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Exploiting Mean-Variance Portfolio Optimization Problems through Zeroing Neural Networks

Author

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  • Spyridon D. Mourtas

    (Department of Economics, Division of Mathematics and Informatics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece)

  • Chrysostomos Kasimis

    (Department of Physics, Electronics Laboratory, University of Patras, 26504 Patras, Greece)

Abstract

In this research, three different time-varying mean-variance portfolio optimization (MVPO) problems are addressed using the zeroing neural network (ZNN) approach. The first two MVPO problems are defined as time-varying quadratic programming (TVQP) problems, while the third MVPO problem is defined as a time-varying nonlinear programming (TVNLP) problem. Then, utilizing real-world datasets, the time-varying MVPO problems are addressed by this alternative neural network (NN) solver and conventional MATLAB solvers, and their performances are compared in three various portfolio configurations. The results of the experiments show that the ZNN approach is a magnificent alternative to the conventional methods. To publicize and explore the findings of this study, a MATLAB repository has been established and is freely available on GitHub for any user who is interested.

Suggested Citation

  • Spyridon D. Mourtas & Chrysostomos Kasimis, 2022. "Exploiting Mean-Variance Portfolio Optimization Problems through Zeroing Neural Networks," Mathematics, MDPI, vol. 10(17), pages 1-20, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3079-:d:898341
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    References listed on IDEAS

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    1. Zhifeng Dai, 2019. "A Closer Look at the Minimum-Variance Portfolio Optimization Model," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-8, August.
    2. Simos, Theodore E. & Katsikis, Vasilios N. & Mourtas, Spyridon D. & Stanimirović, Predrag S., 2022. "Unique non-negative definite solution of the time-varying algebraic Riccati equations with applications to stabilization of LTV systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 164-180.
    3. Wendong Jiang & Chia-Liang Lin & Vasilios N. Katsikis & Spyridon D. Mourtas & Predrag S. Stanimirović & Theodore E. Simos, 2022. "Zeroing Neural Network Approaches Based on Direct and Indirect Methods for Solving the Yang–Baxter-like Matrix Equation," Mathematics, MDPI, vol. 10(11), pages 1-13, June.
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    Citations

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    Cited by:

    1. Katsikis, Vasilios N. & Mourtas, Spyridon D. & Stanimirović, Predrag S. & Li, Shuai & Cao, Xinwei, 2023. "Time-varying minimum-cost portfolio insurance problem via an adaptive fuzzy-power LVI-PDNN," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    2. Stanimirović, Predrag S. & Mourtas, Spyridon D. & Mosić, Dijana & Katsikis, Vasilios N. & Cao, Xinwei & Li, Shuai, 2024. "Zeroing neural network approaches for computing time-varying minimal rank outer inverse," Applied Mathematics and Computation, Elsevier, vol. 465(C).
    3. Houssem Jerbi & Obaid Alshammari & Sondess Ben Aoun & Mourad Kchaou & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2023. "Hermitian Solutions of the Quaternion Algebraic Riccati Equations through Zeroing Neural Networks with Application to Quadrotor Control," Mathematics, MDPI, vol. 12(1), pages 1-19, December.
    4. Houssem Jerbi & Hadeel Alharbi & Mohamed Omri & Lotfi Ladhar & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2022. "Towards Higher-Order Zeroing Neural Network Dynamics for Solving Time-Varying Algebraic Riccati Equations," Mathematics, MDPI, vol. 10(23), pages 1-16, November.
    5. Xingyuan Li & Chia-Liang Lin & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2022. "Computation of Time-Varying {2,3}- and {2,4}-Inverses through Zeroing Neural Networks," Mathematics, MDPI, vol. 10(24), pages 1-13, December.
    6. Rabeh Abbassi & Houssem Jerbi & Mourad Kchaou & Theodore E. Simos & Spyridon D. Mourtas & Vasilios N. Katsikis, 2023. "Towards Higher-Order Zeroing Neural Networks for Calculating Quaternion Matrix Inverse with Application to Robotic Motion Tracking," Mathematics, MDPI, vol. 11(12), pages 1-21, June.

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