The top management team is important to understand as the executives within the top management team would have long-term implications for a firm's investment, operating and financing decisions which would affect the firm value. As these executives may have pre-existing connections outside the current firm, they are likely to be affected by these connections within the top management team. In this dissertation, I draw upon the literature in sociology that discusses different mechanisms of connections; 1) better information transfer, 2) cohesion and better coordination, and 3) favorable treatment to see how the connections within the top management team affects different decisions for the firm using data from 1999 to 2013. First, I find that the executives with connections to the CEO are less likely to be forced out and those with social connections to the CEO enjoy less sensitivity of involuntary turnover to performance. Notably, I find that this is consistent with CEOs favorably treating the connected executives rather than CEOs keeping connected executives for the benefits. Second, I find that firms with greater percentage of executives with connections to the CEO have greater accruals earnings management and lower likelihood of detection of accounting manipulations. I also show that the connections have an effect only when the joint tenure between the CEO and the executives are short. Finally, I document that firms with more closely connected top management team issue management earnings forecasts in a more precise form and issue more frequent and accurate forecasts. I show that this matters more when the top management team’s external network size is small. / Business Administration/Accounting

Prior research indicates that management forecast strategies affect investors' perceptions of management, which, in turn, influence investors' judgments about the firm. The current study hypothesizes and demonstrates that decisions about the completeness and form of management's forecast disclosure affect venture capitalists' (VCs) investment screening judgments. In an experiment, 53 experienced VCs indicate whether they would recommend conducting due diligence on a new venture. I manipulate the completeness (inclusion vs. omission of quantitative data about the components of earnings) and form (point vs. range forecast values) of management's financial forecasts in a 2 X 2 between-subjects design. When management is more (less) complete in its forecast disclosure, participants make more (less) favorable investment screening judgments. Additionally, when managers provide less complete disclosures, the use of point rather than range forecasts leads to particularly unfavorable screening judgments, whereas when managers provide more complete disclosures, the use of point rather than range forecasts leads to particularly favorable screening judgments. Taken together, these results indicate that the completeness of forecast disclosure increases the favorability of screening judgments and decisions about the form of financial forecasts can offset some of the adverse consequences of less complete disclosure. / Ph. D.

Intensity-duration-frequency (IDF) curves are widely used for peak discharge estimation in designing hydraulic structures. The traditional Gumbel probability method entails selecting annual maximum rainfall depths (intensities) conditioned on a fixed time window width (which in general will not coincide with the rainfall event duration) from a continuous record to perform a frequency analysis in terms of the marginal distribution. The digitized database contains annual maximum intensities for selected discrete durations. This method presents problems when intensities are required for arbitrary durations which are not part of the selected durations. Accurate interpolated and especially extrapolated intensity values are hard to obtain. The present study offers two methods both involving a joint probability approach to overcome the deficiencies inherent in the traditional method of IDF analysis. The first joint probability approach employs Box-Cox and modulus transformations to transform original data to near bivariate normality. The second method does not require such a transformation. Instead, it uses the closed-form bivariate Burr III cumulative distribution to fit the data. Another advantage of the joint probability approach is that it allows one to gauge the rarity of certain extreme events, such as probable maximum precipitation, in terms of the joint occurrence of its extremely high intensity and a sufficiently long duration (e.g. 24 hours). The joint probability approach is applied to three data sets. The resulting conditional probability intensity estimates are quite close to those obtained by traditional Gumbel IDF analysis. In addition, reliable interpolated and extrapolated intensities are available because the approach essentially fits a flexible surface to the discrete data with the capability of providing a complete probabilistic structure. / Master of Science

