A study of cross-industry return predictability in the Chinese stock market

Ellington, Michael ORCID: 0000-0003-0264-7572, Stamatogiannis, Michalis P ORCID: 0000-0002-7283-7550 and Zheng, Yawen (2022) A study of cross-industry return predictability in the Chinese stock market. INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS, 83. p. 102249.

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Official URL: http://dx.doi.org/10.1016/j.irfa.2022.102249

Abstract

We investigate cross-industry return predictability for the Shanghai and Shenzhen stock exchanges, by constructing 6- and 26- industry portfolios. The dominance of retail investors in these markets, in conjunction with the gradual diffusion of information hypothesis provide the theoretical background that allows us to employ machine learning methods to test for cross-industry predictability. We find that Oil, Telecommunications and Finance industry portfolio returns are significant predictors of other industries. Our out-of-sample forecasting exercise shows that the OLS post-LASSO estimation outperforms a variety of benchmarks and a long–short trading strategy generates an average annual excess return of 13%.

Item Type:	Article
Uncontrolled Keywords:	Return predictability, Shrinkage, LASSO, Model selection, Industry portfolio
Divisions:	Faculty of Humanities and Social Sciences > School of Management
Depositing User:	Symplectic Admin
Date Deposited:	23 Jun 2022 07:25
Last Modified:	18 Jan 2023 20:57
DOI:	10.1016/j.irfa.2022.102249
Open Access URL:	https://www.sciencedirect.com/science/article/pii/...
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3156983