Abstract
In this article we use a net with a single hidden layer and back-propagation to discriminate between targets and non-target firms. The model is estimated on a state-based sample, though the best net is selected and subsequently analysed on the basis of a cross-validation sample which is representative of the true population. Tests of model performance are constructed on the basis of performance in the cross-validation sample. In addition to the usual asymptotic assumptions commonly made we also use a bootstrap pairs sampling algorithm, and a residual based sampling algorithm to generate alternative standard errors and confidence intervals
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© 1998 Springer Science+Business Media Dordrecht
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Fairclough, D., Hunter, J. (1998). The Ex-ante Classification of Takeover Targets Using Neural Networks. In: Refenes, AP.N., Burgess, A.N., Moody, J.E. (eds) Decision Technologies for Computational Finance. Advances in Computational Management Science, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5625-1_30
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DOI: https://doi.org/10.1007/978-1-4615-5625-1_30
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