Dissertations / Theses on the topic 'Standard and Poor’s Corporation'

Thesis (Ph.D.)--George Mason University, 2008.
Vita: p. 75. Thesis director: Willem Thorbeck. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Economics. Title from PDF t.p. (viewed Aug. 27, 2008). Includes bibliographical references (p. 70-74). Also issued in print.

In this study a two-part mixed probability density function was derived which described the relative changes in the Standard and Poor's 100 Stock Index over various intervals of time. The density function is a mixture of two different halves of normal distributions. Optimal values for the standard deviations for the two halves and the mean are given. Also, a general form of the function is given which uses linear regression models to estimate the standard deviations and the means. The density functions allow stock market participants trading index options and futures contracts on the S & P 100 Stock Index to determine probabilities of success or failure of trades involving price movements of certain magnitudes in given lengths of time.

Made available in DSpace on 2010-04-20T20:20:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2001-07-13T00:00:00Z
Análise dos critérios determinantes dos ratings de risco soberano emitidos pela agência Standard & Poor's, evidenciando variáveis de cunho econômico e político. Realização de testes empíricos de regressão linear e análise dos coeficientes determinantes do risco soberano em moeda estrangeira.

Background: Increasingly, companies choose to finance their business with corporate bonds which has resulted in an increased demand on credit ratings. As such the rating agencies have a very important role in the financial markets. Examining the value of a credit-rating can be very interesting for both issuer and investor.

Purpose: The purpose of the study is to identify and define the value of rating.

Execution: The study is built upon nine interviews with rated and non-rated firms and investors.

Result: The most important value of rating is the greater access to the corporate- and CP market. This infers that additional capital can be issued, in comparison to that available from bank loans, however, not necessarily at a lower rate. Consequently, the rate is not the driving factor concerning the choice of buying a rating. Rating has great effect on the pricing of bonds and CP’s, which is a result of the reliance investors have on the rating agencies. Rating affects the investment decision directly through the investment mandates, and indirectly through the effect on the individual investment decision.

As a result from the latest financial crisis, the banking industry has undergone major modifications during the last years in order to limit banks’ risks. A vast majority of existing literature tends to focus upon credit risk evaluation methods and techniques mainly concerning quantitative measures and large companies. Thus, the lack of research regarding credit risk evaluation of SMEs is profound, especially considering Sweden. With the dominant market share of SMEs compared to large corporations the authors found it interesting to further explore this area of the credit assessment process as SMEs largely impact the Swedish business sector.   The purpose of the thesis at hand is to explore and provide empirical evidence of which criteria banks assess when evaluating credit risk of SMEs in Sweden. In regards to the purpose the authors have chosen to adapt the perspective of the banking industry throughout the thesis. In order to bridge the research gap the following question was established, “How do banks evaluate credit risk of SMEs in Sweden?” In light of the lack of research regarding qualitative assessment of credit risk, the authors found it interesting in terms of developing new theoretical and practical knowledge to establish the following sub-question, “What are the qualitative criteria used by banks when evaluating credit risk of SMEs in Sweden?” Further, as existing literature mainly focus on large companies the authors found it interesting to compare the findings regarding credit risk evaluation on SMEs to the evaluation process of one of the largest credit rating agencies. As a result the second sub-question was established as following, “Are these criteria similar to the criteria used by Standard & Poor’s in their rating model?” These questions were conducted in order to provide the authors and the reader with further insight regarding the criteria used by banks in their evaluation process.   An inductive approach was adopted, in line with the epistemological stance of interpretivism and the ontological belief of constructivism. With this in mind, the authors of the thesis conducted a qualitative exploratory research employing narrative interviews in order to collect the data needed, as of the lack of existing research to address the research questions.   The results of the research were that the criteria used in the assessment of credit risk tend to alter from advisor to advisor. The most commonly used criteria by the advisors are budget, business plan, customer’s customers, internal and external discipline, financial statements, industry specifics, historical accounts, key individuals, relationship, repayment capacity and the owner/individual. It was discovered that the qualitative criterion of assessing the individual majorly impacts the credit risk evaluation. However, what matters in the end is the overall impression of both qualitative and quantitative measures of the firm.   In regards to sub-question one, the authors established a list of qualitative criteria used by advisors in their credit risk evaluation of SMEs, the most widely used criteria among the advisors are the owner/individual, key individuals, internal discipline, industry specifics, external discipline, customer’s customers, relationship and business plan. In comparison with the criteria used by Standard and Poor’s and the banks, it was evident that the criteria used in the evaluation differed a lot between the two.

