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Evolving Classifiers to Model the Relationship between Strategy and Corporate Performance Using Grammatical Evolution

  • Anthony Brabazon
  • Michael O’Neill
  • Conor Ryan
  • Robin Matthews
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2278)

Abstract

This study examines the potential of grammatical evolution to construct a linear classifier to predict whether a firm’s corporate strategy will increase or decrease shareholder wealth. Shareholder wealth is measured using a relative fitness criterion, the change in a firm’s marketvalue- added ranking in the Stern-Stewart Performance 1000 list, over a four year period, 1992-1996. Model inputs and structure are selected by means of grammatical evolution. The best classifier correctly categorised the direction of performance ranking change in 66.38% of the firms in the training set and 65% in the out-of-sample validation set providing support for a hypothesis that changes in corporate strategy are linked to changes in corporate performance.

Keywords

Linear Discriminant Analysis Corporate Performance Corporate Strategy Corporate Investment 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.
    Altman, Edward I. (1993). Corporate Financial Distress and Bankruptcy, New York: John Wiley and Sons Inc.Google Scholar
  2. 2.
    Bauer, R. (1994). GeneticA lgorithms and Investment Strategies, New York: John Wiley & Sons.Google Scholar
  3. 3.
    Bowman, E. and Helfat, C. (2001). Does Corporate Strategy Matter?,Strategic Management Journal, 22:1–23.CrossRefGoogle Scholar
  4. 4.
    Brabazon, T., Glintchak E., Matthews R. (2001). Modelling the relationship between strategy and corporate performance using a hybrid GA/NN model, in Proceedings of the SEAG Annual Conference, 10 September 2001, Oxford.Google Scholar
  5. 5.
    Elfring, T. and Volberda, H. (1996). Schools of Thought in Strategic Management: Fragmentation, Integrating or Synthesis?, in Elfring, T. Jensen, H. and Money, A. (eds), Theory Building in the Business Sciences pp. 11–47, Copenhagen, Copenhagen Business School Press.Google Scholar
  6. 6.
    Hair, Joseph F., Anderson, Rolph E., Tatham, Ronald L. and Black, William C. (1998). Multivariate Data Analysis, Upper Saddle River, Prentice Hall.Google Scholar
  7. 7.
    Klemz, B. (1999). Using genetic algorithms to assess the impact of pricing activity timing, Omega, 27:363–372.CrossRefGoogle Scholar
  8. 8.
    Koza, J. (1992). GeneticProgramming. MIT Press.Google Scholar
  9. 9.
    Levitt, B. and March J. (1988). Organizational Learning, Annual Review of Sociology, 14:319–340.CrossRefGoogle Scholar
  10. 10.
    McKelvey, B. (1999). Avoiding Complexity Catastrophe in Coevolutionary Pockets: Strategies for Rugged Landscapes, Organization Science, 10(3):294–321.CrossRefGoogle Scholar
  11. 11.
    Mintzberg, H. (1990). Strategy Formation: Schools of Thought., in Frederickson, J. (ed.), Perspectives on Strategic Management, pp. 107–108, New York.Google Scholar
  12. 12.
    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
  13. 13.
    Nelson, R. and Winter, S. (1982). An Evolutionary Theory of Economic Change, Cambridge, Massachusetts, Harvard University Press.Google Scholar
  14. 14.
    O’Neill M. (2001) Automatic Programming in an Arbitrary language: Evolving Programs with Grammatical Evolution. Ph.D. thesis, University of Limerick, 2001.Google Scholar
  15. 15.
    O’Neill M., Ryan C. (2001) Grammatical Evolution. IEEE Trans. Evolutionary Computation, Vol. 5 No. 4, August 2001.Google Scholar
  16. 16.
    O’Neill, M., Brabazon, T., Ryan, C. and Collins J.(2001). Evolving Market Index Trading Rules Using Grammatical Evolution, In LNCS 2037: Applications of Evolutionary Computing, pp. 343–35, Springer-Verlag.CrossRefGoogle Scholar
  17. 17.
    Pendharkar, P. (2001). An empirical study of design and testing of hybrid evolutionary-neural approach for classification, Omega, 29:361–374.CrossRefGoogle Scholar
  18. 18.
    Porter, M. (1985). Competitive Advantage:Creating and Sustaining Superior Performance, New York, The Free Press.Google Scholar
  19. 19.
    Porter, M. (1996). What is Strategy?, Harvard Business Review, Nov-Dec, 61-78.Google Scholar
  20. 20.
    Ryan C., Collins J.J., O’Neill M. (1998). Grammatical Evolution: Evolving Programs for an Arbitrary Language. LNCS 1391, Proc. of the First European Workshop on GeneticPr ogramming, pp. 83–95. Springer-Verlag.Google Scholar
  21. 21.
    St. John, C., Balakrishnan, N. and Fiet, O. J. (2000). Modelling the relationship between corporate strategy and wealth creation using neural networks, Computers and operations research, 27:1077–1092.zbMATHCrossRefGoogle Scholar
  22. 22.
    Varetto, F. (1998). Genetic algorithms in the analysis of insolvency risk, Journal of Banking and Finance, 22(10):1421–1439.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Anthony Brabazon
    • 1
  • Michael O’Neill
    • 2
  • Conor Ryan
    • 2
  • Robin Matthews
    • 3
  1. 1.Dept. Of AccountancyUniversity College DublinIreland
  2. 2.Dept. Of Computer Science And Information SystemsUniversity of LimerickIreland
  3. 3.Centre for International Business PolicyKingston Business SchoolLondon

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