Made available in DSpace on 2009-11-18T19:01:04Z (GMT). No. of bitstreams: 1 ACF189.pdf: 1048760 bytes, checksum: 6056ed3b4b46f5ee586cfd69fdbc22bf (MD5) Previous issue date: 2007 / This research evaluated the quality of the management of Brazilian stock funds on the period from January 1997 to October 2006. The analysis was based on the Modern Portfolio Theory measures of performance. In addition, this research evaluated the relevance of the performance measures The sample with 21 funds was extracted from the 126 largest Brasilian stock options funds because they were the only with quotas on the whole period. The monthly mean rate of return and the following indexes were calculated: total return, mean monthly return, Jensen Index, Treynor Index, Sharpe Index, Sortino Index, Market Timing and the Mean Quadratic Error. The initial analysis showed that the funds in the sample had different objectives and limitations. To make valuable comparisons, the ANBID (National Association of Investment Banks) categories were used to classify the funds. The measured results were ranked. The positions of the funds on the rankings based on the mean monthly return and the indexes of Jensen, Treynor, Sortino and Sharpe were similar. All of the ten ACTIVE funds of this research were above the benchmark (IBOVESPA index) in the measures above. Based on the CAPM, the managers of these funds got superior performance because they might have compiled the available information in a superior way. The six funds belonging to the ANBID classification of INDEXED got the first six positions in the ranking based on the Mean Quadratic Error. None of the researched funds have shown market timing skills to move the beta of their portfolios in the right direction to take the benefit of the market movements, at the significance level of 5%. / Esta pesquisa analisou a qualidade da gestão dos fundos de ações brasileiros através da análise do desempenho obtido por eles no período de janeiro de 1997 a outubro de 2006. A análise foi feita com base na Teoria Moderna das Carteiras. Ela analisou também a aplicação dos critérios de desempenho na medida do desempenho deles. A amostra de 21 fundos foi selecionada do universo composto pelos 126 maiores fundos de aços Brasileiros. Os seguintes critérios de desempenho foram calculados a partir das cotas mensais dos fundos: retorno absoluto, retorno Médio Mensal, Índice de Jensen, Índice de Treynor, Índice de Sharpe, Índice de Sortino, capacidade de prever os movimentos do mercad (Market Timing) e o Erro Quadrático Médio. A análise dos dados começou com a classificação dos fundos de acordo com os tipos ANBID, pois os objetivos e as limitações dos fundos da amostra não eram homogêneos. Posteriormente, os resultados obtidos nas medições de desempenho foram ordenados em rankings e analisados. Dez dos 21 fundos da amostra, todos ativos, tiveram desempenho superior ao mercado (representado pelo IBOVESPA) nos critérios do retorno médio mensal e nos Índices de Jensen, Treynor, Sortino e Sharpe. De acordo com o CAPM, esses gestores devem ter compilado as informações disponíveis de uma forma superior, e por isso, conseguiram um desempenho superior ao mercado. Os seis fundos da categoria IBOVESPA INDEXADO ficaram com as seis primeiras posições na calcificação pelo Erro Quadrático Médio, o que já era esperado, pois a missão desses fundos é reproduzir o retorno do índice. Nenhum dos gestores dos 21 fundos demonstrou ter capacidade de prever o movimento do mercado (Market Timing), ao nível de significância de 5%, e movimentar o beta de sua carteira na direção correta a fim de maximizar o retorno do investidor.

