Prediction of stock price movements through regression analysis for Sun Pharma and Cipla

Abstract

Stock price prediction of the company aims to determine the future price movements of the stock. With the advent of data analytics, prediction of stock price movement has become highly popular. The researchers use numerous ways to predict the stock price movements like data mining, artificial neuron networks, technical analysis etc. This study aims to predict the stock prices with the integration of statistical tool known as regression analysis. Regression analysis has become an immense part of financial modelling as it sets to establish the relationship between dependent and independent variables. Thus the study takes into consideration the regression analysis for the data analysis. Due to the onset of Covid 19, pharma stocks have evidenced volatility in their prices. Hence two pharma companies are considered for the study. The changes in stock prices of Sun pharma and Cipla were studied for the span of three years. The probability of price variation was analysed and a robust model was developed to understand the probability of change in prices for the future. The study would be very helpful for the equity investors as it would help them in maximisation of their wealth.