Quantum-inspired meta-heuristic approaches for a constrained portfolio optimization problem

Abstract

Portfolio optimization has always been a challenging proposition and a highly studied problem in finance and management. Portfolio optimization facilitates the selection of the right assets and their distribution according to the set objectives. Often, it has been found that this nonlinear constraint problem cannot be efficiently solved using a traditional approach. In this paper, quantum-inspired incarnations of three evolutionary techniques, viz., (i) genetic algorithm (GA), (ii) differential evolution (DE), and (iii) particle swarm optimization (PSO) are used for the portfolio optimization problem. Experiments have been conducted with more than 10 years of stock price data from NASDAQ, BSE, and Dow Jones. Several enhancements of the evolutionary algorithms have been proposed in this article, viz., (i) enhanced crossover techniques for the portfolio optimization problem, (ii) regularization function to allocate funds efficiently, and (iii) dynamic parameter tuning using sensitivity analysis.