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Predicting Financial Distress in the Indian Banking Sector: A Comparative Study Between the Logistic Regression, LDA and ANN Models

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  • Nandita Mishra
  • Shruti Ashok
  • Deepak Tandon

Abstract

Financial distress is a socially and economically significant issue that affects almost every firm across the world. Predicting financial distress in the banking industry can substantially aid in the reduction of losses and can help avoid misallocation of banks’ financial resources. Models for financial distress prediction of banks are being increasingly employed as important tools to identify early warning signals for the whole banking system. This study attempts to forecast the financial distress of commercial banks by developing a bankruptcy prediction model for banks. The sample size for the study is 75 Indian banks. Logistic, linear discriminant analysis (LDA) and artificial neural network (ANN) models have been applied on the last 5 years’ (2015–2019) data of these banks. Data analysis results reveal the logistic and LDA models exhibiting similar prediction accuracy. The results of the ANN prediction model exhibit better prediction accuracy. It is expected that the results of this study will be useful for managers, depositors, regulatory bodies and shareholders to better manage their interests in the banking sector of the country.

Suggested Citation

  • Nandita Mishra & Shruti Ashok & Deepak Tandon, 2024. "Predicting Financial Distress in the Indian Banking Sector: A Comparative Study Between the Logistic Regression, LDA and ANN Models," Global Business Review, International Management Institute, vol. 25(6), pages 1540-1558, December.
    • Handle: RePEc:sae:globus:v:25:y:2024:i:6:p:1540-1558
    DOI: 10.1177/09721509211026785
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    References listed on IDEAS

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