Dissertação de Mestrado em Engenharia Informática
Com a crescente sensibilização dos problemas energéticos que atravessamos, torna-se evidente a necessidade de adoptar uma atitude mais inteligente frente à utilização que fazemos dos recursos que temos disponíveis. Seja por uma questão financeira, motivos estratégicos ou simplesmente consciência ambiental, as grandes empresas começaram a preocupar-se com a forma como estão a gastar energia. Esta crescente tendência mundial reflecte-se numa oportunidade de negócio que não passou despercebida aos grandes produtores de hardware, e tem sido notória a preocupação em vender produtos “amigos do ambiente” e de “baixo consumo”. Diversos artigos têm sido publicados sobre este tema, que chegou a ser considerado pela conceituada consultora Gartner como a primeira preocupação das grandes empresas de tecnologias de informação para o ano de 2008. Sendo este um tema “fresco”, poucos casos práticos são conhecidos do resultado de uma “atitude verde” no dimensionamento, arquitectura e gestão dos parques tecnológicos. Esta dissertação irá mostrar um caso prático num dos centros de dados de referência em Portugal, do impacto positivo que pode ter a adopção dessa “atitude verde”, e principalmente as vantagens que podemos retirar ao conhecer todos os aspectos relacionados com o consumo energético de cada um dos elementos que constituem a realidade tecnológica de uma empresa. Um sistema de monitorização energética está a ser desenvolvido no âmbito deste Mestrado, e da informação recolhida por esse sistema serão realizados diversos estudos que ajudarão a conhecer qual o estado actual, e que melhorias poderão ser feitas ao centro de dados utilizado.

While much has been written on the Australian film and television industry, little has been presented by actual producers, filmmakers and technicians of their time and experiences within that same industry. Similarly, with historical documentaries, it has been academics rather than filmmakers who have led the debate. This thesis addresses this shortcoming and bridges the gap between practitioner experience and intellectual discussion, synthesising the debate and providing an important contribution from a filmmaker-academic, in its own way unique and insightful. The thesis is presented in two voices. First, my voice, the voice of memoir and recollected experience of my screen adventures over 38 years within the Australian industry, mainly producing historical documentaries for the ABC and the SBS. This is represented in italics. The second half and the alternate chapters provide the industry framework in which I worked with particular emphasis on documentaries and how this evolved and developed over a 40-year period, from 1970 to 2010. Within these two voices are three layers against which this history is reviewed and presented. Forming the base of the pyramid is the broad Australian film industry made up of feature films, documentary, television drama, animation and other types and styles of production. Above this is the genre documentary within this broad industry, and making up the small top tip of the pyramid, the sub-genre of historical documentary. These form the vertical structure within which industry issues are discussed. Threading through it are the duel determinants of production: ‘the market’ and ‘funding’. Underpinning the industry is the involvement of government, both state and federal, forming the three dimensional matrix for the thesis. For over 100 years the Australian film industry has depended on government support through subsidy, funding mechanisms, development assistance, broadcast policy and legislative provisions. This thesis aims to weave together these industry layers, binding them with the determinants of the market and funding, and immersing them beneath layers of government legislation and policy to present a new view of the Australian film industry.

Thesis (Ph.D.)--University of Nebraska-Lincoln, 2009.
Title from title screen (site viewed October 13, 2009). PDF text: 118 p. ; 11 Mb. UMI publication number: AAT 3358959. Includes bibliographical references. Also available in microfilm and microfiche formats.

