InsidersTradesSigma
Quantitative Signals & Scoring
A quantitative technique that assigns differential weights to input variables or signals in a predictive model based on their relative contribution to explaining or predicting insider trading activity and market movements.
In the context of insider trading surveillance and quant scoring platforms, feature importance weighting determines how much each signal, transaction indicator, or behavioral metric influences the final conviction score or risk ranking. Variables such as Form 4 filing patterns, trading volume concentration, sector momentum, and information coefficient metrics receive weights calibrated through machine learning techniques, backtesting, or expert judgment. High-weight features typically exhibit superior predictive lift, signal persistence, and low decay rates, while low-weight features may introduce noise or exhibit regime-dependent reliability.
Effective feature weighting schemes must account for look-ahead bias, signal decay over rolling windows, and time-series stability of factor loadings to avoid overfitting to historical insider activity patterns. Weights are often recalibrated periodically using point-in-time normalization and cross-sectional factor exposure analysis to ensure the model reflects current market microstructure and regulatory enforcement regimes. Portfolio managers and compliance teams leverage feature importance rankings to understand which behavioral or transaction signals drive alerts, enabling more efficient investigations and targeted pre-clearance system reviews.