Year: 1983
Type: article
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.
Source: Journal of Medical Ethics
DOI: 10.1136/jme.9.1.12
Authors: D Brahams, M Brahams
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W2061212911