New drugs and stock market: a machine learning framework for predicting pharma market reaction to clinical trial announcements
Bibliographic record
Abstract
Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of events, hence being closely monitored by the public. Most works focus on retrospective analysis of announcement impact on company stock prices, bypassing the consideration of the problem in the predictive paradigm. In this work, we aim to close this gap by proposing a framework that allows predicting the numerical values of announcement-induced changes in stock prices. In fact, it is a problem of the impact prediction of the specific event on the corresponding time series. Our framework includes a BERT model for extracting the sentiment polarity of announcements, a Temporal Fusion Transformer for forecasting the expected return, a graph convolution network for capturing event relationships, and gradient boosting for predicting the price change. We operate with one of the biggest FDA (the Food and Drug Administration) datasets, consisting of 5436 clinical trial announcements from 681 companies for the years 2018-2022. During the study, we get several significant outcomes and domain-specific insights. Firstly, we obtain statistical evidence for the clinical result promulgation influence on the public pharma market value. Secondly, we witness inherently different patterns of responses to positive and negative announcements, reflected in a stronger and more pronounced reaction to negative clinical news. Thirdly, we discover two factors that play a crucial role in a predictive framework: (1) the drug portfolio size of the company, indicating the greater susceptibility to an announcement in the case of low diversification among drug products and (2) the announcement network effect, manifesting through an increase in predictive power when exploiting interdependencies of events belonging to the same company or nosology. Finally, we prove the viability of the forecast setting by getting ROC AUC scores predominantly greater than 0.7 for the classification of price change on historical data. We emphasize the transferability and generalizability of the developed framework on other datasets and domains but on the condition of the presence of two key entities: events and the associated time series.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".