Regime Conditioning Alters Risk, Not Predictability in Short-Horizon U.S. Equity ETF Models
Somani (2026) studies machine learning return prediction in equities using tree ensembles over 2010–2025. The abstract mentions a Sharpe ratio, drawdowns and an out-of-sample test but not a benchmark, transaction costs, multiple testing or the size of the universe.
“Results show that regime conditioning does not materially improve directional accuracy: both models achieve approximately 55% out-of-sample accuracy.”
AssetEquitiesETFs
StrategyMachine learning
TechniqueTree ensemble
GeographyUS
Also stated: Daily, 2010–2025, Sharpe ratio, Drawdown, out-of-sample test
Missing from abstract: benchmark, costs, multiple testing, universe
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