Adaptation in the Presence of Exogeneous Information in an Artificial Financial Market

  • José L. Gordillo
  • Juan Pablo Pardo-Guerra
  • Christopher R. Stephens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2972)

Abstract

In recent years, agent-based computational models have been used to study financial markets. One of the most interesting elements involved in these studies is the process of learning, in which market participants try to obtain information from the market in order to improve their strategies and hence increase their profits. While in other papers it has been shown how this learning process is determined by factors such as the adaptation period, the composition of the market and the intensity of the signals that an agent can perceive, in this paper we shall discuss the effect of external information in the learning process in an artificial financial market (AFM). In particular, we will analyze the case when external information is such that it forces all participants to randomly revise their expectations of the future. Even though AMFs usually use sophisticated artificial intelligence techniques, in this study we show how interesting results can be obtained using a quite elementary genetic algorithm.

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References

  1. 1.
    Bedau, M., Joshi, S.: An Explanation of Generic Behavior in an Evolving Financial Market. Preprint 98-12-114E, Santa Fe Institute (1998)Google Scholar
  2. 2.
    Farmer, J.D.: Market Force, Ecology and Evolution. Working Paper 98-12-117E, Santa Fe Institute (1998)Google Scholar
  3. 3.
    Chan, N., Lebaron, B., Lo, A., Poggio, T.: Agent Based Models of Financial Markets: a Comparison with Experimental Markets, Brandeis University (1999) (working paper)Google Scholar
  4. 4.
    Gordillo, J.L.: Análisis de Mercados Financieros mediante el Mercado Financiero Artificial NNCP. Tesis de Maestría. IIMAS-UNAM (2000)Google Scholar
  5. 5.
    Palmer, R.G., Arthur, W.B., Holland, J.H., Lebaron, B., Tayler, P.: Artificial Economic Life: a Simple Model of a Stock Market. Physica D 73 (1994)Google Scholar
  6. 6.
    O’Hara, M.: Market Microstructure Theory. Blackwell Publishers Inc., Malden (1997)Google Scholar
  7. 7.
    Goldberg, D.G.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)MATHGoogle Scholar
  8. 8.
    Gordillo, J.L., Stephens, C.R.: Strategy Adaptation and the Role of Information in an Artificial Financial Market. In: Late Breaking Papers, GECCO (2001)Google Scholar
  9. 9.
    Gordillo, J.L., Stephens, C.R.: Analysis of Financial Markets with the Artificial Agent-based Model-NNCP. Encuentro Nacional de Ciencias de la Computación. Sociedad Mexicana de Ciencias de la Computación, Mexico (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • José L. Gordillo
    • 1
  • Juan Pablo Pardo-Guerra
    • 2
  • Christopher R. Stephens
    • 3
  1. 1.Dirección Gral. de Serv. de Cómputo Académico, UNAM 
  2. 2.Facultad de Ciencias, UNAM 
  3. 3.Instituto de Ciencias Nucleares, UNAM 

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