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International Workshop on Learning Classifier Systems

IWLCS 2001: Advances in Learning Classifier Systems pp 151–180Cite as

Explorations in LCS Models of Stock Trading

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2321))

Abstract

In previous papers we have described the basic elements for building an economic model consisting of a group of artificial traders functioning and adapting in an environment containing real stock market information. We have analysed the feasibility of the proposed approach by comparing the final wealth generated by such agents over a period of time, against the wealth of a number of well known investment strategies, including the bank, buy-and-hold and trend-following strategies. In this paper we review classical economic theories and introduce a new strategy inspired by the Efficient Market Hypothesis (named here random walk to compare the performance of our traders. In order to build better trader models we must increase our understanding about how artificial agents learn and develop; in this paper we address a number of design issues, including the analysis of information sets and evolved strategies. Specifically, the results presented here correspond to the stock of IBM.

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Author information

Authors and Affiliations

  1. School of Computing, Napier University, 10 Colinton Road, Edinburgh, EH10 5DT

    Sonia Schulenburg & Peter Ross

Authors
  1. Sonia Schulenburg
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  2. Peter Ross
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Editor information

Editors and Affiliations

  1. Dipartimento di Elettronica e Informazione Artificial Intelligence and Robotics Laboratory, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy

    Pier Luca Lanzi

  2. Research and Technology, Cognition and Robotics, DaimlerChrysler AG, Alt-Moabit 96A, 10559, Berlin, Germany

    Wolfgang Stolzmann

  3. Prediction Dynamics, Concord, MA, 01742, USA

    Stewart W. Wilson

  4. Department of General Engineering, The University of Illinois, Urbana-Champaign, IL, 61801, USA

    Stewart W. Wilson

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© 2002 Springer-Verlag Berlin Heidelberg

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Schulenburg, S., Ross, P. (2002). Explorations in LCS Models of Stock Trading. In: Lanzi, P.L., Stolzmann, W., Wilson, S.W. (eds) Advances in Learning Classifier Systems. IWLCS 2001. Lecture Notes in Computer Science(), vol 2321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48104-4_10

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  • DOI: https://doi.org/10.1007/3-540-48104-4_10

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43793-2

  • Online ISBN: 978-3-540-48104-1

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