Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Shu-HengChen:2001:JEDC,
-
author = "Shu-Heng Chen and Chia-Hsuan Yeh",
-
title = "Evolving traders and the business school with genetic
programming: A new architecture of the agent-based
artificial stock market",
-
journal = "Journal of Economic Dynamics and Control",
-
year = "2001",
-
volume = "25",
-
number = "3-4",
-
pages = "363--393",
-
month = mar,
-
keywords = "genetic algorithms, genetic programming, Agent-based
computational economics, Social learning, Business
school, Artificial stock markets",
-
DOI = "doi:10.1016/S0165-1889(00)00030-0",
-
abstract = "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 = "JEL classification codes: G12; G14; D83 a AI-ECON
Research Group, Department of Economics, National
Chengchi University, Taipei, 11623 Taiwan b AI-ECON
Research Group, Department of Finance I-Shou
University, Kaohsiung County, 84008 Taiwan",
- }
Genetic Programming entries for
Shu-Heng Chen
Chia Hsuan Yeh
Citations