산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제36권4호
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  • Pages.123-129
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  • 2013
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  • 2005-0461(pISSN)
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  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
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유전알고리즘 활용한 실시간 패턴 트레이딩 시스템 프레임워크

Conceptual Framework for Pattern-Based Real-Time Trading System using Genetic Algorithm

  • 투고 : 2013.08.29
  • 심사 : 2013.12.18
  • 발행 : 2013.12.31
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초록

The aim of this study is to design an intelligent pattern-based real-time trading system (PRTS) using rough set analysis of technical indicators, dynamic time warping (DTW), and genetic algorithm in stock futures market. Rough set is well known as a data-mining tool for extracting trading rules from huge data sets such as real-time data sets, and a technical indicator is used for the construction of the data sets. To measure similarity of patterns, DTW is used over a given period. Through an empirical study, we identify the ideal performances that were profitable in various market conditions.

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참고문헌

  1. Lo, A.W., Mamaysky, H., and Wang, J., Foundation of technical analysis : computations, algorithms, statistical inference, and empirical implementation. Journal of Finance, 2000, Vol. 55, p 1705-1765. https://doi.org/10.1111/0022-1082.00265
  2. Leigh, W., Purvis, R., and Ragusa, J.M., Forecasting the NYSE Composite Index with technical analysis, pattern recognizer, neural network, and genetic algorithm : a case study in romantic decision support. Decision Support Systems, 2002, Vol. 32, p 161-174.
  3. Chen, G., Wu, X., and Zhu, X., Sequential pattern mining in multiple streams, in Proceedings of the Fifth International Conference on Data Mining. IEEE Computer Society, 2005, p 585-588.
  4. Dorr, D.H. and Denton, A.M., Establishing relationships among patterns in stock market data. Data and Knowledge Engineering, 2009, Vol. 68, p 318-337. https://doi.org/10.1016/j.datak.2008.10.001
  5. Armano, G., Marchesi, M., and Murru, A., A hybrid genetic-neural architecture for stock indexes forecasting. Information Science, 2005, Vol. 170, p 3-33. https://doi.org/10.1016/j.ins.2003.03.023
  6. Chang, P.C., Wang, Y.W., and Yang, W.N., An investigation of the hybrid forecasting models for stock price variation in Taiwan. Journal of the Chinese Institute of Industrial Engineering, 2004, Vol. 21, p 358-368. https://doi.org/10.1080/10170660409509416
  7. David, E. and Suraphan, T., The use of data mining and neural networks for forecasting stock market returns. Expert System Application, 2005, Vol. 29, p 927-940. https://doi.org/10.1016/j.eswa.2005.06.024
  8. Chavarnakul, T. and Enke, D., Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems with Applications, 2008, Vol. 34, p 1004-1017. https://doi.org/10.1016/j.eswa.2006.10.028
  9. Sevastianov, P. and Dymova, L., Synthesis of fuzzy logic and Dempster-Shafer Theory for the simulation of the decision-making process in stock trading systems. Mathematics and Computers in Simulation, 2009, Vol. 80, p 506-521. https://doi.org/10.1016/j.matcom.2009.06.027
  10. Yudong, Z. and Lenan, W., Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. Expert Systems with Applications, 2009, Vol. 36, p 8849-8854. https://doi.org/10.1016/j.eswa.2008.11.028
  11. Kim, K., Electronic and algorithmic trading technology : The complete guide(Complete technology guides for financial services), Burlington : Academic Press, 2007.
  12. Christoffersen, P., Ghysels, E., and Swanson, N.R., Let's get "real" about using economic data. Journal of Empirical Finance, 2009, Vol. 9, p 343-360.
  13. Anderson, T.G., Bollerslev, T. Diebold, F.X., and Vega, C., Micro effects of macro announcements : Real-time price discovery in foreign exchange. American Economic Review, 2003, Vol. 93, p 38-62. https://doi.org/10.1257/000282803321455151
  14. Clark, T.E. and Kozicki, S., Estimating equilibrium real interest rates in real-time. North American Journal of Economics and Finance, 2005, Vol. 16, p 395-413. https://doi.org/10.1016/j.najef.2005.04.002
  15. Ben, R.M. and Rochester, H.C., Is technical analysis profitable on a stock market which has characteristics that suggest it may be inefficient?. Research in International Business and Finance, 2005, Vol. 19, p 384-398. https://doi.org/10.1016/j.ribaf.2005.05.001
  16. Pawlak, Z., Rough set approach to knowledge-based decision support. European Journal of Operational Research, 1997, Vol. 99, p 48-57. https://doi.org/10.1016/S0377-2217(96)00382-7
  17. Bellman, R., Dynamic Programming, NJ : Princeton University Press, 1957.
  18. Lee, S.J., Ahn, J.J., Oh, K.J., and Kim, T.Y., Using rough set to support investment strategies of real-time trading in futures market. Applied Intelligence, 2010, Vol. 32, p 364-377. https://doi.org/10.1007/s10489-008-0150-y
  19. Ohrn, A., Discernibility and rough sets in medicine : Tools and applications, Ph.D. Thesis, Trondheim : Norwegian University of Science and Technology, 1999.
  20. Sharpe, W.F., The Sharpe ratio. Journal of Portfolio Management, 1994, Vol. 21, p 49-58. https://doi.org/10.3905/jpm.1994.409501