Working Paper: NBER ID: w27867
Authors: Steven J. Davis; Stephen Hansen; Cristhian Seminario-Amez
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.
Keywords: COVID-19; stock returns; risk exposures; machine learning; text analysis
JEL Codes: E44; G12; G14; G18
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| bad COVID-19 news (F44) | lowers stock returns for firms with high exposures to travel, traditional retail, aircraft production, and energy (L93) |
| bad COVID-19 news (F44) | raises stock returns for firms with high exposures to healthcare, e-commerce, web services, drug trials, and materials critical for supply chains in semiconductors, cloud computing, and telecommunications (L63) |
| monetary and fiscal policy responses to the pandemic (E63) | impact firm-level returns (L25) |
| risk factor discussions in 10-K filings (G32) | explains stock price reactions (G14) |
| machine learning approach (C45) | outperforms dictionary methods in predicting returns (G17) |