A internacionalização do setor bancário português

O processo de globalização, na esfera dos mercados financeiros, exigiu às instituições bancárias opções de investimento estratégico na plataforma internacional. O movimento de implantação dos bancos portugueses no estrangeiro acompanhou esse processo, permitindo a oferta de serviços bancários de captação e financiamento nos principais mercados de destino das exportações e emigração. A presente dissertação tem como objetivo o estudo do processo de internacionalização do setor bancário português centrado na seguinte questão geral de investigação: “Quais os fatores determinantes das variáveis que caraterizam a evolução do setor bancário português no exterior?” O desenvolvimento desta questão é conduzido através da construção de um modelo explicativo dos impactos de um conjunto de determinantes, selecionados a partir da revisão de literatura, sobre os indicadores que traduzem a dinâmica do negócio bancário no exterior. Neste contexto, pretendeu-se obter evidência empírica desses efeitos através de uma metodologia que consiste na estimação de modelos de dados em painel, utilizando uma amostra de seis bancos com relevância ao nível de investimento no mercado externo relativos ao período compreendido entre 2004 e 2014. Os resultados empíricos sugerem a existência de relações estatisticamente significativas entre as variáveis consideradas nos modelos. Foram encontrados indícios que associam consistentemente as variáveis emigração, Investimento Direto Estrangeiro, Produto Interno Bruto em Portugal e nos países de acolhimento, ativo bancário e inflação, com a evolução da atividade bancária no exterior. Adicionalmente, os resultados revelam que o desemprego e o rácio do crédito em relação ao ativo são estatisticamente significativos na sua influência sobre o indicador da rendibilidade dos bancos. Conclui-se que a significância dos fatores selecionados permite explicar o comportamento dos indicadores de negócio no exterior para os bancos estudados e, consequentemente, a validade do modelo de análise proposto. No entanto, não se exclui que outros elementos explicativos não ponderados no estudo tenham igualmente preponderância explicativa no processo de internacionalização do setor bancário.

Plano de negócios para empresa de iluminação de emergência

Dissertação de Mestrado em Finanças Empresariais

Alianças estratégicas: análise do sector exportador de produtos português

Dissertação de Mestrado em Finanças Empresariais

Investment optimization in distribution network based on fuzzy outage parameters

This paper presents a methodology that aims to increase the probability of delivering power to any load point of the electrical distribution system by identifying new investments in distribution components. The methodology is based on statistical failure and repair data of the distribution power system components and it uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A mixed integer non-linear optimization technique is developed to identify adequate investments in distribution networks components that allow increasing the availability level for any customer in the distribution system at minimum cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a real distribution network.

Metalearning in ALBidS: A Strategic Bidding System for electricity markets

Metalearning is a subfield of machine learning with special pro-pensity for dynamic and complex environments, from which it is difficult to extract predictable knowledge. The field of study of this work is the electricity market, which due to the restructuring that recently took place, became an especially complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotia-tion entities. The proposed metalearner takes advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that pro-vides decision support to electricity markets’ participating players. Using the outputs of each different strategy as inputs, the metalearner creates its own output, considering each strategy with a different weight, depending on its individual quality of performance. The results of the proposed meth-od are studied and analyzed using MASCEM - a multi-agent electricity market simulator that models market players and simulates their operation in the market. This simulator provides the chance to test the metalearner in scenarios based on real electricity market´s data.

MASCEM: electricity markets simulation with strategic agents

Electricity markets are complex environments, involving numerous entities trying to obtain the best advantages and profits while limited by power-network characteristics and constraints.1 The restructuring and consequent deregulation of electricity markets introduced a new economic dimension to the power industry. Some observers have criticized the restructuring process, however, because it has failed to improve market efficiency and has complicated the assurance of reliability and fairness of operations. To study and understand this type of market, we developed the Multiagent Simulator of Competitive Electricity Markets (MASCEM) platform based on multiagent simulation. The MASCEM multiagent model includes players with strategies for bid definition, acting in forward, day-ahead, and balancing markets and considering both simple and complex bids. Our goal with MASCEM was to simulate as many market models and player types as possible. This approach makes MASCEM both a short- and mediumterm simulation as well as a tool to support long-term decisions, such as those taken by regulators. This article proposes a new methodology integrated in MASCEM for bid definition in electricity markets. This methodology uses reinforcement learning algorithms to let players perceive changes in the environment, thus helping them react to the dynamic environment and adapt their bids accordingly.

