@inproceedings{chen-etal-2025-finhear,
    title = "{F}in{HEAR}: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making",
    author = "Chen, Jiaxiang  and
      Zou, Mingxi  and
      Wang, Zhuo  and
      Wang, Qifan  and
      Sun, Danny Dongning  and
      Chi, Zhang  and
      Xu, Zenglin",
    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.87/",
    doi = "10.18653/v1/2025.findings-emnlp.87",
    pages = "1648--1672",
    ISBN = "979-8-89176-335-7",
    abstract = "Financial decision-making presents unique challenges for language models, requiring them to handle temporally evolving, risk-sensitive, and event-driven contexts. While large language models (LLMs) demonstrate strong general reasoning abilities, they often overlook key behavioral patterns underlying human financial behavior{---}such as expert reliance under information asymmetry, loss-averse risk adjustment, and temporal adaptation. We propose FinHEAR, a multi-agent framework for Human Expertise and Adaptive Risk-aware reasoning. FinHEAR coordinates multiple LLM-based agents to capture historical trends, interpret current events, and incorporate expert knowledge within a unified, event-aware pipeline. Grounded in behavioral economics, FinHEAR features mechanisms for expert-guided retrieval to reduce information asymmetry, dynamic position sizing to reflect loss aversion, and feedback-driven refinement to enhance temporal consistency. Experiments on a curated real-world financial dataset show that FinHEAR consistently outperforms strong baselines in both trend forecasting and decision-making."
}