A ascensão de um grande número de pessoas em países em desenvolvimento para a classe média, no inicio do século XXI, aliado ao movimento político para transferência de base energética para os biocombustíveis vêm aumentando a pressão sobre os preços das commodities agrícolas e apresentando novas oportunidades e cenários administrativos para os produtores agrícolas dessas commodities, em especial aquelas que podem se diversificar em muitos subprodutos para atender diferentes mercados, como o de alimentos, químico, têxtil e de energia. Nesse novo ambiente os produtores podem se beneficiar dividindo adequadamente a produção entre os diferentes subprodutos, definindo o melhor momento para a comercialização através de estoques, e ainda controlar sua exposição ao risco através de posições no mercado de derivativos. A literatura atual pouco aborda o tema da diversificação e seu impacto nas decisões de produção e comercialização agrícola e portanto essa tese tem o objetivo de propor um modelo de decisão fundado na teoria de seleção de portfólios capaz de decidir a divisão da produção entre diversos subprodutos, as proporções a serem estocadas e o momento mais adequado para a comercialização e por fim as posições em contratos futuros para fins de proteção ou hedge. Adicionalmente essa tese busca propor que esse modelo seja capaz de lidar com incerteza em parâmetros, em especial parâmetros que provocam alto impacto nos resultados, como é o caso dos retornos previstos no futuro. Como uma terceira contribuição, esse trabalho busca ainda propor um modelo de previsão de preços mais sofisticado que possa ser aplicado a commodities agrícolas, em especial um modelo híbrido ou hierárquico, composto de dois modelos, um primeiro modelo fundado sob a teoria de processos estocásticos e do Filtro de Kalman e um segundo modelo, para refinar os resultados do primeiro modelo de previsão, baseado na teoria de redes neurais, com a finalidade de considerar variáveis exógenas. O modelo híbrido de previsão de preços foi testado com dados reais do mercado sucroalcooleiro brasileiro e indiano, gerando resultados promissores, enquanto o modelo de decisão de parâmetros de produção, comercialização, estocagem e hedge se mostrou uma ferramenta útil para suporte a decisão após ser testado com dados reais do mercado sucroalcooleiro brasileiro e do mercado de milho, etanol e biodiesel norte-americano. / The rise of a large number of people in developing countries for the middle class at the beginning of the century, combined with the political movement to transfer the energy base for biofuels has been increasing pressure on prices of agricultural commodities and presenting new opportunities and administrative scenarios for agricultural producers of these commodities, especially those who may diversify into many products to meet different markets such as food, chemicals, textiles and energy. In this new environment producers can achieve benefits properly dividing production between different products, setting the best time to market through inventories, and still control their risk exposure through positions in the derivatives market. The literature poorly addresses the issue of diversification and its impact on agricultural production and commercialization decisions and therefore this thesis aims to propose a decision model based on the theory of portfolio selection able to decide the division of production between different products, the proportions to be stored and timing for marketing and finally the positions in futures contracts to hedge. Additionally this thesis attempts to propose that this model is capable of dealing with uncertainty in parameters, especially parameters that cause high impact on the results, as is the case of expected returns in the future. As a third contribution this paper seeks to also propose a model more sophisticated to forecast prices that can be applied to agricultural commodities, especially a hybrid or hierarchical model, composed of two models, a first one based on the theory of stochastic processes and Kalman filter and a second one to refine the results of the first prediction model, based on the theory of neural networks in order to consider the exogenous variables. The hybrid model for forecasting prices has been tested with real data from the Brazilian and Indian sugar ethanol market, generating promising results, while the decision model parameters of production, commercialization, storage and hedge proved a useful tool for decision support after being tested with real data from Brazilian sugar ethanol market and the corn, ethanol and biodiesel market in U.S.A.

