FaVOR: LLM-Based Agentic Framework for Factor Mining via Empirical Validation
2026-08-31 • Artificial Intelligence
Artificial IntelligenceComputational Engineering, Finance, and Science
AI summaryⓘ
The authors notice that current automated methods for finding financial factors focus too much on returns and often miss whether the factors truly match the economic ideas behind them. They introduce FaVOR, a new system that checks if each factor matches its original economic hypothesis through a three-step process: breaking down the hypothesis, validating each part, and then combining them so the result stays understandable. Testing on major stock indices shows FaVOR performs better and stays reliable even when markets change. This approach helps find financial signals that make sense economically and are easier to trust.
financial factorseconomic hypothesisfactor miningmulti-agent systemsreturn optimizationregime shiftsCSI 500S&P 500interpretabilityFaVOR
Authors
Hyeonjin Kim, Minseok Kim, Seunghyeon Jung, Sujin Pyo, Huisu Jang, Woojin Lee
Abstract
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.