英文标题：
《Weighted Elastic Net Penalized Mean-Variance Portfolio Design and
  Computation》
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作者：
Michael Ho, Zheng Sun, Jack Xin
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最新提交年份：
2015
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英文摘要：
  It is well known that the out-of-sample performance of Markowitz\'s mean-variance portfolio criterion can be negatively affected by estimation errors in the mean and covariance. In this paper we address the problem by regularizing the mean-variance objective function with a weighted elastic net penalty. We show that the use of this penalty can be motivated by a robust reformulation of the mean-variance criterion that directly accounts for parameter uncertainty. With this interpretation of the weighted elastic net penalty we derive data driven techniques for calibrating the weighting parameters based on the level of uncertainty in the parameter estimates. We test our proposed technique on US stock return data and our results show that the calibrated weighted elastic net penalized portfolio outperforms both the unpenalized portfolio and uniformly weighted elastic net penalized portfolio.   This paper also introduces a novel Adaptive Support Split-Bregman approach which leverages the sparse nature of $\\ell_{1}$ penalized portfolios to efficiently compute a solution of our proposed portfolio criterion. Numerical results show that this modification to the Split-Bregman algorithm results in significant improvements in computational speed compared with other techniques.
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中文摘要：
众所周知，马科维茨均值-方差投资组合准则的样本外性能会受到均值和协方差估计误差的负面影响。在本文中，我们通过用加权弹性净惩罚正则化均值-方差目标函数来解决这个问题。我们表明，这种惩罚的使用可以由直接解释参数不确定性的均值-方差准则的稳健重新表述来驱动。通过对加权弹性净惩罚的这种解释，我们导出了基于参数估计中的不确定性水平校准加权参数的数据驱动技术。我们在美国股票收益率数据上测试了我们提出的方法，结果表明，经过校准的加权弹性净惩罚投资组合优于未授权投资组合和均匀加权弹性净惩罚投资组合。本文还介绍了一种新的自适应支持分割Bregman方法，该方法利用$\\ell_{1}$惩罚投资组合的稀疏性来有效地计算我们提出的投资组合准则的解。数值结果表明，与其他技术相比，对Split-Bregman算法的这种修改导致了计算速度的显著提高。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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