Skip to main content
SpringerLink
Log in

Bond-Issuer Credit Rating with Grammatical Evolution

  • Conference paper
Applications of Evolutionary Computing (EvoWorkshops 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3005))

Included in the following conference series:

Abstract

This study examines the utility of Grammatical Evolution in modelling the corporate bond-issuer credit rating process, using information drawn from the financial statements of bond-issuing firms. Financial data, and the associated Standard & Poor’s issuer-credit ratings of 791 US firms, drawn from the year 1999/2000 are used to train and test the model. The best developed model was found to be able to discriminate in-sample (out-of-sample) between investment-grade and junk bond ratings with an average accuracy of 87.59 (84.92)% across a five-fold cross validation. The results suggest that the two classifications of credit rating can be predicted with notable accuracy from a relatively limited subset of firm-specific financial data, using Grammatical Evolution.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Dordrecht (2003)

    MATH  Google Scholar 

  2. Ederington, H.: Classification models and bond ratings. Financial Review 20(4), 237–262 (1985)

    Article  Google Scholar 

  3. Gentry, J., Whitford, D., Newbold, P.: Predicting industrial bond ratings with a probit model and funds flow components. Financial Review 23(3), 269–286 (1988)

    Article  Google Scholar 

  4. Huang, Z., Chen, H., Hsu, C., Chen, W., Wu, S.: Credit rating analysis with support vector machines and neural networks: a market comparative study. Decision Support Systems (2003) (Article in press)

    Google Scholar 

  5. Kamstra, M., Kennedy, P., Suan, T.K.: Combining Bond Rating Forecasts Using Logit. The Financial Review 37, 75–96 (2001)

    Article  Google Scholar 

  6. Shin, K., Han, I.: A case-based approach using inductive indexing for corporate bond rating. Decision Support Systems 32, 41–52 (2001)

    Article  Google Scholar 

  7. Brabazon, T., O’Neill, M., Matthews, R., Ryan, C.: Grammatical Evolution and Corporate Failure Prediction. In: Spector, et.al (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), July 9-13, 2002, pp. 1011–1019. Morgan Kaufmann, San Francisco (2002)

    Google Scholar 

  8. Altman, E.: The importance and subtlety of credit rating migration. Journal of Banking & Finance 22, 1231–1247 (1998)

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  11. Ryan, C., Collins, J.J., O’Neill, M.: Grammatical Evolution: Evolving Programs for an Arbitrary Language. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 83–95. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  12. Koza, J.: Genetic Programming. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  15. Dutta, S., Shekhar, S.: Bond rating: a non-conservative application of neural networks. In: Proceedings of IEEE International Conference on Neural Networks, II, pp. 443–450 (1988)

    Google Scholar 

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

    Google Scholar 

  17. Matthews, B.W.: Comparison of the predicited and observed secondary structure of T4 phage lysozyme. Biochemica et Biophysica Acta 405, 442–451 (1975)

    Google Scholar 

  18. Koza, J.: Genetic Programming II. MIT Press, Cambridge (1994)

    MATH  Google Scholar 

  19. Brabazon, T., O’Neill, M.: Anticipating Bankruptcy Reorganisation from Raw Financial Data using Grammatical Evolution. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 368–377. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    Anthony Brabazon

  2. Biocomputing-Developmental Systems Dept. Of Computer Science & 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. Institute of Computer Graphics and Algorithms, Vienna University of Technology, Favoritenstraße 9–11/1861, 1040, Vienna, Austria

    Günther R. Raidl

  2. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  3. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  4. School of Mathematical and Computer Science, Heriot-Watt University, EH14 8AS, Edinburgh, UK

    David Wolfe Corne

  5. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  6. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

  7. Computing Laboratory, University of Kent, Canterbury, UK

    Colin G. Johnson

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. ICIS, Radboud University Nijmegen, The Netherlands

    Elena Marchiori

  10. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Brabazon, A., O’Neill, M. (2004). Bond-Issuer Credit Rating with Grammatical Evolution. In: Raidl, G.R., et al. Applications of Evolutionary Computing. EvoWorkshops 2004. Lecture Notes in Computer Science, vol 3005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24653-4_28

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-24653-4_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-21378-9

  • Online ISBN: 978-3-540-24653-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation