Supply Chain Propagation of Textual Signals: LLM Embeddings and Cross-Sectional Return Predictability
A 2026 paper by Yılkı on news sentiment and text signals in equities, using a large language model on a 2011–2025 sample: the abstract mentions a Sharpe ratio, alpha, t-statistics, a benchmark, an out-of-sample test and the size of the universe and leaves out transaction costs and multiple testing.
“A long-short portfolio sorted on net_pc_5 achieves an annualized Sharpe ratio of 0.86 and a Fama-French five-factor alpha of 7.27% per year (t = 2.30).”
AssetEquitiesindices, single stocks
StrategyNews sentimentmachine learning
TechniqueLarge language modellinear factor model
GeographyUSEurope
Also stated: Annual, 2011–2025, Sharpe ratio, Alpha, t-statistic, Long-short, Text and news, benchmark, out-of-sample test, universe of 500 firms
Missing from abstract: costs, multiple testing
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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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