CLSBE - Dissertações de Mestrado / Master Dissertations
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Browsing CLSBE - Dissertações de Mestrado / Master Dissertations by Field of Science and Technology (FOS) "Ciências Médicas::Outras Ciências Médicas"
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- Avaliação do potencial osteogénico de cimentos biocerâmicosPublication . Oliveira, Ana Isabel Flores; Cardoso, Miguel Agostinho Beco Pinto; Duarte, Ana Sofia Direito dos Santos; Noites, Rita Brandão de PinhoINTRODUÇÃO: Os cimentos de obturação à base de silicato de cálcio têm sido estudados como alternativa à utilização de cimentos de obturação convencionais, uma vez que visam a indução da osteogénese, sendo uma alternativa mais favorável para a prática clínica. Neste estudo, o objetivo principal é analisar a evidência de forma sistemática sobre a avaliação do potencial osteogénico dos cimentos de obturação baseados em silicato de cálcio para que estes materiais sejam utilizados de forma mais segura nos tratamentos endodônticos na prática clínica. MATERIAIS E MÉTODOS: A pesquisa foi realizada nas plataformas Pubmed/MEDLINE, Web of Science e Scopus, foram definidos termos Mesh. De seguida, realizou-se a seleção dos artigos através de critérios de inclusão e exclusão. Os termos MeSH foram interligados com os operadores booleanos AND e OR. RESULTADOS: Foram selecionados 9 artigos neste estudo de revisão sistemática. Foram estudadas várias marcas de cimentos biocerâmicos (iRoot SP, MTA Fillapex, BioRoot RCS, EndoSequence BCS, ProRoot ESS, Endoseal MTA, Nishika Canal Sealer BG, Nano-ceramic Sealer, Wellroot ST) e vários cimentos convencionais (Sealapex, Apatite Root Sealer, Pulp Canal Sealer, Roth Sealer e AH-Plus) utilizando diferentes meios de cultura. CONCLUSÃO: Os cimentos biocerâmicos são uma boa alternativa aos cimentos convencionais, uma vez que apresentam melhor potencial osteogénico que os cimentos convencionais. No entanto, é necessário confirmar estes resultados através da realização de estudos clínicos.
- Earnings prediction using machine learning methods and analyst comparisonPublication . Martins, Alexandre Inês; Trung, Tran HieuIn the course of this dissertation we propose an experimental study on how technical, macroeconomic, and financial variables, alongside analysts’ forecasts, can be used to optimize the prediction for the subsequent quarter’s earnings results using machine learning, comparing the performance of the models to analysts’ forecasts. The dissertation includes three steps. In step one, an event study is conducted to test abnormal returns in firms’ stock prices in the day following earnings announcement, grouped by earnings per share (EPS) growth in classes of size 3, 6 and 9, computed for each quarter. In step two, several machine learning models are built to maximize the accuracy of EPS predictions. In the last step, investment strategies are constructed to take advantage of investors’ expectations, which are closely correlated with analysts’ predictions. In the backdrop of an exhaustive analysis on quarterly earnings predictions using machine learning methods, conclusions are drawn related to the superiority of the CatBoost classifier. All machine learning models tested underperform analyst predictions, which could be explained by the time and privileged information at analysts’ disposal, as well as their selection of firms to cover. Regardless, machine learning models can be used as a confirmation for analyst predictions, and statistically significant investment strategies are pursued with those fundamentals. Importantly, high confidence predictions by machine learning models are significantly more accurate than the average accuracy of forecasts.
- Equity valuation : Target CorporationPublication . Teles, João Duarte; Martins, José Carlos TudelaThis dissertation has as its main objective the valuation of Target Corporation, being its major output the resulting intrinsic value of the company’s stock. To unveil the most appropriate methods to follow and apply in order to obtain this output, a review of the main articles and literature of some of the greatest minds in the finance field was firstly performed. Even though many models were studied and reviewed, only some of them were chosen to execute the valuation, due to its greatest practical applications in “real-life” examples. Resultingly, the first and main method used was the Discounted Cash Flow, which is one if not the most known model to perform valuations. By performing the forecast of the company’s financial statements, it was possible to consequently forecast the free cash flows to the firm, and then achieve the Equity Value. Finally, the fair price of Target’s stock deemed by this model was of $259.83 per share. The second model applied was the Relative Valuation Model. By performing this valuation using P/E, EV/EBITDA, and EV/Sales multiples, intrinsic values of $220.62, $307.28, and $286.09 per share were achieved. The overall conclusion yielded by both these models is that Target’s stock is undervalued, being its intrinsic value greater than its market price. However, the report provided by Morningstar found otherwise, opinionating that its valuations conclude that the stock is overvalued and fairly priced at 159$ per share, being this different fair price the result of different assumptions and report timings.