Determinants of financial distress, a study of listed companies in vietnam

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY TRẦN THỊ KIM PHƯỢNG DETERMINANTS OF FINANCIAL DISTRESS: A STUDY OF LISTED COMPANIES IN VIET NAM ECONOMICS MASTER THESIS Major: Business Administration Ho Chi Minh City - 2012 MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY TRẦN THỊ KIM PHƯỢNG DETERMINANTS OF FINANCIAL DISTRESS: A STUDY OF LISTED COMPANIES IN VIET NAM ECONOMICS MASTER THESIS Major: Business Administration Major Code: 60.34.05 INSTRUCTOR: Võ Xuân Vinh, Ph.D Ho Chi Minh City - 2012 i ABSTRACT This study focuses on researching the relationship between a set financial ratios and the probability of failure companies Through the Logistic regression method, the results show that EPS, Cash per shares and Asset turnover are the most important financial ratios, which help investors to identify the financial distress of listed companies in Vietnam Stock Exchange ii ACKNOWLEGEMENTS During the time for conducting my thesis, I have been strongly supported by many people Through these words, I would like to extend my sincere to all of them The first one I would like to express my sincere gratitude is my direct supervisor PhD Vo Xuan Vinh, who provides me his great guidance day by day until completing the thesis The next ones I would like to express my special gratitude are my family, who are always by my side and encourage me if necessary Last but not least, my thesis would be nothing without the enthusiasm and information from my friends Tran Thi Kim Phuong Ho Chi Minh City, December 2012 iii STUDENT DECLARATION I hereby declare that the content in this thesis is my own, except for special references, quotations and summaries All data, references using in this research are clearly identified The thesis has not been accepted for any degree until now SIGNED: DATE: iv TABLE OF CONTENT ABSTRACT i ACKNOWLEGEMENTS ii STUDENT DECLARATION iii TABLE OF CONTENT iv LIST OF TABLES vi Chapter 1: Introduction of the study 1.1 Rationale of the study 1.2 Research objectives and questions a) Research objectives b) Research questions 1.3 Structure of the study Chapter 2: Literature Review 2.1 Definition of financial distress 2.2 Ratios in designing models 2.3 Techniques used in financial distress predictions 12 2.4 Hypotheses 15 2.5 Conclusions 16 Chapter Research Methods 17 3.1 The model 17 3.2 Selection of predictor variables 18 3.3 Data set 20 Chapter Data analysis and Findings 23 v 4.1 Descriptive Statistics 23 4.2 Correlations 24 4.3 Regression model 25 Chapter Conclusions 30 5.1 Summary 30 5.2 Limitation of the research study 31 REFERENCES 33 APPENDICES 36 vi LIST OF TABLES Table Summary statistics 23 Table Variable correlation 25 Table The performance of logistic regression for models 26 vii LIST OF ABBREVIATIONS HOSE: Ho Chi Minh City Stock Exchange MDA: Multiple Discriminant Analysis ANN: Artificial Neural Networks WOCA: Working capital GROPROM: Gross profit margin EPS: Earnings per share DEBTTOTAL: Total debt to total assets CASPSHARE: Cash flow per share ATURNOVER: Asset turnover SALEPERCA: Sales per cash SALEPERRE: Sales per receivables 29 Besides, from the result of the model and model 5, McFadden Rsquared of the model decreased from 29.25% to 21.74% when replacing EPS with CASPSHARE It is clearly that EPS had stronger effect on the probability of failure stronger in comparison with CASPSHARE As such, analyzing the model and model showed that the influence of ATURNOVER on the probability of failure was more powerful than the effect of SALEPERRE Moreover, the result of logistic regression also demonstrated that the impact of CASPSHARE on the probability of failure seemed to be higher compared to ATURNOVER Finally, with coefficients of independent variables achieved, it is reasonable to assume that EPS, Cash per shares and Asset turnover have the most impact on the probability of the failure 30 Chapter Conclusions 5.1 Summary This study have been conducted to find out the relationship between a set of financial ratios and the probability of financial distress for listed companies in Viet Nam Stock Exchange, especially one year prior to the failure Through applying the logistic regression analysis, the results show that six out of eight financial ratios as Earnings per share, Asset turnover, Sale per receivables, Cash per share, Working capital, Gross profit