A controllable laboratory stock market for modeling real stock markets

  • Kenan An
  • Xiaohui Li
  • Guang Yang
  • Jiping HuangEmail author
Regular Article


Based on the different research approaches, econophysics can be divided into three directions: empirical econophysics, computational econophysics, and experimental econophysics. Because empirical econophysics lacks controllability that is needed to study the impacts of different external conditions and computational econophysics has to adopt artificial decision-making processes that are often deviated from those of real humans, experimental econophysics tends to overcome these problems by offering controllability and using real humans in laboratory experiments. However, to our knowledge, the existing laboratory experiments have not convincingly reappeared the stylized facts (say, scaling) that have been revealed for real economic/financial markets by econophysicists. A most important reason is that in these experiments, discrete trading time makes these laboratory markets deviated from real markets where trading time is naturally continuous. Here we attempt to overcome this problem by designing a continuous double-auction stock-trading market and conducting several human experiments in laboratory. As an initial work, the present artificial financial market can reproduce some stylized facts related to clustering and scaling. Also, it predicts some other scaling in human behavior dynamics that is hard to achieve in real markets due to the difficulty in getting the data. Thus, it becomes possible to study real stock markets by conducting controlled experiments on such laboratory stock markets producing high frequency data.


Statistical and Nonlinear Physics 


  1. 1.
    R.N. Mantegna, H.E. Stanley, Nature 376, 46 (1995)ADSCrossRefGoogle Scholar
  2. 2.
    R.N. Mantegna, H.E. Stanley, Introduction to Econophysics: Correlations and Complexity in Finance (Cambridge University Press, Cambridge, 1999)Google Scholar
  3. 3.
    V. Plerou, P. Gopikrishnan, L.A.N. Amaral, M. Meyer, H.E. Stanley, Phys. Rev. E 60, 6519 (1999)ADSCrossRefGoogle Scholar
  4. 4.
    P. Gopikrishnan, V. Plerou, L.A.N. Amaral, M. Meyer, H.E. Stanley, Phys. Rev. E. 60, 5305 (1999)ADSCrossRefGoogle Scholar
  5. 5.
    V. Plerou, P. Gopikrishnan, B. Rosenow, L.A.N. Amaral, H.E. Stanley, Phys. Rev. Lett. 83, 1471 (1999)ADSCrossRefGoogle Scholar
  6. 6.
    X. Gabaix, P. Gopikrishnan, V. Plerou, H.E. Stanley, Nature 423, 267 (2003)ADSCrossRefGoogle Scholar
  7. 7.
    W.C. Zhou, H.C. Xu, Z.Y. Cai, J.R. Wei, X.Y. Zhu, W. Wang, L. Zhao, J.P. Huang, Physica A 388, 891 (2009)ADSCrossRefGoogle Scholar
  8. 8.
    E. Kantar, B. Deviren, M. Keskin, Eur. Phys. J. B 84, 339 (2011)ADSCrossRefGoogle Scholar
  9. 9.
    W.B. Arthur, Am. Econ. Rev. 84, 406 (1994)Google Scholar
  10. 10.
    D. Challet, Y.-C. Zhang, Physica A 246, 407 (1997)ADSCrossRefGoogle Scholar
  11. 11.
    D. Challet, Y.-C. Zhang, Physica A 256, 514 (1998)CrossRefGoogle Scholar
  12. 12.
    R. Savit, R. Manuca, R. Riolo, Phys. Rev. Lett. 82, 2203 (1999)ADSCrossRefGoogle Scholar
  13. 13.
    L. Tesfatsion, Inf. Sci. 149, 262 (2003)CrossRefGoogle Scholar
  14. 14.
    W. Wang, Y. Chen, J.P. Huang, Proc. Natl. Acad. Sci. 106, 8423 (2009)ADSCrossRefGoogle Scholar
  15. 15.
    L. Zhao, G. Yang, W. Wang, Y. Chen, J.P. Huang, H. Ohashi, H.E. Stanley, Proc. Natl. Acad. Sci. 108, 15058 (2011)ADSCrossRefGoogle Scholar
  16. 16.
    M. Marsili, D. Challet, R. Zecchina, Physica A 280, 522 (2000)ADSCrossRefGoogle Scholar
  17. 17.
    B. LeBaron, Handbook of Computational Economics (Elsevier, Amsterdam, 2005), Vol. 2, pp. 134–151Google Scholar
  18. 18.
    B. LeBaron, W.B. Arthur, R. Palmer, J. Econ. Dyn. Control 23, 1487 (1999)CrossRefzbMATHGoogle Scholar
  19. 19.
    J. Yang, Studies in Fuzziness and Soft Computing 100, 85 (2002)CrossRefGoogle Scholar
  20. 20.
    Y. Liang, K.N. An, G. Yang, J.P. Huang, Phys. Rev. E 87, 012809 (2013)ADSCrossRefGoogle Scholar
  21. 21.
    V.L. Smith, J. Econ. Perspect. 8, 113 (1994)CrossRefGoogle Scholar
  22. 22.
    J.H. Kagel, A.E. Roth, The Handbook of Experimental Economics (Princeton University Press, New Jersey, 1995)Google Scholar
  23. 23.
    D. Friedman, Economic Inquiry 31, 410 (1993)CrossRefGoogle Scholar
  24. 24.
    D.P. Porter, V.L. Smith, J. Behavioral Finance 4, 7 (2003)CrossRefGoogle Scholar
  25. 25.
    S. Hirota, S. Sunder, J. Econ. Dyn. Control 31, 1875 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    J.-P. Bouchaud, M. Potters, Theory of Financial Risk and Derivative Pricing: From Statistical Physics to Risk Management (Cambridge University Press, Cambridge, 2003)Google Scholar
  27. 27.
    J.G. Oliveira, A.-L. Barabási, Nature 437, 1251 (2005)ADSCrossRefGoogle Scholar
  28. 28.
    A.-L. Barabási, Nature 435, 207 (2005)ADSCrossRefGoogle Scholar
  29. 29.
    A. Grabowski, N. Kruszewska, R.A. Kosiński, Eur. Phys. J. B 66, 107 (2008)ADSCrossRefzbMATHGoogle Scholar
  30. 30.
    T. Zhou, H.A.T. Kiet, B.J. Kim, B.-H. Wang, P. Holme, Europhys. Lett. 82, 28002 (2008)ADSCrossRefGoogle Scholar

Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Kenan An
    • 1
  • Xiaohui Li
    • 1
  • Guang Yang
    • 1
  • Jiping Huang
    • 1
    Email author
  1. 1.Department of Physics and State Key Laboratory of Surface PhysicsFudan UniversityShanghaiP.R. China

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