The Black-Litterman model extends traditional portfolio theory by incorporating investors’ views to arrive at a bespoke asset allocation.
Developed by Fisher Black and Bob Litterman in the 1990s, the Black-Litterman model uses mixed estimation techniques to combine the market equilibrium vector of expected returns with an investor-specific, usually Bayesian-derived, vector to form a new, posterior estimate of expected returns. The final vector of expected returns is assumed to have a probability distribution of the product of two multivariate normal distributions.
To overcome the limitations in modern portfolio theory, many asset management companies have adopted the Black-Litterman model to implement practical asset allocation models.
You can use MATLAB® to implement the Black-Litterman model. Sample implementations and extensions are available for download from practitioners such as Attilio Meucci and Jay Walters. You can use these functions with the Financial Toolbox™ portfolio analysis and asset allocation functionality.