An empirical investigation on firm-level performance of cross border mergers and acquisitions

This empirical study investigates the performance of cross border M&A. The first stage is to identify the determinants of making cross border M&A complete. One focus here is to extend the existing empirical evidence in the field of cross border M&A and exploit the likelihood of M&A from a different perspective. Given the determinants of cross border M&A completions, the second stage is to investigate the effects of cross border M&A on post-acquisition firm performance for both targets and acquirers. The thesis exploits a hitherto unused data base, which consists of those firms that are rumoured to be undertaking M&A, and then follow the deal to completion or abandonment. This approach highlights a number of limitations to the previous literature, which relies on statistical methodology to identify potential but non-existent mergers. This thesis changes some conventional understanding for M&A activity. Cross border M&A activity is underpinned by various motives such as synergy, management discipline, and acquisition of complementary resources. Traditionally, it is believed that these motives will boost the international M&A activity and improve firm performance after takeovers. However, this thesis shows that such factors based on these motives as acquirer’s profitability and liquidity and target’s intangible resource actually deter the completion of cross border M&A in the period of 2002-2011. The overall finding suggests that the cross border M&A is the efficiency-seeking activity rather than the resource-seeking activity. Furthermore, compared with firms in takeover rumours, the completion of M&A lowers firm performance. More specifically, the difficulties in transfer of competitive advantages and integration of strategic assets lead to low firm performance in terms of productivity. Besides, firms cannot realise the synergistic effect and managerial disciplinary effect once a cross border M&A is completed, which suggests a low post-acquisition profitability level.

The MG Rover closure and policy response: an evaluation of the Task Force model in the UK

In recent years there have been a number of high-profile plant closures in the UK. In several cases, the policy response has included setting up a task force to deal with the impacts of the closure. It can be hypothesised that task force involving multi-level working across territorial boundaries and tiers of government is crucial to devising a policy response tailored to people's needs and to ensuring success in dealing with the immediate impacts of a closure. This suggests that leadership, and vision, partnership working and community engagement, and delivery of high quality services are important. This paper looks at the case of the MG Rover closure in 2005, to examine the extent to which the policy response to the closure at the national, regional and local levels dealt effectively with the immediate impacts of the closure, and the lessons that can be learned from the experience. Such lessons are of particular relevance given the closure of the LDV van plant in Birmingham in 2009 and more broadly – such as in the case of the downsizing of the Opel operation in Europe following its takeover by Magna.

Simple approximate MAP inference for Dirichlet processes mixtures

The Dirichlet process mixture model (DPMM) is a ubiquitous, flexible Bayesian nonparametric statistical model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibbs sampling are required. As a result, DPMM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here, we develop a simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithm for DPMMs. This algorithm is as simple as DP-means clustering, solves the MAP problem as well as Gibbs sampling, while requiring only a fraction of the computational effort. (For freely available code that implements the MAP-DP algorithm for Gaussian mixtures see http://www.maxlittle.net/.) Unlike related small variance asymptotics (SVA), our method is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate closed-form likelihood which enables out-of-sample calculations and the use of standard tools such as cross-validation. We illustrate the benefits of our algorithm on a range of examples and contrast it to variational, SVA and sampling approaches from both a computational complexity perspective as well as in terms of clustering performance. We demonstrate the wide applicabiity of our approach by presenting an approximate MAP inference method for the infinite hidden Markov model whose performance contrasts favorably with a recently proposed hybrid SVA approach. Similarly, we show how our algorithm can applied to a semiparametric mixed-effects regression model where the random effects distribution is modelled using an infinite mixture model, as used in longitudinal progression modelling in population health science. Finally, we propose directions for future research on approximate MAP inference in Bayesian nonparametrics.