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On distribution of number of trades in different time windows in the stock market

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  • Dremin, I.M.
  • Leonidov, A.V.

Abstract

Properties of distributions of the number of trades in different intraday time intervals for five stocks traded in MICEX are studied. The dependence of the mean number of trades on the capital turnover is analyzed. Correlation analysis using factorial and Hq moments demonstrates the multifractal nature of these distributions as well as some peculiar changes in the correlation pattern. Guided by the analogy with the analysis of particle multiplicity distributions in multiparticle production at high energies, an evolution equation relating changes in capital turnover and a number of trades is proposed. We argue that such equation can describe the observed features of the distribution of the number of trades in the stock market.

Suggested Citation

  • Dremin, I.M. & Leonidov, A.V., 2005. "On distribution of number of trades in different time windows in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 353(C), pages 388-402.
    • Handle: RePEc:eee:phsmap:v:353:y:2005:i:c:p:388-402
    DOI: 10.1016/j.physa.2004.12.048
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    References listed on IDEAS

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    1. Bacry, E. & Delour, J. & Muzy, J.F., 2001. "Modelling financial time series using multifractal random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 299(1), pages 84-92.
    2. Bonanno, Giovanni & Lillo, Fabrizio & Mantegna, Rosario N, 2000. "Dynamics of the number of trades of financial securities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 280(1), pages 136-141.
    3. Andrei Leonidov, 2004. "Long Memory In Stock Trading," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 7(07), pages 879-885.
    4. Bouchaud,Jean-Philippe & Potters,Marc, 2003. "Theory of Financial Risk and Derivative Pricing," Cambridge Books, Cambridge University Press, number 9780521819169, October.
    5. Kaushik Matia & Yosef Ashkenazy & H. Eugene Stanley, 2003. "Multifractal Properties of Price Fluctuations of Stocks and Commodities," Papers cond-mat/0308012, arXiv.org.
    6. Andrei Leonidov, 2003. "Long Memory in Stock Trading," Papers cond-mat/0303222, arXiv.org, revised Feb 2004.
    7. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
    8. Jean-Philippe Bouchaud & Marc Potters & Martin Meyer, 1999. "Apparent multifractality in financial time series," Science & Finance (CFM) working paper archive 9906347, Science & Finance, Capital Fund Management.
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    Cited by:

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    2. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frédéric Abergel, 2011. "Econophysics review: I. Empirical facts," Post-Print hal-00621058, HAL.
    3. Anirban Chakraborti & Ioane Muni Toke & Marco Patriarca & Frederic Abergel, 2011. "Econophysics review: I. Empirical facts," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 991-1012.

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