Incorporating Economic Objectives into Bayesian Priors: Portfolio Choice under Parameter Uncertainty
提出一种让贝叶斯先验反映经济目标的方法，通过将先验施加于问题解而非原始参数，利用Fama-French数据发现基于目标的先验能显著提升投资绩效，年确定性等价收益差异常超10%。
Abstract This paper proposes a way to allow Bayesian priors to reflect the objectives of an economic problem. That is, we impose priors on the solution to the problem rather than on the primitive parameters whose implied priors can be backed out from the Euler equation. Using monthly returns on the Fama-French 25 size and book-to-market portfolios and their 3 factors from January 1965 to December 2004, we find that investment performances under the objective-based priors can be significantly different from those under alternative priors, with differences in terms of annual certainty-equivalent returns greater than 10% in many cases. In terms of an out-of-sample loss function measure, portfolio strategies based on the objective-based priors can substantially outperform both strategies under alternative priors and some of the best strategies developed in the classical framework.