The Augmented Black-Litterman Model: A Ranking-Free Approach to Factor-Based Portfolio Construction and Beyond

Quantitative Finance, Vol. 13, No. 2, 2013, DOI: 10.1080/14697688.2012.714902

Posted: 26 Feb 2009 Last revised: 26 Jan 2016

Wing Cheung

Independent

Date Written: August 30, 2007

Abstract

The Fama and French (1992 and 1993 etc.) factor ranking approach is very popular among quantitative fund managers. However, this approach suffers from hidden factor view, loss of information, etc. issues. Based on the Black-Litterman model (Black and Litterman, 1992; as explained in Cheung, 2009A), we design a technique that endogenises the ranking process and elegantly resolves these issues. This model explicitly seeks forward-looking factor views and smoothly blends them to deliver robust allocation to securities. Our numerical experiments show this is an intuitive and practical framework for factor-based portfolio construction, and beyond.

The paper is featured by:
- A new and unified framework for strategy combination, factor mimicking and security-specific bets
- An elegant and ranking-free approach to factor style construction
- Worked examples based on the FTSE EUROTOP 100 universe
- Insight into the classic issue of confidence parameter setting
- An implementation guidance in the appendix

Keywords: asset allocation, portfolio construction, Bayes' Rule, Black-Litterman, view blending and shrinkage, CAPM, semi-strong market efficiency, Fama-French, factor ranking, factor risk model, mean-variance optimisation, robustness

JEL Classification: C10, C11, C61, G11, G14

Suggested Citation

Cheung, Wing, The Augmented Black-Litterman Model: A Ranking-Free Approach to Factor-Based Portfolio Construction and Beyond (August 30, 2007). Quantitative Finance, Vol. 13, No. 2, 2013, DOI: 10.1080/14697688.2012.714902. Available at SSRN: https://ssrn.com/abstract=1347648 or http://dx.doi.org/10.2139/ssrn.1347648

Wing Cheung (Contact Author)

Independent ( email )

No Address Available

Paper statistics

Abstract Views
10,230