Estimation of Default Probability for Corporate Entities in Republic of Serbia
Abstract
In this paper a quantitative PD model development has been excercised according to the Basel Capital Accord standards. The modeling dataset is based on the financial statements information from the Republic of Serbia. The goal of the paper is to develop a credit scoring model capable of producing PD estimate with high predictive power on the sample of corporate entities. The modeling is based on 5 years of end-of-year financial statements data of available Serbian corporate entities. Weight of evidence (WOE) approach has been applied to quantitatively transform and prepare financial ratios. Correlation analysis has been utilized to reduce long list of variables and to remove highly interdependent variables from training and validation datasets. According to the best banking practice and academic literature, the final model is provided by using adjusted stepwise Logistic regression. The finally proposed model and its financial ratio constituents have been discussed and benchmarked against examples from relevant academic literature.
References
Abdou, H. A., & Pointon, J. (2011). Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intelligent Systems in Accounting, Finance and Management, 18(2-3), 59-88.
Allison, P. D. (2012). Logistic regression using SAS: Theory and application. SAS Institute.
Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4)
Altman, E., & Sabato, G. (2007). Modeling Credit Risk for SMEs: Evidence from the US Market. Abacus, 43(3), 323-357.
Basel Committee on Banking Supervision Publications, Bank for International Settlement (2005), Studies on validation of internal rating systems.
Basel Committee on Banking Supervision Publications, Bank for International Settlement (2006). International convergence of capital measurement and capital standards – A revised framework.
Bijak, K., & Thomas, L. C. (2012). Does segmentation always improve model performance in credit scoring? Expert Systems with Applications, 39, 2433–2442.
Carlehed, M., & Petrov, A. (2012). A methodology for point-in-time-through-the-cycle probability of default decomposition in risk classification systems. The Journal of Risk Model Validation, 6(3), 3.
Chen, F. L., & Li, F. C. (2010). Combination of feature selection approaches with SVM in credit scoring. Expert Systems with Applications, 37(7), 4902-4909.
Fei, F., Fuertes, A. M., & Kalotychou, E. (2012). Credit rating migration risk and business cycles. Journal of Business Finance & Accounting, 39(1‐2), 229-263.
Harrell, F. (2015). Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer.
International Accounting Standards Board (2014). International Financial Reporing Standard 9 – Financial Instruments.
Jain, K., Gupta, P., & Sanjiv, M. (2011). Logistic Predictive Model for SMEs Financing in India. The Journal of Business Perspective, 15(4), 331-346.
Khemais, Z., Nesrine, D., & Mohamed, M. (2016). Credit Scoring and Default Risk Prediction: A Comparative Study between Discriminant Analysis & Logistic Regression. International Journal of Economics and Finance, 8(4), 39.
Kennedy, K., Namee, B. M., Delany, S., O’Sullivan, M., & Watson, N. (2013). A window of opportunity: Assessing behavioural scoring. Expert Systems with Applications, 40, 1372–1380.
Louzada, F., Ferreira-Silva, P. H., & Diniz, C. A. (2012). On the impact of disproportional samples in credit scoring models: An application to a Brazilian bank data. Expert Systems with Applications, 39, 8071–8078.
Malik, M., & Thomas, L. C. (2012). Transition matrix models of consumer credit ratings. International Journal of Forecasting, 28(1), 261-272.
Muminović, S., Pavlović, V., Cvijanović, J.M. (2011) Predictive ability of various bankruptcy prediction z-score models for Serbian publicly listed companies. Industrija, vol. 39, br. 3, str. 1-12
Nie, G., Rowe, W., Zhang, L., Tian, Y., & Shi, Y. (2011). Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications, 38(12), 15273-15285.
Pavlović, V., Muminović, S., Cvijanović, J.M. (2011) Application of Sandin & Porporato's bankruptcy prediction model on Serbian companies. Industrija, vol. 39, br. 2, str. 1-13
Pluto, K., & Tasche, D. (2010). Internal-Ratings‐Based Approach. Encyclopedia of Quantitative Finance
Siddiqi, Naeem. Credit risk scorecards: developing and implementing intelligent credit scoring. Vol. 3. John Wiley & Sons, 2012.
Sohn, S. Y., & Kim, Y. S. (2012). Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking. Small Business Economics, 1-13.