Interpretable Factor Decomposition for Decision Intelligence in Large-Scale Financial Markets: Evidence from China's A-Share Market

2026-06-11 · Source: Takara TLDR - Daily AI Papers · Field: Finance & Economics — Capital Markets & Investment Management, FinTech & Digital Financial Services · Depth: Expert, medium

Summary

A new interpretable machine learning pipeline decomposes Cross-Sectional Equity Return Predictability into auditable factor contributions, specifically applied to China's A-share market. Researchers utilized an XGBoost model with TreeSHAP attribution, stress-tested on 3632 Chinese A-share stocks from 2009 to 2019. Over 55 months of out-of-sample data, the model achieved a mean AUC of 0.547 and a +2.38%/month long-short spread (Annualized Sharpe 2.23), which remained persistent at +2.31%/month after Carhart four-factor adjustment. SHAP Decomposition indicated that behavioral signals, such as turnover and momentum, contributed 58.2% to predictive attribution, significantly more than valuation ratios at 10.7% across 55 industry groups. Ablation analysis further validated these findings and revealed feature substitutability structures.

Key takeaway

For quantitative analysts building predictive models for emerging markets like China's A-share, you should prioritize behavioral signals such as turnover and momentum. These factors account for a significant 58.2% of predictive attribution, outperforming traditional valuation ratios. Integrate interpretable machine learning techniques like TreeSHAP and ablation analysis to audit factor contributions and uncover complex feature interactions, enhancing model transparency and robustness.

Key insights

Interpretable ML reveals behavioral factors dominate Chinese A-share predictability, offering auditable financial insights.

Principles

• Behavioral signals strongly predict equity returns.
• SHAP and ablation reveal feature substitutability.
• Auditable factor decomposition enhances decision intelligence.

Method

The pipeline uses XGBoost with TreeSHAP for attribution, stress-tested on historical stock data, and employs ablation analysis to cross-validate factor rankings and identify feature substitutability.

In practice

• Apply XGBoost with TreeSHAP for factor attribution.
• Prioritize behavioral signals in stock selection models.
• Use ablation alongside SHAP for deeper feature insights.

Topics

• Interpretable Machine Learning
• Factor Decomposition
• XGBoost
• TreeSHAP
• China A-share Market
• Equity Return Prediction

Code references

• Eric991005/Multitask-Stockformer

Best for: Research Scientist, AI Scientist, Data Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.