Comparison of Discriminant Analysis, Logistic Regression and Artificial Neural Networks in Credit Risk Analysis

Mehmet Yazıcı
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Mehmet Yazıcı: Esenyurt University

Journal of Finance Letters (Maliye ve Finans Yazıları), 2018, vol. 33, issue 109, 91-106

Abstract: The aim of this study is to provide an alternative method for estimating the financial failure of SMEs where the risk assessment is difficult. Financial data are insufficient to predict the failure of SMEs, which is the focal point of our banks in recent years. In this study, the results of an application in discriminant analysis, logistic regression and artificial neural network methods were compared. It is observed that the distinction between good and bad credit has been best achieved by artificial neural networks method.

Keywords: Discriminant Analysis; Logistic Regression; Artifi¬al Neural Network; Financial Failure (search for similar items in EconPapers)
JEL-codes: G01 G17 G33 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:acc:malfin:v:33:y:2018:i:109:p:91-106

DOI: 10.33203/mfy.393348

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