What survives honest evaluation? Leakage-safe, search-aware assessment of LLM-driven trading strategy discovery
A 2026 paper by Gençay on news sentiment and text signals in equities, using a large language model: the abstract mentions a Sharpe ratio and an out-of-sample test and leaves out a benchmark, transaction costs, multiple testing and the size of the universe.
“Second, the system records every strategy evaluation its search performs and deflates all reported performance by that trial count, tracing how the best in-sample Sharpe ratio climbs with each trial while the deflation threshold, driven by the agent's own search, climbs faster.”
AssetEquitiesETFs
StrategyNews sentiment
TechniqueLarge language model
GeographyUS
Also stated: Sharpe ratio, Text and news, out-of-sample test
Missing from abstract: benchmark, 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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