IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i11p1797-d822932.html
   My bibliography  Save this article

Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments

Author

Listed:
  • Alexander Musaev

    (St. Petersburg State Technological Institute (Technical University), 190013 St. Petersburg, Russia
    St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences, 199178 St. Petersburg, Russia)

  • Andrey Makshanov

    (Department of Computing Systems and Computer Science, Admiral Makarov State University of Maritime and Inland Shipping, 198035 St. Petersburg, Russia)

  • Dmitry Grigoriev

    (Center of Econometrics and Business Analytics (CEBA), St. Petersburg State University, 199034 St. Petersburg, Russia)

Abstract

We consider the problem of evolutionary self-organization of control strategies using the example of speculative trading in a non-stationary immersion market environment. The main issue that obstructs obtaining real profit is the extremely high instability of the system component of observation series which implement stochastic chaos. In these conditions, traditional techniques for increasing the stability of control strategies are ineffective. In particular, the use of adaptive computational schemes is difficult due to the high volatility and non-stationarity of observation series. That leads to significant statistical errors of both kinds in the generated control decisions. An alternative approach based on the use of dynamic robustification technologies significantly reduces the effectiveness of the decisions. In the current work, we propose a method based on evolutionary modeling, which supplies structural and parametric self-organization of the control model.

Suggested Citation

  • Alexander Musaev & Andrey Makshanov & Dmitry Grigoriev, 2022. "Evolutionary Optimization of Control Strategies for Non-Stationary Immersion Environments," Mathematics, MDPI, vol. 10(11), pages 1-17, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:11:p:1797-:d:822932
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/11/1797/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/11/1797/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang Lu, 2019. "Artificial intelligence: a survey on evolution, models, applications and future trends," Journal of Management Analytics, Taylor & Francis Journals, vol. 6(1), pages 1-29, January.
    2. Tarun Chordia & Amit Goyal & Alessio Saretto, 2017. "p-Hacking: Evidence from Two Million Trading Strategies," Swiss Finance Institute Research Paper Series 17-37, Swiss Finance Institute, revised Apr 2018.
    3. Mohammad Asjad & Shahbaz Khan, 2017. "Analysis of maintenance cost for an asset using the genetic algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 445-457, June.
    4. Ruba Abu Khurma & Ibrahim Aljarah & Ahmad Sharieh & Mohamed Abd Elaziz & Robertas Damaševičius & Tomas Krilavičius, 2022. "A Review of the Modification Strategies of the Nature Inspired Algorithms for Feature Selection Problem," Mathematics, MDPI, vol. 10(3), pages 1-45, January.
    5. Alexander Musaev & Andrey Makshanov & Dmitry Grigoriev, 2022. "Numerical Studies of Channel Management Strategies for Nonstationary Immersion Environments: EURUSD Case Study," Mathematics, MDPI, vol. 10(9), pages 1-20, April.
    6. Han, Chenyu & Wang, Yiming & Ning, Ye, 2019. "Comparative analysis of the multifractality and efficiency of exchange markets: Evidence from exchange rates dynamics of major world currencies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    7. Jia LU & Noor Muhammad SHAZEMEEN & Raimonda MARTINKUTE-KAULIENE, 2020. "Portfolio Decision Using Time Series Prediction and Multi-objective Optimization," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 118-130, December.
    8. Chu, Jeffrey & Zhang, Yuanyuan & Chan, Stephen, 2019. "The adaptive market hypothesis in the high frequency cryptocurrency market," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 221-231.
    9. BenSaïda, Ahmed & Litimi, Houda, 2013. "High level chaos in the exchange and index markets," Chaos, Solitons & Fractals, Elsevier, vol. 54(C), pages 90-95.
    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. Eva Kaslik & Mihaela Neamţu & Anca Rădulescu, 2022. "Preface to the Special Issue on “Advances in Differential Dynamical Systems with Applications to Economics and Biology”," Mathematics, MDPI, vol. 10(19), pages 1-3, September.

