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The Santa Fe Artificial Stock Market Re-Examined - Suggested Corrections

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Author Info

  • Norman Ehrentreich

    (Martin-Luther University of Halle- Wittenberg)

Abstract

This paper rectifies a design problem in the Santa Fe Artificial Stock Market Model. Due to a faulty mutation operator, the resulting bit distribution in the classifier system was systematically upwardly biased, thus suggesting increased levels of technical trading for smaller GA-invocation intervals. The corrected version partly supports the Marimon-Sargent-Hypothesis that adaptive classifier agents in an artificial stock market will always discover the homogeneous rational expectation equilibrium. While agents always find the correct solution of non-bit usage, analyzing the time series data still suggests the existence of two different regimes depending on learning speed. Finally, classifier systems and neural networks as data mining techniques in artificial stock markets are discussed.

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File URL: http://128.118.178.162/eps/comp/papers/0209/0209001.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Computational Economics with number 0209001.

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Length: 22 pages
Date of creation: 23 Sep 2002
Date of revision:
Handle: RePEc:wpa:wuwpco:0209001

Note: Type of Document - Adobe-Pdf; prepared on LaTex on IBM PC (Windows); to print on Postscript; pages: 22; figures: included. submitted to the Journal of Economic Dynamics and Control
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Web page: http://128.118.178.162

Related research

Keywords: Asset Pricing; Learning; Financial Time Series; Genetic Algorithms; Classifier Systems; Agent-Based Simulation;

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References

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  1. repec:att:wimass:9725 is not listed on IDEAS
  2. repec:att:wimass:9625 is not listed on IDEAS
  3. Shareen Joshi & Jeffrey Parker & Mark A. Bedau, 1998. "Technical Trading Creates a Prisoner's Dilemma: Results from an Agent-Based Model," Research in Economics 98-12-115e, Santa Fe Institute.
  4. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
  5. Joshi, Shareen & Parker, Jeffrey & Bedau, Mark A, 2002. "Financial Markets Can Be at Sub-optimal Equilibria," Computational Economics, Society for Computational Economics, vol. 19(1), pages 5-23, February.
  6. Kiyotaki, Nobuhiro & Wright, Randall, 1989. "On Money as a Medium of Exchange," Journal of Political Economy, University of Chicago Press, vol. 97(4), pages 927-54, August.
  7. Chen, Shu-Heng & Lux, Thomas & Marchesi, Michele, 2001. "Testing for non-linear structure in an artificial financial market," Journal of Economic Behavior & Organization, Elsevier, vol. 46(3), pages 327-342, November.
  8. Riechmann, Thomas, 2001. "Genetic algorithm learning and evolutionary games," Journal of Economic Dynamics and Control, Elsevier, vol. 25(6-7), pages 1019-1037, June.
  9. Marimon, Ramon & McGrattan, Ellen & Sargent, Thomas J., 1990. "Money as a medium of exchange in an economy with artificially intelligent agents," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 329-373, May.
  10. repec:att:wimass:9520 is not listed on IDEAS
  11. W. Brian Arthur & John H. Holland & Blake LeBaron & Richard Palmer & Paul Taylor, 1996. "Asset Pricing Under Endogenous Expectation in an Artificial Stock Market," Working Papers 96-12-093, Santa Fe Institute.
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Cited by:
  1. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233 Elsevier.

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