Predicting Corporate Financial Distress in Sri Lanka: An Extension to Z-Score Model

Authors

  • K.G.M. Nanayakkara Department of Commerce & Financial Management, Faculty of Commerce & Management Studies, University of Kelaniya, Sri Lanka
  • A.A. Azeez Department of Finance, Faculty of Management & Finance, University of Colombo, Sri Lanka

DOI:

https://doi.org/10.18533/ijbsr.v5i3.733

Keywords:

Financial distress, market variables, multivariate discriminant analysis, Z-Score.

Abstract

The main purpose of this study is to develop a better financial distress prediction model for the Sri Lankan companies using the Z-score model. Fourteen variables have been selected consisting of accounting, cash flow and market based variables. Multivariate Discriminate Analysis (MDA) was used as the analytical technique and stepwise method was used to select the variables with the best discriminating power to a dataset of sixty-seven matched pairs of failed and non-failed quoted public companies over the period 2002 to 2011. The final models are validated using the cross validation method. The results indicate that a model with four predictors of earnings before interest and taxes, cash flow from operations to total debts, retained earnings to total assets, and firm size have achieved the classification accuracy of 85.8% in one year prior to the distress with a very low type I error. Moreover, the model has correctly classified the cases by 79.9% and 69.4% in two year and three year prior to distress respectively. The study has further revealed that the companies with negative cutoff value fall into distress zone while the companies with positive cutoff values fall into safety area. Hence, the study concluded that the companies with cutoff values approximately zero should be considered on mitigating actions for financial distress not only on the accounting information but also on the cash flow and market data.

References

Agarwal, V., & Taffler, R.J. (2008a). Comparing the performance of market – based and accounting – based bankruptcy prediction models, Journal of Banking and Finance, 32, 1541-1551.

Altman, E.I. (1968). Financial ratios, Discriminant analysis and the prediction of corporate bankruptcy, The Journal of Finance, 23, 589-609.

Altman, E.I. (1983). Corporate financial distress: A complete guide to predicting, avoiding and dealing with bankruptcy, New York: John Wiley & Sons

Altman, E.I. (1993). Corporate financial distress and bankruptcy, 2nd ed., New York: John Wiley & Sons

Altman, E.I. (2000). Predicting financial distress of companies: Revisiting the Z-score and ZETA models, Journal of Banking and Finance

Altman, E.I., Baidya, T.K.N., & Dias, L.M.R. (1979). Assessing potential financial problems for firms in Brazil, Journal of Banking and Finance, 10(2), 9-24.

Beaver, W. H. (1966). Financial ratios as predictors of failures, Journal of Accounting Research, 4, 71-111.

Beaver, W. H., McNichols, M.F., & Rhie, J.W. (2005). Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy, Review of Accounting Studies, 10 (1), 93-122.

Bellovary. J., Giacomino, D., & Akers, M. (2007). A review of bankruptcy prediction studies: 1930 to present, Journal of Financial Education, 33.

Bhunia, A., Khan, S.J.U., & Mukhuti, S. (2011). Predicting of financial distress: A case study of Indian companies, Asian Journal of Business Management, 3(3), 210-218.

Blum, M. (1974). Failing company discriminant analysis, Journal of Accounting Research, 12 (1), 1-25.

Bum, J.K. (2007). Bankruptcy prediction: Book value or market value?. Paper presented at 2007 APRIA Annual Meeting. Retrieved from http:// www.rmi.nccu.edu.tw/apria/docs.

Burns, R.B., & Burns, R.A. (2009). Research Methods and Statistics Using SPSS, (e-publication, 1st ed), Sage Publication Limited.

Campbell, J.Y., Hilscher, J., & Szilagyi,J. (2011), Predicting financial distress and the performance of distressed stocks, Journal of Investment Management, 9 (2), 1-21.

Chava, S., & Jarrow, R.A. (2004). Bankruptcy prediction with industry effects, Review of Finance, 8, 537-569.

Christidis, A.C.Y., & Gregory, A. (2010). Some new models for financial distress prediction in the UK, Discussion paper no: 10/04, Xfi-Centre for Finance and Investment, UK.

Deakin, E.B. (1972). A discriminant analysis of predictors of business failure, Journal of Accounting Research, 10 (1), 167-179.

Gombola, M.J., Haskins, M.E., Ketz, J.E., & Williams, D.D. (1987). Cash flow in bankruptcy prediction, Journal of Financial Management, 16 (4), 55-65.

Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., & Tatham, R.L. (2011). Multivariate Data Analysis, (6th ed), Pearson.

Hillegeist, S. A., Cram, D.P., Keating, E.K., & Lundstedt, K.G. (2004). Assessing the probability of bankruptcy, Review of Accounting Studies, 9 (1), 5-34.

Kosmidis, K., Venetaki, M., Stavropoulos, A., & Terzidis, K. (2011). Predicting Financial distress in Greek business: A viability factors perspective, Oral, 250-262.

Lakshan, A.M.I., & Wijekoon, W.M.H.N. (2012). Predicting corporate failure of listed companies in Sri Lanka, GSTF Journal of Business Review, 2, 180-185.

Mensah, Y.M. (1984). An examination of the stationarity of multivariate bankruptcy prediction models: A methodological study, Journal of Accounting Research, 22(1), 380-395.

Nanayakkara, K.G.M., & Azeez, A.A. (2013), Predicting financial distress of quoted public companies in Sri Lanka; Special reference to Z score model, Conference proceedings of International Conference on Business & Information, Faculty of Commerce and Financial Management, University of Kelaniya, Sri Lanka.

Norton, C.L., & Smith, R.E. (1979). A comparison of general price level and historical cost financial statements in the prediction of bankruptcy, The Accounting Review, 54 (1), 72-87.

Ohlson, J.A. (1980). Financial ratios and the probabilistic prediction of bankruptcy, Journal of Accounting Research, 18(1), 109-131.

Samarakoon, L.P., & Hasan, T. (2003). Altman’s Z-Score models of predicting corporate distress: Evidence from the emerging Sri Lankan stock market, Journal of the Academy of Finance, 1, 119-125.

Sharma, D.S. (2001). The role of cash flow information in predicting corporate failure: the state of the literature, Journal of Managerial Finance, 27(4), 3-28.

Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model, The Journal of Business, 74(1), 101-124.

Taffler, R. J. (1983). The assessment of company solvency and performance using a statistical model, Accounting and Business Research, 15(52), 295-307.

Taffler, R. J., & Tishaw, H. (1977). Going, going, gone: Four factors which predict, Accountancy, 88, 50-54.

Yap, B.C.F., Yong, D.G.F., & Poon, W.C. (2010). How well do financial ratios and multiple discriminant analysis predict company failure in Malaysia, International Research Journal of Finance and Economics, 54, 166-175.

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Published

2015-03-28

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