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http://dx.doi.org/10.3745/KIPSTB.2008.15-B.6.603

Daily Stock Price Prediction Using Fuzzy Model  

Hwang, Hee-Soo (한라대학교 전기전자과)
Publication Information
The KIPS Transactions:PartB / v.15B, no.6, 2008 , pp. 603-608 More about this Journal
Abstract
In this paper an approach to building fuzzy model to predict daily open, close, high, and low stock prices is presented. One of prior problems in building a stock prediction model is to select most effective indicators for the stock prediction. The problem is overcome by the selection of information used in the analysis of stick-chart as the input variables of our fuzzy model. The fuzzy rules have the premise and the consequent, in which they are composed of trapezoidal membership functions, and nonlinear equations, respectively. DE(Differential Evolution) searches optimal fuzzy rules through an evolutionary process. To evaluate the effectiveness of the proposed approach numerical example is considered. The fuzzy models to predict open, high, low, and close prices of KOSPI(KOrea composite Stock Price Index) on a daily basis are built, and their performances are demonstrated and compared with those of neural network.
Keywords
Fuzzy Model; Time Series Prediction; Differential Evolution; Nonlinear Model; Stock Prediction;
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1 H. S. Hwang, “Automatic design of fuzzy rule base for modeling and control using evolutionary programming,” IEEE Proc-Control Theory Appl., 146, (1), pp.9-16, 1996   DOI   ScienceOn
2 H. S. Hwang and K. B. Woo, “Linguistic fuzzy model identification,” IEEE Proc. Control Theory Appl., 142, (6), pp.537-544, 1995   DOI   ScienceOn
3 S. J. Kang, C. H. Woo, H. S. Hwang and K. B. Woo, “Evolutionary design of fuzzy rule base for nonlinear system modeling and control,” IEEE Trans. Fuzzy Systems, 8, (1), pp.37-44, 2000   DOI   ScienceOn
4 B. M. Louis, “Trend forecasting with technical analysis,” Marketplace BOOKS, 2000
5 K. H. Lee and G. S. Jo, “Expert system for predicting stock market timing using a candlestick chart,” Expert System With Applications, 16, pp.357-364, 1999   DOI   ScienceOn
6 L. C. H. Leon, A. Liu and W. S. Chen, “Pattern discovery of fuzzy time series for financial prediction,” IEEE Trans. Knowledge and Data Engineering, 18, (5), pp.613-625, 2006   DOI   ScienceOn
7 Coupelon, “Neural network modeling for stock movement prediction - A state of the art,” 2007(http://olivier.coupelon.free.fr/Neural_network_modeling_for_stock_movement_prediction.pdf)
8 Fan and M. Palaniswami, “Stock selection using support vector machines,” Proc. of Int. Joint Conf. on Neural Networks, 2001   DOI
9 S. M. Kendall and K. Ord, “Time Series,” Oxford, 1997
10 J. L. Wanga and S. H. Chanb, “Stock market trading rule discovery using two-layer bias decision tree,” Expert Systems with Applications 30, (4), pp.605-611, May, 2006   DOI   ScienceOn
11 Giudici P. Applied Data Mining. Statistical Methods for Business and Industry. Wiley, 2003.
12 R. Storn, “Differential evolution, a simple and efficient heuristic strategy for global optimization over continuous spaces,” Journal of Global Optimization, 11, (4), pp.341-359, 1997   DOI
13 M. Sugeno and T. Yasukawa, “A fuzzy logic based approach to qualitative modeling,” IEEE Trans. Fuzzy Syst., 1, (1), pp.7-31, 1993   DOI   ScienceOn
14 T. Takagi and M. Sugeno, “Fuzzy identification of systems and its application to modeling and control,” IEEE Trans. Syst. Man. Cybern., 15, pp.116-132, 1985
15 P. C. Chang and C. H. Liua, “A TSK type fuzzy based system for stock price prediction,” Expert Systems with Applications, 34, (1), pp.135-144, 2008   DOI   ScienceOn
16 K. Nygren, “Stock Prediction: A neural network approach,” Master Thesis, Royal Institute of Technology, KTH, March, 2004
17 Y. Tang, F. Xu, X. Wan and Y. Q. Zhang, “Web-based fuzzy neural networks for stock prediction,” Computational intelligence and applications, pp.169-174, 2002
18 G. Armano, M. Marchesi and A. Murru, “A hybrid geneticneural architecture for stock indexes forecasting,” Information Sciences, 170, 2005   DOI   ScienceOn
19 M. Noor and R. H. Khokhar, “Fuzzy Decision Tree for Data Mining of Time Series Stock Market Databases,” Critical Assessment of Mocroarray Data Analysis 2004 Conference, November, 2004
20 M. H. Zarandi, E. Neshat, I. B. Turksen and B. Rezaee, “A type-2 fuzzy model for stock market analysis,” Fuzzy System Conf., FUZZ-IEEE 2007, pp.1-6, July, 2007   DOI