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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Machine-Learning in the Chinese Factor Zoo
Organization Unit
  • Markus Leippold
  • Qian Wang
  • Wenyu Zhou
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Journal of Financial Economics
Publisher Elsevier
Geographical Reach international
ISSN 0304-405X
Volume 145
Number 2
Page Range 64 - 82
Date 2022
Abstract Text We add to the emerging literature on empirical asset pricing in the Chinese stock market by building and analyzing a comprehensive set of factors with 1,160 signals for return prediction. Using various machine learning algorithms, we investigate which signals dominate in the Chinese market, a market characterized by a large proportion of retail investors with speculative motives, state-owned firms, and short-sales restrictions. Contrary to studies for the U.S. market, liquidity and fundamental factors emerge as the most important predictors, while price trend signals are less significant. We find that retail investors' dominating presence positively affects short-term predictability, particularly for small stocks. Another feature that distinguishes the Chinese from the U.S. market is the high predictability of large stocks and state-owned enterprises over longer horizons. Our portfolio analysis shows that this overall increased predictability leads to significantly higher out-of-sample performance than in other markets, which remains economically significant after transaction costs.
Related URLs
Digital Object Identifier 10.1016/j.jfineco.2021.08.017
Other Identification Number merlin-id:21155
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