Shark Repellents : Predicting the Takeover-Likelihood by Means of Pre-emptive Anti-Takeover Provisions & Key Performance Indicators

This study is concerned with public companies (listed on the stock exchange) which are threatened by an unwanted takeover-attempt. Particularly, the investigation is centered around opportunities for such companies to defend themselves against hostile takeovers. Therefore, this study covers defense strategies, the so-called anti-takeover provisions (ATPs) or shark repellents. More specifically, pre-emptive ATPs were analyzed in order to determine whether they are effective measures for a takeover-target to avoid being acquired. This question is widely discussed by existing literature that is concerned with the overall topic of mergers & acquisitions, whereby findings of prior researchers often are contradicting or inconclusive. Moreover, there is a lack of literature examining the case of takeover-attempts which are characterized by a hostile deal-attitude specifically. As the adoption of pre-emptive ATPs does have its pitfalls, we aimed to find implications for the management of publicly traded companies concerning the question if they should deploy pre-emptive ATPs or not. We analyzed hostile takeover-events which took place within the timeframe of 2003-2018, whereby target-companies where located all over the world. As done by other researchers before, the level of resistance against takeovers has been measured by forming an index (in this study denoted by G-Index*), which accounts for the number of pre-emptive ATPs adopted by the takeover-target. That index was used subsequently to test, if companies with a low/high level of resistance against takeovers were more/less likely to be acquired. As not only the resistance against takeovers is an influencing factor on the outcome of a takeover-event, we also measured the impact of performance- and contextual factors. Those comprise key performance indicators (KPIs) for efficiency and profitability, as well as the context factors region and industry. That measurement has been carried out using the binary logistic regression, whereby all mentioned aspects were included in one model to form a representative model of takeover-events. This model was used to examine the individual impacts of all variables on the one hand, and for predicting the takeover-likelihood for each company on the other hand. Subsequently, the predicted takeover-likelihood was tested via the Pearson correlation with the number of pre-emptive ATPs adopted by the public companies. By using the binary regression, we found that a higher level of managerial resistance against takeovers is decreasing the probability for a company to be acquired. High resistance against takeovers, measured by a high number of pre-emptive anti-takeover provisions adopted, does have a statistically significant negative effect on the takeover-likelihood. Moreover, another key finding is that a company which is more efficient than the average of its industry, is more likely to be acquired. High efficiency (measured by gross profit margin) does have a statistically significant positive effect on the takeover-likelihood. By using the Pearson correlation, we found a statistically significant relation between the number of pre-emptive ATPs adopted and the predicted takeover-likelihood. The predicted takeover-likelihood correlates negatively with the number of pre-emptive ATPs. These results show that pre-emptive ATPs can be an effective measure against hostile takeovers