The Study of the Predictive Power of Meta-heuristic Algorithms to Provide a Model for Bankruptcy prediction

Document Type : Original Article

Authors

1 PhD student, Department of Accounting, Islamic Azad University, Damavand Branch, Damavand, Iran

2 Assistant Professor, Department of Accounting, Islamic Azad University, Damavand Branch, Damavand, Iran.

Abstract

Progress in technology as well as extensive environmental changes have accelerated the economy. Intelligent models are examples of these developments predicting the corporate bankruptcy in the future. Investors and creditors are very interested in predicting the corporate bankruptcy, looking for reliable strategies to identify distressed and bankrupt companies. In the present research, companies active in Tehran Stock Exchange in a 10-years period were investigated in terms of bankruptcy based on localized Kordestani-Tatli model based on Altman model, healthy and bankrupt companies were identified as, and those added to or excluded from the stock market during 10 years were eliminated. The research data were collected and refined by means of secondary data extracted from financial statements and through the databases of the Exchange Organization as well as the Central Bank. The models employed to assess the data and predict bankruptcy included six metaheuristic algorithms including gravitational, gray wolf, genetic, imperialist competition, whale and differential evolution algorithm in combination with neural network; and the quality of the predictive models were compared in terms of accuracy and other assessment criteria. Financial ratios in three groups of profitability, liquidity, and capital structure were considered as the input variables of the models with good predictive power in determining bankruptcy. Hence, from the six algorithms investigated in terms of accuracy, the proposed model of the current research includes the three genetic, imperialist competition, and gray wolf algorithms in combination with artificial neural network method having 86% correct prediction accuracy and being able to provide reliable results.

Keywords

References

  • Jeong, C., Min, J. H., & Kim. M. S, 2012. A tuning method for the architecture of neural network models incorporating GAM and GA as applied to bankruptcy prediction. Expert Systems with Applications. 39(3): 3650-3658.
  • Kim, Myoung-Jong & Dae-Ki Kang, 2012, Classifiers selection in ensembles using GAs for bankruptcy prediction. Expert Systems with Applications, pp. 1-7
  • Kordestani & Tatli, Spring 2014, he Evaluate ability of Altman adjusted Model to Prediction Stages of Financial Distress Newton and Bankruptcy, Quarterly of Investment Knowledge, Issue 9, pp. 83-100.
  • Liou, F. M, 2008, Fraudulent financial reporting detection and business failure prediction models: A comparison. Managerial Auditing Journal. 23(7): 650- 662.
  • Qazanfari, Rahimi Kia, and Asgari, 2017, Corporate bankruptcy prediction based on hybrid intelligent systems, Quarterly Journal of Financial Accounting and Auditing Research, Issue 37, pp. 159-194.
  • Shin K, Lee Y. A GA Application in Bankruptcy prediction modeling, Expert systems with application 2002; 1-8.
  • Wang,G., Ma, J., & Yang, S, 2014,“An Improved Boosting Based on Feature Selection for Corporate Bankruptcy Prediction”,Expert Systems with Applications, 41(5), PP.2353-2361
  • Wu, C.H. Tzeng. G.H., Yeong-Jia G. and Fang W.C., 2007, A Real-Valued GA, To Optimize the Parameters of Support Vector Machine for Prediction Bankruptcy, Journal of Expert Systems with Application, Vol. 32, pp. 397-408

 

 

 

Files

Share

How to cite

Statistics