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

P. K. Viswanathan, Suresh Srinivasan and N. Hariharan

Journal of Emerging Market Finance, 2020, vol. 19, issue 2, 226-261

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

Keywords: Emerging markets; financial inclusion; government policy and regulation; market efficiency (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:emffin:v:19:y:2020:i:2:p:226-261

DOI: 10.1177/0972652720913478

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