Mining Frequent Episodes for Relating Financial Events and Stock Trends (original) (raw)

Abstract

It is expected that stock prices can be affected by the local and overseas political and economic events. We extract events from the financial news of Chinese local newspapers which are available on the web, the news are matched against stock prices databases and a new method is proposed for the mining of frequent temporal patterns.

Preview

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. R. Agrawal and R. Srikant. Mining sequential patterns. 11th International Conf. On Data Engineering, March 1995.
    Google Scholar
  2. M.S. Chen, J.S. Park, and P.S. Yu. Efficient Data Mining for Path Traversal Patterns. IEEE Transactions on Knowledge and Data Engineering, March/April 10:2, 1998.
    Google Scholar
  3. J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. SIGMOD, 2000.
    Google Scholar
  4. P. S. Kam and A. W. C. Fu. Discovering Temporal Patterns for Interval-Based Events. Proc. Second International Conference on Data Warehousing and Knowledge Discovery, 2000.
    Google Scholar
  5. H. Lu, J. Han, and L. Feng. Stock Movement Predication and N-Dimensional Inter-Transaction Association Rules. Proc. of SIGMOD workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD’98), 1998.
    Google Scholar
  6. H. Mannila and H. Toivonen. Discovering generalized episodes using minimal occurrences. 2nd International Conf. On Knowledge Discovery and Data Mining, August 1996.
    Google Scholar
  7. Anny Ng and K.H. Lee. Event Extraction from Chinese Financial News. International Conference on Chinese Language Computing (ICCLC), 2002.
    Google Scholar
  8. J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M.C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. Proceedings of the 12th IEEE International Conference on Data Engineering, 2001.
    Google Scholar
  9. P. C. Wong, W. Cowley, H. Foote, and E. Jurrus. Visualizing Sequential Patterns for Text Mining. Proceedings IEEE Information Visualization, October 2000.
    Google Scholar

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
    Anny Ng & Ada Wai-chee Fu

Authors

  1. Anny Ng
  2. Ada Wai-chee Fu

Editor information

Editors and Affiliations

  1. Computer Science Department, Korea Advanced Institute of Science and Technology, 373-1 Koo-Sung Dong, Yoo-Sung Ku, Daejeon, 305-701, Korea
    Kyu-Young Whang
  2. Department of Statistics, Seoul National University, Sillimdong Kwanakgu, Seoul, 151-742, Korea
    Jongwoo Jeon
  3. School of Electrical Engineering and Computer Science, Seoul National University, Kwanak P.O. Box 34, Seoul, 151-742, Korea
    Kyuseok Shim
  4. Department of Computer Science and Engineering, University of Minnesota, 200 Union St SE, Minneapolis, MN, 55455, USA
    Jaideep Srivastava

Rights and permissions

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ng, A., Fu, A.Wc. (2003). Mining Frequent Episodes for Relating Financial Events and Stock Trends. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8\_4

Download citation

Publish with us