Determinantes da estrutura de capitais do sector bancário português

Este estudo tem como objectivo analisar quais os factores que determinam a estrutura de capitais do sector bancário Português. Com o intuito de atingir o objectivo e assumindo a existência de uma estrutura óptima de capitais, recorrer-se-á ao modelo de regressão linear múltipla para verificar a aderência do processo de decisão às teorias acerca da estrutura de capitais, bem como quais dos factores analisados a afectarão significativamente. Os resultados obtidos sugerem que a rendibilidade, a dimensão, o risco e a tangibilidade são os principais determinantes da estrutura de capitais do sector bancário português. ABSTRACT: The main aim for this study is to verify which determinants influence the Portuguese bank's capital structure. ln order to achieve the above mentioned aim and assuming an optimal capital structure, we will apply a multiple linear regression model with the purpose of proving the capital structure theories existence and to observe which determinants influence it. The obtained results mention that profitability, size, risk and tangibility are the principal determinants of Portuguese bank's capital structure.

Obtaining environmental favourability functions from logistic regression

Logistic regression is a statistical tool widely used for predicting species’ potential distributions starting from presence/absence data and a set of independent variables. However, logistic regression equations compute probability values based not only on the values of the predictor variables but also on the relative proportion of presences and absences in the dataset, which does not adequately describe the environmental favourability for or against species presence. A few strategies have been used to circumvent this, but they usually imply an alteration of the original data or the discarding of potentially valuable information. We propose a way to obtain from logistic regression an environmental favourability function whose results are not affected by an uneven proportion of presences and absences. We tested the method on the distribution of virtual species in an imaginary territory. The favourability models yielded similar values regardless of the variation in the presence/absence ratio. We also illustrate with the example of the Pyrenean desman’s (Galemys pyrenaicus) distribution in Spain. The favourability model yielded more realistic potential distribution maps than the logistic regression model. Favourability values can be regarded as the degree of membership of the fuzzy set of sites whose environmental conditions are favourable to the species, which enables applying the rules of fuzzy logic to distribution modelling. They also allow for direct comparisons between models for species with different presence/absence ratios in the study area. This makes themmore useful to estimate the conservation value of areas, to design ecological corridors, or to select appropriate areas for species reintroductions.