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:: Volume 9, Issue 31 (6-2020) ::
mieaoi 2020, 9(31): 173-204 Back to browse issues page
Investigating the Efficiency of Hybrid Model in Comparison with Logistic Regression and Artificial Neural Network in Credit Risk Evaluation of Companies Listed in Tehran Stock Exchange
Mostafa Hashemi Tilehnouei Dr 1, Saba Hosseinzadeh Ms.
1- Islamic Azad University, East Tehran Branch , mostafahashemi82@gmail.com
Abstract:   (537 Views)
Credit risk evaluation is an integral part of the lending process. Significance of credit rating is increased by the global financial crisis and banks’ capital requirement. The purpose of this research is to find a new and more accurate way to estimate corporate credit scoring. Based on Traditional statistical methods and artificial Intelligence (AI), this research following Lee, et al., (2016) is testing a hybrid model, the model is combing logistic regression and artificial neural network(ANN). Population of the study is companies listed on Tehran Stock Exchange during 2010 to 2016.sampling method is systematic eliminating method that with considering the criteria, number of 90 companies were selected for the study. The results show the hybrid model in comparison with logistic regression and artificial neural network is more efficient in credit rating of companies listed in Tehran Stock Exchange.
Keywords: Credit Risk, Hybrid Model, Logistic Regression Model, Artificial Neural Network Model.
Full-Text [PDF 923 kb]   (123 Downloads)    
Type of Study: Research | Subject: Special
Received: 2020/03/3 | Accepted: 2020/06/20 | Published: 2020/06/30
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Hashemi Tilehnouei M, Hosseinzadeh S. Investigating the Efficiency of Hybrid Model in Comparison with Logistic Regression and Artificial Neural Network in Credit Risk Evaluation of Companies Listed in Tehran Stock Exchange. mieaoi. 2020; 9 (31) :173-204
URL: http://mieaoi.ir/article-1-917-en.html


Volume 9, Issue 31 (6-2020) Back to browse issues page
نشریه اقتصاد و بانکداری اسلامی Islamic Economics & Banking
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