Mining for core patterns in stock market data

Jianfei Wu, Anne Denton, Omar A. El Ariss, Dianxiang Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

We introduce an algorithm that uses stock sector information directly in conjunction with time series subsequences for mining core patterns within the sectors of stock market data. The core patterns within a sector are representative groups of stocks for the sector when it shows coherent behavior. Multiple core patterns may exist in a sector at the same time. In comparison with clustering algorithms, the core patterns are shown to be more stable as the stock price evolves. The proposed algorithm has only one free parameter, for which we provide an empirical choice. We demonstrate the effectiveness of the algorithm through a comparison with the DBScan clustering algorithm using data from the Standard and Poor 500 Index.

Original languageEnglish (US)
Title of host publicationICDM Workshops 2009 - IEEE International Conference on Data Mining
Pages558-563
Number of pages6
DOIs
StatePublished - Dec 1 2009
Event2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009 - Miami, FL, United States
Duration: Dec 6 2009Dec 6 2009

Publication series

NameICDM Workshops 2009 - IEEE International Conference on Data Mining

Other

Other2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009
CountryUnited States
CityMiami, FL
Period12/6/0912/6/09

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition
  • Software

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    Wu, J., Denton, A., El Ariss, O. A., & Xu, D. (2009). Mining for core patterns in stock market data. In ICDM Workshops 2009 - IEEE International Conference on Data Mining (pp. 558-563). [5360472] (ICDM Workshops 2009 - IEEE International Conference on Data Mining). https://doi.org/10.1109/ICDMW.2009.115