  activities  for  planning,  budgeting  and  accounting  purposes.  We  have  compared  the                content of activity and its technical attributes. For this objective we have used Application                Software – Planning (APV-VP) included into Staff Informational System (SIS) managed by                the MoD CR Planning Section. To verify a technical possibility of cost allocation to selected                cost  unit we have  used  applications Data  cube  and  RON  (cost  allocation) included into                Financial Information System (FIS) managed by MoD CR Economic Section. Finally we have                chosen one representative activity, i.e. 14-days battalions training and allocated relevant                costs  to  our  selected  cost  unit  and  its  sub-units.  So  we  have  verified  our  preliminary                assumption as for technical and methodical feasibility of such solution.                                                               804","As main data sources were used scientific papers, relating legal regulations and internal                directions. We also worked with MoD CR databases coming from SIS and FIS systems,                mainly we used data from 2017 and 2018. Besides RON cost allocation method we used                basic  scientific  procedures  as  description,  analysis,  comparison,  and  synthesis  of  main                findings.                 3 Results and Discussion                This chapter summarizes main findings of our research. The first part compares planned,                budgetary and accounting classification of activities within the MoD CR. The second part                analyses costs of selected activity realized by specific cost centre included into MoD CR.                 Analysis of planning and budgeting classification of activities                On the base of planning and budgeting materials analysis we have found a considerably                extensive documentation. There exist planning and budgeting internal orders, directions                or instructions. Within MoD CR a relatively high number of plans is prepared for long-term                time period (15 years), middle-term (5 year) and one –year time period (i.e. Activity plan                which is the basic document to manage all activities within the MoD CR in the calendar                year). All plans of entities directly subordinated to MoD CR are based on this document.                Probably  the  most  important  methodical  document  for  planning  represents  RMO  No                66/2012 (MoD CR, 2012) that besides other defines main terms as objective, measure,                task, activity and so. In fact these terms mean specific levels of aims (or planned activities)                elaborated consequently from the top to the lowest level of management and the lowest                level represent the most detailed activity. Because of limited extent of the paper it is not                possible to depict all terms and levels of aims. We can conclude there exist 8 levels of aims                at minimum from planning point of view and we have found that our selected cost unit                                     th                works with aims of 4  to 8  level.                                           th                Our analysis further proved a proper link between Activity plan and Budgetary structure                (MoD CR, 2018) which serves not only to budget, but also to accounting classification.                Budgetary structure (a list of numerical characteristics) is interconnected with Activity plan                on a certain level. This linkage represents the attribute CIL (column 5, see table 2). In our                opinion this linkage can be well utilized for tracking planned, budgeted and accounting                costs of activities. As table 2 implies an attribute CIL may contain 12 numerical marks that                describe specific levels of aims.                                          Table 1 Budgetary Structure Scheme              UCS  UUS  NS  SU  AU  PVS  CIL  FIN  ZDR  PAR  POL  PRJ  ORG  UZ  ZJ  MD  DAL                            6    3    4   10    12     3    10     6     9    12    15     9    3   15    15                      Source: MoD CR (2018): Methodology of the Budgetary structure, version 1.11. No MO                                                     11294/2018-8201                 Unfortunately, we have also found a certain discrepancy between terminology used for                levels of aims by planning and budgetary methodology. Especially terminology used for                          th                aims  of  4   a  5   levels  that  are  in  the  sphere  of  our  interest  is  confusing.  To  avoid                                 th                misinterpretation, we would recommend to clarify and harmonize it.                Comparing the Activity Plan of our selected cost centre with the Budgetary structure, i.e.                                              th                 th                5  level of Activity plan vs. 5  level of aim (9  and 10  digit) in Budgetary structure, we                                                                        th                                                               th                can confirm the same activity identification, i.e. concretely Preparation and achievement                of full operational capabilities of battalion. Thus, all real activities related to unit training                will be provided (and covered) under this identification.                Within an analysis of our selected cost unit activities we have tracked its Activity plan on                the above described level and identified 896 specific objectives (or partial activities). It is                clear that tracking nearly nine hundred indicators is unrealistic and probably useless. Also                we should take into consideration that meanwhile we operate on the 5th level of aims.                When we would want to monitor activities on 3 lower levels, it would be necessary to carry                                                             805","out a thorough revision of aims (activities) and reduce them for purposes of controlling to                a reasonable number to meet managerial needs.                 Another problem represents evaluation of plan´s fulfilment. The present system is set up                for  verbal  evaluation (fulfilled  /  unfulfilled  /  running,  event.  with  a  brief  explanation  of                activity postponing) in the APV-VP module. It is evident that such evaluation rather does                not meet managerial needs, as a consequence, we could claim that the cost unit works,                employees  are  paid  for  their  work,  defence  is  secured.  Interconnection  of  planning,                budgeting  and  accounting  activities  classification  would  enlarge  a  range  of  evaluation                criteria at least for financial indicators based on costs or others.                As mentioned above, the classification of activities used for planning purposes is very close                to  the  budget  one.  Additionally,  APV  -  VP  planning  module  is  partly  prepared  for  cost                evaluation. In fact some financial flows are tracked by the system even now. The point is                                                                                               rd                these data are recorded on the cash basis only, i.e. only expenditures of the 3  level aims                are monitored at present. Moreover, financial plans are based on historical data and not                on the real needs of MoD CR. So it would be desirable to determine total expenses of each                training, preparation of a unit for a mission, time demand for administrative activities not                directly related to training and meeting, or the main aim, i.e. ensuring defense of the Czech                Republic by the ACR forces. Then, based on these data a plan for next year could be created                and  necessary  funds  for  the  next  time  period  negotiated.  To  use  the  Activity  Plan  for                tracking  costs  of  main  activities,  a  minor  modification  of  the  Budgetary  structure  is                required, because currently it is possible to allocate costs only to a specific cost unit. We                suggest 2 options how to interconnect costs and activities.                 st                1  option: The Budget structure can be extended by a new attribute that would include a                numerical  indication  of  a  specific  activity.  This  would  be  feasible,  but  demanding  on                software  modifications  and  maybe  costly.  Also  it  would  be  necessary  to  create  new                classification of activities, that would reflect specific needs of every single cost centre which                carry out diverse tasks and the scale of branches involved in is huge. This large scale of                activities might be confusing and complicated to manage.                 nd                2  option: Attribute CIL may be extended by a numerical characteristic of an activity. This                option  consists  in  extending  the  attribute  by  another  6  positions,  which  would  exactly                                                           th                correspond  to  a  numerical  code  of  the  8   level  of  activities.  On  this  level  it  would  be                possible to identify clearly costs related to them. This indicator could be inserted into CIL                as  a  part  of  controlling  process  when  invoices  are  liquidated  (in  the  case  of  services                acquired) or material is consumed. Similar process is used at present for determining the                place of origin of costs. In this case, ORG attribute is used for determining cost unit which                is responsible for these costs. So it would be possible to filter out all costs associated with                the activity of a certain level by entering its numerical code into the controlling journal                within Data cube.                Cost allocation of a selected activity                 To determine real costs of any planned activity at present seems relatively time-consuming                and inexact because it is possible only manually. For purposes of our research we used                management accounting data from RON and Data cube and interlinked them manually to                one selected activity in Data cube. We have found that this procedure can be used only in                the case of important activities (i.e. battalion trainings, maintained a high level of military                preparedness, mission preparation, etc.). Only for such (important) activities, the kind of                consumption (material, fuel, ammunition etc.) is recorded into internal evidence, and then                lodged to the logistic system. Cost data then appear in the Data cube (the logistic system                is  linked  to  the  financial  system).  For  purpose  of  our  examining  we  have  chosen  one                representative activity which is 14-day military training. It was necessary to analyse and                identify  attributes  of  all  related  primary  documents  which  we  carried  out  manually  in                cooperation with the responsible accountant and to enter them into the controlling journal.                Then the system allocated all costs to our selected activity.                In fact it was only all direct costs but still they can depict relevant consumption of sources                for one specific activity. Figure 1 below illustrates not only total costs per 14 days training,                                                            806","but also absolute and relative shares of partial sub-centres on the total costs of selected                activity. We can see that the total costs per activity exceeded 5.5 million CZK, while the                highest share on total costs was recorded by cost unit (sub-centre) 3, i.e. 49 % of total,                sub-centre 1, i.e. 15 % and sub-centre 4, i.e. 13 %. On the other hand, the lowest share                is recorded by command and administrative sub-units (take into consideration that only                direct costs were included (consumption of material, fuel, foodstuff, ammunition).                          Figure 1 Costs of Selected Battalion (cost centre) per 14 Days Training                                                         OIC        Command \& staff                                                    40968,620        89884,350                                                       1%                2%                                                                                          Cost unit 6                                                                                         501085,920                                                                                             9%                                                                         24 744                                                                       Cost unit 1                                                                       857757,270                                           487 312                                          Cost unit 3                     15%           857 757                                         2709587,670                                   Cost unit 2                                             49%                                                                                       121895,070                                                                                           2%                                                                                       121 895                                                                                  Cost unit 4                                                                                  754631,0                                                                                    13%                       Source: Processing by authors on the base of internal MoD CR data from Data cube (2018)                 As it was stated before, we are not able to allocate all costs for now because Data cube                does  not  offer  a  possibility  to  allocate  centrally  acquired  or  recorded  costs  to  partial                activities (for example costs of using the training ground etc.). The cost-allocation base                has to be defined firstly by Controlling department of MoD CR which is the main gesture of                controlling implementation. After implementation of proper cost-allocation bases it will be                possible  to  supplement  total  costs  by  indirect  costs  which  definitely  will  increase                information capability of controlling data.                To  determine  the  purpose  of  public  sources  spending  more  precisely,  it  would  be                appropriate, in our opinion, to specify also closer areas (or categories) of consumption. We                have  identified  more  than  one  thousand  ongoing  activities  proceeding  in  selected  cost                centre (based on analysis of Activity Plan). We have found that more than ½ of activities                                                                          th                is identified on the lowest (non-compulsory) 9  and 10  levels. These levels of activity                                                                th                classification  are  too  detailed  in  our  opinion  and  the  value  of  output  information  in                comparison  with  the  burden  for  its  acquiring  is  at  least  debatable;  also  the  utility  for                managerial purposes seems low. For this reason we abstracted from them in our further                analysis. But still there stayed about 500 activities that we divided into four categories:                     1.  administration (51 % of activities);                    2.  public events and representations (19 % of activities);                    3.  training (16 % of activities);                    4.  courses (14 % of activities).                                                              807","th                It was relatively surprising for us that more than 50 % of all planned activities of the 8                level belong to administration and only 16 % to training or courses (14 %). Even public                representation include more activities (19 %). We think that this finding should be a subject                of discussion within MoD CR as far as one of the most important aims of ACR is combat                readiness.                Incorporating this information into the Budgetary structure, we would be able to track how                much funds MoD CR spends in the given areas and for what. For this purpose ZJ attribute                in Budgetary structure could be used. Suggested numerical designation could be inserted                into the ZJ attribute as a part of the controlling process when invoices are liquidated again.                So we could approach to defined long-term goal of MoD CR controlling, i.e. to create a Cost                Plan that would contribute to comparison of planned and real costs with accordance to 3E                principles.                 4 Conclusions                 To conclude our research we could state that controlling in MoD CR is able to monitor total                costs  of  specific  costs  centres  at  present.  However  for  managerial  purposes  (planning,                budgeting, analytical) it would be more beneficial to track costs of proceeding activities                rather  than  static  cost  centres.  On  the  base  of  planning,  budgetary  and  accounting                documents  and  application  analysis  we  claim  there  exist  a  linkage  between  planning,                budgetary and accounting classification of activities that could be comfortably used for this                purpose.                There exist several options how to accomplish it technically, probably the easiest way would                be  to  supplement  attribute  CIL  interconnecting  all  above  mentioned  classification  by                several numeric marks. But firstly it would be necessary to harmonize some terminological                discrepancies and reduce amount of tracked planned activities, in our opinion, by 2 lowest                level.                 In  the  second  part  of  the  paper  we  selected  one  representative  (training)  activity  and                recorded its direct costs, so we proved our preliminary assumption concerning possibility                of the procedure. Total costs allocated to selected activity we further  divided into sub-                centres which may enable to compare and evaluate consumption of sources by sub-centres                or even individuals. Such evidence may be used for planning, budgeting, standard-setting,                and various analytical purposes.                 References                Hirsch,  B.,  Nitzl,  C.,  Schauss,  J.  (2015).  The  Influence  of  Management  Accounting                Departments  Within  German  Municipal  Administrations.  Financial  Accountability  \&                Management, vol. 31(2), pp. 192-218.                Hood,  C.  (1991).  A  public  management  for  all  seasons.  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The  role  of  performance  measures  in  a  federal-state  context:  The                examples of housing and disability services.  Australian Journal of Public Administration,                vol. 62(1), pp. 70-79.                Pavlik, L. (2017). Competition and Controlling in Higher Education, a Crucial Segment of                the Public Sector. Public Finance Quarterly-Hungary, vol. 62(1), pp. 50-73.                Poulsen, B. (2009). Competing Traditions of Governance and Dilemmas of Administrative                Accountability: The Case of Denmark. Public Administration, vol. 87(1), pp. 117-131.                Reichborn-Kjennerud, K. (2013). Political Accountability and Performance Audit: The Case                of Auditor General in Norway. Public Administration, vol. 91(3), pp. 680-695.                 Sanger, M. B., Bryna, M. (2013). Does Measuring Performance Lead to Better Performance?                Journal of Policy Analysis and Management, vol. 32(1), pp. 185-203.                Silva,  A.  M.,  Macedo,  I.  M.  (2015).  The  Influence  of  Management  Frameworks  on  the                Accounting Information System: the Specific Case of Public Hospitals. In: The 8th Annual                Conference of the EuroMed-Academy-of-Business. Verona: Euromed Press.                 De  Vries,  M.  S.  (2010).  Performance  measurement  and  the  search  for  best  practices.                International Review of Administrative Sciences, vol. 76(2), pp. 313-330.                Vodáková,  J.,  Sglundová,  N.  (2017).  Does  proceeding  accounting  reform  contribute  to                better cost control within the Czech state administration units? In: Proceedings of the 21st                International Conference Current Trends in Public Sector Research. Šlapanice: Masarykova                univerzita, p. 8.                                                                                                              809","Banking System Development and Economic Growth: Evidence                from a Sample of High Income Countries and Low \& Middle Income                                                       Countries                                                       Lukáš Vodička                                                 Masaryk University in Brno                                Faculty of Economics and Administration, Department of Finance                                         Lipová 507/41a, 602 00 Brno, Czech Republic                                                E-mail: 421426@mail.muni.cz                 Abstract: This paper evaluates the relationship between banking system development and                economic growth using data from 141 countries over the 1998 – 2015 period. Observed                countries are also divided into two groups: high income countries and low \& middle income                countries. The set of explaining determinants describes three characteristics of the banking                system – depth, efficiency and stability. The data is collected from following databases:                World Development Indicators, Global Financial Development, KOF Index of Globalization                and Financial Access Survey. Two-step system GMM estimator is used to address the issue                of endogeneity. Robustness of the obtained results is tested with fixed-effects estimator.                Using these methods we find that bigger banking system per se does not foster economic                output, however its quality does. In case of high-income countries greater effect of banking                sector instability on economic growth is observed.                 Keywords: banking, GMM, economic growth                JEL codes: E44, G21, O47                 1 Introduction                The earliest work examining the influence of the financial system on the economic growth                                         th                can be traced to the 19  century. Even though the topic has been subject of debate for                a long time, it still attracts the attention of many researchers.                Levine (1997) states that the financial markets and institutions may appear to diminish                the problem arising from the information and transaction frictions. The primary function of                the financial systems is then to mediate the flow of money and to reduce the impact of                information frictions.                However,  some  authors  claim  that  with  improved  financial  systems,  new  problems                regarding  economic  growth  arise.  For  example  cheaper  loans  allow  to  shift  budget                limitations and increase the consumption over a given period. In macroeconomic growth                models, this reduction in savings causes a lower economic growth over a longer period of                time. Rajan (2005), as a reason for the possible negative impact of the finance on the                economic growth, indicates the increase of the volatility and poor allocation of the capital,                especially  the  human  one,  where  the  financial  sector  attracts educated  specialists  who                would otherwise work in the productive sectors of the real economy. The impact of the                banking system on the economic growth according to the existing theoretical framework                does not necessarily have to be positive.                The work of Levine and King (1993) can be viewed as a breakthrough in the empirical                verification  of  these  theoretical  propositions.  They  have  improved  the  system  of  proxy                variables describing the financial market. Thus, the ratios were used as the volume of bank                loans to GDP. This work was absolutely essential for further research, especially by creating                a proxy variables set-up. These variables captured predominantly characteristics of the                banking  system,  and  even in  recent  studies  this  approach is  often favoured  –  banking                system itself is then used as proxy for the whole financial system.                Exploring whether the impact of finance on the economic activity is really positive has been                studied by many academics as a follow-up to Levine. Christopoulos and Tsionas (2004),                using the OLS panel model, observed that this relationship was positive. In their work,                                                              810","they used the variables of the banking sector size. Different approaches have been made                to similar conclusions, for example, by Durusu-Ciftci et al. (2017).                 Especially in the course of years after 2008, studies questioning the positive impact of the                banking development on the economic growth emerged. Cecchetti and Kharroubi (2012)                argue  similarly  to  Rajan  (2005).  According  to  their  results,  the  impact  of  the  banking                system on economic growth becomes negative after the ratio of loans to private sector                reaches a certain threshold value. Berkes et al. (2012) also came to similar conclusions.                There  is  also  a  number  of  studies  that  track  the  negligible  or  unclear  influence  of  the                financial sector on the economic growth, e.g. the work of Hasan et al. (2016) who state                that the financial depth does not have any significant impact on the economic growth, on                the contrary to the effectiveness associated with a long-term economic growth.                The aim of this paper is to contribute to the evolving discussion on the topic and to assess                the impact of the overall banking system and its characteristics on the economic growth of                selected countries. The rest of the paper is organized as follows. Section 2 describes the                methodology and dataset. Section 3 presents the results. To obtain the estimations of the                effects of selected determinants on the economic growth, the GMM (Generalized Method                of Moments) system estimator is used. Robustness of the results is tested with fixed-effects                estimator. The last section discusses the results and compares them with the results of                other works.                  2 Methodology and Data                Barro (1991) has suggested a simple equation to determine the growth determinants:                       Y = α + αF + βX + ε                                                              (1)                                         i,t                                              i,t                                   i,t                        i,t                             0                where  α0,  α  and  β  are  the  coefficients,  Fit  indicates  a  given  indicator  of  the  financial                development of a country i in the course of time t, Xi,t is the matrix of the control variables                for a country i in the course of time t, εi, t, is the regression error.                To estimate the parameters, the GMM estimator proposed by Arellano and Bover (1995)                and Blundell and Bond (1998) is used. Seven and Yetkiner (2016) report the following                three advantages of the GMM (namely the system GMM):                            •  consistent  and  effective  estimates  of  regression  parameters  when  the                            regressors are not strictly exogenous and there are the heteroskedasticity and                            autocorrelation within the units,                            •  overcoming the  problem  of  endogeneity using instrument  variables  which                            are exogenous,                            •  the estimator is suitable for studies with short periods of time, but numerous                            units (small t and large n).                For these reasons, the GMM estimator, namely the GMM two-step estimator, is selected                for this study.                As stated by Bun and Windmeijer (2010), this estimator using the appropriate moment                conditions has the following form:                               ´ −1            ´                                         −1                                                           ̂ =    ´ −1            ´       −1                                              (2)                                                            −1                                                 where qi = (Δyi´, yi´). For a detailed description of the estimator, see Bun and Windmeijer                (2010).                The accuracy of the model specification is verified by Arellano-Bond autocorrelation tests                where the zero hypothesis is the absence of autocorrelation. According to Mileva (2007),                it  is  important  not  to  reject  this  hypothesis  in  the  case  of  AR  (2),  as  it  reveals  the                autocorrelation at levels. Hansen's test then verifies whether the instruments applied are                exogenous, the zero hypothesis is the suitability of the instruments. Both these statistics                together with the number of instruments are reported along with the estimation of the                coefficients in Tables 2 and 3.                                                             811","The fixed-effect model estimator is not described due to limited space and the fact that the                method is basic for panel data analysis, it is well known and widely used, and therefore it                is not necessary to describe it here.                Data                An empirical study in this work is based on data for 141 countries in the course of 1998-                2015. Countries are divided into two groups: high-income countries and low \& middle-                income  countries  according to  the  World  Bank. Using this  approach  more  homogenous                panels are obtained and different results for each group can be observed.                Variables  can  be  categorized  into  the  explained  variables,  explanatory,  and  controlling                variables. For modeling, the data is averaged over the three-year, non-overlapping periods                due to the specification of the estimator. The database was compiled on the basis of the                existing World Development Indicator database of the World Bank, and databases of the                International Financial Statistics (IFS) – IMF, KOF Globalization Index ETH Zürich and GFDD                compiled by Čihák et al. (2012).                                                 