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Despite extensive research on price and earnings estimations, there are still puzzling results that have not been resolved. One of the puzzles in price estimation is that multiples using earnings forecasts outperform multiples using the residual income model (Liu, Nissim and Thomas, 2002). This puzzle undermines the validity of theory-based valuation models, which are originated from valuation theory and have been developed over the century. The first two projects of this thesis address this puzzle and explain mathematically how the pricing error of a multiple is determined by the correlation coefficient between price and a value driver. The projects then demonstrate that the puzzle in Liu, Nissim and Thomas (2002) is caused by the bad selection of residual income models and, in fact, the majority of residual income models (i.e. well-chosen residual income models) actually outperform multiples using earnings forecasts in pricing error. When models are examined in terms of future return generation, residual income models again outperform multiples using earnings forecasts, providing evidence that theory-based valuation models are superior to rule-of- thumb based multiples in price and intrinsic value estimations. The third project addresses an issue in earnings estimation by cross-sectional models. Recently, Hou, van Dijk and Zhang (2012) and Li and Mohanram (2014) introduce cross-sectional models in earnings estimation and argue that their cross-sectional models produce better earnings forecasts than analyst forecasts. However, their models suffer from one fundamental problem of cross-sectional models: the loss of firm-specific information in earnings estimation (Kothari, 2001). In other words, cross-sectional models apply the same coefficients (i.e. the same earnings persistence and future prospects) to all firms to estimate their earnings forecasts. The third project of this thesis addresses this issue by proposing a new model, a conditional cross-sectional model, which allows the coefficient on earnings to vary across firms. By allowing firms to use different earnings coefficients (i.e. different earnings persistence and future prospects), the project shows that a conditional cross-sectional model improves a cross-sectional model in all dimensions: a) bias, accuracy and earnings response coefficient; b) unscaled and scaled earnings estimations; and c) across all forecast horizons. The thesis contributes to the price and earnings estimations literature. First, the thesis addresses the decade-old puzzle in price estimation and rectifies the previous misunderstanding of valuation model performance. By demonstrating the superiority of theory-based valuation models over rule-of-thumb based multiples, the thesis encourages further development of theory-based valuation models. Second, in earnings estimation, the thesis provides future researchers a new model, which overcomes the fundamental problem of cross-sectional models in earnings estimation while keeping their advantages. In sum, the thesis improves the knowledge and practices of price and earnings estimations.

It is the intention of this dissertation to determine the most influential factors affecting the accuracy of design · price forecasts. As a result of the lack of research relating to the accuracy design price forecasts in South Africa, quantity surveyors are unaware of the level of accuracy that they attain. It is proposed that an awareness of their forecasting accuracy.and the factors which affect will contribute towards enhanced performance. By means of an analysis of a sample of quantity surveyors estimates, the factors which exert the most significant influence over identified.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / The purpose of this document is to improve inventory management processes and food processing at a restaurant in the region of Ancash by applying the 5S methodology and using specific indicators for the location and type of work. All this was achieved with the implementation of the menu engineering methodology, which consists basically of forecasts, linear programming, long-term orders, and inventory management. The proper operation of the new processes was experimentally validated. First, the main results were that the use of approximately 4.30 m2 of the work space allocated to unnecessary activities within the work area was optimized, increasing the number of processes that a worker can perform without moving more than 1 m from their work position. Despite the many studies existing on the methodology implemented, there is scarce material that focuses on its application in restaurants, as it is normally developed in industrial areas.

