Beyond Black-Litterman: Letting the Data Speak

Abstract

The Black-Litterman model is a popular approach for asset allocation by blending an investor's proprietary views with the views of the market. However, their model ignores the data-generating process whose dynamics can have significant impact on future portfolio returns. This paper extends the Black-Litterman model to allow Bayesian learning to exploit all available information - the market views, the investor's proprietary views as well as the data. The framework allows practitioners to combine insights from the Black-Litterman model with the data to generate potentially more reliable trading strategies and more robust portfolios.

Further, we show that many Bayesian learning tools can now be readily applied to practical portfolio selections in conjunction with the Black-Litterman model.

Keywords: Black-Litterman, Bayesian, Mean-variance, Portfolio Choice, Views