Table 1 List of Variables                  Variable                                     Definition                 source                                       type                 GDP per capita (constant 2010 US$)       o   ln (GDP per capita t+1/GDP per capita t).                 growth                 World Bank, WDI                               dependent                                                              Consists of outlays on additions to the fixed assets                 Gross capital formation (% of GDP)       y   of the economy plus net changes in the level of                                                              inventories.                 World Bank, WDI                               control                                                              Trade is the sum of exports and imports of goods                 Trade (% of GDP)                         ac  and services measured as a share of gross                                                              domestic product.                 World Bank, WDI                               control                                                              Gross enrollment ratio is the ratio of total                 School enrollment, secondary (%          ad   enrollment, regardless of age, to the population of                 gross)                                       the age group that officially corresponds to the                                                              level of education shown                 World Bank, WDI                               control                                                              Expense is cash payments for operating activities                 Expense (% of GDP)                       af   of the government in providing goods and                                                              services.                 World Bank, WDI                               control                                                              The KOF Globalization Index measures the three                 Overall globalization index              ag  main dimensions of globalization: economic, social                                                              and political.                 KOF                                           control                                                              CPI (Consumer Price Index) after hyperbolic sine                 CPI                                      ah                                                              transformation                 World Bank, WDI                               control                 Initial GDP per capita                   ai   Controlling for convergence effect.                 World Bank, WDI                               control                                                              Ratio of liquid liabilities to GDP. Liquid liabilities                 Liquid liabilities to GDP (%)            ak                                                              are also known as broad money, or M3.                 Cihak et al., GFD                             depth                 Deposit money bank assets to GDP             Total assets held by deposit money banks as a                 (%)                                     am   share of GDP.                 Cihak et al., GFD                             depth                                                               812","The financial resources provided to the private                 Private credit by deposit money                 banks to GDP (%)                         an  sector by domestic money banks as a share of                                                              GDP.                 Cihak et al., GFD                             depth                                                              The total value of demand, time and saving                 Bank deposits to GDP (%)                 ap  deposits at domestic deposit money banks as                                                              a share of GDP.                 Cihak et al., GFD                             depth                                                              Demand, time and saving deposits in deposit                 Financial system deposits to GDP                 (%)                                      cp  money banks and other financial institutions as                                                              a share of GDP.                 Cihak et al., GFD                             depth                                                              First component obtained via Principal Component                 BD (index for the depth of the           au                 banking system)                         (av)   Analysis (ak, am, an, ap, cp), (av – quadratic                                                              form).                 Authors' calculations                         depth                                                              Commercial banks’ after-tax net income to yearly                 Bank ROA                                 bg  averaged total assets after hyperbolic sine                                                              transformation.                 Cihak et al., GFD                             efficiency                                                              Commercial banks’ after-tax net income to yearly                 Bank ROE                                 bh  averaged equity after hyperbolic sine                                                              transformation.                 Cihak et al., GFD                             efficiency                                                              Assets of three largest commercial banks as                 Bank concentration (%)                   bd                                                              a share of total commercial banking assets.                 Cihak et al., GFD                             efficiency                                                              Z-score captures the probability of default of                 Bank Z-score                             bp                                                              a country's commercial banking system.                 Cihak et al., GFD                             stability                                                              The financial resources provided to the private                 Bank credit to bank deposits (%)         bo  sector by domestic money banks as a share of                                                              total deposits.                 Cihak et al., GFD                             stability                 Bank capital to total assets (%)         bq  Ratio of bank capital and reserves to total assets.                 Cihak et al., GFD                             stability                                                              Dummy variable for the presence of banking crisis                 Banking crisis dummy                     bu                                                              (1=banking crisis, 0=none)                 Cihak et al., GFD                             stability                                                Source: Own data processing                 The reasons for the use of these proxy indicators of the financial system are not provided                due to limited space. For the reasoning, see the publications in the introductory section.                The descriptive statistics are available on request.                 3 Results and Discussion                The banking development variables are studied across the single characteristics (depth,                efficiency, and stability). Only the coefficients of the regressors representing the financial                system  will  be  reported  for  clarity  of  results  and  limited  space.  The  impacts  of  these                variables for each group of the countries are shown in the following tables. The regressors                have  not  been  included  in  a  single  model  because  of  the  limitation  of  the  number  of                instruments. The control variables are always used. Each table presents an overview of                several models with the statistics of the respective tests.                                                                813","Table 2 Low-income Countries – Estimated Models. Based on the Analysis Performed on                                                      Data Collected                                         RESULTS                    MODEL SPECIFICATIONS                              Variable  Coefficient     p    AR(2)  Hansen  Instruments  Groups                              au            -0.0204  0.003      0.1   0.287            61      75                    Depth     an            -0.0217  0.008  0.13      0.244            61      75                              av           0.00917  0.023  0.06       0.132            61      75                              bg           0.01357  0.126  0.05        0.34            61      75                  Efficiency   bh          0.01253  0.004  0.08       0.333            61      75                              bd           0.00176  0.989  0.11       0.335            61      73                               bp                   0  0.988  0.12     0.292            61      75                   Stability  bo            -0.0105  0.394  0.13      0.212            61      75                              bq           0.01495  0.409  0.26       0.779            61      55                              bu             0.0009  0.944  0.06      0.121            46      75                                                Source: Own data processing                Variables: au – index for the depth of the banking system, an – private credit by deposit                money banks to GDP, av – au , bg – bank ROA, bh – bank ROE, bd – bank concentration,                                              2                bp – bank Z-score, bo – bank credit to bank deposits, bq – bank capital to total assets,                bu – banking crisis dummy                  Table 3 High-income Countries – Estimated Models. Based on the Analysis Performed on                                                      Data Collected                                        RESULTS                    MODEL SPECIFICATIONS                              Variable  Coefficient     p    AR(2)  Hansen  Instruments  Groups                              au            -0.0156  0.064  0.15       0.82            61      47                    Depth     an            -0.0458       0  0.09      0.76            61      48                              av            -0.0056    0.06  0.12     0.783            61      47                              bg           0.03084        0  0.07     0.794            61      48                  Efficiency   bh          0.01065  0.002       0.1   0.754            61      48                              bd           0.00412  0.815  0.11       0.798            61      48                              bp           0.00347  0.678  0.08       0.754            61      48                   Stability  bo            -0.0321  0.017  0.03      0.789            61      47                              bq            -0.0008  0.938  0.07      0.875            61      43                              bu            -0.0241       0  0.49     0.245            46      48                                                Source: Own data processing                 Variables: au – index for the depth of the banking system, an – private credit by deposit                                              2                money banks to GDP, av – au , bg – bank ROA, bh – bank ROE, bd – bank concentration,                bp – bank Z-score, bo – bank credit to bank deposits, bq – bank capital to total assets,                bu – banking crisis dummy                 The  statistically  significant  negative  impact  of  the  size  of  the  banking  market  on  the                dependent variable can be observed in both groups. The coefficient associated with the                quadratic regressor at the banking market is negative (i.e. concave function and declining                yields)  in  case  of  high-income  countries  (however,  significance  of  this  relationship  is                rejected, even though by a narrow margin) and positive in case of low-income countries                (i.e. after a certain point big banking systems could support economic growth in these                countries). According to the results, the size of the banking system does not foster growth                of the economy, and it may even reduce it.                                                             814","The coefficients of banking system efficiency show that the effectiveness of the banking                system is statistically significant with a positive impact on the economic growth. The bd                variable  (bank  concentration)  representing  the  competition  at  the  banking  market  is                statistically insignificant. This holds in both groups of countries.                The  final  monitored  feature  of  the  banking  systems  is  their  stability.  The  bo  and  bu                regressors  (loans  to  bank  deposits  and  bank  crisis  dummy  variable)  are  statistically                significant  with  a  negative  impact  on  the  economic  growth,  which  is  in  line  with  the                expectations. However, this relationship is significant only in case of high-income countries.                This can be due to the fact that advanced countries with developed financial systems are                more likely to be negatively affected by financial crisis.                 The  robustness  of  the  results  is  checked  with  fixed-effects  estimator.  For  simplicity,                robustness is checked for the collective data. Therefore the results are slightly different                from  a  quantitative  point  of  view,  but  from  a  qualitative  point  of  view  the  results  are                comparable.                   Table 4 Robustness Check – Estimated Models. Based on the Analysis Performed on                                                                    25                                                     Data Collected                                  Regressor                         GMM           FE                                  Depth                                  Credit (% GDP)               an  -0.0274      0.4144                                  BD (PCA index for the depth)   au   -0.015    -0.352                                  Efficiency                                  Bank ROA                     bg  0.015709    0.01042                                  Bank ROE                     bh  0.104636     0.0007                                  Stability                                  Credit to deposits (%)       bo  -0.01519    -0.43114                                                Source: Own data processing                 The  results  obtained  via  fixed-effects  estimator  are  more  or  less  in  line  with  results                obtained via GMM. In case of  an variable, the observed effect is opposite, however  au                variable is better proxy as it comprises more aspects of banking system. Most results are                robust to the FE check.                 4 Conclusions                This work contributes to the existing literature on the finance and economic growth by                analyzing the main sector of the financial system and its characteristics. Using a wide-                ranging spectrum of variables it enables to gain a new insight into the issue and contribute                to an evolving discussion on this subject. Countries are divided into two groups: high-                income countries and low \& middle-income. Using this approach more homogenous panels                are obtained and different results for each group can be observed. The data processing                uses a two-step estimator GMM system that addresses the endogeneity problem of the                variables used – for this reason, it is a popular estimator for a given topic, e.g. work Berkes                et al. (2012).                 Using this method, estimates of the coefficients of individual indicators are obtained, which                show that the quality and quantity represent two conceptually different areas with different                effects on the economic growth. While a larger banking system may even have a negative                impact on the growth, the stability and the effectiveness of the banking system are crucial                to the growth of the economy. The negative impact of the size of the banking system on                the economic growth may, for example, be the result of the \"too big to fail\" policy, where                the banks are motivated to take a higher risk and commit moral hazard. This behavior may                result  in  an  increased  volatility  in  the  financial  markets  or  even  in  the  financial  crisis.                  25  Statistically significant estimates are highlighted using the bold font.                                                            815","Another possible reason for the negative impact is the reduction of the household savings,                as discussed earlier. The results do not provide clear evidence of a concave relationship                between the size of the monitored sector and the performance of the economy, although                this function is observed, it is not statistically significant in case of high-income countries.                In case of low-income countries a convex function is observed. This result contradicts the                work of Berkes et al. (2012). The efficiency then, according to the obtained coefficients                estimates, contributes to the economic growth and the same can be said about the stability                of the given sector. According to the results high income countries are more affected by                the potential instability.                The  results  obtained  are  robust  to  the  fixed  effects  check  and  in  accordance  with  the                conclusions of Hasan et al. (2016). Based on this study, it can be said that not the size,                but the quality of the banking system is an important determinant of the GDP growth.                 Acknowledgments                 The author wishes to thank Mr. Oleg Deev for his helpful comments and remarks. This                research is supported by the Masaryk University grant financing in the Programme of the                Rector.                 References                Arellano, M., Bover, O. (1995). Another look at the instrumental variable estima-tion of                error-components models. Journal of Econometrics, vol. 68, pp. 29-52.                Barro, R. J. (1991). Economic Growth in Cross Section of Countries. Quarterly Journal of                Economics, vol. 106(2), pp. 407-443.                Berkes,  E.,  Panizza,  U.,  Arcand,  J.-L.  (2012).  Too  Much  Finance?  IMF  Working  Papers,                International Monetary Fund, no. 12/161.                 Blundell R., Bond S. (1998). Initial conditions and moment restrictions in dynamic panel                data models. Journal of Econometrics, vol. 87, pp. 115-143.                Bun, M., Windmeijer, F. (2010). The weak instrument problem of the system GMM estimator                in dynamic panel data models. Econometrics Journal, vol. 13(1), pp. 95-126                Cecchetti,  S.,  Kharroubi,  E.  (2012).  Reassessing  the  impact  of  finance  on  growth.  BIS                Working Papers, Bank for International Settlements, no. 381.                Christopoulos,  D.,  Tsionas,  M.  (2004).  Financial  development  and  economic  growth:                evidence from panel unit root and cointegration tests. Journal of Development Economics,                vol. 73(1), pp. 55-74.                Čihák, M., Demirgüç-Kunt, A., Feyen E., Levine R. (2012). Global Financial Development                Database (GFDD). The World Bank.                Durusu-Ciftci, D., Ispir, M. S., Yetkiner, H. (2017). Financial development and economic                growth: Some theory and more evidence. Journal of Policy Modeling, vol. 39(2), pp. 290-                306.                Hasan, I., Horvath, R., Mareš, J. (2016). What type of finance matters for growth? Bayesian                model averaging evidence. Policy Research working paper; Washington, D.C.: World Bank                Group, no. WPS 7645.                 International Monetary Fund. (2017). Financial access survey.                King, R. G., Levine, R. (1993). Finance and growth: Schumpeter might be right. Quarterly                Journal of Economics, vol. 108, pp. 717-738.                 KOF  [Konjunkturforschungsstelle  or  Economic  Research  Centre  of  ETH  Zurich].  (2017).                2017 Index of globalization. Retrieved from: http://globalization.kof.ethz.ch/media/filer_                public/2017/04/19/rankings_2017.pdf                                                               816","Levine, R. (1997). Financial development and economic growth: views and agenda. Journal                of Economic Literature, vol. 35, pp. 688-726.                 Mileva, E. (2007). Using Arellano – Bond Dynamic Panel GMM Estimators in Stata.                Rajan, R. G. (2005). Has financial development made the world riskier? In: Proceedings –                Economic Policy Symposium – Jackson Hole. Federal Reserve Bank of Kansas City, issue                Aug, pp. 313-369.                Seven, Ü., Yetkiner, I. (2016). Financial intermediation and economic growth: Does income                matter? Economic Systems, vol. 40(1), pp. 39-58.                The World Bank (2018). World Development Indicators. Washington, D.C.: The World Bank.                                                                                                                                  817","Equality In Old-Age Security In Poland                                                     Damian Walczak                                            Nicolaus Copernicus University in Torun                      Faculty of Economic Sciences and Management, Department of Financial Management                                             Gagarina 11, 87-100 Torun, Poland,                                                  E-mail: dwalczak@umk.pl                 Abstract: Defining the scope and form of old-age security should be based, among other                things, on the principles of social justice and equality. Equality is the manifestation, and                the instantiation of social justice and it means the equal treatment of equals. Therefore,                distribution, especially the one made in the pension system, does not have to be equal,                but it  is  to  serve  everyone,  so  it has  to  be fair  and  based  on  the  cooperation  system.                Unfortunately, many pension privileges in Poland are not implementing the principle of                equality. The study analysed, on the basis of a nationwide survey conducted in 2016, public                opinions on equality in the pension system. In addition, in the paper, I show expenditures                for retirement benefits of privileged persons in the Polish pension system. The aim of the                article is to present the equality in the pension system in Poland, both in the theoretical                aspect,  and  in  the  practical  aspect.  The  logistic  regression  method  was  applied  while                attempting  to  reach  the  research  aim  of  this  study.  In  the  paper,  also,  the  following                research  methods  were  applied:  statistical  analysis,  economic  analysis  of  the  law  and                analysis of documents.                 Keywords: equality, pension, social security, savings                JEL codes: D14, D31, D63, H23, H55                 1 Introduction                The pension system is a tool for dividing the current GDP between the working generation                and the generation of pensioners (Chybalski, 2016). However, the manner of this division                depends on the adopted regulations within a given system. The current system of defined                benefit or defined contribution, introducing a funded or PAYG system influences the division                of  GDP  in  different  ways.  Also,  actuarial  system  balancing,  as  well  as  social  justice  or                equality  are  dependent  on  detailed  solutions  (Chybalski,  Marcinkiewicz,  Fabian,  2018;                Kurach, 2017; Mešťan, Šebo, Králik, 2017).                 Social justice may be understood as “the form of claims against the incomes and property                of fellow citizens through collective schemes for redistribution”, of course this claims has                to be related to the fairness of exchanges and fairness of contracts (Jordan, 1998). In turn,                equality is the manifestation and the instantiation of social justice and it means the equal                treatment of equals (Antonów, 2015; Pacud, 2015). However, equality may refer to equal                of  meritas,  needs,  conditions,  opportunities,  access  or  outcome  (Ratajczak,  2017).                Therefore the equality should be always the dominant principle of distributive justice, upon                which a pension system is founded (Deutsch, 1975). However, distributive justice may                have three totally different meanings:                    a)  the perception of the individual about fairness of rewards they receive from the                       organization (Iqbal, 2013),                    b)  fair distribution of goods within a group (Schweizer-Ries, 2008). It could be only                       intragenerational fairness (Fenge, 2005) or, also, intergenerational fairness (Zuber,                       2016), or both together,                    c)  equal distribution of income in the whole life (it means adequacy of pension, Attias                       et.  al.  2016).  This  area  obviously  also  correlates  with  the  state’s  duties  to                       appropriately  shape  the  pension  system,  in  order  for  the  abovementioned                       smoothening of consumption within a lifecycle to be realized.                 Thus in the case of equality we are talking about matters related to objective and subjective                equality, as well as equality in terms of individual income within the whole lifecycle.                                                              818","The aim of the article is to present the equality in the pension system in Poland, both in                the theoretical aspect (theory of equality – presented above), and in the practical aspect                (actual pension and public opinion about equality in the Polish pension system).                 2 Methodology and Data                The article has been based on the study entitled “Wiedza i postawy wobec ubezpieczeń                społecznych” (“Knowledge of and attitudes towards social insurance”) conducted by Social                Insurance Institution (the institution in charge of social insurance in Poland, which employs                over  45,000  people),  later  in  the  paper  –  “SII  study”  (SII,  2016d).  The  study  was                conducted in July and August 2016, and its aim was to provide information about the state                of knowledge about social insurance in the Polish society. It was carried out on a random                sample representative of the total population of Poland aged 15-75. The sample size was                N=1,030 people. The sample was a random-quota one and it was selected from the Main                Statistical  Office’s  address  database.  The  process  was  carried  out  by  using  the  CAPI                technique (computer-assisted personal interview).                Due to the low level of knowledge on the topics related to the pension system in Poland                (Olejnik,  Białowąs,  2015;  Musiał,  Świecka,  2017)  the  article  deals  only  with  those                questions  which  refer  to  general  matters  or  subjective  opinions  on  equality-related                matters.                In the article (the description of the research method was fully taken from Krupa, Walczak,                2016) the dependence of the characteristics was determined with the use of the chi-square                test of independence (χ  test). In each of such tests individual indications of respondents                                        2                were compared with potential characteristics that determine their decisions (the relevant                calculations were made with the use of SPSS Statistics 24).                For  each  pair  of  characteristics  a  zero hypothesis  H   was  set  and it  assumed  that  the                                                                      0                compared  characteristics  are  independent,  and  the  hypothesis       assumed  that  these                                                                                    1                                                                   2                characteristics are dependent. If the calculated     is greater than the critical value χ 2 df,α                (for degrees of freedom df: (r-1) (s-1) and a presupposed level of significance    = 0.05),                then H  should be rejected, otherwise there is no reason to reject the null hypothesis.That                       0                                  2                              2                means that    ≥        ,    and we reject H  at the significance level   , and we may suppose                                                       0                                                             2                                                         2                that the characteristics are dependent,    <       ,   , there is no reason to reject H  assuming                                                                                               0                the independence of characteristics.                The work will also include the presentation of figures concerning the basic equality-related                issue in the pension system, which is the average amount of pension benefits in Poland.                The figures will be presented based on the data from the Main Statistical Office, Social                Insurance Institution and the Agricultural Social Insurance Fund.                3 Results and Discussion                 Equality in a pension system can be measured in a number of manners. It can especially                take place in Poland, a country whose whole system of old-age security is very diverse                (Walczak, 2017). The measures of that equality may be the level of pension contribution,                the minimum age of entitlement to pension benefits, or the amount of benefits received.                Due to the scope of this work (the other figures would require a separate, lengthy subject                analysis), it will present only the amounts of benefits in the Polish pension system. As                pointed out in Table 1, Poland demonstrates a lot of variety in the level of aforementioned                benefits. Farmers receive about 50% of the benefit received within the general pension                system.  On  the  other  hand,  soldiers, miners,  police  and  prison  system  officers  receive                almost  twice  the  amount  received  within  the  general  system.  The  highest  pensions,                however,  are  received  by  judges  and prosecutors.  Their  benefits  are  several  times the                amount of the ones received by farmers or beneficiaries of the general system.                