Agentic Factor Engineering: Turning Investment Questions into Factors

Factor construction turns investment ideas into numerical signals for quantitative investment. Automated methods can generate factor formulas and programs, but a single fixed formula cannot fully capture many investment judgments. For example, assessing whether a company's new capacity has begun to translate into deliveries requires examining disclosures, production data, and price records together. The key evidence differs across companies. We propose Agentic Factor Engineering, using investment questions to define natural-language factors. An executor agent follows common execution and scoring rules to read records, perform calculations, and assign each stock a score with supporting evidence. We also explore search over natural-language factors through return-guided evolution, using return evaluations and company cases to revise questions and expand the candidate bank. We then select ensembles from all generations using validation performance. Experiments on fundamental and technical factors in CSI300 and Nasdaq-100 support question-based measurement through formula comparisons, controlled evidence changes, and repeated execution. In chronologically held-out tests, selected ensembles achieve higher information coefficients (IC) and rank information coefficients (RankIC) than initial ensembles in three of four settings, suggesting predictive potential for this approach to factor construction.