碩士
國立臺北大學
統計學系
102
This paper examines the data of 313 sequential weekly samples taking place between 2008/1/1 and 2013/12/31. The data include six variables: S&P 500 index, USD index, Gold index, Oil index, Stoxx Europe 600, and CRB index. This research has been conducted in two phrases. The first phase is to forecast the trend of each variable by adopting ARIMA and GARCH time series models after getting the First Order Difference under Unit Root Test. The model selection result using the minimum error of MAPE has shown that ARIMA(2,1,2)-GARCH(1,1)is the best forecast model for all six variables. The second phase is to study the correlations among S&P 500 index, USD index, Oil index, Gold index, Stoxx Europe 600 and CRB index. Since we have the consistency among six best models, through Unit Root Test, we identify the correlations among underlying models by getting first order difference as stationary then examining the Cointegration test, VECM, Granger causality test, Impulse response Analytics and Forecast Error Variance Decomposition. It is discovered that the variable model combinations have long-term and stable relationships. Granger causality test result shows one-way causality of six pairs; ( gold to S&P 500 index), (gold to Stoxx Europe 600), (gold to CRB index), (Stoxx Europe 600 to oil),( Stoxx Europe 600 to CRB index),and (S&P 500 index to CRB index). Two-way causality is also detected for three pairs (S&P 500 index and Stoxx Europe 600) , (gold and oil), and (CRB index and oil). Additionally, no causality is found between USD index and other five variables. For impulse response analyses, it is discovered that the impact of exogenous factors to response variable stabilizes in 14th period regardless of the long -term, short-term, positive or negative impacts. Forecast Error Variance Decomposition result shows that all variables are with the highest impact in the first-period time and diminish afterwards. In the 16th period, over 50% self-explanatory effect can be found for S&P 500 index, USD index, gold, and oil. For the other two variables(Stoxx Europe 600 and CRB index react), the self-explanatory effect is less than 50% and they are more prone to changes of other instruments or market trends. In summary,, after financial crisis, all models run with different results from those for normal circumstances due to excessive supply of liquid assets.

碩士
國立臺灣師範大學
工業教育學系
100
Information technology (IT) is defined as the obtainment, procedure, storage and propagation of sounding, drawing, and textual information by combining microelectronics-based computing and telecommunications. Nowadays, IT is starting to spread further from the conventional personal computer and network technologies to integrations of other fields of technology such as the use of cell phones, televisions, automobiles, etc. In other words, IT has penetrated in daily life of human beings and become one part of the whole society. The importance of IT has become momentous. Therefore, to understand the performance of efficiency and productivity of the IT firms is critical for managers as well as for personal investors. Until now, there are very few researches tried to analyze final performance of the IT firms. As a result, this research intends to use traditional Data Envelopment Analysis (DEA) CCR or BCC models to evaluate the performance of IT firms. The Decision Making Units (DMUs) on this research are chosen from IT firms in S&;P 500. However, the traditional DEA models are not fair models from the aspect of improper weight derivations. Thus, this paper intends to analyze the efficiency of IT firms in S&;P 500 efficiencies by using multiple objective programming (MOP) based Data Envelopment Analysis (DEA). In a MOP based DEA approach, DMUs will be evaluated based on an equal standard and the results will be evaluated more fairly. The world’s leading IT firms in S&;P 500 will be evaluated based on publicly available financial reports of the fiscal year. In addition, the newly developed MOP can improve the traditional DEA’s unfair weights problems and benchmark the efficiency of IT firms in S&;P 500 correctly. In the future, performance evaluation results can be served as foundations for investment strategies definition.

