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Comparative study of central decision makers versus groups of evolved agents trading in equity markets

  • Cyril Schoreels

    ()

    (School of Computer Science and IT University of Nottingham)

  • Jonathan M. Garibaldi

    (University of Nottingham)

Registered author(s):

    This paper investigates the process of deriving a single decision solely based on the decisions made by a population of experts. Four different amalgamation processes are studied and compared among one another, collectively referred to as central decision makers. The expert, also referred to as reference, population is trained using a simple genetic algorithm using crossover, elitism and immigration using historical equity market data to make trading decisions. Performance of the trained agent population’s elite, as determined by results from testing in an out-of-sample data set, is also compared to that of the centralized decision makers to determine which displays the better performance. Performance was measured as the area under their total assets graph over the out-of-sample testing period to avoid biasing results to the cut off date using the more traditional measure of profit. Results showed that none of the implemented methods of deriving a centralized decision in this investigation outperformed the evolved and optimized agent population. Further, no difference in performance was found between the four central decision makers

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    File URL: http://repec.org/sce2006/up.8228.1141149360.pdf
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    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 410.

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    Date of creation: 04 Jul 2006
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    Handle: RePEc:sce:scecfa:410
    Contact details of provider: Web page: http://comp-econ.org/
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    1. Tesfatsion, Leigh, 2001. "Introduction to the special issue on agent-based computational economics," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 281-293, March.
    2. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    3. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
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