Indian Journal of Science and Technology, ( ISI ), Volume (5), No (9), Year (2012-9) , Pages (3321-3327)

Title : ( Effective Components on the Forecast of Companies Dividends Using Hybrid Neural Network and Binary Algorithm Model )

Authors: Mahdi Salehi , Behzad Kardan , Zohreh Aminifard ,

Citation: BibTeX | EndNote

The issue of accounting profit has been noticed from long time by investors, managers, financial analysts and creditors. Due to the importance of dividend per share is disclosed by companies and the role of dividend in decisions and because the most important source of information for investors and managers and other users in the stock, is the forecasted dividend by companies, this study follows to recognize the affecting factors on 23 chemical companies in the Tehran Stock Exchange dividend using genetic algorithms combined with artificial neural network. Finally, the variables affecting the output are used to predict dividends in the model that is by neural network designed. The error is calculated and be the basis of comparison with other methods. The study included chemical companies accepted in Tehran Stock Exchange during 2006-2010.The independent variables in this study are accounting ratios and stock cash dividend is dependent variable.

Keywords

, Prediction, Dividends, Neural network, binary
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@article{paperid:1029411,
author = {Salehi, Mahdi and Kardan, Behzad and Zohreh Aminifard},
title = {Effective Components on the Forecast of Companies Dividends Using Hybrid Neural Network and Binary Algorithm Model},
journal = {Indian Journal of Science and Technology},
year = {2012},
volume = {5},
number = {9},
month = {September},
issn = {0974-6846},
pages = {3321--3327},
numpages = {6},
keywords = {Prediction; Dividends; Neural network; binary algorithm},
}

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%0 Journal Article
%T Effective Components on the Forecast of Companies Dividends Using Hybrid Neural Network and Binary Algorithm Model
%A Salehi, Mahdi
%A Kardan, Behzad
%A Zohreh Aminifard
%J Indian Journal of Science and Technology
%@ 0974-6846
%D 2012

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