Improved method for detecting acquirer fixed effects

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 fixed effects (permanent abnormal 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 regression based statistics (R- square, adjusted R- square and fixed effects Fisher tests) used to detect the presence and significance of acquirer fixed effects. We introduce a new resampling method to detect acquirer fixed effects 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 fixed effects but only for a marginal fraction of the acquirer population. This result is robust to endogenous attrition and varying time periods between successive transactions.

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