In  the  context  presented  above,  it  can  be  stated  beyond  any  doubt  that  in  terms  of                insurance the system is far from equal. Different professional groups receive benefits in                different amounts. At the same time, if one were to analyze the contents of Table 1, it                                                            819","could not be overlooked that benefits in “The provision pension system” are fully financed                from the state budget, in “The farmers’ pension system” it is so in about 95%, whereas in                “The  general  pension  system”  it  happens  partially.  “The  general  pension  system”  is                theoretically based on the system of defined pension contribution, with the pensions being                a  derivative  of  the  accumulated  capital  (Walczak,  2017;  Walczak,  Pieńkowska-                Kamieniecka,  2015).  However,  due  to  the  minimum  pension  guaranteed  to  those  with                minimum required insurance period (Bielecki, Tyrowicz, Makarski, Waniek, 2015), as well                as individuals who receive benefits on the basis of defined benefits rule (Wojdak, Kopański,                Bojanowska, 2015) (for example, miners, as well as people born before 1 January 1949),                the system needs to be subsidized by the state.                 What  is  the  reason  for  the  diversity  in  the  level  of  pension  benefits  in  Poland?                Unfortunately, it is not equality in terms of opportunities or needs, which would justify such                a significant diversity. According to Golinowska (2009), the existing pension privileges were                shaped in the years 1945-1990,  i.e., during the years of Communist control in Poland.                Then authorities used the system as an instrument to attain their political goals. Thus,                pension benefits in Poland are a derivative of power and privileges acquired in relation to                it, not of equality perceived in any way.                                    Table 1 Average Pensions Value in 2015 (in PLN)                      Type of    Paying institution              Type of benefit               Average                     system                                                                  pensions                         The                                  Average monthly pension           2,096.55                     general      Social Insurance                     pension         Institution                      Miners                  3,994.59                     system                                     Bridging benefits             2,422.06                                                       Compensation benefits for teachers     1,552.45                                   The Ministry of      Soldiers and military intelligence                                  National Defense       and counterintelligence officers     3,395.61                                                          Officers of the Police, Internal                                                          Security Agency, Intelligence                       The         The Ministry of       Agency, Central Anticorruption       3,671.46                    provision      Internal Affairs   Bureau, Border Guards, Government                     pension                              Protection Bureau, State Fire                     system                            Service and State Protection Bureau                                   The Ministry of            Prison Service Officers         3,518.32                                       Justice                                   Last employer                                  (relevant court or         Judges and prosecutors           8,236.12                                 prosecutor's office)                    Farmers’     Agricultural Social                     pension       Insurance Fund                    Farmers                  1,209.81                     system                          Source: own study based on (CSO, 2016; SII, 2016a; SII, 2016 b; SII, 2016c)                In Table 2 it was pointed out that the Poles opt for leveling the income of all citizens. Out                of  1,030  questioned,  only  37  strongly  oppose  the  idea.  Fewer  people,  yet  still  a  vast                majority of the surveyed, opts for common, i.e. state’s responsibility for providing old-age                security. The presented results indicate that a small portion of the respondents believe that                income-related  matters,  as  well  as  old-age  security,  should  be  left  to  one’s  individual                decision. According to most, those matters are of such importance that they should bring                about the state’s action. The Poles thus opt for equality of results, which is a feature of a                socialist state.                                                                820","Table 2 Responses to Questions: “We should aim for the incomes of all people to be                relatively equal” (Q1) and “Security for old-age is a topic of importance for the society as                                        a whole and we should all aim for it” (Q2)                                             I                    I         I                                                    I rather                                                                                    Hard                        Specification  strongly     disagree   rather  strongly     to say   Total                                         disagree              agree      agree                             (Q1)           37         158       465       311        59     1030                              (Q2)           82         246       431       171       100     1030                                           Source: own study based on “SII study”                 Analyzing in more detail which respondents were more in favor of the suggested actions                by the state, in Table 3 the results of Chi-square test of independence were presented. In                contrast to the results of Walczak and Pieńkowska-Kamieniecka (2018), only the variable                “gender” does not have any relation with the responses provided. The remaining variables                are dependent. It means that people of different characteristics within particular variables                have different opinions on the issues presented in the article.                Presenting those issues in more detail (due to its size limitations, the article will not present                the figures concerning individual values), it can be noticed that people who have children                definitely opt for leveling incomes. 81.5% of people with children strongly agree (or rather                agree)  with  the  opinion  that  people’s  incomes  should  be  equal,  whereas  among  the                childless the rate is 72.2%.                People living in the countryside and smaller towns are much more willing to support the                postulate  of  income  leveling.  The  ones  who  strongly  or  rather  agree  are  81.4%  of                countryside inhabitants and 68.5% of big city dwellers (cities above 500,000 inhabitants).                In the case of education, the dependence amounts to lower support for equality by people                with better education. Whereas 75.3% of people with primary education support (strongly                or  rather)  the  necessity  to  level  the  income  of  all  people,  among  those  with  higher                education the rate is merely 63.8%.                 It is the more educated, however, unlike the topic of income leveling, that opt for the role                of  state  in  ensuring  equal  old-age  benefits.  As  many  as  87.2%  of  people  with  higher                education, in contrast to 77.1% of those with primary education, believe in the state’s role                in ensuring old-age pension to all citizens. Single persons (77%) are much less likely to                support the role of state than married ones (85.2%) and divorced (81.6%). In the case of                age, as can be guessed, senior citizens are much more willing to support such a solution.                According to age, 85.2% of those aged over 55 are in favor (strongly or rather agree),                with 77% of those aged below 35.                In both questions the less well-off are in favor of income equality and old-age security                being a topic of common interest. However, in the case of aiming for the incomes of all                people to be equal the difference is greater. As many as 82.3% of people with the lowest                income (0-1,200 PLN per capita in a household) believe that this is the course of action to                be taken, whereas only 68.8% of the wealthiest (income above 2,000 PLN per capita in a                household) believe the same.                 Table 3 Results of Testing of Dependencies Between Equality-related Questions (Q1 and                                                Q2) and Selected Variables                                                     Degrees                                     Phi (V-                  Dependent Variable                     of                 ,      p-value  Decision¹  Cramer                                                                                                      freedom                                        a)                                                       (df)                      We should aim for the incomes of all people to be relatively equal (Q1)                                                                                     No reason                 Gender                     2.699        4       9.4877    0.609                     -                                                                                    to reject                                                                                                  0                                                            821","Age (3 groups: aged                 0-35; aged between         6.245        8      15.5073    0.620     No reason       -                 35 and 55; aged                                                    to reject                                                                                                  0                 above 55)                 Marital status                 (single; married;          14.198       8      15.5073    0.077     No reason                 divorced,                                                          to reject                                                                                                  0                 widow/widower)                 Having children (yes;      11.861       4       9.4877    0.018     Rejection    0.107                 no)                                                                             (0.107)                                                                                          0                 Size of the place of                 residence (4                 categories:                 countryside; towns                 below 100,000              30.635      12      21.0261     0.02     Rejection    0.172                 inhabitants; cities                                                             (0.100)                                                                                          0                 below 500,000                 inhabitants; cities                 over 500,0000                 inhabitants)                 Education level (4                 categories: primary;                                                Rejection    0.169                 vocational;                29.385      12      21.0261     0.03                 (0.098)                 secondary; above                                                         0                 secondary)                 Income (3                 categories: below                                                   Rejection    0.159                 1,200 PLN; below           25.976       8      15.5073     0.01                 (0.112)                 2,000 PLN; over                                                          0                 2,000 PLN)                  Security for old-age is a topic of importance for the society as a whole and we                                               should all aim for it (Q2²)                                                                                     No reason                 Gender                     0.881        4       9.4877    0.927                                                                                    to reject                                                                                                  0                 Age                                                                 Rejection    0.159                                            26.027       8      15.5073     0.01                                                                                            0    (0.112)                 Marital status                                                      Rejection    0.136                                            19.008       8      15.5073    0.015                                                                                            0    (0.096)                 Having children                                                     No reason                                             7.84        4       9.4877    0.097                                                                                    to reject                                                                                                  0                 Size of the place of       14.214      12      21.0261    0.287     No reason                 residence                                                          to reject    0                 Education level                                                     Rejection    0.164                                            27.734      12      21.0261     0.06                 (0.096)                                                                                          0                 Income                                                              Rejection    0.124                                            15.718       8      15.5073    0.047                                                                                                 (0.087)                                                                                          0                                   ¹ Rejection of    0  means dependence of indicated variables.                               ² The variables in Q2 have the same categories like in question Q1.                                           Source: own study based on “SII study”                4 Conclusions                 The Polish pension system is definitely not based on equality, regardless of how the equality                would  be  measured.  Equality  in  Poland  was  transformed  into  an  element  of  power.                Unfortunately, instead of an equality-based system, Poland has an overly diversified one,                which  in  addition  requires  subsidization  from  state  budget.  The  subsidy  is  also  not                                                               822","distributed equally, but goes only to some privileged groups, since the beneficiaries receive                retirement pensions of different amounts.                 Very high variability in the amount of benefits may have negative results in the future, as                then the people may come to the conclusion that the principle of income leveling that they                postulate is not followed in the pension system. As a result, they may demand benefits to                be leveled, even to the extent of taking away the acquired pension rights in the name of                “equality”.                The Poles believe that old-age security should be a topic of common social interest, which                means  that  they indirectly  opt  for increasing the  state’s  activity in  terms  guaranteeing                benefits to senior citizens. Due to that, as mentioned above, they probably also expect                that those benefits would also be made equal. The group especially in favor of this solution                are senior citizens, whose numbers will continuously grow. A similar opinion is also shared                by poorer people, who are a growing group of future pensioners.                The studies described in the paper allow for formulation of two most important conclusions:                    •   the Polish pension system does not follow the principle of equality,                    •   the Poles expect leveling of incomes and activity of the state in ensuring old-age                       security.                 References                 Antonów, K. (2015). Zasady równości i sprawiedliwości społecznej w prawie emerytalnym                [The Principles of Equality and Social Justice in Pension Law]. Annales Universitatis Mariae                Curie - Skłodowska Lublin – Polonia, vol. LXII(2), p. 9.                Attias, A., Arezzo, M. F., Pianese, A., Varga, Z. (2016). A comparison of two legislative                approaches to the pay-as-you-go pension system in terms of adequacy. The Italian case.                Insurance: Mathematics and Economics, vol. 68, pp. 203-211.                 Bielecki, M., Tyrowicz, J., Makarski, K., Waniek, M. (2015). 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In: González-Ricoy, I., Gosseries,                A., ed., Institutions for Future Generations, Oxford: Oxford University Press, pp. 65-81.                                                              824","The Use of Different Methods of the Tax Base Estimation in Income                           Taxes in the Light of SAC Rulings in 2017 in Poland                                                    Radosław Witczak                                                       University of Lodz                                    Faculty of Economics and Sociology, Institute of Finance                                             Narutowicza 68, 90-131 Lodz, Poland                                             E-mail: radoslaw.witczak@uni.lodz.pl                 Abstract: Important issue for public finance is tax evasion. This problem concerns  also                income taxes. In order to protect the interests of the State Treasury and to ensure fair                competition in the economy, measures must be available to prevent the occurrence of such                attempts.  One  way  to  combat  tax  evasion  is  estimation  of  the  tax  base.  The  Polish                legislation points out different methods of the tax base estimation. The provisions provide                tax authorities with a list of six tax base estimation methods they can use. However, tax                authorities  may  use  other  methods  serving  the  same  purpose,  but  the  law  (the  Tax                Ordinance Act) does not indicate what these methods should be. The aim of the article is                to indicate methods of the tax base estimation used by the tax administration in the light                of SAC rulings in 2017. The analyse of using the methods of estimating of tax base by the                tax  authorities  was  given.  The  scope  of  the  analyse  were  different  cases  from  the                administrative court. The cases concerned different types of methods of tax estimation                used by the tax administration. In all analysed cases the other methods than pointed out                in the  provisions  were  used.  The implementation  of  rules  concerning  other  methods  of                estimating of tax base was proposed.                 Keywords: taxation, tax fraud, income taxes, the estimation of tax base, tax collection                JEL codes:H26, H25, H24 H83, K42                 1 Introduction                The tax evasion is an important problem for public finance in Poland. As the data show the                scale of gray economy comparing to GDP is decreasing but still is relevantly high last years                (see table 1).                                     Table 1 The Scale of Gray Economy (2014-2018)                                  Year          2014     2015    2016     2017     2018                                  Bln PLN        361      370     377      396      402                                  Bln EUR       90,25     90,5    94,25     99      105                                  (1EUR=                                  4PLN)                                  Share of      19,7      19,5    19,0     18,7     18,0                                  gray                                  economy                                  in GDP                                  (%)                  Source: developed on the Instytutu Prognoz i Analiz Gospodarczych (Institute of Forecasting and                                      the Economic Analysis) (Cieślak-Wróblewska, 2018)                 The  share  of  gray  economy  in  gross  domestic  product  in  years  2014-2018  decreases                smoothly to the rate 18%. However, the nominal value of gray economy increases every                year.  It  means  the  tax  administration  should  combat  the  scale  of  illegal  activity  of                taxpayers. One way to combat tax evasion is estimation of the tax base. The provisions                provide the tax authorities with a list of six tax base estimation methods they can use.                However, tax authorities may use other methods serving the same purpose, but the law                (the Tax Ordinance Act) does not indicate what these methods should be. Some of the tax                estimation methods are described in the literature data. The previous partial researches                show  that  tax  administration  uses  mainly  the  tax  estimation  methods  which  were  not                described in the provision or in the literature data. It is advisable to research if the Polish                                                             825","tax administration uses the tax estimation method indicated in the provision and if the                provisions require to be improved.                 The aim of this article is to indicate methods of the tax base estimation used by the tax                administration in the light of SAC ruling in 2017.                The  research  hypothesis  is  as  follows:  The  tax  administration  uses  different  income                estimation methods including the methods indicated in the provisions.                 2 Estimation of income in Tax Ordinance Act                According to the Tax Ordinance Act tax authorities may estimate the tax base. The tax law                allows to estimate tax base in following cases (the Tax Ordinance Act, art. 23 item 1):                    •   there are no tax books or other data necessary to determine the tax base,                    •   the taxpayer has breached the conditions entitling him or her to lump-sum taxation                       taxpayers’ taxable incomes,                    •   the  data  resulting  from  the  tax  records  do  not  allow  the  taxable  base  to  be                       determined.                 The reason for estimation is involved with the reliability of tax evidences. The law in force                stats that tax records should be kept reliably and correctly. Tax registers are considered                as reliable if their entries reflect the actual state. The effect of unreliable tax evidences can                be estimation of the tax base (Melezeni, 2017).                According to the tax law the tax authorities refrain from determining the taxable base by                the use tax estimation. Despite the lack of tax books, the tax authority may determine the                tax base if it is possible to obtain evidence during the proceedings or data stemming from                tax books and the evidences enable to determine the tax base (Dzwonkowski, 2018).                The Tax Ordinance Act provides tax authorities following tax base estimation (statutory)                methods they can use (the Tax Ordinance Act, art. 23 item 3):                    •   the internal comparative method,                    •   the external comparative method,                    •   the inventory method,                    •   the production method,                    •   the cost method,                    •   the type of income as a percentage of turnover.                The legislator has defined the tax base estimating methods included in the provisions in a                laconic way (Etel, 2018). One of the tax estimation method is the internal comparative                method. The tax base is determined on the base of comparing the amount of turnover with                the previous years’ turnover figures in the same taxpayer. The next method is external                comparative method. It estimates the tax base comparing the amount of turnover in other                enterprises that are doing business in similar conditions. The inventory method consists in                comparing the value of the assets of the enterprise at the beginning and at the end of the                period, taking into account the turnover speed rate. The production method estimates the                tax base on basis of the production capacity of the taxpayer. According to its legal definition                cost method derives the turnover figure from a company’s expenses based on a coefficient                indicating their share of turnover. The last method is the type of income as a percentage                of  turnover.  The  amount  of income is calculated  from the  sale  of  particular  goods  and                particular  services  based  on  their  share  of  total  turnover  (Dworniak,  2003;  the  Tax                Ordinance  Act,  art.  23  item  3).  Some  authors  maintain  that  those  methods  are  most                popular in use in practice (Brzeziński, et al 2002). Tax authorities may use other methods                to estimate the tax base than those mentioned above. The law does not indicate what                these methods should be. Until the end of 2015 there was priority for the methods specified                in the provisions. The non statuary methods could be applied in particularly justified cases                if the statuary methods could not be applied. Currently, the importance of non-statuary                methods  and  contained  in  the  statutory  catalogue  -  has  been  equated  (Dzwonkowski,                2018). Following methods as ‘non-statutory’ methods are presented as in the literature                (Schneider, 2007; Brzeziński et al., 2007; Brolik et al., 2013; Etel 2018):                                                            826","•   determination the turnover figure from information sources,                    •   determination the percentages of particular products in total production                    •   determination the percentages of some goods in the total turnover of the company,                    •   analysing the formulas used to make particular products,                    •   estimation business expenses in relation to turnover,                    •   examining the consumption of electricity,                    •   estimation a taxpayer’s incomes based on its expenses,                    •   investigating net profits,                    •   utilising an econometric model of costs.                One of the income estimation method mentioned above is a taxpayer’s incomes based on                its expenses method. This method is indicated in fact in the Polish law. This method may                be  used  in  case  of  revenues  unmatched  by  the  disclosed  sources  or  derived  from                undisclosed sources as a part of the Personal Income Tax (Kandut 2017). The provisions                on revenues unmatched by the disclosed sources or  derived from undisclosed sources (the                assessment of not revealed sources of incomes) concern particular issues to be followed                by  the  tax  administration  (Marciniuk,  2017).  There  are  opinions  not  accepting  the                assessment of not revealed sources of incomes as an estimation method determining the                tax base (Dzwonkowski, 2009; Pietrasz, 2007). It means the assessment of not revealed                sources  of  incomes  is  not  an  estimation  itself.  So  a  taxpayer’s  incomes  based  on  its                expenses method can be used by the tax administration while the tax is determined only                on the basis of the provision of income tax in case of not revealed sources of incomes.                Although,  some  authors  (Schneider,  2007)  and  the  German  provisions  foresee  the                calculation of the income based on expenses as one of the income estimation methods                (Carlé, 2006).                Tax authorities should calculate a taxpayer’s liability as accurate as possible. It means that                tax administration should apply such a tax estimation method which allows to determine                the tax base as close as possible to its accurate value (the Tax Ordinance Act, art. 23 item                5; Adamiak et al., 2014).                 3 The identification of use of income tax base estimation methods–                research findings                The empirical part of this article is based on the analysis of case studies derived from the                rulings  of  the  Supreme  Administrative  Court.  Taxpayers  challenging  tax  authorities’                decisions may lodge a complaint with the Voivodeship Administrative Court (VAC). VAC is                competent to assess whether a particular decision is legally sound. A complainant who                disagrees with the ruling issued by the Voivodeship Administrative Court may submit a                cassation  appeal  to  the  Supreme  Administrative  Court.  All  rulings  that  the  Supreme                Administrative Court made in 2017 from 1. January to 30 April and which were available                in the Lex database were analysed. As many as 129 rulings issued in connection with article                23  of  the  Tax  Ordinance  Act  regulating  the  use  of  the  tax  base  estimation  rules  were                examined. By analysing court rulings concerning the article 23 of the Tax Ordinance Act                the  application  of  estimation  methods  may  be  identified.  The  rulings  of  Supreme                Administrative Court which were examined involved different types of taxes (see table 2).                      Table 2 The Types and Numbers of Taxes to which Estimation Provisions Apply              Tax     Income     Income      VAT     VAT cases    Excise      Excise      Other    Other tax                       taxes      taxes                 as a                cases as a    taxes       as a                                cases as a          percentage              percentage            percentage                                percentage             of all                  of all                of all                                   of all             rulings                 rulings               rulings                                  rulings              No.       52         40,3%       48      37,2%         14       10,9%        15        11,6%              of              cases                                               Source: developed by the author                                                               827","More than 40% of the analysed rulings (see table No 2) concerned income taxes. However,                the majority of this rulings were referring to personal income tax (as many as 49 rulings).                