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Topological properties of stock market networks: The case of Brazil

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  • Tabak, Benjamin M.
  • Serra, Thiago R.
  • Cajueiro, Daniel O.

Abstract

This paper investigates the topological properties of the Brazilian stock market networks. We build the minimum spanning tree, which is based on the concept of ultrametricity, using the correlation matrix for a variety of stocks of different sectors. Our results suggest that stocks tend to cluster by sector. We employ a dynamic approach using complex network measures and find that the relative importance of different sectors within the network varies. The financial, energy and material sectors are the most important within the network.

Suggested Citation

  • Tabak, Benjamin M. & Serra, Thiago R. & Cajueiro, Daniel O., 2010. "Topological properties of stock market networks: The case of Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3240-3249.
  • Handle: RePEc:eee:phsmap:v:389:y:2010:i:16:p:3240-3249
    DOI: 10.1016/j.physa.2010.04.002
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    References listed on IDEAS

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    Cited by:

    1. Kanjamapornkul, K. & Pinčák, Richard & Bartoš, Erik, 2016. "The study of Thai stock market across the 2008 financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 117-133.
    2. Gogas, Periklis & Papadimitriou, Theophilos & Matthaiou, Maria-Artemis, 2016. "Bank supervision using the Threshold-Minimum Dominating Set," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 23-35.
    3. Kantar, Ersin & Keskin, Mustafa, 2013. "The relationships between electricity consumption and GDP in Asian countries, using hierarchical structure methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(22), pages 5678-5684.
    4. repec:eee:phsmap:v:490:y:2018:i:c:p:1309-1323 is not listed on IDEAS
    5. Papadimitriou, Theophilos & Gogas, Periklis & Tabak, Benjamin M., 2013. "Complex networks and banking systems supervision," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4429-4434.
    6. Coletti, Paolo, 2016. "Comparing minimum spanning trees of the Italian stock market using returns and volumes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 246-261.
    7. repec:spr:jeicoo:v:12:y:2017:i:3:d:10.1007_s11403-016-0176-x is not listed on IDEAS
    8. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2015. "Optimality problem of network topology in stocks market analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 108-114.
    9. A. Q. Barbi & G. A. Prataviera, 2017. "Nonlinear dependencies on Brazilian equity network from mutual information minimum spanning trees," Papers 1711.06185, arXiv.org.
    10. Kazemilari, Mansooreh & Mardani, Abbas & Streimikiene, Dalia & Zavadskas, Edmundas Kazimieras, 2017. "An overview of renewable energy companies in stock exchange: Evidence from minimal spanning tree approach," Renewable Energy, Elsevier, vol. 102(PA), pages 107-117.
    11. Výrost, Tomáš & Lyócsa, Štefan & Baumöhl, Eduard, 2015. "Granger causality stock market networks: Temporal proximity and preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 262-276.
    12. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2012. "Stock market networks: The dynamic conditional correlation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4147-4158.
    13. Výrost, Tomáš, 2012. "Country effects in CEE3 stock market networks: a preliminary study," MPRA Paper 43481, University Library of Munich, Germany.
    14. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2013. "Minimal spanning tree problem in stock networks analysis: An efficient algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2226-2234.
    15. repec:nea:journl:y:2017:i:35:p:33-52 is not listed on IDEAS
    16. Sensoy, Ahmet & Tabak, Benjamin M., 2014. "Dynamic spanning trees in stock market networks: The case of Asia-Pacific," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 414(C), pages 387-402.
    17. Chu, J. & Nadarajah, S., 2017. "A statistical analysis of UK financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 445-459.
    18. Kocheturov, A. & Batsyn, M. & Pardalos, P., 2015. "Dynamics of Cluster Structures in Stock Market Networks," Journal of the New Economic Association, New Economic Association, vol. 28(4), pages 12-30.
    19. Djauhari, Maman A., 2012. "A robust filter in stock networks analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 5049-5057.
    20. Majapa, Mohamed & Gossel, Sean Joss, 2016. "Topology of the South African stock market network across the 2008 financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 35-47.
    21. Chen, Kun & Luo, Peng & Sun, Bianxia & Wang, Huaiqing, 2015. "Which stocks are profitable? A network method to investigate the effects of network structure on stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 224-235.
    22. Wang, Gang-Jin & Xie, Chi & Han, Feng & Sun, Bo, 2012. "Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4136-4146.
    23. Kazemilari, Mansooreh & Djauhari, Maman Abdurachman, 2015. "Correlation network analysis for multi-dimensional data in stocks market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 62-75.

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