A hybrid model for estimating the probability of default of corporate customers

Document Type : Research Paper

Authors

1 Faculty of Management, University of Tehran

2 Faculty of Management and Accounting, Shahid Beheshti Universit

Abstract

Credit risk estimation is a key determinant for the success of financial institutions. The aim of this paper is presenting a new hybrid model for estimating the probability of default of corporate customers in a commercial bank. This hybrid model is developed as a combination of Logit model and Neural Network to benefit from the advantages of both linear and non-linear models. For model verification, this study uses an experimental dataset collected from the companies listed in Tehran Stock Exchange for the period of 2008–2014. The estimation sample included 175 companies, 50 of which were considered for model testing. Stepwise and Swapwise least square methods were used for variable selection. Experimental results demonstrate that the proposed hybrid model for credit rating classification outperform the Logit model and Neural Network. Considering the available literature review, the significant variables were gross profit to sale, retained earnings to total asset, fixed asset to total asset and interest to total debt, gross profit to asset, operational profit to sale, and EBIT to sale.

Keywords

Main Subjects

References

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Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies)
Volume 9, Issue 3
July 2016
Pages 651-673

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