Only 3 rulings referred to corporate income tax. Whereas, Value-added tax was dealt with                in 48 of the rulings. Excise tax was analysed in almost 11% of cases. Also 11 % of analysed                rulings related to other taxes like fuel duties, lump sum turnover-based tax.                A  more  detailed  analysis  was  applied  to  rulings  made  in  cases  where  the  provisions                concerning tax base estimation in income taxes (both personal income tax and corporate                income tax) were used. The income estimation methods were applied by the tax authorities                in 18 cases (see Table 3).                 Table 3 Numbers of Rulings Concerning Estimation Methods and Other Matters Related                                                     to Income Taxes              No. of               No. of rulings     No. of rulings     No. of rulings      No. of rulings              analysed             in which tax        in which tax        on matters          on other              rulings                   base               base          other than the      matters as a                                    estimation          estimation           use of         percentage of                                  methods were       methods were          estimation        all rulings on                                        used            used as a           methods          income taxes                                                      percentage of                                                      all rulings on                                                      income taxes              52                         18               34,6%                 34               65,4%                                              Source: developed by the author.                 The data in Table 3 show that tax authorities did not use the tax estimation methods in                65,4% of analysed cases. The reasons why there were no tax base estimation methods in                the SAC rulings were varied. One important reason was the problem of dummy invoices                used as a source of deductible costs. Taxpayers lodged a complaint with VAC demanding                using the estimation of tax base. Other reasons were a withdrawal from an estimation                procedure or the non-applicability of estimation methods. In some cases the SAC ruled on                other issues than tax base estimation like for example expiration of tax liabilities.                 Tax estimation is in fact used in some cases when the tax administration uses the rules                allowing to refrain from determining the taxable base by the use tax estimation. It may be                a way of avoiding the provisions  on using tax estimation methods. The tax administration                should have justified the choice of the tax estimation method according to provisions when                the tax decision were taken. It should explain the use of the applied estimation method. It                is easier to formally abandon the tax base estimation and calculate the income tax on base                of other evidences. Moreover, such activity seems to be accepted in the SAC rulings. It is                not a purpose of this article to investigate the use of rules of abandoning the tax base                estimation by the tax authorities. However, this problem should be research in separate                article.                 In 18 analysed cases involving income taxes, tax authorities used different methods to                estimate taxable income. Tax authorities did not used the statutory methods in analysed                cases  at  all.  The  use  of  statutory  methods  requires  to  meet  special  conditions.  If  the                taxpayer does not keep his or her tax records it is impossible or it is very difficult to apply                such  methods  like  the  internal  comparative  method,  the  inventory  method,  the  cost                method. The external comparative method requires to find companies doing business in                similar conditions as the taxpayer does. It is not always possible to find such companies.                In  case  of  the  production  method  the  tax  administration  cannot  recognize  that  the                production  capacity  of  the taxpayer  equals  his  or  her  volume  of  revenues.  In  the  free                market economy is not a problem to produce goods or services, but to sell them. So this                method in the free market economy cannot be used as the only estimation method. The                production method should be only used to determine the maximum range of estimated                income.  In  my  opinion,  this  is  why  statuary  methods  are  not  applied  by  the  tax                administration.                                                              828","The study also shows that the taxpayer’s incomes based on its expenses method was not                used at all. Although, this method seems to be helpful for tax administration in case of lack                of data. The reason for not applying this method may be the fact that this method refers                to  revenues  unmatched  by  the  disclosed  sources.  The  source  of  income  is  known  in                analysed cases while generally the Tax Ordinance provisions on the tax base estimation                are applied. It is usually doing business by the taxpayer. The tax administration estimates                tax base on the basis of Tax Ordinance not PIT and provisions on revenues unmatched by                the disclosed sources or derived from undisclosed sources. This is why the tax authorities                do  not  use  this  method.  It  is  advisable  to  change  the  law.  The  based  on  taxpayer’s                expenses  method  should  be  indicated  as  one  of  the  methods  of  income  estimation                regulated in Tax Ordinance. It could be determined in more detail in the provisions like                provisions of not revealed sources of incomes.                Following sources were used to estimate the income  by the tax authorities in particular                analysed  cases  (in  some  rulings  there  was  no  particular  information  on  applied  tax                estimation methods).                    •   the mixed method,                    •   testimonies of recipients of services, data from bank account, (three cases of the                       same taxpayer, but they concerned different tax years),                    •   average  margins  based  on  data  from  other  authorities,  statistical  office  and                       taxpayer's explanations,                    •   the value of the goods at the beginning and at the end of the year, average price                       and the average margin to estimate the costs,                    •   data  obtained  from  the  tax  records  (the  amount  of  purchased  materials),                       testimonies of recipients of services and data obtained from contractors,                    •   testimonies of witnesses, tax records of the contractors, data from employment and                       social  insurance  records  kept  by  the  taxpayer,  bank  account,    taxpayer's                       explanations on the cost calculation,                    •   tax records for VAT purposes, the share of costs in revenues obtained from the tax                       records from other part of the tax year,                    •   the  mix  of  the  internal  comparative  method  and  the  cost  method,  taxpayer’s                       explanations but with use of other evidences like taxpayer’s tax records,                    •   testimonies of recipients and external market data.                The mix  method was based on the features of the internal comparative method regarding                the share of operating expenses in the audited period in the sales volume of products,                determined  based  on  data  from  the  accounting  system  and  their  comparison  with                information about transactions obtained from taxpayer’s contractors. At the same time,                through its  application, the income from  unauthorized  sales  was  estimated, using  data                from the list of shipments, taking into account the average prices of products in relation to                the documented outflow of products. Formally this method was applied in 8 analysed cases,                but the rulings referred to the same taxpayer, but they concerned different tax years or                the tax advances.                As the study shows the testimonies of recipients (witnesses), taxpayer's explanations are                often used to estimate the income of the taxpayer. In case of taxpayer's explanations it                would case the underestimation of the income. There is a risk that the taxpayer who have                not fulfilled his or her tax obligation properly also could underestimate the data used for                estimation. Due to fact that the non – statuary methods are be applied it is advisable to                elaborate the guidelines for use the non – statuary methods.                 4 Conclusions                In most of analysed cases the tax administration did not use tax estimation methods at                all. If the tax was estimated the tax authorities applied the non – statuary methods. So                the hypothesis was verified that the tax administration does not use the income estimation                methods indicated in provisions. These findings suggest some changes in the tax system.                Due to fact that the methods indicated in Tax ordinance are seldom able to be applied in                the case of taxpayers doing business it is advisable to elaborate the guidelines for the use                                                            829","of  non  –  statuary  methods.  The  tax  estimation  can  be  applied  in  fact  by  the  tax                administration by using the rules of abandoning the tax base estimation. In such a case no                method is  named  by  the  tax  authorities  due  to  fact  that  formally  no  tax  estimation is                applied. However, this problem of should be research in more detail. Another change in                the law concerns the use of the based on taxpayer’s expenses method. This method should                be indicated as one of the methods of income estimation regulated in Tax Ordinance. It                could be determined in more detail in the provisions like provisions of not revealed sources                of incomes.                References                 Cieślak-Wróblewska,  A.  (2018).  A.  Szara  strefa  się  kurczy,  ale  to  wciąż  400  mld  zł.                Rzeczpospolita (electronical edition).                 Adamiak,  B.,  Borkowski,  J.,  Mastalski,  R.,  Zubrzycki,  J.  (2014).  Ordynacja  podatkowa.                Komentarz 2014. Wrocław: Oficyna Wydawnicza „Unimex”, p.232.                Brzeziński, B., Kalinowski, M., Masternak, M., Olesinska, A. (2002). Ordynacja podatkowa.                Komentarz. Legalis.                Brzeziński, B., Kalinowski, M., Masternak, M., Olesinska, A. (2007). Ordynacja podatkowa.                Komentarz T.1., Toruń: Dom Organizatora, p. 242.                Brolik, J., Dowgier, R., Etel, L., Kosikowski, C., Pietrasz, P., Popławski, M., Presnarowicz, S.,                Stachurski, W. (2013). Ordynacja podatkowa. Komentarz. Lex.                Carlé,  T.  (2006).  Geldverkehrs-  und  Vermögenszuwachsrechnung.  Möglichkeit  zur                Überprüfung von Verprobungsmethoden.  Der AO-Steuer-Berater, no. 6. Retrieved from:                https://www.wiso-net.de:443/document/AOB__AOSTB.2006.05.A.02.                Dworniak, K. (2003). Ordynacja podatkowa. Komentarz. Legalis.                 Dzwonkowski,  H.,  ed.  (2009).  Opodatkowanie  dochodów  nieujawnionych.  Praktyka                postępowania podatkowego i odpowiedzialność karna skarbowa, Warszawa: Difin.                Dzwonkowski, H., ed. (2018). Ordynacja podatkowa. Komentarz.  Legalis.                 Etel, L., ed. (2018). Ordynacja podatkowa. Komentarz. Lex.                Kandut,  K.  (2017).  Opodatkowanie  dochodów  nieujawnionych  jako  narzędzie                uszczelniające system podatkowy. Lex.                 Marciniuk, J., ed. (2017). Podatek dochodowy od osób fizycznych Komentarz. Legalis.                Melezini, A., ed. (2017). Ordynacja podatkowa. Wzory pism z komentarzem. Warszawa:                Wolters Kluwer, p. 281.                 Pietrasz, P. (2007). Opodatkowanie dochodów nieujawnionych. Lex.                Schneider, K. (2007). Błędy i oszustwa w dokumentach finansowo-księgowych. Warszawa:                Polskie Wydawnictwo Ekonomiczne.                 Tax Ordinance Act 29.08.1997 r., Dz. U. 2017 no. 201.                                                                              830","Debt Capacity and Capital Structure                            of the Companies Managed by Overconfident CEO                                               Elżbieta Wrońska-Bukalska                                              Maria Curie-Skłodowska University                                                    Faculty of Economics                                            Pl. M. C. Skłodowskiej 5, 20-031 Lublin                                               elzbieta.bukalska@umcs.lublin.pl                 Abstract: Overconfidence is one of the psychological biases that have impact on financial                decisions. Still, as for financing decisions there is no consensus on the direction of the                impact, and in assessing this - some research show using excessive debt while others debt-                conservatism. We, in turn, assume that inconsistency on the capital structure decisions of                overconfident managers might be explained by debt capacity of the their companies. We                think that companies managed by overconfident managers have lower debt capacity and,                at the same time, they use a higher debt ratio than their debt capacity allows. We collected                a unique sample composed of 145 non-listed companies and their financial data for the                2010-2015  period.  We  then  divided  the  sample  into  two  subsamples  depending  on                managerial overconfidence. We found that debt capacity is lower for companies managed                by  overconfident  managers.  There  are also  differences  in  sources  of  debt  capacity:  for                companies managed by overconfident managers the main source is size and tangibility.                Moreover,  companies  managed  by  overconfident  managers  have  lower  debt  ratio.  This                might indicate safe financing strategy, but using a low debt ratio means that companies                managed by overconfident managers are constrained to use equity because they exhaust                their debt capacity.                Keywords: overconfidence, capital structure, debt capacity                 JEL codes: G32, G40, D91                 1 Introduction                Overconfidence  is  one  of  the  biases  and  fallacies  that  affects  a  cognitive  process.                Overconfidence  can  have  some  serious  consequences.  Researchers  have  offered                overconfidence  as  an  explanation  for  medical  misdiagnosis,  mistaken  court  judgement                based on overconfident witnesses’ statements, road accidents, as well as the ill-considered                decisions  made  by  politicians  (Johnson,  2004).  The  notion  of  overconfidence  has  been                studied  since  60ies  of  the  XX  century.  Eventually,  overconfidence  was  identified  as  a                complex  phenomenon.  Moore  and  Healy  (2008)  identified  three  dimensions  of                overconfidence: (1) overestimation of one’s actual performance relative to an objective                standard, (2) overplacement of one’s performance relative to others, and (3) overoptimism                in one’s beliefs.                Much research has shown that overconfidence is an important factor affecting financial                decision-making and financial performance. Overconfident managers are convinced that                they have better knowledge and abilities than do others. This induces them to refrain from                obtaining  new  information,  to  not  listen  to  advice  or  expert  opinion.  If  overconfident                managers actually go gain pertinent information, they use it to confirm their own beliefs                (Paredes, 2004). Reluctance to gain new knowledge makes the overconfident managers                miss investment opportunity or miss the signs of failure (Goel and Thakor, 2008). Hence,                overconfident managers continue their course of action whatever its prospects are (Baker                et al., 2007). Overconfident managers find it difficult to learn from past experience and                correct their behavior (Heaton, 2002). They attribute any success to their own efforts and,                at the same time, they attribute failure to the actions of others. Overconfident managers                are  also  convinced  that  their  future  will  be  better  than  that  of  other  people;  they                overestimate  the  possibility  of  success  and,  at  the  same  time,  they  underestimate the                possibility of failure (Malmendier and Tate, 2005). The afore-mentioned might explain the                                                             831","overconfidence managers’ attitude toward risk – they underestimate risk and engage in                more risky activity (not because it is riskier but because the overconfident manager just                can not see risk).                Overconfident  decision-making has  been  observed  in  many  financial  decisions.  Indeed,                managerial  overconfidence  leads  to  higher  probability  of  failure  for  newly-established                companies (Russo and Schoemaker 1992, Cooper et al. 1988), as well as overinvestment                – even in negative NPV projects (Gervais et al. 2003; Malmendier and Tate, 2005).                Some  research  exists  on  the  capital  structure  of  overconfident  managers.  This  has                demonstrated statistically a significant relation between managerial overconfidence and                capital structure decisions.                Herein, one group of researchers has tackled static capital structure ratios and revealed                that  overconfident  managers  keep  higher  debt  ratios  than  do  managers  showing  no                overconfidence (eg. Barros and Silveira, 2008; Ben-David et al., 2007; Hackbart, 2009;                Park and Kim, 2009; Rihab and Lotfi, 2016). The explanation of higher debt ratios is that                overconfident managers underestimate the probability of failure and they can not see the                risk connected with the use of excessive debt.                A further group of researchers have examined the issue of dynamic decisions with regards                to the of capital by overconfident managers. With reference to this, Lin et al. (2005) have                confirmed that the importance of debt relative to equity use is more pronounced for the                overconfident CEOs. Here, the ratios of net debt issues to total external financing are 53%                and 49% for the optimistic and the non-optimistic, respectively. Furthermore, optimistic                CEOs tend to issue more debt than non-optimistic CEOs.                Yet  another  group  of  research  has  been  centered  upon  the  financing  decisions  of                overconfident managers. Malmendier et al. (2011), for example, found that overconfident                managers use lower debt ratios and called it debt conservatism. In addition, Hackbarth                (2009) found that overconfident managers with “risk perception bias” tend to believe that                their debt is undervalued, while equity is overvalued and, therefore, they prefer equity to                debt financing.                The lack of consistency in research findings on capital structure of overconfident managers                gives a good rationale for further research attempts to explain the financing decision of                companies managed by overconfident managers. With this in mind, we hold that the capital                structure  decisions  of  overconfident  managers  depend  on  the  debt  capacity  of  their                companies.                The aim of the paper is to identify the relation between debt capacity and capital structure                in companies managed by overconfident managers. We think that companies managed by                overconfident managers have lower debt capacity and, at the same time, they use a higher                debt ratio than their debt capacity indicate.                In  our  research,  we  collected  a  unique  sample  of  145  non-listed  companies  by                surveying the CEOs on their overconfidence. We subsequently divided the sample into                two subsamples depending on the overconfidence. We collected the financial data of                surveyed companies covering the 2010-2015 period. The total number of observations                is  870.  We  compared  debt  capacity  levels,  as  well  as  capital  structure  of  the                subsamples.                In our study, we employed the nonparametric U Mann Whitney test (for independent                subsamples). We also implemented regression analysis. We found partial confirmation                of our expectations.                Our  results  are  important  for  business  practice  (for  the  companies  owners  and                cooperating  institutions  –  eg.  banks).  This  is  because  there  is  quite  a  number  of                overconfident  people  who  are  in  managing  positions,  and  their  decisions  affect  the                financial  health  of  the  enterprise.  The originality of our research lies in implementing                thorough  research  not  only  on  the  capital  structure  of  the  companies managed  by the                overconfident managers but also on the overconfidence identification, on the debt structure                                                             832","and the factors affecting the capital structure (debt capacity). Such an approach is the first                known to us in assessing the relation between managerial overconfidence and financing                decisions.                 2 Debt capacity                Debt capacity was originally defined by Myers (1977) as the point at which an increase in                the use of debt reduces the total market value of the firm’s debt.  Debt capacity is the                maximum amount of debt that keeps the firm’s value increasing.                 Indeed,  investors  limit  credit  supply  if  they  expect  that  further  debt  issues  are  not                supported by a firm’s liquidity and profitability, and lenders protect their claims through                bond covenants and loan agreements if the company wants to exceed debt level. Corporate                managers have incentives not to use excessive debt financing to avoid costly renegotiations                with creditors and to maintain control over the firm. Hence, it is in their best interest to                stay below some critical amount of debt. Hess and Immenkotter (2014) call the critical                amount of debt which is determined by these various forces the firm’s debt capacity.                Firms that are most financially secure use the public debt market for financing. Such firms                have the greatest capacity to borrow, and they hold a very low probability of distress and                – and so it is cost effective for them to avoid the intermediation costs incurred with bank                debt. Firms that are more of a risk financially have a lower capacity to borrow, hence they                use the more flexible but nominally more expensive bank debt. Bank debt provides the                cheapest form of flexible financing. Firms that are at the greatest of risk financially are                constrained to use equity (or are unable to obtain any outside funding). Once firms exhaust                their debt capacity, they will eventually resort to equity.                Lemmon  and  Zender  (2010)  define  debt  capacity  as  having  a  credit  rating  and  the                possibility to issue  bonds in  a  public market.  The  presence  (or  absence)  of rated  debt                provides  an  indication  of  the  extent  to  which  a  firm  has  access  to  relatively  low  cost                borrowing and so suggests a relatively large (or small) debt capacity.                According to Lemmon and Zender (2010), a primary indicator of debt capacity is whether                the firm has, based on its underlying characteristics, a high likelihood of being able to                access the public debt markets. Firms that can access the bond market have cash flows                that  are  sufficiently  stable,  hold  sufficiently large  pools  of existing  collateral,  and  have                sufficient informational transparency to allow access to relatively large amounts of arms-                length  debt.  These firms  are  bigger,  have less  volatile  cash  flows,  have  more  tangible                assets, and thus they are likely to demand higher levels of debt financing because these                firms have rated debt.                 There are some factors, however, affecting the likelihood of having rated debt. Firms with                low  levels  of  collateral  have  relatively  low  demand  for  debt  financing  and  seek                intermediated  or  monitored  debt  while  firms  with high levels  of  collateral  use  direct  or                public debt. What is more, smaller and younger firms are likely to have a shorter track                record and be more opaque from the standpoint of lenders, suggesting that they will be                less likely to have bond ratings. Smaller firms also face proportionally higher fixed costs of                issuing bonds in the public debt markets. In contrast, large firms with their higher amount                of tangible assets and longer credit history usually have higher debt capacity. In addition,                companies with low volatility will be more likely to be able to obtain a bond rating. More                profitable firms are also better able to make required payments to debtholders and so can                support more debt. Moreover, firms with more debt outstanding have proven their ability                to borrow (Lemmon and Zender, 2010).                 3 Methodology                 It  is  quite  challenging  to identify  and  to  measure  overconfidence.  Usually, in cognitive                psychology,  overconfidence  is  identified  through  personal  survey.  Surveying  allows  to                identify beliefs and track the rationale for a specific behavior. However, when referring to                managers,  other  ways  of  identifying  overconfidence  were  developed.  By  far  the  most                                                              833","influential proxies for managerial overconfidence have been constructed by Malmendier                and Tate (2005). The proxies and dataset of the aforementioned have been used in many                other  studies  of  overconfidence:  based  on  options (longholder, holder  67),  shares  (net                buyer), and based on articles appearing in the press. Another method of identifying CEO                overconfidence is based on the frequency of upward-biased earnings forecasts (Lin et al.,                2005) or the frequency of M\&A made by managers (Doukas and Petmezas, 2007). A still                further  method  of  identifying  overconfidence  is  built  upon  higher  CEOs’  relative                compensations (Hayward and Hambrick, 1997). Methods commonly used to identify CEO’s                overconfidence allow to evaluate the overconfidence by behavior - not by beliefs. While                human behavior might sometimes result from the strategy implemented by the company                -  not  from  the  personal  beliefs  held  by  managing  personnel  (e.g.  frequency  of  M\&A).                Additionally, the stated measures might be applicable only to listed companies. To identify                and measure overconfidence, we followed the methodology of Wrońska-Bukalska (2016)                who  assumed  (after  Moore  and  Healey,  2008)  that  overconfidence  is  a  complex                phenomenon consisting of overestimation, overplacement and overoptimism. She used a                survey  to  identify  overconfidence  and  developed  an  original  method  of  overconfidence                measuring.  This  methodology  allows  to  identify  the  managerial  overconfidence  and                separate overconfident (OC) managers from the non-overconfident (nonOC).                To identify debt capacity, we followed the methodology of Lemmon and Zender (2010),                who prepared a logit regression model in which the dependent variable is one if a firm has                debt rating in a particular year and zero otherwise. The prepared logit regression predicts                bond ratings and they used a predictive model of whether a firm has a bond rating in a                given year as the primary indication of the extent of a given firm's debt capacity. In their                study, they identified several characteristics used in the logit regression: firm size (log of                total assets, Log TA), profitability (ROA), the fraction of total assets that are tangible (PPE),                the market to book ratio (MB), leverage (Lev), firm age (the natural log of the number of                years since the firm first appeared on Compustat, Log Age)), the variability of cash flow                (the standard deviation of stock returns, SdR). Their logit regression model for companies                with high bond rating is:                       Pr = −10.048 + 1.212 Log TA + 0.028 ROA − 0.136PPE − 0.077 MB + 3.917 Lev                            + 0.363 Log Age − 4.944 SdR                                                 (1)                which is based on the likelihood that a firm has access to public debt markets (Pr). If this                is so, the rating is 1. This logit regression model reveals the characteristics of a company                and the expected signs and all are significant predictors of the likelihood that a firm has a                bond rating. The model fit as measured by the pseudo R-squared is 52%.                We applied the regression model estimates that were presented in Lemmon and Zender’s                (2010)  model,  but  we  corrected  this  model  so  as  to  make  it  applicable  for  non-listed                companies. Ultimately, we implemented the variables as following: size of the company -                SIZE  –  log  of  Total  assets,  age  of  the  company  -  AGE  –  log  of  years  since  company                establishing, tangibility - TANG – the relation of fixed assets to total assets, profitability -                PROF – the relation of EBITDA to total assets, leverage – LEV – the relation of long-term                debt to total assets, variability of the cash flow - sdOCF – standard deviation of OCF, while                OCF is the relation of operating cash flow to total assets.                