Multi-Agent Large Language Models with Reinforcement Learning for Quant Trading
Chen and Kawashima (2026) study news sentiment and text signals using a large language model. The abstract mentions alpha, excess returns, profits, drawdowns and a benchmark but not an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Specifically, we introduce a novel framework that integrates a multi-agent LLM system with reinforcement learning to improve formulaic alpha generation and portfolio allocation.”
StrategyNews sentimentportfolio optimization
TechniqueLarge language modelreinforcement learning
ClaimAlphaexcess return
InputsText and news
Also stated: benchmark
Missing from abstract: 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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