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

• URL: http://arxiv.org/abs/2506.09080v1
• Date: Tue, 10 Jun 2025 04:06:51 GMT
• Title: FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making
• Authors: Jiaxiang Chen, Mingxi Zou, Zhuo Wang, Qifan Wang, Dongning Sun, Chi Zhang, Zenglin Xu,
• Abstract summary: FinHEAR is a framework for Human Expertise and Adaptive Risk-aware reasoning.<n>It orchestrates specialized agents to analyze historical trends, interpret current events, and retrieve expert-informed precedents.<n> Empirical results on financial datasets show that FinHEAR consistently outperforms strong baselines across trend prediction and trading tasks.
• Score: 35.588439039301605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Your AI, Not Your View: The Bias of LLMs in Investment Analysis [55.328782443604986]
  Large Language Models (LLMs) face frequent knowledge conflicts due to discrepancies between pre-trained parametric knowledge and real-time market data.<n>This paper offers the first quantitative analysis of confirmation bias in LLM-based investment analysis.<n>We observe a consistent preference for large-cap stocks and contrarian strategies across most models.
  arXiv  Detail & Related papers  (2025-07-28T16:09:38Z)
• FinDPO: Financial Sentiment Analysis for Algorithmic Trading through Preference Optimization of LLMs [2.06242362470764]
  We introduce FinDPO, the first finance-specific sentiment analysis framework based on post-training human preference alignment.<n>The proposed FinDPO achieves state-of-the-art performance on standard sentiment classification benchmarks.<n>We show that FinDPO is the first sentiment-based approach to maintain substantial positive returns of 67% annually and strong risk-adjusted performance.
  arXiv  Detail & Related papers  (2025-07-24T13:57:05Z)
• Quantitative Risk Management in Volatile Markets with an Expectile-Based Framework for the FTSE Index [0.0]
  The research employs a dataset spanning two decades of FTSE 100 returns, incorporating periods of high volatility, market crashes, and recovery phases.<n>Expectile-based Value-at-Risk (EVaR) consistently outperforms traditional VaR measures across various confidence levels and market conditions.<n>The framework exhibits superior performance during volatile periods, with reduced model risk and enhanced predictive accuracy.
  arXiv  Detail & Related papers  (2025-07-16T08:24:14Z)
• Active Test-time Vision-Language Navigation [60.69722522420299]
  ATENA is a test-time active learning framework that enables a practical human-robot interaction via episodic feedback on uncertain navigation outcomes.<n>In particular, ATENA learns to increase certainty in successful episodes and decrease it in failed ones, improving uncertainty calibration.<n>In addition, we propose a self-active learning strategy that enables an agent to evaluate its navigation outcomes based on confident predictions.
  arXiv  Detail & Related papers  (2025-06-07T02:24:44Z)
• FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation [63.55583665003167]
  We present FinDER, an expert-generated dataset tailored for Retrieval-Augmented Generation (RAG) in finance.<n>FinDER focuses on annotating search-relevant evidence by domain experts, offering 5,703 query-evidence-answer triplets.<n>By challenging models to retrieve relevant information from large corpora, FinDER offers a more realistic benchmark for evaluating RAG systems.
  arXiv  Detail & Related papers  (2025-04-22T11:30:13Z)
• Retrieval-augmented Large Language Models for Financial Time Series Forecasting [29.769616823587594]
  We introduce FinSrag, the first retrieval-augmented generation (RAG) framework with a novel domain-specific retriever FinSeer for financial time-series forecasting.<n>FinSeer leverages a candidate selection mechanism refined by LLM feedback and a similarity-driven training objective to align queries with historically influential sequences while filtering out financial noise.<n>We enrich the retrieval corpus by curating new datasets that integrate a broader set of financial indicators, capturing previously overlooked market dynamics.
  arXiv  Detail & Related papers  (2025-02-09T12:26:05Z)
• Analysis of Financial Risk Behavior Prediction Using Deep Learning and Big Data Algorithms [7.713045399751312]
  This paper explores the feasibility and effectiveness of using deep learning and big data algorithms for financial risk behavior prediction.<n>A deep learning-based big data risk prediction framework is designed and experimentally validated on actual financial datasets.
  arXiv  Detail & Related papers  (2024-10-25T08:52:04Z)
• AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
  We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
  arXiv  Detail & Related papers  (2024-03-19T09:45:33Z)
• Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs [44.53203911878139]
  Large Language Models (LLMs) are increasingly adopted in financial analysis for interpreting complex market data and trends. Financial Bias Indicators (FBI) is a framework with components like Bias Unveiler, Bias Detective, Bias Tracker, and Bias Antidote. We evaluate 23 leading LLMs and propose a de-biasing method based on financial causal knowledge.
  arXiv  Detail & Related papers  (2024-02-20T04:26:08Z)
• FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models [32.7825479037623]
  FinPT is a novel approach for financial risk prediction that conduct Profile Tuning on large pretrained foundation models. FinBench is a set of high-quality datasets on financial risks such as default, fraud, and churn.
  arXiv  Detail & Related papers  (2023-07-22T09:27:05Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.