@inproceedings{5b99cf8e8e2e482db0b794c028adf96c,
title = "Evolving classifiers to model the relationship between strategy and corporate performance using grammatical evolution",
abstract = "This study examines the potential of grammatical evolution to construct a linear classifier to predict whether a firm{\textquoteright}s corporate strategy will increase or decrease shareholder wealth. Shareholder wealth is measured using a relative fitness criterion, the change in a firm{\textquoteright}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.",
author = "Anthony Brabazon and Michael O{\textquoteright}Neill and Conor Ryan and Robin Matthews",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2002.; 5th European Conference on Genetic Programming, EuroGP 2002 ; Conference date: 03-04-2002 Through 05-04-2002",
year = "2002",
doi = "10.1007/3-540-45984-7_10",
language = "English",
isbn = "9783540433781",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "103--112",
editor = "Foster, {James A.} and Evelyne Lutton and Julian Miller and Conor Ryan and Tettamanzi, {Andrea G.B.}",
booktitle = "Genetic Programming - 5th European Conference, EuroGP 2002, Proceedings",
}