碩士
國立雲林科技大學
建築與室內設計系碩士班
100
This study investigated the post-war Taiwan Sugar Corporation the construction book Figure standardized process analysis mainly object to the Taiwan Sugar Corporation, the construction standards Figure hope the standard diagram of the formation, drawing mode, building construction and use of materials clearer understanding. The study found: the standard chart formation was due to post-war Taiwan Sugar Corporation production gradually recovered energy, demand for new buildings of the factory for supply, in 1956, began preparations for the design of the standard map published in the early 1960s and started to implement. The 1990s, the construction and repair standard functionality gradually declined limited reduction in the level of day-to-day repairs, without As for today is completely discarded. And standard drawing characteristics are as follows: A standard graph drawing constituent principles: the standard reference figure as sugar mills, sugar mills will be modified for the drawing in accordance with the actual base status, and thus, the standard no configuration diagram; each set of plans shall contain: plan , elevations, roof trusses figure, foundation plans, sections, and detail the bulk sample. The combination of each set of drawings: a variety of plan is placed with a drawing, section and detail placed another drawing, diagram the face of strong according to sex, easy to interpret blueprints. Customary materials and construction methods in the standard figure: diagram with the building plans, the former listed a variety of structure and practices of the various parts of an encyclopedia database; latter structure strengthened by brick made for the mainstream, with the roof in warehouse type RC, still in dormitory class timber roof. Interior, still a large number of Japanese colonial period the usual bamboo ribs stucco, lath stucco as a material of the walls and ceilings; interior and exterior decoration materials will be in accordance with the position in the hierarchy, select different decoration. Followed by Taiwan Sugar production for sugarcane board applications, traditional construction methods with new materials. The original purpose of the standard map in saving time, effort, and subsequent maintenance spares convenience and cost savings of effect by promoting the construction standardization. However, from the point of view of the preservation of cultural assets, the construction standards of the post-war Taiwan Sugar diagram, on behalf of the wisdom of the Sugar Company engineering professionals for the study of the history of modern architecture usual mainstream elite building across two geographical characteristics of the collective wisdom of orientation, industrial buildings, reflecting the special circumstances in Taiwan in the post-war political and industrial, political and economic implications of this for their one; Second, drawing record the many engineering technology, for important database research the then Sugar facilities construction technology, through this study came out with its technical characteristics, the more important is that it can be used as a future postwar Sugar architectural cultural assets repair important basis for this for a second. Today, Taiwan Sugar Corporation, in addition to saving tangible architectural and cultural assets, whether to restart the fine tradition of the original construction and repair system, through the recovery of intangible cultural assets, establish their own repair mechanisms of their own cultural assets, which is the domestic possible ways of industrial preservation of cultural assets.

碩士
嶺東科技大學
高階主管企管碩士在職專班
105
This article examines the impact of the S & P 500 stock index on the NIE-4 stock market. After study of the 2008 financial crisis, the S & P 500 index analyzes the market risk-return rate of NIE-4 (Taiwan, Hong Kong, Singapore and South Korea)’s stock market for decision-making reference. The data was collected from January 01, 2010 to December 02, 2016. The study adopted the GARCH Model to investigate the impact of the US S&P Index volatility on the Asian Stock Markets and the volatility of each country. The empirical results indicated the eve return of US S&P Index had significant positive influence on the NIE-4’ Stock Market: Hong Kong (0.476), South Korea (0.386), Taiwan (0.384) and Singapore (0.292) respectively.

碩士
東吳大學
法律學系
102
Standard Essential Patent (SEP) is an unfamiliar term for Taiwanese companies, especially for small and medium enterprises (SMEs). In statistic, the percentage of SMEs is considerable high in Taiwan and they are spread from all around the world due to good at product development and having flexibility. However, most of them do not have ability to deal with international patent litigations, especially for the subject matter is focusing on the SEP which is relatively difficult than others. This thesis aims to find out the key factors and techniques which Realtek Semiconductor Corporation used in the patent litigation through discussing the case on Realtek Semiconductor Corporation v. LSI Corporation. Moreover, this thesis also aims to provide the responding strategy of patent litigation for licensee through understanding the SEP. Besides, the conclusion of thesis can be a reference for Taiwanese enterprises to protect their invention.

