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ISRN Artificial Intelligence
Volume 2014 (2014), Article ID 451849, 10 pages
http://dx.doi.org/10.1155/2014/451849
Research Article

Study on the Effectiveness of the Investment Strategy Based on a Classifier with Rules Adapted by Machine Learning

West Pomeranian University of Technology, Żołnierska 49, 71-210 Szczecin, Poland

Received 29 September 2013; Accepted 12 December 2013; Published 3 February 2014

Academic Editors: J. Bajo and K. W. Chau

Copyright © 2014 A. Wiliński 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. K. P. Murphy, Machine Learning: A Probabilistic Perspective, Cambridge, Mass, USA, 2012.
  2. C. Satchwell, Pattern Recognition and Trading Decisions, Irwin Trader’s Edge Series, McGraw-Hill, 2005.
  3. G. Polya, How To Solve It, Garden City, Egypt, 1957.
  4. D. L. Donoho, A. Maleki, M. Shahram, I. U. Rahman, and V. Stodden, “Reproducible research in computational harmonic analysis,” Computing in Science and Engineering, vol. 11, no. 1, pp. 8–18, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. P. Ball, Critical Mass: How One Thing Leads to Another, Farrar Straus Giroux, 2006.
  6. W. Pedrycz, Computational Intelligence: An Introduction, Computer Engineering, Software Programming, CRC Press, 1998.
  7. W. Brock, J. Lakonishok, and B. LeBaron, “Simple technical trading rules and the stochastic properties of stock returns,” Journal of Finance, vol. 47, no. 5, pp. 1731–1764, 1992.
  8. B. M. Cai, C. X. Cai, and K. Keasey, “Market efficiency and returns to simple technical trading rules: further evidence from U.S., U.K., Asian and Chinese stock markets,” Asia-Pacific Financial Markets, vol. 12, no. 1, pp. 45–60, 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Gençay, “Linear, non-linear and essential foreign exchange rate prediction with simple technical trading rules,” Journal of International Economics, vol. 47, no. 1, pp. 91–107, 1999. View at Scopus
  10. B. LeBaron, “Technical trading rules and regime shifts in foreign exchange,” Tech. Rep., 1991.
  11. G. G. Tian, H. U. A. Guang Wan, and G. U. O. Mingyuan, “Market efficiency and the returns to simple technical trading rules: new evidence from U.S. Equity Market and Chinese Equity Markets,” Asia-Pacific Financial Markets, vol. 9, no. 3-4, pp. 241–258, 2002. View at Scopus
  12. A. Muriel, “Short-term predictions in forex trading,” Physica A, vol. 344, no. 1-2, pp. 190–193, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. A. Wilinski, “Prediction models of financial markets based on multiregression algorithms,” CSJ of Moldova, vol. 19, no. 2, pp. 178–188, 2011.
  14. K. Fujimoto and S. Nakabayashi, “Applying GMDH algorithm to extract rules from examples,” Systems Analysis Modelling Simulation, vol. 43, no. 10, pp. 1311–1319, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Raghuraj and S. Lakshminarayanan, “Variable predictive models—a new multivariate classification approach for pattern recognition applications,” Pattern Recognition, vol. 42, no. 1, pp. 7–16, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. P. Klesk and A. Wilinski, “Market trajectory recognition and trajectory prediction using Markov models,” in Artificial Intelligence and Soft Computing, vol. 6113 of Lecture Notes in Computer Science, pp. 405–413, 2010.
  17. J. Krutsinger, Trading Systems: Secrets of the Masters, McGraw-Hill, 1997.
  18. A. G. Ivakhnenko, An Inductive Sorting Method for the Forecast of Multidimensional Random Processes and Analog Events with the Method of Analog Forecast Complexing, Pattern Recognition and Image Analysis, 1991.
  19. D. Kahneman, P. Slovic, and A. Tversky, Judgment Under Uncertainty: Heuristics and Biases, Cambridge University Press, 1982.
  20. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, December 1995. View at Scopus
  21. F. Wang, P. Yu, and D. Cheung, “Complex stock trading strategy based on Particle Swarm Optimization,” in Proceedings of the IEEE Conference on Computational Intelligence for Financial Engineering Economics (CIFEr '12), pp. 1–6, 2012.
  22. K. W. Chau, “Application of a PSO-based neural network in analysis of outcomes of construction claims,” Automation in Construction, vol. 16, no. 5, pp. 642–646, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Zhang and K.-W. Chau, “Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization,” Journal of Universal Computer Science, vol. 15, no. 4, pp. 840–858, 2009. View at Scopus