Predicting Stock Returns Using Linear Regression

Abstract

This study examines the use of linear regression to forecast stock returns, with a particular emphasis on Apple Inc. (AAPL) and its correlation with the market proxy SPY, which represents the S&P 500 index. The paper outlines the methodology for gathering and processing historical stock price data for both AAPL and SPY, calculating daily returns, and applying linear regression to model the relationship between the two variables. We detail the process of training the linear regression model using a training dataset, where we estimate key parameters, and assess its predictive performance on a separate testing set. Performance evaluation is conducted using key metrics such as R-squared and Mean Squared Error (MSE) to measure the model’s accuracy in predicting stock returns. The findings demonstrate the utility of linear regression in financial forecasting, offering insights into its effectiveness as well as its limitations, particularly in capturing complex, non-linear market behaviors. Furthermore, the study touches upon the increasing role of machine learning in finance, highlighting its potential to offer more sophisticated models for stock return prediction and other financial applications.