数据与计算发展前沿 ›› 2020, Vol. 2 ›› Issue (4): 142-154.

doi: 10.11871/jfdc.issn.2096-742X.2020.04.012

所属专题： 下一代互联网络技术与应用

• 技术与应用 • 上一篇    下一篇

基于矩阵值因子算法的企业年金投资组合建模与并行求解

杜首燕1,2(),陆忠华1,*()   

  1. 1.中国科学院计算机网络信息中心,北京 100190
    2.中国科学院大学,北京 100049
  • 收稿日期:2020-05-12 出版日期:2020-08-20 发布日期:2020-09-10
  • 通讯作者: 陆忠华
  • 作者简介:杜首燕,中国科学院计算机网络信息中心,硕士研究生,主要研究方向为投资组合优化、计算金融和高性能计算技术。
    本文主要承担工作为构建并实现了基于矩阵值因子算法的均值-方差投资组合优化模型,进行最优值求解和并行计算。
    Du Shouyan is a master student at Institute of the Computer Network Information Center at Chinese Academy of Sciences. Her research interests include portfolio optimization, computational finance and high performance computing technology.
    In this paper, she mainly undertakes the work of constructing and implementing the mean-variance portfolio optimization model based on matrix-valued factor algorithm, performing optimal value solution and parallel calculation.
    E-mail: dushouyan@hotmail.com|陆忠华,中国科学院计算机网络信息中心,研究员,博士,主要研究方向为高性能技术及其在计算金融中的应用。
    本文主要承担工作为矩阵值因子算法和高性能计算技术指导,并指导完成本论文。
    Lu Zhonghua, Ph.D., is a research fellow at Institute of the Computer Network Information Center at Chinese Academy of Sciences. Her research interests include high performance technology and its applications in computational finance.
    In this paper, she mainly undertakes the work of matrix-valued factor algorithm and high performance computing technology guidance, and guiding the completion of this paper.
    E-mail: zhlu@cnic.cn
  • 基金资助:
    国家自然科学基金“基于高性能计算的养老目标基金投资策略研究”(61873254)

Modeling and Parallel Computed Enterprise Annuity Portfolio Based on Matrix-Valued Factor Algorithm

Du Shouyan1,2(),Lu Zhonghua1,*()   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-05-12 Online:2020-08-20 Published:2020-09-10
  • Contact: Lu Zhonghua

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摘要：

【目的】为了满足我国企业年金的资产配置和实际交易的需求,确定整体的风险和收益目标,得到最佳的资产配置比例和更优的投资决策。【方法】本文在遵循企业年金安全性和收益性前提下,基于矩阵值因子算法构建了带投资约束条件的均值-方差优化模型,并基于CVXOPT求解器、遗传算法和粒子群算法进行最优值求解,综合最好方差、均值方差和均值收益率三个指标,选择最优模型实现并行计算。【结果】研究和实验结果表明,模型实现了对高维协方差矩阵的降维建模和预测,缓解了在资产数量多的情况下,模型的待估参数过多且不易求解的问题,从而更快的收敛到全局最优解;并行计算可使最优模型的计算效率显著提升,有效缩短模型的运行时间。【局限】作为面向我国企业年金的投资组合优化模型,改进均值-方差模型解的不可靠性和考虑职工的风险承受能力的差别是下一步需要解决的重要问题。【结论】投资组合优化模型结合矩阵值因子算法和并行计算有利于解决投资组合选择的计算瓶颈问题,促进企业年金的保值增值,从而缓解社会养老金制度在人口老龄化环境下所面临的平衡难以持续、负担不断加重的问题。

关键词: 企业年金, 矩阵值因子算法, 遗传算法, 高性能计算, 均值-方差优化模型

Abstract:

[Objective] The goal of the study is to meet the needs of asset allocation and actual transactions of enterprise annuity in China, determine the overall risk and return goals, and gain the best asset allocation ratio and better investment decisions. [Methods] Following the premise of security and profitability of enterprise annuity, this paper develops a mean-variance optimization model with investment constraints based on the matrix-valued factor algorithm. The optimal value is obtained based on the CVXOPT solver, genetic algorithm and particle swarm optimization algorithm. Then, considering the three indicators of best variance, mean variance and mean return rate, the optimal model is chosen for calculation in parallel. [Results] Our research and experimental results show that the model is able to reduce and predict the dimensions of high-dimensional covariance matrixes, which alleviates the problem that too many parameters may be difficult to solve when given numerous assets and makes faster convergence to the global optimal solution. By conducting parallel computing, the calculation efficiency of the optimal model is significantly improved, which can effectively shorten the running time of the model. [Limitations] As a portfolio optimization model for Chinese enterprise annuity, mitigating the unreliability of the mean-variance model solution and considering the differences in the risk tolerance of employees are important issues that need to be resolved next. [Conclusions] The portfolio optimization model combined with matrix-valued factor algorithm and parallel computing is beneficial to solving the calculation bottleneck problem of portfolio selection, promoting the preservation and appreciation of enterprise annuity, and alleviating the problems that the balance of social pension system is difficult to sustain and the burden of it is increasing under the circumstance of aging population.

Key words: enterprise annuity, matrix-valued factor algorithm, genetic algorithm, high-performance computing, mean-variance optimization model