A Novel Multi-Objective Quantum-Inspired Algorithm for Portfolio Optimization with Short-Selling in Real-World Market

Abstract

: Portfolio optimization is inherently a multi-objective problem that aims to maximize expected return while minimizing investment risk, while also facing exponential growth in the search space and increasing market complexity. Existing multi-objective optimization approaches often struggle to balance convergence and diversity, particularly under realistic trading conditions such as short-selling. To address these challenges, this paper proposes a novel Multi-objective Quantum-inspired Tabu Search (MoQTS) framework for portfolio optimization with short-selling strategies. The proposed method incorporates a quantum-inspired superposition mechanism to enhance global exploration and introduces an entanglement-driven neighborhood search strategy that systematically generates structured local perturbations by modifying one or two asset-selection states. This mechanism enables effective exploration of the neighborhood of non-dominated solutions, thereby improving both convergence accuracy and solution diversity. In addition, a trend ratio (TR)-based evaluation model is adopted to jointly capture return and risk dynamics under real-world market fluctuations. Experiments are conducted on the U.S. stock market using Dow Jones Industrial Average (DJIA) data from 2013 to 2025. The proposed MoQTS is compared with several state-of-the-art multi-objective algorithms, including NSGA-II, MOEA/D, SMS-EMOA, and MOPSO. Experimental results demonstrate that MoQTS can obtain high-quality Pareto-optimal solutions with strong convergence and diversity performance. Results over multiple independent periods further support the effectiveness and robustness of MoQTS. In addition, computational cost analysis shows that MoQTS substantially reduces the number of evaluations and execution time compared with the comparison algorithms while maintaining high-quality Pareto-optimal solutions.