margin are negatively related to the probability of failure Besides, Earnings per share, Asset turnover, and Cash per share are the most important three financial ratios, which have dramatic effect on the state of firms The two ratios positively correlated to the probability of a firm going into financial distress are Total debt to total assets and Sale per cash By comparison, the findings of this thesis regarding to the profit ratios and activity ratios is consistent with the previous researches, i.e the Z – Score model of Altman (1978) Moreover, the result of logistic regression also indicates that the financial distress of listed companies in Viet Nam Stock Exchange derives from ineffective operation activities leading the loss (EPS) as well as the capacity in managing companies’ assets to generate revenue (Asset turnover) The aforementioned conclusions regarding the relationship between financial ratios and the state of a company seems to be quite helpful for investors in Viet Nam For example, investors should collect information about company’s health with involved ratios It will help them identify firms 31 in financial distress, and reduce risk as much as possible when investing some stocks like that 5.2 Limitation of the research study However, one of some limitations is that independent variables still not contributed enough to the explanation for the financial distress McFadden R-squared of a model fluctuates around 30% One reason is that the data can only be collected in the period from 2007 – 2011, when the number of financial distress listed companies is still not much Although there have been more and more listed firms falling into the financial distress in 2012, gathering financial ratios is impossible due to not having the financial statements at this time The limited data lead to the result that it is difficult to find out more ratios affecting the probability of the failure such as market ratios Besides, financial ratios are computed from financial statements, which have problems inherent in the interpretation of accounting standards This together with the fact that the results of the thesis depend much on the reliability of the financial statements are the another limitation With these limitations mentioned above, they suggest further research to supplement what the thesis could not reach The conclusions involved in the relationship between variables and the probability of failure are used as a base for the further research However, each financial market with different characteristic will react in not exactly the same way Thus, a set of financial ratios continue to be researched as adding more macroeconomic ratios such as inflation In addition, the future researches need to study the data over a much longer period as well as analysis the two or three years prior to the financial 32 distress time Furthermore, apart from the traditional techniques such as Logistic Regression and Multivariate Discriminant Analysis, some new methods have proven the advantage of predicting the financial distress, i.e Neural Networks Thus, these techniques will be taken into consideration in later researches Such studies have a significant contribution to the field of forecasting the failure in Viet Nam 33 REFERENCES Altman, E.I 1968, 'Financial ratios, discriminant analysis and the prediction of corporate failure', Journal of Finance, vol 23, no 4, pp 589-609 Beaver, W.H 1966, 'Financial ratios as predictors of failure', Journal of Accounting Research, vol 4, no 3, pp 71-111 Boritz, K., and Sun 2007, 'The effect of general price level adjustments on the predictability of financial ratios', Journal of Accounting Research, pp 273-84 Bradstreet 1985, 'Classifying Bankrupt Firms with Funds Flow Components', Journal of Accounting Research, pp 146-60 Charalambous, C., Charitou, A & Kaourou, F 2000, 'Comparative analysis of artificial neural network models: Application in bankruptcy prediction', Annals of Operations Research, vol 99, pp 403-25 Chin, F.a 2002, 'The differential bankruptcy predictive ability of specific price level adjustments: some empirical evidence', Accounting Review, vol 58, no 2, pp 228-46 Deakin, E.B 1972, 'A Discriminant Analysis of Predictors of Business Failures', Journal of Accounting Research, vol 10, no 1, pp 167-79 Dougall, G.a 1952, 'Predicting Corporate Failure', Accountants Digest, p Foster 1986, 'Current Cost Accounting