This report aims to make a comparison between the Swedish and Spanish electricity market, the design of new improvements that could achieve a better operation for both markets as well as the price forecasting for both spot markets. These enhancements are oriented to decrease electricity prices, energy use and the system CO2 emissions. Also, the main organizations of the market and their roles has been characterized, clarifying the functions of the Market Operator and the System Operator. In addition, the different markets, the trading products and the price formation have been explained and the picture of the market structure has been achieved with enough depth. Moreover, some of the most used methods in Time Series Analysis has been enumerated to understand which techniques are needed for forecast the electricity prices and the methodology used (Box-Jenkins Method) has been explained in detail. Later, all these methods have been implemented in an own code developed in Python 3.6 (TSAFTools .py) with the help of different statistics libraries mentioned during the method chapter. On the other hand, the description of the market situation has been carried out for both countries. Power installed capacity, electricity generation, average prices, main renewable technologies and policies to increase the renewable energy share has been analysed and corresponding described. Then, to estimate the market’s future spot electricity prices, ARIMA models have been selected to analyse the evolution of the day-ahead price using the TSAFTools.py. The final models show a proper performance in the two markets, especially in the Nordpool, achieving an RMSE: 37.68 and MAPE: 7.75 for the year in 2017 in Nordpool and a RMSE: 270.08 and MAPE: 20.24 in OMIE for 2017. Nordpool spot prices from 2015 to 2016 has been analysed too but obtaining a result not as good as the year 2017 with an RMSE: 49.01 and MAPE: 21.42. After this analysis, the strengths and weaknesses of both markets are presented and the main problems of the Spanish electricity system (power overcapacity, fuel dependency, non-cost-efficient renewable energies policies, lack of interconnexion capacity etc.) and the Swedish electricity system (dependency for nuclear power, uncertainty for solar electricity Generation) are presented. Finally, due to the quick development of the energy sector in the last years and the concern of the European Committee to reach a new design for the electricity market, different kinds of recommendations for the future have been considered.

The field of stock returns and assessing stock returns utilising financial ratios has attracted substantial interest from various stakeholders. In terms of previous research, the role of financial ratios on stock returns has been based on studies in developed markets, with limited research in emerging markets. This research study provides an understanding of two specific financial ratios, namely the Return on Equity (ROE) and Dividend Payout (DPO) ratios and their impact on annual stock returns (ASR) in emerging stock markets in South Africa and Nigeria. A longitudinal analysis was performed from 2000 to 2013 for companies listed on the JSE Top 40 Index and from 2006 to 2013 for companies listed on the NSE 50 Index. The tests between the mean ROE and the mean ASR for companies listed on the JSE Top 40 Index revealed a significant positive correlation. The conclusions drawn from the relationship between the mean ROE and the mean ASR for companies listed on the NSE 50 Index and both the relationships between the mean DPO and the Mean ASR for both companies listed on the JSE top 40 Index and the NSE 50 Index was inconclusive. / Dissertation (MBA)--University of Pretoria, 2014 / lmgibs2015 / Gordon Institute of Business Science (GIBS) / unrestricted

