Machine learning, anomalies, and the expected market return: Evidence from China

Qingjie Du, Yang Wang, Chishen Wei and K.C. John Wei

Pacific-Basin Finance Journal, 2023, vol. 82, issue C

Abstract: We investigate whether machine learning (ML) techniques that forecast overall U.S. market returns using cross-sectional stock return anomalies in Dong et al. (2022) are useful for the China equity market. We successfully forecast out-of-sample R2 of the market return in China using a combined version of ordinary least squares and an elastic net model. However, the other four ML methods cannot forecast the market return. Overall, our exercise highlights the potential of ML techniques, but also calls for future research to rule out the possibility of model mining.

Keywords: Machine learning; Chinese stock market; Anomalies; Return predictability (search for similar items in EconPapers)
JEL-codes: G11 G14 G15 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:82:y:2023:i:c:s0927538x23002391

DOI: 10.1016/j.pacfin.2023.102168

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