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http://dx.doi.org/10.7465/jkdi.2014.25.2.281

Using genetic algorithm to optimize rough set strategy in KOSPI200 futures market  

Chung, Seung Hwan (Department of Information and Industrial Engineering, Yonsei University)
Oh, Kyong Joo (Department of Information and Industrial Engineering, Yonsei University)
Publication Information
Journal of the Korean Data and Information Science Society / v.25, no.2, 2014 , pp. 281-292 More about this Journal
Abstract
As the importance of algorithm trading is getting stronger, researches for artificial intelligence (AI) based trading strategy is also being more important. However, there are not enough studies about using more than two AI methodologies in one trading system. The main aim of this study is development of algorithm trading strategy based on the rough set theory that is one of rule-based AI methodologies. Especially, this study used genetic algorithm for optimizing profit of rough set based strategy rule. The most important contribution of this study is proposing efficient convergence of two different AI methodology in algorithm trading system. Target of purposed trading system is KOPSI200 futures market. In empirical study, we prove that purposed trading system earns significant profit from 2009 to 2012. Moreover, our system is evaluated higher shape ratio than buy-and-hold strategy.
Keywords
Algorithm trading; futures market; genetic algorithm; rough set;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 Byun, H. W., Song, C, W., Han, S. K., Lee, T. K. and Oh, K. J. (2009). Using genetic algorithms to develop volatility index-assisted hierarchical portfolio optimization. Journal of Korean Data & Information Science Society, 20, 1049-1060.   과학기술학회마을
2 Chang, P. C. and Liu, C. H. (2008). A TSK type fuzzy rule based system for stock price prediction. Expert Systems with Applications, 34, 135-144   DOI   ScienceOn
3 Chavarnakul, T. and Enke, D. (2008). Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 34, 1004-1017.   DOI   ScienceOn
4 Dai, J. H. and Li, Y. X. (2002). Study on discretization based on rough set theory, Preceedings of the First International Conference On Machine Learning and Cybernetics, 4-5.
5 Kang, Y. J. and Oh, K. J. (2013). Using rough set to develop a volatility reverting strategy in options market. Journal of Korean Data & Information Science Society, 24, 135-150.   과학기술학회마을   DOI   ScienceOn
6 Dong, M. and Zhou, X. S. (2002). Exploring the fuzzy nature of technical patterns of U.S stock market. ICONIP'02-SEAL'02-FSKD'02, Singapore, 18-22.
7 Fazel Zarzndi, M. H., Rezaee, B., Turksen, I. B. and Neshat, E. (2009). A type-2 fuzzy rule-based expert system model for stock price analysis. Expert Systems with Applications, 36, 139-154.   DOI   ScienceOn
8 Kim, H, H. and Oh, K. J. (2012). Using rough set to develop the optimization strategy of evolving timedivision trading in the futures market. Journal of Korean Data & Information Science Society, 23, 881-893.   DOI   ScienceOn
9 Kim, K. J. and Han, I. G. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications, 19, 125-132.   DOI   ScienceOn
10 Kim, S. and Ahn, H. (2010). Development of an intelligent trading system using support vector machines and genetic algorithms. Journal of Intelligence and Information Systems, 16, 71-92.
11 Kuo, R. J., Chen, C. H. and Hwang, Y. C. (2001). An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems, 118, 21-45.   DOI   ScienceOn
12 Pawlak, Z. (1997). Rough set approach to knowledge-based decision support. European Journal of Operational Research, 99, 48-57.   DOI   ScienceOn
13 Lee, S. J., Ahn, J. J., Oh, K. J. and Kim, T. Y. (2010). Using rough set to support investment strategies of real-time trading in futures market. Applied Intelligence, 32, 364-337.   DOI   ScienceOn
14 Lo, A. W., Mamaysky, H. M. and Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. Journal of Finance, 55, 1705-1770.   DOI   ScienceOn
15 Marc, B. (2004). Khiops: A statistical discretization method of continuous attributes. Machine Learning, 55, 53-69.   DOI
16 Fong, S., Tai, J. and Si, Y. W. (2011). Trend following algorithms for technical trading in stock market. Journal of Emerging Technologies in Web Intelligence, 3, 136-145.