Forecast earnings management based on adjusted Jones model using Artificial Neural Networks and Genetic Algorithms

Subject Areas : Financial engineering

Khosro Faghani Makrani ¹ , S. Hasan Salehnezhad ² , Vahid Amin ³

1 - استادیار، عضو هیات علمی دانشگاه آزاد اسلامی، واحد سمنان، گروه حسابداری، سمنان ، ایران.
2 - استادیار، عضو هیات علمی دانشگاه پیام نور، گروه حسابداری، تهران، ایران.
3 - مربی، عضو هیات علمی دانشگاه پیام نور، گروه حسابداری، تهران، ایران.

Received: 2016-02-14 Accepted : 2016-05-03 Published : 2016-09-22

Keywords: genetic algorithms, Discretionary accruals, Earnings Management, Artificial Neural Networks,

Abstract :

In recent years, earnings management in university research has attracted much attention. The aim of this study is to predict earnings management through discretionary accruals based on adjusted Jones model. In this study, two models of artificial neural networks and genetic algorithms - neural network hybrid model as a successful model to predict earnings management based on adjusted Jones model were used in the Tehran Stock Exchange. The sample used in this study is consisted of 570 firm-year between 2008 to 2013. The results showed that neural networks have a high ability to predict earnings management rather than the adjusted Jones linear model. The findings also suggest that, the genetic algorithm through optimizing artificial neural network weights is able to increase power of artificial neural network to predict earnings management.