The European Physical Journal B

, Volume 38, Issue 2, pp 353–362 | Cite as

Clustering and information in correlation based financial networks

  • J.-P. Onnela
  • K. Kaski
  • J. KertészEmail author


Networks of companies can be constructed by using return correlations. A crucial issue in this approach is to select the relevant correlations from the correlation matrix. In order to study this problem, we start from an empty graph with no edges where the vertices correspond to stocks. Then, one by one, we insert edges between the vertices according to the rank of their correlation strength, resulting in a network called asset graph. We study its properties, such as topologically different growth types, number and size of clusters and clustering coefficient. These properties, calculated from empirical data, are compared against those of a random graph. The growth of the graph can be classified according to the topological role of the newly inserted edge. We find that the type of growth which is responsible for creating cycles in the graph sets in much earlier for the empirical asset graph than for the random graph, and thus reflects the high degree of networking present in the market. We also find the number of clusters in the random graph to be one order of magnitude higher than for the asset graph. At a critical threshold, the random graph undergoes a radical change in topology related to percolation transition and forms a single giant cluster, a phenomenon which is not observed for the asset graph. Differences in mean clustering coefficient lead us to conclude that most information is contained roughly within 10% of the edges.


Empirical Data Correlation Matrix Random Graph Critical Threshold Radical Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    The economy as an evolving complex system II, edited by W.B. Arthur, S.N. Durlauf, D.A. Lane (Addison-Wesley, Reading, Massachusetts, 1997)Google Scholar
  2. 2.
    H.M. Markowitz, J. Finance 7, 77 (1952)Google Scholar
  3. 3.
    R. Albert, A.-L. Barabási, Rev. Mod. Phys. 74, 47 (2002); S.N. Dorogovtsev, J.F.F. Mendes, Evolution of Networks: From Biological Nets to the Internet and WWW (Oxford UP, 2003)ADSCrossRefGoogle Scholar
  4. 4.
    G. Caldarelli, S. Battiston, D. Garlaschelli, M. Catanzaro, in Complex Networks, edited by E. Ben-Naim, H. Frauenfelder, Z. Toroczkai (Springer, 2004)Google Scholar
  5. 5.
    J.-P. Onnela, A. Chakraborti, K. Kaski, J. Kertesz, A. Kanto, Phys. Scr. T 106, 48 (2003)ADSCrossRefGoogle Scholar
  6. 6.
    R.N. Mantegna, Eur. Phys. J. B 11, 193 (1999)ADSCrossRefGoogle Scholar
  7. 7.
    L. Kullmann, J. Kertész, R. Mantegna, Physica A 287, 412 (2000)ADSCrossRefGoogle Scholar
  8. 8.
    J.-P. Onnela, A. Chakraborti, K. Kaski, J. Kertész, Eur. Phys. J. B 30, 285 (2002)ADSMathSciNetCrossRefGoogle Scholar
  9. 9.
    J.-P. Onnela, A. Chakraborti, K. Kaski, J. Kertész, A. Kanto, Phys. Rev. E 68, 056110 (2003)ADSCrossRefGoogle Scholar
  10. 10.
    J.-P. Onnela, A. Chakraborti, K. Kaski, J. Kertész, Physica A 324, 247 (2003)ADSMathSciNetCrossRefGoogle Scholar
  11. 11.
    G. Bonanno, F. Lillo, R.N. Mantegna, Quantitative Finance 1, 96 (2001)CrossRefGoogle Scholar
  12. 12.
    G. Bonanno, N. Vandewalle, R.N. Mantegna, Phys. Rev. E 62, R7615 (2000)Google Scholar
  13. 13.
    L. Kullmann, J. Kertész, K. Kaski, Phys. Rev. E 66, 026125 (2002)ADSCrossRefGoogle Scholar
  14. 14.
    M. Mehta, Random Matrices (Academic Press, New York, 1995)Google Scholar
  15. 15.
    L. Laloux et al. , Phys. Rev. Lett. 83, 1467 (1999)ADSCrossRefGoogle Scholar
  16. 16.
    V. Plerou et al. , Phys. Rev. Lett. 83, 1471 (1999)ADSCrossRefGoogle Scholar
  17. 17.
    S. Pafka, I. Kondor, Physica A 319, 487 (2003)ADSMathSciNetCrossRefGoogle Scholar
  18. 18.
    I.T. Joliffe, Principal Component Analysis (2002, Heidelberg, Springer); R. Brummelhuis, A. Cordoba, M. Quintanilla, L. Seco, Mathematical Finance 12, 23 (2002)MathSciNetCrossRefGoogle Scholar
  19. 19.
    A. Hyvärinen, Neural Computing Surveys 2, 94 (1999); A.D. Back, A. Weigend, Int. J. Neural System 8, 473 (1997)Google Scholar
  20. 20.
    B. Bollobás, Random Graphs, 2nd edn. (Cambridge University Press, 2001)Google Scholar
  21. 21.
    Forbes at, referenced in March-April, 2002Google Scholar

Copyright information

© Springer-Verlag Berlin/Heidelberg 2004

Authors and Affiliations

  1. 1.Laboratory of Computational EngineeringHelsinki University of TechnologyFinland
  2. 2.Department of Theoretical PhysicsBudapest University of Technology and EconomicsBudapestHungary

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