This work deals with the energy management of belt conveyor systems (BCS) under various demandside management (DSM) programmes. The primary objective of this work is to model the energy consumption and energy related cost of operating troughed belt conveyor systems under different electricity pricing tariffs. This research is motivated by the increasing need for energy efficiency and energy cost reduction in the operation of BCS. This is as a result of technological improvements in BCS technology leading to increasingly longer belts being commissioned and as a result of rapidly rising electricity costs. An energy model derived from established industry standards is proposed for long conveyors. The newly proposed model uses a first-order partial differential equation (PDE) in order to capture the state of material on the belt. This new model describes the conveyor's power requirement using an equation with two parameters. A system identification set-up involving a recursive parameter estimating algorithm is simulated for measurements with varying degrees of noise. The results show that the proposed model estimates conveyor power and material delivered by long conveyors more accurately than the existing steady-state models. Downhill conveyors (DHCs) are important potential energy sources that can be tapped to improve the overall energy efficiency of BCSs. A generic optimisation model that is able to optimally schedule three configurations of BCS with DHC is proposed. The economic assessment of implementing dynamic braking and regenerative drives technology on downhill conveyors is undertaken with the help of the model. The assessment shows that combining regenerative drives and optimal operation of BCS with DHC generates energy savings that give attractive payback period of less than 5 years. A chance-constrained model predictive control (cc-MPC) algorithm is proposed for scheduling belt conveyor systems with uncertain material demand on the output storage. The chance-constraints are based on the modelling of material demand by a sum of known mean demand and, zero-mean and normally distributed random component. The cc-MPC algorithm is shown to produce schedules that give a smaller number and smaller magnitude of storage limit violations compared to normal MPC and chance-constrained optimal control algorithms. An equation that gives the amount of effective storage required to meet storage constraints for a given value of standard deviation is established. The optimal scheduling of BCS under the real-time pricing (RTP) tariff is considered. This study develops a methodology for establishing the economic value of price forecasting schemes for loads capable of load-shifting. This methodology is used to show that the economic benefit obtained from a forecast is highly dependent on the volatility of the electricity prices being predicted and not their mean value. The methodology is also used to illustrate why the commonly used indices mean absolute percentage error (MAPE) and root mean square error (RMSE) are poor indicators of economic benefit. The proposed index using Kendall's rank correlation between the actual and predicted prices is shown to be a good indicator of economic benefit, performing far better than RSME and MAPE. / Thesis (PhD)--University of Pretoria, 2016. / Electrical, Electronic and Computer Engineering / PhD / Unrestricted

Within forecasting electricity pricing, we analyse whether adding various vari- ables improves the predictions, and if shorter time intervals between observa- tions enhance accuracy of the forecasting. Next, we focus on proper selection of lagged observations, which has not been thoroughly covered in the past litera- ture. In addition, many papers studied electricity prices in larger markets (e.g. United States, Australia, Nord Pool, etc.) on datasets limited in scope, with 2-3 years timespan. To address these gaps in literature, we obtain one daily and one hourly dataset, both spanning 6 years (January 1, 2015 - December 31, 2020), from four Central and Eastern European countries - the Czech Repub- lic, the Slovak Republic, Hungary, and Romania. These contain information on the electricity prices, and information on our observed added variables - temperature and cross-border electricity flows. For the forecasting, we use two different methods - Autoregression (AR) and Seemingly Unrelated Regression (SUR). The thorough selection of lagged observations, which we accustom to the closing time of the auction-based electricity market system, serves further studies as a guidance on how to avoid possible errors and inconsistencies in their predictions. In our analyses, both AR and SUR models show that...

This dissertation is a study of fundamental questions in macro-finance using modern tools from textual analysis. These questions include how financial constraints affect firm investment and financing decisions when they are not presently binding, and whether stock returns are predictable based on concerns revealed in conversations between firms and investors. The first chapter examines whether financial covenants are an important consideration for firm decisions when they are not presently in violation. A key empirical challenge is measuring the risk of future covenant violations, which is not directly observed. I propose a novel measure of concerns about future violations by distinguishing between discussions of covenants in earnings calls that relate to the future as opposed to the past or present. As validation, I show that the measure predicts future violations and covaries intuitively with earnings, leverage, and default risk. Importantly, I find that concerns about covenants are significantly associated with reductions in investment as well as debt and equity financing activities. These responses persist even after controlling for standard measures of investment opportunities and are economically large relative to the effects of actual violations. The second chapter empirically analyzes two explanations for how covenants concerns relate to a firm's investment decisions. One explanation is that covenant concerns coincide with a deterioration in expected profitability, which dampens firms' incentives to invest. A second explanation is that firms become concerned when they expect violations to be more costly, which indicates future difficulties with funding investments. To shed light on the relevance of these two explanations, I examine empirical patterns in analyst expectations of future earnings, loan amendments in SEC filings, and the stock returns of firms that mention covenant concerns. The evidence suggest that both explanations are relevant mechanisms driving the correlation between covenant concerns and firm activity. However, I find that the second channel is more economically significant, suggesting that covenant concerns are informative about the degree to which firms are constrained by financial covenants. In the third chapter, I investigate how covenant concerns relate to firm policies in a standard model of investments with financial frictions. In the model, the theoretical object that most naturally links to covenant concerns is the expected shadow cost of the borrowing constraint. As in the data, the shadow cost of the borrowing constraint covaries negatively with earnings as well as firm investment and financing activity. Through an analysis of impulse response functions, I show how the empirical correlations between covenant concerns and firm policy arise in the model. One channel is through negative productivity shocks, which raises covenant concerns and leads to a fall in investment, debt, and equity issuance. The second channel is through higher leverage, holding fixed productivity. In the model, firm with higher debt levels are more concerned about covenants when hit by a negative productivity shock, and also choose less investment, debt issuance, and equity issuance. In this chapter, I also discuss several shortcomings of the model and suggest avenues for modifications. The final chapter investigates a new question: are stock returns predictable based on the extent to which firms are concerned about the macroeconomy? We document that firms that pay more attention to the macroeconomy earn lower average returns relative to firms that pay less attention to the macroeconomy. Differences in returns are economically significant and are not explained by traditional asset pricing factors, such as market beta, size, value, and idiosyncratic volatility. To explain the negative macroeconomic attention premium, we propose a model of attention allocation that links analyst attention to fundamental shocks affecting firm cash flows. In the model, attention to the macroeconomy is increasing in the share of earning news explained by the macroeconomic component. Firms with a greater share of cash flow news explained by the macroeconomic component face lower cash flow risk, hence earn lower expected returns.

