A latent factor model for the Chinese stock market

Tian Ma, Wen Jun Leong and Fuwei Jiang

International Review of Financial Analysis, 2023, vol. 87, issue C

Abstract: We propose a new latent factor model for the Chinese stock market based on an instrumented principal component analysis (IPCA). Compared with other common asset pricing models, the new latent factor model explains a larger proportion of individual and portfolio return variation and shows significant out-of-sample predictability. The long-short investment strategy formed by the IPCA factor also presents the highest average return and Sharpe ratio. Subsample and different horizon results are robust. Market beta, profitability and momentum emerge as the most important characteristics in driving the latent factors. We also provide evidence on the economic grounds of the new latent factor model.

Keywords: Big data; Instrumented principal component analysis; Latent factors; Cross section of returns; China's stock market (search for similar items in EconPapers)
JEL-codes: G11 G12 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:87:y:2023:i:c:s1057521923000716

DOI: 10.1016/j.irfa.2023.102555

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