Two-Time-Scale Transfer Learning for Market-by-Order Micro-Return Forecasting
Becerra, Peters and Hu (2026) study machine learning return prediction using a recurrent neural network. The abstract mentions excess returns but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Results demonstrate that the TTSTL framework significantly outperforms standalone deep learning baselines in terms of accuracy (RelMAE), while also enabling volatility modeling and probabilistic forecasting.”
StrategyMachine learning
TechniqueRecurrent neural networkfeedforward network
FrequencyIntraday
ClaimExcess return
Also stated: Long-short, 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
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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