What it measures

Financial analysis is a forecasting-from-evidence task: good analysts read demand from the outside world and translate it into a revenue call weeks before the print. SITW measures exactly that capability, end-to-end, on clean public labels.

• Mine demand, selling, and exogenous signals — plus peer read-through and substitution.
• Predict direction and magnitude vs. point-in-time consensus, with a volume / price / FX / M&A decomposition.
• Cite real, contemporaneous, load-bearing signals.
• Calibrate — express confidence and abstain when signals are insufficient.

Why revenue, not stock

Stock price is a noisy third-order effect. Revenue is the clean, audited, demand-driven line. We predict it and decompose the move by cause. The same signal→demand engine also powers go-to-market: account prioritization, buying intent, churn risk, and pipeline forecasting.

Two tracks, one backbone

• Live rolling track (flagship): freeze predictions before each report; resolve mechanically after — contamination is structurally impossible.
• Frozen historical track: replay past quarters against a date-pinned corpus for fast iteration. The pilot on this site is this track.
• Read-through track: late reporters get peer/customer read-through; first movers get none — a designed difficulty axis.

How it's scored

• Skill vs. consensus and naive persistence
• Direction / magnitude / timing as separate axes
• Driver decomposition (volume vs. price vs. FX)
• Mining recall, feed-utilization, open-web lift
• Temporal-availability provenance (retrievable ≤ T)
• Calibration (Brier / ECE) + selective prediction