In this thesis, the effect of pitch based optimisation was explored for a 80 turbine wind farm. Using a modified Jensen wake model and the Particle Swarm Optimisation (PSO) model, a pitch optimisation strategy was created for the dominant turbulence and atmospheric condition for the wind farm. As the wake model was based on the FLORIS model developed by P.M.O Gebraad et. al., the wake and power model was compared with the FLORIS model and a -0.090% difference was found. To determine the dynamic predictive capability of the wake model, measurement values across a 10 minute period for a 19 wind turbine array were used and the wake model under predicted the power production by 17.55%. Despite its poor dynamic predictive capability, the wake model was shown to accurately match the AEP production of the wind farm when compared to a CFD simulation done in FarmFlow and only gave a 3.10% over-prediction. When the optimisation model was applied with 150 iterations and particles, the AEP production of the wind farm increased by 0.1052%, proving that the pitch optimisation method works for the examined wind farm. When the iterations and particles used for the optimisation was increased to 250, the power improvement between optimised results improved by 0.1144% at a 222.5% increase in computational time, suggesting that the solution has yet to fully converge. While the solutions did not fully converge, they converged sufficiently and an increase in iterations gave diminishing results. From the results, the pitch optimisation model was found to give a significant increase in power production, especially in wake intensive wind directions. However, the dynamic predictive capabilities will have be improved upon before the control strategy can be applied to an operational wind farm.

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There is no clear consensus about whether green investments perform better, worse orequal to conventional brown investments. With the rising popularity of socialinvestments, it becomes increasingly important to understand these investments. Therecent launch of the MSCI Climate Paris Aligned Indices (CPAI) aim to illustrate thedevelopment of an economy that is in line with the requirements and goals of the ParisAgreement from 2015. In this research we aim to find out whether the MSCI Europe,USA and EM Climate Paris Aligned Indices outperform their parent indices. We do thisby comparing performance measures such as the net return, standard deviation of netreturns and Sharpe ratio. We further conduct an ordinary least squares regression to testwhether the betas and Jensen´s alphas of the CPAI differ significantly from their parentindices.The results show that only the USA CPAI clearly outperforms its parent index. This isdue to it having a higher Sharpe Ratio and Jensen’s alpha as well as higher monthly netreturns and a lower standard deviation compared to its parent index. The regressionshows that it does perform better than the parent index. The results for the EM CPAIshow that it performs in a similar way as its parent index. It has a higher monthly netreturn but also slightly higher standard deviation which leads to an equally large Sharperatio. Neither the estimated Jensen’s alpha nor the beta are significantly different fromthose of its parent index and thus the hypothesis of it performing equally as well as itsparent index cannot not be rejected. Lastly, the Europe CPAI has a higher Sharpe ratio,Jensen’s alpha and monthly net returns than its parent index, but it also exhibits a higherstandard deviation. The regression indicated that it performs in a similar way as itsparent index, no difference could be proven. In conclusion, this means that all CPAIperform at least equally as well as their parent indices, if not better.

This thesis presents new models for determining piezoresistive response in long, thin polysilicon beams with either axial and bending moment inducing loads or torsional loads. Microelectromechanical (MEMS) test devices and calibration methods for finding the piezoresistive coefficients are also presented for both loading conditions. For axial and bending moment inducing loads, if the piezoresistive coefficients are known, the Improved Piezoresistive Flexure Model (IPFM) is used to find the new resistance of a beam under stress. The IPFM first discretizes the beam into small volumes represented by resistors. The stress that each of these volumes experiences is calculated, and the stress is used to change the resistance of the representative resistors according to a second-order piezoresistive equation. Once the resistance change in each resistor is calculated, they are combined in parallel and series to find the resistance change of the entire beam. If the piezoresitive coefficients are not initially known, data are first collected from a test device. Piezoresistive coefficients need to be estimated and the IPFM is run for the test device's different stress states giving resistance predictions. Optimization is done until changing the piezoresistive coefficients provides model predictions that accurately match experimental data. These piezoresistive coefficients can then be used to design and optimize other piezoresistive devices. A sensor is optimized using this method and is found to increase voltage response by an estimated 10 times. For torsional loads, the test device consists of a slider-crank connected to two torsional legs. The slider-crank creates torsional stress in the legs which causes a change in the electrical resistance through the legs. A model that predicts the effects of a scissor hinge on the slider-crank is presented. Torsional stresses in the legs are calculated delete{using the membrane analogy.} and the legs are discretized into long parallel resistors and the stresses delete{from the membrane analogy} applied to each resistor. Assuming a second-order piezoresistance, an optimization is then done to find the piezoresistive coefficients by changing them until the model prediction fits the test data. These coefficients can be used to predict angular displacement from resistance measurements in fully integrated torsional sensors. Potential applications are discussed, and a torsional accelerometer is presented.

