Dynamic Asset Allocation with Expected Shortfall via Quantum Annealing

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.

D-Wave Implementation of Quantum Annealing for Optimal Resource Allocation in Disaster Response Operation of Marikina City

“…Quantum annealing, a subfield of quantum computing, has gained attention as a promising approach for solving optimization problems by leveraging quantum bits, or qubits, to explore solution spaces and find optimal configurations. Studies have applied quantum annealing, mainly using systems like the D-Wave, to efficiently solve large-scale combinatorial optimization problems and explore complex solution landscapes for resource allocation [3], [5]. Study [3] examines the benefits of quantum annealing systems compared to classical computing systems and delves into the formulation and discussion of a simulator for multitasking in a quantum annealer (QAMT).…”

“…Studies have applied quantum annealing, mainly using systems like the D-Wave, to efficiently solve large-scale combinatorial optimization problems and explore complex solution landscapes for resource allocation [3], [5]. Study [3] examines the benefits of quantum annealing systems compared to classical computing systems and delves into the formulation and discussion of a simulator for multitasking in a quantum annealer (QAMT). This integration of classical optimization algorithms and problem-specific heuristics has significantly improved the performance and scalability of quantum annealing.…”

“…After the ooding, the Marikina City Disaster Risk Reduction Management Oce (CDRRMO) usually requests an additional workforce to assist aected areas in the fast and ecient delivery of resource aid [2]. Quantum annealing (QA) presents a remarkably parallelized method for optimization problems by using a strategy that involves converting it into a QUBO problem [3]. In another study [4], D-Wave utilizes QA to nd the global minimum of an objective function via a quantum annealing algorithm to solve a problem similar to the vehicle routing problem.…”