Sentiment trading with large language models
Kirtac and Germano (2024) study news sentiment and text signals in equities using a large language model over 2010–2023. The abstract mentions a Sharpe ratio, excess returns and transaction costs but not a benchmark, an out-of-sample test, multiple testing or the size of the universe.
“Strategies based on the Loughran-McDonald dictionary yield the lowest Sharpe ratio of 1.23.”
AssetEquities
StrategyNews sentiment
TechniqueLarge language model
GeographyUS
Also stated: Daily, 2010–2023, Sharpe ratio, Excess return, Long-short, Text and news, costs
Missing from abstract: benchmark, out-of-sample test, 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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