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Path-Based Visibility Graph Kernel and Application for the Borsa Istanbul Stock Network

Author

Listed:
  • Ömer Akgüller

    (Department of Mathematics, Muğla Sıtkı Koçman University, 48000 Muğla, Turkey)

  • Mehmet Ali Balcı

    (Department of Mathematics, Muğla Sıtkı Koçman University, 48000 Muğla, Turkey)

  • Larissa M. Batrancea

    (Department of Business, Babeş-Bolyai University, 400174 Cluj-Napoca, Romania)

  • Lucian Gaban

    (Faculty of Economics, “1 Decembrie 1918” University of Alba Iulia, 510009 Alba Iulia, Romania)

Abstract

Using networks to analyze time series has become increasingly popular in recent years. Univariate and multivariate time series can be mapped to networks in order to examine both local and global behaviors. Visibility graph-based time series analysis is proposed herein; in this approach, individual time series are mapped to visibility graphs that characterize relevant states. Companies listed on the emerging market index Borsa Istanbul 100 (BIST 100) had their market visibility graphs collected. To further account for the local extreme values of the underlying time series, we constructed a novel kernel function of the visibility graphs. Via the provided novel measure, sector-level and sector-to-sector analyses are conducted using the kernel function associated with this metric. To examine sectoral trends, the COVID-19 crisis period was included in the study’s data set. The findings indicate that an effective strategy for analyzing financial time series has been devised.

Suggested Citation

  • Ömer Akgüller & Mehmet Ali Balcı & Larissa M. Batrancea & Lucian Gaban, 2023. "Path-Based Visibility Graph Kernel and Application for the Borsa Istanbul Stock Network," Mathematics, MDPI, vol. 11(6), pages 1-25, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1528-:d:1103452
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    References listed on IDEAS

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    Cited by:

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    3. J. Alberto Conejero & Andrei Velichko & Òscar Garibo-i-Orts & Yuriy Izotov & Viet-Thanh Pham, 2024. "Exploring the Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps," Mathematics, MDPI, vol. 12(7), pages 1-23, March.

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