Work

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

Year: 2025

Type: article

Abstract: 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.

Source: International Review of Economics & Finance

DOI: 10.1016/j.iref.2025.104153

Authors: Ailian Zhang, Mengmeng Pan, Xuan Zhang

Institutions: Nanjing University of Aeronautics and Astronautics, Chinese University of Hong Kong, Chinese University of Hong Kong, Shenzhen

Language: English

Topic: Financial Markets and Investment Strategies

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funders: National Natural Science Foundation of China, Ministry of Education of the People's Republic of China, Young Scientists Fund, Humanities and Social Science Fund of Ministry of Education of China