Empirical evidences on voluntary delisting phenomenon

The paper studies what drives firms to voluntary delist from capital markets and what differs in firms’ behavior and fundamentals between public-to-private transactions and M&A deals with listed corporations. Moreover, I study the relationship between ownership percentage in controlling shareholders’ hands and cumulative returns around the delisting public announcement. I perform my tests both for the Italian and the US markets and I compare the findings to better understand how the phenomenon works in these different institutional environments. Consistent with my expectations, I find that the likelihood of delisting is mainly related to size, underperformance and undervaluation, while shareholders are more rewarded when their companies are involved in PTP transactions than in M&As with public firms.

Contributions towards smart cities : exploring block level census data for the characterization of change in Lisbon

The interest in using information to improve the quality of living in large urban areas and its governance efficiency has been around for decades. Nevertheless, the improvements in Information and Communications Technology has sparked a new dynamic in academic research, usually under the umbrella term of Smart Cities. This concept of Smart City can probably be translated, in a simplified version, into cities that are lived, managed and developed in an information-saturated environment. While it makes perfect sense and we can easily foresee the benefits of such a concept, presently there are still several significant challenges that need to be tackled before we can materialize this vision. In this work we aim at providing a small contribution in this direction, which maximizes the relevancy of the available information resources. One of the most detailed and geographically relevant information resource available, for the study of cities, is the census, more specifically the data available at block level (Subsecção Estatística). In this work, we use Self-Organizing Maps (SOM) and the variant Geo-SOM to explore the block level data from the Portuguese census of Lisbon city, for the years of 2001 and 2011. We focus on gauging change, proposing ways that allow the comparison of the two time periods, which have two different underlying geographical bases. We proceed with the analysis of the data using different SOM variants, aiming at producing a two-fold portrait: one, of the evolution of Lisbon during the first decade of the XXI century, another, of how the census dataset and SOM’s can be used to produce an informational framework for the study of cities.