R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization
Li and co-authors (2025) study machine learning return prediction using a large language model. The abstract mentions excess returns and profits but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“Empirically, RD-Agent(Q) achieves up to 2X higher annualized returns than classical factor libraries using 70% fewer factors, and outperforms state-of-the-art deep time-series models on real markets.”
StrategyMachine learning
TechniqueLarge language model
ClaimExcess returnprofit
InputsFundamentalstext and news
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
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