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| Title | Evolving Traders and the Business School with Genetic Programming: A New Architecture of the Agent-Based Artificial Stock Market |
| Creator | 陳樹衡;葉佳炫 Chen,Shu-Heng;Yeh,Chia-Hsuan |
| Contributor | 政大經濟系 |
| Key Words | Agent-based computational economics;Social learning;Genetic program- ming;Business school;Artificial stock markets |
| Date | 2001-03 |
| Date Issued | 9-Jan-2009 12:17:24 (UTC+8) |
| 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 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. |
| Relation | Journal of Economic Dynamics and Control,25(3/4),363-393 |
| Type | 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) | 2001-03 | en_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 | en | en_US |
| dc.language | en-US | en_US |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Journal of Economic Dynamics and Control,25(3/4),363-393 | en_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 Market | en_US |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1016/S0165-1889(00)00030-0 | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1016/S0165-1889(00)00030-0 | en_US |