Fusões e aquisições: a evidência empírica

info:eu-repo/semantics/publishedVersio

Endogenous mergers and market structure

In this paper we use a two-stage game to model endogenous mergers. In the second stage of the game, firms compete on the product market. In the first stage, anticipating what will happen in the second stage, firms decide whether or not to merge. In the model, merger occurrence is determined by the interplay of the initial number of firms in the industry, the expected competitive intensity, and the possibility to economize on fixed costs through merger. It is shown that the equilibrium market structure concentration is decreasing in the first of these factors and increasing in the other two. Some implications for antitrust policy are discussedinfo:eu-repo/semantics/publishedVersio

Takeover bids : evidence from the Portuguese market

Throughout the nineties, a number of tender offers occurred in the Por- tuguese market. This article employs event study methodology to investigate their effects on the involved firms’ shareholders. On average, these operations increased the market value of the involved firms by 2% to 3%. However, target sharehold- ers appropriated most of this gain, earning 18% over their firms’ previous value, whereas bidder shareholders seem to have gained nothing. These averages bent in bidders shareholders favour, however, when bidders held significant positions in the targets’ capital before the bid.info:eu-repo/semantics/publishedVersio

The Peso Problem : literature review

Peso Problem situations represent a market reaction prior to abrupt events, that although expected to occur may actually never happen. They so correspond to an anticipation of the event by market participants, their behaviour being biased by the expectation of the abrupt event. The “Peso Problem” concept originated in the currency market, but the situation is transversal to any asset in the market place. This thesis aims to give a perspective of how Peso Problem situations affect asset pricing behaviour in the currency, equity, bond and derivatives markets. Acknowledging that a biased market data behaviour can result from people’s attitudes, behavioural finance forwards an alternative to the traditional Efficient Market Hypothesis point of view for the Peso Problem and similar market data behaviours

Digital microfluidic devices: the role of the dielectric layer

Digital microfluidics (DMF) is a field which has emerged in the last decade as a re-liable and versatile tool for sensing applications based on liquid reactions. DMF allows the discrete displacement of droplets, over an array of electrodes, by the application of voltage, and also the dispensing from a reservoir, mixing, merging and splitting fluidic operations. The main drawback of these devices is due to the need of high driving volt-ages for droplet operations. In this work, alternative dielectric layers combinations were studied aiming the reduction of these driving voltages. DMF chips were designed, pro-duced and optimized according to the theory of electrowetting-on-dielectric, adopting different combinations of parylene-C and tantalum pentoxide (Ta2O5) as dielectric ma-terials, and Teflon as hydrophobic layer. With both devices’ configurations, i.e., Parylene as single dielectric, and multilayer chips combining Parylene and Ta2O5, it was possible to perform all the fluidic opera-tions in the microliter down to hundreds of nanoliters range. Multilayer chips presented significant reduction on driving voltages for droplet op-erations in silicone oil filler medium: from 70 V (parylene only) down to 30 V (parylene/Ta2O5) for dispensing; and from 50 V (parylene only) down to 15 V (parylene/Ta2O5) for movement. Peroxidase colorimetric reactions were successfully performed as proof-of-concept, using multilayer configuration devices

Study and optimization of the memory management in Memcached

Over the years the Internet has become more popular than ever and web applications like Facebook and Twitter are gaining more users. This results in generation of more and more data by the users which has to be efficiently managed, because access speed is an important factor nowadays, a user will not wait no more than three seconds for a web page to load before abandoning the site. In-memory key-value stores like Memcached and Redis are used to speed up web applications by speeding up access to the data by decreasing the number of accesses to the slower data storage’s. The first implementation of Memcached, in the LiveJournal’s website, showed that by using 28 instances of Memcached on ten unique hosts, caching the most popular 30GB of data can achieve a hit rate around 92%, reducing the number of accesses to the database and reducing the response time considerably. Not all objects in cache take the same time to recompute, so this research is going to study and present a new cost aware memory management that is easy to integrate in a key-value store, with this approach being implemented in Memcached. The new memory management and cache will give some priority to key-value pairs that take longer to be recomputed. Instead of replacing Memcached’s replacement structure and its policy, we simply add a new segment in each structure that is capable of storing the more costly key-value pairs. Apart from this new segment in each replacement structure, we created a new dynamic cost-aware rebalancing policy in Memcached, giving more memory to store more costly key-value pairs. With the implementations of our approaches, we were able to offer a prototype that can be used to research the cost on the caching systems performance. In addition, we were able to improve in certain scenarios the access latency of the user and the total recomputation cost of the key-value stored in the system

