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Predicting Financial Health of Banks for Investor Guidance Using Machine Learning Algorithms

Author

Listed:
  • P. K. Viswanathan
  • Suresh Srinivasan
  • N. Hariharan

Abstract

While earlier studies have focused excessively on bankruptcy prediction of banks, this study classifies banks based on their financial strength from the perspective of retail depositors who currently do not have an authentic guiding framework that helps them identify banks with higher risk profiles. Using machine learning techniques, we classify 44 Indian banks into distinct categories of financial health based on 12-year data from 2005 to 2017. We first use unsupervised learning to identify a pattern leading to logical groups in terms of financial health and then move to supervised learning for prediction. Using linear discriminant analysis (LDA), Classification and Regression Tree (CART) and Random Forest methods, we predict the cluster membership with the associated explanatory power alongside. We also compare our classification with the credit ratings awarded by rating agencies and highlight certain discrepancies that exist between what is predicted by our models and the credit rating awards. JEL Codes: C53; M10

Suggested Citation

  • P. K. Viswanathan & Suresh Srinivasan & N. Hariharan, 2020. "Predicting Financial Health of Banks for Investor Guidance Using Machine Learning Algorithms," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 19(2), pages 226-261, August.
    • Handle: RePEc:sae:emffin:v:19:y:2020:i:2:p:226-261
    DOI: 10.1177/0972652720913478
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    References listed on IDEAS

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    Cited by:

    1. P. K. Viswanathan & Sandeep Srivathsan & Wayne L. Winston, 2022. "Multiclass Discriminant Analysis using Ensemble Technique: Case Illustration from the Banking Industry," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 21(1), pages 92-115, March.
    2. de Jesus, Diego Pitta & Besarria, Cássio da Nóbrega, 2023. "Machine learning and sentiment analysis: Projecting bank insolvency risk," Research in Economics, Elsevier, vol. 77(2), pages 226-238.
    3. Islam, Md Rafiqul & Liu, Shaowu & Biddle, Rhys & Razzak, Imran & Wang, Xianzhi & Tilocca, Peter & Xu, Guandong, 2021. "Discovering dynamic adverse behavior of policyholders in the life insurance industry," Technological Forecasting and Social Change, Elsevier, vol. 163(C).

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    More about this item

    Keywords

    Emerging markets; financial inclusion; government policy and regulation; market efficiency;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General

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