RT Journal Article
SR Electronic
T1 Factor Investing with Black–Litterman–Bayes: Incorporating Factor Views and Priors in Portfolio Construction
JF The Journal of Portfolio Management
FD Institutional Investor Journals
SP jpm.2020.1.196
DO 10.3905/jpm.2020.1.196
A1 Kolm, Petter N.
A1 Ritter, Gordon
YR 2020
UL http://jpm.pm-research.com/content/early/2020/12/14/jpm.2020.1.196.abstract
AB 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 priors on factor risk premiums, 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 arbitrage pricing theory multifactor 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 and using a number of well-known cross-sectional US equity factors, that the BLB approach can add value to mean–variance-optimal multifactor risk premium portfolios.TOPICS: Factor-based models, factors, risk premia, portfolio construction, portfolio theoryKey Findings▪ The authors propose a general framework referred to as Black–Litterman–Bayes (BLB) for constructing optimal portfolios for factor-based investing.▪ The framework allows for the incorporation of investor views and priors on factor risk premiums, including data-driven and benchmark priors.▪ The authors provide computationally efficient closed-form formulas for the (posterior) expected returns and return covariance matrix.▪ In a realistic empirical example, using a number of well-known cross-sectional US equity factors, they demonstrate that the BLB approach can add value to mean–variance-optimal multifactor risk premium portfolios.