Cross-sector comovements and policy impact in the COVID-19 stock market: A dynamic factor approach

Joy D. Xiuyao Yang

Global Finance Journal, 2023, vol. 56, issue C

Abstract: This study investigates the remarkable comovements in U.S. equity returns during the COVID-19 pandemic. It constructs a dynamic factor model (DFM) to illuminate the sources of the comovements and their implications. Using the Markov Chain Monte Carlo (MCMC) estimation method, the study finds that the comovements had a weak daily oscillation pattern during the pandemic. With that pattern, the study also finds significant monetary policy effects on the equity returns of several key sectors. In addition, it estimates the impact of news shocks, including monetary policy news, fiscal stimulus news, and unemployment news, on cross-sector equity returns. For any given sector, the conventional and unconventional monetary policy news shocked the sector in opposite directions. Among the positive monetary news shocks, the strongest were from interest rate policy surprises. Conversely, fiscal stimulus news had the most substantial positive impact and triggered all sectors to rebound from the bear market at the end of March 2020. Furthermore, by applying Natural Language Processing (NLP) sentiment analysis, this study sheds light on the positive correlation between comovements and news sentiment.

Keywords: Comovements; Monetary policy; Dynamic factor model; Markov Chain Monte Carlo (MCMC); Bayesian inference; Text mining (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:glofin:v:56:y:2023:i:c:s1044028322000746

DOI: 10.1016/j.gfj.2022.100772

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