Factors in Corporate Governance Affect to Default Risk of Companies Listed on Vietnam Stock Market Mai KyQuoc Mbus 2.4 – 2011 Maikyquoc@yahoo.com University of Economics HCM City International School of Business Master Thesis Supervisor Dinh Cong Khai January 2014 i ACKNOWLEDGEMENT First of all, I would like to thank Dr Khai who has supervised with strength, knowledge and vitality that has supported me to finish this research Secondly, I would like to appreciate to my family for moral and financial support, encouragement and motivation Thirdly, I own particular thanks to professors, ISB community and all my classmates who have gone with me so far and helped me to achieve the knowledge Finally, I would like to thank my friends who have helped me in several ways to accomplish this research and authors of previous researches which provided me theoretical framework to support for my findings in researching ii ABSTRACT Corporate governance plays important role in developing of company Good corporate governance ensures strong management performance, efficient capital structure, and constant investment strategy Besides that, default risk is the uncertainty surrounding a firm's ability to service its debts and obligations Corporate governance is regarded as the most important key, affect to defaults risk Using secondary data from 70 companies which represent for all industries, listed on Vietnam Stock market, this research empirically identify the relationship of factors in corporate governance such as the power of CEO, proportion of government ownership, and salaries & remuneration of board of directors to default risk Key words: corporate governance, default risk, EDF score, KMV model iii TABLE OF CONTENT ACKNOWLEDGEMENT i ABSTRACT ii TABLE OF CONTENT iii LIST OF FIGURES v LIST OF TABLES vi LIST OF ABBREVIATIONS vii CHAPTER INTRODUCTION 1.1 Research Background 1.2 Statement of Problem 1.3 Research Objective and Research Question 1.4 Scope of the Research 1.5 Research Structure CHAPTER LITERATURE REVIEW 2.1 Corporate Governance 2.1.1 Overview Corporate Governance 2.1.2 Corporate Governance in Vietnam 2.2 Default Risk 11 2.3 Factors Affect to Default Risk and Hypothesis Development 12 2.3.1 Government Ownership 12 2.3.2 Scale Board 13 2.3.3 CEO power 14 2.3.4 Payment of Board 15 2.3.5 Other Factors Affect to Default Risk 15 2.3.6 Conceptual Framework 18 CHAPTER RESEARCH METHODOLOGY 20 3.1 Research Process 20 3.2 Model Testing 20 3.2.1 Model Selection 20 3.2.2 KMV Model 23 3.3 Target Population and Sample 24 iv 3.3.1 Target Population 24 3.3.2 Sample 25 3.4 Data collection 25 3.5 Method Analysis 28 3.5.1 Descriptive Analysis 28 3.5.2 Multiple Regression Strategy 28 CHAPTER RESULTS 30 4.1 Descriptive Statistic 30 4.2 Estimation of Regression Models and Hypothesis Testing 33 4.2.1 Estimating the Model 33 4.2.2 Inspection of Multicollinearity 36 4.2.3 Check the Autocorrelation 39 4.2.4 Test of the Heteroscedasticity 42 4.2.5 Testing the Assumption of Normal Distribution of the Error Term 42 4.2.6 Ramsey Test for Omitted Variables 43 4.3 Summurize the Result 45 CHATER CONCLUSION AND IMPLICATIONS 47 5.1 Research Finding 47 5.2 Managerial Implications 48 5.3 Limitations and Future Research 51 REFERENCES 53 APPENDIX 58 v LIST OF FIGURES Figure 2.1 The conceptual framework 19 Figure 3.1 Research process 20 vi LIST OF TABLES Table 3.1 Describing variables 22 Table 3.2 The relationship between dependent variable and independent variables 23 Table 3.3 The source of independent variables 27 Table 3.4 Descriptive statistic variables 28 Table 4.1 The result from the first regression 34 Table 4.2 Wald test redundant variables 35 Table 4.3 The result from the second regression 36 Table 4.4 Coefficients between pairs of variables 38 Table 4.5 Value of the Variance- Inflation factor 39 Table 4.6 The result after adjusting endpoints 41 Table 4.7 The result from Ramsey Reset Test 45 vii LIST OF ABBREVIATIONS CEO Chief Executive Officer CO Coefficient EDF Expected Probability of Default HET Heteroscedasticity OLS Ordinary Least Square VAR Variable VARS Variables VIF Variance Inflation Factor CHAPTER INTRODUCTION 1.1 Research Background Throughout many centuries lenders have been concerned whether loans would be paid or not Many strategies are applied to build several models to count and forecast default risk Much effort is applied to identify factors that affect these risks The problem is a still acute, especially in the light of the recent financial, and more researches are done in this discipline On contrast, corporate governance is put under scrutiny for the last three decades The young research discipline deserves attention of theories and practitioners The issues of financial statement, ownership and controlling, compensation and incentives are typically and widely argued in professional society, business circles, and regulatory basic of corporate governance in protecting shareholders’ interests has become more important