This dissertation investigates the effects of changes in the accounting environment on the capital markets. Included are three manuscripts, each of which, make an important contribution to the accounting literature. The first two manuscripts investigate the impact and importance of analysts' forecasts. The third manuscript documents the impact of eliminating an important accounting disclosure. This dissertation makes the following contributions to the accounting literature. The first manuscript documents that investor skepticism towards meet/beat firms appears to have been a temporary phenomenon and investors have resumed rewarding firms that meet/beat analysts' earnings expectations. Further, the study provides evidence that changes in the analyst forecasting environment also contributed to this temporary decline implying that the scandals did not have as strong of an effect on investors' confidence in earnings as previously believed. The second manuscript contributes to the accounting literature by documenting the importance of meeting/beating cash flow forecasts to participants in the debt markets. Finally, the third manuscript contributes to the existing literature regarding the value relevance of the IFRS -- U.S.GAAP reconciliation by documenting a significant decrease in publicly available information to equity investors at the first reporting period following the SEC's decision to eliminate the reconciliation. All of these manuscripts extend what is currently known about the importance of public disclosures to capital market participants. / Ph. D.

A previsão de demanda é uma das principais ferramentas para a eficiência do gerenciamento das organizações, afetando diretamente a lucratividade do negócio. O atual nível competitivo das empresas requer previsões cada vez mais acuradas, sedo estas um diferencial para o sucesso empresarial. Neste contexto, a combinação de previsões se tornou um dos principais métodos empregados no intuito de melhorar a precisão das previsões. Através de uma revisão da literatura sobre as abordagens da combinação de previsões, identificou-se uma carência de estudos comparativos que incorporem modelos de regressão para a combinação de previsões. Assim, o objetivo principal desta dissertação é combinar três previsões individuais (redes neurais, modelos ARIMA e modelos de alisamento exponencial) via média simples, variância mínima e modelos de regressão, comparando as três previsões combinadas com suas previsões individuais. Estas comparações serão avaliadas em duas situações: em séries simuladas (estacionárias) e em uma série de dados reais (não estacionária) de uma empresa que realiza auditorias médicas. As medidas empregadas para a escolha do método mais preciso são MAE, MAPE, RMSE e o coeficiente U de Theil. Os resultados obtidos enfatizam a melhoria das previsões quando estas são combinadas por regressão, tanto para séries convergentes quanto para a série divergente. / Forecasting is a key tool for ensuring the efficiency of management in organizations, directly affecting business profitability. The current competitive corporative level requires increasingly accurate predictions. In this context, the combination of forecasts has improved forecast accuracy. Through a literature review on the approaches of combining forecasts, we identified a lack of comparative studies that incorporate regression models for combining forecasts. Thus, the main objective of this dissertation is to combine three individual forecasts (neural networks, ARIMA models and exponential smoothing models) via simple average, minimum variance and regression models, comparing the three combined forecasts with their individual forecasts. These comparisons are evaluated in two situations: in simulated series (converging) and in series of real data (divergent) from a company that performs medical audits. The measures used to identify the best method are MAE, MAPE, RMSE and Theil’s U coefficient. Results from combined methods improved the predictions in both convergent and divergent series.

A previsão de demanda é uma das principais ferramentas para a eficiência do gerenciamento das organizações, afetando diretamente a lucratividade do negócio. O atual nível competitivo das empresas requer previsões cada vez mais acuradas, sedo estas um diferencial para o sucesso empresarial. Neste contexto, a combinação de previsões se tornou um dos principais métodos empregados no intuito de melhorar a precisão das previsões. Através de uma revisão da literatura sobre as abordagens da combinação de previsões, identificou-se uma carência de estudos comparativos que incorporem modelos de regressão para a combinação de previsões. Assim, o objetivo principal desta dissertação é combinar três previsões individuais (redes neurais, modelos ARIMA e modelos de alisamento exponencial) via média simples, variância mínima e modelos de regressão, comparando as três previsões combinadas com suas previsões individuais. Estas comparações serão avaliadas em duas situações: em séries simuladas (estacionárias) e em uma série de dados reais (não estacionária) de uma empresa que realiza auditorias médicas. As medidas empregadas para a escolha do método mais preciso são MAE, MAPE, RMSE e o coeficiente U de Theil. Os resultados obtidos enfatizam a melhoria das previsões quando estas são combinadas por regressão, tanto para séries convergentes quanto para a série divergente. / Forecasting is a key tool for ensuring the efficiency of management in organizations, directly affecting business profitability. The current competitive corporative level requires increasingly accurate predictions. In this context, the combination of forecasts has improved forecast accuracy. Through a literature review on the approaches of combining forecasts, we identified a lack of comparative studies that incorporate regression models for combining forecasts. Thus, the main objective of this dissertation is to combine three individual forecasts (neural networks, ARIMA models and exponential smoothing models) via simple average, minimum variance and regression models, comparing the three combined forecasts with their individual forecasts. These comparisons are evaluated in two situations: in simulated series (converging) and in series of real data (divergent) from a company that performs medical audits. The measures used to identify the best method are MAE, MAPE, RMSE and Theil’s U coefficient. Results from combined methods improved the predictions in both convergent and divergent series.

