Factors Determining the Financial Performance of Public Sector Banks in India

The Indian banking sector, initially dominated by public sector banks after nationalization in 1969, has undergone significant changes due to financial reforms in the 1990s. These reforms aimed to increase competition, productivity, and efficiency, leading to the entry of private and foreign banks. However, recent global events such as the Russia-Ukraine war and the COVID-19 pandemic have put pressure on banks' profitability and capital. This study aims to investigate the impact of both bank-specific and macroeconomic factors on the financial performance of Indian public sector banks, specifically focusing on return on assets (ROA), return on equity (ROE), and net interest margin (NIM) as key profitability indicators. The study period is 2010-11 to 2021-22, covering a period that encompasses significant economic and regulatory changes.

Existing literature extensively explores factors influencing bank profitability, with studies conducted in various countries including the US, Colombia, Brazil, Greece, China, Taiwan, and many European nations. Research on Indian banks also exists, with studies examining the impact of variables such as interest cost, deposits per branch, credit to total assets, operating expenses, non-interest income, and macroeconomic determinants on profitability. These studies provide a mixed picture, with some identifying positive and others negative relationships between bank profitability and various factors. This study aims to build upon this existing research by providing an updated analysis of the Indian public sector banking sector, considering recent economic and regulatory changes.

This study uses secondary data collected from sources such as Moneycontrol.com, Reserve Bank of India reports, and publications from the Indian Banking Association. The sample consists of 12 public sector banks in India selected based on their market capitalization in 2021-22. The study period is 12 years, from 2010-11 to 2021-22. The dependent variables are ROA, ROE, and NIM. Independent variables include bank-specific factors such as bank size (log of total assets), capital adequacy ratio (CAR), cost-to-income ratio (CTI), non-performing asset ratio (NPA), credit risk ratio (CRR), and credit deposit ratio (CDR), and macroeconomic factors such as GDP growth rate and inflation rate. Multiple linear regression analysis is used to investigate the relationship between the dependent and independent variables. Descriptive statistics, correlation analysis, and ANOVA are also employed. The regression model is specified as: PRO = β0 + β1(Size) + β2(CAR) + β3(CTI) + β4(NPA) + β5(CrR) + β6(CDR) + β7(GDP) + β8(Infl) + ε, where PRO represents ROA, ROE, or NIM; and ε represents the error term. The study uses SPSS software for statistical analysis.

Descriptive statistics show high variability in the profitability measures (ROA, ROE, NIM) among the sampled banks. Correlation analysis reveals significant negative correlations between bank size, cost-to-income ratio, non-performing assets, and profitability measures. Conversely, capital adequacy ratio, credit risk, credit deposit ratio, GDP growth, and inflation exhibit positive correlations with profitability. Regression analysis reveals that: * Bank size has a statistically insignificant negative relationship with ROA, ROE, and NIM. * CAR has a statistically insignificant negative relationship with ROA and NIM, but a positive (though insignificant) relationship with ROE. * CTI shows a statistically insignificant negative association with ROA and ROE and a positive (insignificant) relation with NIM. * NPA has a statistically insignificant negative relationship with all three profitability measures. * Credit Risk shows a statistically insignificant positive relationship with all three profitability measures. * CDR has a statistically insignificant negative relationship with ROA and NIM, and a positive (insignificant) relationship with ROE. * GDP growth has a statistically insignificant positive relationship with all three profitability measures. * Inflation rate shows a statistically insignificant negative relationship with all three profitability measures. While the R-squared values for the regression models are high (above 0.93), indicating a good overall fit, most of the individual coefficients are statistically insignificant at the 5% level. However, the F-statistic suggests that the overall model for ROE and NIM are significant, indicating the importance of the set of variables collectively.

The findings partially support previous research on bank profitability determinants. The negative relationship between non-performing assets and profitability is consistent with expectations. However, many other relationships expected from literature did not reach statistical significance. This might be due to the specific characteristics of the Indian public sector banking context, the chosen time period, or limitations in the data. The insignificance of several bank-specific factors, highlights the importance of macroeconomic conditions in determining profitability. The study suggests that improving asset quality (reducing NPAs), enhancing cost efficiency, and managing credit risk are essential internal factors for improving profitability. While the impact of GDP growth and inflation may not be solely within the bank's control, these macroeconomic factors also play a substantial role in the banks' financial performance.

This study contributes to the understanding of profitability determinants for Indian public sector banks. While many individual relationships were not statistically significant, the study highlights the complex interplay of internal bank factors and macroeconomic conditions affecting profitability. It emphasizes the need for a more holistic approach by banks in managing risks and optimizing cost efficiency to enhance profitability in the current competitive environment. Future research could explore these relationships using a more expanded dataset or applying alternative econometric techniques.

The study's limitations include the use of secondary data, reliance on a specific sample of banks, and the potential influence of unobserved factors affecting bank profitability. The cross-sectional nature of the data does not allow for analysis of dynamic relationships over time. The insignificant results for many variables might be due to multicollinearity among the independent variables.