Diagnosing Corporate Stability using Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Anthony Brabazon and Michael O'Neill",
  title =        "Diagnosing Corporate Stability using Grammatical
  journal =      "International Journal of Applied Mathematics and
                 Computer Science",
  year =         "2004",
  volume =       "14",
  number =       "3",
  pages =        "363--374",
  keywords =     "genetic algorithms, genetic programming, Grammatical
  ISSN =         "1641-876X ?",
  URL =          "http://matwbn.icm.edu.pl/ksiazki/amc/amc14/amc1436.pdf",
  size =         "12 pages",
  abstract =     "Grammatical Evolution (GE) is a novel data-driven,
                 model-induction tool, inspired by the biological
                 gene-to-protein mapping process. This study provides an
                 introduction to GE, and demonstrates the methodology by
                 applying it to construct a series of models for the
                 prediction of bankruptcy, employing information drawn
                 from financial statements. Unlike prior studies in this
                 domain, the raw financial information is not
                 preprocessed into pre-determined financial ratios.
                 Instead, the ratios to be incorporated into the
                 classification rule are evolved from the raw financial
                 data. This allows the creation and subsequent evolution
                 of alternative ratio-based representations of the
                 financial data. A sample of 178 publicly quoted, US
                 firms, drawn from the period 1991 to 2000 are used to
                 train and test the model. The best evolved model
                 correctly classified 86 (77)percent of the firms in the
                 in-sample training set (out-of-sample validation set),
                 one year prior to failure.",

Genetic Programming entries for Anthony Brabazon Michael O'Neill