@inproceedings{wang-etal-2025-investalign,
    title = "{I}nvest{A}lign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes Under Herd Behavior",
    author = "Wang, Huisheng  and
      Pan, Zhuoshi  and
      Zhang, Hangjing  and
      Liu, Mingxiao  and
      Gao, Hanqing  and
      Zhao, H. Vicky",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/test-year-match/2025.acl-long.495/",
    doi = "10.18653/v1/2025.acl-long.495",
    pages = "10021--10052",
    ISBN = "979-8-89176-251-0",
    abstract = "Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental limitation: the scarcity of real-user data needed for Supervised Fine-Tuning (SFT). While SFT can bridge the gap between LLM outputs and human behavioral patterns, its reliance on massive authentic data imposes substantial collection costs and privacy risks. We propose \textbf{InvestAlign}, a novel framework that constructs high-quality SFT datasets by leveraging theoretical solutions to similar and simple optimal investment problems rather than the complex scenarios. Our theoretical analysis demonstrates that training LLMs with \textbf{InvestAlign}-generated data achieves faster parameter convergence than using real-user data, suggesting superior learning efficiency. Furthermore, we develop \textbf{InvestAgent}, an LLM agent fine-tuned with \textbf{InvestAlign}, which shows significantly closer alignment to real-user data than pre-SFT models in both simple and complex investment problems. This highlights our proposed \textbf{InvestAlign} as a promising approach with the potential to address complex optimal investment problems and align LLMs with investor decision-making processes under herd behavior. Our code is publicly available at \url{https://github.com/thu-social-network-research-group/InvestAlign}."
}