Firm-level risk exposures and stock returns in the wake of COVID-19 / Steven J. Davis, Stephen Hansen, Cristhian Seminario-Amez
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..
| Medienart: |
E-Book |
|---|
| Erscheinungsjahr: |
September 2020 |
|---|---|
| Erschienen: |
Munich, Germany: CESifo, Center for Economic Studies & Ifo Institute ; September 2020 |
| Reihe: |
CESifo working paper - no. 8594 (2020) |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Davis, Steven J., 1957- [VerfasserIn] |
|---|
| Links: |
www.cesifo.org [kostenfrei] |
|---|
| Umfang: |
1 Online-Ressource (circa 83 Seiten) ; Illustrationen |
|---|
| Weitere IDs: |
10419/226296 |
|---|
| Förderinstitution / Projekttitel: |
|
|---|
| PPN (Katalog-ID): |
1735411922 |
|---|
| LEADER | 01000cam a2200265 4500 | ||
|---|---|---|---|
| 001 | 1735411922 | ||
| 003 | DE-627 | ||
| 005 | 20230427161419.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 201012s2020 gw |||||o 00| ||eng c | ||
| 024 | 7 | |a 10419/226296 |2 hdl | |
| 035 | |a (DE-627)1735411922 | ||
| 035 | |a (DE-599)KXP1735411922 | ||
| 035 | |a (OCoLC)1225558103 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 044 | |c XA-DE | ||
| 100 | 1 | |a Davis, Steven J. |d 1957- |e verfasserin |0 (DE-588)129367141 |0 (DE-627)393989305 |0 (DE-576)169236692 |4 aut | |
| 245 | 1 | 0 | |a Firm-level risk exposures and stock returns in the wake of COVID-19 |c Steven J. Davis, Stephen Hansen, Cristhian Seminario-Amez |
| 264 | 1 | |a Munich, Germany |b CESifo, Center for Economic Studies & Ifo Institute |c September 2020 | |
| 300 | |a 1 Online-Ressource (circa 83 Seiten) |b Illustrationen | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 490 | 1 | |a CESifo working paper |v no. 8594 (2020) | |
| 520 | |a 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. | ||
| 650 | 7 | |8 1.1\x |a Kapitalmarktrendite |0 (DE-627)742092755 |0 (DE-2867)29640-5 |2 stw | |
| 650 | 7 | |8 1.2\x |a Coronavirus |0 (DE-627)1694209695 |0 (DE-2867)30218-4 |2 stw | |
| 650 | 7 | |8 1.3\x |a Wirkungsanalyse |0 (DE-627)09140116X |0 (DE-2867)10991-6 |2 stw | |
| 700 | 1 | |a Hansen, Stephen |d 1981- |e verfasserin |0 (DE-588)142726451 |0 (DE-627)638520207 |0 (DE-576)333048172 |4 aut | |
| 700 | 1 | |a Seminario-Amez, Cristhian |e verfasserin |4 aut | |
| 810 | 2 | |a CESifo GmbH |t CESifo working papers |v no. 8594 (2020) |9 8594 |w (DE-627)340076984 |w (DE-576)281209073 |w (DE-600)2065232-X |x 2364-1428 |7 ns | |
| 856 | 4 | 0 | |u https://www.cesifo.org/DocDL/cesifo1_wp8594.pdf |x Verlag |z kostenfrei |
| 856 | 4 | 0 | |u https://www.cesifo.org/en/publikationen/2020/working-paper/firm-level-risk-exposures-and-stock-returns-wake-covid-19 |x Verlag |z kostenfrei |
| 856 | 4 | 0 | |u http://hdl.handle.net/10419/226296 |x Resolving-System |z kostenfrei |
| 912 | |a GBV_ILN_26 | ||
| 912 | |a ISIL_DE-206 | ||
| 912 | |a SYSFLAG_1 | ||
| 912 | |a GBV_KXP | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a GBV_ILN_2403 | ||
| 912 | |a ISIL_DE-LFER | ||
| 951 | |a BO | ||
| 980 | |2 26 |1 01 |x 0206 |b 3779013711 |c 08 |f K: |d DS 63 |e --%%-- |j --%%-- |y z1k |z 12-10-20 | ||
| 980 | |2 2403 |1 01 |x DE-LFER |b 3798761361 |c 00 |f --%%-- |d --%%-- |e n |j --%%-- |y l01 |z 11-11-20 | ||
| 981 | |2 2403 |1 01 |x DE-LFER |r https://www.cesifo.org/DocDL/cesifo1_wp8594.pdf | ||