than ever Following that, effective corporate governance ensures strong management performance, efficient allocation of resource and sound investment strategy Altogether these factors improve companies` financial health, reduce information asymmetry between the firm and outside investors, reduce default risk and ultimately benefit the securities holders (Chiang, Yeh and Chen, 2009) However, strong shareholder, oriented corporate governance might ignore the interests of credits Especially on the verge of financial distress, strategies that maximize shareholders outcome may lead to making value – destroying decisions, and result to harm debt holders (Shleifer and Vishny, 1997) Although creditors are priority claimants in cases of a company`s financial distress, highly leveraged firms, on the verge of bankruptcy, have strong incentives to gamble with lender`s money in favor of the stockholders Therefore, it is the serious risk which the securities holder have accepted when the company get involve in high default risk A series of big bankruptcy in the world such as Enron (2001), WorldCom (2002), Lehman Brothers (2008), General Motors (2009), and CIT Group (2009) quite shock many investors and caused several serious problems to society Applying to Vietnam stock market, the relation between corporate governance and defaults risk is still a concern to investors More than a decade of existence and development, Vietnam's stock market has gone through many changes Currently, there are more than 687 companies from different sectors listed on both exchanges in Hanoi and Ho Chi Minh City The stocks are influenced by the economic situation, the financial world and the region, along with the impact of policies and management mechanisms of the government (Toan and Gordon, 2008) The default risk plays the most important role in making long term investment decision Vietnam stock market also experiences many risks in investment In year 2012, 11 securities are placed under special control in year 2012 Also in year 2012, four companies are withdrawn securities brokerage operation Statistical results of the business of securities companies show that more than 50 % loss in 2012 and own more than 70 % of companies have accumulated losses Especially, 21 listed companies are delisted in year 2012, and in first months of 2013, 16 stocks are delisted There are several stocks such as SBS (Sacombank Securities Company), DDM (Vina Corporation Company), VES (Me Ca Veneco Corporation Company), and TLT (Viglacera Thang Long Corporation Company) which used to be attracted by many investors, have been delisted (Vietstock, 2013) Such companies are quite weakness of corporate governance As the result, the share and bond holders of such companies are accepted the risk of losing money Therefore, besides using some information about the company performance such as return of asset, return of equity, cash liquidity, and rate of debt and so on Investors also require 50 Agreement on executive remuneration: The shares of the company have agreed to pay compensation for executives depend largely on the results of operations of the company in the next year or the rights stock options in the future will have better growth potential Of course, all the criteria given above are not entirely complete and accurate for all cases It is merely to suggest adding to the selection criteria of the current stock investors, aims to help investors better understand the impact of these factors on the risk of the company is consider investing, with the right investment decisions and more effective Especially in volatile market situation as bad as this is influenced by a series of bad news about the macroeconomic situation Application of probability of default of the KMV Model As discussed in chapter & 3, the probability of insolvency EDF using the market value of the stock , and the leverage ratio to measure the probability of default of the company So, of course, this model is evaluated as well as reviews the traded companies, the public interest However, according to Moody’s, the model is not only that EDF model is applied well to the company that trading at the stock, and the shares of the company Moody's KMV experiences with small companies on the U.S stock market The study is the securities of small companies and fewer transactions on the market dynamics There are 2000 companies and the observed market value of less than $ 20 million (of which about 1,000 companies with market capitalization less than $ million) of which most have not been listed and traded only deal agreement - which is the most insolvent companies easier And Moody’s model has found EDF model works perfectly well with this type of company Moody's KMV also had a number of applied researches for the company without share trading There is a version RiskCalc to solve this problem RiskCalc is also a model with similar features like Moody KMV model RiskCalc which use financial ratios to