Empirical Asset Pricing with Large Language Model Agents
News sentiment and text signals in equities, studied by Cheng and Chin in 2025 with a large language model. The abstract mentions a Sharpe ratio but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“The experimental results demonstrate that our methodology surpasses traditional machine learning-based baselines in both portfolio optimization and asset pricing errors.”
AssetEquities
StrategyNews sentimentportfolio optimization
TechniqueLarge language model
ClaimSharpe ratio
Also stated: Text 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
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
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