Application of Fama-French five factor model in US clothing industry during COVID-19

Abstract

During the COVID crisis, the US clothing industry has suffered a great plunge due to vast store closures and business discontinuation. People must understand in what aspects this crisis impacted on the clothing industry. This paper aims to analyze the impact of COVID-19 on US clothing industry return and risk. We used data in a one-year span before and during COVID-19, respectively, from the Kenneth French Data Library. This study applies the Fama & French 5-factor asset pricing model and adopts the multiple linear regression method for analysis. We tried to identify which factors in the model explained the COVID returns and how their explanatory power changes before and during COVID. It is interesting to evaluate that size, value, and investment factors were all insignificant before the crisis, but all turned significant during COVID-19. Also, discovering that growth stocks outperformed value stocks during the crisis was not the case before the crisis. It could be attributed to investors' speculation intent becoming stronger amid the distressing environment of the pandemic. Firms in clothing industries were investing conservatively due to a shortage in cash flows in extreme uncertainty. Small stocks performed better than big stocks during the crisis. It speculates that bigger-size firms were shattered more as they occupied greater market share and thus undertook more loss than smaller-size firms. In short, during the COVID period, small stocks, growth stocks, and conservative stocks performed more strongly and were thus more favourable to investors.