Transparent Augmented Black-Litterman Allocation: Simple and Unified Framework for Strategy Combination, Factor Mimicking, Hedging, and Stock-Specific Alphas

Abstract:     
You have some factor, strategy, and/or stock-specific alpha ideas. Without an optimiser, some straightforward linear algebra gives you the diversified and efficient Bayesian allocation that allows greater performance accountability. All you need is just a factor risk model. How does this sound‘ This paper derives a transparent version of the ABL model (Cheung, 2009B) with an explicit allocation expression, including components for all the needed functionalities. In addition to further insights, it allows more tangible implementation of strategy combination, factor mimicking, hedging, and stock-specific bets in a unified framework. More specifically, it enables the following:
- A unified, open, and optimiser-free allocation framework with parsimonious use of risk model
- Modularised alpha generation: 'portable alphas' with Bayesian assembly
- Implied new efficient techniques respectively for strategy combination, composite factor mimicking, hedging, and stock-specific betting
- Deeper insight into the ultimate Bayesian allocation mechanism
- Complete guidance on Black-Litterman practical issues, making the technique genuinely practical
- An interface between the art and science of portfolio construction

Number of Pages in PDF File: 34

Keywords: portfolio construction, Bayes' Rule, Augmented Black-Litterman (ABL), view blending and shrinkage, semi-strong market efficiency, Fama-French, factor ranking, factor risk model, optimisation, robustness, portable alpha, strategy combination, factor mimicking, hedging, stock-specific betting

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

working papers series