Firm-Level Risk Exposures and Stock Returns in the Wake of COVID-19 /

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 return...

Ausführliche Beschreibung

Gespeichert in:
1. Verfasser:
Davis, Steven J.
Körperschaft:
National Bureau of Economic Research
Weitere Verfasser:
Hansen, Stephen, Seminario-Amez, Cristhian
Format:
Elektronisch E-Book
Sprache:
Englisch
Veröffentlicht:
Cambridge, Mass. National Bureau of Economic Research 2020.
Zusammenfassung:
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.
Umfang:
1 online resource: illustrations (black and white);
Anmerkungen:
September 2020.
Schlagworte: