Adaptive Financial Transformer with Regime-Gated Attention for Stock Return Prediction
A 2026 paper by Sarkar on machine learning return prediction in equities, using a transformer: the abstract mentions a Sharpe ratio and leaves out a benchmark, an out-of-sample test, transaction costs, multiple testing and the size of the universe.
“Results demonstrate competitive predictive performance while reducing model complexity by 15.2% and improving parameter efficiency through feature selection, providing an interpretable Transformer architecture for financial time-series forecasting.”
AssetEquities
StrategyMachine learning
TechniqueTransformerrecurrent neural network
ClaimSharpe ratio
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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