Dividend Policy Prediction by Multivariable and Univariate Neural Network Models

Document Type : Original Article

Authors
Mohsen Hamidian 1
M.B. Mohhamadzadeh Moghadam 2
Sajjad Naghdi 3
Javad Esmaeili 4
1 Associate Professor in Accounting, Islamic Azad University
2 Instructor of Accounting & Ph.D in Accounting, Islamic Azad University
3 Ph.D in Accounting, Shahid Beheshti University
4 MS in Accounting, Shahid Beheshti University
Abstract
The topic dividend policy is one of the most leading issues in modern corporate finance affecting the firm value. The results of linear methods and regression could not satisfy researchers in forecasting of financial issues such as dividend policy.
In this paper, we present a comparative analysis of the forecasting accuracy of univariate and multivariate Artificial Neural Network using a sample of 183 companies listed in the Tehran Stock Exchange through for the years 2011_2015.
This study shows that the application of the multivariate neural network model results in forecasts that are more accurate than Univariate neural network forecasting models. Our findings show that forecast of a multivariate ANN incorporating Marsh and Merton (1987) variables is more accurate than univariate ANNs. Therefore, based on the results of the study we suggest that shareholders, investors and other stakeholders use multivariate ANNs to predict dividend policy of companies listed in Tehran Stock Exchange.
 
Keywords
Dividend policy
Multivariate neural network
Univariate neural network
Marsh and Merton model
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