AFFI International Conference 2017

Full Program »

Improved Method for Detecting Acquirer Skills

File
View File
pdf
1.5MB

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, confirming suspicions reported in the extant literature about the validity of fixed-effect regressions based statistics (adjusted R-square and fixed effects Fisher tests) used to detect the presence of skills. 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.

Author(s):

Eric de Bodt    
FFBC-Université de Lille
France

Jean-Gabriel Cousin    
FFBC-Université de Lille
France

Richard Roll    
California Institute of Technology
United States

 

Powered by OpenConf®
Copyright©2002-2016 Zakon Group LLC