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Journal of Zhejiang University SCIENCE A 2006 Vol.7 No.4 P.564~569

http://doi.org/10.1631/jzus.2006.A0564


Simulation of game analysis based on an agent-based artificial stock market re-examined


Author(s):  Liu Cheng, Wu Yi-li, Yan Gang-feng

Affiliation(s):  Department of System Science and Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   aniu288@zju.edu.cn, wuyl@zucc.edu.cn, ygf@zju.edu.cn

Key Words:  Agent-based model, Technical trading, Asset prices, Simulation


Liu Cheng, Wu Yi-li, Yan Gang-feng. Simulation of game analysis based on an agent-based artificial stock market re-examined[J]. Journal of Zhejiang University Science A, 2006, 7(4): 564~569.

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DOI - 10.1631/jzus.2006.A0564


Abstract: 
This work re-examined the simulation result of game analysis (Joshi et al., 2000) based on an agent-based model, Santa Fe Institute Artificial Stock Market. Allowing for recent research work on this artificial model, this paper’s modified game simulations found that the dividend amplitude parameter is a crucial factor and that the original conclusion still holds in a not long period, but only when the dividend amplitude is large enough. Our explanation of this result is that the dividend amplitude parameter is a measurement of market uncertainty. The greater the uncertainty, the greater the price volatility, and so is the risk of investing in the stock market. The greater the risk, the greater the advantage of including technical rules.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[2] Bierman, S.H., Fernandez, L., 1993. Game Theory with Economic Applications. Addison-Wesley, Redwood City, CA.

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[4] Ehrentreich, N., 2005. The Santa Fe artificial stock market re-examined-suggested corrections. Journal of Economic Behavior and Organization (in Press).

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[7] Holland, J., 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor.

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[9] Joshi, S., Parker, J., Bedau, M.A., 2000. Technical Trading Creates a Prisoner’s Dilemma: Results from an Agent-Based Model. Computational Finance 99, MIT Press, Cambridge, MA, p.465-479.

[10] Joshi, S., Parker, J., Bedau, M.A., 2002. Financial markets can be at sub-optimal equilibria. Computational Economics, 19(1):5-23.

[11] LeBaron, B., 2002. Building the Santa Fe Artificial Stock Market. Working paper, Http://people.brandeis.edu/blebaron/wps/sfisum.pdf.

[12] LeBaron, B., Arthur, W.B., Palmer, R., 1999. Time series properties of an artificial stock market. Journal of Economic Dynamics and Control, 23(9-10):1487-1516.

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[14] Palmer, R., Arthur, W.B., Holland, J., LeBaron, B., Tayler, P., 1994. Artificial economic life: Simple model of a stock market. Physica, D75:264-274.

[15] SFI Bulletin, 1999. Http://www.santafe.edu/research/publications/bulletin.php, 14(2):6-7.

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