GAMMA: Market-Grounded Hypothesis Generation and Groupwise Feedback for LLM-Based Alpha Mining

Alpha factors are scoring rules that rank assets for portfolio construction. Recent LLM agents automate factor discovery through three stages: (1) economic hypothesis generation, (2) factor implementation, and (3) evaluation and evolution. However, existing methods face two limitations. First, hypothesis generation often relies on the LLM's general financial knowledge without grounding hypotheses in the target market's training data. Second, evaluating a hypothesis through a single factor often leads to prematurely discarding a valid hypothesis, mistaking a poor implementation for a flawed economic rationale. We introduce **GAMMA**, **G**roupwise **A**lpha **M**ining via **M**arket-grounded **A**gents, to address these limitations. During hypothesis generation, GAMMA analyzes the target market's feature distributions, cross-sectional relationships, and temporal dependencies, grounding each hypothesis in statistically verified evidence. During factor implementation, GAMMA constructs a group of diverse factor implementations for each hypothesis. During evaluation and evolution, GAMMA clusters these implementations by stock-ranking similarity, summarizes feedback at the cluster level, and expands successful clusters into refined sub-hypotheses. On CSI 300, GAMMA achieves the highest net annualized excess return of 13.29% and an information ratio of 1.471 among 17 compared methods. CSI 300 factor expressions also retain predictive value when transferred to CSI 500. Additional experiments show that GAMMA remains effective on both CSI 500 and S&P 500, demonstrating its generalizability across Chinese and U.S. equity markets.