Forecasting stock market return with anomalies: Evidence from China

Jianqiu Wang, Zhuo Wang and Ke Wu

International Journal of Forecasting, 2025, vol. 41, issue 3, 1278-1295

Abstract: We empirically investigate the relation between anomaly portfolio returns and market return predictability in the Chinese stock market. Using 132 long-leg, short-leg, and long-short anomaly portfolio returns, we employ various shrinkage-based statistical learning methods to capture predictive signals of the anomalies in a high-dimensional setting. Our analysis reveals statistically and economically significant return predictability using long- and short-leg anomaly portfolio returns. Moreover, high arbitrage risk enhances forecasting performance, supporting that the predictability stems from mispricing correction persistence. Contrary to findings in the US stock market, we find little evidence that the long-short anomaly portfolios contribute to market return predictability in China, due to the low persistence of asymmetric mispricing corrections. We provide simulation evidence to justify the distinct prediction patterns for the US and Chinese stock markets.

Keywords: Anomalies; Mispricing; Market return predictability; Chinese stock market; Machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:41:y:2025:i:3:p:1278-1295

DOI: 10.1016/j.ijforecast.2024.12.007

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