Bayes-Stein Estimation for Portfolio Analysis
针对投资组合分析中参数估计的不确定性，提出一种经验贝叶斯估计方法，模拟表明该方法优于传统样本均值，能提升投资组合选择效果。
In portfolio analysis, uncertainty about parameter values leads to suboptimal portfolio choices. The resulting loss in the investor's utility is a function of the particular estimator chosen for expected returns. So, this is a problem of simultaneous estimation of normal means under a well-specified loss function. In this situation, as Stein has shown, the classical sample mean is inadmissible. This paper presents a simple empirical Bayes estimator that should outperform the sample mean in the context of a portfolio. Simulation analysis shows that these Bayes-Stein estimators provide significant gains in portfolio selection problems.