%0 Conference Paper %1 SHChen:1999:gpabmsm %A Chen, Shu-Heng %A Yeh, Chia-Hsuan %B Fifth International Conference: Computing in Economics and Finance %C Boston College, MA, USA %D 1999 %E Belsley, David A. %E Baum, Christopher F. %K Agent-Based Annealing, Artificial Business Computational Economics, Learning, Markets, Peer Pressure School, Simulated Social Stock algorithms, genetic programming, %P 77 %T Genetic Programming in the Agent-Based Modeling of Stock Markets %U http://fmwww.bc.edu/cef99/papers/ChenYeh.pdf %X 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.

@inproceedings{SHChen:1999:gpabmsm, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Boston College, MA, USA}, author = {Chen, Shu-Heng and Yeh, Chia-Hsuan}, biburl = {https://www.bibsonomy.org/bibtex/208e8cd2120283bd58ba8630a493016d4/brazovayeye}, booktitle = {Fifth International Conference: Computing in Economics and Finance}, editor = {Belsley, David A. and Baum, Christopher F.}, interhash = {85ae4db9bca33531f674b525e6b96443}, intrahash = {08e8cd2120283bd58ba8630a493016d4}, keywords = {Agent-Based Annealing, Artificial Business Computational Economics, Learning, Markets, Peer Pressure School, Simulated Social Stock algorithms, genetic programming,}, month = {24-26 June}, note = {Book of Abstracts}, notes = {PDF and abstract on paper differ in detail. Using PDF info}, pages = 77, size = {22 pages}, timestamp = {2008-06-19T17:37:42.000+0200}, title = {Genetic Programming in the Agent-Based Modeling of Stock Markets}, url = {http://fmwww.bc.edu/cef99/papers/ChenYeh.pdf}, year = 1999 }