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Market Efficiency and Learning in an Endogenously Unstable Environment

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  • David Goldbaum

Abstract

\tTraders in this model of an asset market have the opportunity to conduct individual research to acquire a noisy signal of a security's future value, or they can employ least-squares learning in an attempt at extracting the private information of other traders through observing the price. For a fixed proportion of the traders using fundamental research, n, the model converges to a stable fixed point equilibrium. At the fixed point, the regression traders outperform the fundamental traders for all values of n > 0. The equilibrium suffers from a Grossman and Stiglitz (1980) type paradox of efficient markets. Endogenize n based on performance and the Grossman-Stiglitz paradox is alleviated. The model is characterized by an unstable fixed point. As the model converges towards the fixed point, the regression traders perform well. As n falls, the regression traders begin to have a substantial impact on the price, causing greater fluctuations in profits and in n. Inevitably, the actual n is significantly different than the value of n implicit in the regression traders' coefficient values, introducing error in the regression trader's forecast. This leads to substantial mispricing that results in losses to the regression traders. It also throws the model far from the fixed point, starting the convergence process over.

Suggested Citation

  • David Goldbaum, 2001. "Market Efficiency and Learning in an Endogenously Unstable Environment," Computing in Economics and Finance 2001 105, Society for Computational Economics.
  • Handle: RePEc:sce:scecf1:105
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    References listed on IDEAS

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    Cited by:

    1. Brock, W.A. & Hommes, C.H. & Wagener, F.O.O., 2009. "More hedging instruments may destabilize markets," Journal of Economic Dynamics and Control, Elsevier, vol. 33(11), pages 1912-1928, November.
    2. Diks, Cees & Dindo, Pietro, 2008. "Informational differences and learning in an asset market with boundedly rational agents," Journal of Economic Dynamics and Control, Elsevier, vol. 32(5), pages 1432-1465, May.
    3. Hommes, C.H., 2005. "Heterogeneous Agent Models in Economics and Finance, In: Handbook of Computational Economics II: Agent-Based Computational Economics, edited by Leigh Tesfatsion and Ken Judd , Elsevier, Amsterdam 2006," CeNDEF Working Papers 05-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    4. Goldbaum, David & Panchenko, Valentyn, 2010. "Learning and adaptation's impact on market efficiency," Journal of Economic Behavior & Organization, Elsevier, vol. 76(3), pages 635-653, December.
    5. Hommes, Cars H., 2006. "Heterogeneous Agent Models in Economics and Finance," Handbook of Computational Economics,in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 23, pages 1109-1186 Elsevier.
    6. Goldbaum, David, 2006. "Self-organization and the persistence of noise in financial markets," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1837-1855.
    7. Goldbaum, David, 2017. "Divergent Behavior in Markets with Idiosyncratic Private Information," Review of Behavioral Economics, now publishers, vol. 4(2), pages 181-213, September.
    8. Hommes, C.H. & Wagener, F.O.O., 2008. "Complex evolutionary systems in behavioral finance," CeNDEF Working Papers 08-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    9. David Goldbaum, 2004. "On the Possibility of Informationally Efficient Markets," Computing in Economics and Finance 2004 139, Society for Computational Economics.
    10. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    11. David Goldbaum, 2003. "Profitable technical trading rules as a source of price instability," Quantitative Finance, Taylor & Francis Journals, vol. 3(3), pages 220-229.
    12. David Goldbaum, 2013. "Learning and Adaptation as a Source of Market Failure," Working Paper Series 14, Economics Discipline Group, UTS Business School, University of Technology, Sydney.

    More about this item

    Keywords

    Least squares learning; efficient markets; chaos;

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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