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.

ML Prediction Track Record

Current Archive

Live results from our current, leakage-controlled models (features strictly pre-event; validated out-of-sample at ~0.68 AUC). We restarted the public record on May 31, 2026 so every figure here reflects only the models running today — it fills in as upcoming catalysts resolve.

Last updated: 10/3/2026 | Period: Since relaunch

Direction Accuracy

Correct up/down/neutral predictions

77.7%

of 349 predictions

Return Accuracy

Within 10% of actual return

84.0%

magnitude accuracy

vs Naive Baseline

Direction accuracy minus the “always neutral” guess

-11.4 ptsbaseline 89.1%

~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.

Accuracy by Specialized Model

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

Regulatory (PDUFA/NDA/BLA)

69.2%

9/13

Model: impact_pdufa

Types: pdufa, nda_filing, regulatory

Clinical Trials (Phase 1/2/3)

78.4%

134/171

Model: impact_trial

Types: phase_3, phase_2, data_readout, phase_1

Other Events

77.6%

128/165

Model: impact_other

Types: conference, earnings, acquisition, 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

45.6%

run-up ended positive · 428 calls

Meaningful run-up

38.3%

run-up beat +2% (beyond noise)

Median run-up (realized)

-0.9%

median of 428 calls; mean -0.3% (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 -1.9%28/7238.9%

Low (<0.5)

avg 0%167/35646.9%

Coverage by catalyst type

Entry: Earnings

174 calls

Entry: Phase 2

100 calls

Entry: Phase 3

87 calls

Entry: Conference

15 calls

Entry: Offering

13 calls

Entry: Data Readout

11 calls

Entry: Acquisition

8 calls

Entry: Regulatory

5 calls

Entry: Pdufa

5 calls

Entry: Nda Filing

5 calls

Entry: Phase 1

3 calls

Entry: Trial Start

2 calls

For reference, 3-way direction accuracy is 15.9% — 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.

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

resolved against SEC filings

Validation (held-out history)

11% vs 33%

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

low bucket — live

13% raised

predicted 16% · 38 graded

elevated bucket — live

21% raised

predicted 28% · 34 graded

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

Up56

Down20

Neutral273

Actual Outcomes

Up16

Down22

Neutral311

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.

Regulatory

3/3 correct

100.0%

Phase 1

1/1 correct

100.0%

Nda Filing

5/6 correct

83.3%

Offering

14/17 correct

82.4%

Acquisition

8/10 correct

80.0%

Data Readout

Limited Data

8/10 correct

80.0%

Phase 3

Limited Data

70/88 correct

79.5%

Earnings

95/123 correct

77.2%

Phase 2

Limited Data

55/72 correct

76.4%

Conference

Limited Data

11/15 correct

73.3%

Pdufa

Limited Data

1/4 correct

25.0%

Prediction Confusion Matrix

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

	Up	Down	Neutral
Predicted Up	6	8	42
Predicted Down	0	8	12
Predicted Neutral	10	6	257

Diagonal values (highlighted) represent correct predictions

Recent Accurate Predictions

Successfully verified predictions

NVSEFphase_2

Pred: 0.0% → Actual: +3.0%

neutral

IRONphase_2

Pred: +0.6% → Actual: -2.4%

neutral

ALK-B.COphase_3

Pred: 0.0% → Actual: 0.0%

neutral

UCBJYphase_3

Pred: 0.0% → Actual: +1.0%

neutral

UCBJYphase_3

Pred: 0.0% → Actual: -0.6%

neutral

Recent Inaccurate Predictions

Predictions that missed the mark

BEIGFphase_2

Pred: +6.3% → Actual: 0.0%

up/neutral

BHVNphase_2

Pred: +6.3% → Actual: -2.4%

up/neutral

SCNIearnings

Pred: -3.5% → Actual: +0.6%

down/neutral

SCNIearnings

Pred: -10.8% → Actual: +0.6%

down/neutral

SCNIearnings

Pred: -23.2% → Actual: +0.6%

down/neutral

Weekly Accuracy Trend

Historical accuracy by week

Week	Predictions	Accurate	Accuracy
10/12/2026	1	1	100.0%
9/21/2026	12	11	91.7%
9/14/2026	19	18	94.7%
9/7/2026	13	9	69.2%
8/31/2026	21	19	90.5%
8/24/2026	12	9	75.0%
8/17/2026	25	20	80.0%
8/10/2026	47	41	87.2%
8/3/2026	40	33	82.5%
7/27/2026	17	13	76.5%

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: 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: 349 verified predictions