Behavior and Consequences of COVID-19-related Voluntary Disclosure: Evidence from Pharmaceutical Companies
Bibliographic record
Abstract
With the explosion of COVID-19, a number of publicly traded companies in the pharmaceutical industry have thrown themselves into the development of novel coronavirus vaccines and therapeutics, and have voluntarily disclosed information about the development process. In this paper, six companies with different quality ratings of information disclosure in the pharmaceutical industry (refer to the results of the 2019 Shenzhen Stock Exchange quality assessment of information disclosure) were selected to explore the behavior and consequences of voluntary disclosure of information by listed companies in the pharmaceutical industry in China. The results show that voluntary disclosure of positive news will have a positive impact on the company’s share price. Companies with high disclosure quality ratings have lower price volatility before and after disclosure. Low-rated companies have volatile stock prices before and after disclosure, and the price gains are unsustainable for long periods of time, even falling back to lower levels than they were before disclosure. It is not the case that companies’ share prices do not fluctuate due to poor disclosure appraisal results, but rather they may cause a larger market reaction for the purpose of misleading investors. To some extent, this paper enriches the research on voluntary information disclosure of listed companies and will make listed companies understand the consequences of voluntary information disclosure on COVID-19-related issues, provide evidence for relevant supervisory authorities to regulate voluntary information disclosure further, and create a better voluntary information disclosure environment for China’s stock market.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".