Obtaining to the method with the least prediction error is one of the challenging issues of financial and investment markets analyzers. Investors often use two different views of technical and fundamental analysis of prices for buying and selling their desired shares. But each of these two methods alone may have not enough performance due to differences between the actual value of the share and its market price. This paper presents a predictive model named extended Kalman filter which simultaneously fuses information and parameters of technical and fundamental analysis. Then as a real test, the model implemented for the shares of one of industrial company in Iran. Finally, the obtained results will be compared with other methods results such as regression and neural networks which shows its desirability in short-term predictions Index Terms-Stock exchange, data fusion, Extended Kalman filter, technical and fundamental analysis.

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Abstract. In this study, we develop a synergistic forecasting model using

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