and the Prediction of Small Company Performance', Journal of Business Finance and Accounting, vol 13, pp 51-70 Goudie 1987, 'Funds Flow Components, Financial Ratios and 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Models: A Review of Their Usefulness', British Journal of Management, vol 2, pp 89 - 102 35 Sharda, W 1994, 'Predicting Corporate Bankruptcy and Financial Distress: Information Value Added by Multinomial Logit Models', Journal of Economics and Business, pp 269-86 Somerville 1989, 'Failing company discriminant analysis', Journal of Accounting Research, vol 12, no 1, pp 1-25 Taffler 1985 ' Industrial classification in UK capital markets: a test of economic homogeneity', Journal: Applied Economics, vol 17, no 2, pp 291-308 Udo 1993, 'An Empirical Comparison of Bankruptcy Models', The Financial Review, vol 33, pp 35 – 54 Zavgren 1983, 'Predicting bankruptcy resolution', Journal of Business Finance & Accounting, vol 29, no 3, pp 497-520 36 APPENDICES Appendix 1: The model Dependent Variable: Y Method: ML - Binary Logit (Quadratic hill climbing) Date: 12/20/12 Time: 20:51 Sample: 982 Included observations: 982 Convergence achieved after iterations Covariance matrix computed using second derivatives Variable C WOCA GROPROM EPS ATURNOVER SALEPERCA McFadden R-squared S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter LR statistic Prob(LR statistic) Obs with Dep=0 Obs with Dep=1 Coefficient Std Error 1.905946 0.323463 -0.316477 345.0324 1.428423 -0.002377 0.298966 0.188021 0.232597 0.262472 0.243961 92.29115 36 946 0.338145 0.781103 0.494623 58.08971 0.423241 0.001504 Mean dependent var S.E of regression Sum squared resid Log likelihood Restr log likelihood Avg log likelihood Total obs z-Statistic 5.636473 0.414111 -0.639835 5.939647 3.374964 -1.579853 Prob 0.6788 0.5223 0.0007 0.1141 0.96334 0.172039 28.88697 -108.205 -154.351 -0.11019 982 37 Appendix 2: The model Dependent Variable: Y Method: ML - Binary Logit (Quadratic hill climbing) Date: 12/20/12 Time: 20:35 Sample: 982 Included observations: 982 Convergence achieved after iterations Covariance matrix computed using second derivatives Variable C GROPROM EPS DEBTTOTAL ATURNOVER SALEPERCA McFadden R-squared S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter LR statistic Prob(LR statistic) Obs with Dep=0 Obs with Dep=1 Coefficient Std Error 1.903546 -0.37075 354.9522 0.104449 1.444411 -0.002467 0.298455 0.188021 0.232757 0.262632 0.244122 92.13349 36 946 0.525369 0.503576 56.9017 0.858377 0.422483 0.001454 Mean dependent var S.E of regression Sum squared resid Log likelihood Restr log likelihood Avg log likelihood Total obs z-Statistic 3.623257 0.736234 6.23799 0.121682 3.418864 1.697041 Prob 0.0003 0.4616 0.9032 0.0006 0.0897 0.96334 0.172443 29.02307 -108.284 -154.351 -0.11027 982 38 Appendix 3: The model Dependent Variable: Y Method: ML - Binary Logit (Quadratic hill climbing) Date: 12/20/12 Time: 20:49 Sample: 982 Included observations: 982 Convergence achieved after iterations Covariance matrix computed using second derivatives Variable C WOCA GROPROM EPS SALEPERCA SALEPERRE McFadden R-squared S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter LR statistic Prob(LR statistic) Obs with Dep=0 Obs with Dep=1 Coefficient Std Error 1.828481 1.437497 -0.029329 324.4049 -0.002101 0.198604 0.292518 0.188021 0.234624 0.264499 0.245988 90.30054 36 946 0.372062 0.85208 0.569854 62.24051 0.001421 0.069535 Mean dependent var S.E of regression Sum squared resid Log likelihood Restr log likelihood Avg log likelihood Total obs z-Statistic 4.914455 1.687044 0.051467 5.212119 1.477884 2.856167 Prob 0.0916 0.959 0.1394 0.0043 0.96334 0.172391 29.0055 -109.2 -154.351 -0.1112 982 39 Appendix 4: The model Dependent Variable: Y Method: ML - Binary Logit (Quadratic hill climbing) Date: 12/20/12 Time: 20:49 Sample: 982 Included observations: 982 Convergence achieved after iterations Covariance matrix computed using second derivatives Variable C GROPROM EPS DEBTTOTAL SALEPERCA SALEPERRE McFadden R-squared S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter LR statistic Prob(LR statistic) Obs with Dep=0 Obs with Dep=1 Coefficient Std Error 2.233043 -0.187938 360.2532 -0.105499 -0.002043 0.171723 0.283148 0.188021 0.237569 0.267444 0.248934 87.40812 36 946 0.525691 0.563743 59.97996 0.873924 0.001346 0.065661 Mean dependent var S.E of regression Sum squared resid Log likelihood