This paper aims to review prior studies and presents a synthesis of the takeover prediction literature spanning the period 1968–2018.
The paper adopts a narrative review approach. It explores prior studies on takeover target prediction from a historical perspective, focusing on the evolution and development of the literature over the 50-year period.
From a historical development perspective, prior studies in the area can be partitioned into four distinct eras. Studies in the first era (1968–1985) mainly established that takeover targets share common characteristics which can be captured with financial ratios. Studies in the second era (1986–2002) developed and extended formal target prediction hypotheses. These studies concluded that it was impossible to build a successful investment strategy around takeover target prediction. Studies in the third era (2003–2009) explored similar questions using alternative modelling techniques but arrive at similar results – targets can be predicted with limited accuracy and target prediction is unlikely to lead to abnormal returns. Studies in the fourth era (2010–2018) explore implications of M&A predictability on share valuation, governance and bond prices (amongst others), but most importantly, provide some evidence that takeover prediction can lead to abnormal returns when combined with appropriate screening strategies.
This presents the first in-depth review of the literature on takeover target prediction. It highlights the development of the literature over four distinct eras and identifies several limitations, research gaps and opportunities for future research. Given the recent decline in the literature (i.e. fourth era), this study may stimulate new research in the area.
The author is grateful to the editor (Bruce M. Burton) and an anonymous reviewer for feedback and constructive comments that have helped to improve the quality of the paper.
Tunyi, A. (2021), "Fifty years of research on takeover target prediction: a historical perspective", Qualitative Research in Financial Markets, Vol. 13 No. 4, pp. 482-502. https://doi.org/10.1108/QRFM-08-2020-0169
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