A genetic programming model for bankruptcy prediction: Empirical evidence from Iran |
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| Authors: | Hossein Etemadi Ali Asghar Anvary Rostamy Hassan Farajzadeh Dehkordi |
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| Affiliation: | 1. School of Management, Xi''an Jiaotong University, Xi''an, Shaanxi 710049, China;2. Computer Information Network Center, Changchun University of Technology, Changchun, Jilin 130012, China;3. School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran;4. Department of Computer Science, School of Computing, National University of Singapore, Singapore, Singapore;5. Office of Educational Administration, Changchun University of Technology, Changchun, Jilin 130012, China;6. School of Software, Nanchang Hangkong University, Nanchang 330063, China;7. Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam;8. College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China;1. Department of Industrial Engineering, University of Kurdistan, Pasdaran Boulevard, Sanandaj, Iran;2. Department of Software Engineering & Information Technology, University of Kurdistan, Pasdaran Boulevard, Sanandaj, Iran;3. Department of Computer Engineering, Technological Institute of Epirus, Arta, Greece;1. BEACON Center for the Study of Evolution in Action, 1450 BPS, Michigan State University, East Lansing, MI 48824, USA;2. Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA;3. Department of Civil Engineering, University of Tehran, Tehran, Iran;4. Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Canada |
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| Abstract: | Prediction of corporate bankruptcy is a phenomenon of increasing interest to investors/creditors, borrowing firms, and governments alike. Timely identification of firms’ impending failure is indeed desirable. By this time, several methods have been used for predicting bankruptcy but some of them suffer from underlying shortcomings. In recent years, Genetic Programming (GP) has reached great attention in academic and empirical fields for efficient solving high complex problems. GP is a technique for programming computers by means of natural selection. It is a variant of the genetic algorithm, which is based on the concept of adaptive survival in natural organisms. In this study, we investigated application of GP for bankruptcy prediction modeling. GP was applied to classify 144 bankrupt and non-bankrupt Iranian firms listed in Tehran stock exchange (TSE). Then a multiple discriminant analysis (MDA) was used to benchmarking GP model. Genetic model achieved 94% and 90% accuracy rates in training and holdout samples, respectively; while MDA model achieved only 77% and 73% accuracy rates in training and holdout samples, respectively. McNemar test showed that GP approach outperforms MDA to the problem of corporate bankruptcy prediction. |
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