On Accelerating Large-Scale Robust Portfolio Optimization

TL;DR

This paper introduces an extended hyperplane approximation method that significantly accelerates large-scale robust portfolio optimization, enabling practical application to extensive asset sets with improved computational efficiency and robust out-of-sample performance.

Contribution

It presents a novel hyperplane approximation technique that reduces computational time for large-scale robust portfolio problems involving many assets and market uncertainties.

Findings

Reduces optimization time from thousands to a few seconds.

Demonstrates robust out-of-sample trading performance on S&P 500 data.

Provides a scalable solution for large-scale portfolio optimization.

Abstract

Solving large-scale robust portfolio optimization problems is challenging due to the high computational demands associated with an increasing number of assets, the amount of data considered, and market uncertainty. To address this issue, we propose an extended supporting hyperplane approximation approach for efficiently solving a class of distributionally robust portfolio problems for a general class of additively separable utility functions and polyhedral ambiguity distribution set, applied to a large-scale set of assets. Our technique is validated using a large-scale portfolio of the S&P 500 index constituents, demonstrating robust out-of-sample trading performance. More importantly, our empirical studies show that this approach significantly reduces computational time compared to traditional concave Expected Log-Growth (ELG) optimization, with running times decreasing from several thousand seconds to just a few. This method provides a scalable and practical solution to large-scale robust portfolio optimization, addressing both theoretical and practical challenges.