Distributionally Robust End-to-End Portfolio Construction

TL;DR

This paper introduces a novel end-to-end portfolio construction system that incorporates distributional robustness, explicitly accounting for model risk, and learns parameters directly from data for improved decision-making.

Contribution

It develops a distributionally robust end-to-end framework that integrates prediction and optimization, and learns robustness parameters directly from data.

Findings

Efficient training via convex duality reformulation.

Explicit modeling of model risk improves portfolio robustness.

End-to-end training enhances decision accuracy.

Abstract

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.