Genetic Programming in the Agent-Based Modeling of Stock Markets   [GP] [AM]

by

Chen, S.-H. and Yeh, C.-H.

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Info: Fifth International Conference: Computing in Economics and Finance (Conference proceedings), 1999, p. 77
Keywords:genetic algorithms, genetic programming, Agent-Based Computational Economics, Social Learning, Business School, Artificial Stock Markets, Simulated Annealing, Peer Pressure
Abstract:
In this paper, we propose a new architecture to study artificial stock markets. [ASM] 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 behavior. By simulated annealing, [SA] traders' search density can be connected to psychological factors, such as peer pressure [PP] or economic factors such as the standard of living. This market architecture was then implemented in a standard artificial stock market. [ASM] [SM] Our econometric study of the resultant artificial time series [TS] evidences that the return series is independently and identically distributed (iid), and hence supports the efficient market hypothesis [EMH] (EMH). What is interesting though is that this iid 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, [BS] even though these signals were short-lived.
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BibTex:
@InProceedings{SHChen:1999:gpabmsm,
  author =       "Shu-Heng Chen and Chia-Hsuan Yeh",
  title =        "Genetic Programming in the Agent-Based Modeling of
                 Stock Markets",
  booktitle =    "Fifth International Conference: Computing in Economics
                 and Finance",
  year =         "1999",
  editor =       "David A. Belsley and Christopher F. Baum",
  pages =        "77",
  address =      "Boston College, MA, USA",
  month =        "24-26 " # jun,
  note =         "Book of Abstracts",
  keywords =     "genetic algorithms, genetic programming, Agent-Based
                 Computational Economics, Social Learning, Business
                 School, Artificial Stock Markets, Simulated Annealing,
                 Peer Pressure",
  URL =          "http://fmwww.bc.edu/cef99/papers/ChenYeh.pdf",
  size =         "22 pages",
  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 behavior. 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 iid 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 =        "PDF and abstract on paper differ in detail. Using PDF
                 info",
}