发表机构
Dongguk University-Seoul; Seoul National University; Soongsil University（东国大学首尔校区; 首尔大学; 崇实大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FaVOR是围绕假设证据的LLM智能体框架，通过三阶段一致性循环挖掘因子，在中证500、标普500上优于基准，可生成可解释、鲁棒的经济信号。
AI 中文摘要
传统金融依赖专家通过基于经济原理的原则性流程手工构建因子。近期基于大语言模型（LLM）的多智能体系统已实现该流程的自动化，将因子挖掘规模扩展至远超人工的水平。然而，这些自动化方法直接以收益为优化目标，极少检查生成的因子是否仍体现其产生时所依据的经济假设。我们将这种数学形式与经济意义之间的不一致视为收益导向型自动化的结构性失效模式，导致生成的因子模糊了真实信号与伪相关的界限，并在市场制度转换时失效。我们提出FaVOR（通过可观测推理进行因子验证），这是一个围绕假设层面证据而非收益结果重构因子挖掘的智能体框架。FaVOR替代了从假设到公式的标准跳跃，实施了一个三阶段一致性循环，将数学形式与经济原理全程关联：（1）分解将宽泛的经济假设拆分为独立的可观测条件；（2）验证检查每个因子是否反映其预期条件；（3）整合将这些因子合并为结构可解释的复合因子。在2025年的中证500和标普500指数上，FaVOR的表现优于现有基准方法，且在不同市场制度下均保持有效。FaVOR表明，基于假设的因子发现可产生天然具备可解释性、制度鲁棒性且符合经济逻辑的信号，代码可在此URL获取。
英文摘要
Traditional finance relies on experts to hand-craft factors through a principled process grounded in economic rationale. Recent LLM-based multi-agent systems have automated this process, scaling factor mining far beyond manual effort. However, these automated approaches optimize directly for returns and rarely check whether a generated factor still expresses the economic hypothesis that motivated it. We identify this inconsistency between mathematical form and economic meaning as a structural failure mode of return-oriented automation. The resulting factors blur the line between real signals and spurious correlations and break down across regime shifts. We propose FaVOR (Factor Validation through Observable Reasoning), an agentic framework that restructures factor mining around hypothesis-level evidence rather than return outcomes. In place of the standard hypothesis-to-formula leap, FaVOR enforces a three-stage consistency loop tying mathematical form to economic rationale throughout. (1) Decomposition splits a broad economic hypothesis into independent observable conditions. (2) Validation checks whether each factor reflects its intended condition. (3) Integration merges them into a composite whose structure remains interpretable. On the CSI 500 and S&P 500 in 2025, FaVOR outperforms existing baselines while remaining effective across regimes. FaVOR shows that hypothesis-grounded factor discovery produces signals that are interpretable by construction, regime-robust, and economically faithful. The code is available at https://github.com/damilab/FaVOR.
