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

Paper:

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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

Received:
October 14, 2008
Accepted:
April 6, 2009
Published:
November 20, 2009
Creative Commons License
Keywords:
technical rule, learning, genetic algorithm, in-sample data, over-fitting
Abstract
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.
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