Does Machine Learning Outperform Simple Investment Rules? Comparative Performance and Strategy Robustness in European Equity Markets

Abstract

Artificial intelligence is increasingly used in asset management, although evidence that greater model complexity consistently improves investment performance remains limited. This study examines whether machine-learning methods generate incremental value in European equity selection beyond transparent investment rules. The analysis used 562 eligible monthly price series drawn from the March 2026 STOXX Europe 600 constituents and applied retrospectively as a common ex post reference universe. The models were trained on observations from January 2011 to December 2020 and evaluated out of sample using signals formed from January 2021 to March 2026, with corresponding portfolio returns realized from February 2021 to April 2026. Logistic regression, random forest, XGBoost, and an equal-probability ensemble are compared with momentum, low-volatility, and equal-weight strategies under common portfolio-construction rules. Logistic regression recorded the strongest classification results and the highest gross cumulative portfolio return. The transparent momentum strategy recorded the highest observed Sharpe ratio and substantially lower target-weight turnover, while the three-model ensemble underperformed both logistic regression and momentum. Newey–West inference did not establish a statistically significant difference in mean monthly returns between logistic regression and momentum. The results indicate that additional algorithmic complexity did not generate incremental investment value under the restricted technical information set and portfolio framework considered. The findings support evaluating model complexity jointly through predictive quality, gross performance, statistical uncertainty, implementation sensitivity, interpretability, and governance requirements.