Accuracy by Specialized Model

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

Regulatory (PDUFA/NDA/BLA)

0.0%

0/2

Model: impact_pdufa

Types: pdufa

Clinical Trials (Phase 1/2/3)

68.0%

34/50

Model: impact_trial

Types: data_readout, phase_2, phase_3

Other Events

35.7%

5/14

Model: impact_other

Types: conference, earnings, offering

Each 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.

Entry-Timing (pre-catalyst run-up)

Entry predictions forecast the run-up — the move from ~30 days before to ~3 days before the catalyst — a different target than the event-day predictions above, so it's tracked separately. The honest yardstick is the hit-rate (did the run-up materialise) and whether higher conviction lifts that hit-rate.

Run-up hit-rate

56.7%

run-up ended positive · 30 calls

Meaningful run-up

56.7%

run-up beat +2% (beyond noise)

Median run-up (realized)

+3.6%

median of 30 calls; mean +9.4% (outlier-skewed on small n)

Hit-rate by conviction

If the signal carries an edge, higher-conviction calls hit more often.

High conviction (≥0.7)

avg -8%0/10.0%

Low (<0.5)

avg +10%17/2958.6%

Coverage by catalyst type

Entry: Phase 2

11 calls

Entry: Phase 3

9 calls

Entry: Conference

5 calls

Entry: Data Readout

2 calls

Entry: Earnings

2 calls

Entry: Pdufa

1 calls

For reference, 3-way direction accuracy is 6.7% — 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.

Dilution Risk (new model — live tracking)self-graded vs SEC filings

Predicts whether a company prices a share offering within 90 days after its catalyst (SEC 424B filing). Every prediction is timestamped before the event and automatically graded against real SEC filings once the 90-day window elapses — no self-reporting involved.

Live predictions

1,000

upcoming catalysts scored

Graded so far

0

first grades expected from 2026-09-13

Validation (held-out history)

11% vs 33%

realized raise rate, LOW vs ELEVATED bucket — backtest, not live results

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.

Prediction vs Actual Distribution

Compare predicted directions against actual outcomes

Predictions Made

Up20

Down8

Neutral38

Actual Outcomes

Up6

Down10

Neutral50

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.

Data Readout

Limited Data

5/6 correct

83.3%

Phase 3

Limited Data

14/20 correct

70.0%

Phase 2

Limited Data

15/24 correct

62.5%

Conference

Limited Data

4/8 correct

50.0%

Offering

1/3 correct

33.3%

Earnings

Limited Data

0/3 correct

0.0%

Pdufa

Limited Data

0/2 correct

0.0%

Prediction Confusion Matrix

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

	Up	Down	Neutral
Predicted Up	4	4	12
Predicted Down	1	2	5
Predicted Neutral	1	4	33

Diagonal values (highlighted) represent correct predictions

Weekly Accuracy Trend

Historical accuracy by week

Week	Predictions	Accurate	Accuracy
6/22/2026	8	3	37.5%
6/15/2026	25	13	52.0%
6/8/2026	3	3	100.0%
6/1/2026	16	11	68.8%
5/25/2026	10	7	70.0%
5/18/2026	3	1	33.3%
5/11/2026	1	1	100.0%

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 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: Real-time and historical stock prices from Financial Modeling Prep API
Catalyst Data: FDA announcements, clinical trial results, earnings from SEC filings and biotech news sources
Total Records: 66 verified predictions

Important Disclaimer

ML Prediction Track Record

Regulatory (PDUFA/NDA/BLA)

Clinical Trials (Phase 1/2/3)

Other Events

Hit-rate by conviction

Coverage by catalyst type

Predictions Made

Actual Outcomes

How This Track Record Works

Real ML Predictions

Prediction Types

Specialized ML Models

Outcome Classification

Accuracy Metrics

How This Validates Opportunities

Data Sources