To prevent loss of lives during seasonal disasters, relief agencies distribute critical supplies and provide lifesaving services to the affected populations. Despite agencies' efforts, frequently occuring disasters increase the cost of relief operations. The purpose of our study is to minimize the cost of relief operations, considering that such disasters cause random demand. To achieve this, we have formulated a series of models, which are distinct from the current studies in three ways. First, to the best of our knowledge, we are the first ones to capture both perishable and durable products together. Second, we have aggregated multiple products in a different way than current studies do. This unique aggregation requires less data than that of other types of aggregation. Finally, our models are compatible with the practical data generated by FEMA. Our models offer insights on the impacts of various parameters on optimum cost and order size. The analyses of correlation of demand and quality of information offer interesting insights; for instance, under certain cases, the quality of information does not influence cost. Our study has considered both risk averse and risk neutral approaches and provided insights. The insights obtained from our models are expected to help agencies reduce the cost of operations by choosing cost effective suppliers.

Population prospects of Kazakhstan till 2030 Abstract Population change affects national income, national expenditure, and the demand for services such as education, health and transport. Therefore, information about future population size and structure obtained with the help of population forecasts, which can be used for a wide range of decision-making purposes, is of paramount importance. The primary aim of this dissertation is to produce three different types of population forecasts for Kazakhstan till 2030 and by comparing and analysing the differences to find out the most important factors determining the population development process in the country. Kazakhstan is a country with significant size and regional diversity which makes it relevant to consider those dimensions in population forecasting. Most southern oblasts of the country have a young population structure meaning that much of future population growth, particularly of working age, will come from these regions. Also, native population tends to concentrate in rural areas, while industrialized cities are mostly populated by non-natives with considerably different nuptiality and fertility behaviour. Despite such regional and residential demographic differences, presently the country is experiencing an overall increase in birth rates. Many claims...

Bachelor Thesis, Program of Master of Business and Economics, 15 hp School of Business and Economics – Linnaeus University in Växjö 2FE30E:3 Spring, 2017 Authors: Sofie Areskoug and Niklas Karlén Supervisor: Damai Nasution Examiner: Natalia Semenova Keywords: Financial Analyst, Gender, Determinants of forecast accuracy, Sweden Background: The search of finding analysts who make the best forecasts has been an ongoing process since the 1930's. Determinants that can help predict the forecast accuracy of the analysts are in the interest of both investors and brokerage houses. Newer research in this area has taken gender of the analyst into consideration. Women are widely under-represented in the analyst occupation and there is evidence that investors are apprehensive toward women in the financial sector. Purpose: The aim of this thesis is to examine determinants of forecast accuracy regarding analysts covering Swedish companies. The authors have confidence in the research to benefit investors in their decisions on the Swedish stock market. In addition, the authors aim to shed light on the unequal gender representation of female analysts. Method: This thesis has examined 519 individual scores of forecast accuracy from 284 financial analysts covering stocks on the Swedish Index OMXS30. The forecasts are from the years 2016 and 2017. This study has a quantitative strategy and the data have been tested by an OLS estimates regression. Results: The empirical evidence shows that being a female analyst have a statistically significant positive effect on forecast accuracy. Female analysts covering Swedish stocks seem to outperform their male colleagues. Furthermore, insignificant results were found for firm complexity, industry complexity, brokerage house and analyst experience.

Population prospects of Kazakhstan till 2030 Abstract Population change affects national income, national expenditure, and the demand for services such as education, health and transport. Therefore, information about future population size and structure obtained with the help of population forecasts, which can be used for a wide range of decision-making purposes, is of paramount importance. The primary aim of this dissertation is to produce three different types of population forecasts for Kazakhstan till 2030 and by comparing and analysing the differences to find out the most important factors determining the population development process in the country. Kazakhstan is a country with significant size and regional diversity which makes it relevant to consider those dimensions in population forecasting. Most southern oblasts of the country have a young population structure meaning that much of future population growth, particularly of working age, will come from these regions. Also, native population tends to concentrate in rural areas, while industrialized cities are mostly populated by non-natives with considerably different nuptiality and fertility behaviour. Despite such regional and residential demographic differences, presently the country is experiencing an overall increase in birth rates. Many claims...

