Title: Predicting Greek mergers and acquisitions: a new approach

Authors: Athanasios Tsagkanos, Antonios Georgopoulos, Costas Siriopoulos

Addresses: University of Patras, Department of Business Administration, University Campus – Rio, P.O. Box 1391, Patras 26500, Greece. ' University of Patras, Department of Business Administration, University Campus – Rio, P.O. Box 1391, Patras 26500, Greece. ' University of Patras, Department of Business Administration, University Campus – Rio, P.O. Box 1391, Patras 26500, Greece

Abstract: In this paper, we investigate the possibility of predicting takeover targets in Greece, which is an incipient market for acquisitions. Our work is based on recursive partitioning techniques, that is decision-tree models, given that takeover likelihood models (such as logit) are not robust over time (Powell, 1997). However, we adopt a new technique with respect to Espahbodi and Espahbodi (2003), the machine learning algorithm J4.8 that is a new application in the sector of mergers and acquisitions. The results show that J4.8 outperforms the classical regression tree, although the predictive accuracy is not promising.

Keywords: mergers; acquisitions; recursive partitioning techniques; Greece; financial services management; takeover targets; takeover prediction.

DOI: 10.1504/IJFSM.2007.016286

International Journal of Financial Services Management, 2007 Vol.2 No.4, pp.289 - 303

Available online: 15 Dec 2007 *

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