To measure capital structure we followed Rajan and Zingales (1995) and we implemented                same ratios describing the capital structure and the structure of debt and equity: Debt                ratio – DEBT - the relation of total liabilities to the sum of the equity and total liabilities =                1 – the relation of equity to total assets (the equity ratio), Equity to fixed assets – EQUITY                –  the  relation  of  equity  to  fixed  assets,  Share  capital  ratio  –  SHARE  -  the  relation  of                registered capital gained from owners in the relation to total assets, Reserved capital ratio                – RES - the relation of reserved capital (retained earnings) to total assets, Long-term ratio                – LONG- the relation of long-term liabilities to total assets, Short-term ratio – SHORT- the                relation of short-term liabilities to total assets.                The study sample we utilized comes from Polish companies that are non-listed. The data                refer to the companies that were willing to take part in the survey on overconfidence. Our                                                             834","research covers the specific features of managers (overconfidence) and financial data of                2010-2015 and includes only those companies that meet the following requirements: the                business being established before 2010, the business being run for the whole 2010-2015                period, president being the same for the whole period of 2010-2015, complete financial                statement being available, insurance and banking companies being excluded. We collected                145  surveys  and  were  able  to  divide  the  sample  into  two  samples:  non  overconfident                managers (nonOC – 78 companies and 468 observations) and overconfident managers (OC                – 67 companies and 402 observations).                4 Results and Discussion                 Table 1 presents the descriptive statistics of factors affecting debt capacity, but also the                descriptive statistics of debt capacity for the sample and subsamples.                  Table 1 The Descriptive Statistics of Factors Affecting Debt Capacity and Debt Capacity                              sample                nonOC                  OC                   U Mann                                                                                                Whitney                                                                                                statistics                               mean   median   SD    mean   median   SD    mean   median   SD                 SIZE           8.7     8.5    2.4    8.3     8.5    1.8    9.2    8.7    3.2    -1.630*                                                                                                 (0.103)                 TANG          34.0    28.8   0.28   21.6     8.7    0.24   43.6   41.4   0.29   -6.699***                                                                                                 (0.000)                 PROF          -2.2    2.63   0.43   4.36    3.34    0.16   -15.5   2.2   0.84   -2.621***                                                                                                 (0.009)                 LEV           9.48    0.15   0.22   7.18    0.79    0.12   9.84   0.03   0.13   -0.353                                                                                                 (0.724)                 AGE            18      17     8.7    17     16.5    5.8   19.5     18    12.2   -0.723                                                                                                  0.470                 OCF            5.6     6.2   0.67    6.9     7.1    0.27   -4.8   5.2    0.74   -0.804                                                                                                  0.422                 DEBT CAP      0.72    0.83    4.3   1.07    0.87    3.5   0.32    0.83   5.1    -1.583*                                                                                                  0.113                                 Source: own calculations based on data collected by surveying                Our results indicate that debt capacity is lower for companies managed by overconfident                managers.  This  is  consistent  with  our  expectations.  The  sources  of  debt  capacity  for                companies  managed  by  non-overconfident  and  overconfident  managers  are  different.                Companies managed by overconfident managers are bigger, older, have higher tangibility                and  lower  profitability.  What  is  the  most  important,  the  source  of  interest  coverage  –                EBITDA – is lower for companies managed by overconfident managers.                Table 2 presents the descriptive statistics of capital structure ratios for the sample and                subsamples.                Herein, it is noticeable that debt ratio is lower for companies managed by overconfident                managers.  This  is  quite  surprising  because  some  research  held  that  such  companies                demonstrated higher debt ratios (eg. Barros and Silveira, 2008). However, we conjecture                that this lower leverage might be explained by lower debt capacity (especially profitability).                This is contradictory to our expectations because we assumed that companies managed by                overconfident managers have higher debt ratios.                                  Table 2 The Descriptive Statistics of Capital Structure                            sample                nonOC                 OC                    U Mann                                                                                              Whitney                                                                                              statistics                             mean   median   SD    mean   median   SD   mean   median   SD                 DEBT        66.1    63.3   0.46   72.4    49.4   2.06   48.0   36.8    0.59   -4.663***                                                                                               0.000***                 EQUITY      5.61     1.6   17.5    7.8     3.1   17.9   2.6     1.3    3.5      -2.924                                                                                                 0.003                 SHARE       20.5     6.1   0.48   12,3     4.7   0.22   34.1    7.8    0.83   -3,435***                                                                                                 0.001                 RES          9.3    32.9   2.20   17,3    22.7   0.52   33.1   36.8    0.50    -2.559**                                                            835","0.011                 LONG        9.48    0.15   0.22   7.18    0.79   0.12   9.84   0.03    0.13     -0.353                                                                                                (0.724)                 SHORT       62.9    38.0   2.06   68.9    54.0   0.45   38.8   27.3    0.58   -6.078***                                                                                                (0.000)                                 Source: own calculations based on data collected by surveying                 That the debt ratio is much lower for companies managed by overconfident managers may                be support for the results drawn by Malmendier et al. (2011). Yet, as evident in the table,                at the same time the ratio of equity to fixed assets is lower. This shows that companies                managed by overconfident mangers implement more risky financing strategies. According                to  the  theory  proposed  by  Lemmon  and  Zender,  2010,  the  firms  at  greatest  risk  are                constrained to use equity because they have exhausted their debt capacity. It seems that                companies managed by overconfident managers use lower debt ratios because the position                of the company is too risky to obtain extra debt and they are forced to use equity, but this                equity is not enough to finance fixed assets.                We prepared two regression models that include overconfidence and debt capacity as the                independent variables and some controlling variables: tangibility, profitability, size, age,                operating cash flow in relation to total assets. The dependent variables are: debt ratio                (DEBT) and equity to fixed assets ratio (EQUITY), respectively.                       DEBT =   0+β1OC+ β2DEBT CAP+β3TANG+β4PROF+β5AGE+β6SIZE+β7OCF    (2)                        EQUITY =   0+β1OC+ β2DEBT CAP+β3TANG+β4PROF+β5AGE+β6SIZE+β7OCF (3)                Table 3 presents the regression analysis results.                        Table 3 Regression Models of Debt Ratio and Equity to Fixed Assets Ratio                 Independent\\Dependent                     DEBT                         EQUITY                 variable                 OC                                 -0.198; 0.001***               -1.080; 0.031**                 DEBT CAP                             0.393; 0.063*                -5.365; 0.002**                 TANG                                 -0.390; 0.171               -16.105; 0.000***                 PROF                                -0.505; 0.008**                3.000; 0.056*                 AGE                                  -0.091; 0.632                  -0.794; 0.616                 SIZE                               -0.183; 0.000***                0.800; 0.036**                 OCF                                   0.079; 0.513                  -0.462; 0.644                 F statistics; p-value                 7.495; 0.000                 12.450; 0.000                 R square                                  0.068                         0.108                                 Source: own calculations based on data collected by surveying                 As seen in the table, the debt ratio is negatively related with overconfidence. This means                that higher overconfidence is accompanied by lower debt ratio. Moreover, the debt ratio is                positively related with debt capacity - which reveals that the higher debt ratio the higher                debt capacity.                 Furthermore, as evident, the relation of equity to fixed assets is negatively related with                overconfidence.  This  means  that  the  higher  overconfidence  the  lower  the  equity  ratio                (because tangibility is higher). Still, the ratio is negatively related with debt capacity, which                implies  that  the  higher  the  debt  capacity,  the  lower  relation  of  equity  to  fixed  assets                (because companies with higher debt capacity are able to get extra debt and do not need                equity). Again, companies managed by overconfident managers seems to take on more                risk, yet the companies holding the greatest risk are constrained to use equity because                they have exhausted their debt capacity (Lemmon and Zender, 2010). Thus, companies                managed by overconfident managers use lower debt ratios because they are in too great                a position of risk to obtain extra debt and they are forced to use equity - but this equity is                not enough to finance fixed assets.                                                               836","5 Conclusions                We aimed to find the relation between debt capacity and capital structure for companies                managed  by  overconfident  managers.  We  found  that  such  companies  have  lower  debt                capacity - which means that they are able to obtain less debt and they have to rely on                equity. Our research also shows that companies managed by overconfident managers have                lower debt ratios - which demonstrates that they utilize more equity. At the same time,                companies  managed  by  overconfident  managers  while  employing  more  equity,  have  a                lower relation of equity to fixed assets. Thus, the debt capacity of the companies managed                by  overconfident  managers  depends  more  on  tangibility than  on  profitability and  other                factors. Still, this tangibility is so high that higher equity is not sufficient to finance fixed                assets.                We also found support for the notion that companies managed by the overconfident have                lower  debt  ratios  (Malmendier  et  al.,  2011),  but  we  would  not  call  this  practice  “debt                conservatism”. Herein, lower debt ratios by such companies are justified by having lower                debt capacity and holding higher risk. The firms displaying the greatest risk are constrained                to  use  equity  because  they  have  exhausted  their  debt  capacity  (Lemmon  and  Zender,                2010).  Thus,  we  are  more  prone  to  agree  with  Hackbarth  (2009)  who  found  that                overconfident managers demonstrate “risk perception bias” and they believe that their debt                is undervalued, while equity is overvalued and, therefore they prefer to utilize equity to                debt financing.                 References                 Baker, M., Ruback, R. S., Wurgler J. (2007). Behavioral Corporate Finance: a Survey. In:                Eckbo,  B.  E.,  ed.,  Handbook  of  Empirical  Corporate  Finance.  Amsterdam,  Holland:                Elsevier/North, pp. 145–186.                Barros,  L.  A.  B.  C.  de,  Miceli  da  Silveira,  A.  di.  (2008).  Overconfidence,  Managerial                Optimism and the Determinants of Capital Structure. Brazilian Review of Finance, vol 6(3),                pp. 293–335.                 Ben-David,  I.,  Graham,  J.  R.,  Harvey,  C.  R.  (2007).  Managerial  Overconfidence  and                Corporate Policies. NBER Working Paper, no. 13711.                Cooper, A. C., Woo, C. Y., Dunkelberg, W. C. (1988). Entrepreneurs’ Perceived Chances                for Success. Journal of Business Venturing, vol. 3(2), pp. 97–108.                Doukas,  J.  A.,  Petmezas,  D.  (2007).  Acquisitions,  Overconfident  Managers  and  Self-                attribution Bias. European Financial Management, vol. 13(3), pp. 531–577.                 Gervais,  S.,  Heaton,  J.  B.,  Odean,  T.  (2002).  The  Positive  Role  of  Overconfidence  and                Optimism in Investment Policy. Rodney L. White Center for Financial Research Working                Papers, no. 02.15.                Goel,  A.  M.,  Thakor,  A.  V.  (2008).  Overconfidence,  CEO  Selection,  and  Corporate                Governance. Journal of Finance, vol. 63(6), pp. 2737–2784.                Hackbarth,  D.  (2009).  Determinants  of  Corporate  Borrowing:  A  Behavioral  Perspective.                Journal of Corporate Finance, vol. 15(4), pp. 389–411.                 Hayward,  M.  L.  A.,  Hambrick,  D  .C.  (1997).  Explaining  the  Premiums  Paid  for  Large                Acquisitions:  Evidence  of  CEO  Hubris.  Administrative  Science  Quarterly,  vol.  42(1),                pp. 103–127.                Heaton, J. B. (2002). Managerial Optimism and Corporate Finance. Financial Management,                vol. 31, pp. 33–45.                 Hess, D., Immenkötter, P. (2014). How Much Is Too Much? Debt Capacity and Financial                Flexibility. SSRN Working Paper, no. 1990259.                 Johnson,  D.  D.  P.  (2004).  Overconfidence  and  War:  The  Havoc  and  Glory  of  Positive                Illusions. Cambridge: Harvard College.                                                             837","Lemmon, M. L., Zender, J. F. (2010). Debt Capacity and Tests of Capital Structure Theories.                The Journal of Financial and Quantitative Analysis, vol. 45(5), pp. 1161-1187.                 Lin, Y. H., Hu, S. Y., Chen, M. S. (2005). Managerial Optimism and Corporate Investment:                Some Empirical Evidence from Taiwan. Pacific-Basin Finance Journal, vol. 13, pp. 523–546.                Malmendier, U., Tate, G. (2005). CEO Overconfidence and Corporate Investment. Journal                of Finance, vol. 60(6), pp. 2661–2700.                Malmendier, U., Tate, G., Yan, J. (2011). Overconfidence and Early‐Life Experiences: The                Effect of Managerial Traits on Corporate Financial Policies. Journal of Finance, vol. 66(5),                pp. 1687–1733.                 Moore, D.A., Healy, P.J. (2008). The Trouble with Overconfidence. Psychological Review,                vol. 115, pp. 502–517.                Myers, C. (1977). Determinants of Corporate Borrowing. Journal of Financial Economics,                vol. 5, pp. 147-175.                Park,  C.,  Kim,  H.  (2009).  The  Effect  of  Managerial  Overconfidence  on  Leverage.                International Business \& Economics Research Journal, vol. 8(12), pp. 115–126.                Paredes, T. A. (2004). Too Much Pay, Too Much Deference: Is CEO Overconfidence the                Product of Corporate Governance?. Washington U. School of Law Working Paper, no. 04-                08-02.                Rajan, R.  G.,  Zingales,  L.  (1995).  What  Do  We  Know  About  Capital  Structure?  Some                Evidence from International Data. Journal of Finance, vol. 50(5), pp. 1421-1460.                Rihab, B. A., Lotfi, B. J. (2016). Managerial Overconfidence and Debt Decisions. Journal of                Modern Accounting and Auditing, vol. 12(4), pp. 225-241.                Russo,  J.  E.,  Schoemaker,  P.  J. H.  (1992).  Managing  Overconfidence.  Magazine:  Sloan                Management Review, vol. 33(2), pp. 7–17.                 Wrońska-Bukalska, E. (2016). Overconfidence of Students and Managers – Comparative                Analysis. Proceedings of the Economics \& Finance Conferences, pp. 349-361.                                                                                                   838","Application of Selected Machine Learning Methods to Companies’                                               Insolvency Prediction                                                     Joanna Wyrobek                                               The Cracow University of Economics,                                              Department of Corporate Finance                                              Rakowicka St. 27, 31-510 Kraków                                                  wyrobekj@uek.krakow.pl                 Abstract: The purpose of the paper was to test the efficiency of various modern machine                learning methods on the representative sample of Polish companies for the time period                2008 – 2017. The novelty factor in the paper is that it uses a representative sample of                companies,  which  seems  to improve the  efficiency  of  the  models  and  that  the  training                sample and the validation sample include data from different time periods and different                companies (the training sample data covered the period of 2008 – 2013 and the validation                sample covered the period 2014 – 217). The hypothesis verified in the paper is [H1] that:                the  most  efficient  algorithms  in  bankruptcy  prediction  are:  Gradient  Boosting  Decision                Trees and Random Decision Forest.                Keywords: machine learning, bankruptcy prediction, corporate finance                JEL codes: G33, C55, C63                 1 Introduction                 According  to  Lewis    (Lewis,  2017),  ‘Machine  learning  is  a  collection  of  algorithms  that                generate insight from data. That insight might be used by humans or other machines to                make  a  decision’.  In  other  words,  machine  learning  involves  machines  ‘learning  from                examples, rather than being explicitly programmed for a particular outcome’ (Brynjolfsson,                Mcafee, 2017, p. 5) which are capable of either forecasting future outcomes or classifying                objects into classes (Lewis, 2017).                 Currently, machine learning is one of the most popular research areas in banks, various                sorts of financial institutions, international corporations, and ministries (judging by the job                advertisements). Examples of current research directions include (Brynjolfsson, Mcafee,                2017,  p.  5)  robo-advisory,  i.e.,  automatic  systems  that  advise  on  asset  management,                artificial intelligence trading, risk management, insurance underwriting and claims, i.e.,                personalized underwriting. Another popular application of machine learning techniques is                bankruptcy prediction (Barboza, Kimura, Altman, 2017, p. 406).                 Generally speaking, bankruptcy can be defined as a legal term meaning the situation when                a physical or legal person cannot repay its obligations. Bankruptcy is a legal status, which                can be only imposed by court order and can be initiated either by a debtor or the person                itself.                 In line with the current regulations of the Polish law (Bankruptcy and Insolvency Act from                the  28th  of  February  2003),  bankruptcy  (pl.  upadłość,  bankructwo)  (Bankruptcy  and                Insolvency  Act from the  28th  of  February  2003,  paragraphs:  10,  20) is  defined  as  the                proceedings commenced by way of Bankruptcy Application to the High Court concerning a                company that became insolvent in order to commence a joint investigation of all claims by                its creditors. Since in the Polish law bankruptcy is the result of insolvency, it is important                to define also the term insolvency. According to the Polish Bankruptcy and Insolvency Act                from the 28th of February 2003 paragraphs: 11 and 12, a company is considered insolvent                in two cases:                Case 1: it is presumed that a company is insolvent if a company is unable to meet its                obligations and the delay in payment is longer than three months, or                                                                 839","Case 2: it is presumed that a company is insolvent if the balance sheet value of all its                liabilities (without provisions and liabilities towards its subsidiaries) exceeds the value of                its assets and this condition persists for a period longer than 24 months.                In any of the above cases, the insolvency situation means that a company is unable to pay                back its obligations and if any liabilities are going to be repaid, it is going to take a long                time and it is not certain. This is why the financial situation of any business partners and                clients is carefully checked by a company to make sure that it is not giving trade credit or                any other forms of a loan to a partner with a high risk of bankruptcy.                The purpose of the paper was to test the usefulness of various modern machine learning                techniques  in  the  Polish  companies’  bankruptcy  prediction.  In  particular,  the  following                algorithms were tested: support vector machines (SVM), Random Forest (RF), Gradient                Boosting Decision  Trees  (GB),  Neural Network  with  1  hidden layer  (NN),  Convolutional                Neural Network (CNN) and Naïve Bayes (NB). The research hypothesis was [H1]: the most                efficient  algorithms  in  bankruptcy  prediction  are  Gradient  Boosting  Decision  Trees  and                Random  Decision  Forest.  Contrary  to  many  publications  (compare  remarks  about  the                research method in Appendix 1) the paper clearly divides the dataset into a training sample                (which  is  further  divided  into  the  train  sample  and  test  sample  within  10  fold  cross-                validation procedure) and a validation sample to test the efficiency of the trained models                on a separate sample of companies and separate time period.                 2 Related Works                Table 1 presents a brief summary of previous results concerning the accuracy of machine                learning methods in bankruptcy prediction. The list of papers is presented in the Appendix                1, Table 1 only shows a brief summary of the average accuracy for different algorithms                (based  on  these  papers).  Only  1  paper  in  the  list  used  a  representative  sample  of                companies – it was the paper written by Min and Jeong (Min, Yeong, 2009).                 As it can be seen from Table 1, only naïve Bayes had accuracy above 95% but the average                accuracy was based only on 2 papers, so it is not very reliable.                      Table 1 Summary of the Previous Research Concerning the Accuracy of Selected                                    Machine Learning Methods in Bankruptcy Prediction                              Technique       Base Classifiers  Av. Accuracy  No of papers                             Single model            DT              80,05            19                             Single model            NN              85,3              7                             Single model       Naive Bayes          95,25             2                             Single model           SVM              81,13            21                             GB                      DT              82,93             7                             Random Forest           DT              89,37             4                                                    Source: own research                 Among the publications listed in Appendix 1, one can find some papers which were based                on  Polish  companies.  Pawełek  and  Grochowina  (2017)  used  a  sample  of  42  bankrupt                companies  and  7181  active  companies  and  applied  the  techniques  described in  Gatnar                (2008, p. 63) to train (among other models) the Random Forest model. The best accuracy                the researchers managed to achieve was found for the unbalanced sample of companies                and it was equal to 90,3%. Another publication based on Polish companies is the book by                Pociecha, Pawełek, Baryła and Augustyn (2014) who (among other methods) used 3-layer                neural (input, hidden, output) networks on the sample of 7147 active companies and 182                bankrupt companies. The best accuracy of their models was equal to 78,6%.                Another publication which used Polish data and machine learning algorithms in bankruptcy                prediction was the book written by Korol and Prusak (2014). Korol and Prusak’s models                were trained on 39 bankrupt companies and 39 active companies. Korol and Prusak used                                                              840","many different neural networks, one of them was a neural network with 1 layer of hidden                neurons. Researchers tested different combinations of neurons in the input layer and in                the hidden layer.  For  10:1  proportions  between the  training  and  validation  sample  the                researchers achieved 100% of the accuracy of their model.                A  list  of  analyzed  previous  publications  in  Appendix  1  shows  a  brief  description  of  the                validation technique used in each paper As it can be seen, not always the researchers used                a separate validation sample and in many cases it was unclear what they meant by the                “test sample” – whether it was an internal division for the purpose of the cross-validation                or whether the training sample was used for training and the test sample was used only                for  the  validation  of  the  already  trained  model.  Only  in  2  papers  the  authors  clearly                explained  that  the  validation  sample  was  not  used  for  training  and  that  the  validation                sample comprised companies different to companies included in the training sample and                from the different time period. In many cases, the description of the model validation was                unclear and too general.                 Another important remark about the previous research is that all analyzed publications                used  for  model  training  only  a  selected  number  of  financial  ratios  and  non-financial                measures. On one hand, this approach allowed to train different machine learning  models                on exactly the same variables. On the other hand, however, decision trees based models                (specifically:  RF,  GBDT)  and  neural  network  methods  deal  very  well  with  correlated                features (independent variables) and work very well on data (after normalization) taken                directly from a financial statement. They create their own ratios as a result of their training.                Therefore, it was possible that the approach adopted in the previous papers did not allow                to fully test the capabilities of decision trees based models and neural networks.                 3 Model Specification                Dataset chosen for the research included years 2008 – 2017 and initially included 147 340                companies (all of the companies in the data sample used double-sided accounting). Data                were downloaded from the database Orbis which belongs to Bureau Van Dijk company.                Data included the balance sheet (statement of financial position) and the income statement                elements. Data were tested whether the sum of assets was equal to the sum of equity and                liabilities. Moreover, any suspicious or wrong records or columns were also removed. The                final  sample  included  1415  bankrupt  companies  and  1450  active  companies.  Active                companies were randomly chosen, but we tried to match the type of the economic activity                between  each  bankrupt  company  and  a  similar  active  ‘sister’  company.  As  a  company                status change date (active→bankrupt or active->insolvent), we assumed the year when                the company had a negative equity for the first time (although according to the Polish law                it was overzealous, we should take the next year) or the Orbis company status change                data, whichever was first (if the latter was available in the database). In relation to the                assumed status change date, we assumed that the insolvency petition must have been                filed  one year earlier  and  that the  model  should  be  able  to  warn  about the insolvency                petition yet another year earlier (a similar approach was adopted in Aktan 2011, p. 57).                Therefore, the time difference between the status change date and the moment when the                model should signalize upcoming problems was at least 2 years (for the companies where                we  estimated  the  status  change  date  based  on  negative  equity,  the  real  moment  of                insolvency announcement by the court could have been later). What is worth mentioning                is  that  we  considered  as  bankrupt  or  insolvent  only  these  companies,  which  had                appropriate  status  in  the  Orbis  database  (analysis  of  the  dataset  showed  that  not  all                companies with negative equity were bankrupt or insolvent, but all bankrupt companies                had negative equity).                The data sample was then divided into the training set including the companies data for                years 2008-2013 and the validation set which included the companies data for years 2014-                2017. Companies in each data set were different (companies in the validation set did not                belong to the training set).                                                               