This study examines the total cumulative return and the total risk-adjusted return for OMXSPI and the two investment strategies The Magic Formula and The Acquirer’s Multiple. The aim is to find out if it’s possible to beat the market over time in contradiction to the efficient market hypothesis. Data has been collected to cover a 15-year period between 2005 and 2020. The results end up challenging the efficient market hypothesis with higher total cumulative returns, but also fairly convincingly higher risk-adjusted returns from the strategies. The study also found that the Acquirer’s Multiple outperformed the Magic Formula with regards to both measures.

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Cette thèse présente trois contributions en lien avec l'apprentissage et la détection de dissimilarité statistique multivariée, problématique d'importance primordiale pour de nombreuses méthodes d'apprentissage utilisées dans un nombre croissant de domaines. La première contribution introduit la notion de taille d'effet multivariée non-paramétrique, éclairant la nature de la dissimilarité détectée entre deux jeux de données, en deux étapes. La première consiste en une décomposition d'une mesure de dissimilarité (divergence de Jensen-Shannon) visant à la localiser dans l'espace ambiant, tandis que la seconde génère un résultat facilement interprétable en termes de grappes de points de forte discrépance et en proximité spatiale. La seconde contribution présente le premier test non-paramétrique d'homogénéité séquentiel, traitant les données issues de deux jeux une à une--au lieu de considérer ceux-ci- in extenso. Le test peut ainsi être arrêté dès qu'une évidence suffisamment forte est observée, offrant une flexibilité accrue tout en garantissant un contrôle del'erreur de type I. Sous certaines conditions, nous établissons aussi que le test a asymptotiquement une probabilité d'erreur de type II tendant vers zéro. La troisième contribution consiste en un test de détection de changement séquentiel basé sur deux fenêtres glissantes sur lesquelles un test d'homogénéité est effectué, avec des garanties sur l'erreur de type I. Notre test a une empreinte mémoire contrôlée et, contrairement à des méthodes de l'état de l'art qui ont aussi un contrôle sur l'erreur de type I, a une complexité en temps constante par observation, le rendant adapté aux flux de données. / In this thesis, we study problems related to learning and detecting multivariate statistical dissimilarity, which are of paramount importance for many statistical learning methods nowadays used in an increasingly number of fields. This thesis makes three contributions related to these problems. The first contribution introduces a notion of multivariate nonparametric effect size shedding light on the nature of the dissimilarity detected between two datasets. Our two step method first decomposes a dissimilarity measure (Jensen-Shannon divergence) aiming at localizing the dissimilarity in the data embedding space, and then proceeds by aggregating points of high discrepancy and in spatial proximity into clusters. The second contribution presents the first sequential nonparametric two-sample test. That is, instead of being given two sets of observations of fixed size, observations can be treated one at a time and, when strongly enough evidence has been found, the test can be stopped, yielding a more flexible procedure while keeping guaranteed type I error control. Additionally, under certain conditions, when the number of observations tends to infinity, the test has a vanishing probability of type II error. The third contribution consists in a sequential change detection test based on two sliding windows on which a two-sample test is performed, with type I error guarantees. Our test has controlled memory footprint and, as opposed to state-of-the-art methods that also provide type I error control, has constant time complexity per observation, which makes our test suitable for streaming data.

The thesis deals with the influence of free various technological grinding processes of pure clinker minerals. The goals is to synthetically prepare pure clinker minerals and monitor the effect of the duration of the grinding process and monitor the impact of grinding technology on their crystallinity. A laser granulometry and XRD analysis are used for evaluation.