Financial distress and bankruptcy prediction an appropriate models for listed firms in vietnam

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNIVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS FINANCIAL DISTRESS AND BANKRUPTCY PREDICTION: AN APPROPRIATE MODEL FOR LISTED FIRMS IN VIETNAM BY PHAM VO NINH BINH MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, DECEMBER 2017 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIET NAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS FINANCIAL DISTRESS AND BANKRUPTCY PREDICTION: AN APPROPRIATE MODEL FOR LISTED FIRMS IN VIETNAM A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS BY PHAM VO NINH BINH Academic Supervisor Dr VO HONG DUC HO CHI MINH CITY, DECEMBER 2017 DECLARATION I declare that the thesis report entitled, “The financial distress and bankruptcy prediction: An appropriate model for listed firms in Vietnam” composed and submitted by myself in fulfillment of the requirements for the degree of Master of Art in Development Economics to the Vietnam – Netherlands Programme This is my basic work and conclusions drawn are based on the material collected by my own I confirm that this work has not previously been submitted to any other university for the award of any other degree, diploma or equivalent course HCMC, December 2017 Phạm Võ Ninh Bình i ACKNOWLEDGEMENTS First and foremost, I am so appreciative and grateful to all the people who supported in some way to the work made in this thesis I would like to express the immeasurable appreciation and deepest gratitude to my academic supervisor – Dr Võ Hồng Đức, for his contribution of time, invaluable guidance, encouragement, and enthusiasm It is really my privilege to work with him Moreover, I wish to acknowledge all of the teachers and staves at Vietnam – The Netherlands Programme for their useful knowledge, advice, and suggestions while I learned at University Special thanks to Prof Nguyễn Trọng Hoài, Dr Phạm Khánh Nam and Dr Trương Đăng Thụy inspiring me to write my thesis as well as their believability play a key role in the success of my study I would like also to thank my friends at class 22 for their friendship and constant support Finally, I would like to thank my parents and an older sister who is always siding by side me in the difficult and happy time HCMC, December 2017 Phạm Võ Ninh Bình ii ABBREVIATIONS AFC: Asian Financial Crisis ASEAN: Association of Southeast Asian Nations AUC: Area under the Receiver Operating Characteristics Curve BSM: Black–Scholes–Merton CPV: Communist Party of Vietnam DD: Distance to Default model EDF: Expected default frequently EMS: Emerging market score model GFC: Global financial crisis HOSE: Ho Chi Minh City Stock Exchange HNX: Hanoi stock exchange M&A: Merger and Acquisition MDA: Multivariate discriminant analysis ROC: Receiver Operating Characteristics Curve TPP: Trans-Pacific Partnership ABSTRACT In this fast-changing world, it is likely that potential exposures are present in all economic sectors In the emerging markets such as Vietnam, financial stability is always an important topic to attract attention from academics, practitioners, and policymakers The Global Financial Crisis in 2008/2009 was the most recent event in which financial stability of countries has been tested During or even after the crisis, many nations have been still facing macroeconomic problems in relation to unemployment, a reduction of output, firms’ bankruptcy and a sharp increase of firms’ default risk which all lead to a serious instability Although Vietnam is the country with the second highest economic growth rate in Asia and one of a few new emerging markets in the world, the economy has also been suffered to the financial risk This study is conducted to obtain the following three objectives First, this study is to identify early warning indicators of corporate financial distress (or the financial risk) using the accounting- based and market-based models Second, the study is to build an appropriate bankruptcy prediction model, one of its first kind in Vietnam, using market data for listed firms in Vietnam Third, the above bankruptcy prediction model is then extended by incorporating macroeconomic factors which are widely considered as key factors affecting the financial distress and bankruptcy of firms, to be named “a