Strategic bidding methodology for electricity markets using adaptive learning

The very particular characteristics of electricity markets, require deep studies of the interactions between the involved players. MASCEM is a market simulator developed to allow studying electricity market negotiations. This paper presents a new proposal for the definition of MASCEM players’ strategies to negotiate in the market. The proposed methodology is implemented as a multiagent system, using reinforcement learning algorithms to provide players with the capabilities to perceive the changes in the environment, while adapting their bids formulation according to their needs, using a set of different techniques that are at their disposal. This paper also presents a methodology to define players’ models based on the historic of their past actions, interpreting how their choices are affected by past experience, and competition.

Strategic bidding in electricity markets: an agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Plano de negócios para a construção de uma unidade fabril de misturas complementares

A Cooperativa Agrícola de Vila do Conde desenvolve um negócio de fabrico e comercialização de misturas complementares para alimentação bovina, sobretudo para vacas leiteiras. Há alguns anos a esta parte, esta Cooperativa sabe que terá que deslocalizar a unidade fabril existente devido a imposições da Direção Geral de Alimentação e Veterinária, relacionadas com questões de natureza ambiental. A necessidade de ser realizado um novo investimento, para garantir a sustentabilidade do negócio mais rentável gerido por esta Cooperativa, levou a pensar-se na possibilidade de construção de uma nova unidade fabril, de dimensão superior, capaz de servir outras cooperativas, visando o desejado entendimento das cooperativas em torno de um objetivo comum, logrando a obtenção de economias de escala, de extrema importância para a sobrevivência do setor leiteiro na região do Entre Douro e Minho. Para o efeito será constituída uma nova sociedade por quotas, designada por AGRIVIL XXI, Lda., de capital exclusivamente cooperativo, possibilitando que, em cada momento, se possa aferir a situação económica e financeira do negócio de forma mais rigorosa e autónoma. Esta realidade foi conducente à elaboração do presente Plano de Negócios que se espera profícuo para definição dos objetivos e metas a atingir num futuro próximo pela Cooperativa Agrícola de Vila do Conde. As análises de viabilidade e do risco do projeto demonstraram estarem criadas as condições de aceitação do mesmo, sendo expectável um VAL de 1.371.764 euros, uma TIR de 12,04% e um pay-back period próximo dos 11 anos. No entanto é notório a existência de um risco inerente ao investimento na medida em que o montante dos fluxos gerados tende a aproximar-se dos fluxos investidos, não gerando um excedente de riqueza significativo.

Estratégia de internacionalização da Grande Porto: seleção, modos de entrada e marketing-mix na abordagem dos mercados internacionais

Dissertação apresentada ao Instituto Politécnico do Porto para obtenção do Grau de Mestre em Gestão das Organizações, Ramo de Gestão de Empresas Orientada pelo Professor Doutor José Freitas Santos

Strategic Planning and Organizational Effectiveness in Social Service Organizations in Portugal

This study aims to understand the reality of social service organizations, the level of implementation of the strategic planning as well as the impact of its application on organizational effectiveness. At first, we will group organizations in clusters according to the level of strategic planning implementation and its degree of effectiveness. Secondly, we will analyse all the different groups. Given the growing number of social service organizations and the consequent complexity of their structures, it turns out the need for these organizations adopt formal management techniques. Strategic planning is a valuable strategic management tool and one of its main objectives is to make organizations more effective. Therefore, the research has been conducted in order to determine if strategic planning is implemented in social service organizations and which effects has its application on organizational effectiveness. The survey, applied to 220 social service organizations, allowed us to gather them into different clusters, showing that different levels of strategic planning determine distinct degrees of organizational efficiency. Finally, it should be noted that findings of this research may be essential to decision makers of these organizations, because it was shown that the adoption of strategic planning has a positive influence on organizational effectiveness of social service organizations.

Stochastic Framework for Strategic Decision-making of Load-serving Entities for Day-ahead Market

The deregulation of electricity markets has diversified the range of financial transaction modes between independent system operator (ISO), generation companies (GENCO) and load-serving entities (LSE) as the main interacting players of a day-ahead market (DAM). LSEs sell electricity to end-users and retail customers. The LSE that owns distributed generation (DG) or energy storage units can supply part of its serving loads when the nodal price of electricity rises. This opportunity stimulates them to have storage or generation facilities at the buses with higher locational marginal prices (LMP). The short-term advantage of this model is reducing the risk of financial losses for LSEs in DAMs and its long-term benefit for the LSEs and the whole system is market power mitigation by virtually increasing the price elasticity of demand. This model also enables the LSEs to manage the financial risks with a stochastic programming framework.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.