Large Language Models and Return Prediction in China
Tan, Wu and Zhang (2024) study news sentiment and text signals in equities using a large language model. The abstract mentions profits but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“The value-weighted long-minus-short portfolios yield annualized returns between 35% and 67%, depending on the model.”
AssetEquities
StrategyNews sentimentmachine learning
TechniqueLarge language model
GeographyChina
Also stated: Profit, Fundamentals, 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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