Adaptability Analysis of Quantitative Hedging Strategies in the A-Share Market

DOI

10.2991/978-94-6239-801-6_44How to use a DOI?

Keywords

Quantitative Hedging; Machine Learning; Market Adaptability

Abstract

This paper examines the A-share market from January 2021 to December 2025. It applies machine learning methods, including Random Forest, AdaBoost, and Gradient Boosting to build a multi-factor quantitative hedging strategy from three dimensions: momentum, volatility, and turnover. The study systematically tests how the strategy performs under different market conditions-bull markets, bear markets, and sideways markets. The results show several findings. First, the strategy delivered an annualized return of -1.83% with a Sharpe ratio of -0.70 over the full sample period. The overall performance was unsatisfactory, mainly because the model relied solely on technical factors, the market was in a downtrend during the sample period, and transaction costs eroded returns significantly. Second, the strategy showed strong dependence on market states. It performed best in bull markets and worst in bear markets. In sideways markets, it was roughly break-even. Third, the Gradient Boosting model achieved the best prediction results. Fourth, SHAP feature importance analysis indicates that the 12–1-month momentum factor contributed the most, confirming the existence of significant medium-to-long-term momentum effects in the A-share market. The paper suggests improving the adaptability of quantitative hedging strategies through three approaches: strategy optimization, market development, and regulatory refinement.

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  - Tianyu Huang
PY  - 2026
DA  - 2026/10/23
TI  - Adaptability Analysis of Quantitative Hedging Strategies in the A-Share Market
BT  - Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)
PB  - Atlantis Press
SP  - 407
EP  - 415
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-801-6_44
DO  - 10.2991/978-94-6239-801-6_44
ID  - Huang2026
ER  -