The mean-variance model remains the most prevalent investment framework, built on diversification principles. However, it consistently struggles with estimation errors in expected returns and the covariance matrix, its core parameters. To address this concern, this research evaluates the performance of mean variance (MV) and global minimum-variance (GMV) models across various shrinkage estimators designed to improve these parameters. Specifically, we examine five shrinkage estimators for expected returns and eleven for the covariance matrix. To compare multiple portfolios, we employ a super efficient data envelopment analysis model to rank the portfolios according to investors risk-return preferences. Our comprehensive empirical investigation utilizes six real world datasets with different dimensional characteristics, applying a rolling window methodology across three out of sample testing periods. Following the ranking process, we examine the chosen shrinkage based MV or GMV portfolios against five traditional portfolio optimization techniques classical MV and GMV for sample estimates, MiniMax, conditional value at risk, and semi mean absolute deviation risk measures. Our empirical findings reveal that, in most scenarios, the GMV model combined with the Ledoit Wolf two parameter shrinkage covariance estimator (COV2) represents the optimal selection for a broad spectrum of investors. Meanwhile, the MV model utilizing COV2 alongside the sample mean (SM) proves more suitable for return oriented investors. These two identified models demonstrate superior performance compared to traditional benchmark approaches. Overall, this study lays the groundwork for a more comprehensive understanding of how specific shrinkage models perform across diverse investor profiles and market setups.

翻译：均值-方差模型作为基于分散化原则的主流投资框架，其核心参数——预期收益与协方差矩阵的估计误差问题始终存在。为应对此问题，本研究评估了采用不同收缩估计量改进参数估计的均值-方差（MV）与全局最小方差（GMV）模型的表现。具体而言，我们检验了五种预期收益收缩估计量及十一种协方差矩阵收缩估计量。为比较多元投资组合，采用超效率数据包络分析模型，依据投资者风险-收益偏好对投资组合进行排序。我们通过六个具有不同维度特征的真实市场数据集展开全面实证研究，采用滚动窗口方法跨越三个样本外测试期。在排序完成后，将选定的基于收缩估计的MV或GMV投资组合与五种传统组合优化技术（基于样本估计的经典MV与GMV、MiniMax、条件风险价值及半均值绝对偏差风险度量模型）进行对比。实证结果表明：在多数情境下，GMV模型结合Ledoit-Wolf双参数协方差收缩估计量（COV2）构成了面向广大投资者的最优选择；而采用COV2与样本均值（SM）的MV模型则更适用于收益导向型投资者。这两种模型均展现出优于传统基准方法的性能。总体而言，本研究为深入理解特定收缩模型在不同投资者类型与市场环境中的表现奠定了理论基础。