@inproceedings{kou-etal-2025-automate,
    title = "Automate Strategy Finding with {LLM} in Quant Investment",
    author = "Kou, Zhizhuo  and
      Yu, Holam  and
      Luo, Junyu  and
      Peng, Jingshu  and
      Li, Xujia  and
      Liu, Chengzhong  and
      Dai, Juntao  and
      Chen, Lei  and
      Han, Sirui  and
      Guo, Yike",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1005/",
    doi = "10.18653/v1/2025.findings-emnlp.1005",
    pages = "18517--18533",
    ISBN = "979-8-89176-335-7",
    abstract = "We present a novel three-stage framework leveraging Large Language Models (LLMs) within a risk-aware multi-agent system for automate strategy finding in quantitative finance. Our approach addresses the brittleness of traditional deep learning models in financial applications by: employing prompt-engineered LLMs to generate executable alpha factor candidates across diverse financial data, implementing multimodal agent-based evaluation that filters factors based on market status, predictive quality while maintaining category balance, and deploying dynamic weight optimization that adapts to market conditions. Experimental results demonstrate the robust performance of the strategy in Chinese {\&} US market regimes compared to established benchmarks. Our work extends LLMs capabilities to quantitative trading, providing a scalable architecture for financial signal extraction and portfolio construction. The overall framework significantly outperforms all benchmarks with 53.17{\%} cumulative return on SSE50 (Jan 2023 to Jan 2024), demonstrating superior risk-adjusted performance and downside protection on the market."
}