The pricing ability of factor model based on machine learning: Evidence from high-frequency data in China

Ailian Zhang, Mengmeng Pan and Xuan Zhang

International Review of Economics & Finance, 2025, vol. 101, issue C

Abstract: The existing literature mainly documents the asset pricing models estimated on low-frequency data, lacking the empirical evidence for exploring the “right” systematic factors based on high-frequency (HF) level. This study develops a revised HF factor model and evaluates the asset pricing performance. Using machine learning algorithms, we find that HF factor model includes three very persistent systematic factors, well-approximated by a portfolio of market, finance, and information. Sharpe ratios and out-of-sample tests prove that the HF revised factor model has the best explanatory power compared to the CAPM, Fama-French three-factor and five-factor models. The findings contribute to an in-depth understanding of the characteristics and mechanisms of risk and return from an HF perspective in the Chinese stock market.

Keywords: Asset pricing; Machine learning; High-frequency; Chinese stock market (search for similar items in EconPapers)
JEL-codes: E31 E32 E52 G28 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reveco:v:101:y:2025:i:c:s1059056025003168

DOI: 10.1016/j.iref.2025.104153

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