From Index to Equity: Pre-Training Transformers for Stock Return Prediction
Machine learning return prediction in equities, studied by Coolsaet, Gallardo and Gao in 2026 with a transformer. The abstract does not mention a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Pre-training on the market index improved the binary cross-entropy loss for individual stock prediction from 0.69 to 0.64.”
AssetEquitiesindices
StrategyMachine learning
TechniqueTransformerrecurrent neural network
GeographyGlobal
Also stated: Intraday, Long-short, Price and volume
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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