Factor Neutralization

Factor neutralization is the process of removing unintended factor exposures from a portfolio or signal so that performance reflects the desired sources of return. In quantitative investing, stocks often load on multiple factors simultaneously. For example, Momentum stocks may also have Growth characteristics, or Value stocks may cluster in specific sectors. Without neutralization, a strategy designed to capture one factor may unknowingly become dominated by another. This can distort performance attribution and increase risk during regime shifts. Neutralization works by statistically adjusting factor scores or portfolio weights to eliminate exposure to unwanted variables such as market beta, sector bias, size, or secondary factors. This is commonly done using regression techniques, where the target factor is isolated from other correlated influences. The goal is clarity: if you are running a Momentum strategy, returns should primarily come from momentum, not from hidden bets on small caps or cyclical sectors. Factor neutralization also improves diversification in multi-factor models. By reducing overlap between factors, each signal contributes more independently to overall performance, increasing robustness. However, over-neutralization can weaken signals. Removing too many exposures may strip away genuine return drivers. Practical implementations therefore balance purity with effectiveness. Used correctly, factor neutralization enhances interpretability, reduces unintended risk, and ensures strategies behave as designed across changing market environments.