USER PERCEPTION TOWARDS STOCK PRICE PREDICTION USING LINEAR REGRESSION MODEL

Abstract

Stock market prediction is a critical area of research with profound implications for investors, financial analysts, and policymakers. This study explores the efficacy of linear regression analysis within the domain of business analytics for forecasting stock market movements. Leveraging historical stock prices and relevant economic indicators, we employ a rigorous methodology to construct and evaluate predictive models using linear regression techniques. Technological advancement increases the study on stock and share market industry. Decision making is enhanced by various statistical and machine learning algorithms. Enormous research work has been concentrated on the feature prediction of stock prices based on historical prices and volume. Performance measures are analyzed in this work with S&P 500 Index using statistical methods in Python environment. Results obtained in this study are superior than the existing methods. The conventional methods for financial market analysis are based on linear regression. This paper focuses on the best independent variables to predict the closing value of the stock market. This study is used to determine specific factors which are providing the most impact on the prediction of the closing price.