A Neural Network of Dividend Payout Ratio Determinants
Subject Areas :Mahdi Moradzadeh Fard 1 , Parisa Attary Motlagh 2
1 - استادیار دانشگاه آزاد اسلامی واحد کرج
2 - کارشناس ارشد حسابداری دانشگاه آزاد اسلامی واحد علوم و تحقیقات
Keywords: Neural network, Earning Per Share, Dividend Per Share, Dividend Payout Ratio,
Abstract :
This research analyzes the determinants of dividend payout ratio, using a sample of Tehran Stock Exchange companies and it also compares the predictive power of neural networks and regression model to estimate dividend payout ratio. The purpose of this research is identification and explanation of the determinants of dividend payout ratio, evaluation of the importance of these determinants, and presentation of a descriptive model of dividend payout ratio determinants. Among the different theories and ideas researchers in the field of finance about dividend, in this study, signaling theory, agency theory and residual theory of dividend have been studied. In order to review the theories, first substituting variables were determined and then the necessary information for 133 companies was gathered during six years(83-88). The information was analyzed via statistical methods based on correlation coefficient, multivariate regression, and artificial neural networks. The results of this research indicate the existence of a significant and positive relationship between dividend payout ratio and “last year dividend payout ratio, ownership dispersion and free cash flow(FCF)”. A comparison between regression and neural network models indicating more accurate prediction of dividend payout ratio using neural network model. Also in structure(7-13-1) of neural network, a model with “learning initial=0/15 and momentum= 0/9” shows that revenue growth, last year dividend payout ratio, and ownership dispersion are the most important determinants of dividend payout ratio.
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