Dynamic Asset Allocation with Expected Shortfall via Quantum Annealing

TL;DR

This paper introduces a hybrid quantum-classical algorithm for dynamic asset allocation that optimizes portfolios based on expected shortfall risk, leveraging quantum annealing hardware to handle complex optimization problems.

Contribution

It presents a novel approach combining quantum annealing with portfolio optimization to incorporate extreme market risks via expected shortfall, demonstrating practical feasibility with real data.

Findings

Quantum annealers achieved over 80% of classical optimal returns.

Higher asset correlations improved quantum annealing performance.

The method effectively models extreme market events using expected shortfall.

Abstract

Recent advances in quantum hardware offer new approaches to solve various optimization problems that can be computationally expensive when classical algorithms are employed. We propose a hybrid quantum-classical algorithm to solve a dynamic asset allocation problem where a target return and a target risk metric (expected shortfall) are specified. We propose an iterative algorithm that treats the target return as a constraint in a Markowitz portfolio optimization model, and dynamically adjusts the target return to satisfy the targeted expected shortfall. The Markowitz optimization is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. The use of the expected shortfall risk metric enables the modeling of extreme market events. We compare the results from D-Wave's 2000Q and Advantage quantum annealers using real-world financial data. Both quantum annealers are able to generate portfolios with more than 80% of the return of the classical optimal solutions, while satisfying the expected shortfall. We observe that experiments on assets with higher correlations tend to perform better, which may help to design practical quantum applications in the near term.