Generalized Dasymetric Mapping Algorithm for Accessing Land-Use Change

Contém resumo

CEO overconfidence and leverage ratio: Evidence from the founder members of Mila

The study investigates the impact of the managerial overconfidence bias on the capital structure of a sample of 78 firms from Chile, Peru and Colombia, during the years 1996-2014. We infer that there is a positive relation between the leverage ratio and a) the overconfidence; b) the experience and c) the male gender of the executive. Overconfidence is measured according to the status of the CEO (entrepreneur or not-entrepreneur) and the hypotheses are tested through dynamic panel data model. The empirical results show a highly significant positive correlation between overconfidence and leverage ratio and between gender and leverage ratio while, in contrast, the relation between experience and leverage ratio is negative.

Using artificial neural networks to generate trading signals for crude oil, copper and gold futures

In this thesis, a feed-forward, back-propagating Artificial Neural Network using the gradient descent algorithm is developed to forecast the directional movement of daily returns for WTI, gold and copper futures. Out-of-sample back-test results vary, with some predictive abilities for copper futures but none for either WTI or gold. The best statistically significant hit rate achieved was 57% for copper with an absolute return Sharpe Ratio of 1.25 and a benchmarked Information Ratio of 2.11.

Genetic algorithm for shipping route estimation with long-range tracking data : automatic reconstruction of shipping routes based on the historical ship positions for maritime safety applications

Ship tracking systems allow Maritime Organizations that are concerned with the Safety at Sea to obtain information on the current location and route of merchant vessels. Thanks to Space technology in recent years the geographical coverage of the ship tracking platforms has increased significantly, from radar based near-shore traffic monitoring towards a worldwide picture of the maritime traffic situation. The long-range tracking systems currently in operations allow the storage of ship position data over many years: a valuable source of knowledge about the shipping routes between different ocean regions. The outcome of this Master project is a software prototype for the estimation of the most operated shipping route between any two geographical locations. The analysis is based on the historical ship positions acquired with long-range tracking systems. The proposed approach makes use of a Genetic Algorithm applied on a training set of relevant ship positions extracted from the long-term storage tracking database of the European Maritime Safety Agency (EMSA). The analysis of some representative shipping routes is presented and the quality of the results and their operational applications are assessed by a Maritime Safety expert.