Asian Journal of Research in Business Economics and Management
  • Year: 2014
  • Volume: 4
  • Issue: 9

Predicting Real earning management using PSO, ICA, MLP and SVM: A case study in firms listed in Tehran Security Exchange

  • Author:
  • Fatemeh Sadat Ghotbia, Hamid Khademb, Mostafa Bagherib, Abulfazl Sadat Aghaeec
  • Total Page Count: 17
  • Page Number: 336 to 352

aM.A Student, Financial Management, University of Sistan and Baluchestan, Zahedan, Iran

bM.A in Accounting, University of Ferdosi, Mashad

cM.A in Accounting, University of Sistan and Baluchestan, Zahedan, Iran

Online published on 6 September, 2014.

Abstract

The main aim of this research is to predict real earnings management using colonial competitive algorithm(ICA), Particle Swarm Optimization (PSO), support vector machine(SVM) and multi-layer Perceptron (MLP)network algorithm combined with Particle Swarm Optimization (PSO). For this purpose, the fourteen variables affecting earnings management were used as independent variables and real earnings management were used as dependent variables. Research sample include 113 firms for 7 years between 2008–2012. The results show that multi-layer Perceptron network is more efficient than other algorithms in real earnings management prediction. Eviews was used for earning management models and Matlab and SPSS used for their implementation.

Keywords

Predicting real earning management, Colonial competitive algorithm, algorithm birds move, Support Vector Machine, Multi-layer Perceptron network