Comparative Analysis of the BRIC Countries Stock Markets Using Network Approach

  • Arsenii VizgunovEmail author
  • Andrey Glotov
  • Panos M. Pardalos
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 59)


The paper presents the analysis of the network model referred to as market graph of the BRIC countries stock markets. We construct the stock market graph as follows: each vertex represents a stock, and the vertices are adjacent if the price correlation coefficient between them over a certain period of time is greater than or equal to specified threshold. The market graphs are constructed for different time periods to understand the dynamics of their characteristics such as correlation distribution histogram, mean value and standard deviation, size and structure of the maximum cliques. Our results show that we can split the BRIC countries into two groups. Brazil, Russia and India constitute the first group, China constitutes the second group.


Comparative analysis BRIC Stock market Network approach Market graph 


  1. 1.
    Boginski, V., Butenko, S., Pardalos, P.M.: On structural properties of the market graph. In: Nagurney, A. (ed.) Innovations in Financial and Economic Networks, pp. 29–45. Edward Elgar, Cheltenham Glos (2003) Google Scholar
  2. 2.
    Boginski, V., Butenko, S., Pardalos, P.M.: Statistical analysis of financial networks. Comput. Stat. Data Anal. 48, 431–443 (2005) MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Boginski, V., Butenko, S., Pardalos, P.M.: Mining market data: a network approach. Comput. Oper. Res., 3171–3184 (2006) Google Scholar
  4. 4.
    Huang, W.-Q., Zhuang, X.-T., Shuang, Y.: A network analysis of the Chinese stock market. Physica A 388, 2956–2964 (2009) CrossRefGoogle Scholar
  5. 5.
    Jallo, D., Budai, D., Boginski, V., Goldengorin, B., Pardalos, P.M.: Network-based representation of stock market dynamics: an application to American and Swedish stock markets. In: Goldengorin, B., Kalyagin, V., Pardalos, P. (eds.) Models, Algorithms, and Technologies for Network Analysis. Springer Proceedings in Mathematics & Statistics, vol. 32, pp. 91–98 (2012) Google Scholar
  6. 6.
    Mantegna, R.N., Stanley, H.E.: An Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge University Press, Cambridge (2000) Google Scholar
  7. 7.
    Salter-Townshend, M., White, A., Gollini, I., Murphy, T.: Review of statistical network analysis: models, algorithms, and software. Stat. Anal. Data Min. 5(4), 243–264 (2012) MathSciNetCrossRefGoogle Scholar
  8. 8.
    Vizgunov, A., Goldengorin, B., Kalyagin, V., Koldanov, A., Koldanov, P., Pardalos, P.M.: Network approach for the Russian stock market. Comput. Manag. Sci. (2013). doi: 10.1007/s10287-013-0165-7 Google Scholar
  9. 9.
    Batsyn, M.V., Goldengorin, B.I., Maslov, E.V., Pardalos, P.M.: Improvements to MCS algorithm for the maximum clique problem. J. Comb. Optim. (2013). doi: 10.1007/s10878-012-9592-6 Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Arsenii Vizgunov
    • 1
    Email author
  • Andrey Glotov
    • 1
  • Panos M. Pardalos
    • 2
  1. 1.National Research University Higher School of EconomicsNizhny NovgorodRussian Federation
  2. 2.Department of Industrial and Systems EngineeringUniversity of FloridaGainesvilleUSA

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