How much electricity is going to be consumed for the next 24 hours? What will be the temperature for the next three days? What will be the number of sales of a certain product for the next few months? Answering these questions often requires forecasting several future observations from a given sequence of historical observations, called a time series. <p><p>Historically, time series forecasting has been mainly studied in econometrics and statistics. In the last two decades, machine learning, a field that is concerned with the development of algorithms that can automatically learn from data, has become one of the most active areas of predictive modeling research. This success is largely due to the superior performance of machine learning prediction algorithms in many different applications as diverse as natural language processing, speech recognition and spam detection. However, there has been very little research at the intersection of time series forecasting and machine learning.<p><p>The goal of this dissertation is to narrow this gap by addressing the problem of multi-step-ahead time series forecasting from the perspective of machine learning. To that end, we propose a series of forecasting strategies based on machine learning algorithms.<p><p>Multi-step-ahead forecasts can be produced recursively by iterating a one-step-ahead model, or directly using a specific model for each horizon. As a first contribution, we conduct an in-depth study to compare recursive and direct forecasts generated with different learning algorithms for different data generating processes. More precisely, we decompose the multi-step mean squared forecast errors into the bias and variance components, and analyze their behavior over the forecast horizon for different time series lengths. The results and observations made in this study then guide us for the development of new forecasting strategies.<p><p>In particular, we find that choosing between recursive and direct forecasts is not an easy task since it involves a trade-off between bias and estimation variance that depends on many interacting factors, including the learning model, the underlying data generating process, the time series length and the forecast horizon. As a second contribution, we develop multi-stage forecasting strategies that do not treat the recursive and direct strategies as competitors, but seek to combine their best properties. More precisely, the multi-stage strategies generate recursive linear forecasts, and then adjust these forecasts by modeling the multi-step forecast residuals with direct nonlinear models at each horizon, called rectification models. We propose a first multi-stage strategy, that we called the rectify strategy, which estimates the rectification models using the nearest neighbors model. However, because recursive linear forecasts often need small adjustments with real-world time series, we also consider a second multi-stage strategy, called the boost strategy, that estimates the rectification models using gradient boosting algorithms that use so-called weak learners.<p><p>Generating multi-step forecasts using a different model at each horizon provides a large modeling flexibility. However, selecting these models independently can lead to irregularities in the forecasts that can contribute to increase the forecast variance. The problem is exacerbated with nonlinear machine learning models estimated from short time series. To address this issue, and as a third contribution, we introduce and analyze multi-horizon forecasting strategies that exploit the information contained in other horizons when learning the model for each horizon. In particular, to select the lag order and the hyperparameters of each model, multi-horizon strategies minimize forecast errors over multiple horizons rather than just the horizon of interest.<p><p>We compare all the proposed strategies with both the recursive and direct strategies. We first apply a bias and variance study, then we evaluate the different strategies using real-world time series from two past forecasting competitions. For the rectify strategy, in addition to avoiding the choice between recursive and direct forecasts, the results demonstrate that it has better, or at least has close performance to, the best of the recursive and direct forecasts in different settings. For the multi-horizon strategies, the results emphasize the decrease in variance compared to single-horizon strategies, especially with linear or weakly nonlinear data generating processes. Overall, we found that the accuracy of multi-step-ahead forecasts based on machine learning algorithms can be significantly improved if an appropriate forecasting strategy is used to select the model parameters and to generate the forecasts.<p><p>Lastly, as a fourth contribution, we have participated in the Load Forecasting track of the Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem where we were required to backcast and forecast hourly loads for a US utility with twenty geographical zones. Our team, TinTin, ranked fifth out of 105 participating teams, and we have been awarded an IEEE Power & Energy Society award.<p> / Doctorat en sciences, Spécialisation Informatique / info:eu-repo/semantics/nonPublished

Purpose: The purpose of this paper is to examine if underlying psychological factors have an impact on shareholders' investment decisions and if forecasting quarterly reports have an impact on shareholders' investment decisions. Methods: The study combines an application of two different methods, survey and an event study. The purpose of the survey was to investigate if psychological factors interfere with shareholders' investment decisions. The Event study is designed to measure equity market reaction to the forecasts and if any changes occurred in the companies' share price. Theoretical: This study is based on established theories in the fields of behavioral finance, the efficient market hypothesis and the economic man. Conlusion: Underlying psychological factors have a certain impact on the shareholders' investment decisions. Both events study and survey, in particularly the part of the survey, which includes forecasts, showed that the shareholders are not affected by the forecasts published by market analysts.

