How bank business models relate with risk?: The case of Portugal in times of financial crisis

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Bank-firm relationships and financial crisis

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Rural Vitality Check: proposta de metodologia para a avaliação da sustentabilidade de uma aldeia

Dissertação para obtenção do Grau de Mestre em Engenharia do Ambiente perfil Ordenamento do Território e Impactes Ambientais

Bacterial peptidoglycan biosynthesis and recognition by the innate immune system

Dissertation presented to obtain the Ph.D degree in Biology

Bank capital and stock performance around the subprime lending risis

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Design of a balanced scorecard-type management system for a back-office department in a bank

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Software for pattern recognition of the larvae of Aedes aegypti and Aedes albopictus

Software for pattern recognition of the larvae of mosquitoes Aedes aegypti and Aedes albopictus, biological vectors of dengue and yellow fever, has been developed. Rapid field identification of larva using a digital camera linked to a laptop computer equipped with this software may greatly help prevention campaigns.

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.

Basel III new capital requirements, impacts and bank behavior

For some years, researchers could not find a clear effect of capital adequacy on the risk profile of banks, as shareholders could increase the riskiness of the assets (qualitative effect), crowding-out the effect of reduced leverage (volume effect). Some shareholders might have the will to increase the riskiness of the assets, but they may lack the power to do so. Considering only ”powerful” shareholders, definitive conclusions were drawn but with constant ownership profile. In this paper I investigate whether there is a significant change in the type of shareholders in response to regulatory capital shocks and, if so, will the banking system be in the hands of more “desired” shareholders. I find that ownership profile responds to a regulatory shock, changing the risk appetite of the ruling power at the bank. I find more banks and the government in the ownership of undercapitalised banks and much less institutional shareholders and free float. I claim that these new shareholders may not the desired ones, given the objective of the regulatory change, as they are associated with a preference for more leverage. One possible explanation for this crowding-out effect is that regulators are trying to contain idiosyncratic risk (more linked to the riskiness of the assets) with a rule that contains systematic risk (capital adequacy). This has a distorting effect on ownership. Another insight can be drawn from the tests: supervisors should be aware of significant ownership movements that cause the crowding-out.

A CHECK-LIST ON THE INVASIVE SPECIES OF FORESTRY PLANTATIONS IN LOWER AMAZON, NW.

The work conelete of a survey of the mvaewe plants which appeared during the first six months of seedling stage in foreet plantations of second and third rotation, in the Jari area, Brazil. The resulting list showed two types of invasives: (1) true weeds - those cosmopolitan species that are typical of disturbed habitats; and (2) pioneer species - those which were probably remnants from the original natural vegetation of the studied area.

Improving Millennium Bank's personal finance manager

Double Degree

O "Check-up" do conhecimento: aprender e avaliar

Relatório de estágio de mestrado em Educação Pré-Escolar e Ensino do 1.º do Ensino Básico

Using customer lifetime value to improve the prediction of bank deposit subscription in telemarketing campaigns

Customer lifetime value (LTV) enables using client characteristics, such as recency, frequency and monetary (RFM) value, to describe the value of a client through time in terms of profitability. We present the concept of LTV applied to telemarketing for improving the return-on-investment, using a recent (from 2008 to 2013) and real case study of bank campaigns to sell long- term deposits. The goal was to benefit from past contacts history to extract additional knowledge. A total of twelve LTV input variables were tested, un- der a forward selection method and using a realistic rolling windows scheme, highlighting the validity of five new LTV features. The results achieved by our LTV data-driven approach using neural networks allowed an improvement up to 4 pp in the Lift cumulative curve for targeting the deposit subscribers when compared with a baseline model (with no history data). Explanatory knowledge was also extracted from the proposed model, revealing two highly relevant LTV features, the last result of the previous campaign to sell the same product and the frequency of past client successes. The obtained results are particularly valuable for contact center companies, which can improve pre- dictive performance without even having to ask for more information to the companies they serve.