Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence

Authors:
F. Mokhatab Rafiei;S. M. Manzari;S. Bostanian
Affiliations:
Department of Industrial Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran;Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran;Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

The purpose of this study is to design a model to predict financial health of companies. Financial ratios for 180 manufacturing companies quoted in Tehran Stock Exchange for one year (year ended March 21, 2008) have been used. Three models; based on artificial neural networks (ANN), genetic algorithm (GA), and multiple discriminant analysis (MDA) are utilized to classify the bankrupt from non bankrupt corporations. ANN model achieved 98.6% and 96.3% accuracy rates in training and holdout samples, respectively. To evaluate the reliability of the model, the data were examined with genetic algorithm and Multivariate discriminate analysis method. GA model attained only 92.5% and 91.5% accuracy rates and MDA reached 80.6% and 79.9 in training and holdout samples, respectively.