Genetic programming in the agent-based artificial stock market
Shu-Heng Chen
Chia-Hsuan Yeh
Nat. Chengchi Univ., Taipei;
This paper appears in: Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Publication Date: 1999
Volume: 2,
On page(s): -841 Vol. 2
Meeting Date: 07/06/1999 - 07/09/1999
Location: Washington, DC, USA
ISBN: 0-7803-5536-9
References Cited: 6
INSPEC Accession Number: 6338893
Digital Object Identifier: 10.1109/CEC.1999.782509
Current Version Published: 2002-08-06
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 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
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