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

DOI

10.2991/978-94-6239-699-9_51How to use a DOI?

Keywords

industry rotation; market regime; deep factor; XGBoost; risk parity

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.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Qiuyu Shen
AU  - Hongsen Cheng
AU  - Haifei Liu
PY  - 2026
DA  - 2026/06/02
TI  - An End-to-End Regime-Dependent Industry Rotation Strategy in China’s A-Share Market
BT  - Proceedings of the 2026 4th International Conference on Digital Economy and Management Science (CDEMS 2026)
PB  - Atlantis Press
SP  - 473
EP  - 480
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-699-9_51
DO  - 10.2991/978-94-6239-699-9_51
ID  - Shen2026
ER  -