Efficient Bayesian Factor Mimicking: Methodology, Tests and Comparison

Abstract:     
When investment or hedging views are generated on a factor which is not directly investible, creating a quality factor proxy or mimicking portfolio becomes a basic implementation requirement. For fundamental factors, traditional factor-mimicking techniques include the Fama-French (FF) factor-ranking approach (Fama-French, 1993), and constrained optimisation that controls portfolio exposure to factors. In a seemingly different connection, Cheung (2009B) shows how to construct factor portfolios in the Augmented Black-Litterman (ABL) framework, which makes its intrinsic choice of factor-mimicking technique. In this paper, we test the performance of this technique, along with traditional techniques. Our results show that the ABL factor-mimicking technique is more efficient.

This article features: -
- A brief review of two families of traditional and the new ABL FM techniques;
- A simulation-based testing methodology that isolates the FM quality issue from peripheral risk model and view quality issues, thereby avoiding unnecessary joint tests; and
- Numerical comparison between these techniques, leading to concrete evidence that the ABL technique is more efficient.

Keywords: factor mimicking, portfolio construction, Augmented Black-Litterman (ABL), factor tilt, Fama-French, factor ranking, factor risk model, optimisation, OLS, GLS

JEL Classifications: C10, C11, C61, G11

Working Paper Series