发表机构
National University of Singapore; Huazhong University of Science and Technology; Zhejiang University（新加坡国立大学; 华中科技大学; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有多模态金融交易模型的跨模态依赖捕捉不足、抗噪性差的问题，提出F²Agent框架，通过专业化智能体提取模态信号、模态感知自适应融合机制及抗噪正则化，在6种资产上较16个基线实现超20%年化回报率提升，表现出优异性能。
AI 中文摘要
随着信息来源日益多样化和异质化，有效利用多模态数据对高质量金融交易愈发关键。尽管近期基于大语言模型（LLM）的智能体进展已能实现多模态输入的摄入，但现有方法因多模态建模能力有限、融合机制低效、鲁棒性不足，无法捕捉细微的跨模态依赖关系，且易受市场噪声影响。为应对这些挑战，我们提出F²Agent，这是一种由智能体智能金融融合驱动的新型多模态智能体范式。F²Agent首先部署一组专业化智能体，以全面提取各模态特有的信号；进一步引入模态感知自适应融合机制，结合抗噪声一致性正则化，动态捕捉细粒度跨模态依赖关系，生成抗噪声交易信号。对6种股票和加密货币资产开展的大量实验表明，F²Agent在多项交易指标上始终优于16个竞争性基线模型，年化平均回报率的相对提升超过20%。值得注意的是，F²Agent在GOOG上实现120.48%的回报率，在TSLA上实现148.41%的回报率，展现出其在不同市场动态下的有效性与鲁棒性。
英文摘要
With increasingly diverse and heterogeneous information sources, effectively leveraging multimodal data is becoming pivotal for high-quality financial trading. Although recent advancements in Large Language Model (LLM)-based agents have enabled the ingestion of multimodal inputs, existing methods fail to capture nuanced cross-modal dependencies and remain vulnerable to market noise, due to limited multimodal modeling, ineffective fusion mechanisms, and inadequate robustness. To address these challenges, we propose F$^2$Agent, a novel multimodal agentic paradigm driven by the Financial Fusion of Agentic Intelligence. F$^2$Agent first deploys a hierarchy of specialized agents to comprehensively extract modality-specific signals. It further introduces a modality-aware adaptive fusion mechanism coupled with noise-robust consistency regularization to dynamically capture fine-grained inter-modality dependencies and generate noise-resilient trading signals. Extensive experiments on six stocks and cryptocurrency assets demonstrate that F$^2$Agent consistently outperforms 16 competitive baselines across multiple trading metrics, with over 20% relative improvement in annualized return on average. Notably, F$^2$Agent delivers returns of 120.48% on GOOG and 148.41% on TSLA, demonstrating its efficacy and robustness in varying market dynamics.
Comments32 pages, 12 figures, 19 tables
