—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
—Stock exchange, data fusion, Extended
Kalman filter, technical and fundamental analysis.
H. Haleh is with Faculty of Industrial and Mechanical Engineering;
Islamic Azad University-Qazvin Branch, Qazvin, Iran; e-mail:
B. Akbari Moghaddam, is with Faculty of Management and Accounting
Science, Islamic Azad University-Qazvin Branch ,Qazvin, Iran;
S. Ebrahimijam is with Faculty of Industrial and Mechanical Engineering;
Islamic Azad University-Qazvin Branch , Qazvin, Iran; e-mail:
Cite:H. Haleh, B. Akbari Moghaddam, and S. Ebrahimijam, "A New Approach to Forecasting Stock Price with EKF Data Fusion," International Journal of Trade, Economics and Finance vol.2, no.2, pp. 109-114, 2011.