Bharat Kumar Meher

The study aims to determine the impact of Capital Adequacy Ratio, Credit Losses Ratio and Efficiency Ratio on the two significant profitability ratios, namely Return on Assets (ROA) and Return on Equity (ROE), during the pandemic. Panel Data Regression is used to model the effects of Capital Adequacy, Credit Losses and Efficiency Ratio on Return on Assets and Return on Equity of Indian banks. A suitable model has been developed by analyzing the results of the Hausman test and the p-values. It has been found that Capital Adequacy Ratio (CAR) with coefficient value of –0.664, CET1 with coefficient value of 1.83 and efficiency ratio with coefficient value of 1.825 have significantly affected the return on assets as their p-values are less than 0.05. However, the accepted relationship between CAR and ROA, efficiency ratio and ROA were inverse, but their coefficients were significant. The provision for credit losses (PCL) was not affecting the ROA significantly during the pandemic and hence was not considered while framing the model. Again, the dependent variable is the return on equity, except CAR. Other ratios, i.e., CET1, efficiency ratio, and PCL ratio have unacceptable correlations and are even non-significant as their p-values are less than 0.05.

Many investors in order to predict stock prices use various techniques like fundamental analysis and technical analysis and sometimes rely on the discussions provided by various stock market analysts. ARIMA is a part of time-series analysis under prediction algorithms, and this paper attempts to predict the share prices of selected pharmaceutical companies in India, listed under NIFTY100, using the ARIMA model. A sample size of 782 time-series observations from January 1, 2017 to December 31, 2019 for each selected pharmaceutical firm has been considered to frame the ARIMA model. ADF test is used to verify whether the data are stationary or not. For ARIMA model estimation, significant spikes in the correlogram of ACF and PACF have been observed, and many models have been framed taking different AR and MA terms for each selected company. After that, 5 best models have been selected, and necessary inculcation of various AR and MA terms has been made to adjust the models and choose the best adjusted ARIMA model for each firm based on Volatility, adjusted R-squared, and Akaike Information Criterion. The results could be used to analyze the stock prices and their prediction in-depth in future research efforts.