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.
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