Track Record

Track the accuracy of our AI intelligence for economic indicators

Last 90 days

Overall Accuracy

41.5%

Total Predictions

405

High Confidence Accuracy

44.4%

Edge Accuracy

41.8%

Coverage Rate

35%

Outcome Breakdown

168/237

Correct / Incorrect

Accuracy Over Time

Last 365 days

Accuracy by Confidence Tier

n=55n=58n=509

Accuracy by Signal Type

Accuracy by Country

Best & Worst Indicators

Best Performing

#	Indicator	Country	Accuracy	n
1	Initial Jobless Claims	الولايات المتحدة	72.7%	11
2	Unemployment Rate	منطقة اليورو	60%	5
3	ANZ Business Confidence	نيوزيلندا	40%	5
4	New Home Sales	الولايات المتحدة	40%	5
5	EIA Weekly Petroleum Report	الولايات المتحدة

Recent Predictions

Date	Event	Country	Tier	Direction	Outcome	Result
2026-05-13 09:00:00+00	Industrial Production MoM	منطقة اليورو	★☆☆	↓	In-line	\u2717
2026-06-25 12:30:00+00	Personal Spending MoM	الولايات المتحدة	★☆☆	↑	Beat	\u2713
2026-07-30 09:00:00+00	GDP Growth Rate YoY	منطقة اليورو	★★☆	↓	Beat

Methodology

How It Works

The system analyzes 7 types of economic signals for each indicator before release: leading indicators, revisions, seasonal patterns, momentum, mean reversion, spillover effects, and macro context. These signals are combined into a final prediction with a 4-tier confidence level.

Signal Types

Leading: data that precedes the indicator temporally. Revisions: direction of prior data adjustments. Seasonal: historical patterns in the same period. Momentum: continuation of surprise trends. Mean Reversion: extreme values returning to average. Spillover: other countries' data influencing the indicator.

Confidence Tiers

★★★ High: multiple aligned signals with historical accuracy above 60%. ★★☆ Medium: good signals with some conflict. ★☆☆ Low: weak or conflicting signals. ☆☆☆ Context: reference information only without directional prediction.

Academic Basis

The system is based on econometric and machine learning research, including leading indicator models (Stock & Watson), revision analysis (Mankiw & Shapiro), and adjusted seasonal patterns.