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題名 Can Artificial Traders Learn and Err Like Human Traders? A New Direction for Computational Intelligence in Behavioral Finance
作者 陳樹衡
Chen, Shu-Heng ; Shih, Kuo-Chuan ; Tai, Chung-Ching
貢獻者 經濟系
日期 2012
上傳時間 20-三月-2014 16:56:36 (UTC+8)
摘要 The microstructure of markets involves not only human traders’ learning and erring processes but also their heterogeneity. Much of this part has not been taken into account in the agent-based artificial markets, despite the fact that various computational intelligence tools have been applied to artificial-agent modeling. One possible reason for this little progress is due to the lack of good-quality data by which the learning and erring patterns of human traders can be easily archived and analyzed. In this chapter, we take a pioneering step in this direction by, first, conducting double auction market experiments and obtaining a dataset involving about 165 human traders. The controlled laboratory setting then enables us to anchor the observing trading behavior of human traders to a benchmark (a global optimum) and to develop a learning index by which the learning and erring patterns can be better studied, in particular, in light of traders’ personal attributes, such as their cognitive capacity and personality. The behavior of artificial traders driven by genetic programming (GP) is also studied in parallel to human traders; however, how to represent the observed heterogeneity using GP remains a challenging issue.
關聯 Financial Decision Making Using Computational Intelligence, Springer Series Optimization and Its Applications, 70, 2012, 35-69
資料類型 book/chapter
dc.contributor 經濟系en_US
dc.creator (作者) 陳樹衡zh_TW
dc.creator (作者) Chen, Shu-Heng ; Shih, Kuo-Chuan ; Tai, Chung-Chingen_US
dc.date (日期) 2012en_US
dc.date.accessioned 20-三月-2014 16:56:36 (UTC+8)-
dc.date.available 20-三月-2014 16:56:36 (UTC+8)-
dc.date.issued (上傳時間) 20-三月-2014 16:56:36 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/64747-
dc.description.abstract (摘要) The microstructure of markets involves not only human traders’ learning and erring processes but also their heterogeneity. Much of this part has not been taken into account in the agent-based artificial markets, despite the fact that various computational intelligence tools have been applied to artificial-agent modeling. One possible reason for this little progress is due to the lack of good-quality data by which the learning and erring patterns of human traders can be easily archived and analyzed. In this chapter, we take a pioneering step in this direction by, first, conducting double auction market experiments and obtaining a dataset involving about 165 human traders. The controlled laboratory setting then enables us to anchor the observing trading behavior of human traders to a benchmark (a global optimum) and to develop a learning index by which the learning and erring patterns can be better studied, in particular, in light of traders’ personal attributes, such as their cognitive capacity and personality. The behavior of artificial traders driven by genetic programming (GP) is also studied in parallel to human traders; however, how to represent the observed heterogeneity using GP remains a challenging issue.en_US
dc.format.extent 991360 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Financial Decision Making Using Computational Intelligence, Springer Series Optimization and Its Applications, 70, 2012, 35-69en_US
dc.title (題名) Can Artificial Traders Learn and Err Like Human Traders? A New Direction for Computational Intelligence in Behavioral Financeen_US
dc.type (資料類型) book/chapteren