A Takagi–Sugeno fuzzy model combined with a support vector regression for stock trading forecasting |
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| Affiliation: | 1. Collins College of Business, The University of Tulsa, 800 South Tucker Drive, Helmerich Hall 118B, Tulsa, OK, 74104, United States;2. Department of Engineering Management and Systems Engineering, Laboratory for Investment and Financial Engineering, Intelligent Systems Center, Missouri University of Science and Technology, 221 Engineering Management, 600 W. 14th Street, Rolla, MO, 65409-0370, United States;3. SphereXX.com, 9142 S. Sheridan, Tulsa, OK, 74133, United States;4. Microsoft Corporation, 205 108th Ave NE #400, Bellevue, WA, 98004, United States;1. Department of Computer Science, Xiamen University, Xiamen, 361005, China;2. Department of Computer and Information Science, University of Macau, Macau, China\n;3. Faculty of Software and Information Science, Iwate Prefectural University, Iwate, Japan |
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| Abstract: | The turning points prediction scheme for future time series analysis based on past and present information is widely employed in the field of financial applications. In this research, a novel approach to identify turning points of the trading signal using a fuzzy rule-based model is presented. The Takagi–Sugeno fuzzy rule-based model (the TS model) can accurately identify daily stock trading from sets of technical indicators according to the trading signals learned by a support vector regression (SVR) technique. In addition, when new trading points are created, the structure and parameters of the TS model are constantly inherited and updated. To verify the effectiveness of the proposed TS fuzzy rule-based modeling approach, we have acquired the stock trading data in the US stock market. The TS fuzzy approach with dynamic threshold control is compared with a conventional linear regression model and artificial neural networks. Our result indicates that the TS fuzzy model not only yields more profit than other approaches but also enables stable dynamic identification of the complexities of the stock forecasting system. |
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| Keywords: | Model identification Takagi–Sugeno model Support vector regression Stock trading forecasting |
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