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Fundamentalists vs. chartists: learning and predictor choice dynamics

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  • Michele Berardi

Abstract

In a simple, forward looking univariate model of price determination we investigate the evolution of endogenous predictor choice dynamics in presence of two types of agents: fundamentalists an chartists. We find that heterogeneous equilibria in which fundamentalists and chartists coexist are possible, even when the fraction of agents is endogenized. We then combine evolutionary selection among heterogeneous classes of models with adaptive learning in the form of parameter updating within each class of rules and find that equilibria in which chartists constantly outperform fundamentalists seem never to be learnable. Simulations also show that, in general, interactions between learning and predictor choice dynamics do not prevent convergence of both processes towards their equilibrium values when the equilibrium is E-stable.

Suggested Citation

  • Michele Berardi, 2008. "Fundamentalists vs. chartists: learning and predictor choice dynamics," Centre for Growth and Business Cycle Research Discussion Paper Series 104, Economics, The University of Manchester.
    • Handle: RePEc:man:cgbcrp:104
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    Cited by:

    1. Xing Gao & Daniel Ladley, 2022. "Noise trading and market stability," The European Journal of Finance, Taylor & Francis Journals, vol. 28(13-15), pages 1283-1301, October.
    2. Thomas Holtfort, 2019. "From standard to evolutionary finance: a literature survey," Management Review Quarterly, Springer, vol. 69(2), pages 207-232, June.
    3. ap Gwilym, Rhys, 2009. "The Monetary Policy Implications of Behavioral Asset Bubbles," Cardiff Economics Working Papers E2009/18, Cardiff University, Cardiff Business School, Economics Section.
    4. Rhys ap Gwilym, 2013. "The Monetary Policy Implications of Behavioral Asset Bubbles," Southern Economic Journal, Southern Economic Association, vol. 80(1), pages 252-270, July.
    5. Michele Berardi, 2011. "Heterogeneous sunspots solutions under learning and replicator dynamics," Centre for Growth and Business Cycle Research Discussion Paper Series 160, Economics, The University of Manchester.
    6. Berardi, Michele, 2015. "On the fragility of sunspot equilibria under learning and evolutionary dynamics," Journal of Economic Behavior & Organization, Elsevier, vol. 112(C), pages 251-265.
    7. Thierry Warin & Aleksandar Stojkov, 2021. "Machine Learning in Finance: A Metadata-Based Systematic Review of the Literature," JRFM, MDPI, vol. 14(7), pages 1-31, July.
    8. Rhys ap Gwilym, 2013. "The Monetary Policy Implications of Behavioral Asset Bubbles," Southern Economic Journal, John Wiley & Sons, vol. 80(1), pages 252-270, July.
    9. repec:wly:soecon:v:80:1:y:2013:p:252-270 is not listed on IDEAS

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    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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