Evidence of Abnormal Trading on COVID-19 Pfizer Vaccine Development Information
Bibliographic record
Abstract
The 2019 COVID-19 pandemic led to an economic slowdown worldwide and shook the investment world. Pharmaceutical investments were influenced by the anticipation of COVID-19 vaccine developments. Our study examines the real-time impact of public announcements concerning COVID-19 vaccine developments on stock returns and volatilities for Pfizer, Moderna, and the S&P 500. Market Return and Information Event methodology were used to analyze stock activities immediately before important public COVID-19 vaccine development announcements related to Pfizer and Moderna vaccines. This methodology was employed for vaccine news announcements between 2 January 2020 and 4 March 2022. Stock returns and volatility were analyzed with time-series regression analysis. Findings demonstrated that increased trade volatilities occurred immediately prior to COVID-19 vaccine development news was made public. Specifically, Pfizer stock returns were significantly higher (above the mean) immediately before positive COVID-19 vaccine development information was made public. Also, increased volume volatility was observed for Pfizer, Moderna, and the S&P 500 index stocks immediately before positive vaccine development information concerning Pfizer and Moderna vaccines were made public. These findings suggest that the vaccine information may have been leaked before being made public. If so, the findings may indicate that investors were taking advantage of insider information while trying to mitigate the appearance that they engaged in insider trading.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".