[pt] A utilização de um modelo eficiente para o abastecimento de clientes VMI (Vendor Management Inventory) é um dos fatores mais importantes para que as empresas promovam uma relação equilibrada entre o nível de serviço oferecido ao cliente e o custo logístico associado a operação. Dado a importância do assunto, e a possibilidade por parte da autora de participar da implementação de um novo sistema para o abastecimento de clientes VMI em uma empresa de gases industriais e medicinais, o objetivo desta dissertação foi analisar a adoção deste novo sistema e os principais resultados obtidos após sua implementação. Foram ressaltados os pontos relacionados com a previsão de vendas, a programação de entregas e os indicadores de desempenho. A metodologia utilizada para a elaboração desse trabalho incluiu: pesquisa bibliográfica, dados de fontes primárias extraídos de sistemas de informação da empresa e de entrevistas não estruturadas com funcionários envolvidos na operação, além de visitas in loco para observação direta. Os resultados desta dissertação foram uma análise da operação antes e depois da implementação do novo sistema na Empresa de Gases Alfa e uma comparação com a operação da matriz americana da Empresa. Os motivos pelos quais a operação no Brasil não atingiu os mesmos patamares da operação americana, mesmo após a adoção do novo sistema, assim como ações de melhoria, também foram expostos na dissertação. / [en] The use of an efficient model for costumer VMI (Vendor Management Inventory) supply is one of the most important factors for companies to promote a balanced trade-off between the service level offered to the costumer and the logistic cost associated to the operation. Given the importance of the subject, and the possibility of the author to take part of the implementation of a new system to supply VMI costumers of an industrial and medicine gases company, the aim of this dissertation was to analyze the adoption of this system and the main results obtained after its implementation. It shall be highlighted spots related to sales prevision, delivery programming and performance indexes. The methodology used for elaborate this dissertation has included: bibliographic research, data of primary sources of the information system of the company and non-structured interviews with employees involved in the operation, besides in situ visits for direct observation. The results of this dissertation were an analysis of the operation before and after the implementation of the new system and a comparison with the operation of the company`s American office. The reasons through which the operation in Brazil has not reached the levels of the American operation, even after the adoption of the new system, as well as improvement actions, were also herein exposed.

O turismo entrou em um período de forte expansão após a Segunda Guerra Mundial que perdura até os dias atuais. O aumento da circulação de turistas repercute na geração de renda e empregos para os países visitados, além do enriquecimento adquirido através das trocas culturais. Este crescimento tem despertado o interesse da comunidade científica, bem como profissional, com o intuito de explorar as metodologias para a modelagem e previsão da demanda turística. Estimativas acuradas da demanda servem de apoio para corretas tomadas de decisão por parte dos gestores quanto ao dimensionamento adequado de recursos financeiros, especialmente frente à realização de um evento de grandes proporções. Neste sentido, este trabalho tem por objetivos verificar quais são as técnicas atualmente mais utilizadas para previsão de demandas turísticas através de revisão da literatura, desde 2005 até 2015; utilizar dois métodos de modelagem (ARIMA e RNA) para modelar e prever a demanda turística de duas sedes olímpicas recentes; comparar essas previsões com as previsões obtidas por cinco métodos de combinação de previsões (médias aritmética, harmônica e geométrica, variância mínima e regressão linear) e; aplicar o método mais acurado para prever a demanda turística do Brasil. Os resultados foram avaliados através de três medidas de acurácia. Em virtude da realização dos Jogos Olímpicos em 2016, a demanda brasileira para este período foi modelada e prevista e a previsão foi ajustada segundo um ajuste matemático sazonal, objetivando ganho de acurácia. Foi observado ganho de acurácia quando as previsões foram combinadas e, na série brasileira, o ajuste adotado indicou um acréscimo de 175% na demanda original para agosto de 2016. / Tourism has experienced a strong increase since the end of World War II. The increase in tourist circulation results in income and employment expansion, besides the cultural enrichment involved in such experiences. This growth has attracted attention from the scientific community as well as professional, with the objective of exploring the methodologies for tourism demand modelling and forecasts. Accurate demand estimates serve as support for correct decision making by managers especially considering financial resource scaling for major events. In this sense, this study aims to verify which techniques are more currently used for forecasting tourism demand through review of the literature from 2005 to 2015; using two modeling methods (ARIMA and ANN) to make models and forecasting the tourism demand of two recent Olympic hosts; comparing these forecasts with the forecasts obtained for five methods of combining forecasts (arithmetic, harmonic and geometric means, minimum variance and linear regression) and; applying the most accurated method to forecast the tourism demand in Brazil. The results were evaluated using three different accuracy measurements. By virtue of the 2016 Olympic Games, the Brazilian tourism demand was modeled and the forecast was adjusted by a seasonal mathematical adjustment, designed for better precision. A gain in preciseness was observed when forecasts were combined and, for the Brazilian series, the adopted adjustment indicated an increase of 175% when compared with the original demand for August 2016.

