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Machine Learning in the Chinese Stock Market

Journal: Journal of Financial Economics

Date: 20220801

Author: Leippold, Markus; Wang, Qian; Zhou, Wenyu

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.

Link: Google Scholar

Machine Learning Performance

Neural networks consistently outperform other methods, with monthly out-of-sample R² up to 2.58% compared to 0.40% for US market

Key Predictors

Liquidity factors are most important, followed by fundamental signals - contrasting with US market where trend indicators dominate

Retail Impact

Strong predictability for small stocks due to retail investor dominance (99.8% of accounts), particularly in short-term trading

Feature	Chinese Market	US Market
Top Predictors	Liquidity, Fundamentals	Trend Indicators
Monthly R² (Neural Networks)	2.58%	0.40%
Small Stock Predictability	High	Low