ML Prediction Track Record

Accuracy by Specialized Model

Performance of our category-specific ML models trained on different catalyst types

Regulatory (PDUFA/NDA/BLA)

0.0%

0/0

Model: impact_pdufa

Types:

Clinical Trials (Phase 1/2/3)

87.9%

Prediction vs Actual Distribution

Compare predicted directions against actual outcomes

Predictions Made

Up0

Down0

Neutral58

Actual Outcomes

Up0

Down

Accuracy by Catalyst Type

Performance breakdown for each catalyst category

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.

phase_3

Limited Data

13/13 correct

100.0%

Prediction Confusion Matrix

How predictions compare to actual outcomes (rows = predicted, columns = actual)

	Up	Down	Neutral
Predicted Up	0	0	0
Predicted Down	0	0	0

Weekly Accuracy Trend

Historical accuracy by week

Week	Predictions	Accurate	Accuracy
12/22/2025	8	8	100.0%
12/15/2025	50	43	86.0%

Methodology

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 predict up/down/neutral? This validates the Win Rate shown on Opportunities
Return Accuracy: Was the predicted return within 10% of actual? This validates the shown on Opportunities

Important Disclaimer