Na trajetória da economia mundial, destaca-se a importância do agronegócio, que exerce um papel essencial no desenvolvimento econômico e social dos países, devido principalmente à sua capacidade de geração de renda e empregos. Entretanto, o agronegócio possui um obstáculo para a sua sustentabilidade, que é sua natureza cíclica, sofrendo influências de vários fatores de mercado e apresentando elevada volatilidade nos preços das commodities. Nesse sentido, vê-se a necessidade de explorar o aspecto cíclico dos preços das commodities, com o intuito de captar a dinâmica dos fatores de mercado que influenciam a formação do preço, para o seu monitoramento antecipado. Dentro desse contexto, o objetivo do presente estudo foi propor o desenvolvimento de uma ferramenta para prever o comportamento dos ciclos de crescimento e retração de uma commodity, especificamente o açúcar, com base no modelo de indicador antecedente. Para isso, foi construído, primeiramente, o ciclo de preços agrícolas, com base nos ciclos de negócios e na exposição das estruturas que representam os principais fatores de alteração nos preços das commodities: econômica, fundamentalista, climática e relacionada. O próximo passo foi datar os pontos de mudança do preço do açúcar, utilizando um modelo de cadeia de Markov e confrontando seus resultados com os acontecimentos históricos do setor. Posteriormente, um modelo de fator dinâmico foi utilizado para extrair movimentos cíclicos comuns a um conjunto de variáveis que apresentam poder de previsão, fora de amostra, com relação ao preço do açúcar. Como resultado, foram encontrados três indicadores antecedentes, que sinalizaram consistentemente a maioria dos picos e vales do ciclo do preço do açúcar, num horizonte de dois anos de antecedência. Cada indicador selecionado é composto por uma combinação linear entre os coeficientes e quatro variáveis independentes, as quais representam, respectivamente, as estruturas setoriais analisadas: fundamentalista, econômica, climática e relacionada. Em seguida, os indicadores foram combinados com o preço em um vetor bivariado auto-regressivo para obter previsões lineares do preço da commodity açúcar. As previsões obtidas revelam que os indicadores apresentaram um desempenho de previsão bem superior ao do modelo base, em todos os horizontes, e muito próximo aos valores reais dos preços. Portanto, da análise de previsão de pontos de mudança e de previsão linear, conclui-se que os indicadores antecedentes da commodity açúcar (IAC) constituem-se em um instrumento informativo para sinalizar o comportamento futuro do preço do açúcar, mesmo quando apenas dados preliminares e não revisados estão disponíveis. A ferramenta proposta, além de servir como um instrumento para compreender a natureza das flutuações dos preços das commodities, pretende tornar-se fonte de subsídios para o projeto de diretrizes, ações e formulação de estratégias de desenvolvimento, tanto no âmbito das políticas públicas, quanto daquelas iniciativas que deveriam ser adotadas pelo setor privado, servindo como um instrumento essencial para o planejamento das instituições integrantes do agronegócio. / In the course of the world economy, underscoring the importance of agribusiness, which plays a key role in economic and social development of countries, mainly due to its ability to generate income and jobs. However, agribusiness has an obstacle to its sustainability, which is its cyclical nature, is influenced by various market factors and a very high volatility in commodity prices. In this sense, we see the need to explore the cyclical aspect of commodity prices, in order to capture the dynamics of market factors that influence the pricing for its monitoring anticipated. Within this context, the objective of this study was to propose the development of a tool to predict the behavior of cycles of growth and shrinkage of a commodity, specifically sugar, based on the type of leading indicator. For that was built first, the cycle of agricultural prices, based on business cycles and exposure of the structures that represent the main factors of change in commodity prices: economic fundamentalism, climate and related. The next step was dating the turning points of the price of sugar, using a model of Markov chain, comparing their results with historical events in the industry. Subsequently, a dynamic factor model was used to extract common cyclical movements in a set of variables that have predictive power, out of the sample to the price of sugar. As a result, there were three leading indicators, which signaled consistently most of the peaks and valleys of the cycle of the price of sugar, a horizon of two years in advance. Each indicator selected is composed of a linear combination of the coefficients and four independent variables, which represent, respectively, industry structures analyzed: fundamentalist, economic, climate and related. Then, the indicators were combined with the price in a bivariate vector autoregressive forecasts for linear price of crude sugar. The predictions show that the indicators showed a predictive performance far superior to the base model at all horizons, and very close to the actual values of prices. Therefore, the analysis of forecasting turning points and linear prediction, it is concluded that the leading indicators of crude sugar (IAC) is based on an informative tool for signaling future behavior of the price of sugar, even when only preliminary data not reviewed are available. The proposed tool, besides serving as a tool to understand the nature of fluctuations in commodity prices, hopes to become a source of input for the draft guidelines, actions and formulation of development strategies, both in the public policies and those initiatives that should be adopted by the private sector, serving as an essential tool for planning of institutions of agribusiness.

