Who Benefitted From the Covid-19 Pandemic?
Bibliographic record
Abstract
Using different tests on different datasets, this paper empirically examines how healthcare firms and insiders used the Covid-19 pandemic opportunistically to raise capital and maximize their wealth. The research design consists of three parts. The first part examines biotechnology and Covid-19 related firms’ stock price to the onset on the pandemic using event studies and also examines long-term abnormal returns using the buy and hold abnormal returns (BHAR) approach. We find that the whole healthcare industry, and not just biomedical firms, produced high short-term and long-term abnormal returns. In the second part, we run an ordinary least squares regression and a two-way standard error clustered approach on the healthcare industry within Fama French 17 industries, propensity-matched score sample, and our own hand-collected dataset. Our results show that four industries within the healthcare industry capitalized on the opportunities provided by the pandemic; they are biomedical (SIC: 2836), pharmaceutical preparations firms (SIC: 2834), industrial organic chemicals and electromedical industry (SIC: 2860), and electrotherapeutic apparatus (SIC: 3845). The last section analyzes insider trading activities during three and four quarters before and after the pandemic. Using our collected sample firms and WHO Covid firms’ data, our results confirm that the purchases by insiders significantly increased during the first three quarters after the start of the pandemic. However, this does not last long, and we find strong selling in the fourth quarter after the start of the pandemic.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".