The Impact of Acquisitions on Share Pricing and Long Term Performance – Empirical Evidence from the Global Automotive Industry

Automotive industry has faced intense consolidation pressure, which has lead to increasing number of M&As. However, empirical evidence has given controversial results suggesting that most of M&As are value destructive for acquiring companies and for acquiring companies’ shareholders. The objective of this master’s thesis is to examine how acquiring companies’ shareholders react to acquisition announcement and is the reaction in line with the long-term performance. This study uses empirical evidence from automotive industry, which has been characterized as an industry that holds large amount of vertical and horizontal synergies. Transaction data consists of 65 acquisitions made by publicly listed companies between 2008-2010. The short-term impact is tested by applying event study methodology while the long term operative performance is examined with accounting study methodology. The event study results indicate that during the three days after acquisition (t= 0-2), the acquiring firms’ stocks generate an abnormal return of 1.22% on average across all acquisitions. When long term performance is studied it is evident that acquiring companies perform better than the industry median pre- and post-transaction but there is no statistically significant evidence that the performance has increased. The only performance ratio indicating statistically significant decrease is Return on Equity (ROE). On long-term horizontal acquisitions seem to outperform conglomerate ones but otherwise deal characteristics do not have any statistically significant impact.

Parameter estimation for Chaotic or Stochastic Dynamics

Since its discovery, chaos has been a very interesting and challenging topic of research. Many great minds spent their entire lives trying to give some rules to it. Nowadays, thanks to the research of last century and the advent of computers, it is possible to predict chaotic phenomena of nature for a certain limited amount of time. The aim of this study is to present a recently discovered method for the parameter estimation of the chaotic dynamical system models via the correlation integral likelihood, and give some hints for a more optimized use of it, together with a possible application to the industry. The main part of our study concerned two chaotic attractors whose general behaviour is diff erent, in order to capture eventual di fferences in the results. In the various simulations that we performed, the initial conditions have been changed in a quite exhaustive way. The results obtained show that, under certain conditions, this method works very well in all the case. In particular, it came out that the most important aspect is to be very careful while creating the training set and the empirical likelihood, since a lack of information in this part of the procedure leads to low quality results.