F in HEAR : Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making (2025)

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.

## introduction large language models (llms) @xcite have demonstrated strong capabilities in general reasoning tasks, including chain-of-thought prompting @xcite , incontext learning @xcite , and code understanding @xcite . however, applying llms to financial domains remains challenging due to the dynamic, uncertain, and eventdriven nature of real-world markets. effective financial decision-making requires temporal reasoning, adaptive risk assessment, and responsiveness to external shocks-far beyond the scope of static reasoning benchmarks. these challenges are further amplified by the complexity of financial data, which is highfrequency, multi-source, and nonlinear @xcite . crucially, financial decisions are not purely rational computations-they reflect behavioral patterns shaped by human cognition. under uncertainty, individuals rely on expert heuristics @xcite @xcite , adapting decisions through bounded rationality and accumulated experience. moreover, risk-sensitive behaviors such as loss aversion and feedback-based adjustment, as formalized in prospect theory @xcite , remain largely unmodeled in existing ai systems. bridging this gap is essential for aligning llm reasoning with real-world, high-stakes decision-making. prior work has explored rule-based systems, deep learning models, and more recently, llmbased frameworks for financial decision-making. rule-based approaches encode expert heuristics but struggle with generalization in dynamic, eventdriven environments. deep learning models, including lstm @xcite , transformer @xcite , and their variants @xcite , improve temporal modeling but often lack interpretability and adaptivity. reinforcement learning (e.g., ppo @xcite ) offers policy learning under uncertainty, yet remains data-hungry and hard to align with human intuition. despite these advancements, existing methods still fall short of key requirements for reliable financial decision-making. first, they lack structured mechanisms to integrate accumulated human expertise-critical for mitigating information asymmetry and supporting decisions under bounded rationality. second, they exhibit limited sensitivity to risk, often ignoring the loss-averse and risk-adjusted behaviors emphasized by behavioral finance theories such as prospect theory. third, they fail to ensure temporal consistency: unlike static reasoning tasks, financial decisions unfold over time, where early-stage errors can compound if not continually revised. addressing these gaps is vital for building systems that are not only accurate, but also robust, interpretable, and adaptive to real-world market dynamics. we introduce finhear-a multi-agent framework for human expertise and adaptive riskaware temporal reasoning. finhear addresses three key challenges in financial decision-making: incomplete information, risk sensitivity, and temporal consistency.to reduce information asymmetry, one agent retrieves relevant expert cases to emulate human-guided decisions under bounded rationality-mirroring how investors rely on heuristics under cognitive constraints. to enhance risk awareness, another agent estimates market risk via interagent disagreement and adjusts position sizes based on prediction confidence, modeling the loss-averse behavior described by prospect theory. a feedback mechanism further refines earlier predictions using realized outcomes, promoting temporal coherence and limiting error propagation.these components are embedded in a structured, event-driven pipeline, enabling finhear to produce reasoning that is both effective and consistent with behavioral economic theories of human decision-making under uncertainty. we construct an event-driven financial dataset linking multi-stock price movements with macroeconomic events, firm-level news, and expert commentary. empirical results show that finhear achieves substantial gains in both trend forecasting and decision making, consistently outperforming strong baselines across key metrics such as acc, mcc, cr, sr, mdd and calmarr. our key contributions are summarized as follows: ## related work deep learning methods. deep learning has been extensively applied to financial forecasting, with models such as lstm @xcite , gru @xcite , and alstm (wang and hao, 2020) capturing sequential dependencies and emphasizing salient time steps. transformer-based models @xcite improved long-range modeling, while informer @xcite and timesnet @xcite enhanced efficiency and temporal variation modeling. extensions like stocknet @xcite integrated sentiment features, and reinforcement learning methods @xcite introduced reward-driven decision-making, further refined by logic-q @xcite with structured priors. however, these models primarily focus on pattern extraction and lack mechanisms for event understanding and risk-adaptive reasoning-gaps that finhear bridges through expert-informed retrieval, dynamic risk modeling, and feedback-based refinement. llm-based methods. recent advances in large language models (llms) have enabled structured financial reasoning through prompting and memory-augmented techniques. methods like chain-of-thought @xcite @xcite , react @xcite , and their extensions @xcite @xcite @xcite promote stepwise reasoning and agent coordination. finmem @xcite and fincon @xcite incorporate memory and multi-agent structures for risk-aware decision-making, while tradin-gagents @xcite mimics real-world organizational hierarchies to improve trading strategies. while these methods introduce reasoning and coordination capabilities, finhear advances further by unifying expert-guided retrieval, dynamic risk modeling, and feedback-based refinement in a behavior-aware framework. ## experiments in this section, we empirically evaluate the performance of finhear by addressing the following research questions: • rq1: forecasting accuracy. how accurately can finhear predict the directional trend of major financial assets? • rq2: backtest performance. how well does finhear perform in historical market simulations, in terms of return and risk? • rq3: component contribution. what is the individual impact of each module within finhear on the overall trading outcome? ## conclusion this paper has proposed finhear to address three key challenges in financial decision-making: incomplete information, risk sensitivity, and temporal consistency. to reduce information asymmetry, it retrieves expert cases to emulate bounded rationality. to manage risk, it adjusts positions based on prediction uncertainty. a feedback mechanism further refines past decisions using outcomedriven updates. these components form a structured, event-driven pipeline grounded in behavioral finance. experiments on single-asset and portfolio tasks show that finhear consistently outperforms rule-based, deep learning, and llm-based baselines across forecasting and decision-making metrics. overall, finhear offers a promising step toward interpretable, resilient, and behavior-aware financial decision-making.