Feature Scope and Cross-Sectional Return Prediction: Evidence from US Large-Cap Equities
Machine learning return prediction in equities, studied by Rocchi in 2026 with reinforcement learning. The abstract mentions alpha, t-statistics, an out-of-sample test and the size of the universe but not a benchmark, transaction costs or multiple testing.
“The study's most robust finding is a monotonic feature ablation: expanding from 25 price factors to 67 GKX-style characteristics at the 21day forward horizon produces monotonically increasing OOS AUC (0.500 → 0.525) and alpha (+2.08% → +8.99%).”
AssetEquitiessingle stocks
StrategyMachine learningnews sentiment
TechniqueReinforcement learninglinear factor model
GeographyUSEurope
Also stated: Alpha, t-statistic, Price and volume, Text and news, out-of-sample test, universe of 846 stocks
Missing from abstract: benchmark, costs, multiple testing
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The paper's neighbourhoodMachine learning return prediction by year
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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