Diagnosing Corporate Stability using Grammatical Evolution

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@Article{BrabazonONeill:2004:IJAMCSDCSuGE,

• author = "Anthony Brabazon and Michael O'Neill",
• title = "Diagnosing Corporate Stability using Grammatical Evolution",
• journal = "International Journal of Applied Mathematics and Computer Science",
• year = "2004",
• volume = "14",
• number = "3",
• pages = "363--374",
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution, corporate failure prediction",
• ISSN = "1641-876X",
• URL = "http://eudml.org/doc/207703",
• 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.",
• notes = "Also known as \cite{Brabazon2004}",

}

Genetic Programming entries for Anthony Brabazon Michael O'Neill

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