Conditional Expectancy Score

The Conditional Expectancy Score synthesizes multiple concurrent signals, insider transaction patterns, beneficial ownership changes, and market regime indicators to produce a forward-looking assessment of expected value. In insider trading detection and quant platforms, this score conditions the expectancy calculation on observed Form 4 filings, Rule 10b5-1 plan adoptions, blackout window proximity, and sector momentum regime, creating a dynamic ranking that reflects both event-driven catalysts and statistical mean reversion or continuation patterns.

Computation typically involves Bayesian updating or logistic regression where prior expectancy estimates are adjusted by the conditional information set, often including PDMR transaction reporting, closely associated person activity, information coefficient decay, and rolling hit rate validation. The score is point-in-time normalized to avoid look-ahead bias, ensuring that only information available at the observation date influences the conditional expectancy estimate, and is calibrated against historical prediction lift and signal persistence metrics.