841","After validity checks and preprocessing, the data sample was normalized. We also applied                the one-hot encoding to discrete data. For preprocessing, we took advantage of the skleran                Python library. Training data sample was divided into 10 parts and for each iteration, we                used 9 parts for training and 1 part for testing (cross-validation).                 As it was explained earlier, the following models were trained (all with skleran library):                support  vector  machines  (SVM),  Random  Forest  (RF,  100  trees),  Gradient  Boosting                Decision Trees (GB), Neural Network with 1 hidden layer (NN, the number of input neurons                and  in  the  hidden  layer  was  equal  to  the  number  of  variables),  Convolutional  Neural                Network (CNN) and Naïve Bayes (NB). The code was written in Jupyter Notebook (Python                version 3.6). Application method was based on the following publications: Rashka (2015),                Kirk (2017), Mueller and Guido (2016), Tripathi (2017), Lantz (2015), Lesmeister (2015),                Julian (2016), Swamynathan (2017), Lewis (2017), Bali and Sarkar (2016), Viswanathan                (2016), Chiu (2015).                For the Gradient Boosting Decision Trees model, the number of estimators was assumed                to be 100, the function which measured the quality of split was mean squared error with                improvement  score  by  Friedman, learning  rate  was 10%,  the improvement  of  the loss                function was calculated as the deviation between the value for the out-of-the-bag samples                and  the  value  for  the  previous  iteration.  Maximum  depth  of  the  individual  regression                estimators was set to 3.                For the neural network (NN) model we assumed 100 trees and there were 681 features                (number of features was the same for all models – it represents the number of variables                after one-hot encoding). As the activation function for the Random Forest model, we used                Scaled Exponential Linear Unit (but we also tested ReLU). As the initializer, we used the                LeCun uninform initializer.                The  convolutional  neural  network  (CNN)  was  also  based  on  SeLU  function  and  LeCun                uinformed initializer and sigmoid activation function. The loss function was binary cross-                entropy. The Random Forest (RF) model was based on 100 decision trees, nodes were split                based on the Gini criteria.                 4 Results and Discussion                Table 2 shows the results of the model training. In Table 2 one can find accuracies (its                definition can be found in Nagaraj, Sridhar, 2015 p.35) calculated for every training subset,                the  average  training  accuracy  and  finally,  the  accuracy  coefficient  calculated  for  the                validation sample. The last coefficient should be considered as the most important measure                of the predictive power of the models because it shows the efficiency of the model for the                companies and years which were not previously shown to the model.                As it can be seen, the calculated accuracy coefficients for every training subset were quite                similar and except for the RF, the validation sample accuracy was always lower than the                average training accuracy.                        Table 2 Comparison of the Accuracy of SVM, RF, GB, NN, CNN and NB Models                              training     SVM     RF im    GB im    NN im    CNN im     NB im                              subset      im acc    acc      acc       acc       acc       acc                                 1        70,51    99,52    99,52    83,83     83,95     72,53                                 2        72,41    99,41    99,52    83,35     83,83     70,75                                 3        69,56    99,88    99,76    85,97     82,28     74,44                                 4        68,37    99,76    99,88    83,47     82,05     74,55                                 5        69,17    99,17    99,64     80,6     80,83     72,98                                 6        70,95    98,93    98,93    81,55     82,14     71,31                                 7        69,17    99,17    99,76    84,52     81,55     73,69                                 8        68,57    99,76    99,76    84,64     83,93     75,36                                                              842","training     SVM     RF im    GB im    NN im    CNN im     NB im                              subset      im acc    acc      acc       acc       acc       acc                                 9         67,5    99,05    99,05    82,26      83,1     71,79                                10        72,86    99,76    99,88    87,14      82,5     72,26                             average      69,91  99,44      99,57    83,73     82,62     72,97                            validation                              sample      69,54  98,91      99,11    83,35     82,23      72,6                                                    Source: own research                 As it can be seen in Table 2, the most accurate model proved to be the gradient boosting                decision trees. The validation sample accuracy of the model was 99,11%, which means,                that for 1000 companies, on average, the model would wrongly classify (active as bankrupt                or bankrupt as active) only 9 companies. The second best validation accuracy could be                observed for the random forest model with the accuracy equal to 98,91%. In terms of                classifying 1000 companies between bankrupt and non-bankrupt classes, on average, the                model would be wrong for 11 companies.                 The third best model turned out to be the neural network with 1 hidden layer with the                validation sample accuracy equal to 82,23%. According to Table 2, the fourth best model                in terms of validation accuracy was the Naïve Bayes model with the accuracy of 72,6%.                The least efficient model was the Support Vector Machines model with the accuracy equal                to 69,54%. If one assumes a standard confidence level p = 5%, only two models had the                confidence level below 5%: it was the Gradient  Boosted Decision Trees model and the                Random Forest Decision Trees model.                Even though it should not be necessary, we tested whether the calculation of financial                ratios  based  on  the  available  raw  data  from  financial  statements  would  improve  the                accuracy of the models, but it turned out that algorithms efficiently created all necessary                ratios internally, without the intervention of the programmer.                5 Conclusions                 Efficient bankruptcy prediction is extremely important for every participant in the market                system. Based on the presented model training results, it seems that modern computer                science created at least two very efficient tools which work almost out of the box – gradient                boosted decision trees and random forest (also based on decision trees) models. Presented                research was designed to observe the behavior of various machine learning methods when                they were trained with a representative sample of the companies.       Despite   the  fact                that the training sample was based on different companies from different years than the                validation  sample,  at  least  two  models  –  the  GBDT  and  the  RFDT  proved  to  be  very                accurate. The relatively low accuracy of other analyzed machine learning models must be,                however, treated with caution, because there are many variants of these models which                potentially can significantly change their accuracy.                Decision trees are often considered as the algorithm which is not dealing well with time. In                other words, decision trees are considered to be very precise in classifying new companies                (companies which were not shown previously to the model) but from the same time period                which was used to train the model and not very good with classifying new companies from                new time periods (time period which representatives were not used to train the model                before).                Presented results seem to contradict this opinion. It was exactly the decision-tree based                models which predicted almost perfectly which companies would go bankrupt and which                companies would remain active in the validation sample which contained years beyond the                time period used for their training.                                                                  843","References                Aktan S. 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Appendix                       Table 3 List of publications where the authors used SVM, RF, GB, NN, CNN or NB                                     algorithms for companies’ bankruptcy prediction                                                         Base                 Studies                Techniques                 Accuracy(%)                                                      classifiers                    Validation method                 Ghodselahi,                GB            DT           72,77                 Amirmadhi (2011)                                                  10 fold cross-validation                 Kim Kang (2012)            GB            DT            75,1       10 fold cross-validation                 Kim Kang (2012)            GB            DT             76        10 fold cross-validation                 Heo and Yang (2014)        GB            DT           78,52       10 fold cross-validation                 Kim and Upneja                 (2014)                     GB            DT            98,1           80/20 partition                 Marques, Garcia, and       GB            DT           75,42                 Sanchez (2012)                                                        80/20 partition                 Sun, Jia, and Li                 (2011)                     GB            DT           96,46        5 fold cross-validation                 Alfaro, Garcia, Games,                                              5 and 4 fold cross-                 and Elizondo (2008)        GB            DT            91,1             validation                                                                     846","449 active and bankrupt                                                                                  companies in the training                                                                                    set, 133 bankrupt and                 Barboza, Kimura,           RF            DT           87,06       13300 active companies                 Altman (2016)                                                     in the test set, different                                                                                   years and companies, no                                                                                     normalization, only                                                                                   selected financial ratios                 Liao, Shih, Chen, and                 Hsu (2014)                 RF            DT           94,91         60/20/20 partition                 Nagaraj, Sridhar           RF            DT            97,4                 (2015)                                                             80/20, 70/30 partition                 Pawełek, Grochowina                 (2017)                     RF            DT            90,3        5 fold cross-validation                 Korol (2010)               RF            DT           88,88        5 fold cross-validation                 Tsai, Cheng (2012)     Single Model      DT            67,6        5 fold cross-validation                 Aktan (2011)           Single Model      DT            87,2           50/50 partition                                                                                     17 bankrupt and 17                 Alfaro, Garcia, Games,   Single Model    DT           87,29       active banks for training                 and Elizondo (2008)                                                                                     and validation sets                 Cho, Kim, and Bae      Single Model      DT           72,38                 (2009)                                                             5 fold cross-validation                 Cho, Hong, Ha(2010)    Single Model      DT           66,73        5 fold cross-validation                 Geng, Bose, and Chen   Single Model      DT            74,4                 (2015)                                                                80/20 partition                 Ghodselahi,                 Amirmadhi (2011)       Single Model      DT           70,35            not explained                 Heo and Yang (2014)    Single Model      DT            73,1            not explained                 Kim Kang (2012)        Single Model      DT            70,3            not explained                 Kim and Upneja                 (2014)                 Single Model      DT             97            80/20 partition                 Lee (2018)             Single Model      DT            73,6           80/20 partition                 Liao, Shih, Chen, and                 Hsu (2014)            Single model       DT           94,68           80/20 partition                 Marques, Garcia, and                                             80/20, 70/30, 60/40 and                 Sanchez (2012)         Single Model      DT           68,75           50/50 partition                 Min J., Jeong C.       Single Model      DT            76,8                 (2009)                                                            10 fold cross-validation                 Ramakrishnan,                 Mirzaei, Naveed        Single Model      DT           74,79                 (2015)                                                            10 fold cross-validation                                                                                   10 fold cross-validation,                 Sun and Li (2009)      Single Model      DT            80,9       the paper mentions the                                                                                      validation sample                                                                                   10 fold cross-validation,                 Sun, Jia, and Li                 (2011)                 Single Model      DT           96,54       the paper mentions the                                                                                      validation sample                                                                                   10 fold cross-validation,                 Sun, Jia, and Li       Single Model      DT           96,54       the paper mentions the                 (2011)                                                                                      validation sample                                                                                   10 fold cross-validation,                 Tsai (2014)            Single Model      DT           83,07       the paper mentions the                                                                                      validation sample                 Aktan (2011)           Single Model   Naive Bayes      92,2           50/50 partition                 Nagaraj, Sridhar       Single Model   Naive Bayes      98,3                 (2015)                                                                50/50 partition                                                                                    test set: 2010-2014,                 Tsai, Cheng (2012)     Single Model      NN            73,9                                                                                  validation set: 2013-2015                                                             847","Aktan (2011)           Single Model      NN             90         5 fold cross-validation                 Blanco-Oliver et al    Single Model      NN            82,7                 (2015)                                                             5 fold cross-validation                 Ecer (2013)            Single Model      NN           97,06        5 fold cross-validation                 Ghodselahi,            Single Model      NN             74                 Amirmadhi (2011)                                                   5 fold cross-validation                 Hu, Tseng (2007)       Single Model      NN           81,78        5 fold cross-validation                 Nanni, Lumini (2009)   Single Model      NN           82,95         60/20/20 partition                 Korol and Prusak       Single Model      NN            100       60/20/20 partition (5 fold                 (2015)                                                               cross-validation)                                                                                   66/33 partition (10 fold                 Pociecha et al (2014)   Single Model     NN           95,83                                                                                      cross-validation)                                                                                   66/33 partition (10 fold                 Tsai, Cheng (2012)     Single Model      SVM            77                                                                                      cross-validation)                                                                                   66/33 partition (10 fold                 Aktan (2011)           Single Model      SVM           92,7                                                                                      cross-validation)                 Altinirmak, Karamasa                                                 70/30 partition, 3                 (2016)                 Single Model      SVM           91,9          different data sets                 Arieshanti et at       Single Model      SVM          70,42          70/30 partition, 3                 (2013)                                                               different data sets                                                                                   66/33 partition (10 fold                 Chaudhuri, De (2011)   Single Model      SVM          74,75                                                                                      cross-validation)                                                                                   train set: 145, validation                 Cho, Kim, and Bae      Single Model      SVM          78,01        set: 89 (10 fold cross-                 (2009)                                                                                         validation)                 Geng, Bose, and Chen   Single Model      SVM           71,1          70/30 and 60/40                 (2015)                                                                   partition                 Ghodselahi,                 Amirmadhi (2011)       Single Model      SVM            71             not explained                 Heo and Yang (2014)    Single Model      SVM          73,29            not explained                                                                                   66/33 partition (10 fold                 Kim Kang (2012)        Single Model      SVM          72,45                                                                                      cross-validation)                 Marques, Garcia, and   Single Model      SVM          71,25       66/33 partition (10 fold                 Sanchez (2012)                                                       cross-validation)                                                                                   66/33 partition (10 fold                 Min, Lee (2005)        Single Model      SVM          88,01                                                                                      cross-validation)                 Nagaraj, Sridhar       Single Model      SVM           99,6       66/33 partition (10 fold                 (2015)                                                               cross-validation)                                                                                   66/33 partition (10 fold                 Nanni, Lumini (2009)   Single Model      SVM          80,74                                                                                      cross-validation)                 Pena, Martinez, Abudu   Single model     SVM           82,3       66/33 partition (10 fold                 (2009)                                                               cross-validation)                 Ramakrishnan,                 Mirzaei, Naveed        Single Model      SVM          84,79       66/33 partition (10 fold                 (2015)                                                               cross-validation)                                                                                   66/33 partition (10 fold                 Sun and Li (2009)      Single Model      SVM          82,64                                                                                      cross-validation)                 Sun, Jia, and Li       Single Model      SVM          96,06                 (2011)                                                             5 fold cross-validation                 Sun, Jia, and Li                 (2011)                 Single Model      SVM          96,06       10 fold cross-validation                 Sun and Li (2012)      Single Model      SVM          81,16       10 fold cross-validation                 Xiao, Yang, Pang, and                 Dang (2012)            Single Model      SVM           64,4       10 fold cross-validation                                                                                       Train set: 80%,                 Ecer (2013)            Single Model     SVMs          85,29                                                                                     validation set: 20%                                                               848","Methodical Approaches to Assessing the Budget Potential of the                  Region Taking into Account the Innovative Development of High-                                                   tech Industries                                    1                                                         2                                                                                               3                  Sergey Yashin , Nadezhda Yashina , Nataliya Pronchatova-Rubtsova , Oksana                                                         Kashina  4                                    1, 2, 3, 4  Lobachevsky State University of Nizhny Novgorod                Institute of Economics and Entrepreneurship,  Management and Public Administration Department,                                                          1                                            2, 3, 4  Department of Finance and Credit                          Bolshaya Pokrovskaya street 37, 603000 Nizhny Novgorod, Russian Federation                  E-mail:  jashinsn@yandex.ru,  sitnicof@mail.ru,  pronat89@mail.ru,  oksana_kashina@mail.ru                                              2                         1                                                                                  4                                                               3                 Abstract: The basis for successful social and economic regional development is a stable                and flexible financial system. It is required to conduct state financial policy, taking into                account the innovative character of changes in all spheres of life. It is necessary to find a                balance  between  the  effective  functioning  of  the  budget  system  in  the  conditions  of                economic volatility, limited financial resources, fierce competition and often unavailability                of borrowed funds and the inevitable transition to the model of innovative development in                the knowledge economy. The study aims at developing a methodology for assessing the                region’s  budget  potential,  taking  into  account  the innovative  development  of high-tech                industries to increase the population’s well-being. The authors developed a methodology                for assessing the region’s budget potential, taking into account the innovative development                of high-tech industries based on a number of principles and a specially developed system                of indicators. Indicators should comprehensively characterize the region's budget potential                from  the  standpoint  of  financial  sustainability  and  the  region's  economic  development                through high-tech industries. It is proposed to form two blocks of indicators: \"Revenues,                Economy,  Innovations\"  and  \"Expenses,  Socio-Economic  Indicators\".  The  methods  of                economic,  system  analysis  and  mathematical  statistics  were  used.  Approbation  of  the                methodology was carried out on the official open data of the Federal State Statistics Service                and the Federal Treasury of Russia for 2016. Practical implementation of the methodology                makes it possible to rank the regions on the basis of a consolidated standardized indicator                of  the  region’s  budget  potential  including  the  innovative  development  of  high-tech                industries.  Monitoring  the  rank  and  analysis  of  the  numerical  value  of  the  region's                consolidated indicator allow: to establish that the problems of the region's budget potential                development are related to the lack of a developed innovation environment and a unified                productive  system  for  managing  budget  resources;  to  develop  effective  managerial                decisions considering innovative character’s transformations to increase the population’s                well-being.                Keywords: budget potential, innovative development, high-tech industries, consolidated                standardized indicator, rating                JEL codes: H2, H5, H6, O3, H7                 1 Introduction                 According to the Concept of long-term social and economic development of Russia until                2020,  the  expected  new  wave  of  technological  changes  is  to  strengthen  the  role  of                innovations  in  the  social  and  economic  development  and  reduce  the  impact  of  many                common  growth  factors.  The  speed  and  quality  of  transfer  from  the  exportable  raw                materials to the innovative path of development to a large extent are determined by the                efficiency of scientific and technological activities. In the context of the knowledge economy                it is exactly  strengthening  of  comparative  advantages in  the  scientific,  educational  and                high-tech  fields  that  will  allow  to  involve  new  sources  of  economic  growth,  ensure                progressive  development  of  the  society  and  transfer  to  a  new  technological  paradigm,                which in the long run will result in growth of the population’s welfare and well-being.                 The research investment level is commonly considered an indicator of the maturity of an                                                             849","economy. Expenditures should be understood as financial and human resources required                to carry out this activity. The result is new knowledge, technologies, inventions, theories                and discoveries. The emergence of the ‘expenditures and results’ model inevitably results                in the need to assess the impact of resource investment. As the social and economic impact                it is suggested to consider the influence of innovative development through knowledge-                intensive and high-tech industries on the economic development of a state, the country’s                GDP  growth,  which  will  represent  a  source  of  additional  resources  to  finance  public                expenses and increase the population’s welfare.                Innovative development of an economy of any country in the world is absolutely impossible                without  a  stable  and  developed  financial  system  as  a  basis  of  not  only  financing                transformations of innovative nature, but also of a reproduction processes regulator. The                basis of a country’s financial system is its budget system. It becomes important to study                the  peculiarities  and  challenges  of  the  budget  system  efficient  functioning  taking  into                account the innovative orientation. The financial stabilization problem in Russia cannot be                solved without strengthening the importance of the government regulation of the financial                system  elements.  Reforming  the  budget  system  in  the  context  of  the  economy  of                knowledge is impossible without innovative development of high-tech industries. In the                conditions of the capital market volatility, insufficiency of financial resources, inaccessibility                of borrowings it becomes relevant to assess the budget potential of a region taking into                account  the  innovative  development  of  high-tech  industries  to  increase  the  country’s                population’s  welfare.  The  budget  policy  that  takes  into  consideration  innovative                development  provides  for  ensuring  the  priority  of  innovation-related  expenditures  and                determination of the parameters and pattern of changing the budget  main expenditure                items  necessary  to  develop  the  innovations.  The  regional  policy  provides  for  setting  a                higher priority of support of those regions that invest in innovative development. In the                long term, the share of the Russian high-tech commodities and services in terms of volume                on  the  global  markets  may  reach  5-10  %  by  2020,  and  Russia  may  take  the  leading                positions in the sector of fundamental and applied R\&D and technologies related thereto.                A lot of researchers in their works traditionally pay attention to the economy innovative                development issue (De Marchi, 2012; Cappelen et. al. 2012; Hewitt-Dundas, 2006, 2013;                Ganotakis, 2012; Ganotakis and Love, 2011). At the same time, in the current conditions                of increased economic volatility many countries have significantly reduced their budget                expenditures for multiple programs aimed at innovation development support (European                Commission, 2010; European Commission, 2011). In doing so, many authors also note                that innovative development success is determined not at all by the amount of innovation                financing, but by the human capital level. The higher the human capital level, the more                significant impact it positively provides on innovations. So, Dakhli and De Clercq (2004) in                their work considered the influence of the two forms of capital, human and social ones, on                the innovation development level in 59 countries, providing an overall view on the role                these forms of capital play in generating material and non-material values. The authors                have  established  that  there  is  a  positive  correlation  between  the  human  capital                development level and innovation development. However, Griliches (1979, 1990) notes                that  the  interrelation  between  human  capital  and  the  volume  of  produced  innovative                products  is  rather  a  complex  one,  in  particular,  a  topical  issue  in  this  regard  is                understanding  how  much  impact  human  capital  provides  on  innovations  in  high-tech                sectors. Akhmetshin et. al. (2018), Mustafin and Ignateva (2016), Qi Dong et. al. (2012)                also highlight the importance of human capital as a factor of economic growth, as well as                of generation and implementation of new concepts and innovations. So, there is a need to                create a new concept of innovative human capital as an innovation determinant. McGuirk                et al. (2015) note that innovative human capital is the key concept that needs to be taken                in consideration when developing state programs of innovation support in a country.                 