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)


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.


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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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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