Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecasting
A 2025 paper by Yang and co-authors on machine learning return prediction in equities, using a feedforward neural network: the abstract does not mention a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Numerous deep learning-based models have been developed for forecasting stock returns, leveraging the powerful representation capabilities of neural networks to identify patterns and factors influencing stock prices.”
AssetEquitiessingle stocks
StrategyMachine learningnews sentiment
TechniqueFeedforward networklinear factor model
InputsText and news
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
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