Company Stock Prices Before and After Public Announcements Related to Oncology Drugs
Bibliographic record
Abstract
BACKGROUND: Phase III clinical trials and Food and Drug Administration (FDA) regulatory decisions are critical for success of new drugs and can influence a company's market valuation. Knowledge of trial results before they are made public (ie, "inside information") can affect the price of a drug company's stock. We examined the stock prices of companies before and after public announcements regarding experimental anticancer drugs owned by the companies. METHODS: We identified drugs that were undergoing evaluation in phase III trials or for regulatory approval by the US FDA from January 2000 to January 2009. Stock prices of companies that owned such drugs were analyzed for 120 trading days before and after the first public announcement of 1) results of clinical trials with positive and negative outcomes and 2) positive and negative regulatory decisions. All statistical tests were two-sided. RESULTS: We identified public announcements from 23 positive trials and 36 negative trials and from 41 positive and nine negative FDA regulatory decisions. The mean stock price for the 120 trading days before a phase III clinical trial announcement increased by 13.7% (95% confidence interval = -2.2% to 29.6%) for companies that reported positive trials and decreased by 0.7% (95% confidence interval = -13.8% to 12.3%) for companies that reported negative trials (P = .09). In a post hoc analysis comparing the stock price averaged over 60 trading days before and after day -60 relative to the clinical trial announcement, the mean stock price increased by 9.4% for companies that reported positive trials and decreased by 4.5% for companies that reported negative trials (P = .03). Changes in company stock prices before FDA regulatory decisions did not differ statistically between companies with positive decision and companies with negative decisions. CONCLUSIONS: Trends in company stock prices before the first public announcement differ for companies that report positive vs negative trials. This finding has important legal and ethical implications for investigators, drug companies, and the investment industry.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".