Year: 2020
Type: report
Abstract: Firm-level stock returns differ enormously in reaction to COVID-19 news.We characterize these reactions using the Risk Factors discussions in pre-pandemic 10-K filings and two text-analytic approaches: expert-curated dictionaries and supervised machine learning (ML).Bad COVID-19 news lowers returns for firms with high exposures to travel, traditional retail, aircraft production and energy supply-directly and via downstream demand linkages-and raises them for firms with high exposures to healthcare policy, e-commerce, web services, drug trials and materials that feed into supply chains for semiconductors, cloud computing and telecommunications.Monetary and fiscal policy responses to the pandemic strongly impact firm-level returns as well, but differently than pandemic news.Despite methodological differences, dictionary and ML approaches yield remarkably congruent return predictions.Importantly though, ML operates on a vastly larger feature space, yielding richer characterizations of risk exposures and outperforming the dictionary approach in goodness-of-fit.By integrating elements of both approaches, we uncover new risk factors and sharpen our explanations for firm-level returns.To illustrate the broader utility of our methods, we also apply them to explain firm-level returns in reaction to the March 2020 Super Tuesday election results.
DOI: 10.3386/w27867
Language: English
Topic: COVID-19 Pandemic Impacts
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3087918459