FinSentiment Alpha: NLP-Based Sentiment Signals for Long-Short Equity Strategies in US Markets Working Paper
Saluja (2026) studies news sentiment and text signals in equities using a large language model. The abstract mentions a Sharpe ratio, alpha, profits, drawdowns, an out-of-sample test and transaction costs but not a benchmark, multiple testing or the size of the universe.
“Under walk-forward validation across four folds, the strategy produces an out-of-sample Sharpe ratio of 1.054 and annualized return of 17.67% with a maximum drawdown of-9.38%.”
AssetEquities
StrategyNews sentiment
TechniqueLarge language modeltransformer
GeographyUSChina
Also stated: Monthly, Sharpe ratio, Alpha, Profit, Drawdown, Long-short, Price and volume, Text and news, out-of-sample test, costs
Missing from abstract: benchmark, 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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