IC-Weighted Signal Blending

A quant strategy often has several independent signals predicting the same thing — say, future stock returns — and simply averaging them equally ignores the fact that some signals are consistently better predictors than others. The information coefficient (IC) measures a signal's predictive skill: it's the correlation between the signal's ranking of stocks and those stocks' subsequent realized returns, typically re-measured each period. IC-weighted blending uses each signal's historical IC as its weight when combining signals into one composite score, so a signal with an IC of 0.08 contributes proportionally more to the blend than one with an IC of 0.02, rather than the two counting equally.

Concretely, if signal A has historically averaged an IC of 0.06 and signal B an IC of 0.02, a simple IC-weighted blend assigns weights proportional to those numbers — roughly 75% to A and 25% to B (0.06 and 0.02 sum to 0.08, and 0.06/0.08 = 0.75) — rather than the 50/50 split an unweighted average would use. This lets a blended signal capture more of the genuinely predictive information in the mix while still benefiting from diversification across signals that aren't perfectly correlated with each other.