InsidersTradesSigma
Quantitative Signals & Scoring
A quantitative signal that isolates the alpha contribution of a fundamental or behavioral factor by removing sector-level performance bias, enabling identification of stock-specific alpha independent of sector momentum or cyclicality.
In insider-trading and quant platforms, sector-neutral factor scoring prevents false positives from sector-wide rallies masking individual stock momentum or distorting insider-activity signals. The methodology typically involves cross-sectional regression where raw factor exposures (e.g., insider transaction concentration, earnings surprise, technical momentum) are orthogonalized against sector membership or sector-level factor values. This ensures that a high score reflects genuine stock-specific alpha rather than riding a sector wave, critical for conviction scoring and pre-clearance systems where sector rotation can artificially inflate signal quality.
Practical implementation in surveillance systems computes sector residuals by subtracting the sector median (or weighted average) factor value from each stock's raw score, or via Fama-Macbeth two-stage regression where sector dummies absorb common variation. The resulting z-standardized residuals rank stocks within their sector context, preventing overweighting of cyclical sectors during bull markets. For insider-transaction analysis, this approach reveals true abnormal trading activity by filtering out sector-wide buying waves driven by sector momentum or macro catalysts, thereby improving signal persistence and reducing look-ahead bias in backtests.