Every new product launching industrial company faces the difficulties of forecasting future success or failure of a new product before launch. Before launch it is common to develop a business case in order to estimate future quantities and set prices. In the present paper the challenges of developing a standardized business case tool for a large industrial construction and mining company are presented. Few academic studies have been conducted on the challenges and complexities of developing business cases. The research question under which this study is done is: What are the challenges associated with developing an effective standardized business case tool for a large industrial construction and mining company? Due to the different subject areas of the business case for new product launch, the challenges are categorized by topics developed by the researcher in the course of this project: process and team, data gathering and validation, quantity forecast and price forecast. The main challenges found in these categories by the researcher are: finding and motivating experts for the project of developing a standardized business case, gathering and selecting all data necessary without including redundant data, ensuring that different potential new products can be forecasted and designing the price forecast to be profit-maximizing. Solutions to these challenges are provided in the context of a case company by using methods suggested by the academic literature and the evaluation of expert interviews inside the case company

Many methods have been used to forecast real-time electricity prices in various regions around the world. The problem is difficult because of market volatility affected by a wide range of exogenous variables from weather to natural gas prices, and accurate price forecasting could help both suppliers and consumers plan effective business strategies. Statistical analysis with autoregressive moving average methods and computational intelligence approaches using artificial neural networks dominate the landscape. With the rise in popularity of convolutional neural networks to handle problems with large numbers of inputs, and convolutional neural networks conspicuously lacking from current literature in this field, convolutional neural networks are used for this time series forecasting problem and show some promising results. This document fulfills both MSEE Master's Thesis and BSCPE Senior Project requirements.

