FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making

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.