Network Contagion of COVID-19 on US Industries

Abstract The applications of Network Science have been limited in finance, but recently Zhu et. al (2016) have created a methodology in which co-movements between a given industry and the overall stock market can be represented by nodes and transitions between these movement states can be represented as edges. This modeling approach creates network structures that provide information on the susceptibility of markets to contagious risk that can be studied with network methods. This paper builds on this methodology to study the effect of the COVID-19 pandemic on contagious financial risk across industrial sectors. I propose a novel metric to measure an industry’s susceptibility to contagion at a point in time. I apply this metric to a sample of U.S. industrial ETFs before and after the onset of the pandemic. My results suggest that COVID-19 led to increases in the susceptibility to contagious risk across all sectors.