Market informed portfolio optimization methods with hybrid quantum computing

Giancarlo Martínez Salirrosas, Jinglun Gao, Arthur Yu and Anish Ravi Verma

Review of Financial Economics, 2025, vol. 43, issue 1, 62-77

Abstract: This document presents a portfolio optimization framework that employs a hybrid quantum computing algorithm and a futures market sentiment indicator—The Market Sentiment Meter (MSM) variable, developed jointly by CME Group and 1QBit. The methodology used was the Variational Quantum Eigensolver (VQE). The work presented here is divided into four portfolio optimization problem formulations, of binary and continuous variable formulations, determining which assets to pick their weights. This work demonstrates that adding the MSM variable can improve the performance of hybrid quantum solutions, by informing the asset selection problem with market environment information through the four MSM states.

Date: 2025
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https://doi.org/10.1002/rfe.1219

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Persistent link: https://EconPapers.repec.org/RePEc:wly:revfec:v:43:y:2025:i:1:p:62-77

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