On the Predictability of Risk Box Approach by Genetic Programming Method for Bankruptcy Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Alireza Bahiraie and Noor {Akma bt Ibrahim} and 
                 A. K. M. Azhar",
  title =        "On the Predictability of Risk Box Approach by Genetic
                 Programming Method for Bankruptcy Prediction",
  journal =      "American Journal of Applied Sciences",
  year =         "2009",
  volume =       "6",
  number =       "9",
  pages =        "1748--1757",
  keywords =     "genetic algorithms, genetic programming, ratios
                 analysis, risk box, bankruptcy prediction",
  ISSN =         "1546-9239",
  URL =          "http://www.scipub.org/fulltext/ajas/ajas691748-1757.pdf",
  oai =          "oai:doaj-articles:dbcbc387f7a40da02a20dffdfbef123f",
  size =         "10 pages",
  abstract =     "{\bf Problem statement: }Theoretical based data
                 representation is an important tool for model selection
                 and interpretations in bankruptcy analysis since the
                 numerical representation are much less transparent.
                 Some methodological problems concerning financial
                 ratios such as non-proportionality, non-asymetricity,
                 non-scalicity are solved in this study and we presented
                 a complementary technique for empirical analysis of
                 financial ratios and bankruptcy risk. {\bf Approach:}
                 This study presented new geometric technique for
                 empirical analysis of bankruptcy risk using financial
                 ratios. Within this framework, we proposed the use of a
                 new ratio representation which named Risk Box measure
                 (RB). We demonstrated the application of this geometric
                 approach for variable representation, data
                 visualization and financial ratios at different stages
                 of corporate bankruptcy prediction models based on
                 financial balance sheet ratios. These stages were the
                 selection of variables (predictors), accuracy of each
                 estimation model and the representation of each model
                 for transformed and common ratios. {\bf Results: }We
                 provided evidence of extent to which changes in values
                 of this index were associated with changes in each axis
                 values and how this may alter our economic
                 interpretation of changes in the patterns and direction
                 of risk components. Results of Genetic Programming (GP)
                 models were compared as different classification models
                 and results showed the classifiers outperform by
                 modified ratios.{\bf Conclusion/Recommendations:} In
                 this study, a new dimension to risk measurement and
                 data representation with the advent of the Share Risk
                 method (SR) was proposed. Genetic programming method is
                 substantially superior to the traditional methods such
                 as MDA or Logistic method. It was strongly suggested
                 the use of SR methodology for ratio analysis, which
                 provided a conceptual and complimentary methodological
                 solution to many problems associated with the use of
                 ratios. Respectively, GP will provide heuristic non
                 linear regression as a tool in providing forecasting
                 regression for studies associated with financial data.
                 Genetic programming as one of the modern classification
                 method out performs by the use of modified ratios. Our
                 new method would be a general methodological guideline
                 associated with financial data analysis.",
  ISSN =         "15469239",
  bibsource =    "OAI-PMH server at www.doaj.org",

Genetic Programming entries for Alireza Bahiraie Noor Akma Ibrahim Mohamed Abdul Karim Azhar