论文标题
分配强大的端到端投资组合构造
Distributionally Robust End-to-End Portfolio Construction
论文作者
论文摘要
我们为投资组合构建提供了一个端到端分配的稳健系统，该系统将资产回报预测模型与分配强大的投资组合优化模型集成在一起。我们还展示了如何直接从数据中学习易耐受性参数和鲁棒性程度。端到端系统可以在培训期间在预测层和决策层之间传达该信息的优势，从而使参数可以接受最终任务的培训，而不仅仅是预测性能。但是，现有的端到端系统无法量化和纠正模型风险对决策层的影响。我们提出的分配在良好的端到端投资组合选择系统明确说明了模型风险的影响。决策层通过解决最小值问题来选择投资组合，其中假定资产返回的分布属于围绕名义分布的歧义集。使用凸双重性，我们以允许对端到端系统进行有效训练的形式重新提出了最小问题。
We propose an end-to-end distributionally robust system for portfolio construction that integrates the asset return prediction model with a distributionally robust portfolio optimization model. We also show how to learn the risk-tolerance parameter and the degree of robustness directly from data. End-to-end systems have an advantage in that information can be communicated between the prediction and decision layers during training, allowing the parameters to be trained for the final task rather than solely for predictive performance. However, existing end-to-end systems are not able to quantify and correct for the impact of model risk on the decision layer. Our proposed distributionally robust end-to-end portfolio selection system explicitly accounts for the impact of model risk. The decision layer chooses portfolios by solving a minimax problem where the distribution of the asset returns is assumed to belong to an ambiguity set centered around a nominal distribution. Using convex duality, we recast the minimax problem in a form that allows for efficient training of the end-to-end system.
