Company failure prediction in the construction industry

dc.contributor.author Isabel Horta en
dc.contributor.author Ana Camanho en
dc.date.accessioned 2018-01-10T10:04:36Z
dc.date.available 2018-01-10T10:04:36Z
dc.date.issued 2013 en
dc.description.abstract This paper proposes a new model to predict company failure in the construction industry. The model includes three major innovative aspects. The use of strategic variables reflecting the key specificities of construction companies, which are critical to explain company failure. The use of data mining techniques, i.e. support vector machine to predict company failure. The use of two different sampling methods (random undersampling and random oversampling with replacement) to balance class distributions. The model proposed was empirically tested using all Portuguese contractors that operated in 2009. It is concluded that support vector machine, with random oversampling and including strategic variables, is a very robust tool to predict company failure in the context of the construction industry. In particular, this model outperforms the results obtained with logistic regression. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/5805
dc.identifier.uri http://dx.doi.org/10.1016/j.eswa.2013.05.045 en
dc.language eng en
dc.relation 6045 en
dc.relation 5990 en
dc.rights info:eu-repo/semantics/embargoedAccess en
dc.title Company failure prediction in the construction industry en
dc.type article en
dc.type Publication en
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