【作者(必填)】PetterN. Kolm and Gordon Ritter

【文题(必填)】Factor Investing with Black–Litterman–Bayes: Incorporating Factor Views and Priors in Portfolio Construction

【年份(必填)】TheJournal of Portfolio Management Quantitative Special Issue 2021, 47 (2) 113-126

【全文链接或数据库名称(选填)】DOI:https://doi.org/10.3905/jpm.2020.1.196

摘要：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.