论文标题
深度加权子集投资组合
Deeply Equal-Weighted Subset Portfolios
论文作者
论文摘要
优化投资组合对估计错误的高度敏感性阻止了其实际应用。为了减轻这种敏感性，我们提出了一种新的投资组合模型，称为深度加权子集投资组合（DEWSP）。 DEWSP是资产宇宙中最高N排名的子集，其成员是根据深度学习算法的预测回报选择的，并且同样加权。在本文中，我们评估了不同尺寸n的DEWSP的性能，与其他类型的投资组合（例如优化的投资组合和历史上相等的子集投资组合（HEWSP））的性能相比，这些投资组合（HEWSPS）是基于历史平均回报的Top-N排名资产的子集。我们发现DEWSP的以下优势：首先，与基准HEWSP相比，DEWSP提供的提高率为0.24％至5.15％。此外，DEWSP是使用纯粹由数据驱动的方法构建的，而不是依靠专家的努力。 DEWSP还可以针对相对风险，并通过调整尺寸N的大小N尺寸来定位资产宇宙EWP的基线。这些优势使DEWSP在实践中具有竞争力。
The high sensitivity of optimized portfolios to estimation errors has prevented their practical application. To mitigate this sensitivity, we propose a new portfolio model called a Deeply Equal-Weighted Subset Portfolio (DEWSP). DEWSP is a subset of top-N ranked assets in an asset universe, the members of which are selected based on the predicted returns from deep learning algorithms and are equally weighted. Herein, we evaluate the performance of DEWSPs of different sizes N in comparison with the performance of other types of portfolios such as optimized portfolios and historically equal-weighed subset portfolios (HEWSPs), which are subsets of top-N ranked assets based on the historical mean returns. We found the following advantages of DEWSPs: First, DEWSPs provides an improvement rate of 0.24% to 5.15% in terms of monthly Sharpe ratio compared to the benchmark, HEWSPs. In addition, DEWSPs are built using a purely data-driven approach rather than relying on the efforts of experts. DEWSPs can also target the relative risk and return to the baseline of the EWP of an asset universe by adjusting the size N. Finally, the DEWSP allocation mechanism is transparent and intuitive. These advantages make DEWSP competitive in practice.