    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. Ciurea Iulia-Cristina, 2024. "The Impact of the EU AI Act on the UN Sustainable Development Goals for 2030 – A Text Analysis," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 2857-2870.
    2. Moeeni , Shahram & Tayebi , Komeil, 2018. "Is It Necessary to Restrict Forex Financial Trading? A Modified Model," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 13(1), pages 63-80, January.
    3. Vasilios Plakandaras & Rangan Gupta & Luis A. Gil-Alana & Mark E. Wohar, 2019. "Are BRICS exchange rates chaotic?," Applied Economics Letters, Taylor & Francis Journals, vol. 26(13), pages 1104-1110, July.
    4. Hong Jiang & Shuyu Sun & Hongtao Xu & Shukuan Zhao & Yong Chen, 2020. "Enterprises' network structure and their technology standardization capability in Industry 4.0," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 749-765, July.
    5. Carmen López-Martín & Sonia Benito Muela & Raquel Arguedas, 2021. "Efficiency in cryptocurrency markets: new evidence," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 403-431, September.
    6. Yuanyuan Zhang & Stephen Chan & Jeffrey Chu & Hana Sulieman, 2020. "On the Market Efficiency and Liquidity of High-Frequency Cryptocurrencies in a Bull and Bear Market," JRFM, MDPI, vol. 13(1), pages 1-14, January.
    7. Aslam, Faheem & Aziz, Saqib & Nguyen, Duc Khuong & Mughal, Khurrum S. & Khan, Maaz, 2020. "On the efficiency of foreign exchange markets in times of the COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    8. Viktor Manahov, 2024. "The rapid growth of cryptocurrencies: How profitable is trading in digital money?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2214-2229, April.
    9. Tsionas, Mike G. & Michaelides, Panayotis G., 2017. "Bayesian analysis of chaos: The joint return-volatility dynamical system," MPRA Paper 80632, University Library of Munich, Germany.
    10. Li Da Xu, 2020. "The contribution of systems science to Industry 4.0," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 618-631, July.
    11. Tsionas, Mike G. & Michaelides, Panayotis G., 2017. "Neglected chaos in international stock markets: Bayesian analysis of the joint return–volatility dynamical system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 95-107.
    12. Lucía Inglada-Pérez & Sandra González y Gil, 2024. "A Study on the Nature of Complexity in the Spanish Electricity Market Using a Comprehensive Methodological Framework," Mathematics, MDPI, vol. 12(6), pages 1-21, March.
    13. Manahov, Viktor & Urquhart, Andrew, 2021. "The efficiency of Bitcoin: A strongly typed genetic programming approach to smart electronic Bitcoin markets," International Review of Financial Analysis, Elsevier, vol. 73(C).
    14. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    15. José A. Roldán-Casas & Mª B. García-Moreno García, 2022. "A procedure for testing the hypothesis of weak efficiency in financial markets: a Monte Carlo simulation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1289-1327, December.
    16. Julio E. Sandubete & León Beleña & Juan Carlos García-Villalobos, 2023. "Testing the Efficient Market Hypothesis and the Model-Data Paradox of Chaos on Top Currencies from the Foreign Exchange Market (FOREX)," Mathematics, MDPI, vol. 11(2), pages 1-29, January.
    17. Yuxin Fang & Hongjun Cao & Jihui Sun, 2022. "Impact of Artificial Intelligence on Regional Green Development under China’s Environmental Decentralization System—Based on Spatial Durbin Model and Threshold Effect," IJERPH, MDPI, vol. 19(22), pages 1-27, November.
    18. Ao Shu & Feiyang Cheng & Jianlei Han & Zini Liang & Zheyao Pan, 2023. "Arbitrage across different Bitcoin exchange venues: Perspectives from investor base and market related events," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5183-5210, December.
    19. Naeem, Muhammad Abubakr & Farid, Saqib & Ferrer, Román & Shahzad, Syed Jawad Hussain, 2021. "Comparative efficiency of green and conventional bonds pre- and during COVID-19: An asymmetric multifractal detrended fluctuation analysis," Energy Policy, Elsevier, vol. 153(C).
    20. Haefner, Naomi & Wincent, Joakim & Parida, Vinit & Gassmann, Oliver, 2021. "Artificial intelligence and innovation management: A review, framework, and research agenda✰," Technological Forecasting and Social Change, Elsevier, vol. 162(C).

    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:jmathe:v:10:y:2022:i:11:p:1797-:d:822932. 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.