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Numerical Studies of Channel Management Strategies for Nonstationary Immersion Environments: EURUSD Case Study

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

This article considers a short-term forecasting of a process that is an output signal of a nonlinear system observed on the background of additive noise. Forecasting is made possible thanks to the technique of nonparametric estimation of local trends. The main problem in this case is the instability of the time of the existence of these local trends. The average duration of relatively stable intervals can be estimated from earlier observation history. Such approaches are called channel strategies. The task of constructing such strategies for EURUSD asset management in the conditions of market chaos is considered, as well as the potential capabilities of these management strategies via computational experiments. We demonstrated the fundamental possibility of achieving profit even for areas with complex dynamics with abrupt changes in the considered process. We propose improved channel strategies and also denote the main directions of increasing their effectiveness.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1408-:d:799868
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    References listed on IDEAS

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    1. Noemi Schmitt & Frank Westerhoff, 2017. "Heterogeneity, spontaneous coordination and extreme events within large-scale and small-scale agent-based financial market models," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1041-1070, November.
    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. Frank McGroarty & Ash Booth & Enrico Gerding & V. L. Raju Chinthalapati, 2019. "High frequency trading strategies, market fragility and price spikes: an agent based model perspective," Annals of Operations Research, Springer, vol. 282(1), pages 217-244, November.
    4. Qianwei Ying & Tahir Yousaf & Qurat ul Ain & Yasmeen Akhtar & Muhammad Shahid Rasheed, 2019. "Stock Investment and Excess Returns: A Critical Review in the Light of the Efficient Market Hypothesis," JRFM, MDPI, vol. 12(2), pages 1-22, June.
    5. Achilleas Zapranis & Prodromos E. Tsinaslanidis, 2012. "Identifying and evaluating horizontal support and resistance levels: an empirical study on US stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 22(19), pages 1571-1585, October.
    6. Taco de Wolff & Alejandro Cuevas & Felipe Tobar, 2020. "Gaussian process imputation of multiple financial series," Papers 2002.05789, arXiv.org.
    7. Mirjana Pejić Bach & Živko Krstić & Sanja Seljan & Lejla Turulja, 2019. "Text Mining for Big Data Analysis in Financial Sector: A Literature Review," Sustainability, MDPI, vol. 11(5), pages 1-27, February.
    8. Timothy King & Dimitrios Koutmos, 2021. "Herding and feedback trading in cryptocurrency markets," Annals of Operations Research, Springer, vol. 300(1), pages 79-96, May.
    9. Bartram, Söhnke M. & Grinblatt, Mark, 2018. "Agnostic fundamental analysis works," Journal of Financial Economics, Elsevier, vol. 128(1), pages 125-147.
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    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.
    2. 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.

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