Quantum beetle antennae search:a novel technique for the constrained portfolio optimization problem  

作　　者：Ameer Tamoor KHAN Xinwei CAO Shuai LI Bin HU Vasilios N.KATSIKIS 

机构地区：[1]Department of Computing,Hong Kong Polytechnic University,Hong Kong 999077,China [2]School of Management,Shanghai University,Shanghai 201900,China [3]School of Information Science and Engineering,Lanzhou University,Lanzhou 730000,China [4]Department of Economics,Division of Mathematics and Informatics,National and Kapodistrian University of Athens,Athens 15772,Greece

出　　处：《Science China(Information Sciences)》2021年第5期117-130,共14页中国科学（信息科学）（英文版）

摘　　要：In this paper,we have formulated quantum beetle antennae search(QBAS),a meta-heuristic optimization algorithm,and a variant of beetle antennae search(BAS).We apply it to portfolio selection,a well-known finance problem.Quantum computing is gaining immense popularity among the scientific community as it outsmarts the conventional computing in efficiency and speed.All the traditional computing algorithms are not directly compatible with quantum computers,for that we need to formulate their variants using the principles of quantum mechanics.In the portfolio optimization problem,we need to find the set of optimal stock such that it minimizes the risk factor and maximizes the mean-return of the portfolio.To the best of our knowledge,no quantum meta-heuristic algorithm has been applied to address this problem yet.We apply QBAS on real-world stock market data and compare the results with other meta-heuristic optimization algorithms.The results obtained show that the QBAS outperforms swarm algorithms such as the particle swarm optimization(PSO)and the genetic algorithm(GA).

关 键 词：quantum computing beetle antennae search portfolio selection optimization finance problem 

分 类 号：F830.91[经济管理—金融学] TP18[自动化与计算机技术—控制理论与控制工程]