Exploring the Synergy of Quantitative Factors and Newsflow Representations from Large Language Models for Stock Return Prediction
News sentiment and text signals in equities, studied by Guo and Hauptmann in 2025 with a large language model. The abstract does not mention a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Finally, our experiments on real investment universes yield several insights into effective multimodal modeling of factors and news for stock return prediction and selection.”
AssetEquities
StrategyNews sentimentmachine learning
TechniqueLarge language model
InputsText and news
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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The paper's neighbourhoodNews sentiment and text signals by year
FamilyNews sentiment and text signals148Momentum and trend following289Factor investing240Machine learning return prediction15318 more · 854
AssetEquities97
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