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

Author

Listed:
  • Juan Gabriel Brida

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

  • W. Adrian Risso

    (University of Siena)

Abstract

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.

Suggested Citation

  • Juan Gabriel Brida & W. Adrian Risso, 2009. "Dynamic and Structure of the Italian stock market based on returns and volume trading," Economics Bulletin, AccessEcon, vol. 29(3), pages 2417-2423.
    • Handle: RePEc:ebl:ecbull:eb-09-00306
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    References listed on IDEAS

    as
    1. Juan Gabriel Brida & Wiston Adrian Risso, 2007. "Dynamics And Structure Of The Main Italian Companies," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 18(11), pages 1783-1793.
    2. Barone, E., 1990. "The italian stock market : Efficiency and calendar anomalies," Journal of Banking & Finance, Elsevier, vol. 14(2-3), pages 483-510, August.
    3. 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.
    4. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    5. Michaely, Roni & Vila, Jean-Luc, 1995. "Investors' Heterogeneity, Prices, and Volume around the Ex-Dividend Day," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 30(2), pages 171-198, June.
    6. Lo, Andrew W & Wang, Jiang, 2000. "Trading Volume: Definitions, Data Analysis, and Implications of Portfolio Theory," The Review of Financial Studies, Society for Financial Studies, vol. 13(2), pages 257-300.
    7. 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(1), pages 109-126, March.
    8. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    9. Rogalski, Richard J, 1978. "The Dependence of Prices and Volume," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 268-274, May.
    10. 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(5), pages 743-765, December.
    11. 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," The Review of Financial Studies, Society for Financial Studies, vol. 8(2), pages 369-399.
    12. Suominen, Matti, 2001. "Trading Volume and Information Revelation in Stock Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 36(4), pages 545-565, December.
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    Cited by:

    1. Deviren, Seyma Akkaya & Deviren, Bayram, 2016. "The relationship between carbon dioxide emission and economic growth: Hierarchical structure methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 429-439.
    2. Brida, Juan Gabriel & London, Silvia & Rojas, Mara, 2012. "Convergencia interregional en dinámica de regimenes: el caso del Mercosur [Regional convergence of dynamic of regimens: the case of Mercosur]," MPRA Paper 36863, University Library of Munich, Germany.
    3. Brida, Juan Gabriel & Matesanz, David & Seijas, Maria Nela, 2016. "Network analysis of returns and volume trading in stock markets: The Euro Stoxx case," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 751-764.
    4. Juan Gabriel Brida & Nicolás Garrido & Silvia London, 2013. "Estudio del desempeno económico regional: el caso argentino," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, December.
    5. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    6. Brida, Juan Gabriel & London, Silvia & Rojas, Mara, 2013. "Una aplicación de los árboles de expansión mínima y árboles jerárquicos al estudio de la convergencia interregional en dinámica de regímenes || An Application of Minimum Spanning Trees and Hierarchica," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 15(1), pages 3-28, June.
    7. Coletti, Paolo, 2016. "Comparing minimum spanning trees of the Italian stock market using returns and volumes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 246-261.

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    More about this item

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G1 - Financial Economics - - General Financial Markets

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