Major Determinants of Bank Profitability in India: A Machine Learning Approach

Abstract

From reforms and fin-tech revolutions to macro-economic shocks, the Indian banking sector has witnessed rapid changes over the last two decades, which has significant implications for banks’ profitability. Viewing bank profitability from three different dimensions, Net Interest Margins (NIM), Return on Assets (RoA) and Return on Equity (RoE), this study has explored the key determinants with the help of machine learning algorithms. It has used a pooled data set of domestic and commercial banks covering 2005–2021. As a dependent variable, profitability by each measure (NIM, RoA and RoE) is reclassified into three categories, above average, average and below average, based on their quartiles. Twenty-one explanatory variables comprising bank-specific, macroeconomic and policy variables are chosen after due validation using feature selection methodology and multicollinearity check. The random forest (RF) classification algorithm is executed using the CARET package in R. The results obtained from feature selection are corroborated with the RF classification findings. The results are robust and give clear-cut visibility of unique and common factors influencing three profitability measures at varying levels. The classification estimates suggest that the bank-specific variables are major determinants of NIM, while macroeconomic and policy variables are the key determinants of RoA and RoE. Further, the results also suggest that the ratio of non-performing assets to total assets and business per employee are two such bank-specific determinants that play an important role in all three dimensions of profitability. Thus, recapitalization and automation will play an important role in bank profitability.