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An empirical case against the use of genetic-based learning classifier systems as forecasting devices

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  • Jaqueson K. Galimberti

    ()
    (The University of Manchester and The Capes Foundation)

  • Sergio da Silva

    ()
    (Department of Economics, Federal University of Santa Catarina)

Abstract

We adapt a genetic-based learning classifier system to a forecast evaluation exercise by making its key parameters endogenous and taking into account the need of convergence of the learning algorithm, an issue usually neglected in the literature. Doing so, we find it hard for the algorithm to beat simpler ones based on recursive regressions and on the random walk in forecasting stock returns. We then argue that our results cast doubts on the plausibility of using learning classifier systems to represent agents process of expectations formation, an approach commonly found into the agent-based computational finance literature.

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File URL: http://www.accessecon.com/Pubs/EB/2012/Volume32/EB-12-V32-I1-P32.pdf
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Bibliographic Info

Article provided by AccessEcon in its journal Economics Bulletin.

Volume (Year): 32 (2012)
Issue (Month): 1 ()
Pages: 354-369

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Handle: RePEc:ebl:ecbull:eb-11-00608

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Related research

Keywords: genetic-based learning classifier systems; genetic algorithms; stock returns forecasting;

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  1. Chen, Shu-Heng & Yeh, Chia-Hsuan, 2001. "Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 363-393, March.
  2. Thomas Brenner, 2004. "Agent Learning Representation - Advice in Modelling Economic Learning," Papers on Economics and Evolution 2004-16, Max Planck Institute of Economics, Evolutionary Economics Group.
  3. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  4. Beltrametti, Luca & Fiorentini, Riccardo & Marengo, Luigi & Tamborini, Roberto, 1997. "A learning-to-forecast experiment on the foreign exchange market with a classifier system," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1543-1575, June.
  5. Branch, William A. & Evans, George W., 2006. "A simple recursive forecasting model," Economics Letters, Elsevier, vol. 91(2), pages 158-166, May.
  6. Tay, Nicholas S. P. & Linn, Scott C., 2001. "Fuzzy inductive reasoning, expectation formation and the behavior of security prices," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 321-361, March.
  7. Ashley, Richard, 2003. "Statistically significant forecasting improvements: how much out-of-sample data is likely necessary?," International Journal of Forecasting, Elsevier, vol. 19(2), pages 229-239.
  8. Vriend, Nicolaas J., 2000. "An illustration of the essential difference between individual and social learning, and its consequences for computational analyses," Journal of Economic Dynamics and Control, Elsevier, vol. 24(1), pages 1-19, January.
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