Mabe, Queen Magadi and Lin, Wei (2018): Determinants of Corporate Failure: The Case of the Johannesburg Stock Exchange.

PDF
MPRA_paper_88485.pdf Download (610kB)  Preview 
Abstract
The aim of this paper is to estimate the probability of default for JSE listed companies. Our distinctive contribution is to use the multisector approach in estimating corporate failure instead of estimating failure in one sector, as failing companies are faced with the same challenge regardless of the sectors they operate in. The study creates a platform to identify the effect of Bookvalue to Marketvalue ratio on the probability to default, as this variable is often used as a proxy for corporate default in asset pricing models. Moreover, the use of Classification and Regression Trees uncovers other variables as reliable predictors to estimate corporate failure as the model is designed to choose the covariates with respect to classification ability. Our study also serves to add to the literature on how Logistic model performance compares to Machine Learning methods such as Classification and Regression Trees and Support Vector Machines. The study is the first to apply Support Vector Machines to predict failure on South African listed companies.
Item Type:  MPRA Paper 

Original Title:  Determinants of Corporate Failure: The Case of the Johannesburg Stock Exchange 
English Title:  Determinants of Corporate Failure: The Case of the Johannesburg Stock Exchange 
Language:  English 
Keywords:  Corporate default, Logistic Regression, Support Vector Machines, Classification and Regression Trees. 
Subjects:  C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61  Optimization Techniques ; Programming Models ; Dynamic Analysis G  Financial Economics > G3  Corporate Finance and Governance > G33  Bankruptcy ; Liquidation 
Item ID:  88485 
Depositing User:  Ms Magadi Mabe 
Date Deposited:  31 Aug 2018 01:50 
Last Modified:  26 Sep 2019 16:34 
References:  Altman, E.I., 1968. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), pp.589–609. Bandyopadhyay, A., 2006. Predicting Probability of Default of Indian Corporate Bonds: Logistic and ZScore Model Approaches. The Journal of Risk Finance, 7(3), pp.255–272. Available at: ban. Beaver, W.H., 1966. Financial Ratios As Predictors of Failure. Journal of Accounting Research, 4(1966), pp.71–111. Berk, J. & Demarzo, P., 2013. Corporate finance Third Edit., New York: Pearson. Brîndescuolariu, D., 2015. The Potential of the Debt Ratio in the Prediction of Corporate Bankruptcy. Journal of Public Administration, Finance and Law, 2013(2), pp.37–46. Bruwer, B.W.S. & Hamman, W.D., 2006. Company failure in South Africa : classification and prediction by means of recursive partitioning. South African Journal of Business Management, 37(4). Canicio, D. & Blessing, K., 2014. Determinants of Bank Failures in MultipleCurrency Regime in Zimbabwe (20092012). International Journal of Economics and Finance, 6(8), pp.229–246. Charalambakis, E.C., 2015. On the prediction of financial distress in developed and emerging markets : Does the choice of accounting and market information matter ? A comparison of UK and Indian Firms. Review of Quantitative Finance and Accounting, 47(1), pp.1–28. Cortes, C. & Vapnik, V., 1995. SupportVector Networks. Machine Learning, 297(973), pp.273–297. Eisenbeis, R.A., 1977. Pitfalls in the Application of Discriminant Analysis in Business, Finance, and Economics. The Journal of Finance, 32(3), pp.875–900. Eisenbeis, R.A., 1996. Recent developments in the application of creditscoring techniques to the evaluation of commercial loansf. IMA Journal of Mathematics Applied in Business and Industry, (September 1995), pp.271–290. Fama, Eugene F & French, K., 1995. Size and BooktoMarket Factors in Earnings and Returns. The Journal of Finance, 50(1), pp.131–155. Gilson, S.C., 2009. Transactions Costs and Capital Structure Choice : Evidence from Financially Distressed Firms. The Journal of Finance, 52(1), pp.161–196. Hand, D.J. & Henly, W.., 1997. Statistical Classification Methods in Consumer Credit Scoring : a Review. Journal of Royal Statistical Society, 160, pp.523–541. Hastie, T., Tibsharani, R. & Friedman, J., 2009. The Elements of Statistical Learning Second., SpringerVerlag New York Inc. John, Y., Hilscher, J.D. & Szilagyi, J., 2017. Predicting Financial Distress and the Performance of Distressed Stocks, Karminsky, A.M. & Kostrov, A., 2014. The probability of default in Russian banking. Eurasian Economic Review, 4(1), pp.81–98. Kidane, H.W., 2004. Predicting financial distress in IT and services companies in South Africa. University of the Free State. Kousenidis, D. V & Negakis, C.I., 2000. Size and booktomarket factors in the relationship between average stock returns and average book returns : some evidence from an emerging market. The European Accounting Review, 9:2(1968), pp.225–243. Kukuk, M. & Ronnberg, M., 2013. Corporate credit default models: A mixed logit approach. Review of Quantitative Finance and Accounting, 40(3), pp.467–483. Lee, T.S. , Chiub . C, Chou, Y., & Lud C., 2006. Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics and Data Analysis, 50(4), pp.1113–1130. Lyandres, E. & Zhdanov, A., 2013. Investment opportunities and bankruptcy prediction. Journal of Financial Markets, 16(3), pp.439–476. Memic, D., 2015. Assessing Credit Default using Logistic Regression and Multiple Discriminant Analysis:Empirical Evidence from Bosnia and Herzegovina. Interdisciplinary Description of Complex Systems, 13(1), pp.128–153. Nie, G., Wei, R, Lingling. Z., Yingjie. T., & Yong. S., 2011. Expert Systems with Applications Credit card churn forecasting by logistic regression and decision tree. Expert Systems With Applications, 38(12), pp.15273–15285. Ohlson, J.A., 1980. Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(No.1). Rafiei, F.M., Manzari, S.M. & Bostanian, S., 2011. Expert Systems with Applications Financial health prediction models using artificial neural networks , genetic algorithm and multivariate discriminant analysis : Iranian evidence. Expert Systems With Applications, 38(8), pp.10210–10217. Salubi, I.L., 2016. Corporate Borrowing and Tax Shield Among Listed Companies in Nigeria. Journal of Academic Research in Economics, 8(2), pp.239–252. South African Reserve Bank, 2015. SARB Quartaly Bulletin. Available at: www.sarb.co.za Tinoco, M.H. & Wilson, N., 2013. International Review of Financial Analysis Financial distress and bankruptcy prediction among listed companies using accounting , market and macroeconomic variables. International Review of Financial Analysis, 30, pp.394–419. Tserng, H.P. , Chen, P. , Huang, W, Lei, M. C., & Tran, Q. H., 2014. Prediction of default probability for construction firms using the logit model. Journal of Civil Engineering and Management, 20(2), pp.247–255. Wilcox, J.W., 1971. A Simple Theory of Financial Ratios as Predictors of Failure. Journal of Accounting Research, 9(2), pp.389–395. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/88485 