A Takagi–Sugeno fuzzy model combined with a support vector regression for stock trading forecasting

• A novel approach for identify turning points of the stock trading signal is presented.
• SVRs classifier was applied to learn the trading signals from set of Technical indices.
• The TS fuzzy approach with dynamic threshold control is developed.
• Different stocks from the US market were selected to be compared for the model performances.

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.

The framework of the stock trading forecasting system using TS fuzzy model.Figure optionsDownload as PowerPoint slide