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