Tese de mestrado, Matemática Financeira, Universidade de Lisboa, Faculdade de Ciências, 2020
A previsão dos preços dos índices bolsistas é uma das mais desafiadoras, complexas e fascinantes tarefas, uma vez que os conjuntos de dados onde estes se inserem, chamadas séries temporais, apresentam várias irregularidades (ruído, não-estacionariedade, não linearidades, entre outras). Vários têm sidos os estudos feitos ao longo dos anos com vista a encontrar técnicas mais eficazes, que sejam capazes de contornar essas irregularidades. Com o crescimento exponencial dos dados e a não homogeneidade dos mesmos, torna-se cada vez mais difícil a verificação dos pressupostos nos modelos econométricos. Tendo em conta os presentes desafios, a presente dissertação terá como principal objetivo comparar os métodos clássicos de econometria com os novos métodos de machine learning, para tal ir-se-á recorrer aos dados do índice bolsista S&P 500, no qual pretende-se prever no final os preços de fecho da série. Numa primeira fase, com vista a uma melhor compreensão das temáticas que serão abordadas faz-se uma contextualização sustentada na literatura científica e num conjunto de conceitos considerados essenciais para a compreensão dos temas abordados. Numa segunda fase, prossegue-se com o estudo empírico, onde ir-se-á analisar as estatísticas descritivas, os gráficos, os pressupostos dos modelos e depois escolhidos os potenciais modelos. Este capítulo será divido em dois subcapítulos. No primeiro subcapítulo o estudo será feito sob a alçada do programa estatístico Eviews onde serão abordadas as técnicas clássicas da econometria. No segundo subcapítulo o estudo será feito no software Python, considerado atualmente um dos softwares mais populares no mundo científico, académico e empresarial. No Eviews, uma vez obtida a estacionariedade da série procede-se com a modelização através da metodologia de Box-Jenkins, mais especificamente o modelo Autorregressivo Integrado de Médias Móveis – ARIMA. Uma vez escolhido o modelo, procede-se com a previsão dos preços de fecho da série. Por outro lado, no Python, serão abordadas vertentes mais inovadoras, sendo uma delas a aplicação das feature engineering que resultarão em trinta e uma (31) novas variáveis. Ao contrário dos modelos clássicos, os modelos obtidos pelos algoritmos de machine learning não necessitam da verificação dos pressupostos habituais econométricos, uma vez que a máquina aprende de forma “autónoma” a contornar certas irregularidades. Os algoritmos utilizados serão o de Regressão Linear/Linear Regression (LR), Suport Vector Regression (SVR) e Random Forest (RF). Por fim, é feita uma interpretação critica dos resultados obtidos ao longo de todo o estudo e comparam-se os resultados, atingindo assim o objetivo inicialmente delineado para a dissertação.
Forecasting the prices of stock market indexes is one of the most challenging, complex and fascinating tasks, since the data sets where they are inserted, called time series, exhibit various irregularities (noise, non-stationarity, non-linearity, among others). Several studies have been carried out over the years with a view to finding more effective techniques that are capable to work around these irregularities. With the exponential growth of the data and the heterogeneity, it becomes more and more difficult to verify the assumptions in the econometric models. Taking into account the present challenges, this dissertation will have as main objective to compare the classic econometrics methods with the new machine learning algorithms, and for this we will use the data of the S&P 500 stock index, from which it is intended to predict at the end the closing prices of the series. In a first phase, with a view to a better understanding of the themes that will be approached, a contextualization based on scientific literature and on a set of concepts considered essential for the comprehension of the topics covered is made. In a second phase, we proceed with the empirical study, where we will analyze the descriptive statistics, the graphs, the assumptions of the models and then the potential models will be chosen. This chapter will be divided into two sub-chapters. In the first sub-chapter, the study will be carried out under the statistical program Eviews, where the classical econometrics techniques will be approached. In the second sub-chapter the study will be done in Python software, currently considered one of the most popular software in the scientific, academic and business world. In Eviews, once the time series is stationary, it is proceeded with the modeling through the Box-Jenkins methodology, more specifically the Integrated Autoregressive Moving Average model - ARIMA. After establishing the final model, the closing prices for the S&P 500 series are forecasted. On the other hand, in Python, more innovative aspects will be addressed, one of which is the application of feature engineering that will result in thirty-one (31) new variables. Unlike the classic models, the algorithms obtained from machine learning do not need to check the usual econometric assumptions, since the machine learns “autonomously” to work around certain irregularities. The algorithms used in this dissertation are the following: Linear Regression (LR), Support Vector Regression (SVR) and Random Forest (RF). Finally, a critical interpretation of the obtained results it is made and the results are compared, thus reaching the objective initially outlined for the dissertation.

