Interpretable Company Similarity with Sparse Autoencoders
Molinari and co-authors (2025) study news sentiment and text signals in equities using 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.
“This is evidenced by their superior performance in correlating logged monthly returns - a proxy for similarity - and generating higher Sharpe ratios in co-integration trading strategies, which underscores deeper fundamental similarities among companies.”
AssetEquities
StrategyNews sentiment
TechniqueLarge language model
GeographyUS
Also stated: Monthly, Sharpe ratio, 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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