An End-to-End Regime-Dependent Industry Rotation Strategy in China’s A-Share Market

Qiuyu Shen, Hongsen Cheng () and Haifei Liu
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Qiuyu Shen: Nanjing University, School of Management and Engineering
Hongsen Cheng: Nanjing University, School of Management and Engineering
Haifei Liu: Nanjing University, School of Management and Engineering

A chapter in Proceedings of the 2026 4th International Conference on Digital Economy and Management Science (CDEMS 2026), 2026, pp 473-480 from Springer

Abstract: Abstract This paper develops an integrated framework for industry rotation in China’s A-share market by combining industry-level factor construction, deep-factor extraction, market-regime prediction, directional classification, and regime-dependent risk parity. Industry signals are first formed from Alpha-style predictors aggregated from representative constituent stocks and then enriched by a feed-forward neural network that learns latent deep factors. The market is subsequently classified into four interpretable states defined by volatility and rotation speed, and next-period regime probabilities are forecast with XGBoost. These probabilities affect both the directional signal layer and the covariance structure used in the allocation layer. Out-of-sample evidence for 2023-2024 shows that the full framework delivers stronger return stability, a higher Sharpe ratio, and better drawdown control than simpler equal-weight or non-regime alternatives. The empirical results indicate that regime information is valuable not only for return prediction, but also for dynamic portfolio risk allocation.

Keywords: industry rotation; market regime; deep factor; XGBoost; risk parity (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-94-6239-699-9_51

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