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Dynamic and Structure of the Italian stock market based on returns and volume trading

  • Juan Gabriel Brida

    ()

    (School of Economics and Management - Free University of Bolzano)

  • W. Adrian Risso

    ()

    (University of Siena)

In this paper we introduce a new method to describe dynamical patterns of multidimensional time. The method combines the tools of Symbolic Time Series Analysis with the nearest neighbor single linkage clustering algorithm. Data symbolization allows to obtain a metric distance between two different time series that is used to construct an ultrametric distance. The methodology is applied to examine the dynamics and structure of the Italian stock market considering both asset returns and volume trading to model the market. We derive a hierarchical organization, constructing minimal-spanning and hierarchical trees, both in normal and extreme situations of the market. From these trees we detect four clusters of firms according to their proximity. We show that the financial cluster is in a central position of the minimal spanning tree, both in normal and extreme situations, reflecting that financial companies represent more than 30% of the Italian market capitalization. We also show that the derived clusters corresponds with companies sharing common economic activities.

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Article provided by AccessEcon in its journal Economics Bulletin.

Volume (Year): 29 (2009)
Issue (Month): 3 ()
Pages: 2417-2423

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Handle: RePEc:ebl:ecbull:eb-09-00306
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  1. Westerfield, Randolph, 1977. "The Distribution of Common Stock Price Changes: An Application of Transactions Time and Subordinated Stochastic Models," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 12(05), pages 743-765, December.
  2. J. Pérez, 2005. "Empirical identification of currency crises: differences and similarities between indicators," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 1(1), pages 41-46, January.
  3. Suominen, Matti, 2001. "Trading Volume and Information Revelation in Stock Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 36(04), pages 545-565, December.
  4. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(01), pages 109-126, March.
  5. Rosario N. Mantegna, 1998. "Hierarchical Structure in Financial Markets," Papers cond-mat/9802256, arXiv.org.
  6. Lo, Andrew W & Wang, Jiang, 2000. "Trading Volume: Definitions, Data Analysis, and Implications of Portfolio Theory," Review of Financial Studies, Society for Financial Studies, vol. 13(2), pages 257-300.
  7. Michaely, Roni & Murgia, Maurizio, 1995. "The Effect of Tax Heterogeneity on Prices and Volume around the Ex-dividend Day: Evidence from the Milan Stock Exchange," Review of Financial Studies, Society for Financial Studies, vol. 8(2), pages 369-99.
  8. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-55, January.
  9. Rogalski, Richard J, 1978. "The Dependence of Prices and Volume," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 268-74, May.
  10. Barone, E., 1990. "The italian stock market : Efficiency and calendar anomalies," Journal of Banking & Finance, Elsevier, vol. 14(2-3), pages 483-510, August.
  11. Brida, Juan G. & Anyul, Martin Puchet & Punzo, Lionello F., 2003. "Coding economic dynamics to represent regime dynamics. A teach-yourself exercise," Structural Change and Economic Dynamics, Elsevier, vol. 14(2), pages 133-157, June.
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