文件名称:12_-Fallahi-AAM-R358-SF-012211

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The cement industry is one of the most important and profitable industries in Iran and great

content of financial resources are investing in this sector yearly. In this paper a GMDH-type

neural network and genetic algorithm is developed for stock price prediction of cement sector.

For stocks price prediction by GMDH type-neural network, we are using earnings per share

(EPS), Prediction Earnings Per Share (PEPS), Dividend per share (DPS), Price-earnings ratio

(P/E), Earnings-price ratio (E/P) as input data and stock price as output data. For this work, data

of ten cement companies is gathering Tehran stock exchange (TSE) in decennial range

(1999-2008). GMDH type neural network is designed by 80 of the experimental data. For

testing the appropriateness of the modeling, reminder of primary data were entered into the

GMDH network. The results are very encouraging and congruent with the experimental results.-The cement industry is one of the most important and profitable industries in Iran and great

content of financial resources are investing in this sector yearly. In this paper a GMDH-type

neural network and genetic algorithm is developed for stock price prediction of cement sector.

For stocks price prediction by GMDH type-neural network, we are using earnings per share

(EPS), Prediction Earnings Per Share (PEPS), Dividend per share (DPS), Price-earnings ratio

(P/E), Earnings-price ratio (E/P) as input data and stock price as output data. For this work, data

of ten cement companies is gathering Tehran stock exchange (TSE) in decennial range

(1999-2008). GMDH type neural network is designed by 80 of the experimental data. For

testing the appropriateness of the modeling, reminder of primary data were entered into the

GMDH network. The results are very encouraging and congruent with the experimental results.
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12_ Fallahi AAM-R358-SF-012211.pdf

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