Predicting Stock Price Changes of Tehran Artmis Company Using Radial Basis Function Neural Networks
Journal Title: International Research Journal of Applied and Basic Sciences - Year 2016, Vol 10, Issue 8
Abstract
Analyzing and predicting changes in stock prices are of most significant capabilities to enter the stock market. Due to their relative abilities in recognizing the behavior and changes of stock prices, technical and fundamental analyses are among the most basic and most used statistical methods in the stock market. Due to the ability of neural networks to understand different behavior patterns, the results of technical analysis are predicted on a daily basis with a higher speed and accuracy using radial basis function (RBF) neural networks and Levenberg learning algorithm.
Authors and Affiliations
Amirhossein Ghaznavi*| Department of Computer, Science and Research Branch, Islamic Azad University, Tehran, Iran, email: A.Ghaznavi@srbiau.ac.ir, Mohammad Aliyari| Department of Computer, Science and Research Branch, Islamic Azad University, Tehran, Iran, Mohammad Reza Mohammadi| Department of Computer, Science and Research Branch, Islamic Azad University, Tehran, Iran
Keywords
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