Forecasting Weekly S&P 500 Returns Using Machine Learning: Evidence from Technical and Volume-Based Indicators

Abstract

Forecasting stock market returns remains a central challenge in finance, and much of the existing literature has relied on linear econometric models, which often struggle to capture nonlinear and time-varying patterns in equity markets. Prior studies have shown mixed evidence regarding the predictive value of technical indicators, leaving uncertainty about whether they contain meaningful information for short-term forecasting. This study addresses that gap by examining the predictability of weekly S&P 500 returns using traditional market-based indicators combined with modern machine learning methods. Weekly price and volume data from January 2000 to October 2025 are used to evaluate whether momentum, moving averages, volatility, and trading volume provide forecasting power for aggregate returns. Two ensemble algorithms, Random Forest and Histogram Based Gradient Boosting, are applied within an expanding window validation design. Results indicate that Gradient Boosting explains approximately 70% of the variation in weekly returns, demonstrating that adaptive learning approaches can reveal meaningful short-term market dynamics.