Anticipating bankruptcy reorganisation from raw financial data using grammatical evolution

Authors:
Anthony Brabazon;Michael O'Neill
Affiliations:
Dept. of Accountancy, University College Dublin, Ireland;Dept. of Computer Science and Information Systems, University of Limerick, Ireland
Venue:
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Year:
2003

Citing 4
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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 financial 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.