Analyzing Sentiment in Financial News: A Quantitative Study for the Advancement of Trading Strategies
Diana (2026) studies news sentiment and text signals in equities using a feedforward neural network. The abstract mentions excess returns and t-statistics but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“The sentiment-driven trading strategy consistently fails to outperform either benchmark across a range of standard performance metrics.”
AssetEquities
StrategyNews sentimentmomentum
TechniqueFeedforward network
ClaimExcess returnt-statistic
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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