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Mathematical Problems in Engineering
Volume 2015, Article ID 178197, 9 pages
Research Article

A Hybrid Analysis Approach to Improve Financial Distress Forecasting: Empirical Evidence from Iran

Department of Industrial & Systems Engineering, Isfahan University of Technology, Isfahan 84156 83111, Iran

Received 11 October 2014; Revised 5 March 2015; Accepted 6 March 2015

Academic Editor: Gerhard-Wilhelm Weber

Copyright © 2015 Shakiba Khademolqorani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Bankruptcy prediction is an important problem facing financial decision support for stakeholders of firms, including auditors, managers, shareholders, debt-holders, and potential investors, as well as academic researchers. Popular discourse on financial distress forecasting focuses on developing the discrete models to improve the prediction. The aim of this paper is to develop a novel hybrid financial distress model based on combining various statistical and machine learning methods. Then multiple attribute decision making method is exploited to choose the optimized model from the implemented ones. Proposed approaches have also been applied in Iranian companies that performed previous models and it can be consolidated with the help of the hybrid approach.