In chapter one, I examine the predictive content of subjective return expectations derived from price targets issued by equity analysts. Equity price targets are an ubiquitous feature of the financial information landscape, but it is not clear how informative they actually are. In this chapter, I show that the cross-section of price-target implied subjective return expectations contains rich informational content for forecasting returns. In-sample, I find that expected returns correlate strongly with average cross-sectional returns to a large panel of portfolios formed on the basis of observable firm characteristics. In out-of-sample exercises, forecasting models using subjective expectations are shown to offer more accurate predictions for portfolio returns than several other commonly employed, cross-sectional predictors, including the book-to-market and dividend-price ratios, momentum, and forward-looking cash-flow measures. Furthermore, these differences are shown to be economically relevant, with conditional portfolios formed on the basis of subjective expectations offering substantially improved risk-adjusted returns compared to many of the other predictors considered. The relative informational content, as well as the production by analysts, of subjective return expectations is found, however, to peak during recessions, with negligible predictive advantage discernible in expansions. In chapter two, my coauthors (Adam Rej, with CFM; David Thesmar, with MIT, CEPR, and NBER) and I empirically analyze a large panel of firm sales growth expectations. We find that the relationship between forecast errors and lagged revision is non-linear. Forecasters underreact to typical (positive or negative) news about future sales, but overreact to very significant news. To account for this non-linearity, we propose a simple framework, where (1) sales growth dynamics have a fat-tailed high frequency component and (2) forecasters use a simple linear rule. This framework qualitatively fits several additional features of data on sales growth dynamics, forecast errors, and stock returns. In chapter three, my coauthor (Ken Teoh, with Columbia) and I construct a novel text-based measure of firm-level attention to macroeconomic conditions and document that stocks associated with higher macroeconomic attention earn lower returns. Moving from the bottom decile to top decile of macroeconomic attention decreases a stock’s average return by 11.6\% per year. We propose a risk-based explanation in which stocks with higher macroeconomic attention contribute less idiosyncratic cash flow risk to the investor’s portfolio, hence earn lower expected returns. Decomposing the unexpected returns of macroeconomic attention-sorted portfolios into cash flow and discount rate news, we find that portfolios with higher macroeconomic attention stocks have lower cash flow risk.

by Ng Wai-Leung Anthony. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 83-87). / Abstracts in English and Chinese. / Chapter I. --- INTRODUCTION --- p.1 / Chapter II. --- LITERATURE REVIEW --- p.5 / Chapter 2.1 --- The Importance of Stock Index and Index Futures --- p.6 / Chapter 2.2 --- Importance of Index Forecasting --- p.6 / Chapter 2.3 --- Reasons for the Lead-Lag Relationship between Stock and Futures Markets --- p.9 / Chapter 2.4 --- Importance of the lead-lag relationship --- p.10 / Chapter 2.5 --- Some Empirical Findings of the Lead-Lag Relationship --- p.10 / Chapter 2.6 --- New Approach to Financial Forecasting - Artificial Neural Network --- p.12 / Chapter 2.7 --- Artificial Neural Network Architecture --- p.14 / Chapter 2.8 --- Evidence on the Employment of ANN in Financial Analysis --- p.20 / Chapter 2.9 --- Hong Kong Securities and Futures Markets --- p.25 / Chapter III. --- GENERAL GUIDELINE IN DESIGNING AN ARTIFICIAL NEURAL NETWORK FORECASTING MODEL --- p.28 / Chapter 3.1 --- Procedure for using Artificial Neural Network --- p.29 / Chapter IV. --- METHODOLOGY --- p.37 / Chapter 4.1 --- ADF Test for Unit Root --- p.38 / Chapter 4.2 --- "Error Correction Model, Error Correction Model with Short- term Dynamics, and ANN Models for Comparisons" --- p.38 / Chapter 4.3 --- Comparison Criteria of Different Models --- p.39 / Chapter 4.4 --- Data Analysis --- p.39 / Chapter 4.5 --- Data Manipulations --- p.41 / Chapter V. --- RESULTS --- p.42 / Chapter 5.1 --- The Resulting Models --- p.42 / Chapter 5.2 --- The Prediction Power among the Models --- p.45 / Chapter 5.3 --- ANN Model of Input Variable Selection Using Contribution Factor --- p.46 / Chapter VI. --- CAUSALITY ANALYSIS --- p.54 / Chapter 6.1 --- Granger Casuality Analysis --- p.55 / Chapter 6.2 --- Results Interpretation --- p.56 / Chapter VII --- CONSISTENCE VALIDATION --- p.61 / Chapter VIII --- ARTIFICIAL NEURAL NETWORK TRADING SYSTEM --- p.67 / Chapter 7.1 --- Trading System Architecture --- p.68 / Chapter 7.2 --- Simulation Runs using the Trading System --- p.77 / Chapter XI. --- CONCLUSIONS AND FUTURE WORKS --- p.79