Record Details

Investigating determinants of disclosure quality using Artificial Neural Network

The Journal of Accounting and Management

View Archive Info
 
 
Field Value
 
Title Investigating determinants of disclosure quality using Artificial Neural Network
 
Creator Sarikhani, Mehdi
Saif, Seyed Mojtaba
 
Subject Disclosure Quality, Artificial Neural Network, feature selection
 
Description The purpose of this research is to propose a model for predicting disclosure quality using artificial neural network. Toward this end, this research has used the variables related to liquidity, profitability, leverage, company size, corporate governance and other effective variables by using the artificial neural network. Minimal-Redundancy-Maximal-Relevance criterion and sequential feature selection as two Feature selection methods are used to preprocess the data that could improve the accuracy of model. Results show that in the model where all variables are applied, the linear regression correlation between network output and scope data is %87.8 while in the model where the seven variables of Ownership concentration, Assets-in-place, Age, Profit margin, Percentage of non-executive board members, Institutional ownership ratio, and Number of employees are used, this correlation stands at %92.26. Also, these results show the significant effect of the corporate governance variables on disclosure quality.
 
Publisher The Journal of Accounting and Management
 
Contributor —
 
Date 2017-10-10
 
Type Peer-reviewed Article
 
Format application/pdf
 
Identifier http://journals.univ-danubius.ro/index.php/jam/article/view/4096
 
Source The Journal of Accounting and Management; Vol 7, No 2 (2017): JAM
 
Language en
 
Relation http://journals.univ-danubius.ro/index.php/jam/article/download/4096/14197
http://journals.univ-danubius.ro/index.php/jam/article/download/4096/14379
 
Rights The author fully assumes the content originality and the holograph signature makes him responsible in case of trial.