Sažetak
The aim of this paper is to present corporate default prediction models constructed in the specific market conditions that prevail in the Republic of Serbia, and to compare their prediction accuracy with the most frequently used model – Altman’s Z-score. Many authors have constructed models for the purpose of bankruptcy prediction, but predominantly in stable market conditions or in times of economic growth. We have presented three models that use standard ratios and some specific variables in order to predict corporate bankruptcy in emerging and distressed markets. For that purpose, we have used the following statistical and machine learning methods on a training sample (130 companies): Logistic Regression, Decision Trees and Artificial Neural Networks. Finally, we have compared accuracies of predictions of our models to those of the Altman’s Z-score models using an independent hold-out sample (102 companies). Results show that, out of the aforementioned three models, only the one relying on the artificial neural network algorithm performs better when applied on the hold-out sample, compared to Altman’s Z-score models.
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Reference
Altman , E. (1968). Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy. Journal of Finance, 23 (4), pp. 589-609.
Altman, E. (2002). Corporate Distress Prediction Models in a Turbulent Economic and Basel II Environment. Accessed on January 13, 2013, at NYU Stern School of Business: http://pages.stern.nyu.edu/~ealtman/Corp-Distress.pdf
Beaver, W., et al. (2005). Have Financial Statements Become Less Informative? Evidence From the Ability of Financial Ratios to Predict Bankruptcy. Review of Accounting Studies, 10 (1), pp. 93-122.
Bharath, S., & Shumway, T. (2008). Forecasting Default with the Merton Distance to Default Model. Review of Financial Studies, 21 (1), pp. 1339-69.
Boritz, E., et al. (2007). Predicting Business Failures in Canada. Accounting Perspectives, 6 (2), pp. 141-165.
Campbell, J., Hilscher, J., & Szilagyi, J. (2008). In Search of Distress Risk. The Journal of Finance, 63 (1), pp. 2899-939.
Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting Corporate Failure: Empirical Evidence for the UK. European Accounting Review, 13 (3), pp. 465 – 497.
Dakovic, R., et al. (2010). Bankruptcy Prediction in Norway: A Comparison Study. Applied Economics Letters, 17 (17), pp. 1739-46.
Dechow, P., et al. (2011). Predicting Material Accounting Misstatements. Contemporary Accounting Research, pp. 17-82.
Deventer, D., & Imai, K. (2003). Credit Risk Models and the Basel Accords. Singapore: John Wiley and Sons.
Jayadev, M. (2006). Predictive Power of Financial Risk Factors: An Empirical Analysis of Default Companies. Vikalpa, 31 (3), pp. 45-56.
Lee, W. (2006). Genetic Programming Decision Tree for Bankruptcy. Proceedings of the 2006 Joint Conference on Information Sciences (pg. 2568-2583). Kaohsiung: JCIS.
Mizdrakovic, V. (2012). Komparativna analiza ekonomskih aspekata stečaja (Comparative analysis of the economic aspects of bankruptcy). Accessed on December 28, 2012, at Singipedia – Singidunum University: http://www.singipedia.com/content/3276-Komparativna-analiza-ekonomskih-aspekata-ste%C4%8Daja
Nanda, S., & Pendharkar, P. (2001). Linear Models for Minimizing Misclassification Costs in Bankruptcy Prediction. International Journal of Intelligent Systems in Accounting, Finance and Management, 10 (3), pp. 155-168.
Nguyen, H. G. (2005). Using Neutral Network in Predicting Corporate Failure. Journal of Social Sciences 1 (4), pp. 199-202.
Ohlson, J. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18 (1), pp. 109-131.
Santos, M., Cortez, P., Pereira, J., & Quintela, H. (2006). Corporate Bankruptcy Prediction Using Data Mining Techniques. WIT Transactions on Information and Communication Technologies, 37 (1), pp. 349-357.
Sen, T., et al. (2004). Improving Prediction of Neural Networks: A Study of Two Financial Prediction Tasks. Journal of Applied Mathematics and Decision Sciences, 8 (4), pp. 219-233.
Shumway. T. (2001). Forecasting Bankruptcy More Accurately: A Simple Hazard Model. Journal of Business, 74 (1), pp. 103-224.
