Evaluating Machine Learning Models for S&P 500 Return Prediction

Machine learning (ML) is increasingly applied in finance. In trading in particular, researchers aim to determine whether ML can generate consistent alpha. Reference [1] revisits this question. The authors utilize a long sample dataset from 1970 to 2024 and evaluate a wide range of ML models, including linear, nonlinear, and ensemble methods, to assess whether they can predict one-day-ahead S&P 500 returns and generate trading profits.

In the models, the inputs are price, returns, volume, volatility, market breadth, and simple sentiment proxies. The models include linear methods, such as OLS and elastic net, and nonlinear methods, such as random forest, XGBoost, SVM, and kNN, along with ensemble approaches. They are trained on rolling 10-year windows with recalibration every 2 to 5 years. The trading strategy takes a long position when the forecast is positive and otherwise holds Treasury bills.

The authors pointed out,

This study examined the performance of machine learning architectures applied to financial forecasting within the framework of the adaptive market hypothesis. By comparing 12 models and three ensemble systems over 55 years of daily S&P 500 data, we demonstrated that machine learning with adaptive retraining can be used to obtain stock market profits over an extended period from 1970 to 2024. However, the extent of abnormal returns is limited by transaction costs and the fact that financial markets quickly adapt, reducing the profitability of any machine learning system. Three architectures—elastic nets, logit, and XGboost—consistently outperformed the others, while ensemble designs provided the most resilient returns and stability across changing market regimes. Nevertheless, system profitability declined after 1987, indicating that even sophisticated systems face diminishing arbitrage opportunities as markets learn and adapt. This dynamic is consistent with both adaptive market and adaptive systems perspectives.

In short, without transaction costs, many models generate positive abnormal returns. However, once realistic trading costs are incorporated, these profits largely disappear, particularly in the post-1987 period. There is also a strong regime effect, with high profitability of around 20% or more during 1970 to 1987, followed by a significant decline to approximately 5% to 9% from 1988 to 2024, often rendering the strategies unprofitable after costs.

This paper finds that while ML strategies can generate abnormal returns and remain adaptive over time, profitability declines in later periods and exhibits significant variability, raising concerns about real-time performance.

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References

[1] Olson, D., Nusair, S., & Galariotis, E. (2026). Profitability of Machine Learning Models in Forecasting the S&P 500 Index. Information Systems Frontiers.

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