One  of  the  key  challenges  is  financing  of  high-tech  sectors  and  innovations.  Economic                researchers are of the opinion that innovative activities are hard to finance using the funds                of the open competitive market participants (Hewitt-Dundas, 2006, 2013). In this regard                an important role is assigned to public financing of the innovative development of regions                taking into account their budget potential (Vertakova and Vlasova (2015) aimed to increase                                                            850","the country citizens’ social and economic welfare. So, the innovations themselves are to                contribute to human capital development and well-being of the population. This problem                statement makes it necessary to develop a methodology of assessing the budget potential                of the country’s regions taking into account the innovative development of their high-tech                industries to increase the population’s welfare and well-being.                Within  this  study,  the  budget  potential  (BP)  of  a  region  taking  into  account  the                innovative  development  of  high-tech  (HT)  industries  should  be  understood  as  the                development level of the regional capacities to increase income and expenditure financing                quality  that  will  ensure  efficient  functioning  of  the  budget  system  in  the  conditions  of                economy volatility, limitedness of financial resources, competition and difficulties related                to  attraction  of  borrowings  based  on  innovative  development  in  the  conditions  of  the                knowledge economy.                 The objective of this work is to study the budget potential of a region taking into account                the innovative development of high-tech industries to increase the population’s welfare                and well-being (using the example of the Russian Federation constituent entities) using                the methodology developed by the authors.                Despite the fact that there is a number of in-depth studies on this matter, we think that                studying the budget potential of a region taking into account the innovative development                of high-tech industries as a system of quantitative indicators is not sufficiently covered.                There is no any integral, mutually interconnected quantitative assessment system of the                budget potential that would allow to characterize it comprehensively in terms of regional                financial stability and economic development through high-tech industries.                 2 Methodology and Data                The authors have developed methodological principles of studying the BP of a region taking                into  account the innovative  development  of HT industries that include  the  principles  of                assessment, system of indicators and procedure for assessing the BP of a region taking                into account the innovative development of HT industries. The concept of innovative state                of social and economic systems lies at the basis of our developments. Out study is based                on  the  statistical  analysis  methods,  as  well  as  on  the  systematic  approach  principles                suggesting a holistic approach to a matter under consideration.                We suggest the following principles to assess the budget potential of a region taking into                account the innovative development of high-tech industries:                 1. The principle of balanced reference values. It means that the input reference  values                regarding  the  BP  of  a  region  taking  into  account  the  innovative  development  of  HT                industries  are  quantitatively  interconnected.  Any  and  all  accepted  reference  indicator                values are satisfactory for growth of the BP of a region taking into account the innovative                development of HT industries.                2. The breadth of coverage principle. The number of indicators must be necessary and                sufficient to assess the BP of a region taking into account the innovative development of                HT industries.                3. The indicator quality principle. To assess the innovative state, one needs to use the                indicators allowing to compare the system state over time and space that can be expressed                in relative units.                4. The indicators shall be reliable.                5.  Conclusions  on  the  current  state  of  the  social  and  economic  system  are  formed  by                assessing the uniformity of approximation to the reference level by separate indicators.                 The result of assessing the BP of a region taking into account the innovative development                of  HT  industries  is  identification  of  problems  in  development  of  certain  elements  and                indicators, as well as development of recommendations for the budget potential growth.                We suggest to carry out the development and practical use of the methods of assessing                the  BP  of  a  region  taking  into  account  the  innovative  development  of  HT  industries                according to the following procedure: ‘Research object selection’ → ‘Initial data collection’                                                             851","→  ‘Clarification  and  adjustment  of  reference  values’  →  ‘Calculation  of  indicators’  →                ‘Innovative  development level  analysis’  →  ‘Detailed  analysis  of  the  developed indicator                units’. To achieve the objective of this study and obtain a comprehensive assessment, we                have developed a methodology used to determine a consolidated standardized indicator of                a regional BP taking into account the innovative development of HT industries.                Procedure for assessing the budget potential of a region taking into account the                innovative development of high-tech industries (stages).                I. Based on the objectives of our study we suggest to form  two units of indicators that                characterize  the  budget  potential  of  a  region  taking  into  account  the  innovative                development of high-tech industries: the first unit is ‘Income, Economy, Innovations’, and                the second one is ‘Expenditures, Social and Economic Indicators’ (table 1).                 Table 1 The System of Indicators Used to Assess the Budget Potential (BP) of a Region                Taking into Account the Innovative Development of High-tech (HT) Industries (fragment)                                                                                          Impact on the                                  Indicator / indicator unit name                                                                                               BP                 1.     ‘Income, Economy, Innovations’ (IEI) unit                 1.1.   Motivation of enterprises to invest their own funds in R\&D              +                 1.2.   Budget and tax return of the regional economy                           +                 1.3.   Regional economy innovativeness indicator                               +                 1.4.   Motivation  of  enterprises  to  invest  their  own  funds  in                 technological innovations                                                      +                 1.5.   Regional scientific and staffing resource level                         +                 1.6.   Consumer price index                                                    -                 1.7.   Innovative  activity  (the  share  of  enterprises  engaged  in         +                 innovative activities)                 1.8.   Regional enterprise investment innovativeness indicator                 +                 1.9.   The  share  of  and  high-tech  and  knowledge-intensive                +                 industries in GRP                 1.10.  The indicator of efficiency of using public funds to increase           +                 regional income in high-tech and knowledge-intensive fields                                                             …                 2.     ‘Expenditures, Social and Economic Indicators’ (ESEI)                   +                 unit                 2.1.   Healthcare public financing indicator                                   +                 2.2.   Share of healthcare public financing in GDP                             +                 2.3.   Share of education public financing in GDP                              +                 2.4.   Share of culture public financing in GDP                                +                 2.5.   Average population income per capita                                    +                 2.6.   Birth rate                 2.7.   Death rate                                                              -                 2.8.   Life expectancy at birth                                                +                                                             …                                               Source: prepared by the authors                 II.  Calculated  indicators  allowing  to  assess  the  BP  of  a  region  taking  into  account  the                innovative  development  of  HT  industries  may  have  various  dimension,  importance  or                weight. And the need to carry out a comprehensive assessment based on the whole set of                indicators requires to bring them to a non-dimensional form, to a unified zero reference                point, to a unified variation interval. In this regard the authors use a method based on                linear conversion of initial indicators, within which the values of transformed (standardized                indicators) will fall within the specified interval from 0 to 1. Upon conversion the indicator                dimension disappears, but a fine structure of changes in certain indicators remains. Now                one has the possibility to compare them and display in a unified coordinate system, as well                as to aggregate the calculated indicators (Kornilov et al., 2017).                                                             852","Standardization of the developed indicators of the assessment system is carried out taking                into  account  the  impact  on  the  regional  BP  level  taking  into  account  the  innovative                development of HT industries (table 1). If an increase in the value of indicators results in                improvement of the comprehensive assessment of the BP (positive impact (+), the bigger                the coefficient value, the better), formula (1) is used, and if an increase in the value of                indicators results in deterioration of the comprehensive assessment (negative impact (—),                the lower the coefficient value, the better), then formula (2) is used.                                             ∗                         ∗                       K =   K i max −K ij  , 0 ≤ K ≤ 1                                                  (1)                         ij                                            ij                            K i max −K i min                         ∗                                            ∗                       K =   K ij −K i min  , 0 ≤ K ≤ 1                                                  (2)                                            ij                         ij                            K i max −K i min                                                        th                where Kij is the calculated value of the i  indicator from the IEI or ESEI units of the system                                                            th                                                                       *                                                                                th                of indicators used to assess the BP in the j region, K ij is the i standardized indicator of                the  system  of  indicators  used  to  assess  the  BP  in  the  j th  region,  Ki  max  is  the  highest                                         th                                                                                                th                calculated value of the i indicator, Ki min is the lowest calculated value of the i  indicator                used to assess the BP, i ∈ [1; N], N = 26.                III. To determine the BP of a region taking into account the innovative development of HT                industries in  the  conditions  of  the  economy  volatility  one has  to  take into  account  the                variability of the values of indicators characterizing the regional social and economic state.                To take into account the economy volatility factor we suggest using the formula (3):                             K ∗ ij                          ∗                       KV =                                                                              (3)                          ij                             σ i                where KV ij is the standardized indicator of a region taking into account the volatility K ij,                          *                                                                                                         *                                                                                        *                σi is the mean square deviation of the regional standardized indicator K ij.                IV. To obtain a quantitative comprehensive assessment of the BP of a region taking into                account  the innovative  development  of  HT industries in  the  conditions  of volatility  and                heterogeneity of social and economic conditions, horizontal and vertical disbalance in the                development of the regions one should calculate a consolidated standardized indicator of                                             *                                                          *                the  budget  potential  (CSIV ).  The  CSIV   of  a  region  is  calculated  (4)  as  the  sum  of                standardized indicators taking into account the volatility in the region that are included in                the system of criteria used to assess the BP (IEI or ESEI units) taking into account the                innovative development of HT industries in the regions.                                     ∗                           ∗                       CSIV = ∑ n  KV                                                                    (4)                                i=1                                     ij                           j                where CSIV j is the consolidated standardized indicator of the BP of the j region taking                            *                                                                                            th                into account the innovative development of HT industries in the conditions of volatility.                The lower the value of the CSIV j, the more efficient the state innovation policy, the higher                                                *                the quality of reforming the budget system in the knowledge-based economy and the more                efficient public finance administration to increase the country’s population’s welfare are.                3 Results and Discussion                Practical implementation of the suggested methodology includes 71 regions of the Russian                Federation of the 85 possible. The sampling was conditioned by the public availability of                official information for  analysis.  The  methodology  approbation  resulted in the  following                rating of the constituent entities by the regional budget potential level taking into account                the  innovative  development  of  high-tech  industries  for  2016.  Ranking  was  carried  out                based on the values of calculated consolidated standardized indicators (CSIV ) (table 2).                                                                                              *                 Table 2 Rating of Constituent Entities of the Russian Federation Based on the Results of                                     the Methodology Approbation, 2016 (a fragment)                                                                                  *                 Name of the Russian regions                                CSIV                    Rank                 Saint Petersburg                                           106.39                    1                 Nizhny Novgorod region                                     106.58                    2                                                            853","Moscow                                                     107.53                    3                 Republic of Tatarstan                                      107.81                    4                                                             …                 Tver region                                                112.17                    14                 Sakhalin region                                            112.81                    15                 Novosibirsk region                                         112.82                    16                 Republic of Udmurtia                                       112.97                    17                                                             …                 Sevastopol                                                 115.86                    30                 Republic of Mari El                                        115.94                    31                 Kamchatka Krai                                             116.32                    32                 Stavropol Krai                                             116.84                    33                                                             …                 Murmansk region                                            117.37                    38                 Zabaikalsky Krai                                           117.41                    39                 Kirov region                                               117.66                    40                 Samara region                                              117.69                    41                 Republic of Crimea                                         117.81                    42                 Vladimir region                                            117.93                    43                 Russian Federation                                         117.99                    44                                                             …                 Kurgan region                                              120.83                    59                 Arkhangelsk region                                         120.96                    60                 Pskov region                                               121.29                    61                 Primorsky Krai                                             121.33                    62                                                             …                 Amur region                                                126.25                    72                   Source: authors’ calculations based on the data of the Federal State Statistics Service and the                                          Federal Treasury of the Russian Federation.                 The peculiarities of the historical and economic development of different Russian regions                explain  the  ambiguity  of  the  country’s  current  situation.  The  level  of  scientific  and                technological development is both a result of the economic situation and, at the same time,                provides impact on economic processes itself.                 The  leading  positions  in  the  2016  rating  are  held  by  the  regions  (for  instance,  Saint                Petersburg,  Moscow,  Republic  of  Tatarstan,  Nizhny  Novgorod  region)  with  the  most                efficient state innovation policy. This is confirmed by the values of indicators (budget and                tax return of the regional economy, regional economy innovativeness, innovative activity,                life expectancy at birth) that are above, while the death rate is below the country average.                In  these  regions  the  values  of  the  following  indicators  are  also  high:  motivation  of                enterprises to invest their own funds in technological innovations, regional scientific and                staffing resource level, the share of and high-tech and knowledge-intensive industries in                GRP,  efficiency  of  using  public  funds  to  increase  regional  income  in  high-tech  and                knowledge-intensive fields, average population income per capita. The rating leaders are                characterized by the concentration of R\&D institutions, availability of leading universities                and broad financial opportunities. So, in the Nizhny Novgorod region not only R\&D centers,                but  knowledge-intensive  production  facilities  in  the  fields  of  mechanical  engineering,                chemistry and defense industry are concentrated as well. This is one of the most important                regions in terms of IT development, characterized by a close link between fundamental                science and education and applied research and IT-enterprises. In the outsider regions (for                instance,  Amur  and  Arkhangelsk  regions,  Primorsky  Krai)  the  values  of  the  following                indicators are below the country average: regional economy innovativeness, motivation of                enterprises to invest their own funds in technological innovations, the share of and high-                tech and knowledge-intensive industries in GRP, life expectancy at birth. The indicators of                public financing of education, culture and healthcare in regions do not have a certain trend                in relation to the country average, they can be both above and below it. As a result, the                quantitative assessment has clearly shown that the first-class or lacking regions are not                                                            854","necessarily characterized by only the best or only the worst indicators, respectively. It is                quite obvious that successful innovative development of a territory contributes not only to                GDP growth, but also ensures qualitative and progressive changes thereof. A number of                factors remains restraining the development of science and innovations, the key one of                which is still the lack of financing (the level of wage of scientists, material and technical                facilities). The return on financing of high-tech and fundamental developments due to its                nature is deferred through time, which is a significant disadvantage in comparison with                receipt of profit from mineral extraction.                No doubt that a transfer to an innovative path of development is quite a long and laborious                process requesting significant financial expenses. To successfully implement it one needs                to ensure the demand for innovations within the country, improvement of the innovation                related infrastructure, efficient interrelation of the commercial sector and science, using                the experience of prosperous regions.                 4 Conclusions                The methodology of assessing the budget potential of a region taking into account the                innovative development of high-tech industries was used to assess the social and economic                policy  of  the  Russian  Federation  regions.  The  developed  consolidated  standardized                                *                indicator (CSIV ) allows to quantitatively assess territorial leaders and outsiders. It has                been  established  that  the  problems  of  regional  budget  potential  development  are                connected with the lack of both a developed innovative environment and a single efficient                budget resource administration system. Analyzing the values of a consolidated indicator in                the  context  of  specific  indicators  of  the  assessment  system,  one  can  develop  efficient                managerial  decisions  taking  into  account  innovative  transformations  to  increase  the                population’s welfare and well-being.                Innovative development taking into account its competitive advantages both in traditional                sectors and in new knowledge-intensive fields and the economy of knowledge suggests a                breakthrough in increasing the human capital efficiency, development of high-tech sectors                and  transformation  of  innovation  factors  into  the  main  source  of  territorial  economic                growth. The social result of public policy will represent the opportunity to ensure the human                capital development and improve the population’s life quality, that is, an increase in the                erudition, longevity, birth rate, rise of morality and spiritual wealth of the population.                 It has been found out that a critical value of a specific indicator of the assessment system                does  not  pose  a  threat  to  the  budget  potential  taking  into  account  the  innovative                development  of  high-tech  sectors  in  a  region  in  whole.  However,  this  is  a  signal  to                strengthen the control over spending budgetary funds and implement a responsible social                and economic policy in this field. The best possible value of all coefficients in a specific                region is just one case among potential specific cases. This means that there are a lot of                efficient ways of state innovation policy.                The key areas of analysis and control shall be the following: development of the                R\&D  potential  and  high-tech  sectors  taking  into  account  the  required  financial  and                organizational resources; structural diversification of the economy based on innovative and                technological development; increasing the efficiency of the budget expenditures in whole                and innovation-related expenses in particular; permanent monitoring both of each separate                indicator of the budget potential assessment system and of the whole system of indicators                in their interrelation; timely analysis of the growth drivers and problematic regions to take                preventive measures to increase the efficiency of their activities.                The  analysis  of  the  results  of  the  suggested  methodology  approbation  confirms  its                consistency in whole. The methodology can be easily adapted, and the system of indicators                can be meaningfully added in accordance with the terms and objectives of an assessment.                The formed system of indicators to a large extent characterizes the budget potential of a                region taking into account the innovative development of high-tech industries, while truly                depicting the state and trends of current state innovation policy.                                                               855","Acknowledgments                The reported study was funded by the Russian Foundation for Basic Research according to                the research project № 18-010-00909.                 References                Akhmetshin, E. M., Sharafutdinov, R. I., Gerasimov, V. O., Dmitrieva, I. S., Puryaev, A.                S.,  Ivanov, E.  A.,  Miheeva,  N.  M.  (2018).  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Procedia                Economics and Finance, vol. 27, pp. 692-701.                                                               856","Methodical Approaches to the Formation of Model Budgets in order                    to Improve the Effectiveness of the Budget Process in Russia                                       1                                                                            2                  Nadezhda Yashina , Nataliya Pronchatova-Rubtsova , Sergey Petrov , Oksana                                                                                               3                                                         Kashina  4                                    1, 2, 3, 4  Lobachevsky State University of Nizhny Novgorod                         Institute of Economics and Entrepreneurship, Department of Finance and Credit                          Bolshaya Pokrovskaya street 37, 603000 Nizhny Novgorod, Russian Federation                                           2                                                                                4                   E-mail:  sitnicof@mail.ru,  pronat89@mail.ru,  petrov_ss@list.ru,  oksana_kashina@mail.ru                          1                                                              3                 Abstract: The purpose of the study is the formation of model budgets based on assessing                the effectiveness of budget resources management of the territories for improving inter-                budgetary relations. A methodology for assessing the effectiveness of budget resources                management of the territories, considering the formed indicators in order to develop the                model  budgets,  is  proposed.  The  integrated  indicator  of  the  effectiveness  of  budget                resources management is adopted as a basic criterion for classifying budget stability, which                will allow to determine the type of model budget for the purpose of forming inter-budgetary                relations in the region. The methodological basis was the methods of economic, system                analysis and mathematical statistics. The methodology has been tested on the official data                of the Finance Ministry of the Nizhny Novgorod region for 2014-2016. The result of the                assessment  is  the  rating  of  municipalities,  and  two  classes  of  model  budgets  are                established. The first class is characterized: by high financial stability due to a growth of                the distributions' norms of regulatory taxes to municipalities, increasing the share of own                revenues  to  80-90%,  a  reduction  in  subsidies,  subventions,  increased  dotations,  the                possible growth of municipal borrowing; by norms of social standards per unit of consumers                of  budget  services,  established  with  an  increased  coefficient.  