Network Based Evidence of the Financial Impact of COVID-19 Pandemic

Abstract

How much the largest worldwide companies, belonging to different sectors of the economy, are suffering from the pandemic? Are economic relations among them changing? In this paper, we address such issues by analysing the top 50 S\&P companies by means of market and textual data. Our work proposes a network analysis model that combines such two types of information to highlight the connections among companies with the purpose of investigating the relationships before and during the pandemic crisis. In doing so, we leverage a large amount of textual data through the employment of a sentiment score which is coupled with standard market data. Our results show that the COVID-19 pandemic has largely affected the US productive system, however differently sector by sector and with more impact during the second wave compared to the first.

Keywords: COVID-19 Pandemic, Textual analysis, Financial risk, Network model, Bayesian inference

JEL Classification: C11, C52, C55, C58, G01, G12