Performance Comparison of Multiple Discriminant Analysis and Logit Models in Bankruptcy Prediction
In this study, the attention is dedicated to the development of bankruptcy prediction model in Slovak Republic. The presented paper focuses on the comparison of overall prediction performance of the two developed models. The first one is estimated via discriminant analysis, while the another is based on a logistic regression. The sample is made up of 236 firms operating in Slovakia, divided into two groups - failed and non-failed firms. The results of the study suggest that the model based on a logit function outperforms the classification accuracy of the discriminant model. The most significant predictors of impeding firms´ failure appear to be Net Income to Total Assets, Current Ratio and Current liabilities to Total Assets. (original abstract)
- Agarwal, V., Taffler, R. (2008), Comparing the performance of market-based and accountingbased bankruptcy prediciton models, Journal of Banking and Finance, Vol. 32, No. 8, pp. 1541-1551.
- Ahn, H., Kim, K. (2009), Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach, Applied Soft Computing, 9.2, pp. 599-607.
- Altman, E. I. (1968), Financial ratios, discriminant analysis and the prediction of corporate bankruptcy, The journal of finance, Vol. 23, No.4, pp. 589-609.
- Altman, E. I., et al. (2014), Distressed Firm and Bankruptcy Prediction in an International Context: A Review and Empirical Analysis of Altman's Z-Score Model, available at SSRN 2536340.
- Aziz, M. A., Dar, H. A. (2006), Predicting corporate bankruptcy: Where we stand? Corporate Governance, Vol. 6, No. 1, pp. 18-33.
- Back, B., Laitinen, T., Sere, K. (1996), Neural networks and genetic algorithms for bankruptcy predictions, Expert Systems with Applications, 11.4, pp. 407-413.
- Bauer, J., Agarwal, V. (2014), Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test, Journal of Banking & Finance, 40, pp. 432-442.
- Beaver, W. H. (1966), Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy, The journal of Accounting Research, Vol. 4, pp. 71-111, ISSN 0021-8456.
- Bellovary, J. L., Giacomino, D. E., Akers, M. D. (2007), A review of bankruptcy prediction studies: 1930 to present, Journal of Financial Education, Vol. 33, No. 4, pp. 3-41.
- Bryant, S. M. (1997), A case-based reasoning approach to bankruptcy prediction modeling, Intelligent Systems in Accounting, Finance and Management, 6.3, pp. 195-214.
- Cortes, C., Vapnik, V. (1995), Support-vector networks, Machine learning, 20.3, pp. 273-297.
- Dimitras, A. I., Zanakis, S. H., Zopounidis, C. (1996), A survey of business failures with na emphasis on prediction methods and industrial applications, European Journal of Operational Research, Vol. 90, No. 3, pp. 487-513.
- 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.
- Hair, J. F., et al. (2006), Multivariate data analysis, Upper Saddle River, NJ: Pearson Prentice Hall.
- Hosmer, J. R, Lemeshow, S., Sturdivant, R. X. (2013), Applied logistic regression, John Wiley & Sons.
- Charitou, A., Neophytou, E., Charalambous, C. (2004), Predicting corporate failure: Empirical evidence for the UK, European Accounting Review, Vol. 13, No. 3, pp. 465-497.
- Jardin, P. (2009), Bankruptcy prediction models: How to choose teh most relevant variables? Bankers, Markets & Investors, No. 98, pp. 39-46.
- Karas, M., Režňáková, M. (2012), Financial Ratios as Bankruptcy Predictors: The Czech Republic Case, Proceedings of the 1st WSEAS International Conference on Finance, Accounting and Auditing (FAA'12), pp. 86-91.
- Keasey, K., Watson, R. (1991), Financial distress prediction models: A review of their usefulness, British Journal of Management, Vol. 2, pp. 89-102.
- Kim, M. J., Kang, D. K. (2010), Ensemble with neural networks for bankruptcy prediction, Expert Systems with Applications, 37.4, pp. 3373-3379.
- Kim, K. S. et al. (2011), Comparison of k-nearest neighbor, quadratic discriminant and linear discriminant analysis in classification of electromyogram signals based on the wristmotion directions, Current Applied Physics, 11.3, pp. 740-745.
- Kolari, J. et al. (2002), Predicting large US commercial bank failures, Journal of Economics and Business, 54.4, pp. 361-387.
- Kuumar, P. R., Ravi, V. (2007), Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review, European Journal of Operational Research, Vol. 180, No. 1, pp. 1-28.
- Laitinen, E. K., Lukason, O., Suvas, A. (2014), Behaviour of Financial Ratios in Firm Failure Process: An International Comparison, International Journal of Finance and Accounting, 3.2, pp. 122-131.
- Lasisi, T. A., Shangodouin, D. K. (2014), Robust Test for detecting Outliers in Periodic Processes using Modified Hampel's Statistic.
- Lee, S., Choi, W. S. (2013), A multi-industry bankruptcy prediction model using backpropagation neural network and multivariate discriminant analysis, Expert Systems with Applications, 40.8, pp. 2941-2946.
- Odom, M. D., Sharda, R. (1990), A neural network model for bankruptcy prediction, 1990 IJCNN International Joint Conference on neural networks, pp. 163-168.
- Ohlson, J. A. (1980), Financial Ratios and the Probabilistic Prediction of Bankruptcy. Blackwell Publishing, Journal of Accounting Research, Vol. 18, No. 2, pp. 109-131.
- Olson, D. L., Delen, D., Meng, Y. (2012), Comparative analysis of data mining methods for bankruptcy prediction, Decision Support Systems, 52.2, pp. 464-473.
- Piotroski, J. D. (2000), Value investing: The use of historical financial statement information to separate winners from losers, Journal of Accounting Research, pp. 1-41.
- Premachandra, I. M., Bhabra, G. S., Sueyoshi, T. (2009), DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique, European Journal of Operational Research, 193.2, pp. 412-424.
- Psillaki, M., Daskalakis, N. (2009), Are the determinants of capital structure country or firm specific? Small Business Economics, 33.3, pp. 319-333.
- Reisz, A. S., Perlich, C. (2007), A market-based framework for bankruptcy prediction, Journal of Financial Stability, 3.2, pp. 85-131.
- Tabachnick, B. G.; Fidell, L. S. (2001), Using multivariate analysis, California State University Northridge: Harper Collins College Publishers.
- Tinoco, M. H., Wilson, N. (2013), Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables, International Review of Financial Analysis, Vol. 30, pp. 394-419.
- Wilson, R. L., Sharda, R. (1994), Bankruptcy prediction using neural networks, Decision support systems, 11.5, pp. 545-557.
- Zanganeh, T., Rabiee, M., Zarei, M. (2011), Applying adaptive neuro-fuzzy model for bankruptcy prediction, International Journal of Computer Applications, 20.3, pp. 15-21.
- Zavgren, C. V. (1985), Assessing the vulnerability to failure of American industrial firms: a logistic analysis, Journal of Business Finance & Accounting, 12.1, pp. 19-45.