Integrating Large Language Models and Reinforcement Learning for Sentiment-Driven Quantitative Trading
Long and co-authors (2025) study news sentiment and text signals using a large language model. The abstract does not mention a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“The results suggest that sentiment signals generated by FinGPT offer value when combined with traditional technical indicators, and that reinforcement learning algorithm presents a promising approach for effectively integrating heterogeneous signals in dynamic trading environments.”
StrategyNews sentiment
TechniqueLarge language modelreinforcement learning
InputsPrice and volumetext 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
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