Improved Method for Detecting Acquirer Skills

41 Pages Posted: 31 Mar 2016

See all articles by Eric de Bodt

Eric de Bodt

NHH-Caltech

Jean-Gabriel Cousin

Univ. Lille, ULR 4112 - LSMRC

Richard Roll

California Institute of Technology

Date Written: February 2016

Abstract

Large merger and acquisition (M&A) samples feature the pervasive presence of repetitive acquirers. They offer an attractive empirical context for revealing the presence of acquirer skills (persistent superior performance). But panel data M&A are quite heterogeneous: just a few acquirers undertake many M&As. Does this feature affect statistical inference? To investigate the issue, our study relies on simulations based on real data sets. The results suggest the existence of a bias, such that extant statistical support for the presence of acquirer skills appears compromised. We introduce a new resampling method to detect acquirer skills with attractive statistical properties (specification and power) for samples of acquirers that complete at least five acquisitions. The proposed method confirms the presence of acquirer skills but only for a marginal fraction of the acquirer population. This result is robust to endogenous attrition and varying time periods between successive transactions. Claims according to which acquirer skills are a first order factor explaining acquirer cross-sectional cumulated abnormal returns appears overstated.

Keywords: mergers and acquisitions, skills, attrition, panel data

JEL Classification: G34

Suggested Citation

de Bodt, Eric and Cousin, Jean-Gabriel and Roll, Richard W., Improved Method for Detecting Acquirer Skills (February 2016). Available at SSRN: https://ssrn.com/abstract=2756391 or http://dx.doi.org/10.2139/ssrn.2756391

Eric De Bodt

NHH-Caltech ( email )

18B AVENUE BECHET
Kraainem, 1950
Belgium
+32 475 24 01 69 (Phone)

Jean-Gabriel Cousin (Contact Author)

Univ. Lille, ULR 4112 - LSMRC ( email )

1 place Déliot - BP381
Lille Cedex, 59020
France
33-3-2090-7606 (Phone)
33-3-2090-7629 (Fax)

Richard W. Roll

California Institute of Technology ( email )

1200 East California Blvd
Mail Code: 228-77
Pasadena, CA 91125
United States
626-395-3890 (Phone)
310-836-3532 (Fax)

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