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Anticipating Bankruptcy Reorganisation from Raw Financial Data Using Grammatical Evolution

  • Anthony Brabazon
  • Michael O’Neill
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2611)

Abstract

This study using Grammatical Evolution, constructs 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 .nancial ratios. Instead, the ratios to be incorporated into the predictive 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 publically quoted, US firms, drawn from the period 1991 to 2000 are used to train and test the model. The best evolved model in each time period correctly classified 78 (70)% of the firms in the out-of-sample validation set, one (three) year(s) prior to failure. The utility of a number of different Grammars for the problem domain is also examined.

Keywords

Sales Revenue Grammatical Evolution Model Development Process Bankrupt Firm Compustat Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Anthony Brabazon
    • 1
  • Michael O’Neill
    • 2
  1. 1.Dept. of AccountancyUniversity College DublinIreland
  2. 2.Dept. of Computer Science and Information SystemsUniversity of LimerickIreland

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