Interpretable Factor Decomposition for Decision Intelligence in Large-Scale Financial Markets: Evidence from China's A-Share Market
Researchers have developed an interpretable machine learning pipeline to break down stock market predictability into factor contributions. Applying an XGBoost model with TreeSHAP attribution to Chinese A-share stocks from 2009-2019, the system achieved a significant alpha of +2.38%/month, persistent even after accounting for the Carhart four-factor model. SHAP Decomposition revealed that behavioral signals like turnover and momentum were the primary drivers of predictive attribution, accounting for 58.2% compared to valuation ratios at 10.7%. AI
IMPACT Demonstrates how interpretable AI can uncover hidden patterns in financial markets, potentially improving investment strategies.