Daily Market Return Prediction with Transformer
Han and co-authors (2026) study machine learning return prediction using a transformer. The abstract mentions an out-of-sample test but not a benchmark, transaction costs, multiple testing or the size of the universe.
“Relative to linear benchmarks, the Transformer delivers substantial improvements in out-of-sample R-squared and economic gains available to a mean-variance investor.”
StrategyMachine learningnews sentiment
TechniqueTransformer
FrequencyDaily
InputsPrice and volumetext and news
Also stated: out-of-sample test
Missing from abstract: benchmark, 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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