Factor Investing with Black-Litterman-Bayes : Incorporating Factor Views and Priors in Portfolio Construction

The authors propose a general framework referred to as Black-Litterman-Bayes (BLB) for constructing optimal portfolios for factor-based investing. In the spirit of the classical Black-Litterman model, the framework allows for the incorporation of investor views and different priors on factor risk premia, including data-driven and benchmark priors. Computationally efficient closed-form formulas are provided for the (posterior) expected returns and return covariance matrix that result from integrating factor views into an APT multi-factor model. In a step-by-step procedure, the authors show how to build the prior and incorporate the factor views, demonstrating in a realistic empirical example, using a number of well-known cross-sectional U.S. equity factors, that the BLB approach can add value to mean-variance optimal multi-factor risk premia portfolios