A previsão de demanda é uma das principais ferramentas para a eficiência do gerenciamento das organizações, afetando diretamente a lucratividade do negócio. O atual nível competitivo das empresas requer previsões cada vez mais acuradas, sedo estas um diferencial para o sucesso empresarial. Neste contexto, a combinação de previsões se tornou um dos principais métodos empregados no intuito de melhorar a precisão das previsões. Através de uma revisão da literatura sobre as abordagens da combinação de previsões, identificou-se uma carência de estudos comparativos que incorporem modelos de regressão para a combinação de previsões. Assim, o objetivo principal desta dissertação é combinar três previsões individuais (redes neurais, modelos ARIMA e modelos de alisamento exponencial) via média simples, variância mínima e modelos de regressão, comparando as três previsões combinadas com suas previsões individuais. Estas comparações serão avaliadas em duas situações: em séries simuladas (estacionárias) e em uma série de dados reais (não estacionária) de uma empresa que realiza auditorias médicas. As medidas empregadas para a escolha do método mais preciso são MAE, MAPE, RMSE e o coeficiente U de Theil. Os resultados obtidos enfatizam a melhoria das previsões quando estas são combinadas por regressão, tanto para séries convergentes quanto para a série divergente. / Forecasting is a key tool for ensuring the efficiency of management in organizations, directly affecting business profitability. The current competitive corporative level requires increasingly accurate predictions. In this context, the combination of forecasts has improved forecast accuracy. Through a literature review on the approaches of combining forecasts, we identified a lack of comparative studies that incorporate regression models for combining forecasts. Thus, the main objective of this dissertation is to combine three individual forecasts (neural networks, ARIMA models and exponential smoothing models) via simple average, minimum variance and regression models, comparing the three combined forecasts with their individual forecasts. These comparisons are evaluated in two situations: in simulated series (converging) and in series of real data (divergent) from a company that performs medical audits. The measures used to identify the best method are MAE, MAPE, RMSE and Theil’s U coefficient. Results from combined methods improved the predictions in both convergent and divergent series.

The transmission of many infectious diseases is affected by climate variations, particularly for diseases spread by arthropod vectors such as malaria and dengue. Previous epidemiological studies have demonstrated statistically significant associations between infectious disease incidence and climate variations. Such research has highlighted the potential for developing climate-based epidemic early warning systems. To establish how much variation in disease risk can be attributed to climatic conditions, non-climatic confounding factors should also be considered in the model parameterisation to avoid reporting misleading climate-disease associations. This issue is sometimes overlooked in climate related disease studies. Due to the lack of spatial resolution and/or the capability to predict future disease risk (e.g. several months ahead), some previous models are of limited value for public health decision making. This thesis proposes a framework to model spatio-temporal variation in disease risk using both climate and non-climate information. The framework is developed in the context of dengue fever in Brazil. Dengue is currently one of the most important emerging tropical diseases and dengue epidemics impact heavily on Brazilian public health services. A negative binomial generalised linear mixed model (GLMM) is adopted which makes allowances for unobserved confounding factors by including spatially structured and unstructured random effects. The model successfully accounts for the large amount of overdispersion found in disease counts. The parameters in this spatio-temporal Bayesian hierarchical model are estimated using Markov Chain Monte Carlo (MCMC). This allows posterior predictive distributions for disease risk to be derived for each spatial location and time period (month/season). Given decision and epidemic thresholds, probabilistic forecasts can be issued, which are useful for developing epidemic early warning systems. The potential to provide useful early warnings of future increased and geographically specific dengue risk is investigated. The predictive validity of the model is evaluated by fitting the GLMM to data from 2001-2007 and comparing probabilistic predictions to the most recent out-of-sample data in 2008-2009. For a probability decision threshold of 30% and the pre-defined epidemic threshold of 300 cases per 100,000 inhabitants, successful epidemic alerts would have been issued for 94% of the 54 microregions that experienced high dengue incidence rates in South East Brazil, during February - April 2008.

