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2004 | 14 | 3 | 363-374

Tytuł artykułu

Diagnosing corporate stability using grammatical evolution

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
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.

Rocznik

Tom

14

Numer

3

Strony

363-374

Opis fizyczny

Daty

wydano
2004
otrzymano
2004-03-01
poprawiono
2004-06-01

Twórcy

  • Department of Accountancy, University College Dublin, Belfield, Dublin 4, Ireland
  • Department of Computer Science and Information Systems, University of Limerick, Limerick, Ireland

Bibliografia

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  • Altman E. (1993): Corporate Financial Distress and Bankruptcy. — New York: Wiley.
  • Altman E. (2000): Predicting financial distress of companies: Revisiting the Z-score and Zeta models. — available at: http://www.stern.nyu.edu/ ealtman/Zscores.pdf.
  • Argenti J. (1976): Corporate Collapse: The Causes and Symptoms. — London: McGraw-Hill.
  • Back B., Laitinen T., Sere K. and van Wezel M. (1996): Choosing bankruptcy predictors using discriminant analysis, logit analysis and genetic algorithms. — Techn. Rep. No. 40, Turku Centre for Computer Science, Turku School of Economics and Business Administration.
  • Beaver W. (1966): Financial ratios as predictors of failure. — J. Accounting Res., Supplement: Empirical Research in Accounting, Vol. 4, pp. 71–102.
  • Brabazon A., O’Neill M., Matthews R. and Ryan C. (2002): Grammatical evolution and corporate failure prediction. — Proc. Genetic and Evolutionary Computation Conf. (GECCO 2002), New York: Morgan Kaufmann, pp. 1011– 1019.
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  • Easterbrook F. (1990): Is corporate bankruptcy efficient? — J. Finan. Econ., Vol. 27, pp. 411–417.
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  • Fitzpatrick P. (1932): A Comparison of the Ratios of Successful Industrial Enterprises with Those of Failed Companies.— Washington: The Accountants’ Publishing Company.
  • Gentry J., Newbold P. and Whitford D. (1985): Classifying bankrupt firms with funds flow components.— J. Account. Res., Vol. 23, No. 1, pp. 146–160.
  • Hambrick D. and D’Aveni R. (1988): Large corporate failures as downward spirals. — Admin. Sci. Quart., Vol. 33, pp. 1–23.
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  • Horrigan J. (1965): Some empirical bases of financial ratio analysis. — Account. Rev., Vol. 40, pp. 558–568.
  • Koza J. (1992): Genetic Programming. — Massachusetts: MIT Press.
  • Kumar N., Krovi R. and Rajagopalan B. (1997): Financial decision support with hybrid genetic and neural based modelling tools. — Europ. J. Oper. Res., Vol. 103, No. 2, pp. 339–349.
  • Levinthal D. (1991): Random walks and organisational mortality. — Admin. Sci. Quart., Vol. 36, No. 3, pp. 397–420.
  • Lewin B. (2000): Genes VII. — Oxford University Press.
  • Morris R. (1997): Early Warning Indicators of Corporate Failure: A Critical Review of Previous Research and Further Empirical Evidence.— London: Ashgate Publishing Limited.
  • Moulton W. and Thomas H. (1993): Bankruptcy as a deliberate strategy: Theoretical considerations and empirical evidence. — Strat. Manag. J., Vol. 14, No. 2, pp. 125–135.
  • Ohlson J. (1980): Financial ratios and the probabilistic prediction of bankruptcy. — J. Account. Res., Vol. 18, No. 1, pp. 109–131.
  • O’Neill M. and Brabazon A. (2004): Grammatical swarm. — Proc. Genetic and Evolutionary Computation Conf. GECCO 2004, Seattle, USA, Vol. 1, pp. 163–174.
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  • O’Neill M. (2001): Automatic programming in an arbitrary language: Evolving programs with grammatical evolution.— Ph.D. thesis, University of Limerick, Ireland.
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Typ dokumentu

Bibliografia

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