學術產出-專書/專書篇章

文章檢視/開啟

書目匯出

Google ScholarTM

政大圖書館

引文資訊

TAIR相關學術產出

題名 Market Microstructure: A Self-Organizing Map Approach to Investigate Behavior Dynamics under an Evolutionary Environment
作者 陳樹衡
Kampouridis, Michael ; Chen, Shu-Heng ; Tsang, Edward
貢獻者 經濟系
日期 2012
上傳時間 20-三月-2014 16:57:55 (UTC+8)
摘要 This chapter presents a market microstructure model, which investigates the behavior dynamics in financial markets. We are especially interested in examining whether the markets’ behavior is non-stationary, because this implies that strategies from the past cannot be applied to future time periods, unless they have co-evolved with the markets. In order to test this, we employ Genetic Programming, which acts as an inference engine for trading rules, and Self-Organizing Maps, which is used for clustering the above rules into types of trading strategies. The results on four empirical financial markets show that their behavior constantly changes; thus, agents’ trading strategies need to continuously adapt to the changes taking place in the market, in order to remain effective.
關聯 Natural Computing in Computational Finance Studies in Computational Intelligence Volume 380, 2012, pp 181-197
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) Kampouridis, Michael ; Chen, Shu-Heng ; Tsang, Edwarden_US
dc.date (日期) 2012en_US
dc.date.accessioned 20-三月-2014 16:57:55 (UTC+8)-
dc.date.available 20-三月-2014 16:57:55 (UTC+8)-
dc.date.issued (上傳時間) 20-三月-2014 16:57:55 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/64754-
dc.description.abstract (摘要) This chapter presents a market microstructure model, which investigates the behavior dynamics in financial markets. We are especially interested in examining whether the markets’ behavior is non-stationary, because this implies that strategies from the past cannot be applied to future time periods, unless they have co-evolved with the markets. In order to test this, we employ Genetic Programming, which acts as an inference engine for trading rules, and Self-Organizing Maps, which is used for clustering the above rules into types of trading strategies. The results on four empirical financial markets show that their behavior constantly changes; thus, agents’ trading strategies need to continuously adapt to the changes taking place in the market, in order to remain effective.en_US
dc.format.extent 427761 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Natural Computing in Computational Finance Studies in Computational Intelligence Volume 380, 2012, pp 181-197en_US
dc.title (題名) Market Microstructure: A Self-Organizing Map Approach to Investigate Behavior Dynamics under an Evolutionary Environmenten_US
dc.type (資料類型) book/chapteren