Application of Linear Regression in the Stock Market in Machine Learning

Abstract

This paper explores the application of linear regression (a machine learning technique) in stock market analysis, focusing on Apple (AAPL) stock and the S&P 500 Index (SPY), aiming to predict AAPL’s stock returns and assess the model’s value for investment decisions. It includes data processing, training the linear regression model (with SPY as the independent variable and AAPL as the dependent variable), model evaluation via cross-validation, and hedging return calculation. Key results show AAPL underperformed SPY during the test period, the model effectively shows their correlation, and hedging returns help assess investment outcomes. The study confirms linear regression’s feasibility in stock analysis, notes its limitation in handling nonlinear relationships, and suggests using more complex algorithms like neural networks for better accuracy in the future.