Factor Orthogonalization

In quantitative insider-trading detection, multiple signals often exhibit hidden dependencies. Form 4 filing velocity, insider concentration metrics, and sector momentum may all respond to the same underlying market condition, creating redundant or inflated conviction scores. Factor orthogonalization, typically via Gram-Schmidt decomposition or principal component analysis, transforms correlated factors into an uncorrelated basis set. This ensures each signal component contributes unique explanatory power rather than amplifying shared systematic exposure, critical for robust predictive ranking in competitive markets.

Practitioners apply orthogonalization post-normalization but pre-weighting, ensuring that factor loadings and information coefficients reflect true incremental signal value. This is particularly important in insider-activity concentration scoring, where temporal clustering and volume-based signals may be mechanically linked. By decoupling these components, the model prevents phantom conviction inflation and stabilizes backtest hit-rates across market regimes, especially during low-liquidity or high-volatility periods when multicollinearity becomes acute.