发表机构
National University of Singapore; Asian Institute of Digital Finance; KTH Royal Institute of Technology; University of Oxford（新加坡国立大学; 亚洲数字金融研究院; KTH皇家理工学院; 牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出MUFASA分层多智能体框架，通过解耦方程发现、元协调器推理和记忆机制，在非平稳金融条件下实现可解释符号回归，跨多国数据集达到最先进估值性能。
AI 中文摘要
虽然符号回归（SR）已在科学领域成功用于发现新方程，但其在金融估值中的应用受到若干限制。自然科学提供客观正确的关系，而金融估值构成一类独特的符号发现问题，因为它允许多种有效视角，在非平稳市场条件下运行，并涉及嘈杂、连续的绩效信号。在这项工作中，我们提出了具有符号自适应学习的多智能体基本面分析（MUFASA），一个用于金融符号发现的分层多智能体框架。MUFASA引入了（1）通过代表不同估值视角的专门智能体进行解耦方程发现，（2）一个元协调器，对市场背景信息进行分层推理，以及（3）一个记忆机制，对统计绩效摘要（如准确性、稳定性和尾部风险）进行推理，以在嘈杂反馈下指导学习。跨多个国家数据集的实验表明，与经典金融方法、金融大语言模型和符号回归方法相比，MUFASA在估值任务上实现了最先进的性能，同时产生可解释的方程，我们与社区共享这些方程。我们还公开了跨进化迭代的提炼学习成果和上下文相关策略权重，这可能为未来金融基本面分析研究提供有用的见解。
英文摘要
While symbolic regression (SR) has been successfully used in science to discover new equations, its use in financial valuation is hindered by several limitations. Whereas the natural sciences provide objectively correct relationships, financial valuation constitutes a distinct class of symbolic discovery problems, as it admits multiple valid perspectives, operates under non-stationary market conditions, and involves noisy, continuous performance signals. In this work, we propose Multi-Agent Fundamental Analysis with Symbolic Adaptive learning (MUFASA), a hierarchical multi-agent framework for symbolic discovery in finance. MUFASA introduces (1) disentangled equation discovery via specialized agents representing distinct valuation perspectives, (2) a meta-coordinator that performs hierarchical-level reasoning over market context information, and (3) a memory mechanism that reasons over statistical performance summaries (e.g., accuracy, stability, and tail risk) to guide learning under noisy feedback. Experiments across datasets from multiple countries show that MUFASA achieves state-of-the-art performance on the valuation task compared to classical finance methods, financial large language models, and SR approaches, while simultaneously producing interpretable equations, which we share with the community. We also make publicly available the distilled learnings across evolution iterations and context-dependent strategy weights, which might offer useful insights for future research on financial fundamental analysis.
