Tests of the Fama-French five-factor model in the US stock market under the COVID-19 pandemic

Meng Gao ()
Additional contact information
Meng Gao: Beijing Normal University - Hong Kong Baptist University United International College, Faculty of Sicence and Technology

A chapter in Proceedings of the 2023 2nd International Conference on Economics, Smart Finance and Contemporary Trade (ESFCT 2023), 2024, pp 221-226 from Springer

Abstract: Abstract At the end of 2019, the COVID-19 spread widely around the world, leading to a global economic recession. The data for this article is taken from a database created by Kenneth R. French. Given that the COVID-19 pandemic was widespread in the United States in March 2020 and remained stable through February 2023. In this paper, multiple linear regression method is adopted to conduct empirical analysis and test on the Fama-French five-factor model, and explore the explanatory strength of the five-factor model on the US stock market before and after the epidemic. The results showed that the volatility and average return of the market increased significantly after the epidemic. The epidemic enhanced the market value effect, and also enhanced the explanatory power of the profit factor and investment style factor.

Keywords: Multiple linear regression analysis; Fama-french five-factor model; American stock market; COVID-19 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-268-2_26

Ordering information: This item can be ordered from
http://www.springer.com/9789464632682

DOI: 10.2991/978-94-6463-268-2_26

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().