Cross-Sectional Return Prediction Using Machine Learning: A Practitioner Pipeline
Machine learning return prediction in equities, studied by Lama in 2026 with tree ensembles. The abstract mentions a Sharpe ratio, excess returns, an out-of-sample test and transaction costs but not a benchmark, multiple testing or the size of the universe.
“Ridge Regression achieves the highest out-of-sample R 2 (0.80%), outperforming XGBoost (0.39%) and LightGBM (0.38%), consistent with linear models performing competitively on short data panels with limited signal-to-noise ratios.”
AssetEquities
StrategyMachine learningmomentum
TechniqueTree ensemblelinear factor model
GeographyUS
Also stated: Daily, Sharpe ratio, Excess return, Long-short, Price and volume, out-of-sample test, costs
Missing from abstract: benchmark, multiple testing, universe
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