comprehensive model of bankruptcy prediction” for Vietnam A key objective of this study is to develop a comprehensive model, which is the first of its kind in Vietnam, for the purpose of financial distress and bankruptcy prediction for listed firms Using a sample of more than 800 Vietnamese listed firms on the Vietnam’s stock exchanges in the period from 2003-2016, which is then sub-divided into the pre-global financial crisis (GFC) period (2003-2009) and the post-GFC period (2010-2016) to consider the financial distress likelihood in different scenarios The Emerging Market Score Model (EMS) and the Distance to Default model (DD) are used to identify early the signal of financial distress A new model is then proposed by incorporating various factors including (i) Accounting factors obtained from the EMS model; (ii) Market factors from the DD model; and (iii) Two macroeconomic indicators, the inflation and short-term interest rate which are widely used in the empirical analysis of the topic The Area Under the Receiver Operating Characteristics (ROC) Curve (the AUC) is utilized to compare the usefulness of various default prediction models Empirical findings from this study present evidence to support the view that factors derived from the accounting variables, market variables, and typical macroeconomic fundamental factors have all contributed effect to the financial distress of the Vietnamese listed firms for the research period when they are considered in isolation However, when a comprehensive model is developed, the effect of accounting factors appear to be stronger in comparison with the market factors Findings from this study also confirm that the model of default prediction including (i) accounting factors and (ii) macroeconomic indicators appear to be performing much better than the model including market factors and macroeconomic fundamentals In addition, market variables are less likely to affect the financial distress than accounting and macroeconomic factors in both pre- and post-crisis periods When the attention is on the sectors of the economy, findings from this study present evidence to support the view that Vietnam’s sectors have faced a high degree of financial risk Among various industries, the largest exposure belongs to Consumer Staples sector whereas Health & Education sector is relatively safe in terms of financial risk Findings from this study shed lights to meaningful policies from the Government in relation to the financial distress of firms in order to achieve a financial stability for the nation as a whole Listed firms are also advised that their accounting indicators have also provided reliable indicators to minimize financial distresses and appropriate policies at the firms’ level should be considered Keywords: Financial Distress, Bankruptcy, Distance to Default, Macroeconomic Fundamentals, Vietnam JEL Classification: F62, F65, G01, G31, G33, G34 TABLE OF CONTENTS DECLARATION .i ACKNOWLEDGEMENTS ii ABBREVIATIONS iii ABSTRACT .iv LIST OF TABLES viii LIST OF FIGURES ix Chapter 1: INTRODUCTION .1 1.1 Problem statement 1.2 Research Objectives 1.3 Research questions 1.4 Structure of the thesis Chapter 2: BACKGROUND AND LITERATURE REVIEW 2.1 Why Vietnam? 2.2 Background to the Global Financial Crises .8 2.3 Literature review on credit models 11 2.3.1 Background to corporate financial distress models 11 2.3.2 Comparison of accounting-based and market-based models 18 2.3.3 Studies on financial distress in the context of Asia and Vietnam 20 Chapter 3: RESEARCH METHODOLOGY 23 3.1 Data .23 3.2 Analytical framework 24 3.3 Estimating financial distress 25 3.3.1 Emerging market scoring model (EMS) 25 3.3.2 Distance to default model (DD) .27 3.4 Variable selection 32 3.4.1 Dependent variables .32 3.4.2 Independent variables 32 3.4.3 A Comprehensive model 35 3.5 Logit model 40 3.6 Comparing Emerging market score model (EMS) and Distance to Default (DD) models 42 Chapter 4: EMPIRICAL RESULTS AND ANALYSIS 44 4.1 Data descriptions and Signal of financial distress 44 4.2 Factors affect the financial distress 49 4.3 Financial distress in various scenarios 54 Chapter 5: CONCLUSIONS AND IMPLICATIONS 60 5.1 Conclusions 60 5.2 Policy implications 62 5.2.1 For academics 62 5.2.2 For the Vietnamese Government 63 5.2.3 For practitioners and investors .64 5.3 Limitation and