Uncovering the Internal Structure of the Indian Financial Market: Large Cross-correlation Behavior in the NSE

  • Sitabhra Sinha
  • Raj Kumar Pan
Part of the New Economic Windows book series (NEW)


The cross-correlations between price fluctuations of 201 frequently traded stocks in the National Stock Exchange (NSE) of India are analyzed in this paper. We use daily closing prices for the period 1996–2006, which coincides with the period of rapid transformation of the market following liberalization. The eigenvalue distribution of the cross-correlation matrix, C, of NSE is found to be similar to that of developed markets, such as the New York Stock Exchange (NYSE): the majority of eigenvalues fall within the bounds expected for a random matrix constructed from mutually uncorrelated time series. Of the few largest eigenvalues that deviate from the bulk, the largest is identified with market-wide movements. The intermediate eigenvalues that occur between the largest and the bulk have been associated in NYSE with specific business sectors with strong intra-group interactions. However, in the Indian market, these deviating eigenvalues are comparatively very few and lie much closer to the bulk. We propose that this is because of the relative lack of distinct sector identity in the market, with the movement of stocks dominantly influenced by the overall market trend. This is shown by explicit construction of the interaction network in the market, first by generating the minimum spanning tree from the unfiltered correlation matrix, and later, using an improved method of generating the graph after filtering out the market mode and random effects from the data. Both methods show, compared to developed markets, the relative absence of clusters of co-moving stocks that belong to the same business sector. This is consistent with the general belief that emerging markets tend to be more correlated than developed markets.


Random Matrix Large Eigenvalue Minimum Span Tree Business Sector Price Movement 
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  1. 1.
    Rouwenhorst K G (2005) The origins of mutual funds. In: Goetzmann, W N, Rouwenhorst, K G (eds) The Origins of Value: The financial innovations that created modern capital markets. Oxford Univ Press, New YorkGoogle Scholar
  2. 2.
    Laloux L, Cizeau P, Bouchaud J P, Potters M (1999) Noise dressing of financial correlation matrices, Phys. Rev. Lett. 83: 1467–1470CrossRefADSGoogle Scholar
  3. 3.
    Plerou V, Gopikrishnan P, Rosenow B, Amaral L A N, Stanley H E (1999) Universal and nonuniversal properties of cross correlations in financial time series, Phys. Rev. Lett. 83: 1471–1474CrossRefADSGoogle Scholar
  4. 4.
    Gopikrishnan P, Rosenow B, Plerou V, Stanley H E (2001) Quantifying and interpreting collective behavior in financial markets, Phys. Rev. E 64: 035106CrossRefADSGoogle Scholar
  5. 5.
    Plerou V, Gopikrishnan P, Rosenow B, Amaral L A N, Guhr T, Stanley H E (2002) Random matrix approach to cross correlations in financial data, Phys. Rev. E 65: 066126CrossRefADSGoogle Scholar
  6. 6.
    Noh J D (2000) Model for correlations in stock markets, Phys. Rev. E 61: 5981–5982CrossRefADSMathSciNetGoogle Scholar
  7. 7.
    Kim D-H, Jeong H (2005) Systematic analysis of group identification in stock markets, Phys. Rev. E 72: 046133CrossRefADSMathSciNetGoogle Scholar
  8. 8.
    Utsugi A, Ino K, Oshikawa M (2004) Random matrix theory analysis of cross correlations in financial markets, Phys. Rev. E 70: 026110CrossRefADSGoogle Scholar
  9. 9.
    Morck R, Yeung B, Yu W (2000) The information content of stock markets: Why do emerging markets have synchronous stock price movements?, J. Financial Economics 58: 215–260CrossRefGoogle Scholar
  10. 10.
    Wilcox D, Gebbie T (2004) On the analysis of cross-correlations in South African market data, Physica A 344: 294–298; Wilcox D, Gebbie T (2007) An analysis of cross-correlations in an emerging market, Physica A 375:584–598CrossRefADSGoogle Scholar
  11. 11.
    Kulkarni V, Deo N (2005) Volatility of an Indian stock market: A random matrix approach, In: Chatterjee A, Chakrabarti B K (eds) Econophysics of Stock and Other Markets. Springer, Milan, p 35Google Scholar
  12. 12.
    Jung W-S, Chaea S, Yanga J-S, Moon H-T (2006) Characteristics of the Korean stock market correlations, Physica A 361: 263–271CrossRefADSGoogle Scholar
  13. 13.
    Cukur S, Eryigit M, Eryigit R (2007) Cross correlations in an emerging market financial data, Physica A 376: 555–564CrossRefADSGoogle Scholar
  14. 14.
    Sinha S, Pan R K (2006) The power (law) of indian markets: Analysing NSE and BSE trading statistics, In: Chatterjee A, Chakrabarti B K (eds) Econophysics of Stock and Other Markets. Springer, Milan, pp. 24–34CrossRefGoogle Scholar
  15. 15.
    Pan R K, Sinha S (2007) Self-organization of price fluctuation distribution in evolving markets, Europhys. Lett. 77: 58004CrossRefADSGoogle Scholar
  16. 16.
    Pan R K, Sinha S (2006) Inverse cubic law of index fluctuation distribution in Indian markets, physics/0607014Google Scholar
  17. 17.
    National Stock Exchange (2004) Indian securities market: A review. (http://www.nseindia.com/content/us/ismr2005.zip)
  18. 18.
  19. 19.
    Sengupta A M, Mitra P P (1999) Distribution of singular values for some random matrices, Phys. Rev. E 60: 3389–3392CrossRefADSGoogle Scholar
  20. 20.
    Mantegna R N (1999) Hierarchical structure in financial markets, Eur. Phys. J. B 11: 193–197CrossRefADSGoogle Scholar
  21. 21.
    Onnela J-P, Chakraborti A, Kaski K, Kertesz J (2002) Dynamic asset trees and portfolio analysis, Eur. Phys. J. B 30:285–288CrossRefADSMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Italia 2007

Authors and Affiliations

  • Sitabhra Sinha
    • 1
  • Raj Kumar Pan
    • 1
  1. 1.The Institute of Mathematical Sciences, C.I.T. CampusTaramani, ChennaiIndia

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