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Abstract
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 theory
Key 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.
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US and Overseas: +1 646-931-9045
UK: 0207 139 1600