Crene Research
Four frontier AI models independently forecast 1,091 active events across Revenue beats, EPS surprises, macro releases, and central bank decisions — each scored with Brier metrics against verified outcomes. CRENE is building the first large-scale benchmark of frontier AI models forecasting real-world economic events. When models show high confidence, accuracy reaches 73%.
Four frontier LLMs forecast independently — no anchoring. Cross-model spread reveals uncertainty that single-model systems miss.
Every prediction has named resolution criteria and authoritative sources (SEC filings, BLS, Fed statements). Not crowd-sourced — verified.
Brier scores computed per model per event. Enables model-level analysis: which LLM forecasts best in which domain?
Are the probabilities meaningful? A well-calibrated model predicts 70% and is correct 70% of the time. Points near the dashed line indicate good calibration.
Automated scanners detect upcoming earnings (Polygon.io financials ), macro releases (CPI, NFP, PMI), central bank meetings, and market events. Each gets structured binary resolution criteria and a named authoritative source.
GPT-4o, Gemini 2.0 Flash, Claude Haiku 3.5, and Grok 3 each forecast independently — no model sees another's output. Ensemble consensus is the mean probability. Spread (max − min) measures disagreement.
Low spread + high consensus = high confidence. These predictions historically achieve 73%+ accuracy. The spread-based signal is a core dataset output that enables downstream signal construction.
Daily automated resolution against SEC filings, BLS data, central bank statements. Polygon.io financials API auto-resolves earnings. Brier scores computed per model per event. All data served via public REST API.