Dissertation submitted in compliance with the requirements for the Masters Degree in Business Administration, Durban University of Technology, 2008.
Increasing competitive pressure from global markets and technological developments has resulted in the continual demand for business improvement philosophies and methodologies to address this challenge. The LSS approach to business improvement has emerged in both the practitioner and academic literature as having a significant role in this area. In 2006, The Valspar Corporation embarked on a LSS initiative as a way to improve the business globally, to achieve sustained profitable growth and to enhance customer value. Valspar (SA) found the implementation of LSS a challenge because the organisation could not afford the appointment of a full-time Black Belt to manage the programme locally. Green Belts were appointed to lead LSS projects part-time. Management wanted to know if they have applied the LSS methodology correctly within the scope of the business, especially since not all organisations were successful in the implementation of LSS. The objective of this study was to determine the critical factors that affect the successful implementation of LSS at Valspar (SA) and to assess the degree to which these critical factors exist at Valspar (SA). In a census, the researcher used the questionnaire to gain information about the current views of employees on the LSS programme at Valspar (SA). The research highlighted the critical success factors for LSS implementation and the results of the evaluation revealed both the positive and negative aspects of the LSS programme at Valspar (SA).
Valspar Corporation

碩士
國立臺北商業大學
企業管理系研究所
107
This study contemplates on the runner experience, and attempt to explore the correlation of event image, quality of experience, value of experience, brand recognition, satisfaction and loyalty in participating Standard Charter Taipei Charity Marathon. With the six previous designated variables, this study constructed ten research frameworks based on relevant literature theory. The objects of this study are the runners who attended Standard Charter Taipei Charity Marathon on January 13th, 2019, and completed research questionnaires. 408 research questionnaires were deployed from January 13th to January 26th, 2019. The study uses the structural equation model to make a hypothesis test: (1) The activity image has a significant positive impact on the experience value and participant satisfaction. (2) Experience quality has a significant positive impact on experience value, participant satisfaction and brand identity. (3) Experience value has a significant positive impact on satisfaction and brand identity. (4) Satisfaction has a significant positive impact on brand identity and loyalty. (5) Brand identity has a significant positive impact on loyalty. This study discloses the findings and shows the restriction and suggestion for the corporate and future study reference.

Tese de mestrado em Matemática Financeira, Universidade de Lisboa, Faculdade de Ciências, 2021
A presente dissertação resulta da necessidade de se classificar empresas consoante o seu nível de risco de crédito. Para tal, será desenvolvido um modelo que tem como input as demonstrações financeiras de uma empresa, classificando-a através do rating da sua dívida com a nomenclatura da Standard & Poor’s. Com o intuito de alcançar o objetivo anteriormente definido, considerei um conjunto de dados da CRSP (Center for Research in Security Prices, LLC), sendo a amostra inicial composta por 3320 observações das demonstrações financeiras anuais de diversas empresas que constituem o índice bolsista S&P500, no intervalo temporal de 2010 a 2018. Estes dados foram trabalhados na linguagem de programação Python, utilizando a aplicação Jupyter Notebook, com objetivo de criar, treinar e testar este modelo de Credit Scoring, procedendo à utilização de diversos algoritmos de Machine Learning. Para obter uma melhor performance no modelo, foram usados métodos para a seleção das variáveis pela importância que tinham na classificação do modelo, tendo reduzido as variáveis numéricas de 69 para 20. A capacidade de previsão/acerto dos diversos algoritmos foram comparadas e o melhor algoritmo (Random Forest, o que teve maior percentagem de accuracy) foi escolhido e utilizado para a previsão do modelo. Devido à pouca diversidade de ratings das empresas do S&P500, uma vez que existem poucas empresas com ratings baixos, próximos do nível de default, o modelo criado tornou-se num modelo binário e a classificação foi reduzida a Investment grade (de AAA até BBB-) e Non-Investment grade (de BB+ até CC).
This thesis results from the need to classify companies according to their level of credit risk. And, for this purpose, a model will be developed that taking as input the financial statements of a certain company will return the rating of its debt using the nomenclature from Standard & Poor’s. In order to achieve the previously defined goal, a dataset from CRSP (Center for Research in Security Prices, LLC) was considered with an initial sample of 3320 values of the annual financial statements of several companies that are integrated in the S&P500 stocks index, in the time interval from 2010 to 2018. This dataset was prepared and modified in the programming language Python, using the application Jupyter Notebook, with the goal of creating, training and testing this Credit Scoring model, proceeding with the use of several Machine Learning algorithms. With the purpose of obtaining a better performance in the model, it was produced features selection models, based on their importance for the classification model, and the features were reduced from 69 to only 20 variables. The prediction/accuracy of the various algorithms were compared, and the best algorithm (Random Forest, which had the highest percentage of accuracy) was chosen and used to predict the model. Due to the little diversity of ratings of the S&P500 companies, since there are few companies with low ratings, close to the default level, the model created became a binary model, and the rating was reduced to Investment grade (from AAA to BBB-) and Non-Investment grade (from BB+ to CC).

