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Asset price bubbles and crashes with near-zero-intelligence traders

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  • John Duffy
  • M. Ünver

Abstract

We examine whether a simple agent-based model can generate asset price bubbles and crashes of the type observed in a series of laboratory asset market experiments beginning with the work of Smith, Suchanek and Williams (1988). We follow the methodology of Gode and Sunder (1993, 1997) and examine the outcomes that obtain when populations of zero-intelligence (ZI) budget constrained, artificial agents are placed in the various laboratory market environments that have given rise to price bubbles. We have to put more structure on the behavior of the ZI-agents in order to address features of the laboratory asset bubble environment. We show that our model of “near-zero-intelligence” traders, operating in the same double auction environments used in several different laboratory studies, generates asset price bubbles and crashes comparable to those observed in laboratory experiments and can also match other, more subtle features of the experimental data. Copyright Springer-Verlag Berlin/Heidelberg 2006

Suggested Citation

  • John Duffy & M. Ünver, 2006. "Asset price bubbles and crashes with near-zero-intelligence traders," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 27(3), pages 537-563, April.
  • Handle: RePEc:spr:joecth:v:27:y:2006:i:3:p:537-563
    DOI: 10.1007/s00199-004-0570-9
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    Citations

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    Cited by:

    1. Iori, G. & Porter, J., 2012. "Agent-Based Modelling for Financial Markets," Working Papers 12/08, Department of Economics, City University London.
    2. Giusti, Giovanni & Jiang, Janet Hua & Xu, Yiping, 2012. "Eliminating Laboratory Asset Bubbles by Paying Interest on Cash," MPRA Paper 37321, University Library of Munich, Germany.
    3. Miller, Ross M., 2008. "Don't let your robots grow up to be traders: Artificial intelligence, human intelligence, and asset-market bubbles," Journal of Economic Behavior & Organization, Elsevier, vol. 68(1), pages 153-166, October.
    4. Baghestanian, S. & Lugovskyy, V. & Puzzello, D., 2015. "Traders’ heterogeneity and bubble-crash patterns in experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 82-101.
    5. HIGASHIDA Keisaku & TANAKA Kenta & MANAGI Shunsuke, 2018. "Losses on Asset Returns Caused by Perception Gaps of Fundamental Values: Evidence from laboratory experiments," Discussion papers 18008, Research Institute of Economy, Trade and Industry (RIETI).
    6. Feldman, Todd & Friedman, Daniel, 2008. "Humans, Robots and Market Crashes: A Laboratory Study ∗," Santa Cruz Department of Economics, Working Paper Series qt4kf382p6, Department of Economics, UC Santa Cruz.
    7. repec:spr:decfin:v:40:y:2017:i:1:d:10.1007_s10203-017-0200-1 is not listed on IDEAS
    8. repec:eee:dyncon:v:82:y:2017:i:c:p:223-256 is not listed on IDEAS
    9. Monira Essa Aloud, 2016. "Profitability of Directional Change Based Trading Strategies: The Case of Saudi Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 87-95.
    10. Jakob Grazzini, 2013. "Information dissemination in an experimentally based agent-based stock market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 179-209, April.
    11. Annalisa Fabretti & Tommy Gärling & Stefano Herzel & Martin Holmen, 2017. "Convex incentives in financial markets: an agent-based analysis," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 40(1), pages 375-395, November.
    12. Baghestanian, Sascha & Walker, Todd B., 2015. "Anchoring in experimental asset markets," Journal of Economic Behavior & Organization, Elsevier, vol. 116(C), pages 15-25.
    13. 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.
    14. Ladley, Dan & Schenk-Hoppé, Klaus Reiner, 2009. "Do stylised facts of order book markets need strategic behaviour?," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 817-831, April.
    15. 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.
    16. repec:jas:jasssj:2016-192-2 is not listed on IDEAS
    17. Baghestanian, Sascha & Walker, Todd B., 2014. "Thar she blows again: Reducing anchoring rekindles bubbles," SAFE Working Paper Series 54, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    18. Hong, Jieying & Moinas, Sophie & Pouget, Sébastien, 2018. "Learning in Speculative Bubbles: An Experiment," TSE Working Papers 18-882, Toulouse School of Economics (TSE).

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