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

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@Article{Bahiraie:2009:AJAS,
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

Citations