further research 65 REFERENCE 66 APPENDIX .73 REFERENCE Agarwal, V., & Taffler, R (2008) Comparing the performance of market-based and accountingbased bankruptcy 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Market value of equity 1659 12.267 2.036 Volatility of equity 1659 4815756 106000000 Leverage 1659 1.454 1.349 Inflation 1659 12.748 7.255 Treasury bill 1659 8.298 3.084 Note: This table reports the descriptive statistics of independent variables as WCTA: The working capital over total asset; RETA: Retained earnings over total asset; EBITTA: Earnings before interest and taxes (operating profit) to Total assets; BVETA: Book value of equity to Total liabilities; MVE: Market value of equity; PRICE: Stock price; VOL_MVE: Volatility of market value of equity; LEVERAGE: Leverage ratio; INFLATION: treasury bill in one year Inflation; SHTBRDEF: Short-term Appendix B Binary logistic regression: ��� �� ��� � �� � = �� + �� � + �� + � � � �� � + ����(���) + ������������ + ���� + ������� + �� ��������� + ����������� ���� + � Marginal effect in pre-crisis period (2003-2009) Variable Model WCTA Model Model Model 0.000321 -0.000009 RETA -0.011928 EBITTA BVETL Model Model Model Model 0.000043 -0.000100 -0.000070 -0.010768 -0.010726 -0.009055 -0.009404 -0.265729 -0.242140 -0.256682 -0.239353 -0.252109 0.000081 0.000025 0.000055 0.000123 0.000123 PRICE 0.000000 0.000000 0.000000 0.000000 0.000000 MVE 0.005914 0.006037 0.005285 -0.000123 -0.000148 VOL_MVE 0.000000 0.000000 0.000000 0.000000 0.000000 LEVERAGE -0.011753 -0.011121 -0.009725 -0.000077 -0.000046 INFLATION SHTBRDEF 0.000089 0.001139 0.000215 0.000080 0.004190 0.000197 Note: This table presents the marginal effect for accounting, market and macroeconomic variables in pre-crisis Model and Model show only accounting variables as well as market variables respectively Model and Model reveal the accounting variables plus two macroeconomic variables while Model and Model indicate the market variables plus macroeconomic variables Model and Model present comprehensive model including all of the variables are accounting, market and macroeconomic variables The marginal effect is employed to interpret directly the effect of the regressors on the response variable Appendix C Binary logistic regression: � � � � ��� � � � � � � � � � = �� �� + �� �� � � + � � + ����(���) + � + ������������ + �� � � + ������� + �� ��������� + ����������� ���� + � Summary of statistics for independent variables in post-crisis period (2010-2016) Variable Obs Mean Std D Working capital/total asset 5077 0.198 0.243 Retained earnings /asset 5077 0.032 0.148 EBIT /Total assets 5077 0.024 0.071 Book value of equity / Total liabilities 5077 0.649 0.313 Price 5077 17300 11874 Market value of equity 5077 12.129 1.872 Volatility of equity 5077 683631 98964 Leverage 5077 0.886 1.012 Inflation 5077 7.228 5.352 Treasury bill 5077 7.354 Note: This table reports the descriptive statistics of independent variables as WCTA: The working capital over total asset; RETA: Retained earnings over total asset; EBITTA: Earnings before interest and taxes (operating profit) to Total assets; BVETA: Book value of equity to Total liabilities; MVE: Market value of equity; PRICE: Stock VOL_MVE: LEVERAGE: Volatility Leverage of market ratio; value INFLATION: SHTBRDEF: Short-term treasury bill in one year of price; equity; Inflation; 3.105 Appendix D Binary logistic regression: � � � � ��� � � � � � � � �� = �� �� + �� �� � � + � � � + + ����(���) + ������������ + �� � � + ������� + �� ��������� + ����������� ���� + � Marginal effect in post-crisis period (2010-2016) Variable Model WCTA Model Model Model -0.018123 -0.016384 -0.016199 RETA -0.052113 -0.056582 -0.057523 EBITTA -3.608555 -3.439809 -3.431021 BVETL -0.012977 -0.012338 -0.012458 Model PRICE -0.000004 -0.000004 MVE -0.036388 -0.037685 VOL_MVE 0.000000 0.000000 LEVERAGE -0.022332 -0.022296 INFLATION 0.000997 -0.002463 SHTBRDEF 0.001754 Note: This table presents the marginal effect for accounting, market and macroeconomic variables in post-crisis Model and Model show only accounting variables as well as market variables respectively Model and Model reveal the accounting variables plus two macroeconomic variables while Model and Model indicate the market variables plus macroeconomic variables Model and Model present comprehensive