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題名 Evolving Traders and the Business School with Genetic Programming: A New Architecture of the Agent-Based Artificial Stock Market
作者 陳樹衡;葉佳炫
Chen,Shu-Heng;Yeh,Chia-Hsuan
貢獻者 政大經濟系
關鍵詞 Agent-based computational economics;Social learning;Genetic program-
     ming;Business school;Artificial stock markets
日期 2001-03
上傳時間 9-Jan-2009 12:17:24 (UTC+8)
摘要 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 arti"cial 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.
關聯 Journal of Economic Dynamics and Control,25(3/4),363-393
資料類型 article
DOI http://dx.doi.org/10.1016/S0165-1889(00)00030-0
dc.contributor 政大經濟系-
dc.creator (作者) 陳樹衡;葉佳炫zh_TW
dc.creator (作者) Chen,Shu-Heng;Yeh,Chia-Hsuan-
dc.date (日期) 2001-03en_US
dc.date.accessioned 9-Jan-2009 12:17:24 (UTC+8)-
dc.date.available 9-Jan-2009 12:17:24 (UTC+8)-
dc.date.issued (上傳時間) 9-Jan-2009 12:17:24 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/23277-
dc.description.abstract (摘要) 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 arti"cial 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.-
dc.format application/en_US
dc.language enen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) Journal of Economic Dynamics and Control,25(3/4),363-393en_US
dc.subject (關鍵詞) Agent-based computational economics;Social learning;Genetic program-
     ming;Business school;Artificial stock markets		-
dc.title (題名) Evolving Traders and the Business School with Genetic Programming: A New Architecture of the Agent-Based Artificial Stock Marketen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/S0165-1889(00)00030-0en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1016/S0165-1889(00)00030-0en_US