Deep Reinforcement Trading with Predictable Returns
Brini and Tantari (2023) study machine learning return prediction using reinforcement learning. The abstract mentions excess returns, a benchmark and transaction costs but not an out-of-sample test, multiple testing or the size of the universe.
“On the other hand, the performances on real financial trading problems are strongly affected by the goodness of the signal used to predict returns.”
StrategyMachine learningportfolio optimization
TechniqueReinforcement learning
ClaimExcess return
Also stated: benchmark, costs
Missing from abstract: out-of-sample test, 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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