Important Disclaimer
Past performance is not indicative of future results. These predictions are for informational purposes only and should not be considered financial advice. Always conduct your own research and consult with a qualified financial advisor before making investment decisions.
Archived results from before our May 31, 2026 model recalibration. We reset the public record then — the earlier models were trained partly on contaminated data (earnings / large-caps) that inflated their numbers and would not generalize, so we deliberately do not headline them. Kept here in full for transparency.
Last updated: 10/2/2026 | Period: Before relaunch (archived)
~80% of catalyst moves are neutral, so a model only adds value if it beats the “always-neutral” guess. We show this so the headline can't flatter itself.
Regulatory (PDUFA/NDA/BLA)
impact_pdufaClinical Trials (Phase 1/2/3)
impact_trialOther Events
impact_otherEach category uses a specialized model trained on its catalyst type. Categories fill in as their catalysts resolve; ones with nothing resolved yet show “No data yet” rather than a 0% that would read as failure. Every figure is measured against the same actual market outcomes.
Hit-rate by conviction
If the signal carries an edge, higher-conviction calls hit more often.
Coverage by catalyst type
For reference, 3-way direction accuracy is 40.9% — but direction isn't the model's objective (it forecasts run-up conviction and almost always expects a positive run-up), so the hit-rate and conviction lift above are the meaningful measures.
Graded against: SEC EDGAR 424B filings (priced offerings), resolved ~95 days after each event. Model: calibrated XGBoost on cash runway, filing cadence (time since last raise, active shelf), recent run-up and market cap — holdout AUC ~0.65. An offering is not always negative (it extends the runway); the exact timing within the window is not predictable.
Predictions Made
Actual Outcomes
Model Performance Varies by Catalyst Type
Our ML model performs best on PDUFA events (FDA decisions) where historical patterns are more predictable. Phase 2/3 clinical trial predictions have limited accuracy due to binary outcomes and market efficiency. We are actively working to improve non-PDUFA predictions.
Up | Down | Neutral | |
|---|---|---|---|
| Predicted Up | 3 | 9 | 93 |
| Predicted Down | 5 | 3 | 22 |
| Predicted Neutral | 7 | 18 | 314 |
Diagonal values (highlighted) represent correct predictions
| Week | Predictions | Accurate | Accuracy |
|---|---|---|---|
| 9/21/2026 | 1 | 1 | 100.0% |
| 9/14/2026 | 16 | 5 | 31.3% |
| 8/31/2026 | 3 | 2 | 66.7% |
| 8/10/2026 | 9 | 5 | 55.6% |
| 7/27/2026 | 8 | 5 | 62.5% |
| 7/13/2026 | 14 | 6 | 42.9% |
| 7/6/2026 | 5 | 5 | 100.0% |
| 6/29/2026 | 28 | 13 | 46.4% |
| 6/22/2026 | 8 | 7 | 87.5% |
| 6/15/2026 | 68 | 38 | 55.9% |
How This Track Record Works
Real ML Predictions
This track record shows actual predictions made by our ML model on historical biotech catalyst events. Each prediction is compared against the real market outcome to measure accuracy.
Predictions include price direction (up/down/neutral) and expected return percentage. The same ML models power our Entry Timing and Opportunities features.
Prediction Types
- Impact Prediction: Expected price movement direction and magnitude after catalyst
- Entry Timing: Optimal days before catalyst to enter a position (Pro tier)
- LOA Score: Likelihood of Approval probability for drug catalysts
Specialized ML Models
- PDUFA Model (impact_pdufa): Trained on FDA regulatory decisions (PDUFA, NDA, BLA filings). Achieves highest accuracy due to predictable FDA decision patterns.
- Trial Model (impact_trial): Trained on clinical trial data readouts (Phase 1/2/3). Uses trial-specific features like phase difficulty, indication complexity, and company track record.
- Other Model (impact_other): Trained on other catalyst types (conferences, earnings, AdCom). Handles diverse event types with varying prediction difficulty.
Outcome Classification
- Up: Stock price moved >5% within 5 days after catalyst
- Down: Stock price moved <-5% within 5 days after catalyst
- Neutral: Stock price moved between -5% and +5%
Accuracy Metrics
- Direction Accuracy: Did the model correctly classify up/down/neutral? This validates the Hist. positive rate shown on Opportunities
- Return Accuracy: Was the modelled move within 10% of actual? This validates the Avg. historical move shown on Opportunities
- High Accuracy Rate: Percentage of predictions with accuracy score ≥ 80%
How This Validates Opportunities
The metrics you see on the Catalyst Entry Analysis page are powered by the same ML models validated here. The Hist. positive rate corresponds to our Direction Accuracy, while theAvg. historical move corresponds to our Return Accuracy. This track record provides transparency into how well those models have performed historically.
Data Sources
- Price Data: Daily historical stock prices from our market-data providers
- Catalyst Data: FDA announcements, clinical trial results, earnings from SEC filings and biotech news sources
- Total Records: 474 verified predictions