D18-F09-A03 · INTEGRATED EQUITY SCORING · SYNTHETIC DATA

Fundamental Metric Direction and Peer Normalization

Learn how one normalized score can preserve heterogeneous metric meaning.

The learning loop

1 · PredictName the direction before moving a driver.
2 · InspectFind the ratio, signal, contribution, or residual that changed.
3 · ReconcileTie the components to the raw score or model residual.
4 · BoundInterpret the screen without turning it into a finding.
Make a prediction before stepping

Base → current bridge

Scenario base
Current state
Headline delta

Keyboard: Left/Right step, Home resets, and Space plays or pauses when focus is outside a form control.

Current model output

Frozen equation

p_i = 100·(#{peer<x_i}+0.5·#{peer=x_i})/n; s_i = p_i if higher-better else 100-p_i; S=mean(s_i)

Component and diagnostic ledger

Changed cards are purple. Start here before interpreting the headline.

Response across 61 states

Model output across the selected accounting scenarioA deterministic synthetic sensitivity line with the current state marked.

Interpretation gate

Boundary: midrank ties and minimum peer count

Visible intermediate: per-metric percentile and direction-adjusted score

Open exact point-in-time input and structured output

Input

Output


Synthetic teaching data only. A model score or residual is a historically specified screen—not an audit conclusion, rating, fraud finding, default forecast, or investment recommendation.