Aplicação de técnicas de Clustering ao contexto da Tomada de Decisão em Grupo

Nowadays, decisions made by executives and managers are primarily made in a group. Therefore, group decision-making is a process where a group of people called participants work together to analyze a set of variables, considering and evaluating a set of alternatives to select one or more solutions. There are many problems associated with group decision-making, namely when the participants cannot meet for any reason, ranging from schedule incompatibility to being in different countries with different time zones. To support this process, Group Decision Support Systems (GDSS) evolved to what today we call web-based GDSS. In GDSS, argumentation is ideal since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect Based Sentiment Analysis (ABSA) is a subfield of Argument Mining closely related to Natural Language Processing. It intends to classify opinions at the aspect level and identify the elements of an opinion. Applying ABSA techniques to Group Decision Making Context results in the automatic identification of alternatives and criteria, for example. This automatic identification is essential to reduce the time decision-makers take to step themselves up on Group Decision Support Systems and offer them various insights and knowledge on the discussion they are participants. One of these insights can be arguments getting used by the decision-makers about an alternative. Therefore, this dissertation proposes a methodology that uses an unsupervised technique, Clustering, and aims to segment the participants of a discussion based on arguments used so it can produce knowledge from the current information in the GDSS. This methodology can be hosted in a web service that follows a micro-service architecture and utilizes Data Preprocessing and Intra-sentence Segmentation in addition to Clustering to achieve the objectives of the dissertation. Word Embedding is needed when we apply clustering techniques to natural language text to transform the natural language text into vectors usable by the clustering techniques. In addition to Word Embedding, Dimensionality Reduction techniques were tested to improve the results. Maintaining the same Preprocessing steps and varying the chosen Clustering techniques, Word Embedders, and Dimensionality Reduction techniques came up with the best approach. This approach consisted of the KMeans++ clustering technique, using SBERT as the word embedder with UMAP dimensionality reduction, reducing the number of dimensions to 2. This experiment achieved a Silhouette Score of 0.63 with 8 clusters on the baseball dataset, which wielded good cluster results based on their manual review and Wordclouds. The same approach obtained a Silhouette Score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset.Atualmente, as decisões tomadas por gestores e executivos são maioritariamente realizadas em grupo. Sendo assim, a tomada de decisão em grupo é um processo no qual um grupo de pessoas denominadas de participantes, atuam em conjunto, analisando um conjunto de variáveis, considerando e avaliando um conjunto de alternativas com o objetivo de selecionar uma ou mais soluções. Existem muitos problemas associados ao processo de tomada de decisão, principalmente quando os participantes não têm possibilidades de se reunirem (Exs.: Os participantes encontramse em diferentes locais, os países onde estão têm fusos horários diferentes, incompatibilidades de agenda, etc.). Para suportar este processo de tomada de decisão, os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) evoluíram para o que hoje se chamam de Sistemas de Apoio à Tomada de Decisão em Grupo baseados na Web. Num SADG, argumentação é ideal pois facilita a utilização de justificações e explicações nas interações entre decisores para que possam suster as suas opiniões. Aspect Based Sentiment Analysis (ABSA) é uma área de Argument Mining correlacionada com o Processamento de Linguagem Natural. Esta área pretende classificar opiniões ao nível do aspeto da frase e identificar os elementos de uma opinião. Aplicando técnicas de ABSA à Tomada de Decisão em Grupo resulta na identificação automática de alternativas e critérios por exemplo. Esta identificação automática é essencial para reduzir o tempo que os decisores gastam a customizarem-se no SADG e oferece aos mesmos conhecimento e entendimentos sobre a discussão ao qual participam. Um destes entendimentos pode ser os argumentos a serem usados pelos decisores sobre uma alternativa. Assim, esta dissertação propõe uma metodologia que utiliza uma técnica não-supervisionada, Clustering, com o objetivo de segmentar os participantes de uma discussão com base nos argumentos usados pelos mesmos de modo a produzir conhecimento com a informação atual no SADG. Esta metodologia pode ser colocada num serviço web que segue a arquitetura micro serviços e utiliza Preprocessamento de Dados e Segmentação Intra Frase em conjunto com o Clustering para atingir os objetivos desta dissertação. Word Embedding também é necessário para aplicar técnicas de Clustering a texto em linguagem natural para transformar o texto em vetores que possam ser usados pelas técnicas de Clustering. Também Técnicas de Redução de Dimensionalidade também foram testadas de modo a melhorar os resultados. Mantendo os passos de Preprocessamento e variando as técnicas de Clustering, Word Embedder e as técnicas de Redução de Dimensionalidade de modo a encontrar a melhor abordagem. Essa abordagem consiste na utilização da técnica de Clustering KMeans++ com o SBERT como Word Embedder e UMAP como a técnica de redução de dimensionalidade, reduzindo as dimensões iniciais para duas. Esta experiência obteve um Silhouette Score de 0.63 com 8 clusters no dataset de baseball, que resultou em bons resultados de cluster com base na sua revisão manual e visualização dos WordClouds. A mesma abordagem obteve um Silhouette Score de 0.59 com 16 clusters no dataset das marcas de carros, ao qual usamos esse dataset com validação de abordagem