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Formulating the Concept of an Investment Strategy Adaptable to Changes in the Market Situation

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  • Vera Ivanyuk

    (Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 125993 Moscow, Russia
    Department of Higher Mathematics, Bauman Moscow State Technical University, 105005 Moscow, Russia)

Abstract

The study aims to develop a dynamic model for the management of a strategic investment portfolio, taking into account the impact of crisis processes on asset value. A mathematical model of a dynamic portfolio strategy is developed, and guidelines for framing a long-term investment strategy based on the current state of the investment market are formalized. An efficient method of long-term ensemble forecasting to increase the accuracy of predicting financial time series is elaborated. A methodology for constructing and rebalancing a dynamic strategic investment portfolio based on a changing portfolio strategy that results from assessing the current market state and forecast is developed. The obtained strategic portfolio model has been estimated empirically based on historical data and its rate-of-return characteristics have been compared with those of the existing conventional models used in strategic investment.

Suggested Citation

  • Vera Ivanyuk, 2021. "Formulating the Concept of an Investment Strategy Adaptable to Changes in the Market Situation," Economies, MDPI, vol. 9(3), pages 1-19, June.
    • Handle: RePEc:gam:jecomi:v:9:y:2021:i:3:p:95-:d:580663
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    References listed on IDEAS

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    1. Lu, Ya-Nan & Li, Sai-Ping & Zhong, Li-Xin & Jiang, Xiong-Fei & Ren, Fei, 2018. "A clustering-based portfolio strategy incorporating momentum effect and market trend prediction," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 1-15.
    2. Hadhri, Sinda & Ftiti, Zied, 2019. "Asset allocation and investment opportunities in emerging stock markets: Evidence from return asymmetry-based analysis," Journal of International Money and Finance, Elsevier, vol. 93(C), pages 187-200.
    3. Golosnoy, Vasyl & Gribisch, Bastian & Seifert, Miriam Isabel, 2019. "Exponential smoothing of realized portfolio weights," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 222-237.
    4. Chu, Gang & Zhang, Wei & Sun, Guofeng & Zhang, Xiaotao, 2019. "A new online portfolio selection algorithm based on Kalman Filter and anti-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    5. Benita, Francisco & López-Ramos, Francisco & Nasini, Stefano, 2019. "A bi-level programming approach for global investment strategies with financial intermediation," European Journal of Operational Research, Elsevier, vol. 274(1), pages 375-390.
    6. Mishra, Sasmita & Padhy, Sudarsan, 2019. "An efficient portfolio construction model using stock price predicted by support vector regression," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    7. Wu, Yu-Xi & Wu, Qing-Biao & Zhu, Jia-Qi, 2019. "Improved EEMD-based crude oil price forecasting using LSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 114-124.
    8. Mo, Guoli & Tan, Chunzhi & Zhang, Weiguo & Liu, Fang, 2019. "International portfolio of stock indices with spatiotemporal correlations: Can investors still benefit from portfolio, when and where?," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 168-183.
    9. Wang, Minggang & Zhao, Longfeng & Du, Ruijin & Wang, Chao & Chen, Lin & Tian, Lixin & Eugene Stanley, H., 2018. "A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 220(C), pages 480-495.
    10. de Leeuw, Tim & Gilsing, Victor & Duysters, Geert, 2019. "Greater adaptivity or greater control? Adaptation of IOR portfolios in response to technological change," Research Policy, Elsevier, vol. 48(6), pages 1586-1600.
    11. Dai Pra, Paolo & Tolotti, Marco, 2009. "Heterogeneous credit portfolios and the dynamics of the aggregate losses," Stochastic Processes and their Applications, Elsevier, vol. 119(9), pages 2913-2944, September.
    12. Bagrat Yerznkyan & Svetlana Bychkova & Timur Gataullin & Sergey Gataullin, 2019. "The Sufficiency Principle as the Ideas Quintessence of the Club of Rome," Montenegrin Journal of Economics, Economic Laboratory for Transition Research (ELIT), vol. 15(1), pages 21-29.
    13. Zhu, Shushang & Zhu, Wei & Pei, Xi & Cui, Xueting, 2020. "Hedging crash risk in optimal portfolio selection," Journal of Banking & Finance, Elsevier, vol. 119(C).
    14. Cesarone, Francesco & Mango, Fabiomassimo & Mottura, Carlo Domenico & Ricci, Jacopo Maria & Tardella, Fabio, 2020. "On the stability of portfolio selection models," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 210-234.
    15. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
    16. Ma, Guiyuan & Siu, Chi Chung & Zhu, Song-Ping, 2019. "Dynamic portfolio choice with return predictability and transaction costs," European Journal of Operational Research, Elsevier, vol. 278(3), pages 976-988.
    17. Bi, Junna & Cai, Jun, 2019. "Optimal investment–reinsurance strategies with state dependent risk aversion and VaR constraints in correlated markets," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 1-14.
    18. Wei, Jiuchang & Bu, Bing & Liang, Liang, 2012. "Estimating the diffusion models of crisis information in micro blog," Journal of Informetrics, Elsevier, vol. 6(4), pages 600-610.
    19. Roll, Richard, 1977. "A critique of the asset pricing theory's tests Part I: On past and potential testability of the theory," Journal of Financial Economics, Elsevier, vol. 4(2), pages 129-176, March.
    20. Chen, Shun & Ge, Lei, 2021. "A learning-based strategy for portfolio selection," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 936-942.
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    Cited by:

    1. Vera Ivanyuk, 2022. "Proposed Model of a Dynamic Investment Portfolio with an Adaptive Strategy," Mathematics, MDPI, vol. 10(23), pages 1-19, November.
    2. Vera Ivanyuk, 2021. "Modeling of Crisis Processes in the Financial Market," Economies, MDPI, vol. 9(4), pages 1-17, October.
    3. Sergey Korchagin & Ekaterina Romanova & Petr Nikitin & Denis Serdechnyy & Konstantin V. Bublikov & Irina Bystrenina, 2022. "Mathematical Modeling of Dielectric Permeability and Volt-Ampere Characteristics of a Semiconductor Nanocomposite Conglomerate," Mathematics, MDPI, vol. 10(4), pages 1-7, February.
    4. Oleg Krakhmalev & Nikita Krakhmalev & Sergey Gataullin & Irina Makarenko & Petr Nikitin & Denis Serdechnyy & Kang Liang & Sergey Korchagin, 2021. "Mathematics Model for 6-DOF Joints Manipulation Robots," Mathematics, MDPI, vol. 9(21), pages 1-11, November.
    5. Apostolos Chalkis & Emmanouil Christoforou & Ioannis Z. Emiris & Theodore Dalamagas, 2021. "Modeling asset allocations and a new portfolio performance score," Digital Finance, Springer, vol. 3(3), pages 333-371, December.

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