Prediction stock price using artificial neural network
(Case study: chemical industry firms accepted in Tehran stock exchange)
Abstract
The present paper aims to provide an efficient model to predict stock prices using neural networks. Therefore the chemical industry companies accepted in Tehran Stock Exchange for the study were selected. Data for the period 2014 - 2010 prepared by using feed forward neural network with back-propagation algorithm to predict the stock price of the study were discussed. To evaluate the effectiveness of neural networks as compared to the classical methods of prediction, a comparison with regression (panel data) was performed. Both methods artificial neural network and regression results are consistent with But the total square error of neural network method is 0.29 and 1.68 in the regression that demonstrate the advantages and effectiveness of the neural network method than regression in predicting stock prices and chemical industry companies are listed on the Tehran Stock Exchange.