A previsão de demanda desempenha um papel de fundamental importância dentro das organizações, pois através dela é possível obter uma declaração antecipada do volume demandado no futuro, permitindo aos gestores a tomarem decisões mais consistentes e alocarem os recursos de modo eficaz para atender esta demanda. Entretanto, a eficiência na tomada de decisões e alocação dos recursos requer previsões cada vez mais acuradas. Diante deste contexto, a combinação de previsões tem sido amplamente utilizada com o intuito de melhorar a acurácia e, consequentemente, a precisão das previsões. Este estudo tem por objetivo fazer a adaptação de um modelo de previsão para estimar a demanda de produtos destinados à suplementação animal através da combinação de previsões, considerando as variáveis que possam impactar na demanda e a opinião de especialistas. O trabalho está estruturado em dois artigos, sendo que no primeiro buscou-se priorizar e selecionar, através do Processo Hierárquico Analítico (AHP), variáveis que possam impactar na demanda para que estas pudessem ser avaliadas na modelagem via regressão do artigo 2. Por sua vez, no segundo artigo, realizou-se a adaptação do modelo composto de previsão idealizado por Werner (2004), buscando uma previsão final mais acurada. Os resultados obtidos reforçam que as previsões, quando combinadas, apresentam desempenhos superiores para as medidas de acurácia MAPE, MAE e MSE, em relação às previsões individuais. / The demand prediction has a role of fundamental importance inside the organizations, because trough it is possible to obtain a previous declaration of the demanded amount in the future, allowing the managers to take more consistent decisions and to allocate the resources in an efficient manner in order to satisfy this demand. However, the efficiency in the support decision and resource allocation demands accurated predictions. So, the combination of predictions have been used with the aim of improving the accuracy and, consequently, the precision of the prediction. This study has as objective to do an adaptation of a prediction model to estimate the demand of products designated to animal supplementation through the combination of prediction, considering the variables that can impact in the demand and in the expert opinion. The work is structured in two papers, considering that the first searches to priorize and select through the Analitic Hierarch Process (AHP), variables that can impact in the demand, so they could be evalute in the regression modelling of the paper 2. By the way, in the second paper, it was done an adaptation of the composed prediction model proposed by Werner (2004), searching for a more accurated final prediction. The obtained results reinforce that the prediction, when combined, present superior performance to the accuracy metrics MAPE, MAE and MSE, in relation to the individual predictions.

Evidence that firms adjust accruals to just meet or beat analyst forecasts is pervasive. However, the implications for earnings quality are not clear. Managers can use this practice either to mislead investors, resulting in lower quality earnings, or to signal future earnings growth and thereby improve the decision usefulness of earnings. Assuming that boards are concerned about providing higher quality financial information and that they can discern the proper earnings signal, they should discourage managers from adjusting earnings to beat the analyst forecast target if such adjustment diminishes earnings quality. Consistent with this prediction, I find a significantly negative relation between the probability that a firm beats the target by adjusting accruals and the presence of at least one independent audit committee financial expert for firms with poor future performance. I also find that the negative impact of an independent financial expert on the odds of beating the target by adjusting accruals is significantly stronger for firms with poor future performance than for firms with strong future performance. These findings are consistent with financial expertise on the audit committees improving corporate governance by protecting shareholders from accruals management that reduces the decision usefulness of earnings.

Atmospheric particles represent a component of air pollution that has been identified as a major contributor to adverse health effects and mortality. Aerosols also interact with solar radiation and clouds perturbing the atmosphere and generating responses in a wide range of scales, such as changes to severe weather and climate. Thus, being able to accurately predict aerosols and its effects on atmospheric properties is of upmost importance. This thesis presents a collection of studies with the global objective to advance in science and operations the use of WRF-Chem, a regional model able to provide weather and atmospheric chemistry predictions and simultaneously representing aerosol effects on climate. Different strategies are used to obtain accurate predictions, including finding an adequate model configuration for each application (e.g., grid resolution, parameterizations choices, processes modeled), using accurate forcing elements (e.g., weather and chemical boundary conditions, emissions), and developing and applying data assimilation techniques for different observational sources. Several environments and scales are simulated, including complex terrain at a city scale, meso-scale over the southeast US for severe weather applications, and regional simulations over the three subtropical persistent stratocumulus decks (off shore California and southeast Pacific and Atlantic) and over North America. Model performance is evaluated against a large spectrum of observations, including field experiments and ground based and satellite measurements. Overall, very positive results were obtained with the WRF-Chem system once it had been configured properly and the inputs chosen. Also, data assimilation of aerosol and cloud satellite observations contributed to improve model performance even further. The model is proven to be an excellent tool for forecasting applications, both for local and long range transported pollution. Also, advances are made to better understand aerosol effects on climate and its uncertainties. Aerosols are found to generate important perturbations, ranging from changes in cloud properties over extensive regions, up to playing a role in increasing the likelihood of tornado occurrence and intensity. Future directions are outline to keep advancing in better predictions of aerosols and its feedbacks.