Scientific Beta

This white paper introduces Scientific Beta's well-diversified "top-down" multi factor approaches and compares them with "bottom-up" score-weighting approaches that target high factor intensity. It finds that focusing solely on increasing factor intensity leads to inefficiency in capturing factor premia, as exposure to unrewarded risks more than offsets the benefits of increased factor scores.

In this white paper we introduce Scientific Beta's well-diversified "top-down" multi factor approaches and compare them with "bottom-up" score-weighting approaches that target high factor intensity. We find that focusing solely on increasing factor intensity leads to inefficiency in capturing factor premia, as exposure to unrewarded risks more than offsets the benefits of increased factor scores. High factor scores in "bottom-up" approaches also come with high instability and high turnover. We introduce an approach that considers cross-factor interactions in "top-down" portfolios through an adjustment at the stock selection level. While producing lower factor intensity than "bottom-up" methods, this approach leads to higher levels of diversification and produces higher returns per unit of factor intensity. We show that it dominates "bottom-up" approaches in terms of relative performance, while considerably reducing extreme relative losses and turnover.