碩士
國立中山大學
企業管理學系研究所
94
Nowadays, all kind of enterprises have to face the tough environment-the low margin age. With the factors like liberalization of global trade, emergence of the newly industrialized countries and the blooming BRICs, the worse situation makes them to tolerate more pressure than before. One of all concern important items is cost system that we cannot put aside while composing a good business strategy, no matter how important low-cost leadership strategy, differentiation strategy, or focus differentiation strategy is. The key ingredients make enterprises more competitive including the implement of strategies and the control of cost. But what is the analysts’ great responsibility for providing the right and instant information to the top managers is the cost information matched with the strategies and delivered precisely in the right time. According to the current cost system in this studied case, neither can the information supplied becomes the key source to assist top managers to make the vital decision, nor can performance appraisal system be used. How to create a feasible cost system which can provide the precise and instant information is the main purpose of the essay. The studied case in the essay is one of the public paper manufacturers in Taiwan and almost produces all kind of paper. To simplify the compliexity, focusing on the fine paper, the most competitive field of paper industry in the world becomes my main part of discussion in the essay. And hopefully it will become the trigger to get lots of people involved to continue the study further.

Tese de mestrado, Matemática Financeira, Universidade de Lisboa, Faculdade de Ciências, 2019
O intuito desta dissertação é identificar o método que proporciona resultados mais fidedignos na previsão de séries temporais financeiras, de entre os modelos tradicionais e as novas metodologias de Machine Learning. A fim de alcançar esse objetivo, foi considerada uma base de dados com 4842 valores diários do fecho do índice bolsista Standard & Poor’s 500 (SP500), no intervalo temporal compreendido entre 3 de janeiro de 2000 e 1 de abril de 2019, excluindo os fins- de-semana e feriados. Primeiramente, os dados foram trabalhados no programa Eviews, de forma a obter o modelo econométrico ARIMA mais adequado e através da metodologia de Box-Jenkins procedeu-se para a previsão da série financeira em estudo. Contudo, para obtenção de melhores resultados, a série foi reduzida, iniciando no dia 2 de março de 2009, devido à crise financeira de 2007-2008. Para comparação de modelos, foi realizada a previsão dos dados da bolsa SP500, através de um modelo Deep Neural Network – Long Short-Term Memory (código programado em Python). Conclui-se que os melhores resultados de previsão foram obtidos com os modelos de redes neuronais, tanto para curto como para longo-prazo.
The aim of this dissertation is to identify the method that provides the most reliable results in forecasting financial time series, between the traditional models and the new Machine Learning methodologies. In order to achieve this objective, were considered 4842 daily closing values of the Standard & Poor’s 500 (SP500) stock index, since January 3, 2000 until April 1, 2019, excluding weekends and holidays. First, we use Eviews software, in order to obtain the right ARIMA econometric model, and by using the Box-Jenkins methodology, we forecast the SP500 financial time series. However, for best results, the series was split, starting on March 2, 2009, due to the 2007-2008 financial crisis. For model comparison, was realized the prediction of the SP500 stock index data, using a Deep Neural Network - Long Short-Term Memory model (code programmed in Python). It is concluded that the best results were obtained with neural network models, for both short and long term forecast.

