Stock Market Prediction Using Historical Stock Prices And Dependence On Other Companies In Automotive Sector

Abstract

Stock market investment, due to its volatile nature and dependence on many factors like own company policies, dependence on other companies' stock value, people's outlook on the company, etc., is a big gamble. However, if understood, it can heap in big rewards to investors. This is one of the reasons why stock market analysis has been such a hot topic and a highly researched field. Fundamental and Technical analysis are two ways to study and predict future company stocks. A lot of work has been done previously to predict stock prices using either sentiment analysis or historical stock data, but a very little emphasis has been put on combining multiple factors to predict stock prices. In this study, we will work on companies registered in the automotive sector in NSE. We have focused on historical companies' stock details and the dependence of stock price of one company on other companies in the same sector to predict future stocks. Both of these factors were studied and analyzed, and then a comparative analysis was done to see which model better predicts the closing stock price of Tata Motors, our target company. We have used Autoregressive integrated moving average, Artificial Neural Network, Long Short-Term Memory (LSTM), a type of Recurrent Neural Network models in our research and a comparative analysis among them will be done. © 2022 IEEE.