Factor Investing with Black–Litterman–Bayes: Incorporating Factor Views and Priors in Portfolio Construction
提出Black-Litterman-Bayes框架，将投资者对因子风险溢价的观点和先验融入投资组合构建，通过闭式公式计算后验期望收益和协方差矩阵，实证表明能提升均值-方差最优多因子组合的价值。
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. <b>TOPICS:</b>Factor-based models, portfolio construction, portfolio theory <b>Key Findings</b> ▪ 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.