PT - JOURNAL ARTICLE
AU - Kolm, Petter N.
AU - Ritter, Gordon
TI - Factor Investing with Black–Litterman–Bayes: Incorporating Factor Views and Priors in Portfolio Construction
AID - 10.3905/jpm.2020.1.196
DP - 2020 Dec 31
TA - The Journal of Portfolio Management
PG - 113--126
VI - 47
IP - 2
4099 - http://jpm.pm-research.com/content/47/2/113.short
4100 - http://jpm.pm-research.com/content/47/2/113.full
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 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 and using a number of well-known cross-sectional US equity factors, that the BLB approach can add value to mean–variance-optimal multi-factor risk premium portfolios.TOPICS: Factor-based models, 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 multi-factor risk premium portfolios.