model including all of the variables are accounting, market and macroeconomic variables The marginal effect is employed to interpret directly the effect of the variable regressors on the response Appendix E Comparison ROC curve model 3,5,7 (2003-2016) Model (Acc + Macro): � = � � �� � � + � ��� + � �� � + � �� � + ��� (���) + �� �� ��������� + � � �� �� Model (Mak + Macro): � = ��� (���) + ������������ + ���� + ������� + �� ��������� + � �� Model (Acc + Mak + Macro): � = �� + �� � �� + �� ��� � � � +� ��� �� + � � � � (���) + � ���������� + ���� + ������� + �� ��������� + ����������� ���� + � S en sit ivi ty 00 75 50 25 00 0.00 0.25 0.50 1-Specificity Model ROC area: 0.9354 Model ROC area: 0.9341 0.75 1.00 Model ROC area: 0.6849 Reference Note: This figure reveals the Areas under the receiver operating characteristic curve of the model 3, model and model The good model may have the ROC curve straight up from (0,1) to (1,1) afterward across (1,1) Moreover, the accurate ratio (AR) of the model has defined the area under the ROC curve (AUC) The perfect model may have AR = or the ROC score=1 while the model that has no discriminatory power has AR = or ROC =0 The Model including the accounting and macroeconomic indicators are the best model possessing AR is 0.9354 unit and this model also has the ROC curve go further on the top left Appendix F Comparison ROC curve model 3,5,7 in pre-crisis period (2003-2009) Model (Acc + Macro): � = � � �� � � + ��� + � + � � � � �� �� + ��� (���) + �� �� ��������� + � � �� �� Model (Mak + Macro): � = ��� (���) + ������������ + ���� + ������� + �� ��������� + � �� Model 7: (Acc + Mak + Macro): � = �� + �� � �� + �� ��� � � +� � ��� � + � � (���) ++� ���������� � � ���� + ������� + �� ��������� + ����������� ���� + � S en sit ivi ty 00 75 50 25 00 0.00 0.25 Model ROC area: 0.9091 Model ROC area: 0.9089 0.75 0.50 1-Specificity 1.00 Model ROC area: 0.5957 Reference Note: This figure reveals the Areas under the receiver operating characteristic curve of model 3, model and model in the pre-crisis period The good model may have the ROC curve straight up from (0,1) to (1,1) afterward across (1,1) Moreover, the accurate ratio (AR) of the model has defined the area under the ROC curve (AUC) The perfect model may have AR = or the ROC score=1 while the model that has no discriminatory power has AR = or ROC =0 The Model including the accounting and macroeconomic indicators are the best model possessing AR is 0.9091 unit and this model also has the ROC curve go further on the top left Appendix G Comparison ROC curve model 3,5,7 in post-crisis period (2010-2016) Model (Acc + Macro): � = � � �� � � + ��� + � � � � + � �� �� �� + ��� (���) + �� �� ��������� � �� +� Model (Mak + Macro): � = ��� (���) + ������������ + ���� + ������� + �� ��������� + � �� Model 7: (Acc + Mak + Macro): � = �� + �� � �� + �� ��� � � � +� ��� � + � � (���) ++� ���������� � � ���� + ������� + �� ��������� + ����������� ���� + � 00 75 50 25 00 S en sit ivi 0.00 0.25 0.50 1-Specificity Model ROC area: 0.9394 Model ROC area: 0.9383 0.75 1.00 Model ROC area: 0.682 Reference Note: This figure reveals the Areas under the receiver operating characteristic curve of model 3, model and model in the post-crisis period The good model may have the ROC curve straight up from (0,1) to (1,1) afterward across (1,1) Moreover, the accurate ratio (AR) of the model has defined the area under the ROC curve (AUC) The perfect model may have AR = or the ROC score =1 while the model that has no discriminatory power has AR = or ROC =0 The Model including the accounting and macroeconomic indicators are the best model possessing AR is 0.9394 unit and this model also has the ROC curve go further on the top left ... in case of financial distress firms and he found that high leveraged lead high financial distress state due to high financial expenses and high financial obligations Moreover, the financial distress. .. kind in Vietnam, for the purpose of financial distress and bankruptcy prediction for listed firms Using a sample of more than 800 Vietnamese listed firms on the Vietnam? ??s stock exchanges in the... fundamental indicators using accounting data (the Altman model) which can be used to measure financial distress and bankruptcy for listed firms in Vietnam?  What are the most relevant indicators using