英文标题：
《Reducing Estimation Risk in Mean-Variance Portfolios with Machine
  Learning》
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作者：
Daniel Kinn
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最新提交年份：
2018
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英文摘要：
  In portfolio analysis, the traditional approach of replacing population moments with sample counterparts may lead to suboptimal portfolio choices. I show that optimal portfolio weights can be estimated using a machine learning (ML) framework, where the outcome to be predicted is a constant and the vector of explanatory variables is the asset returns. It follows that ML specifically targets estimation risk when estimating portfolio weights, and that \"off-the-shelf\" ML algorithms can be used to estimate the optimal portfolio in the presence of parameter uncertainty. The framework nests the traditional approach and recently proposed shrinkage approaches as special cases. By relying on results from the ML literature, I derive new insights for existing approaches and propose new estimation methods. Based on simulation studies and several datasets, I find that ML significantly reduces estimation risk compared to both the traditional approach and the equal weight strategy.
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中文摘要：
在投资组合分析中，用样本替代总体矩的传统方法可能会导致次优的投资组合选择。我证明了可以使用机器学习（ML）框架估计最优投资组合权重，其中要预测的结果是一个常数，解释变量的向量是资产回报。因此，在估计投资组合权重时，ML专门针对估计风险，并且“现成”ML算法可用于在存在参数不确定性的情况下估计最优投资组合。该框架将传统方法和最近提出的收缩方法嵌套为特例。通过依赖ML文献的结果，我对现有方法有了新的见解，并提出了新的估计方法。基于仿真研究和多个数据集，我发现与传统方法和等权策略相比，ML显著降低了估计风险。
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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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