Evolution with Individual and Social Learning in an Agent-Based Stock Market
AbstractRecent 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 doesnâ€™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
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2005 with number 228.
Date of creation: 11 Nov 2005
Date of revision:
Individual learning; Social learning; Evolution; Asset pricing; Financial time series;
Find related papers by 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, and Information
This paper has been announced in the following NEP Reports:
- NEP-ALL-2005-11-19 (All new papers)
- NEP-CBE-2005-11-19 (Cognitive & Behavioural Economics)
- NEP-CMP-2005-11-19 (Computational Economics)
- NEP-EVO-2005-11-19 (Evolutionary Economics)
- NEP-FIN-2005-11-19 (Finance)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Arifovic, Jasmina, 1996. "The Behavior of the Exchange Rate in the Genetic Algorithm and Experimental Economies," Journal of Political Economy, University of Chicago Press, vol. 104(3), pages 510-41, June.
- 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.
- repec:att:wimass:9625 is not listed on IDEAS
- 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.
- 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.
- Blake LeBaron, 1999.
"Evolution and Time Horizons in an Agent-Based Stock Market,"
Computing in Economics and Finance 1999
1342, Society for Computational Economics.
- 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.
- repec:cup:macdyn:v:5:y:2001:i:2:p:225-54 is not listed on IDEAS
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- repec:att:wimass:9725 is not listed on IDEAS
- 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.
- Arifovic, Jasmina, 2001. "Evolutionary dynamics of currency substitution," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 395-417, March.
- Arifovic, Jasmina, 2001. "Performance Of Rational And Boundedly Rational Agents In A Model With Persistent Exchange-Rate Volatility," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 204-224, April.
- John R. Graham, 1999. "Herding among Investment Newsletters: Theory and Evidence," Journal of Finance, American Finance Association, vol. 54(1), pages 237-268, 02.
- 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.
- repec:att:wimass:9520 is not listed on IDEAS
- Sushil Bikhchandani & David Hirshleifer & Ivo Welch, 1998. "Learning from the Behavior of Others: Conformity, Fads, and Informational Cascades," Journal of Economic Perspectives, American Economic Association, vol. 12(3), pages 151-170, Summer.
- W. Brian Arthur & John H. Holland & Blake LeBaron & Richard Palmer & Paul Taylor, 1996.
"Asset Pricing Under Endogenous Expectation in an Artificial Stock Market,"
96-12-093, Santa Fe Institute.
- Goodhart, Charles A. E. & O'Hara, Maureen, 1997. "High frequency data in financial markets: Issues and applications," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 73-114, June.
- 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.
- repec:cup:macdyn:v:5:y:2001:i:2:p:204-24 is not listed on IDEAS
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