CLUSTERING BASED ON BETWEENNESS CENTRALITY IN PERIOD: TRANSFORMATION OF CORRELATION COEFFICIENT VALUE INTO DISTANCE IN MATRIC SPACE

  • Mokhammad Ridwan Yudhanegara Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Singaperbangsa Karawang, Indonesia https://orcid.org/0000-0002-7316-3359
  • Edwin Setiawan Nugraha Department of Actuarial Science, Faculty of Business, President University, Indonesia https://orcid.org/0000-0002-3043-0031
  • Sisilia Sylviani Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Indonesia https://orcid.org/0000-0002-7480-7742
  • Karunia Eka Lestari Department of Mathematics Education, Faculty of Teacher Training and Education, Universitas Singaperbangsa Karawang, Indonesia https://orcid.org/0000-0003-1555-5933
  • Ebenezer Bonyah Department of Mathematics Education, Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development, Ghana
DOI: https://doi.org/10.30598/barekengvol19iss2pp1109-1118
Keywords: Dynamic Network, Euclidean Distance, Stock Price, Vertex Betweenness

Abstract

The main information of this research is the transformation of the correlation coefficient value for stock price into the distance. It is done to create a representation in metric space that can be used in cluster analysis on the correlation network, which is a dynamic network. The dynamic network is generated from the weighted edges in the form of distances in each period. Finding the cluster members of the network can be analyzed using a simple technique called a minimum spanning tree. The central cluster member is the vertex betweenness. Vertex betweenness represents banking companies with a high degree of proximity and correlation. It means that the banks that are members of the central cluster are banks with high investment value. Clustering based on betweenness centrality in the case study of stock price correlation becomes useful when transforming the value of the correlation coefficient to distance. The effort to build a network with the edge weight being the distance makes the minimum spanning tree a simple, valuable method for cluster analysis on bank stock prices. In particular, the benefit to investors, i.e., it can reveal which assets are closely correlated, indicating that they may respond to market events in a similar way and make decisions in stock purchases

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References

E. Kalani, A. Elhami, R. B. Kazem-Zadeh, and E. Kamrani, “SELECTION OF INVESTMENT BASIS USING NEURAL NETWORKS IN STOCK EXCHANGE,” Am. J. Ind. Bus. Manag., vol. 08, no. 03, pp. 548–562, 2018, doi: 10.4236/ajibm.2018.83036.

I. Barnett and J. P. Onnela, “CHANGE POINT DETECTION IN CORRELATION NETWORKS,” Sci. Rep., vol. 6, no. June 2015, pp. 1–11, 2016, doi: 10.1038/srep18893.

T. Millington and M. Niranjan, “PARTIAL CORRELATION FINANCIAL NETWORKS,” Appl. Netw. Sci., vol. 5, no. 1, p. 11, 2020, doi: 10.1007/s41109-020-0251-z.

T. Isogai, “DYNAMIC CORRELATION NETWORK ANALYSIS OF FINANCIAL ASSET RETURNS WITH NETWORK CLUSTERING,” Appl. Netw. Sci., vol. 2, no. 8, pp. 1–30, 2017, doi: 10.1007/s41109-017-0031-6.

R. I. Tuffaha and A. Almaktoom, “USING MINIMUM SPANNING TREE TO REDUCE COST OF THE CABLE FOR THE INTERNET,” PalArch’s J. Archaeol. Egypt, vol. 18, no. 15, pp. 215–223, 2021.

P. Tewarie, E. van Dellen, A. Hillebrand, and C. J. Stam, “THE MINIMUM SPANNING TREE: AN UNBIASED METHOD FOR BRAIN NETWORK ANALYSIS,” Neuroimage, vol. 104, pp. 177–188, 2015, doi: 10.1016/j.neuroimage.2014.10.015.

X. Huang, D. Chen, T. Ren, and D. Wang, A SURVEY OF COMMUNITY DETECTION METHODS IN MULTILAYER NETWORKS, vol. 35, no. 1. Springer US, 2021. doi: 10.1007/s10618-020-00716-6.

M. Yildirimoglu and J. Kim, “IDENTI FI CATION OF COMMUNITIES IN URBAN MOBILITY NETWORKS USING MULTI-LAYER GRAPHS OF NETWORK TRAFFIC,” Transp. Res. Part C, vol. 89, no. November 2017, pp. 254–267, 2018, doi: 10.1016/j.trc.2018.02.015.

