Multiple dimensioned mining of financial fluctuation through radial basis function networks

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• 作者：Yi Xiao (1)
  John J. Liu (2)
  Shouyang Wang (3)
  Yi Hu (4)
  Jin Xiao (5)
  
  1. School of Information Management ; Central China Normal University ; Wuhan ; 430079 ; China
  2. Center for Transport Trade and Financial Studies ; City University of Hong Kong ; Kowloon ; Hong Kong
  3. Academy of Mathematics and Systems Science ; Chinese Academy of Sciences ; Beijing ; 100190 ; China
  4. School of Management ; University of Chinese Academy of Sciences ; Beijing ; 100190 ; China
  5. Business School ; Sichuan University ; Chengdu ; 610064 ; China
  
• 关键词：Financial market fluctuation ; Multiple dimensioned mining ; Radial basis function network
• 刊名：Neural Computing & Applications
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：26
• 期：2
• 页码：363-371
• 全文大小：513 KB
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• 刊物类别：Computer Science
• 刊物主题：Simulation and Modeling
  
• 出版者：Springer London
• ISSN：1433-3058

文摘

Fluctuation mining is one of the greatest challenging tasks in the field of finance market. The main contribution of this research was to propose a multiple dimensioned model for financial market fluctuation mining. In this approach, the original financial time series is broken down into different information by the wavelet filtering technique, and then, all this information is handled through radial basis function networks due to its universal approximation abilities and more robust than the ordinary networks. An experimental analysis is conducted with the proposed model using stock index future time series, revealing consistent performance improvement of this kind of multidimensional approach.