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Adaptive Trend Decomposition Method in Financial Time Series Analysis

Author

Listed:
  • Dmitry Aleksandrovich Endovitsky*

    (Voronezh State University Voronezh, Russia)

  • Valery Vladimirovich Davnis

    (Voronezh State University Voronezh, Russia)

  • Viacheslav Vladimirovich Korotkikh

    (Voronezh State University Voronezh, Russia)

Abstract

Purpose: dynamic reproduction of multi-trend stock market processes. Discussion: the authors consider adaptation principles as the basis of the mechanism of the effective stock market. Considering the behavior of the stock market as the behavior of a single social and economic system, having the properties of self-adjustment, self-regulation, adaptation to new, continuously changing conditions, the stock market theories recognized by the scientific community, but disparate and opposing stock market theories, can be considered as a complementary. The fact that the stock market is volatile and follows variable rules at different time intervals formed the understanding of the multi-trend processes of the stock market. Results: the authors introduce the concept of a basis trend and make suggestions concerning its properties. A formal statistical model of the multi-trend process has been proposed, it is introduced as a set of trend components. This model formed the basis of dynamic technology of the adaptive trend decomposition of financial time series, demonstrated in the empirical part.

Suggested Citation

  • Dmitry Aleksandrovich Endovitsky* & Valery Vladimirovich Davnis & Viacheslav Vladimirovich Korotkikh, 2018. "Adaptive Trend Decomposition Method in Financial Time Series Analysis," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 104-109:3.
    • Handle: RePEc:arp:tjssrr:2018:p:104-109
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