Feature-enriched deep learning approach for stock market return forecasting using time-variant Hurst exponent and feature selection
A 2026 paper by Kumbure and co-authors on machine learning return prediction in equities, using a feedforward neural network: the abstract mentions excess returns, a benchmark, an out-of-sample test and transaction costs and leaves out multiple testing and the size of the universe.
“Empirical results show that the inclusion of fractal features improves predictive accuracy, particularly when combined with the proposed feature selection method, which outperforms alternative approaches.”
AssetEquities
StrategyMachine learningmacro signals
TechniqueFeedforward network
FrequencyIntraday
Also stated: Excess return, Long-short, Long-only, benchmark, out-of-sample test, costs
Missing from abstract: multiple testing, universe
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The paper's neighbourhoodMachine learning return prediction by year
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