Approved for public release, distribution is unlimited / This study investigates severe weather events occurring in the Midwest, Central, and Northeastern United States from May through September 2004. Severe weather events are pinpointed using tornado and hail reports and correlating them with NEXRAD radar data to determine maximum intensity of the event. Severe storms that occur within 30 minutes of a model forecast hour are catalogued for further investigation. Once these events are diagnosed, ETA-212 and MM5 model data is regridded, centered on the storm. Divergence values at 300 hPa are extracted from the model data for each storm event. These storms are then grouped in three ways: all storms, tornadic storms, and hail producing storms. The averaged maximum divergence values from the ETA-212 for each group are examined from the 0 hour analysis through the 21 hour forecast. From these averaged divergence values, a matrix of recommended divergence threshold values is derived. For the MM5 data, a subset of storms is examined. The MM5 and ETA-212 are run on an identical set of storms, and the divergence forecasts are compared. / Captain, United States Air Force

Cette thèse s'intéresse au rôle des analystes financiers lors de la couverture des introductions en bourse dans un contexte international. Nous traitons de la valeur informationnelle des couvertures des analystes et de leur conséquence sur la performance à long terme des entreprises nouvellement introduites en bourse. Nous examinons si les recommandations des analystes financiers permettent de réduire le comportement irrationnel des investisseurs en situation de forte incertitude. Nous espérons qu'en réduisant les asymétries d'informations, les analystes financiers aident les investisseurs à mieux cibler la valeur de l'IPO. Cette thèse s'articule autour de deux parties : la première est consacrée au positionnement théorique et à nos hypothèses de recherche ; la seconde se focalise sur la vérification empirique d'un échantillon d'IPOs internationales (Etats-Unis, Angleterre, Allemagne et France) sur la période 1991 à 2005. Les résultats révèlent une sous performance des IPOs plus sévères pour les orphelines (sans couverture des analystes) que pour les non orphelines. Il apparaît que la couverture des analystes est importante pour les IPOs mais que le marché n'en perçoit pas toute la valeur. D'autres analyses soulignent que cette meilleure performance des non orphelines provient du nombre élevé de couvertures. Nous établissons que les recommandations des analystes sont significativement reliées à la performance à long terme des IPOs. Ainsi, nous vérifions le rôle crucial des analystes financiers dans la production et l'interprétation des informations. / This thesis explores the role of financial analysts' coverage on IPOs in an international context. We deal with the informational value of research coverage and the consequence on long run performance of newly public firms. We examine whether financial analyst recommendations allow alleviating the irrational investors' behaviour in the context of strong uncertainty. We expect that by reducing the information asymmetry, financial analyst recommendations help investors to define progressively the true value of the IPO. The thesis is organized in two main parts: the first part presents a survey of literature and define research hypothesis. The second part consists in an empirical validation of an international sample of IPOs (US, United Kingdom, Germany and France) over the 1991-2005 period. The results reveal that long run underperformance is much severe for orphans' IPOs (without financial recommendation) than non orphans' IPOs. The evidence suggests that analyst coverage is indeed important to issuing firm but the market do not fully incorporate the perceived value of this coverage. Further analysis reveals that this outperformance by non orphan stems from high coverage. We establish that analyst recommendations are significantly related to long run performance of IPOs. Hence, we corroborate the crucial role of financial analysts in producing and interpreting IPOs' financial releases.

