Bay Street Meets Machine Learning: Predicting Stock Risk Premium
Machine learning return prediction in equities, studied by Attig and Attig in 2025 with a feedforward neural network. The abstract mentions a Sharpe ratio and excess returns but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“We also show that elevated net anonymous buying and greater broker-level dispersion of anonymous order flow augment forecast accuracy, yielding 60 to 80 basis points higher monthly returns and Sharpe ratios that approach or exceed 0.9 for flexible learners.”
AssetEquities
StrategyMachine learningvalue investing
TechniqueFeedforward networktree ensemble
FrequencyIntraday
Also stated: Sharpe ratio, Excess return, Price and volume, Order book
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodMachine learning return prediction by year
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