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

Ömer Akgüller, Mehmet Ali Balcı (), Larissa M. Batrancea () and Lucian Gaban
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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

Mathematics, 2023, vol. 11, issue 6, 1-25

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.

Keywords: visibility graphs; stock market network; graph kernel (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (5)

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