Comparative Analysis of Credit Risk Models in Relation to SME Segment

Vol.9,No.1(2018)

Tomáš Plíhal Martina Sponerová Miroslav Sponer

https://doi.org/10.5817/FAI2018-1-3

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Abstract
The importance of credit risk management is well known and was deeply investigated by the banking industry. There is a pressure on financial institutions to still improve their credit risk management systems, so the credit risk of a bank is an unflagging object of discussion. The aim of this article to compare the predicting abilities of several bankruptcy models to the SME segment in the Czech Republic and its subsegments - medium sized, small and micro enterprises. We have focused on small and medium sized enterprises (SMEs) considering their fundamental role played in the Czech economy and the considerable attention placed on SMEs. We have chosen popular bankruptcy models that are often applied, namely the Altman Z-score, Altman model developed especially for SMEs in 2007, the Ohlson O-score, the Zmijewski’s model, the Taffler’s model, and the IN05 model. The basic form of the models was used as proposed by their authors. The results were compared using the contingency table and ROC curve. We have found that the best prediction models are Zmijewski´s and Ohlson´s models which use probit and logit methodologies and according to our analysis, their prediction ability is better than that of models based on discriminant analysis. Surprisingly, model IN05 designed for Czech companies provides average results only. One of the worst performing models is Altman 2007, which was created specifically for SMEs, but according to our analysis it only provides subordinates results.

Keywords:
credit risk; bankruptcy prediction; SME; bankruptcy model; probability of default
Author biography

Martina Sponerová

Masaryk University Faculty of Economics and Administration Department of Finance Lipová 41a, 602 00 Brno

Plíhal, T., Sponerová, M. and Sponer, M. (2018). Comparative Analysis of Credit Risk Models in Relation to SME Segment. Financial Assets and Investing, 9(1), pp. 35-50.
References

Altman, E. I. and Sabato, G. (2007). Modelling Credit Risk for SMEs: Evidence from the U.S. Market. ABACUS, 43(3), pp. 332-357. https://doi.org/10.1111/j.1467-6281.2007.00234.x

Altman, E. I., Sabato, G. and Wilson, N. (2010). The Value of Non-Financial Information in Small and Medium-sized Enterprise Risk Management. The Journal of Credit Risk, 6(2), pp. 1-33. https://doi.org/10.21314/JCR.2010.110

Angelini, E., Tollo, Di., G. and Roli, A. (2007). A Neutral Network Approach for Credit Risk Evaluation. The Quarterly Review of Economics and Finance, 48(4), pp. 733-755.

Barnes, P. (1982). Methodological Implications of Non-Normality Distributed Financial Ratios. Journal of Business Finance and Accounting, 9(1), pp. 51-62. https://doi.org/10.1111/j.1468-5957.1982.tb00972.x

Diakomihalis, M. (2012). The Accuracy of Altman's Models in Predicting Hotel Bankruptcy. International Journal of Accounting and Financial Reporting, 2(2), pp. 96-113. https://doi.org/10.5296/ijafr.v2i2.2367

Dietsch, M. and Petey, J. (2004). Should SME Exposures Be Treated as Retail or Corporate Exposures? A Comparative Analysis of Default Probabilities and Asset Correlation in French and German SMEs'. Journal of Banking and Finance, 28(7), pp. 773-788. https://doi.org/10.1016/S0378-4266(03)00199-7

EUROSTAT. (2017). Available at: http://ec.europa.eu/eurostat/.

Headd, B. (2003). Redefining Business Success: Distinguish between Closure and Failure. Small Business Economics, 21(1), pp. 51-61. https://doi.org/10.1023/A:1024433630958

Grunert, J., Norden, L. and Weber, M. (2005). The Role of Non-Financial Factors in Internal Credit Ratings. Journal of Banking & Finance, 29(2), pp. 509-531. https://doi.org/10.1016/j.jbankfin.2004.05.017

Gulnur, D. and Fikret, G. (2011). Knowledge Discovery Using Neural Approach for SME's Credit Risk Analysis Problem in Turkey. Expert Systems with Applications, 38, pp. 9313-9318. https://doi.org/10.1016/j.eswa.2011.01.012

Gupta, J., Wilson, N. and Gregoriou, A. (2014). The Effect of Internationalization on Modelling Credit Risk for SMEs: Evidence from UK Market. Journal of International Financial Markets Institutions & Money, 31, pp. 397-413. https://doi.org/10.1016/j.intfin.2014.05.001

Karels, G. V. and Prakash, A. J. (1987). Multivariate Normality and Forecasting of Business Bankruptcy. Journal of Business Finance and Accounting, 14(4), pp. 573-593. https://doi.org/10.1111/j.1468-5957.1987.tb00113.x

Lifschutz, S. and Jacobi, A. (2010). Predicting Bankruptcy: Evidence from Israel. International Journal of Business and Management, 5(4), pp. 133-141. https://doi.org/10.5539/ijbm.v5n4p133

Neumaierová, I. and Neumaier, I. (2005). Index IN05. In: Evropské finanční systémy. Brno, Masaryk University, pp. 143-146. ISBN 80-210-3753-9.

Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), pp. 109-131. https://doi.org/10.2307/2490395

Shumway, T. (2001). Forecasting Bankruptcy More Accurately: A Simple Hazard Model. Journal of Business, 74(1), pp. 101-124. https://doi.org/10.1086/209665

Taffler, R. and Tishaw, H. (1977). Going, Going, Gone – Four Factors which Predict. Accountancy, 3, pp. 50-54.

Watson, J. and Everett, J. E. (1993). Defining Small Business Failure. International Small Business Journal, 11(3), pp. 34-48. https://doi.org/10.1177/026624269301100302

Zmijewski, M. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22, pp. 59-82. https://doi.org/10.2307/2490859

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