标题： 分布鲁棒端到端投资组合构建 显示英文标题

标题： Distributionally Robust End-to-End Portfolio Construction

摘要： 我们提出了一种端到端的分布鲁棒系统用于投资组合构建，该系统将资产回报预测模型与分布鲁棒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风险容忍度参数和稳健性的程度。 端到端系统的优势在于，在训练过程中预测层和决策层之间可以传递信息，允许参数针对最终任务进行训练，而不仅仅是针对预测性能。 然而，现有的端到端系统无法量化并纠正模型风险对决策层的影响。 我们提出的分布鲁棒端到端投资组合选择系统明确考虑了模型风险的影响。 决策层通过解决一个极小极大问题来选择投资组合，其中假设资产回报的分布属于以名义分布为中心的模糊集。 利用凸对偶性，我们将极小极大问题重新表述为一种形式，从而能够高效地训练端到端系统。 显示英文摘要

摘要： 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.