A Comparison of Key Determinants on Profitability of India’s Largest Public and Private Sector Banks

The banking sector in India has come under the scanner following some key changes in monetary policy. With the Reserve bank of India (RBI) raising interest rates to support the falling Indian currency the Rupee, the cost of funds of banks has increased significantly. This could manifest itself in rising non-performing assets (NPAs) and declining profitability. The profitability of banks is impacted by both internal and external factors. This paper is an attempt to compare the key drivers of profits at India’s largest public and private sector banks. Bank specific metrics and risk factors were important drivers of profits at both banks. Productivity measures were key drivers of profits at India’s largest public sector bank SBI but had no effect on profits at India’s largest private sector bank, HDFC bank. Asset usage efficiency measures were key determinants of profitability at HDFC bank but not at SBI. The single most important determinant of SBI proved to be business per employee, a productivity measure while advances and bank size which are traditional bank metrics were key drivers of profits at HDFC bank. Managers at both banks and their share holders thus can look at these drivers to develop a broad understanding of profitability at the two banks. Keywords: Net Profit, Determinants, Key Performance Indicators, Indian Bank, Step Wise Multiple Regressio

Modeling the NPA of a Midsized Indian Nationalized Bank as a Function of Advances

Non-performing assets (NPA) occur in loans given by a bank or a financial institution where in the borrower defaults or delays interest and or principal payment. The management of NPAs therefore, is a very important part of credit management of banks and financial institutions in the Country. Currently NPA estimates in India are predominantly obtained from the figures published by the Reserve Bank of India (RBI). However it would be helpful for banks and financial institutions to have an estimate of the NPA as soon as loan amounts are disbursed. This study attempted to develop a predictive model for the NPA% at both the gross and net level from the advances made at a midsized Indian national bank. A strong correlation was observed between gross and net NPA% and the advances made suggesting that estimates of gross and net NPA can be made from advances. Linear and non linear models were fit to predict the NPA% from the amounts advanced. A non linear model linking both Gross and net NPA to advances provided the best curve fit and the least deviation from actual values. Thus by simply looking at advances an overall picture of the banks NPA level can be ascertained. Keywords:, Advances, Gross NPA, Indian nationalized Bank, Linear Model, Net NPA, Non Linear Models, NPA Management

Examining the Drivers of Stock Prices of Private Sector Banks in India

Indian private sector banking stocks have been rebounding from a massive selloff. Private sector banks which have traditionally maintained their asset quality and profitability when compared to their public-sector counterparts, have recently seen their asset quality erode. Given that private sector banks play an influential role in the Indian economy, it would be helpful to understand the key drivers of private bank stock prices. This paper studied the influence of some key micro and macro determinants on the stock prices of all listed private sector banks in India over the 12 years from 2008-2019. Panel regression models were fit to study the relationship between the variables. Liquidity, profitability, growth, inflation and exchange rate indicators proved to be important influencers of the stock prices of private sector banks in India. The study provides evidence for the Fisher effect suggesting that private bank stocks can provide good inflation adjusted returns over time. Stakeholders of these banks can focus on these determinants which will contribute to shareholder value over time. Keywords: Indian Private Banks, Stock Market capitalization, Liquidity, Exchange rates, GDP, Panel regression DOI: 10.7176/EJBM/13-11-04 Publication date:June 30th 202