Diagnosing corporate stability using grammatical evolution

Anthony Brabazon; Michael O'Neill

International Journal of Applied Mathematics and Computer Science (2004)

  • Volume: 14, Issue: 3, page 363-374
  • ISSN: 1641-876X

Abstract

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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)% of the firms in the in-sample training set (out-of-sample validation set), one year prior to failure.

How to cite

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Brabazon, Anthony, and O'Neill, Michael. "Diagnosing corporate stability using grammatical evolution." International Journal of Applied Mathematics and Computer Science 14.3 (2004): 363-374. <http://eudml.org/doc/207703>.

@article{Brabazon2004,
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)% of the firms in the in-sample training set (out-of-sample validation set), one year prior to failure.},
author = {Brabazon, Anthony, O'Neill, Michael},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {corporate failure prediction; grammatical evolution},
language = {eng},
number = {3},
pages = {363-374},
title = {Diagnosing corporate stability using grammatical evolution},
url = {http://eudml.org/doc/207703},
volume = {14},
year = {2004},
}

TY - JOUR
AU - Brabazon, Anthony
AU - O'Neill, Michael
TI - Diagnosing corporate stability using grammatical evolution
JO - International Journal of Applied Mathematics and Computer Science
PY - 2004
VL - 14
IS - 3
SP - 363
EP - 374
AB - 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)% of the firms in the in-sample training set (out-of-sample validation set), one year prior to failure.
LA - eng
KW - corporate failure prediction; grammatical evolution
UR - http://eudml.org/doc/207703
ER -

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