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Bond-Issuer Credit Rating with Grammatical Evolution

  • Anthony Brabazon
  • Michael O’Neill
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3005)

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.

Keywords

Total Asset Credit Rating Debt Ratio Bond Rating Grammatical Evolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Dordrecht (2003)zbMATHGoogle Scholar
  2. 2.
    Ederington, H.: Classification models and bond ratings. Financial Review 20(4), 237–262 (1985)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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. 5.
    Kamstra, M., Kennedy, P., Suan, T.K.: Combining Bond Rating Forecasts Using Logit. The Financial Review 37, 75–96 (2001)CrossRefGoogle Scholar
  6. 6.
    Shin, K., Han, I.: A case-based approach using inductive indexing for corporate bond rating. Decision Support Systems 32, 41–52 (2001)CrossRefGoogle Scholar
  7. 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. 8.
    Altman, E.: The importance and subtlety of credit rating migration. Journal of Banking & Finance 22, 1231–1247 (1998)CrossRefGoogle Scholar
  9. 9.
    O’Neill, M.: Automatic Programming in an Arbitrary Language: Evolving Programs in Grammatical Evolution. PhD thesis, University of Limerick (2001)Google Scholar
  10. 10.
    O’Neill, M., Ryan, C.: Grammatical Evolution. IEEE Trans. Evolutionary Computation (2001)Google Scholar
  11. 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)CrossRefGoogle Scholar
  12. 12.
    Koza, J.: Genetic Programming. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  13. 13.
    Altman, E.: Corporate Financial Distress and Bankruptcy. John Wiley and Sons Inc., New York (1993)Google Scholar
  14. 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. 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. 16.
    Hair, J., Anderson, R., Tatham, R., Black, W.: Multivariate Data Analysis. Prentice Hall, Upper Saddle River (1998)Google Scholar
  17. 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. 18.
    Koza, J.: Genetic Programming II. MIT Press, Cambridge (1994)zbMATHGoogle Scholar
  19. 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)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Anthony Brabazon
    • 1
  • Michael O’Neill
    • 2
  1. 1.Dept. Of AccountancyUniversity College DublinIreland
  2. 2.Biocomputing-Developmental Systems Dept. Of Computer Science & Information SystemsUniversity of LimerickIreland

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