Search results

This paper aims to address the question: What is the distribution of value (in pounds) created in a sample of domestic takeovers in the United Kingdom from 2013 to 2020 among acquirer and target stockholders?

The author employs a traditional event study methodology to calculate the percentage excess returns of companies on the announcement date. These returns are then converted into pound-denominated excess returns using the companies' market capitalizations. This allows the author to estimate the synergies of the mergers and acquisitions (M&As) and how they are allocated between acquirers and targets. This innovative transformation from percentage to pound excess returns establishes a new ratio methodology for addressing the paper's objective.

This paper reveals that in UK takeovers, 40 percent of the synergies in pounds are allocated to the stockholders of acquiring companies, while 60 percent go to the stockholders of target companies. In other words, acquirers retain a significant portion—more than half—of the synergies generated in these domestic deals. This original finding is statistically significant at the one percent level and strongly contradicts the hypothesis that acquirers, at best, merely break even.

The evidence that UK takeovers distribute value gains nearly equally between domestic deal parties challenges the enduring conventional insight in the M&A literature. This conventional wisdom suggests that the value created by business combinations is entirely distributed to target company stockholders. Consequently, this reexamination may have broader implications, offering an alternative perspective on the motives behind business combinations. This perspective differs from the “managerial hubris hypothesis,” which aligns with the prevailing conventional insight but receives limited support in the original finding reported here.

The purpose of this paper is to demonstrate with real data the enhanced statistical power of a GLS‐based event study methodology that requires the same input data as the traditional tests.

The paper uses full sample, subsample and simulated modified sample analyses to compare the statistical power of the GLS methodology with traditional methods.

The paper finds that it is often the case that traditional tests will not reject the null when a GLS‐based test may (strongly) reject the null. The power of the former is poor.

There are many published event studies where the null is not rejected. This may be because of the phenomenon being tested but it may also be because of the lack of power of traditional estimators. Hence, rerunning them with the authors' more powerful test is likely to reject some currently well‐accepted null hypotheses of no event effect, stimulating new research ideas. Moreover, as individual stocks have become more volatile, the additional power of the authors' methodology to detect abnormal performance for recent and future events becomes even more important.

There are more than 500 event studies in the top finance journals, which can broadly be split into two subgroups: contemporaneous shocks like changes in regulation and non‐contemporaneous events like mergers. GLS contemporaneous modeling of covariances in the former showed little efficiency gains. The paper's GLS modeling of variances for the latter demonstrates potentially huge effects. Practitioners should be skeptical of prior results accepting the null of no event effect and incorporate GLS to be confident of their future findings.