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Financial data modeling by using asynchronous parallel evolutionary algorithms

  • Wang Chun
  • Li Qiao-yun
Article
  • 23 Downloads

Abstract

In this paper, the high-level knowledge of financial data modeled by ordinary differential equations (ODEs) is discovered in dynamic data by using an asynchronous parallel evolutionary modeling algorithm (APHEMA). A numerical example of Nasdaq index analysis is used to demonstrate the potential of APHEMA. The results show that the dynamic models automatically discovered in dynamic data by computer can be used to predict the financial trends.

Key Words

financial data mining asynchronous parallel algorithm knowledge discovery evolutionary modeling 

CLC number

TP 301 

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Copyright information

© Springer 2003

Authors and Affiliations

  1. 1.School of BusinessHuazhong University of Science and TechnologyWuhan, HubeiChina
  2. 2.Network and Software Technology Center of AmericaSony CorporationSan JoseUSA

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