El presente proyecto de investigación tiene como finalidad el desarrollo de una propuesta basada en la mejora de la gestión de inventarios en una empresa de la industria de minería y construcción. Por ello se emplearán conceptos básicos de gestión de inventarios y pronósticos. Hoy en día, son muchas las empresas pertenecientes a diferentes industrias las cuales buscan conseguir mejorar en la administración de sus inventarios, entre ella se encuentran empresas mineras y de construcción. Las empresas tienen muy en claro que los inventarios son recursos financieros que en lugar de proporcionar algún beneficio se encuentran inmovilizados. Es por ello que gestionar de una mejor manera los inventarios es un tema de gran índole en la actualidad. La empresa en estudio se dedica a la venta de repuestos para maquinaria pesada dirigida hacia el sector de minería y construcción. A la vez, brinda servicios post venta de reparación y mantenimiento. Posee una gama de miles de productos enfocados en un máximo de 10 proveedores. Al encontrar problemas con el stock, se realizó el diagnóstico a la empresa encontrándose el problema de un nivel excesivamente elevado de inventarios. Para ello se propone mejoras en la gestión de inventarios comenzando con la técnica de priorización ABC para centrarse en aquellos productos que generan mayores ingresos. Luego se hará uso de pronósticos de demanda para determinar la cantidad que se va a ofertar para el siguiente periodo y por último se realiza el cálculo de la cantidad a pedir. Al finalizar la parte cuantitativa del proyecto se desarrolla y define los procesos que van a dar soporte a la gestión de inventarios. Además, también se determinó los costos asociados que se incurrirían al desarrollar el proyecto y su análisis de costo beneficio. Finalmente se presenta las conclusiones y recomendaciones que la empresa debe de tener en cuenta en caso de colocar en práctica el proyecto. / The purpose of this research project is to develop a proposal based on the improvement of inventory management in a company in the mining and construction industry. Therefore, basic concepts of inventory management and forecasts will be used. Today, there are many companies belonging to different industries which seek to improve the management of their inventories, including mining and construction companies. The companies are very clear that inventories are financial resources that instead of providing some benefit are immobilized. That is why managing inventories in a better way is a very important issue at present. The company under study is dedicated to the sale of spare parts for heavy machinery directed towards the mining and construction sector. At the same time, it provides post-sale repair and maintenance services. It has a range of thousands of products focused on a maximum of 10 suppliers. When finding problems with the stock, the diagnosis was made to the company finding the problem of an excessively high level of inventories. To do this, we propose improvements in the management of inventories starting with the ABC prioritization technique to focus on those products that generate the highest income. Then demand forecasts will be used to determine the amount that will be offered for the next period and finally the calculation of the amount to be ordered is made. At the end of the quantitative part of the project, the processes that will support the inventory management are developed and defined. In addition, the associated costs that would be incurred when developing the project and its cost-benefit analysis were also determined. Finally, the conclusions and recommendations that the company should take into account in case of putting the project into practice are presented. / Tesis