Option Return Predictability via Machine Learning: New Evidence From China

Yuxiang Huang, Zhuo Wang and Zhengyan Xiao

Journal of Futures Markets, 2025, vol. 45, issue 9, 1232-1252

Abstract: We extend the literature on empirical asset pricing to the Chinese options market by building and analyzing a comprehensive set of return prediction factors using various machine learning methods. In contrast to previous studies for the US market, we emphasize the uniqueness of this emerging market, investigate daily hedging strategies to construct delta‐neutral portfolios, and identify the most important characteristics for return prediction. Short‐selling restrictions in China's financial market diminish the effectiveness of spot hedging, whereas delta‐neutral portfolios based on futures hedging deliver substantial improvements in both annual returns and Sharpe ratios. Machine learning models not only outperform the IPCA benchmark, but also demonstrate strong generalization ability when applied to newly issued option contracts. The out‐of‐sample performance remains economically significant after accounting for transaction costs.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/fut.22604

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:jfutmk:v:45:y:2025:i:9:p:1232-1252

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0270-7314

Access Statistics for this article

Journal of Futures Markets is currently edited by Robert I. Webb

More articles in Journal of Futures Markets from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().