JACIII Vol.13 No.6 pp. 726-730
doi: 10.20965/jaciii.2009.p0726


Learning and Technical Market -Effects of In-Sample Data Selection-

Tomio Kurokawa

Department of Information Science, Aichi Institute of Technology, Yagusa-cho, Toyota 470-0392, Japan

October 14, 2008
April 6, 2009
November 20, 2009
technical rule, learning, genetic algorithm, in-sample data, over-fitting
For a learning system, nothing is more influential than data to be learned. Any data, whether in-sample or out-of-sample, is intrinsically particular. This is typically true for complex market data. Data for the last year on a single stock name, for instance, is only a small segment of data patterns if compared to unseen out-of-sample data having potentially numerous patterns. Any technical system trained with limited in-sample data is more or less particular, thus likely becomes to be overfitted. If in-sample data is particular, its performance for out-of-sample data is expected to be poor, no matter how well the technical system is trained. In other words, a better trained technical system frequently has lower performance for out-of sample data. If not well controlled, training is generally a particularity-seeking process of in-sample data, requiring some mechanism outside of the learning process. Possible solutions may exist in in-sample data selection, learning sophistication, or technical indicator effectiveness. This study is intended to provide a better understanding of technical markets and learning, and suggestions on in-sample data selection. Experiments examine how the selection of in-sample data affects the performance of a target system for out-of-sample data and show technical market and learning phenomena using genetic algorithms (GA).
Cite this article as:
T. Kurokawa, “Learning and Technical Market -Effects of In-Sample Data Selection-,” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.6, pp. 726-730, 2009.
Data files:
  1. [1] J. Wang and S. Chen, “Evolutionary stock trading decision support system using sliding window,” Proc. of the IEEE Conf. on Evolutionary Computation, pp. 253-258, 1998
  2. [2] S. S. Lam, K. P. Lam and H. S. N., “Genetic fuzzy expert trading system for NASDAQ stock market timing,” in Genetic Algorithms and Genetic Programming in Computational Finance, Ed. Shu-Heng, Kluwer Academic Publishers, Boston, pp.197-217, 2002
  3. [3] C. Neely, P. Weller and R. Ditmar, “Is technical analysis in the foreign exchange market profitable? -- a genetic programming approach,” Journal of Financial and Quantitative Analysis, Vol.32, No.4, pp. 405-427 (1997)
  4. [4] L. A. Becker and M. Seshadri, “GP-evolved technical trading rules can outperform buy and hold,” Worcester Polytechnic Institute, Computer Science Technical Report WPI-CS-TR-03-16, 2003
  5. [5] L. A. Becker and M. Seshadri, “Comprehensibility and over-fitting avoidance in genetic programming,” Worcester Polytechnic Institute, Computer Science Technical Report WPI-CS-TR-03-09, 2003
  6. [6] L. Lin, L. Cao, and C. Zhang, “Genetic Algorithms for Robust Optimization in Financial Application,” Proc. of the Fourth IASTED Inter. Conf. of Computational Intelligence, pp. 387-391, 2005
  7. [7] J. Y. Potvin, P. Soriano and M. Vallee: “Genetic trading rules on the stock markets with genetic programming,” Computers & Operations Research, Vol. 31, pp. 1033-1047, 2004
  8. [8] N.G. Pavlidis, E.G. Pavlidis, M.G. Epitropakis, V.P. Plagianakos, and M.N. Vrahatis, “Computational Intelligence Algorithms For Risk-Adjusted Trading Strategies,” (Eds) Dipti Srinivasan and Lipo Wang, IEEE Congress on Evolutionary Computation (CEC), 2007
  9. [9] S. Mabu, K. Hirasawa, and T. Furuzuki, “Trading Rules on Stock Markets Using Genetic Network Programming with Reinforcement Learning and Importance Index,” Transaction of IEE Japan C, Vol.127, No.7, pp. 1061-1067, 2007
  10. [10] T. Kurokawa, “Evolutionary method to optimize composite indicator for market timing,” The Proc. of the 8th Inter. Symposium on Advanced Intelligent Systems, pp. 264-269, 2007
  11. [11] T. Takizawa, “SP-Wave Method,” Tokyo: Pan-Rolling Publishing, 1999
  12. [12] H. He, J. Chen, H. Jin, and S. H. Chen, “Trading Strategies Based on K-means Clustering and Regression Models,” in Computational Intelligence in Economics and Finance, Vol.II, Eds. S. H. Chen, P. P. Wang, and T. W. Kuo, pp. 123-134, Springer, 2007
  13. [13] K. Yamada, GLIBW32, Copyright(c) K. Yamada, 1998-2004,∼uc3k-ymd/

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 03, 2024