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Evolution with Individual and Social Learning in an Agent-Based Stock Market

Author

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  • Ryuichi YAMAMOTO

    () (International Business School Brandeis University)

Abstract

Recent research has shown a variety of computational techniques to describe evolution in an artificial stock market. One can distinguish the techniques based on at which level the learning of agents is modeled. The previous literature describes learning at either individual or social level. The level of learning is exogenously given, and agents involve only a particular level of learning when they update their rules. But such a setting doesn’t say anything about why agents choose a particular level of learning to update their trading rules. This paper introduces a learning mechanism which allows agents to choose one rule at each period among a set of ideas updated through both individual and social learning. A trading strategy performed well in the past is more likely to be selected by agents regardless it is created at individual or social level. This framework allows agents to choose a decision rule endogenously among a wider set of ideas. With such evolution, the following two questions are examined. First, since agents who have a wider set of ideas to choose are more intelligent, a question would arise if the time series from an economy with intelligent agents would converge to a rational expectation equilibrium (REE). Previous literature like LeBaron (2000) and Arthur et al. (1996) investigates the convergence property to the REE by looking at different time-horizons. It finds that the more information from the market agents get before updating their rules, the market is more likely to converge to the REE. But this paper investigates the convergence property by looking at different degrees of intelligence given a time horizon. The second investigates which level of learning is likely to dominate in the market. This is analyzed by investigating who chooses which level of learning and what proportion of the agents often uses individual or social learning. We analyze a hypothesis that wealthy agents often choose an idea from a set of her private ideas (from individual learning) while some with less wealth frequently imitate ideas from others (from social learning). The result eventually indicates that the agent-based stock market in this paper would possibly explain the mechanism of herding behavior which is often observed in financial markets

Suggested Citation

  • 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.
  • Handle: RePEc:sce:scecf5:228
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    References listed on IDEAS

    as
    1. LeBaron, Blake, 2001. "Evolution And Time Horizons In An Agent-Based Stock Market," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 225-254, April.
    2. 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.
    3. 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.
    4. Arthur, W.B. & Holland, J.H. & LeBaron, B. & Palmer, R. & Tayler, P., 1996. "Asset Pricing Under Endogenous Expectations in an Artificial Stock Market," Working papers 9625, Wisconsin Madison - Social Systems.
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    6. Arifovic, Jasmina & Gencay, Ramazan, 2000. "Statistical properties of genetic learning in a model of exchange rate," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 981-1005, June.
    7. Chia-Hsuan Yeh & Shu-Heng Chen, 2000. "Toward An Integration Of Social Learning And Individual Learning In Agent-Based Computational Stock Markets:The Approach Based On Population Genetic Programming," Computing in Economics and Finance 2000 338, Society for Computational Economics.
    8. LeBaron, Blake & Arthur, W. Brian & Palmer, Richard, 1999. "Time series properties of an artificial stock market," Journal of Economic Dynamics and Control, Elsevier, vol. 23(9-10), pages 1487-1516, September.
    9. 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.
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    More about this item

    Keywords

    Individual learning; Social learning; Evolution; Asset pricing; Financial time series;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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