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Heterogeneous Agent Model And Numerical Analysis Of Learning


  • Miloslav Vošvrda
  • Lukáš Vácha


The Efficient Markets Hypothesis provides a theoretical basis for trading rules. Technical trading rules provide a signal of when to buy or sell an asset based on such price patterns to the user. Technical traders tend to put little faith in strict efficient markets. Fundamentalists rely on their model employing fundamental information basis to forecast the next price period. The traders determine whether current conditions call for the acquisition of fundamental information in a forward looking manner rather than relying on past performance. This approach relies on heterogeneity in the agent information and subsequent decisions either as fundamentalists or as chartists. Changing of the chartist's profitability and fundamentalist's positions is the basis of cycles behaviour. It was shown that a level of profitability for particular agent patterns is very sensitive on the structure of memory weights and the memory lengths. It was shown that different values of these memory coefficients can significantly change the preferences of trader strategies. This paper shows an influence of the learning agents process on a level of agent pattern profitability.

Suggested Citation

  • Miloslav Vošvrda & Lukáš Vácha, 2002. "Heterogeneous Agent Model And Numerical Analysis Of Learning," Bulletin of the Czech Econometric Society, The Czech Econometric Society, vol. 9(17).
  • Handle: RePEc:czx:journl:v:9:y:2002:i:17:id:112

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    References listed on IDEAS

    1. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    2. Haltiwanger, John & Waldman, Michael, 1985. "Rational Expectations and the Limits of Rationality: An Analysis of Heterogeneity," American Economic Review, American Economic Association, vol. 75(3), pages 326-340, June.
    3. Lucas, Robert E, Jr, 1978. "Asset Prices in an Exchange Economy," Econometrica, Econometric Society, vol. 46(6), pages 1429-1445, November.
    4. Barucci, Emilio, 2000. "Exponentially fading memory learning in forward-looking economic models," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 1027-1046, June.
    5. William A. Brock, 2001. "Growth Theory, Nonlinear Dynamics and Economic Modelling," Books, Edward Elgar Publishing, number 1491 edited by W. D. Dechert.
    6. Chiarella, Carl & He, Xue-Zhong, 2003. "Heterogeneous Beliefs, Risk, And Learning In A Simple Asset-Pricing Model With A Market Maker," Macroeconomic Dynamics, Cambridge University Press, vol. 7(04), pages 503-536, September.
    7. Carl Chiarella, 1992. "The Dynamics of Speculative Behaviour," Working Paper Series 13, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    8. Zeeman, E. C., 1974. "On the unstable behaviour of stock exchanges," Journal of Mathematical Economics, Elsevier, vol. 1(1), pages 39-49, March.
    9. Gaunersdorfer, Andrea, 2000. "Endogenous fluctuations in a simple asset pricing model with heterogeneous agents," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 799-831, June.
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    Cited by:

    1. Lukáš Vácha & Miloslav Vošvrda, 2006. "Wavelet Applications to Heterogeneous Agents Model," Working Papers IES 2006/21, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Apr 2006.
    2. Lukáš Vácha & Jozef Barunik & Miloslav Vošvrda, 2009. "Smart Agents and Sentiment in the Heterogeneous Agent Model," Prague Economic Papers, University of Economics, Prague, vol. 2009(3), pages 209-219.
    3. Lukáš Vácha & Miloslav Vošvrda, 2007. "Wavelet Decomposition of the Financial Market," Prague Economic Papers, University of Economics, Prague, vol. 2007(1), pages 38-54.
    4. Jozef Barunik & Lukas Vacha & Miloslav Vosvrda, 2009. "Smart predictors in the heterogeneous agent model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 4(2), pages 163-172, November.

    More about this item


    efficient markets hypothesis; technical trading rules; heterogeneous agent model with memory and learning; asset price behaviour;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations


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