Effect of COVID-19 on Stock Market Prediction Using Machine Learning

ABSTRACT

Share market is a chaotic and ever-changing place for making predictions, as there is no defined procedure to evaluate or forecast the value of a share of a company. Methods like time series, technical, statistical and fundamental analysis are used to predict the price of a share. However, these methods have not proven to be very consistent and precise for making predictions. COVID-19 has further deteriorated the chances to find such a tool as the markets have taken a huge hit in the first quarter of 2020. In this paper, support vector machine and multiple regression algorithms will be implemented for predicting stock market prices. Our aim is to find the machine learning algorithm which can predict the stock prices most accurately before and during the pandemic. The accuracy for every algorithm will be compared and the algorithm which is the most accurate would be considered ideal. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.