Working Paper: CEPR ID: DP15314
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 \textit{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.
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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) | reduced investor confidence (G31) |
| bad COVID-19 news (F44) | lowers returns for firms with high exposures to travel (R41) |
| bad COVID-19 news (F44) | raises returns for firms with high exposures to healthcare policy (I18) |
| monetary and fiscal policy responses (E63) | firm-level returns (D22) |
| monetary policy easing on jump days (E60) | firm-level returns (D22) |
| supervised ML approach outperforms dictionary approach in goodness-of-fit (C52) | accurate representation of risk exposures and effects on returns (G17) |