Machine learning in the Chinese stock market

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 U.S. market, liquidity emerges as the most important predictor, leading us to examine the impact of transaction costs closely. The 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. The out-of-sample performance remains economically significant after transaction costs.

# Financial Markets and Investment Strategies # Economics, Econometrics and Finance # Stock Market Forecasting Methods # Decision Sciences # Financial Analysis and Corporate Governance # Business, Management and Accounting

Markus Leippold, Qian Wang, Wenyu Zhou (2021). "Machine learning in the Chinese stock market". Journal of Financial Economics. https://doi.org/10.1016/j.jfineco.2021.08.017