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Co evolution of Genetic Programming Based Agents in an Artificial Stock Market

Author

Listed:
  • Martinez Jaramillo Serafin.

    (CCFEA University of Essex)

  • Tsang Edward P. K.

    (Department of Computer Science University of Essex)

  • Markose, Sheri.

    (Department of Economics University of Essex)

Abstract

The complexity of the financial markets, represents a big challenge to the specialist in the area. The traditional way of coping with the analysis of such systems is the use of analytical models. However, the analytical models present some difficulties and this has leaded to the development of alternative methods for the analysis of such markets. In this paper we analyze the different conditions under which the statistical properties of an artificial stock market resembles those of the real financial markets. The different types of agents that we use in the simulations are technical, fundamental and noisy. Changes in some parameters and agents’ behavior produce different properties of the stock price series. We analyze the wealth distribution of the agents after several periods of trading in the different simulation cases.

Suggested Citation

  • Martinez Jaramillo Serafin. & Tsang Edward P. K. & Markose, Sheri., 2006. "Co evolution of Genetic Programming Based Agents in an Artificial Stock Market," Computing in Economics and Finance 2006 398, Society for Computational Economics.
  • Handle: RePEc:sce:scecfa:398
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    References listed on IDEAS

    as
    1. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
    2. Elhorst, J. Paul, 2001. "Panel data models extended to spatial error autocorrelation or a spatially lagged dependent variable," Research Report 01C05, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    3. Giuseppe Arbia & Gianfranco Piras, 2004. "Convergence in per-capita GDP across European regions using panel data models extended to spatial autocorrelation effects," ERSA conference papers ersa04p524, European Regional Science Association.
    4. repec:dgr:rugsom:01c05 is not listed on IDEAS
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    More about this item

    Keywords

    Artificial Markets; Genetic Programming;

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