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Improved Methods for Detecting Acquirer Skills

de Bodt, Eric and Cousin, Jean-Gabriel and Roll, Richard (2016) Improved Methods for Detecting Acquirer Skills. Social Science Working Paper, 1419. California Institute of Technology , Pasadena, CA. (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20170726-082321868

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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 (size 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.


Item Type:Report or Paper (Working Paper)
Group:Social Science Working Papers
Subject Keywords:mergers and acquisitions, skills, attrition, panel data
Classification Code:JEL: G34
Record Number:CaltechAUTHORS:20170726-082321868
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20170726-082321868
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:79388
Collection:CaltechAUTHORS
Deposited By: Hanna Storlie
Deposited On:07 Aug 2017 21:23
Last Modified:07 Aug 2017 21:23

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