Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (4): 142-154.

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

Special Issue: 下一代互联网络技术与应用

• Technology and Applicaton • Previous Articles     Next Articles

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 E-mail:dushouyan@hotmail.com;zhlu@cnic.cn

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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