Comparative analysis between traditional momentum and machine learning (random forest): evidence from the S&P 500 (2000-2024)

Introduction: This study examines the validity and persistence of the momentum effect in the S&P 500 index (2000–2024), a developed equity market with high informational efficiency. It analyzes whether the empirical evidence supports the continuity of momentum across different time horizons. Objective: To compare the performance of traditional momentum (TM) with a supervised learning model based on Random Forest (RF), assessing predictive ability, risk-adjusted performance, and out-of-sample stability. Methodology: Long–short TM strategies were implemented for horizons of 1, 3, 6, and 12 months, and the RF model was trained using equivalent cumulative returns. Out-of-sample validation was applied through an expanding window, homogeneous backtesting, and temporal stability tests. Results: TM showed limited performance over short horizons and greater consistency over long horizons. RF exhibited greater predictive ability and profitability, especially over long horizons, although with episodes of volatility and overfitting risk. Discussion: Machine learning models capture nonlinear patterns not identifiable by traditional methods, but depend on market conditions and show lower temporal stability, evidencing a trade-off between profitability and robustness. Conclusions: The findings confirm the persistence of momentum and highlight the value of machine learning in financial prediction, underscoring the importance of rigorous validation and risk control.