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Distributions and Long-Range Correlations in the Trading of US Stocks

  • Plamen Ch. Ivanov
  • Ainslie Yuen
  • Boris Podobnik
  • Youngki Lee
Conference paper

Summary

We analyze the sequence of time intervals between consecutive stock trades of five large companies representing different sectors of the US economy over a period of four years. We show that independent of the industry sector and average level of activity, the series of intertrade times exhibit common statistical features. Specifically, we find that: (1) the tail of the probability density function of intertrade times may be fit by a stretched exponential form; (ii) the probability densities of the intertrade times for all five companies collapse onto a single curve when appropriately resealed, and (iii) the intertrade times exhibit correlated behaviour over hundreds of trades within a trading day and an even greater degree of correlation over longer time scales.

Keywords

Trading Activity Detrended Fluctuation Analysis Myosin Heavy Chain Gene Persistent Behaviour Detrended Fluctuation Analysis Method 
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.

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References

  1. 1.
    Bachelier L (1900) Theorie de la speculation. Thesis, Sorbonne, Paris.Reprinted in Cootner P (1964) The Random Character of Stock Prices. MIT Press, CambridgeGoogle Scholar
  2. 2.
    Mandelbrot B (1963) The variation of certain speculative prices. Journal of Business 36:394–419CrossRefGoogle Scholar
  3. 3.
    Dothan M (1990) Prices in Financial Markets. Oxford University Press, New YorkMATHGoogle Scholar
  4. 4.
    Mantegna R, Stanley HE (2000) An introduction to econophysics. Cambridge University Press, Cambridge, EnglandGoogle Scholar
  5. 5.
    Gopikrishnan P, Plerou V, Gabaix X, Stanley HE (2000) Statistical Propertiesof share volume traded in financial markets. Phys. Rev. E 62:4493–4496ADSCrossRefGoogle Scholar
  6. 6.
    Plerou V, Gopikrishnan P, Amaral LAN, Gabaix X, Stanley HE (2000) Eco-nomic fluctuations and anomalous diffusion. Phys. Rev. E 62:3023–3026ADSCrossRefGoogle Scholar
  7. 7.
    Honanno G, Lillo F, Mantegna R (2000) Dynamics of the number of trades of financial securities. Physica A 280:136–141ADSCrossRefGoogle Scholar
  8. 8.
    Engle R, Russell J (1998) Autoregressive conditional duration: A new model for irregularly spaced transaction data. Econotnetrica 66:1127–1163MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Gourieroux C, Jasiak J, LeFol G (1999) Intra-day Market Activity. Journal of Financial Markets 2:193–226CrossRefGoogle Scholar
  10. 10.
    Grammig. J, Maurer K (2000) Non-monotonie hazard functions and the Autoregressive Conditional Duration model. Econometrics Journal 3:16–38MATHCrossRefGoogle Scholar
  11. 11.
    Celia S (2001) Long memory effects in ultra-high frequency data. Quaderni di Statistica 3:43–52Google Scholar
  12. 12.
    Raberto M, Scalas E, Mainardi F (2002) Waiting-times and returns in high-frequency financial data: an empirical study. Physica A 312:749–755ADSCrossRefGoogle Scholar
  13. 13.
    Sabatelli L, Keating S, Dudley J, Richmond P (2002) Waiting time distributions in financial markets. Eur. Phys. J. B 27:273–275MathSciNetADSGoogle Scholar
  14. 14.
    Bunde A, Havlin S (1994) Fractals in Science. Springer, HeidelbergMATHGoogle Scholar
  15. 15.
    Buldyrev SV, Goldberger AL, Havlin S, Peng CE, Stanley HE, Simons M (1993) F actal landscapes and molecular evolution: modeling the myosin heavy chain gene family. Biophysics Journal 65:2673–2679ADSCrossRefGoogle Scholar
  16. 16.
    Peng CE, Buldyrev SV, Havlin S, Simons; M, Stanley HE, Goldberger AL (1994) Mosaic Organization of DNA nucleotides. Phys. Rev. E 4:1685–1689ADSCrossRefGoogle Scholar
  17. 17.
    Hu K, Ivanov PCh, Chen Z, Carpena P, Stanley BE (2001) Effect of trends on detrendcd fluctuation analysis. Phys. Rev. E 64, 011114ADSCrossRefGoogle Scholar
  18. 18.
    Chen Z, Ivanov PCli, Hu K, Stanley HE (2002) Effect of uonstationarities on detrended. fluctuation analysis. Phys. Rev. E 65, 041107ADSCrossRefGoogle Scholar
  19. 19.
    Lux T (1996) The Stable Paretian Hypothesis and the Frequency of Large Returns: An Examination of Major German Stocks. Appl. Financial Economics 6:463–475CrossRefGoogle Scholar
  20. 20.
    Gopikrishnan P, Meyer M, Amaral LAN, Stanley BE (1998) Inverse cubic law for the distribution of stock price variations. Eur. Phys. J. B 3:139–140ADSCrossRefGoogle Scholar
  21. 21.
    Liu Y,Gopikrishnan P, Gizeau P, Meyer M, Peng C-K, Stanley HE (1999) Statistical properties of the volatility of price fluctuations. Phys. Rev. E 60:1390–1400ADSCrossRefGoogle Scholar
  22. 22.
    Plerou V, Gopikrislinan P, Amaral LAN, Meyer M, Stanley HE (1999) Scaling of the distribution of price fluctuations of individual companies. Phys. Rev. E 60:6519–6529Google Scholar

Copyright information

© Springer Japan 2004

Authors and Affiliations

  • Plamen Ch. Ivanov
    • 1
  • Ainslie Yuen
    • 2
  • Boris Podobnik
    • 3
  • Youngki Lee
    • 4
  1. 1.Center for Polymer Studies and Department of PhysicsBoston UniversityBostonUSA
  2. 2.Signal Processing Laboratory, Department of EngineeringCambridge UniversityUK
  3. 3.Faculty of Civil EngineeringUniversity of RijekaRijekaJapan
  4. 4.Yanbian University of Science and TechnologyYanji CityChina

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