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Study on Evolvement Complexity in an Artificial Stock Market

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  • Chun-Xia Yang
  • Tao Zhou
  • Pei-Ling Zhou
  • Jun Liu
  • Zi-Nan Tang

Abstract

An artificial stock market is established based on multi-agent . Each agent has a limit memory of the history of stock price, and will choose an action according to his memory and trading strategy. The trading strategy of each agent evolves ceaselessly as a result of self-teaching mechanism. Simulation results exhibit that large events are frequent in the fluctuation of the stock price generated by the present model when compared with a normal process, and the price returns distribution is L\'{e}vy distribution in the central part followed by an approximately exponential truncation. In addition, by defining a variable to gauge the "evolvement complexity" of this system, we have found a phase cross-over from simple-phase to complex-phase along with the increase of the number of individuals, which may be a ubiquitous phenomenon in multifarious real-life systems.

Suggested Citation

  • Chun-Xia Yang & Tao Zhou & Pei-Ling Zhou & Jun Liu & Zi-Nan Tang, 2004. "Study on Evolvement Complexity in an Artificial Stock Market," Papers cond-mat/0406168, arXiv.org, revised Dec 2004.
    • Handle: RePEc:arx:papers:cond-mat/0406168
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