Dispersion of News Sentiment and Stock Price Return: Applying a Deep Learning Approach
A 2026 paper by Alam and Isakin on news sentiment and text signals in equities, using a recurrent neural network: the abstract mentions excess returns, t-statistics and an out-of-sample test and leaves out a benchmark, transaction costs, multiple testing and the size of the universe.
“The sequence-based strategy generates economically large and statistically significant long-short return spreads out of sample, outperforming a benchmark based on sentiment dispersion.”
AssetEquitiessingle stocks
StrategyNews sentimentevent-driven strategies
TechniqueRecurrent neural networkfeedforward network
FrequencyDaily
Also stated: Excess return, t-statistic, Long-short, Fundamentals, 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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