让AI像专家一样动态判断风险，做出更稳的金融决策
FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making
- 用多个AI代理分工处理历史、事件和专家经验
- 在测试中准确率更高，风险调整后收益优于基线模型
- 适合量化交易、投资顾问等需理性决策的场景
金融决策对语言模型提出独特挑战，要求具备时间推理、动态风险评估和对突发事件的响应能力。尽管大语言模型（LLMs）具备强大通用推理能力，却常无法捕捉人类金融决策的核心行为模式，如信息不对称下的专家依赖、损失厌恶敏感性以及基于反馈的时间调整。我们提出FinHEAR，一种基于人类专家与自适应风险感知的多智能体框架。该框架通过事件中心化流程，调度专用的基于LLM的智能体分析历史趋势、解读当前事件，并检索专家指导的历史先例。结合行为经济学理论，引入专家引导的检索、置信度调整的仓位大小设定及基于结果的优化机制，提升可解释性与鲁棒性。在精选金融数据集上的实证结果显示，FinHEAR在趋势预测与交易任务中持续优于强基线模型，展现出更高的准确率与更好的风险调整后回报。
原文摘要 · Abstract (English)
Financial decision-making presents unique challenges for language models, demanding temporal reasoning, adaptive risk assessment, and responsiveness to dynamic events. While large language models (LLMs) show strong general reasoning capabilities, they often fail to capture behavioral patterns central to human financial decisions-such as expert reliance under information asymmetry, loss-averse sensitivity, and feedback-driven temporal adjustment. We propose FinHEAR, a multi-agent framework for Human Expertise and Adaptive Risk-aware reasoning. FinHEAR orchestrates specialized LLM-based agents to analyze historical trends, interpret current events, and retrieve expert-informed precedents within an event-centric pipeline. Grounded in behavioral economics, it incorporates expert-guided retrieval, confidence-adjusted position sizing, and outcome-based refinement to enhance interpretability and robustness. Empirical results on curated financial datasets show that FinHEAR consistently outperforms strong baselines across trend prediction and trading tasks, achieving higher accuracy and better risk-adjusted returns.
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