- About this Journal ·
- Aims and Scope ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
ISRN Artificial Intelligence
Volume 2014 (2014), Article ID 451849, 10 pages
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.
- K. P. Murphy, Machine Learning: A Probabilistic Perspective, Cambridge, Mass, USA, 2012.
- C. Satchwell, Pattern Recognition and Trading Decisions, Irwin Trader’s Edge Series, McGraw-Hill, 2005.
- G. Polya, How To Solve It, Garden City, Egypt, 1957.
- 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.
- P. Ball, Critical Mass: How One Thing Leads to Another, Farrar Straus Giroux, 2006.
- W. Pedrycz, Computational Intelligence: An Introduction, Computer Engineering, Software Programming, CRC Press, 1998.
- 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.
- 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.
- 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.
- B. LeBaron, “Technical trading rules and regime shifts in foreign exchange,” Tech. Rep., 1991.
- 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.
- A. Muriel, “Short-term predictions in forex trading,” Physica A, vol. 344, no. 1-2, pp. 190–193, 2004.
- A. Wilinski, “Prediction models of financial markets based on multiregression algorithms,” CSJ of Moldova, vol. 19, no. 2, pp. 178–188, 2011.
- 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.
- 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.
- 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.
- J. Krutsinger, Trading Systems: Secrets of the Masters, McGraw-Hill, 1997.
- 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.
- D. Kahneman, P. Slovic, and A. Tversky, Judgment Under Uncertainty: Heuristics and Biases, Cambridge University Press, 1982.
- 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.
- 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.
- 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.
- 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.