JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (1): 101-110.doi: 10.6040/j.issn.1671-9352.0.2021.564

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Portfolio research based on temporal network and random matrix theory

ZHAO Xia1,2, ZHU Yi-pin1*, YANG Ya-jie3, XU Lan-tao1   

  1. 1. School of Statistics and Information, Shanghai University of International Business and Economics, Shanghai 201620, China;
    2. Research Center for Financial Big Data and Actuarial Science, Shanghai University of International Business and Economics, Shanghai 201620, China;
    3. School of Management, Northwestern Polytechnical University, Xian 710129, Shaanxi, China
  • Published:2023-02-12

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Abstract: Considering the complex linear and non-linear dynamic relationship and evolutional information among assets, temporal networks are constructed based on Pearson correlation coefficient, Kendall rank correlation coefficient and Tail correlation coefficient, and further deal with their noise reductions through random matrix, employ conditional centrality of temporal network as selection standard of optimal asset portfolio. Nine network models for asset screening are constructed from three aspects of dependency relationship, matrix noise reduction and central measure, and the mean-variance optimal strategy is further calculated based on SSE 180 indices. The in-sample and out-of-sample performances of investment strategies under different models are compared through indicators such as portfolio turnover and Omega ratio. The results show that the portfolio selected by the model with Kendall and Tail correlation coefficients has lower transaction costs. The application of random matrix theory can significantly improve investment returns and the introduction of temporal conditional centrality is helpful to screen out better portfolios.

Key words: temporal network, random matrix theory, temporal conditional centrality, portfolio optimization

CLC Number: 

  • F830.9
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