O objetivo desta tese é analisar os determinantes políticos e econômicos dos desvios de execução orçamentária no Brasil, no período de 2002 a 2015. Desvios de execução são definidos como a diferença entre o valor observado da variável e o valor previsto no orçamento. A partir do uso de dados em tempo real, isto é, dados disponíveis aos formuladores de política no momento em que as decisões de política fiscal são tomadas, busca-se compreender os desvios de execução da política fiscal à luz de alguns fatores atuantes na fase de planejamento e execução da política, confrontando a intencionalidade declarada pelo executor da política no planejamento orçamentário com os resultados finais obtidos. Para contornar a ausência de uma base de dados em tempo real organizada para a economia brasileira, a base utilizada foi construída a partir dos documentos oficiais gerados ao longo das fases do processo orçamentário. A construção dessa base, no entanto, limitou-se em função da insuficiência de dados em tempo real de acesso público. Para atingir o objetivo proposto, os desvios de execução fiscal foram inicialmente avaliados a partir da análise dos eventos que condicionaram o planejamento e a execução do orçamento. À esta análise somou-se uma análise empírica, na qual buscou-se identificar os determinantes da fase de planejamento, a partir da análise dos fatores que influenciam na previsão de arrecadação, e os condicionantes da fase de execução orçamentária, a partir da identificação dos fatores que contribuem para os desvios de execução. Os resultados indicam que a previsão fiscal distanciou-se mais significativamente dos valores realizados a partir do ano de 2012 e que os desvios nas previsões dos fatores macroeconômicos, sobretudo na previsão de crescimento do PIB ajudam a explicar esse distanciamento. Verificou-se também um otimismo persistente nas projeções de crescimento do PIB utilizadas para a elaboração do orçamento. Adicionalmente, verificou-se que a adoção de subterfúgios contábeis adotados pelo Governo para atingir artificialmente as metas de superávit primário resultam em uma execução orçamentária desvinculada do orçamento planejado, dificultando a análise dos desvios de execução e transformando o processo de planejamento orçamentário em mera exigência legal, sem aplicação prática. / This thesis aims to analyze the politic and economic determinants of the budget execution deviation in Brazil, from 2002 to 2015. Execution deviation is defined as the difference between the observed value of the variable and the forecast budget value. From the use of real-time data, i.e., data available to policy makers at the time the fiscal policy decisions are taken, we seek to understand the execution deviations from fiscal policy in the light of some active factors during the planning phase and policy execution, confronting the intention declared by the policy maker in the budget planning with the final results. To work around the lack of a real-time dataset organized for the Brazilian economy, the dataset used was built from the official documents generated throughout the phases of the budget process. The construction of this dataset, however, was limited due to insufficient data in real-time public access. To achieve this goal, the fiscal execution deviations were initially evaluated from the analysis of the events that conditioned the planning and execution of the budget. To this analysis we added up an empirical analysis, in which we attempted to identify the determinants of the planning phase from the analysis of the factors that influence in the revenue forecast and the conditioners of the budget execution phase from the identification of factors contributing to the execution deviations. The results indicate that the realized values drifted away significantly more from the fiscal forecast from the year 2008 and that the deviations in the forecasts of macroeconomic factors, particularly in the GDP growth forecast, help to explain this gap.

Le Dynamic Line Rating est la modification dynamique des contraintes de courant sur une ligne électrique aérienne, en accord avec la météorologie. De telles modifications permettent alors d’avoir des réductions des phénomènes de congestion près de 99% du temps.De manière similaire aux énergies renouvelables, il est possible de générer des prévisions de ces contraintes modifiées, en accord avec des observations historiques, des prévisions météorologiques et des méthodes d’intelligence artificielle.Dans cette thèse, nous proposons le développement de modèles de prévision probabilistes à court terme du DLR. Nous nous concentrons plus particulièrement sur des méthodes fournissant des prévisions ayant de très faibles probabilités d’être surestimées. Cela passe par le développement et la comparaison de plusieurs méthodes de prévision, ainsi que des améliorations comme des modifications de prévisions à très bas quantile à l’aide de remodélisations des queues de distribution.Par la suite, une réflexion est faite sur l’utilisation en pratique de ces prévisions, d’abord par des cas d’étude simplifié, puis à l’aide de simulations de réseaux électrique. Ces approches nous permettent de développer de nouvelles stratégies d’utilisation des prévisions DLR, optimisant le bien-être social tout en maintenant les risques associés aux erreurs de prévision à un niveau faible.Finalement, nous évaluons les modèles de prévisions développés en fonction de leurs performances économiques à l’aide des modèles de réseaux électriques, et nous démontrons la valeur des améliorations des modèles de prévision que nous proposons. / Dynamic Line Rating is the modification of the maximal current capacity of an overhead electrical line, depending on weather characteristics. Such modifications allow important decreases of congestion phenomena, around 99% of the time.Similarly to renewable generation, it is possible to forecast the modified constraints, accordingly to some historic observations, weather predictions and artificial intelligence methods.In this document, the development of short-term probabilistic DLR forecast models. A focus is especially made on methods providing forecasts having a very low probability of being overestimated. This is made through the development and the comparison of several forecast methods, and some improvements such as the remodelling of very low quantile forecasts with tail density modelling.Following that, a reflection is proposed on the use of such forecasts in practice, first with some simplified test cases, then with electrical grid simulations. These approaches allow us developing new strategies for the use of the DLR forecasts, maximizing the social welfare while keeping risks associated with forecasts errors at low levels.Finally, an evaluation of the forecast models function of their economic value is made with the electrical grids models, and the value of the proposed modifications of the forecast models is then demonstrated.

