[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/ebl/ecbull/eb-11-00608.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • Jaqueson K. Galimberti & Sergio da Silva, 2012. "An empirical case against the use of genetic-based learning classifier systems as forecasting devices," Economics Bulletin, AccessEcon, vol. 32(1), pages 354-369.
    • Handle: RePEc:ebl:ecbull:eb-11-00608
    as

    Download full text from publisher

    File URL: http://www.accessecon.com/Pubs/EB/2012/Volume32/EB-12-V32-I1-P32.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lettau, Martin, 1997. "Explaining the facts with adaptive agents: The case of mutual fund flows," Journal of Economic Dynamics and Control, Elsevier, vol. 21(7), pages 1117-1147, June.
    2. 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.
    3. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    4. Branch, William A. & Evans, George W., 2006. "A simple recursive forecasting model," Economics Letters, Elsevier, vol. 91(2), pages 158-166, May.
    5. 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.
    6. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    7. 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.
    8. Brenner, Thomas, 2006. "Agent Learning Representation: Advice on Modelling Economic Learning," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 18, pages 895-947, Elsevier.
    9. 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.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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.
    2. Ryuichi YAMAMOTO, 2005. "Evolution with Individual and Social Learning in an Agent-Based Stock Market," Computing in Economics and Finance 2005 228, Society for Computational Economics.
    3. Shu-Heng Chen & Chung-Ching Tai, 2006. "Republication: On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Springer;Society for Computational Economics, vol. 28(4), pages 313-331, November.
    4. repec:zbw:iamodp:109915 is not listed on IDEAS
    5. Renaud Bourlès & Juliette Rouchier, 2012. "Evolving Informal Risk-Sharing Cooperatives and Other-Regarding Preferences," Working Papers halshs-00793706, HAL.
    6. Victorien Barbet & Renaud Bourlès & Juliette Rouchier, 2020. "Informal risk-sharing cooperatives: the effect of learning and other-regarding preferences," Journal of Evolutionary Economics, Springer, vol. 30(2), pages 451-478, April.
    7. Min Zheng & Duo Wang & Xue-Zhong He, 2009. "Asymmetry of technical analysis and market price volatility," Published Paper Series 2009-6, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    8. Graubner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM)," IAMO Discussion Papers 109915, Institute of Agricultural Development in Transition Economies (IAMO).
    9. Leigh Tesfatsion, 2002. "Agent-Based Computational Economics," Computational Economics 0203001, University Library of Munich, Germany, revised 15 Aug 2002.
    10. Graupner, Marten, 2011. "The Spatial Agent-based Competition Model (SpAbCoM) [Das räumliche agenten-basierte Wettbewerbsmodell SpAbCoM]," IAMO Discussion Papers 135, Leibniz Institute of Agricultural Development in Transition Economies (IAMO).
    11. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
    12. Gradojevic, Nikola, 2007. "Non-linear, hybrid exchange rate modeling and trading profitability in the foreign exchange market," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 557-574, February.
    13. Ludo Waltman & Nees Eck & Rommert Dekker & Uzay Kaymak, 2011. "Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies," Journal of Evolutionary Economics, Springer, vol. 21(5), pages 737-756, December.
    14. Waltman, L. & van Eck, N.J.P., 2009. "A Mathematical Analysis of the Long-run Behavior of Genetic Algorithms for Social Modeling," ERIM Report Series Research in Management ERS-2009-011-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    15. Adão, Luiz F.S. & Silveira, Douglas & Ely, Regis A. & Cajueiro, Daniel O., 2022. "The impacts of interest rates on banks’ loan portfolio risk-taking," Journal of Economic Dynamics and Control, Elsevier, vol. 144(C).
    16. Haijun Yang & Shuheng Chen, 2018. "A heterogeneous artificial stock market model can benefit people against another financial crisis," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-25, June.
    17. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011, Elsevier.
    18. Shu-Heng Chen & Chung-Ching Tai, 2006. "On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach," Computational Economics, Springer;Society for Computational Economics, vol. 28(1), pages 51-69, August.
    19. Georges, Christophre & Wallace, John C., 2009. "Learning Dynamics And Nonlinear Misspecification In An Artificial Financial Market," Macroeconomic Dynamics, Cambridge University Press, vol. 13(5), pages 625-655, November.
    20. Smith, Peter, 2004. "Reworking the Standard Model of Competitive Markets: The Role of Fuzzy Logic and Genetic Algorithms in Modelling Complex Non-Linear Economic System," General Discussion Papers 30569, University of Manchester, Institute for Development Policy and Management (IDPM).
    21. Haijun Yang & Harry Wang & Gui Sun & Li Wang, 2015. "A comparison of U.S and Chinese financial market microstructure: heterogeneous agent-based multi-asset artificial stock markets approach," Journal of Evolutionary Economics, Springer, vol. 25(5), pages 901-924, November.

    More about this item

    Keywords

    genetic-based learning classifier systems; genetic algorithms; stock returns forecasting;
    All these keywords.

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • G1 - Financial Economics - - General Financial Markets

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ebl:ecbull:eb-11-00608. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: John P. Conley (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.