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Portfolio Insurance through Error-Correction Neural Networks

Author

Listed:
  • Vladislav N. Kovalnogov

    (Laboratory of Inter-Disciplinary Problems of Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Ruslan V. Fedorov

    (Laboratory of Inter-Disciplinary Problems of Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Dmitry A. Generalov

    (Laboratory of Inter-Disciplinary Problems of Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Andrey V. Chukalin

    (Laboratory of Inter-Disciplinary Problems of Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia)

  • Vasilios N. Katsikis

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

  • Spyridon D. Mourtas

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

  • Theodore E. Simos

    (Laboratory of Inter-Disciplinary Problems of Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan
    Data Recovery Key Laboratory of Sichun Province, Neijing Normal University, Neijiang 641100, China
    Department of Mathematics, University of Western Macedonia, 52100 Kastoria, Greece)

Abstract

Minimum-cost portfolio insurance (MCPI) is a well-known investment strategy that tries to limit the losses a portfolio may incur as stocks decrease in price without requiring the portfolio manager to sell those stocks. In this research, we define and study the time-varying MCPI problem as a time-varying linear programming problem. More precisely, using real-world datasets, three different error-correction neural networks are employed to address this financial time-varying linear programming problem in continuous-time. These neural network solvers are the zeroing neural network (ZNN), the linear-variational-inequality primal-dual neural network (LVI-PDNN), and the simplified LVI-PDNN (S-LVI-PDNN). The neural network solvers are tested using real-world data on portfolios of up to 20 stocks, and the results show that they are capable of solving the financial problem efficiently, in some cases more than five times faster than traditional methods, though their accuracy declines as the size of the portfolio increases. This demonstrates the speed and accuracy of neural network solvers, showing their superiority over traditional methods in moderate-size portfolios. To promote and contend the outcomes of this research, we created two MATLAB repositories, for the interested user, that are publicly accessible on GitHub.

Suggested Citation

  • Vladislav N. Kovalnogov & Ruslan V. Fedorov & Dmitry A. Generalov & Andrey V. Chukalin & Vasilios N. Katsikis & Spyridon D. Mourtas & Theodore E. Simos, 2022. "Portfolio Insurance through Error-Correction Neural Networks," Mathematics, MDPI, vol. 10(18), pages 1-14, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3335-:d:914983
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    References listed on IDEAS

    as
    1. Aliprantis, C. D. & Brown, D. J. & Werner, J., 2000. "Minimum-cost portfolio insurance," Journal of Economic Dynamics and Control, Elsevier, vol. 24(11-12), pages 1703-1719, October.
    2. Katsikis, Vasilios N. & Mourtas, Spyridon D., 2019. "A heuristic process on the existence of positive bases with applications to minimum-cost portfolio insurance in C[a, b]," Applied Mathematics and Computation, Elsevier, vol. 349(C), pages 221-244.
    3. Mariya Kornilova & Vladislav Kovalnogov & Ruslan Fedorov & Mansur Zamaleev & Vasilios N. Katsikis & Spyridon D. Mourtas & Theodore E. Simos, 2022. "Zeroing Neural Network for Pseudoinversion of an Arbitrary Time-Varying Matrix Based on Singular Value Decomposition," Mathematics, MDPI, vol. 10(8), pages 1-12, April.
    4. 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.
    5. Kentaro Imajo & Kentaro Minami & Katsuya Ito & Kei Nakagawa, 2020. "Deep Portfolio Optimization via Distributional Prediction of Residual Factors," Papers 2012.07245, arXiv.org.
    6. 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.
    7. Vasilios N. Katsikis & Spyridon D. Mourtas & Predrag S. Stanimirović & Shuai Li & Xinwei Cao, 2021. "Time-Varying Mean-Variance Portfolio Selection under Transaction Costs and Cardinality Constraint Problem via Beetle Antennae Search Algorithm (BAS)," SN Operations Research Forum, Springer, vol. 2(2), pages 1-26, June.
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    Cited by:

    1. 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.
    2. Catalina Lozano-Murcia & Francisco P. Romero & Jesus Serrano-Guerrero & Jose A. Olivas, 2023. "A Comparison between Explainable Machine Learning Methods for Classification and Regression Problems in the Actuarial Context," Mathematics, MDPI, vol. 11(14), pages 1-20, July.
    3. 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.
    4. 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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