calculate 51 the financial statement only a synthetic form of the index from the financial statements Then Moody's conducted a study based on the huge amount of their data about the company and industry data to calculate the default threshold and the default distance From EDF is also calculated by Moody's own distribution I still can use EDF to the risk of a loan that is guaranteed based on property assets that are traded in the market This is a very interesting application if applied in practice in the banking and credit institutions 5.3 Limitations and Future Research This research has several limitations: The first is to use EDF model - the probability of default by Moody's KMV is a factor representing the probability of default However, there is much debate on this issue Although the KMV of model has many advantages in Vietnam but currently there is no research to verify the accuracy of the EDF forecasts than other models On the other hand, Vietnam's stock market is still small, not large scale; the level of investment is not equivalent to each other, so the efficiency of markets is always conservative The study knows that KMV model predict well in large markets, efficient, transparent information However, Vietnam's market is still small, the number of shares to be listed on stock exchange market only 687 companies in 2013 Second, the pattern observed for the 70 largest companies on the stock exchange This sample is statistically significant However representative of the whole market is limited Time is limited, the amount of computation is quite large, and the research hardly can more calculations in the number of such shares Thirdly, the variables in the model are taken out in the financial statements mainly However, not every company announced a full range of information about "corporate governance mass" in the annual report So, in the process of gathering data, the study has 52 also re- record all the information possible on the corporate mass media Finally, the model can calculate the autocorrelation phenomena, the study have sought to overcome, and the model of Ramsay RESET test is the phenomenon of missing variables, however, adjusted coefficient of determination is high above 80 %, the study could not find missing variables are variables that are statistically significant On the other hand most, a lot of changes in the governance of public companies have no statistical significance in the model Indeed this is the most unfortunate of the model However, the study still find variables unique to the market model in Vietnam, is shown in the model, especially variable GOV Expanding the model to calculate the probability of insolvency EDF, the study have to use the standard distribution, additional other studies demonstrated normal distribution is consistent with Vietnam stock market or find a distribution determined other manufacturing 53 REFERENCES Anderson, R.C., Sattar, M A., & Reeb, D M (2004) Board characteristics, accounting 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Listed Companies in Ghana Journal of Finance and Accounting, 3(2), 16872222 Yermack, D (1996) Higher market valuation of companies with a small board of directors Journal of Financial Economics, 40, 185-211 Zahra, S A., & Pearce, J A (1989) Boards of Directors and Corporate Financial Performance Journal of management, 15, 291-334 Zeitun, R., & Tian, G G (2007) Capital structure and corporate performance: evidence from Jordan Australasian Accounting Business and Finance Journal 1(4), 1-24 58 APPENDIX Statistics result from Jaque-Bera variables BIG 12 Series: BIG Sample 140 Observations 140 10 12.5 25.0 37.5 50.0 62.5 75.0 87.5 Mean Median Maximum Minimum Std Dev Skewness Kurtosis 50.19171 49.36000 91.25000 10.77000 18.71792 0.072682 2.343004 Jarque-Bera Probability 2.641183 0.266977 Inspection of the normal distribution assumption of U 16 Series: Residuals Sample 140 Observations 139 12 Mean Median Maximum Minimum Std Dev Skewness Kurtosis Jarque-Bera Probability -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 -2.19E-13 -0.024454 1.896851 -1.425724 0.567321 0.222501 3.545184 2.868335 0.238314 59 List of companies chosen in model Unit Stock Unit Stock Unit Stock Unit Stock Unit Stock 10 11 12 13 14 15 VNM FPT HPG PVD KDC PPC GMD REE DHG VSH MPC TRC BMP PAC SAM 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 PET HRC VIS TCM RIC BT6 VTO VNE VIP IMP HBC BHS ABT SMC TAC 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 TTP COM SC5 SC6 SJD TMS KHP PGC SSC TNC DHA SFC HMC TCR CDC 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 BBC AGF VID ACL LAF SAV GIL DCT TS4 DIC NSC RAL KHA TPC CLC 61 62 63 64 65 66 67 68 69 70 TNA LGC BMC SCD MCV SFI TYA UIC GMC HTV 60 The result from the first regression Dependent Variable: EDF Method: Least Squares Date: 11/23/13 Time: 19:45 Sample: 140 Included observations: 140 Variable Coefficient Std Error t-Statistic Prob C BIG CR DEBT DIR DIV DM FCF GOV INF MAN SA STA SUM