Genetic Programming in the Agent-Based Artificial Stock Market

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Genetic Programming in the Agent-Based Artificial
                 Stock Market",
  booktitle =    "Proceedings of the Congress on Evolutionary
  year =         "1999",
  editor =       "Peter J. Angeline and Zbyszek Michalewicz and 
                 Marc Schoenauer and Xin Yao and Ali Zalzala",
  volume =       "2",
  pages =        "834--841",
  address =      "Mayflower Hotel, Washington D.C., USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "Congress on Evolutionary Computation, IEEE / Neural
                 Networks Council, Evolutionary Programming Society,
                 Galesia, IEE",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, algorithms,
                 agent-based, agent-based computational economics,
                 artificial stock markets, business school, peer
                 pressure, simulated annealing, social learning, time
                 series, economics, simulated annealing, software
                 agents, stock markets",
  ISBN =         "0-7803-5536-9 (softbound)",
  ISBN =         "0-7803-5537-7 (Microfiche)",
  DOI =          "doi:10.1109/CEC.1999.782509",
  abstract =     "In this paper, we propose a new architecture to study
                 artificial stock markets. This architecture rests on a
                 mechanism called 'school' which is a procedure to map
                 the phenotype to the genotype or, in plain English, to
                 uncover the secret of success. We propose an
                 agent-based model of school, and consider school as an
                 evolving population driven by single-population GP
                 (SGP). The architecture also takes into consideration
                 traders' search behaviour. By simulated annealing,
                 traders' search density can be connected to
                 psychological factors, such as peer pressure or
                 economic factors such as the standard of living. This
                 market architecture was then implemented in a standard
                 artificial stock market. Our econometric study of the
                 resultant artificial time series evidences that the
                 return series is independently and identically
                 distributed (iid), and hence supports the efficient
                 market hypothesis (EMH). What is interesting though is
                 that this lid series was generated by 'traders' who do
                 not believe in the EMH at all. In fact, our study
                 indicates that many of our traders were able to find
                 useful signals quite often from business school, even
                 though these signals were short-lived",
  notes =        "CEC-99 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 Library of Congress Number = 99-61143",

Genetic Programming entries for Shu-Heng Chen Chia Hsuan Yeh