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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
  • Altman E. (1968): Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. — J. Finance, Vol. 23, No. 1, pp. 589–609.
  • Altman E., Haldeman R. and Narayanan P. (1977): ZETA Analysis: A new model to identify bankruptcy risk of corporations. — J. Banking Finance, Vol. 1, No. 1, pp. 29–54.
  • 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.
  • Dimitras A., Zanakis S. and Zopounidis C. (1996): A survey of business failures with an emphasis on prediction methods and industrial applications. — Europ. J. Oper. Res., Vol. 90, No. 3, pp. 487–513.
  • Easterbrook F. (1990): Is corporate bankruptcy efficient? — J. Finan. Econ., Vol. 27, pp. 411–417.
  • Fan A. and Palaniswami M. (2000): A new approach to corporate loan default prediction from financial statements. — Proc. Computational Finance / Forecasting Financial Markets Conf. (CF/FFM-2000), London.
  • Ferris S., Jayaraman N. and Makhija A. (1996): The impact of Chapter 11 filings on the risk and return of security holders, 1979–1989. — Adv. Finan. Econ., Vol. 2, pp. 93–118.
  • 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.
  • Hair J., Anderson R., Tatham R. and Black W. (1998): Multivariate Data Analysis. — Upper Saddle River: Prentice Hall.
  • 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.
  • O’Neill M. and Ryan C. (2003): Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. — Boston: Kluwer Academic Publishers.
  • O’Neill M. (2001): Automatic programming in an arbitrary language: Evolving programs with grammatical evolution.— Ph.D. thesis, University of Limerick, Ireland.
  • O’Neill M. and Ryan C. (2001): Grammatical evolution. — IEEE Trans. Evolut. Comput., Vol. 5, No. 4, pp. 349–358.
  • O’Sullivan J. and Ryan C. (2002): An investigation into the use of different search strategies with Grammatical Evolution, In: Lecture Notes in Computer Science (2278): Genetic Programming (J. Foster, E. Lutton, J. Miller, C. Ryan and A. Tettamanzi, Eds.).— Berlin: Springer-Verlag, pp. 103– 113.
  • Russel P., Branch B. and Torbey V. (1999): Market valuation of bankrupt firms: Is there an anomaly? — Quart. J. Bus. Econ., Vol. 38, pp. 55–76.
  • Ryan C., Collins J.J. and O’Neill M. (1998): Grammatical evolution: Evolving programs for an arbitrary language, In: Lecture Notes in Computer Science 1391, Proceedings of the First European Workshop on Genetic Programming (W. Banzhaf, R. Poli, M. Schoenauer, T.C. Fogarty, Eds.). — Berlin: Springer-Verlag, pp. 83–95.
  • Schumpeter J. (1934): The Theory of Economic Development. — Cambridge, MA: Harvard Business Press.
  • Serrano-Cinca C. (1996): Self organizing neural networks for financial diagnosis. — Dec. Supp. Syst., Vol. 17, No. 3, pp. 227–238.
  • Shah J. and Murtaza M. (2000): A neural network based clustering procedure for bankruptcy prediction.— Amer. Bus. Rev., Vol. 18, No. 2, pp. 80–86.
  • Smith R. andWinakor A. (1935): Changes in the financial structure of unsuccessful corporations.— University of Illinois, Bureau of Business Research, Bulletin No. 51.
  • Tam K. (1991): Neural network models and the prediction of bank bankruptcy. — Omega, Vol. 19, No. 5, pp. 429–445.
  • Varetto F. (1998): Genetic algorithms in the analysis of insolvency risk. — J. Bank. Fin., Vol. 22, No. 10, pp. 1421– 1439.
  • Wilson N., Chong K. and Peel M. (1995): Neural network simulation and the prediction of corporate outcomes: Some empirical findings. — Int. J. Econ. Bus., Vol. 2, No. 1, pp. 31–50.
  • Zhang G., Hu M., Patuwo B. and Indro D. (1999): Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. — Europ. J. Oper. Res., Vol. 116, No. 1, pp. 16–32.
  • Zmijewski M. (1984): Methodological issues related to the estimation of financial distress prediction models. — J. Account. Res. – Supplement, Vol. 22, pp. 59–82.
  • Zopounidis C. and Dimitras A. (1998): Multicriteria Decision Aid Methods for the Prediction of Business Failure. — Dordrecht: Kluwer Academic Publishers.
  • Zopounidis C., Slowinski R., Doumpos M., Dimitras A. and Susmaga R. (1999): Business failure prediction using rough sets: A comparision with multivariate analysis techniques. — Fuzzy Econ. Rev., Vol. 4, No. 1, pp. 3–33.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv14i3p363bwm
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