TOBINSQ -8.237202 0.003920 -0.066460 -0.028189 -0.007621 -0.003046 0.122401 -0.130686 0.014996 -0.050030 0.000681 0.113630 20.46295 0.006434 0.050116 0.567661 0.004174 0.033694 0.002073 0.005519 0.006713 0.126794 0.292938 0.004239 0.123066 0.003905 0.050050 0.828118 0.025794 0.094395 -14.51077 0.939280 -1.972445 -13.59505 -1.380783 -0.453747 0.965355 -0.446123 3.537620 -0.406531 0.174414 2.270343 24.71017 0.249426 0.530913 0.0000 0.3494 0.0508 0.0000 0.1698 0.6508 0.3362 0.6563 0.0006 0.6850 0.8618 0.0249 0.0000 0.8034 0.5964 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat 0.858444 0.842590 0.615736 47.39132 -122.8272 1.514467 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic) 2.153421 1.551953 1.968959 2.284135 54.14609 0.000000 61 The result from the second regression Dependent Variable: EDF Method: Least Squares Date: 11/23/13 Time: 19:51 Sample: 140 Included observations: 140 Variable Coefficient Std Error t-Statistic Prob C CR DEBT DM GOV SA STA -8.250901 -0.063547 -0.028499 0.145717 0.012588 0.092492 20.57640 0.429458 0.032705 0.001917 0.106452 0.002639 0.041361 0.792013 -19.21234 -1.943017 -14.86938 1.368858 4.769770 2.236215 25.97988 0.0000 0.0541 0.0000 0.1734 0.0000 0.0270 0.0000 R-squared R-Adjusted squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat 0.855561 0.849045 0.602979 48.35662 -124.2386 1.509119 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic) 2.153421 1.551953 1.874838 2.021920 131.3008 0.000000 The result after adjusting endpoints Dependent Variable: EDF Method: Least Squares Date: 11/23/13 Time: 19:57 Sample(adjusted): 140 Included observations: 139 after adjusting endpoints Convergence achieved after 10 iterations Variable Coefficient Std Error t-Statistic Prob C CR DEBT DM GOV SA STA AR(1) -8.129707 -0.055203 -0.025742 0.229177 0.012282 0.140258 20.09613 0.316533 0.409619 0.033281 0.001928 0.113689 0.002848 0.052507 0.743098 0.085049 -19.84699 -1.658677 -13.35188 2.015824 4.312465 2.671245 27.04370 3.721788 0.0000 0.0996 0.0000 0.0459 0.0000 0.0085 0.0000 0.0003 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat 0.867161 0.860062 0.582281 44.41576 -117.9413 2.064193 Inverted AR Roots 32 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic) 2.158136 1.556559 1.812105 1.980996 122.1648 0.000000 62 The result from Ramsey Reset Test Ramsey RESET Test: F-statistic Log likelihood ratio 12.38339 24.41183 Probability Probability 0.000012 0.000005 Test Equation: Dependent Variable: EDF Method: Least Squares Date: 11/23/13 Time: 22:45 Sample: 140 Included observations: 139 Convergence achieved after 11 iterations Variable Coefficient Std Error t-Statistic Prob C CR DEBT DM GOV SA STA FITTED^2 FITTED^3 AR(1) -2.990258 0.034488 -0.012612 0.018891 0.002714 0.034101 8.031652 0.238050 -0.019340 -0.131011 1.278713 0.031704 0.003463 0.092530 0.003085 0.037960 2.833830 0.040494 0.003007 0.100798 -2.338490 1.087813 -3.642400 0.204158 0.879659 0.898339 2.834204 5.878636 -6.430772 -1.299730 0.0209 0.2787 0.0004 0.8386 0.3807 0.3707 0.0053 0.0000 0.0000 0.1960 R-squared Adjusted R-squared S.E of regression Sum squared resid Log likelihood Durbin-Watson stat 0.888557 0.880782 0.537449 37.26183 -105.7354 1.995283 Inverted AR Roots -.13 Mean dependent var S.D dependent var Akaike info criterion Schwarz criterion F-statistic Prob(F-statistic) 2.158136 1.556559 1.665258 1.876371 114.2822 0.000000 63 Table shows the way to calculate EDF form KMV Model Data/ Asumption Equity value Et Column1 177.389.975.700,000 Equity volatility бt Short-term DEBT 0,454 Long-term DEBT Liabilities Lt Risk-free rate r Horizon ( T-t) Unknowns Assets Value At 43.038.053.907 43.038.053.907,000 11,00% date 31/12 ( Adjusted Price ) * numbers of stock Financial statement Financial statement Column1 220.428.029.607 Assets Volatility бA Model values from Black-Scholes fomular d1 d2 Equity value Note 0,374535698 = ( B6+B2) = ( B3*B2)/B11 Column1 4,842328921 4,467793224 181.873.082.718,17 (LN(B11/B6)+(B7+1/2*B12^2)*B8)/(B12*SQRT(B8)) B15-B12*SQRT(B8) 0,453932554 B11*NORMSDIST(B15)-B6*EXP(-B7*B8)*NORMSDIST(B16) (B11/B17)*B12*NORMSDIST(B15) 0,000638705 (B17/B2-1)^2+(B18/B3-1)^2 → Run solver to find Equity Volatility Objective : minimize deviation data-model Square rel.erros Column1 Default Point 43.038.053.907,00 B4+1/4*B5 Distant to default Prob(default) 2,15 (B11-B24)/(B11*B12) NORMSDIST(-B25) 1,58% 64 ... factors from macroeconomic instability The second relates to subjective factors of corporate governance This paper only focuses on finding how some factors of corporate governance affecting to. .. Vietnam stock market, aim to identify default risk Therefore, the study focuses on answering two questions: What factors in corporate governance affect to default risk of companies listed on Vietnam. .. change of stock price It is no easy to calculate the default risk Besides that, there are many reasons to explain the cause of the default risk of companies listed on the stock market in Vietnam