Machine learning in the Chinese stock market

Markus Leippold, Qian Wang and Wenyu Zhou

Journal of Financial Economics, 2022, vol. 145, issue 2, 64-82

Abstract: We add to the emerging literature on empirical asset pricing in the Chinese stock market by building and analyzing a comprehensive set of return prediction factors using various machine learning algorithms. Contrasting previous studies for the US market, liquidity emerges as the most important predictor, leading us to closely examine the impact of transaction costs. The retail investors’ dominating presence positively affects short-term predictability, particularly for small stocks. Another feature that distinguishes the Chinese market from the US market is the high predictability of large stocks and state-owned enterprises over longer horizons. The out-of-sample performance remains economically significant after transaction costs.

Keywords: Chinese stock market; Factor investing; Machine learning; Model selection (search for similar items in EconPapers)
JEL-codes: C52 C55 C58 G0 G1 G17 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (79)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:145:y:2022:i:2:p:64-82

DOI: 10.1016/j.jfineco.2021.08.017

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