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

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  • Mehmet Yazıcı

    (Esenyurt University)

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.

Suggested Citation

  • Mehmet Yazıcı, 2018. "Comparison of Discriminant Analysis, Logistic Regression and Artificial Neural Networks in Credit Risk Analysis," Journal of Finance Letters (Maliye ve Finans Yazıları), Maliye ve Finans Yazıları Yayıncılık Ltd. Şti., vol. 33(109), pages 91-106, April.
    • Handle: RePEc:acc:malfin:v:33:y:2018:i:109:p:91-106
    DOI: https://doi.org/10.33203/mfy.393348
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    More about this item

    Keywords

    Discriminant Analysis; Logistic Regression; Artifi¬al Neural Network; Financial Failure;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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