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Evolvement Complexity in an Artificial Stock Market |
| YANG Chun-Xia;ZHOU Tao;ZHOU Pei-Ling;LIU Jun;TANG Zi-Nan |
| Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230026 |
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| Cite this article: |
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YANG Chun-Xia, ZHOU Tao, ZHOU Pei-Ling et al 2005 Chin. Phys. Lett. 22 1014-1017 |
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Abstract An artificial stock market is established based on the multi-agent model. Each agent has a limited memory of the history of stock price, and will choose an action according to its memory and trading strategy. The trading strategy of each agent evolves ceaselessly as a result of a self-teaching mechanism. The 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 a Lévy 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.
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89.90.+n
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Published: 01 April 2005
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89.90.+n
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(Other topics in areas of applied and interdisciplinary physics)
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02.50.Le
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(Decision theory and game theory)
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64.60.Cn
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(Order-disorder transformations)
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87.10.+e
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