A genetic programming model for bankruptcy prediction: Empirical evidence from Iran

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@Article{Etemadi20093199,

• title = "A genetic programming model for bankruptcy prediction: Empirical evidence from Iran",
• author = "Hossein Etemadi and Ali Asghar Anvary Rostamy and Hassan Farajzadeh Dehkordi",
• journal = "Expert Systems with Applications",
• volume = "36",
• number = "2, Part 2",
• pages = "3199--3207",
• year = "2009",
• ISSN = "0957-4174",
• DOI = "DOI:10.1016/j.eswa.2008.01.012",
• URL = "http://www.sciencedirect.com/science/article/B6V03-4RSRDDN-4/2/acecffea7c551388162fae4dfbe2a6e2",
• keywords = "genetic algorithms, genetic programming, Bankruptcy prediction, Financial ratios, Multiple discriminant analysis, Iranian companies",
• 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 94percent and 90percent accuracy rates in training and holdout samples, respectively; while MDA model achieved only 77percent and 73percent accuracy rates in training and holdout samples, respectively. McNemar test showed that GP approach outperforms MDA to the problem of corporate bankruptcy prediction.",

}

Genetic Programming entries for Hossein Etemadi Ali Asghar Anvary Rostamy Hassan Farajzadeh Dehkordi

Citations