The  second  class  is                characterized by sufficient financial stability of municipalities, requires a balanced approach                to establishing the structure of income and financing of expenditures. This methodology                will  help  to  match  the  amount  of  expenditure  obligations  to  the  amount  of  disposable                income  and  balanced  budgets.  The  formation  of  model  budgets  will  increase  the                responsibility of the regional state authorities and local governments for ensuring effective                spending  of  funds,  for  creating  a  base  for  expanding  their  own  revenue  potential;  will                contribute  to  improving  the  budgetary  process  management  effectiveness  and  the                introduction of budgeting principles based on results.                Keywords:  budget  resources,  effectiveness,  model  budget,  inter-budgetary  relations,                integrated indicator                JEL codes: H7, H21, H61, H63, H68                 1 Introduction                Stability and firmness of public budget ensuring coherent functioning of all sectors and                fields of life with their complex links and close interdependence of specific fields have been                called into  question  by  the  global  financial  system  turbulence.  In the  conditions  of  the                market economy volatility it has become quite obvious that a public budget system has                high flexibility to changes in the macroeconomic situation. The main efforts of a state are                focused on achieving its stable development, increasing the public expenditures efficiency,                improvement  of  the  quality  and  accessibility  of  public  services,  elimination  of                disproportions of territorial social and economic development, growth of the population’s                welfare and well-being. Reforming of public finances at all levels of the budget system                must result in long-term balance and sustainability of territorial budgets. In this regard                special attention is required to be paid to organization and implementation of the budget                process to increase its efficiency based on the assessment of public finance administration                quality.  In  promoting  the  efficiency  of  solving  current  tasks  of  territorial  budgets  an                important role is played by inter-budgetary transfers that are necessary for the budgets of                most regions and municipalities to fulfill their social obligations: ensuring a minimum of                social services guaranteed to each citizen of a country and a decent standard of living.                                                             857","That’s why the issues of improving the model of building inter-budgetary relations remain                relevant  (Yashina  et  al.,  2013).  The  system  of inter-budgetary transfers is intended  to                provide an optimal balance between common regional interests and the interests of specific                municipalities, to facilitate rational distribution of financial resources in a region.                A large number of studies is dedicated to assessment of budget resources administration                efficiency.  Special  attention  is  paid  to  studying  territorial  budget  sustainability  as  an                indicator of the budget resources administration efficiency.                The priority settings of the public financial policy include ensuring long-term sustainability                of  territorial  budgets.  An  indicator  of  territorial  budget  sustainability  assessment  is  a                ‘budget gap’ reflecting long-term conformity of the budget expenditure and revenue with                the accumulated public debt amount. A lot of works are dedicated to assessment of the                budget gap in relation to some European and American countries (Auerbach et al., 1991;                2003; 2004; Evans et al., 2012; Gokhale, Raffelhuschen, 1999; Gokhale and Smetters,                2003; Kotlikoff, Burns, 2012). In Russia the assessment of this value was for the first time                carried out under the guidance of L. Kotlikoff (Goryunov et al., 2013). Budget sustainability                allows to clarify whether it is possible to maintain budget expenditures on a planned level                and to fulfill one’s obligations to creditors to the full extent with the key characteristics of                the  tax  and  budget  policy  remaining  unchanged  (Goryunov  et  al.,  2015).  Budget                sustainability describes a specific fiscal regime with a specific structure and budget revenue                and expenditure amount.                 Methodological  aspects  of  the  budget  sustainability  assessment  were  elaborated  in  the                works by J. Horne (Horne, 1991) and O. Blanchard et al. (Blanchard, 1990; Blanchard et                al., 1990), revealing the overall concept of budget sustainability and suggesting specific                indicators to assess it. The ‘intertemporal budget constraint’ of a state serves as a basis                and indicators are needed to determine whether chronical deviations between revenue and                expenditure will emerge provided that an economy will develop in accordance with a certain                trend.                In the conditions of economic volatility special attention should be paid to studying the                factors that are able to lead to the risk  of emergence of public budget crisis situations.                Such  factors  may  include:  currency  and  time  structure  of  public  debt,  availability  of                contingent liabilities, banking sector sustainability, access to international capital markets                (Reinhart  and  Rogoff,  2009).  In  such  situations  to  assess  public  budget  sustainability                methods are required that mainly represent a comprehensive stress testing of finance. In                the context of crisis phenomena transformation of a budget mechanism into an efficient                tool of macroeconomic stabilization and using all budget expenditure reserves to finance                the economy and human capital development become the key aspect of the budget policy.                (Bogolib, 2015).                There is no a single methodological approach to determine and assess budget sustainability                in  the  Russian  practice.  There  is  no  systematic  legal  regulation  of  this  issue,  and  this                concept  is  not  explained  in  regulatory  instruments.  However,  compliance  with  the                requirements of the Budget Code of the Russian Federation to the state of regional and                local  budgets  (restrictions  by  the  level  of  debt  and  budget  deficiency,  standards  of                expenditures for maintenance of public authorities, etc.) are often interpreted as the basis                of ensuring budget sustainability (Yashina et al., 2013). The methodologies are widespread                of assessing the budget financial sustainability based on an analysis of characteristics of                the revenue and expenditure budget items, accounts payables on the basis of: integral                indicators  (see,  for  example,  Kolesov,  2000;  Yashina  et  al.,  2013),  arched  logarithmic                elasticity indicators (Lapushinskaya, 2001), based on a correlation analysis (Yashin and                Yashina, 2003).                Currently the issue of lack of transparent and unified methodologies to determine budget                sustainability  for  the  purpose  of  increasing  budget  resources  administration  efficiency                requires  to  study  and  develop  new  methodological  approaches  to  carrying  out  a                comprehensive qualitative and quantitative assessment of territorial budget sustainability.                 The  objective  of  this  study  is  to  form  model  budgets  based  on  the  assessment  of  the                                                            858","territorial budget resources administration efficiency to improve inter-budgetary relations.                 2 Methodology and Data                The  authors  have  offered  a  methodology  of  assessing  the  territorial  budget  resources                administration  efficiency  to  form  model  budgets.  A  comprehensive  indicator  of  the                territorial budget sustainability may be used as a criterion to assess the budget resources                administration efficiency. Depending on the assigned category of budget sustainability, the                model budget type is determined and a corresponding inter-budgetary relations policy in                a region is formed.                The  high  rate  of  current  changes  and  the  need  of  an  immediate  response  lead  to                development  of  new  approaches,  models  and  methods  even  in  conservative  areas  of                activity,  including  public  administration.  In  this  regard  the  authors  have  developed  a                methodology of the budget sustainability express analysis based on the system consisting                of 8 reference indicators.                For the purposes of analysis, it is necessary to:                     •   Determine the scope of required financial information;                    •   Form  the  groups  of  revenue  based  on  the  commonly  accepted  budget  revenue                       classification  by  tax,  non-tax  revenue  and  non-refundable  transfers,  and  of                       expenditures taking into account the expenditure functional classification;                    •   Develop coefficients.                Information from official reports on implementation of municipal and regional budgets will                be used as initial data.                The  system  of  coefficients  shall  meet  the  following  requirements:  maximal                informativeness; reliability and accessibility — determination on the basis of official budget                implementation  reports;  comparability  —  an  opportunity  to  carry  out  assessment  of                municipalities and constituent entities of the Russian Federation in space (in comparison                with other territories) and in time (frequency); availability of numerical reference standards                of minimum satisfactory level or variation range.                 The  system  of  indicators  represents  relative  indicators/coefficients  of  the  budget  state                (IBS) and includes:                 1.  Financial  independence  coefficient  (IBS1)  is  calculated  as  the  ratio  of  budget  own                revenue  (OR)  to  the  total  revenue  amount  (R)  and  shows  to  what  extent  the  budget                depends on financial support of a higher-level budget or what is the share of the budget                own revenue (1):                              OR   TR+NTR                       IBS =     =                                                                       (1)                          1                               R     R                where TR is tax revenue, NTR is non-tax revenue.                2. Financial sustainability coefficient (IBS2) is calculated as the ratio of tax revenue (TR)                to the total revenue amount (R) and shows how much taxes go into the budget (2):                              TR                       IBS =   R                                                                         (2)                          2                3. Coefficient of own revenue and non-refundable transfers ratio (IBS3) is calculated as the                ratio of tax revenue (TR) and non-tax revenue (NTR) to non-refundable transfers (NRT),                which allows to compare the amount of one’s own funds and financial support (3):                              OR    TR+NTR                       IBS =  NRT  =  NRT                                                                (3)                          3                4. Coefficient of sufficiency of one’s own revenue to finance social expenditures (IBS4) is                calculated  as  the  ratio  of  one’s  own  revenue  (OR)  to  social  sector  expenditures  (Es):                healthcare, education, social policy, physical training, culture and cinema; shows the level                                                               859","of budget own funds sufficiency (the degree of self-sufficiency) to finance the social sector                (4):                               OR   TR+NTR                       IBS =     =                                                                       (4)                          4                              E s    E s                5. Coefficient of sufficiency of one’s own revenue to finance production industries (IBS5) is                calculated as the ratio of one’s own revenue (OR) to expenditures for national economy                (Ene) and housing and utility sector (Ehus), shows the level of budget own funds sufficiency                (the degree of self-sufficiency) to finance the production industries (5):                                 OR      TR+NTR                       IBS =         =                                                                   (5)                          5                              E ne +E hus  E ne +E hus                6.  Administration  expenditures  financing  coefficient  (IBS6)  is  calculated  as  the  ratio  of                expenditures for nationwide issues (ENI) to the total budget expenditure amount (E), shows                how much budgetary funds is used to finance the administrative apparatus (6):                        IBS =   E NI                                                                      (6)                          6                               E                7. Human capital investment coefficient (IBS7) is calculated as the ratio of expenditures                for the social sector (Es) to the total budget expenditure amount (E), shows how much                budgetary funds is used to finance the human capital development (7):                        IBS =  E s                                                                        (7)                          7                              E                8. Interest expenditures coefficient (IBS8) is calculated as the ratio of expenditures for                servicing state and municipal indebtedness (Ei) to tax revenue (TR) and shows to what                extent the expenditures for servicing state and municipal indebtedness are covered by tax                revenue (8):                              E i                       IBS =  TR                                                                         (8)                          8                Upon carrying out the coefficient express analysis the municipal budget sustainability level                is determined by determining the coefficient reference values for the three sustainability                categories. The  threshold  coefficient  reference  values  determining  budget  sustainability                categories  are  formed  by  an  expert  method  based  on  an  analysis  of  the  array  of  the                calculated values of indicators taking into account the peculiarities of territorial social and                economic  state  and  development,  analysis  of  the  most  successfully  developing  and                efficiently functioning municipalities, as well as the coefficient variation range (IBSi max, IBSi                min) within the aggregate of the territorial budgets under consideration.                 The  coefficient  analysis  does  not  allow  to  assess  the  budget  sustainability  in  an                unequivocal, quantitative and summarized manner. In this regard, we offer a methodology                of assessing the territorial budget resources administration efficiency taking into account                the  formed  reference  indicators  by  determining  an  integrated  standardized  budget                                                                              *                sustainability  indicator  (indicator  of  the  budget  state),  IBS integ.  The  calculation  of  the                integrated standardized indicator includes the following three stages:                1. Dividing the coefficients used for analysis into two groups according to the impact they                provide on the territorial budget resources administration efficiency (table 1);                2. Bringing the indicators to a comparable form according to formulas (9) and (10) (table                1). Separate budget state indicators may have various dimension, importance or weight,                which requires to bring them to a unified form, to a unified zero reference point, to a unified                variation interval  when  carrying  out  a comprehensive  assessment  of territorial  budgets                based on an aggregate of indicators. The authors use a method based on linear conversion                of initial indicators, within which the indicators values fall within the specified interval from                0 to 1 (Kornilov et al., 2017).                                                                  860","Table 1 Grouping Indicators by the Territorial Budget Resources Administration                                                            Efficiency                    Group 1. ‘Improvement of the budget               Group 2. ‘Deterioration of the                   sustainability with an increase in the            budget sustainability with an                               indicator values’                    increase in the indicator values’                    ∗                                                                    ∗                 IBS =   IBS i max −IBS ij                                      (9)  IBS =  IBS ij −IBS i min                             (10)                    ij                                                                    ij                        IBS i max −IBS i min                            IBS i max −IBS i min                 where IBS  is the value of the i  standardized indicator in the assessment coefficient                                                  th                           ∗                           ij                                th                                                                                     th                 system in the j  municipality; IBS i max  is the maximum value of the i  indicator within the                                                                                              th                 whole sampling of municipalities; IBS  is a separate calculated value of the i  indicator;                                                       ij                 IBS i min   is  the  minimum  value  of  the  i th     indicator  within  the  whole  sampling  of                 municipalities; 0 ≤ IBS ≤ 1.                                       ∗                                       ij                 Financial independence coefficient (IBS1)        Administration expenditures financing                                                                  coefficient (IBS6)                 Financial sustainability coefficient (IBS2)      Interest expenditures coefficient (IBS8)                 Coefficient of own revenue and non-                 refundable transfers ratio (IBS3)                 Coefficient of sufficiency of one’s own                 revenue to finance social expenditures (IBS4)                 Coefficient of sufficiency of one’s own                 revenue to finance production industries                 (IBS5)                 Human capital investment coefficient (IBS7)                                              Source: prepared by the authors.                                      *                3.  Calculation  of  IBS integ  j  is  carried  out  by  aggregating  the  converted  indicators  of  a                specific municipal budget (11):                                      ∗                       IBS ∗ integ j  = ∑ IBS                                                          (11)                                      ij                Ranking  of  municipalities  is  carried  out  in  accordance  with  the  value  of  an  integrated                standardized  budget  sustainability  indicator.  The  lower  the  value  of  the  integrated                standardized  budget  sustainability  indicator  IBS integ  j  the  more  efficient  the  territorial                                                                  *                budget resources administration is.                Simultaneously  with  making  a  rating  one  has  an  opportunity  to  form  the  budget                                                                                                       *                sustainability categories by ‘cutting them off’ by the threshold reference values of IBS integ                for budget sustainability categories.                The following categories are formed by the efficiency of budget resources administration                based on budget sustainability:                                                       *                4. ‘Best budgets’ have the lowest IBS integ values in a sampling not exceeding the threshold                reference values to be assigned to this category;                                                        *                5.  ‘Satisfactory  budgets’  have  the  IBS integ values exceeding  the  I  category  values,  but                which are lower than the II category threshold reference values;                                                        *                6. ‘Worst budgets’ have the highest IBS integ values, the worst ones in a sampling.                The final stage of assessing the budget resources administration efficiency is forming model                budgets of the following two categories:                    •   1 category of model budgets: municipalities considered to be the best ones based                       on  the  budget  sustainability  criterion  and,  accordingly,  with  the  most  efficient                       administration of the budget resources;                    •   2  category  of  model  budgets:  municipalities  with  satisfactory  and  worst  budget                       sustainability values  and,  consequently,  with  a  poor  quality  of  budget  resources                       administration.                 3 Results and Discussion                Practical implementation of this methodology was carried out based on the sampling of                data of 52 municipalities of the Nizhny Novgorod region, regional and consolidated budget                                                            861","of the region for 2014-2016. A comparative rating of municipalities of the Nizhny Novgorod                region by assessment of the budget resources administration efficiency is shown in Table                2.                  Table 2 Rating of Municipalities Based on the Results of the Methodology Approbation,                                                 2014-2016 (a fragment)                                                                     Position     Position      Position                               Municipality name                                                                     in 2014      in 2015       in 2016                 Regional budget                                         1            1             1                 Consolidated budget                                     2            2             2                 1 category ‘Best budgets’                               3            3             3                 Dzerzhinsk city                                         4            5             4                 Bor city                                                5            7            10                 Vyksa city                                              6           12            14                 Nizhny Novgorod city                                    7           37            46                                                            …                 2 category ‘Satisfactory budgets’                      26           26            26                                                            …                 Vachsky district                                       36           39            36                 Bolsheboldinsky district                               37           43            40                 Dalnekonstantinovsky district                          38           48            41                 Urensky district                                       39           33            34                                                            …                 Sokolsky district                                      51           51            45                 Bolshemurashkinsky district                            52           56            56                 Vadsky district                                        53           57            54                 Spassky district                                       54           52            52                 Sharangsky district                                    55           53            55                 Krasnooktyabrsky district                              56           55            57                 Tonkinsky district                                     57           54            50                 3 category ‘Worst budgets’                             58           58            58                  Source: authors’ calculations based on the data of the Finance Ministry of the Nizhny Novgorod                                                          region.                 The  analysis  of  the  results  of  the  suggested  methodology  approbation  confirms  its                consistency. The rating of the Nizhny Novgorod regional municipalities during these three                years is headed by the regional and consolidated budget forming the ‘best category’ of                sustainability.  Such  situation  is  expectedly  reflected  in  the  following  development                indicators:  GRP  grows  in  average  by 6%,  the  most  profitable  sources  remain  personal                income tax that accounts for nearly 40%, then comes the corporate profit tax and property                tax. These indicators grow, which shows an increase in corporate profits, investments in                fixed assets and regional average wage. Even the availability of significant expenditures                for servicing state and municipal indebtedness in these budgets rather shows a positive                trend.  Not  the  amount  of  indebtedness  is  of  importance,  but  how  efficiently  it  is                administered  (Yashina  et  al.,  2015),  whether  these  funds  are  used  to  increase  the                population’s standard of living and welfare and human capital development or not. When                analyzing the expenditure item it is worth noting that primarily these are investments in                human  development,  that  is,  healthcare,  education  and  social  policy  development                programs. The positive aspect in this regard is that the budgetary funds are not just wasted                but  rationally used, including  for  capital  expenditures: investments in  building  schools,                kindergartens or underground development. The majority of the territorial budgets have                preserved their development orientation in the course of this study, which has reflected in                a comparatively stable composition of the budget sustainability categories (table 2). The                drastic  change in the  Nizhny  Novgorod  city  position is  mainly  due to  a  poor  quality  of                human  capital financing, indebtedness administration  and insufficient  self-sufficiency in                supporting production industries.                                                            862","For territories with the ‘best budgets’ according to the sustainability criterion (regional and                consolidated budget of the Nizhny Novgorod region) it is recommended to develop the ‘1                category  model  budgets’,  for  territories  with  the  ‘satisfactory  and  worst  budgets’  —  ‘2                category model budgets’ (Nizhny Novgorod city, Bor city, Dzerzhinsk city, etc.).                4 Conclusions                 The integral assessment of the budget resources administration efficiency is the basis of                budget administration, since it allows to analyze the quality of planning, implementation                of local and regional budgets, as well as the level of regulating and controlling thereof. The                developed methodology  of  budget  sustainability  express  analysis  based  on  8 indicators                allows to: analyze the rationality of using budget resources and a model of inter-budgetary                relations in a region; reveal the main problems related to shortfalls in receipt of revenue,                expenditures ‘scatteration’ and peculiarities in financing a specific sector; determine the                reference points to increase the quality of budget process administration; develop a policy                to implement set objectives and tasks of territorial social and economic development.                Forming ‘model budgets’ will provide an opportunity to:                     •   Ensure the balance between common regional interests and the interests of specific                       municipalities;                    •   Rationally distribute financial resources in a region;                    •   Ensure conformity of expenditures with the amount of available revenue, as well as                       budget balance;                    •   Increase the responsibility of public authorities for the efficiency of spending funds                       and growth of one’s own revenue potential;                    •   Increase the efficiency of budget process administration;                    •   Implement into the budget process result-oriented budgeting principles.                For territories with the first category model budgets it is recommended to: increase the                standards of regulating taxes distribution and the share of one’s own revenue up to 80-90                %;  lower  subsidies,  subventions,  increase  grants;  use  borrowed  resources;  establishe                increased  social  standards  per  budget  service  consumer  unit.  For  territories  classified                within  the  second  category  of  model  budgets it is  suggested  to  gradually increase  the                standards of regulating taxes distribution and ensure the share of one’s own revenue not                lower than at 50%, decrease grants, not to resort to borrowings.                The optimal differentiation of expenditure obligations between different levels of authority                will ensure efficient provision of budgetary services, reduce the number of subventions and                subsidies  not  related  to  implementation  of  national  priorities,  reduce  doubling  and                scaterring of budgetary funds. Transfer on the municipal level of an additional standard of                deductions from profit tax, personal income tax and excises will solve the problem of the                budget  revenue  item  replenishment,  provide  local  self-government  bodies  with  an                opportunity to influence the receipt of such taxes, eliminate the problem of a low specific                weight of local budget own revenue.                 Acknowledgments                 The reported study was funded by the Russian Foundation for Basic Research according to                the research project № 18-010-00909.                 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