Stanisic, N., Radojevic, T., Mizdrakovic, V., & Stanic, N. (2012). Capital Efficiency Analysis of Serbian Companies. Singidunum Journal of Applied Sciences, 9 (2), pp. 41-49.
Youn, H., & Gu, Z. (2010). Predict US Restaurant Firm Failures: The Artificial Neural Network Model Versus Logistic Regression Model. Tourism and Hospitality Research, 10 (3), pp. 171-187.
Altman , E. (1968). Financial Ratios, Discriminant Analysis and Prediction of Corporate Bankruptcy. Journal of Finance, 23 (4), pp. 589-609.
Altman, E. (2002). Corporate Distress Prediction Models in a Turbulent Economic and Basel II Environment. Accessed on January 13, 2013, at NYU Stern School of Business: http://pages.stern.nyu.edu/~ealtman/Corp-Distress.pdf
Beaver, W., et al. (2005). Have Financial Statements Become Less Informative? Evidence From the Ability of Financial Ratios to Predict Bankruptcy. Review of Accounting Studies, 10 (1), pp. 93-122.
Bharath, S., & Shumway, T. (2008). Forecasting Default with the Merton Distance to Default Model. Review of Financial Studies, 21 (1), pp. 1339-69.
Boritz, E., et al. (2007). Predicting Business Failures in Canada. Accounting Perspectives, 6 (2), pp. 141-165.
Campbell, J., Hilscher, J., & Szilagyi, J. (2008). In Search of Distress Risk. The Journal of Finance, 63 (1), pp. 2899-939.
Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting Corporate Failure: Empirical Evidence for the UK. European Accounting Review, 13 (3), pp. 465 – 497.
Dakovic, R., et al. (2010). Bankruptcy Prediction in Norway: A Comparison Study. Applied Economics Letters, 17 (17), pp. 1739-46.
Dechow, P., et al. (2011). Predicting Material Accounting Misstatements. Contemporary Accounting Research, pp. 17-82.
Deventer, D., & Imai, K. (2003). Credit Risk Models and the Basel Accords. Singapore: John Wiley and Sons.
Jayadev, M. (2006). Predictive Power of Financial Risk Factors: An Empirical Analysis of Default Companies. Vikalpa, 31 (3), pp. 45-56.
Lee, W. (2006). Genetic Programming Decision Tree for Bankruptcy. Proceedings of the 2006 Joint Conference on Information Sciences (pg. 2568-2583). Kaohsiung: JCIS.
Mizdrakovic, V. (2012). Komparativna analiza ekonomskih aspekata stečaja (Comparative analysis of the economic aspects of bankruptcy). Accessed on December 28, 2012, at Singipedia – Singidunum University: http://www.singipedia.com/content/3276-Komparativna-analiza-ekonomskih-aspekata-ste%C4%8Daja
Nanda, S., & Pendharkar, P. (2001). Linear Models for Minimizing Misclassification Costs in Bankruptcy Prediction. International Journal of Intelligent Systems in Accounting, Finance and Management, 10 (3), pp. 155-168.
Nguyen, H. G. (2005). Using Neutral Network in Predicting Corporate Failure. Journal of Social Sciences 1 (4), pp. 199-202.
Ohlson, J. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18 (1), pp. 109-131.
Santos, M., Cortez, P., Pereira, J., & Quintela, H. (2006). Corporate Bankruptcy Prediction Using Data Mining Techniques. WIT Transactions on Information and Communication Technologies, 37 (1), pp. 349-357.
Sen, T., et al. (2004). Improving Prediction of Neural Networks: A Study of Two Financial Prediction Tasks. Journal of Applied Mathematics and Decision Sciences, 8 (4), pp. 219-233.
Shumway. T. (2001). Forecasting Bankruptcy More Accurately: A Simple Hazard Model. Journal of Business, 74 (1), pp. 103-224.
Stanisic, N., Radojevic, T., Mizdrakovic, V., & Stanic, N. (2012). Capital Efficiency Analysis of Serbian Companies. Singidunum Journal of Applied Sciences, 9 (2), pp. 41-49.
Youn, H., & Gu, Z. (2010). Predict US Restaurant Firm Failures: The Artificial Neural Network Model Versus Logistic Regression Model. Tourism and Hospitality Research, 10 (3), pp. 171-187.