A hybrid model for estimating the probability of default of corporate customers
Publish place: Iranian Journal of Management Studies، Vol: 9، Issue: 3
Publish Year: 1395
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JIJMS-9-3_010
تاریخ نمایه سازی: 6 شهریور 1402
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 ۲۰۰۸–۲۰۱۴. The estimation sample included ۱۷۵ companies, ۵۰ 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.
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Authors
رضا راعی
Faculty of Management, University of Tehran
مهدی سعیدی کوشا
Faculty of Management, University of Tehran
سعید فلاح پور
Faculty of Management, University of Tehran
محمد فدائی نژاد
Faculty of Management and Accounting, Shahid Beheshti Universit
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