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中国工业与应用数学学会会刊
主管：中华人民共和国教育部
主办：西安交通大学
ISSN 1005-3085  CN 61-1269/O1
工程数学学报

工程数学学报 ›› 2017, Vol. 34 ›› Issue (1): 21-30.doi: 10.3969/j.issn.1005-3085.2017.01.003

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基于混合量子粒子群优化的投资组合模型及实证分析

高岳林,   余雅萍   

  1. 北方民族大学信息与系统科学研究所，银川  750021
  • 收稿日期:2014-10-15 接受日期:2016-01-12 出版日期:2017-02-15 发布日期:2017-04-15
  • 基金资助:
    国家自然科学基金(61561001)；北方民族大学重点科研项目(2015KJ10).

Portfolio Model Based on Hybrid Quantum Particle Swarm Optimization with Empirical Research

GAO Yue-lin,   YU Ya-ping   

  1. Institute of Information & System Science, Beifang University of Nationalities, Yinchuan 750021
  • Received:2014-10-15 Accepted:2016-01-12 Online:2017-02-15 Published:2017-04-15
  • Supported by:
    The National Natural Science Foundation of China (61561001); the Foundation of Research Projects of Beifang University of Nationalities (2015KJ10).

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摘要：

本文在Markowitz均值-方差模型的基础上，建立了带有资产数目和投资比例约束的投资组合模型，使得新模型更加切合实际．为了求解这个模型和仿真实际投资，构造了基于量子粒子群优化的差分进化和混沌搜索混合算法．数值实验表明所提算法是有效的，优于其他改进的粒子群算法、差分进化算法、遗传算法、模拟退火算法、禁忌搜索算法．同时实证表明，所提出的算法很好地求解了这个投资组合模型，模拟仿真产生了较好的结果．

关键词: 投资组合, 量子粒子群, 混合算法, 实证分析

Abstract:

In this paper, the portfolio model with the constraints of a number of assets and the proportion of investment is established on the basis of the Markowitz mean-variance model. For solving the model and the simulated actual investment, a quantum particle swarm hybrid algorithm is constructed by differential evolution and chaotic search. Numerical experiments show that the proposed algorithm is effective, and that the proposed hybrid algorithm performs better than other improved particle swarm optimization algorithm, differential evolution algorithm, genetic algorithm, simulated annealing algorithm and tabu search algorithm. Besides, the empirical results show that the proposed algorithm is a good solution to the portfolio model, and the simulation results show that the proposed model is effective.

Key words: portfolio, quantum particle swarm, hybrid algorithm, empirical analysis

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