Comprehensive Study of Machine learning Algorithms for Stock Market Prediction during COVID-19

Abstract

The stock market has always drawn investors' attention. Stock trend forecasting tools are in high demand because they facilitate the direct acquisition of profits. The more precise the results, the greater the likelihood of generating a greater profit. Statistically, only 3% of the Indian population invests in stocks, and at the time of COVID-19, that number was even lower, as the stock market was not based on general patterns and equations but rather on the emotional quotient of the people. Such a circumstance increased the stock market's vulnerability. The pandemic factor, such as the case of COVID-19, influences the stock market's trends. Technical analysis of market trends is a technique for interpreting past and present prices to forecast likely future prices. Several deep learning and machine learning algorithms are utilized to generate stock market forecasts, wherein LSTM and ARIMA models have been proven to produce reasonably accurate results. The prior works focused on individual models and their components to provide forecasts. Thus, this paper aims to compare the two well-established models and provide investors with models that work well with data and have appropriate parameter values. The LSTM and ARIMA models are presented because they provide appropriate results using technical analysis of the data set, and the results are to be compared. The closing values from the historic stock prices of the top three Indian hospitality industries were used as the data set. The results show that the individual models work well when the data matches the model and the appropriate parameter is used.