Stock Price Prediction and Stock Behaviour using GA and LR with NIFTY50 index Based on Daily Charts

Abstract

The price of a company's stock, which can increase in lockstep with the price of a single share, is the one of the indicator to measure its performance. Clients or stockholding companies find it challenging to make long-term projections regarding the value of specific stocks due to the unpredictable nature of stock prices. Consequently, there is no business-related subject more talked about than stock market predictions. It is crucial to resolve this issue in a way that benefits buyers and investors because they frequently experience investment losses. Machine learning is useful in developing model for stock value predictions. We are utilizing Python and Linear Regression, one of the Machine Learning statistical techniques for predictive analysis, to create a stock price prediction website in order to address this issue. Our study primarily focuses on the NIFTY50 index's performance in distributed lag with the purpose of predicting stock prices in the Indian stock market. Several useful characteristics of the NIFTY50 lag index were extracted by means of a genetic algorithm. After that, we uncovered hidden correlations between the stock index and a given stock's trend by using the linear regression classifier. For the purpose of testing our approach, we used it to forecast the future of three distinct equities. In comparison to state-of-the-art forecasting methodologies, our experimental results demonstrated an accuracy of 82.55%. For the purpose of predicting daily changes in stock prices, the NIFTY50 stock index proved to be useful.