Robustness of Sign Correlation in Market Network Analysis

  • Grigory A. BautinEmail author
  • Alexander P. Koldanov
  • Panos M. Pardalos
Part of the Springer Optimization and Its Applications book series (SOIA, volume 100)


Financial market can be modeled as network represented by a complete weighted graph. Different characteristics of this graph (minimum spanning tree, market graph, and others) give an important information on the network. In the present paper it is studied how the choice of measure of similarity between stocks influences the statistical errors in the calculation of network characteristics. It is shown that sign correlation is a robust measure of similarity in contrast with Pearson correlation widely used in market network analysis. This gives a possibility to get more precise information on stock market from observations.


Pearson Correlation Minimum Span Tree Sample Sign Heavy Tail Mixture Distribution 
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.



This work is partly supported by RF government grant, ag. 11.G34.31.0057 and RFFI grant 14-01-00807.


  1. 1.
    Anderson, T.W.: An Introducion to Multivariate Statistical Analysis, 3rd edn. Wiley-Interscience, New York (2003)Google Scholar
  2. 2.
    Bautin, G.A. Kalyagin, V.A., Koldanov, A.P.: Comparative analysis of two similarity measures for the market graph construction. Springer Proceedings in Mathematics and Statistics, vol. 59, pp. 29–41 (2013).
  3. 3.
    Bautin, G.A., Kalyagin, V.A., Koldanov, A.P., Koldanov, P.A., Pardalos, P.M.: Simple measure of similarity for the market graph construction. Comput. Manage. Sci. 10, 105–124 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Carlyle, J.W., Thomas, J.B.: On nonparametric signal detectors. IEEE Trans. Inf. Theory 10(2), 146–152 (1964)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Gupta, F.K. Varga, T., Bodnar, T.: Elliptically Contoured Models in Statistics and Portfolio Theory. Springer (2013). ISBN: 978-1-4614-8153-9Google Scholar
  6. 6.
    Kalyagin, V.A., Koldanov, A.P., Koldanov, P.A., Pardalos, P.M., Zamaraev, V.A.: Measures of uncertainty in market network analysis. (2013) arXiv:1311.2273Google Scholar
  7. 7.
    Koldanov, A.P., Koldanov, P.A., Kalyagin, V.A., Pardalos, P.M.: Statistical procedures for the market graph construction. Comput. Stat. Data Anal. 68, 17–29 (2013)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Mantegna, R.N., Stanley, H.E.: An Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge University Press, Cambridge (2000)Google Scholar
  9. 9.
    Puri, M.L., Sen, P.K.: Nonparametric Methods in Multivariate Analysis. Wiley, New York/London/Sydney/Toronto (1971)zbMATHGoogle Scholar
  10. 10.
    Shiryaev, A.N.: Essential of Stochastic Finance: Facts, Models, Theory. Word Scientific, New Jersey (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Grigory A. Bautin
    • 1
    Email author
  • Alexander P. Koldanov
    • 1
  • Panos M. Pardalos
    • 1
  1. 1.National Research University Higher School of Economics, Laboratory LATNAMoscowRussia

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