Submitted by Filipe dos Santos (fsantos@pucsp.br) on 2018-01-22T11:34:25Z No. of bitstreams: 1 Rodrigo Lucas da Silva Pereira da Gama Alves.pdf: 1005971 bytes, checksum: 703910354b6d38c372bcf94d50e23665 (MD5) / Made available in DSpace on 2018-01-22T11:34:25Z (GMT). No. of bitstreams: 1 Rodrigo Lucas da Silva Pereira da Gama Alves.pdf: 1005971 bytes, checksum: 703910354b6d38c372bcf94d50e23665 (MD5) Previous issue date: 2017-12-19 / The present work is dedicated to the presentation of an attempt to systematize the Brazilian appeal system instituted by the Code of Civil Procedure of 2015. This is because the new law does not discipline the appeal plan according to the content of judicial decision-making. Therefore, there are equal situations in substance that have been disciplined differently by the legislator, a fact that leads to a situation that violates the isonomy and the legal process. Suffice is to imagine that the legislator did two little alterations, but that had a great impact on the system. The first alteration was to establish a criterion to define the sentence, i.e., observing the content of the pronouncement and its purpose, that is, whether or not a phase that takes to the procedure end. From that point on, it conferred on the decisions a nature of interlocutory decision. In another aspect, it also allowed a partial retention of merit, in the cases of requests made in cumulation. The Code also maintained the principle of correspondence to the establishment that is the appropriate appeal against the sentence, it is an appeal and against an interlocutory decision and interlocutory appeal. However, in disciplining the legal regime of the grievance, there was not the necessary adequacy so that the appeal could in fact challenge the decisions of merit, insofar as the original discipline of the resource was maintained as if it existed, for to attack incidental decisions without regard to the merits of the case. Moreover, the entire recursal system is based on the sentence, as if this is the only pronunciation capable of resolving a pretension of the parties, but, of course, the interlocutory decision can also resolve the merits. Thus, our investigation will go through the nature of judicial decision-making, appeals and at the and the final, it will purpose that these institutes are interpreted according to the Federal Constitution, in order that we can adapt the recursal system in an isonomic way and in attention to the legal process / O presente trabalho se dedica em apresentar uma tentativa de sistematização do sistema recursal brasileiro instituído pelo Código de Processo Civil de 2015. Isto porque a nova lei não disciplinou o plano recursal de acordo com o conteúdo dos pronunciamentos judiciais decisórios. Por isso, há situações iguais em substância que foram disciplinadas de forma absolutamente diferente pelo legislador, fato que leva a uma situação que viola a isonomia e o devido processo legal. Basta imaginarmos que olegislador fez duas alterações pontuais, mas que foram de grande impacto nosistema. A primeira foi estabelecer um duplo critério para definir sentença, ou seja, observando o conteúdo do pronunciamento e a sua finalidade, vale dizer, se encerra ou não uma fase do procedimento. A partir desse ponto, conferiu às demais decisões natureza de decisão interlocutória. Noutra vertente, permitiu, também, a prolação de decisões parciais de mérito, nas hipóteses de pedidos feitos em cumulação. O Código manteve, ainda, o princípio da correspondência ao estabelecer que o recurso cabível contra a sentença é a apelação e contra a decisão interlocutória é o agravo de instrumento. No entanto, ao disciplinar o regime jurídico do agravo de instrumento, não houve a adequação necessária para que o recurso pudesse, de fato, impugnar as decisões de mérito, na medida em que se manteve a disciplina originária do recurso como se existisse, tão somente, para atacar decisões incidentais sem relação com o mérito do processo. Além do mais, todo o sistema recursal é baseado na sentença, como se se esta fosse o único pronunciamento capaz de resolver a pretensão das partes, mas, evidentemente, a decisão interlocutória também pode resolver o mérito. Assim, nossa investigação passará pela natureza dos pronunciamentos judiciais decisórios, dos recursos e, ao final, proporá que esses institutos sejam interpretados conforme a Constituição Federal, a fim de que possamos adequar o sistema recursal de forma isonômica e em atenção ao devido processo legal