Multifactor Models

Multifactor models combine several factors, such as Momentum, Value, Quality, and Low Volatility, into a single investment framework. Instead of relying on one return driver, multifactor strategies diversify across multiple sources of alpha. Each stock receives scores for individual factors, which are then blended into a composite ranking. Portfolio construction is based on these combined scores. The main advantage of multifactor models is robustness. Since factors perform differently across market cycles, combining them helps smooth returns and reduce reliance on any single anomaly. Designing multifactor models requires careful weighting, normalization, and correlation analysis. Highly correlated factors provide limited diversification benefits, while complementary factors enhance stability. Multifactor strategies also incorporate risk controls, sector limits, and turnover constraints to ensure real-world implementability. By balancing growth-oriented and defensive signals, multifactor models aim to deliver more consistent performance across varying market regimes.