Genetic programming in the agent-based artificial stock market
Shu-Heng Chen; Chia-Hsuan Yeh
Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Volume 2, Issue , 1999 Page(s): - 841 Vol. 2
Digital Object Identifier 10.1109/CEC.1999.782509
Summary: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
View citation and abstract |
Cart
