Efficient Bayesian Factor Mimicking: Methodology, Tests and Comparison

Authors: Cheung, W. and Mittal, N.

Publisher: SSRN


When investment or hedging views are generated on a factor which is not directly investible, creating a quality factor mimicking portfolio becomes a basic implementation requirement. For fundamental factors, traditional factor-mimicking techniques include the Fama-French (FF) factor-ranking approach (Fama and French, 1993), and constrained optimisation that controls portfolio exposure to factors. In a seemingly different connection, Cheung (2012) shows how to construct factor portfolios in the Augmented Black-Litterman (ABL) framework, which makes its endogenous choice of factor-mimicking technique. In this article, we test the performance of this technique, along with traditional techniques. Our results show that the ABL factor-mimicking technique is more efficient.

Source: Manual