A Hype-Adjusted Probability Measure for NLP Stock Return Forecasting
Cao and Geman (2025) study news sentiment and text signals in equities. The abstract does not mention a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“This work improves the forecast accuracy by addressing news bias, memory, and weight, and incorporating shifts in sentiment direction.”
AssetEquities
StrategyNews sentimentmachine learning
GeographyUS
FrequencyIntraday
Also stated: 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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