S. K. R and K. Balakrishnan, “BETWEENNESS CENTRALITY IN CARTESIAN PRODUCT OF GRAPHS BETWEENNESS CENTRALITY IN CARTESIAN PRODUCT OF GRAPHS,” AKCE Int. J. Graphs Comb., vol. 17, no. 1, pp. 571–583, 2020, doi: 10.1016/j.akcej.2019.03.012.

J. Matta, G. Ercal, and K. Sinha, “COMPARING THE SPEED AND ACCURACY OF APPROACHES TO BETWEENNESS CENTRALITY APPROXIMATION,” Comput. Soc. Networks, vol. 6, no. 1, pp. 1–30, 2019, doi: 10.1186/s40649-019-0062-5.

M. R. Yudhanegara and K. E. Lestari, “NETWORK CLUSTERING METHOD FOR PREVENTING THE SPREAD OF COVID-19 IN INDONESIAN SCHOOLS,” Commun. Math. Biol. Neurosci., vol. 2023, pp. 1–15, 2023, doi: 10.28919/cmbn/7922.

M. R. Yudhanegara, S. W. Indratno, and R. K. N. Sari, “DYNAMIC ITEMS DELIVERY NETWORK: PREDICTION AND CLUSTERING,” Heliyon, vol. 7, no. 5, p. e06934, 2021, doi: 10.1016/j.heliyon.2021.e06934.

S. W. Indratno, K. N. Sari, and M. R. Yudhanegara, “OPTIMIZATION IN ITEM DELIVERY AS RISK MANAGEMENT: MULTINOMIAL CASE USING THE NEW METHOD OF STATISTICAL INFERENCE FOR ONLINE DECISION,” Risks, vol. 10, no. 6, 2022, doi: 10.3390/risks10060122.

P. Sacks, “METRIC SPACES,” IN TECHNIQUES OF FUNCTIONAL ANALYSIS FOR DIFFERENTIAL AND INTEGRAL EQUATIONS, Academic Press, 2017, pp. 35–50. doi: 10.1016/b978-0-12-811426-1.00003-9.

M. P. A. Starmans, S. R. va. der Voort, J. M. C. Tovar, J. F. Veenland, S. Klein, and W. J. Niessen, RADIOMICS. Elsevier Inc., 2019. doi: 10.1016/B978-0-12-816176-0.00023-5.

M. R. Yudhanegara and K. E. Lestari, “PREDICTIVE DISTRIBUTION TO DETERMINE LEARNING MODEL AT THE STRATEGIC COMPETENCE LEVEL OF STUDENTS IN STATISTICS GROPUS COURSE,” BAREKENG: J. Math. App., vol. 18, no. 1, pp. 313–322, 2024, doi: https://doi.org/10.30598/barekengvol18iss1pp0313-0322.

K. E. Lestari, U. S. Pasaribu, S. W. Indratno, and H. Garminia, “GENERATING ROOTS OF CUBIC POLYNOMIALS BY CARDANO’S APPROACH ON CORRESPONDENCE ANALYSIS,” Heliyon, vol. 6, no. 6, p. e03998, 2020, doi: 10.1016/j.heliyon.2020.e03998.

K. E. Lestari, M. R. Utami, and M. R. Yudhanegara, “SIMPLE ALGORITHM TO CONSTRUCT CIRCULAR CONFIDENCE REGIONS IN CORRESPONDENCE ANALYSIS,” BAREKENG: J. Math. App., vol. 16, no. 1, pp. 65–74, 2022.

Published
2025-04-01
How to Cite
[1]
M. R. Yudhanegara, E. S. Nugraha, S. Sylviani, K. E. Lestari, and E. Bonyah, “CLUSTERING BASED ON BETWEENNESS CENTRALITY IN PERIOD: TRANSFORMATION OF CORRELATION COEFFICIENT VALUE INTO DISTANCE IN MATRIC SPACE”, BAREKENG: J. Math. & App., vol. 19, no. 2, pp. 1109-1118, Apr. 2025.
Issue
Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Section
Articles

Copyright (c) 2025 Mokhammad Ridwan Yudhanegara, Edwin Setiawan Nugraha, Sisilia Sylviani, Karunia Eka Lestari, Ebenezer Bonyah

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