The stock market response to COVID-19 : evidence from five developed markets
Bibliographic record
Abstract
This study evaluates the connection between stock returns and the COVID-19 pandemic in five developed markets, including Canada, France, Germany, the United Kingdom, and the United States. The analysis is based on observations ranging from December 2019, when the first official cases of the new virus were discovered, to April 2022 and uses data from 3,120 firms. Stock returns reacted negatively to the growth of cumulative cases and deaths in the overall sample as well as across four of the five countries, except for the United Kingdom. While the relation between lockdowns and stock performance is also negative, fiscal stimuli seem to have a positive impact. Furthermore, I find that higher perceived risk and rising uncertainty, measured by Google search volume and a policy uncertainty index based on news, are also related to lower performance in most regression specifications. It can be observed that smaller companies in my sample suffer more from a higher growth rate of cumulative cases than medium-sized ones and the largest firms even experience a positive effect. Finally, I show that industry affiliation matters. The pandemic-related change in stock returns across industries varies in statistical and economic significance, with some coefficients being positive and others negative.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".