Restr log likelihood Avg log likelihood Total obs zStatistic Prob 4.247825 0.333374 6.006227 0.120718 1.518495 2.615304 0.7389 0.9039 0.1289 0.0089 0.96334 0.174029 29.55923 -110.647 -154.351 -0.11268 982 40 Appendix 5: The model Dependent Variable: Y Method: ML - Binary Logit (Quadratic hill climbing) Date: 12/20/12 Time: 20:55 Sample: 982 Included observations: 982 Convergence achieved after iterations Covariance matrix computed using second derivatives Variable C WOCA GROPROM CASPSHARE SALEPERCA SALEPERRE McFadden R-squared S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter LR statistic Prob(LR statistic) Obs with Dep=0 Obs with Dep=1 Coefficient Std Error 1.085779 3.487755 1.253483 77.79161 -0.002601 0.191384 0.217475 0.188021 0.258214 0.28809 0.269579 67.13468 36 946 0.328923 0.789957 0.542987 19.78064 0.001315 0.063976 Mean dependent var S.E of regression Sum squared resid Log likelihood Restr log likelihood Avg log likelihood Total obs z-Statistic 3.301014 4.415119 2.308494 3.932715 -1.977925 2.99149 Prob 0.001 0.021 0.0001 0.0479 0.0028 0.96334 0.177531 30.761 -120.783 -154.351 -0.123 982 41 Appendix 6: The model Dependent Variable: Y Method: ML - Binary Logit (Quadratic hill climbing) Date: 12/20/12 Time: 20:57 Sample: 982 Included observations: 982 Convergence achieved after iterations Covariance matrix computed using second derivatives Variable C WOCA GROPROM CASPSHARE ATURNOVER SALEPERCA McFadden R-squared S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter LR statistic Prob(LR statistic) Obs with Dep=0 Obs with Dep=1 Coefficient Std Error 1.164864 2.444658 0.677108 96.75102 1.163216 -0.002794 0.317413 0.720135 0.430495 20.99639 0.361959 0.001373 0.210934 0.188021 0.26027 0.290146 0.271635 65.1154 Mean dependent var S.E of regression Sum squared resid Log likelihood Restr log likelihood Avg log likelihood 36 946 Total obs z-Statistic Prob 3.669864 3.394724 1.572858 4.607984 3.213669 -2.035174 0.0002 0.0007 0.1158 0.0013 0.0418 0.96334 0.179527 31.45642 121.7928 154.3505 0.124025 982 42 Appendix 7: The model Sample: 982 Included observations: 982 Convergence achieved after iterations Covariance matrix computed using second derivatives Variable C GROPROM DEBTTOTAL CASPSHARE ATURNOVER SALEPERCA Coefficient Std Error 2.530663 0.847624 -2.013271 92.87835 1.257175 -0.003141 0.533118 0.436244 0.824441 20.66037 0.364339 0.001238 McFadden R-squared S.D dependent var Akaike info criterion 0.193098 0.188021 0.265877 Mean dependent var S.E of regression Sum squared resid Schwarz criterion 0.295752 Log likelihood Hannan-Quinn criter 0.277242 Restr log likelihood LR statistic Prob(LR statistic) 59.60965 Avg log likelihood Obs with Dep=0 Obs with Dep=1 36 946 Total obs z-Statistic 4.746907 1.943003 -2.441983 4.495483 3.450562 -2.53649 Prob 0.052 0.0146 0.0006 0.0112 0.96334 0.182324 32.44427 124.5457 154.3505 0.126829 982 43 Appendix 8: The model Dependent Variable: Y Method: ML - Binary Logit (Quadratic hill climbing) Date: 12/20/12 Time: 20:32 Sample: 982 Included observations: 982 Convergence achieved after iterations Covariance matrix computed using second derivatives Variable C GROPROM DEBTTOTAL CASPSHARE SALEPERCA SALEPERRE McFadden R-squared S.D dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter LR statistic Prob(LR statistic) Obs with Dep=0 Obs with Dep=1 Coefficient Std Error 2.747885 1.340299 -1.722875 78.46636 -0.002334 0.132492 0.529652 0.561107 0.819993 19.76388 0.001215 0.056802 0.167191 0.188021 0.274021 0.303897 0.285386 51.61202 Mean dependent var S.E of regression Sum squared resid Log likelihood Restr log likelihood Avg log likelihood 36 946 Total obs z-Statistic 5.188094 2.388671 2.101085 3.97019 -1.92054 2.332535 Prob 0.0169 0.0356 0.0001 0.0548 0.0197 0.96334 0.184105 33.08126 128.5445 154.3505 0.130901 982 ... from stable and unstable firms However, performing financial ratios need to pay more attention because of the interpretation of accounting standards as a base for financial reports As regards collecting... being examined For instance, in the study related to Malaysia Stock Exchange, the financial distress companies are defined according to the following options: a) Closing down under Companies Act... 2.1 Definition of financial distress It can be said that there are many definitions used in researches regarding financial distress Dun and Bradstreet (1985) clarify the financial distress as the