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Computational Intelligence and Neuroscience
Volume 2014, Article ID 318524, 6 pages
http://dx.doi.org/10.1155/2014/318524
Research Article

Hybrid Machine Learning Technique for Forecasting Dhaka Stock Market Timing Decisions

School of Engineering and Computer Science, Independent University, Dhaka 1229, Bangladesh

Received 7 May 2013; Revised 17 December 2013; Accepted 9 January 2014; Published 19 February 2014

Academic Editor: Simone Fiori

Copyright © 2014 Shipra Banik et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C. F. Tsai and S. P. Wang, “Stock price forecasting by hybrid machine learning techniques,” in Proceedings of the International Multiconference of Engineers and Computer Scientists, Hongkong, China, 2009.
  2. S. M. Shamsuddin, S. H. Jaaman, and M. Darus, “Neuro-Rough trading rules for mining Kuala Lumpur composite index,” European Journal of Scientific Research, vol. 28, no. 2, pp. 278–286, 2009. View at Google Scholar · View at Scopus
  3. J. Yao and J. P. Herbert, “Financial time-series analysis with rough sets,” Applied Soft Computing Journal, vol. 9, no. 3, pp. 1000–1007, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Shen and H. T. Loh, “Applying rough sets to market timing decisions,” Decision Support Systems, vol. 37, no. 4, pp. 583–597, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. S. H. Jaaman, S. M. Shamsuddin, B. Yusob, and M. Ismail, “A predictive model construction applying rough set methodology for Malaysian stock market returns,” International Research Journal of Finance and Economics, vol. 30, pp. 211–218, 2009. View at Google Scholar · View at Scopus
  6. F. E. H. Tay and L. Shen, “Economic and financial prediction using rough sets model,” European Journal of Operational Research, vol. 141, no. 3, pp. 641–659, 2002. View at Publisher · View at Google Scholar · View at Scopus
  7. Y.-F. Wang, “Mining stock price using fuzzy rough set system,” Expert Systems with Applications, vol. 24, no. 1, pp. 13–23, 2003. View at Publisher · View at Google Scholar · View at Scopus
  8. T. G. Smolinski, D. L. Chenoweth, and J. M. Zurada, “Application of rough sets and neural networks to forecasting university facility and administrative cost recovery,” in Proceedings of the 7th International Conference on Artificial Intelligence and Soft Computing (ICAISC '04), pp. 538–543, June 2004. View at Scopus
  9. M. Zhang and J. T. Yao, “A rough sets based approach to feature selection,” in Proceedings of the 23rd International Conference of NAFIPS, pp. 434–439, June 2004. View at Scopus
  10. L. I. Kuncheva, “Fuzzy rough sets: application to feature selection,” Fuzzy Sets and Systems, vol. 51, no. 2, pp. 147–153, 1992. View at Google Scholar · View at Scopus
  11. J. Yao, C. L. Tan, and H. Poh, “Neural networks for technical analysis: a study on KLCI,” International Journal of Theoretical and Applied Finance, pp. 221–241, 1999. View at Google Scholar
  12. B. K. Wong, V. S. Lai, and J. Lam, “A bibliography of neural network business applications research: 1994–1998,” Computers and Operations Research, vol. 27, no. 11-12, pp. 1045–1076, 2000. View at Publisher · View at Google Scholar · View at Scopus
  13. W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The Bulletin of Mathematical Biophysics, vol. 5, no. 4, pp. 115–133, 1943. View at Publisher · View at Google Scholar · View at Scopus
  14. Z. Pawlak, “Rough sets,” International Journal of Computer & Information Sciences, vol. 11, no. 5, pp. 341–356, 1982. View at Publisher · View at Google Scholar · View at Scopus
  15. J. W. Wilder, “New concepts in technical trading systems,” Trend Research, 1978.
  16. The Rosetta Rough Set Toolkit, http://www.lcb.uu.se/tools/rosetta.