IDEAS home Printed from https://ideas.repec.org/a/gam/jecomi/v9y2021i3p95-d580663.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7099/9/3/95/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7099/9/3/95/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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).
    11. 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.
    12. 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).
    13. Chen, Shun & Ge, Lei, 2021. "A learning-based strategy for portfolio selection," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 936-942.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. Zhu, Shushang & Zhu, Wei & Pei, Xi & Cui, Xueting, 2020. "Hedging crash risk in optimal portfolio selection," Journal of Banking & Finance, Elsevier, vol. 119(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. He, Huizi & Sun, Mei & Li, Xiuming & Mensah, Isaac Adjei, 2022. "A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features," Energy, Elsevier, vol. 244(PA).
    2. Fereydooni, Ali & Barak, Sasan & Asaad Sajadi, Seyed Mehrzad, 2024. "A novel online portfolio selection approach based on pattern matching and ESG factors," Omega, Elsevier, vol. 123(C).
    3. Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    4. Xu, Kunliang & Niu, Hongli, 2023. "Denoising or distortion: Does decomposition-reconstruction modeling paradigm provide a reliable prediction for crude oil price time series?," Energy Economics, Elsevier, vol. 128(C).
    5. Xu, Kunliang & Niu, Hongli, 2022. "Do EEMD based decomposition-ensemble models indeed improve prediction for crude oil futures prices?," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    6. Xu, Qifa & Li, Mengting & Jiang, Cuixia, 2021. "Network-augmented time-varying parametric portfolio selection: Evidence from the Chinese stock market," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    7. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    8. Jiangwei Liu & Xiaohong Huang, 2021. "Forecasting Crude Oil Price Using Event Extraction," Papers 2111.09111, arXiv.org.
    9. Zhao, Geya & Xue, Minggao & Cheng, Li, 2023. "A new hybrid model for multi-step WTI futures price forecasting based on self-attention mechanism and spatial–temporal graph neural network," Resources Policy, Elsevier, vol. 85(PB).
    10. Zhao, Yuan & Zhang, Weiguo & Gong, Xue & Wang, Chao, 2021. "A novel method for online real-time forecasting of crude oil price," Applied Energy, Elsevier, vol. 303(C).
    11. Abdollahi, Hooman, 2020. "A novel hybrid model for forecasting crude oil price based on time series decomposition," Applied Energy, Elsevier, vol. 267(C).
    12. Vera Ivanyuk, 2022. "Proposed Model of a Dynamic Investment Portfolio with an Adaptive Strategy," Mathematics, MDPI, vol. 10(23), pages 1-19, November.
    13. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    14. Radosław Kurach, 2013. "Does Beta Explain Global Equity Market Volatility – Some Empirical Evidence," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 7(2), June.
    15. Dumas, Bernard & Gabuniya, Tymur & Marston, Richard C, 2020. "Firms' Exposures to Geographic Risks," CEPR Discussion Papers 15503, C.E.P.R. Discussion Papers.
    16. Hertrich Markus, 2019. "A Novel Housing Price Misalignment Indicator for Germany," German Economic Review, De Gruyter, vol. 20(4), pages 759-794, December.
    17. Turan G. Bali & Robert F. Engle & Yi Tang, 2017. "Dynamic Conditional Beta Is Alive and Well in the Cross Section of Daily Stock Returns," Management Science, INFORMS, vol. 63(11), pages 3760-3779, November.
    18. Migliavacca, Milena & Goodell, John W. & Paltrinieri, Andrea, 2023. "A bibliometric review of portfolio diversification literature," International Review of Financial Analysis, Elsevier, vol. 90(C).
    19. Jorge Braga de Macedo & Jeffrey Goldstein & David Meerschwam, 1984. "International Portfolio Diversification: Short-Term Financial Assets and Gold," NBER Chapters, in: Exchange Rate Theory and Practice, pages 199-238, National Bureau of Economic Research, Inc.
    20. Figge, Frank & Hahn, Tobias & Barkemeyer, Ralf, 2014. "The If, How and Where of assessing sustainable resource use," Ecological Economics, Elsevier, vol. 105(C), pages 274-283.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jecomi:v:9:y:2021:i:3:p:95-:d:580663. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.