IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v369y2006i2p780-788.html
   My bibliography  Save this article

The co-evolutionary dynamics of directed network of spin market agents

Author

Listed:
  • Horváth, Denis
  • Kuscsik, Zoltán
  • Gmitra, Martin

Abstract

The spin market model [S. Bornholdt, Int. J. Mod. Phys. C 12 (2001) 667] is generalized by employing co-evolutionary principles, where strategies of the interacting and competitive traders are represented by local and global couplings between the nodes of dynamic directed stochastic network. The co-evolutionary principles are applied in the frame of Bak–Sneppen self-organized dynamics [P. Bak, K. Sneppen, Phys. Rev. Lett. 71 (1993) 4083] that includes the processes of selection and extinction actuated by the local (node) fitness. The local fitness is related to orientation of spin agent with respect to the instant magnetization. The stationary regime is formed due to the interplay of self-organization and adaptivity effects. The fat tailed distributions of log-price returns are identified numerically. The non-trivial model consequence is the evidence of the long time market memory indicated by the power-law range of the autocorrelation function of volatility with exponent smaller than one. The simulations yield network topology with broad-scale node degree distribution characterized by the range of exponents 1.3<γin<3 coinciding with social networks.

Suggested Citation

  • Horváth, Denis & Kuscsik, Zoltán & Gmitra, Martin, 2006. "The co-evolutionary dynamics of directed network of spin market agents," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 780-788.
  • Handle: RePEc:eee:phsmap:v:369:y:2006:i:2:p:780-788
    DOI: 10.1016/j.physa.2006.01.067
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437106001427
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2006.01.067?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bornholdt, Stefan, 2022. "A q-spin Potts model of markets: Gain–loss asymmetry in stock indices as an emergent phenomenon," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    2. Stefan Bornholdt, 2021. "A q-spin Potts model of markets: Gain-loss asymmetry in stock indices as an emergent phenomenon," Papers 2112.06290, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:369:y:2006:i:2:p:780-788. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.