The Intrinsic Logic of the Augmented Black-Litterman Model

Abstract

Portfolio management is an art as well as science. We argue that portfolio managers eventually face two fundamental scientific challenges: (a) how to allocate; and (b) how to mimic. These require scientific answers. Other challenges in portfolio management are arts where different tastes, preferences, judgments and beliefs should be allowed. A general portfolio management framework should provide principles for solving the scientific problems, but remain neutral and facilitate interactions with the art part. The Augmented Black-Litterman (ABL) model rightly meets these requirements. This article reveals its intrinsic logic. It features: - An exploration of the motivation and general requirements of portfolio construction; - A dissection of the ABL allocation model; - Three ABL endogenous techniques and their efficiency evidences; - The intrinsic logic of the ABL model; and - Justifications of the model as a unified Bayesian allocation framework. Practitioners can consider this framework a unified allocation theory, which can be used to understand, evaluate and improve existing portfolio construction practices.