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This paper introduces F$^2$Agent, a novel multimodal trading framework that leverages a hierarchy of specialized agents to extract modality-specific signals and employs a modality-aware adaptive fusion mechanism with noise-robust consistency regularization. The approach effectively addresses the limitations of existing methods by capturing nuanced cross-modal dependencies and enhancing resilience to market noise. Experimental results show that F$^2$Agent outperforms 16 competitive baselines, achieving over 20% relative improvement in annualized returns, with exceptional performance on individual stocks like GOOG and TSLA.
F$^2$Agent achieves over 20% better annualized returns than existing models by dynamically capturing inter-modality dependencies and resisting market noise.
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.
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