Stacked Generalization Framework For The Prediction Of Corporate Acquisitions

Over the past decade the number of corporate acquisitions has increased rapidly worldwide. This has mainly been due to strategic reasons, since acquisitions play a prominent role in corporate growth. The prediction of acquisitions is of major interest to stockholders, investors, creditors and generally to anyone who has established a relationship with the acquired and non-acquired firm. Most of the previous studies on the prediction of corporate acquisitions have focused on the selection of an appropriate methodology to develop a predictive model and the comparison with other techniques to investigate the relative efficiency of the methods. On the contrary, this study proposes the combination of different methods in a stacked generalization context. Stacked generalization is a general framework for combining different classification models into an aggregate estimate that is expected to perform better than the individual models. This approach is employed to combine models for predicting corporate acquisitions which are developed through different methods into a combined model. Four methods are considered, namely linear discriminant analysis, probabilistic neural networks, the rough set theory and the UTADIS multicriteria decision aid method. An application of the proposed stacked generalization approach is presented involving a sample of 96 UK firms.