Skip to main content
SpringerLink
Log in

Anticipating Bankruptcy Reorganisation from Raw Financial Data Using Grammatical Evolution

  • Conference paper
  • First Online:
Applications of Evolutionary Computing (EvoWorkshops 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2611))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Altman, E. (1993). Corporate Financial Distress and Bankruptcy, New York: John Wiley and Sons Inc.

    Google Scholar 

  2. Morris, R. (1997). Early Warning Indicators of Corporate Failure: A critical review of previous research and further empirical evidence, London: Ashgate Publishing Limited.

    Google Scholar 

  3. Brabazon, T., O’Neill, M., Matthews, R., and Ryan, C. (2002). ‘Grammatical Evolution and Corporate Failure Prediction’, In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), Spector et. al. Eds., New York, USA, July 9–13, 2002, pp. 1011–1019, Morgan Kaufmann.

    Google Scholar 

  4. Fitzpatrick, P. (1932). A Comparison of the Ratios of Successful Industrial Enterprises with Those of Failed Companies, Washington: The Accountants’ Publishing Company.

    Google Scholar 

  5. Smith, R. and Winakor, A. (1935). ‘Changes in the Financial Structure of Unsuccessful Corporations’, University of Illinois, Bureau of Business Research, Bulletin No. 51.

    Google Scholar 

  6. Beaver, W. (1968). ‘Financial Ratios as Predictors of Failure’, Journal of Accounting Research-Supplement: Empirical Research in Accounting, 71–102.

    Google Scholar 

  7. Altman, E. (1968). ‘Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy’, Journal of Finance, 23:589–609.

    Article  Google Scholar 

  8. Gentry, J., Newbold, P. and Whitford, D. (1985). ‘Classifying Bankrupt Firms with Funds Flow Components’, Journal of Accounting Research, 23(1):146–160.

    Article  Google Scholar 

  9. Zmijewski, M. (1984). ‘Methodological Issues Related to the Estimation of Financial Distress Prediction Models’, Journal of Accounting Research-Supplement, 59–82.

    Google Scholar 

  10. Ohlson, J. (1980). ‘Financial Ratios and the Probabilistic Prediction of Bankruptcy’, Journal of Accounting Research, 18:109–131.

    Article  Google Scholar 

  11. Shah, J. and Murtaza, M. (2000). ‘A Neural Network Based Clustering Procedure for Bankruptcy Prediction’, American Business Review, 18(2):80–86.

    Google Scholar 

  12. Serrano-Cina, C. (1996). ‘Self organizing neural networks for financial diagnosis’, Decision Support Systems, 17:227–238.

    Article  Google Scholar 

  13. Varetto, F. (1998). ‘Genetic algorithms in the analysis of insolvency risk’, Journal of Banking and Finance, 22(10):1421–1439.

    Article  Google Scholar 

  14. Kumar, N., Krovi, R. and Rajagopalan, B. (1997). ‘Financial decision support with hybrid genetic and neural based modelling tools’, European Journal of Operational Research, 103:339–349.

    Article  MATH  Google Scholar 

  15. Altman, E. (2000). ‘Predicting Financial Distress of Companies: Revisiting the Zscore and Zeta models’, http://www.stern.nyu.edu/ ealtman/Zscores.pdf, October 2001.

  16. Russel, P., Branch, B. and Torbey, V. (1999). ‘Market Valuation of Bankrupt Firms: is there an anomaly?’, Quarterly Journal of Business and Economics, 38:55–76.

    Google Scholar 

  17. Ferris, S., Jayaraman, N. and Makhija, A. (1996). ‘The Impact of Chapter 11 filings on the Risk and Return of Security Holders, 1979-1989’, Advances in Financial Economics, 2:93–118.

    Google Scholar 

  18. Moulton, W. and Thomas, H. (1993). ‘Bankruptcy As a Deliberate Strategy: Theoretical Considerations and Empirical Evidence’, Strategic Management Journal, 14:125–135.

    Article  Google Scholar 

  19. Ryan C., Collins J.J., O’Neill M. (1998). Grammatical Evolution: Evolving Programs for an Arbitrary Language. Lecture Notes in Computer Science 1391, Proceedings of the First European Workshop on Genetic Programming, 83–95, Springer-Verlag.

    Google Scholar 

  20. O’Neill M., Ryan C. (2001) Grammatical Evolution, IEEE Trans. Evolutionary Computation. 2001.

    Google Scholar 

  21. O’Neill, M. (2001). Automatic Programming in an Arbitrary Language: Evolving Programs in Grammatical Evolution. PhD thesis, University of Limerick, 2001.

    Google Scholar 

  22. Koza, J. (1992). Genetic Programming. MIT Press.

    Google Scholar 

  23. Hair, J., Anderson, R., Tatham, R. and Black, W. (1998). Multivariate Data Analysis, Upper Saddle River, New Jersey: Prentice Hall.

    Google Scholar 

  24. Argenti, J. (1976). Corporate Collapse: The Causes and Symptoms, London: McGraw-Hill.

    Google Scholar 

  25. Smith, T. (1992). Accounting for Growth. London: Century Business.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Accountancy, University College Dublin, Ireland

    Anthony Brabazon

  2. Dept. of Computer Science and Information Systems, University of Limerick, Ireland

    Michael O’Neill

Authors
  1. Anthony Brabazon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael O’Neill
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. of Computer Engineering, University of Parma, Parco Area delle Scienze 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  2. Computing Laboratory, University of Kent, Canterbury, Kent, CT2 7NF, UK

    Colin G. Johnson

  3. Dept. of Information and Communications Technologies Faculty of Computer Science, University of A Coruña, A Coruña, CP 15071, Spain

    Juan J. Romero Cardalda

  4. Dept. of Mathematics and Computer Science, Free University of Amsterdam, de Boelelaan 1081a, 1081 HV, Amsterdam, The Netherlands

    Elena Marchiori

  5. Schol of Systems Engineering, University of Reading, PO Box 225, Whiteknights, Reading, RG6 6AY, UK

    David W. Corne

  6. AnimatLab, 8 rue du capitaine Scott, 75015, Paris, France

    Jean-Arcady Meyer  & Agnès Guillot  & 

  7. SAP AG, Neurottstrasse 16, 69190, Walldorf, Germany

    Jens Gottlieb

  8. Parallel Computing and Complex Systems Group, University of Leipzig, Augustusplatz 10/11, 04109, Leipzig, Germany

    Martin Middendorf

  9. Algorithms and Data Structures Group Institute of Computer Graphics, Vienna University of Technology, Favoritenstrasse 9-11/186, 1040, Vienna, Austria

    Günther R. Raidl

  10. School of Computing, Napier University, 219 Colinton Road, Edinburgh, EH14 1DJ, UK

    Emma Hart

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Brabazon, A., O’Neill, M. (2003). Anticipating Bankruptcy Reorganisation from Raw Financial Data Using Grammatical Evolution. In: Cagnoni, S., et al. Applications of Evolutionary Computing. EvoWorkshops 2003. Lecture Notes in Computer Science, vol 2611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36605-9_34

Download citation

  • DOI: https://doi.org/10.1007/3-540-36605-9_34

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00976-4

  • Online ISBN: 978-3-540-36605-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation