• ISSN: 2301-3567 (Print), 2972-3981 (Online)
    • Abbreviated Title: J. Econ. Bus. Manag.
    • Frequency: Quarterly
    • DOI: 10.18178/JOEBM
    • Editor-in-Chief: Prof. Eunjin Hwang
    • Executive Editor: Ms. Fiona Chu
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HOME > Archive > 2023 > Volume 11 Number 1 (Feb. 2023) >
JOEBM 2023 Vol.11(1): 17-20 ISSN: 2301-3567
DOI: 10.18178/joebm.2023.11.1.731

Portfolio Establishment Based on Fama-French Five-Factor Model in China Stock Market

Abstract—In the field of modern finance, investors like to use the ACPM model to analyze their portfolios to reduce risks and maximize returns. And the main purpose of our investigation is to choose the six stocks and use the R-studio to analyze the data to see whether the five-factor model can be applied well in China stock market. We start our investigation by collecting data and setting up multiple linear regression models. Then we observe the correlations between the five factors and the excess returns of different stocks and test if all the values of the population parameters and some certain parameters are equal to 0. Finally, we test if multicollinearity existed. We can conclude from the analysis that HML is the most significant factor in all of the portfolios. Besides, the factor CMA has the least significance in portfolios 1 and 2 and the factor SMB is the least significant factor in the rest of the regressions of portfolios. Based on the result, we find that the five-factor model is also applicable in China stock market. So Chinese stock investors can use the five-factor model to help them achieve better investment returns.

Index Terms—ACPM, five-factor model, portfolios, SMB

Zihui Gong, Qianqian Shi, and Yuzhi Zhou are with Lancaster University, UK.
Guangjie Xu is with Hohai University, China.
*Correspondence: y.zhou28@lancaster.ac.uk

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Cite:Zihui Gong, Qianqian Shi, Guangjie Xu, and Yuzhi Zhou, "Portfolio Establishment Based on Fama-French Five-Factor Model in China Stock Market," Journal of Economics, Business and Management vol. 11, no. 1, pp. 17-20, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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