À l’heure actuelle, la régulation des chaines mondiales de production dépend principalement de mécanismes non étatiques tels que les codes de conduite et les audits sociaux implantés par les entreprises, les ONG et les organisations internationales. Dans cette recherche, nous évaluons si les audits menés par Nike, Adidas et Puma peuvent contribuer à renforcer les droits des travailleurs chinois. À l’aide d’entrevues réalisées sur le terrain auprès d’auditeurs et d’ONG, nous avons conceptualisé quatre conditions de base, lesquelles concernent la participation directe des travailleurs, le développement de capacités locales, le respect de la liberté d’association et la professionnalisation du travail d’auditeur social. Notre étude conclut que des étapes restent à franchir afin que les audits deviennent des outils d’avancement des droits fondamentaux. L’enjeu prioritaire demeure le développement de structures démocratiques afin de permettre aux ouvriers de former des syndicats libres et de négocier leurs propres conventions de travail.
Current regulation of global supply chains mainly relies on non state mechanisms such as codes of conduct and social audits implemented by corporations, non governmental organizations (NGOs) and international organizations. This research seeks to assess if the audits carried out by Nike, Adidas and Puma can help strengthen the rights of Chinese workers. Through field work interviews conducted with auditors and NGOs, four basic conditions were defined and involve direct participation of workers, local capacity building, respect of freedom of association and professionalization of social auditors. The outcome of the study reveals progress still needs to be achieved for audits to become tools for the advancement of fundamental rights. The primary issue remains the development of democratic systems to allow workers to form free unions and negotiate their own collective labour agreements.

Questa tesi è volta ad investigare due importanti fenomeni, uno naturale ed uno umano. Il primo riguarda i terremoti, mentre il secondo è legato al contenuto dei discorsi ufficiali dei presidenti americani. Per il primo caso, il nostro obiettivo è quello di definire un indicatore dei danni economici causati dai terremoti, proponendo un indice calibrato su una lunga serie di magnitudo rilevate in lunghi periodi di tempo. Mentre per il caso dei discorsi presidenziali, vogliamo quantificare il loro impatto sul mercato finanziario, in particolare studiamo l’effetto che essi hanno sull’indice “Standard and Poor’s 500”. Il nostro obiettivo principale è quello di contribuire nell’ambito delle scelte di politica economica prendendo in considerazione tali fenomeni ed analizzandoli con un approccio diverso ed innovativo. L’analisi esposta in questa tesi è sviluppata per mezzo di strumenti econofisici strettamente collegati all’ambito dell’analisi “rank-size”. Tale analisi consiste nell’uso di una serie di funzioni particolarmente utili per l’esplorazione delle proprietà di grandi dataset, anche quando essi sono distribuiti nel tempo e hanno bande di errore non perfettamente definite per via di particolari condizioni di campionamento. Nei capitoli che riguardano i terremoti così come in quelli dedicati all’analisi dei discorsi dei presidenti americani sono mostrati e commentati i risultati di regressioni non lineari impiegate per stimare i coefficienti di varie leggi “rank-size”. Tali stime sono state manipolate in modo tale da poter giungere a conclusioni dal rilievo economico. I risultati più robusti sono stati raggiunti grazie alla straordinaria capacità di interpretare i dati da parte delle leggi “rank-size”. Nell’ambito della valutazione dell’impatto economico dei discorsi presidenziali, un’analisi aggiuntiva è stata svolta valutando diverse distanze tra serie storiche. In particolare considerando la serie storica delle parole semanticamente legate all’economica e pronunciate dai presidenti americani nel corso della storia e le serie storiche del volume, dei prezzi e dei rendimenti dell’indice “Standard & Poor's 500”. Per questa analisi abbiamo impiegato un approccio probabilistico ed anche uno meramente topologico. Infatti abbiamo misurato l’entropia delle serie storiche e comparato le conclusioni valutando le differenze fra diverse misure di distanza vettoriale.