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

Document Type: Research Paper


1 Faculty of Management, University of Tehran

2 Faculty of Management and Accounting, Shahid Beheshti Universit


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.


Main Subjects

Article Title [Persian]

ارائة مدل ترکیبی برآورد احتمال نکول مشتریان حقوقی بانک‌ها

Authors [Persian]

  • رضا راعی 1
  • مهدی سعیدی کوشا 1
  • سعید فلاح پور 1
  • محمد فدائی نژاد 2
1 دانشکده مدیریت ،دانشگاه تهران
2 دانشکده مدیریت و حسابداری، دانشگاه شهید بهشتی
Abstract [Persian]

تخمین ریسک اعتباری یکی از معیارهای کلیدی در موفقیت بانک یا مؤسسة اعتباری است. هدف این مقاله ارائة مدلی ترکیبی برای تخمین احتمال نکول مشتریان حقوقی در بانک‌های تجاری است. این مدل ترکیبی از ترکیب مدل لوجیت و شبکة عصبی توسعه یافته است که باعث استفاده از مزایای مدل خطی و غیرخطی به‌صورت هم‌زمان می‌شود. برای تأیید مدل از داده‌های شرکت‌های بورسی در دورة زمانی 1387 تا 1392 استفاده شده است که حجم نمونة 175 شرکت بوده است که 125 داده برای مدل‌سازی و 50 داده به عنوان داده‌های برون نمونه‌ای برای تست مدل استفاده شده‌اند. برای انتخاب متغیرهای از روش حداقل مربعات قدم به قدم (پیش‌رونده- پس‌رونده) استفاده شده است. نتایج نشان می‌دهد مدل ترکیبی جدید از مدل لوجیت و شبکة عصبی بهتر عمل می‌کند. برخلاف تحقیقات گذشته، متغیرهای اثرگذار عبارت است از سود ناخالص به فروش، سود انباشته به دارایی، دارایی ثابت به کل دارایی، بهره به کل بدهی، سود ناخالص به دارایی، سود عملیاتی به فروش و سود قبل از بهره و مالیات به فروش.

Keywords [Persian]

  • ریسک اعتباری
  • شبکة عصبی
  • مدل ترکیبی
  • مدل لوجیت
  • نکول
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