Machine learning in the Chinese stock market

• 成果类型: Article
• 署名作者: Leippold, Markus; Wang, Qian; Zhou, Wenyu
• 署名单位: University of Zurich; Zhejiang University; Zhejiang University
• 刊物名称: JOURNAL OF FINANCIAL ECONOMICS
• ISSN/ISSBN: 0304-405X
• DOI: 10.1016/j.jfineco.2021.08.017
• 发表日期: 2022
• 页码: 64-82
• 关键词: Chinese stock market; Factor investing; Machine Learning; model selection

摘要

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. (C) 2021 The Author(s). Published by Elsevier B.V.