[pt] Esta tese consiste em três ensaios sobre previsão de inflação, com foco na inflação brasileira. No primeiro ensaio, examinamos a eficácia de vários métodos de previsão para prever a inflação, com foco na agregação de previsões desagregadas. Consideramos diferentes níveis de desagregação para a inflação e empregamos uma variedade de técnicas tradicionais de séries temporais, bem como modelos lineares e não lineares de aprendizado de máquina que lidam com um número grande de preditores. Para muitos horizontes de previsão, a agregação de previsões desagregadas performa tão bem quanto expectativas baseadas em coleta e modelos que geram previsões a partir do agregado. No geral, os métodos de aprendizado de máquina superam os modelos de séries temporais tradicionais em precisão preditiva, com excelente desempenho para os desagregados da inflação. Em nosso segundo ensaio, investigamos os potenciais benefícios de combinar previsões de inflação individuais ao propor uma correção para viés variável no tempo da média de previsões. Nossa análise inclui estimações empregando janelas rolantes e modelos em espaço de estados que usam a recursividade do filtro de Kalman. Obtivemos um bom desempenho de previsão para modelos baseados em janelas rolantes pequenas em horizontes de previsão curtos e intermediários, enquanto um modelo em espaço de estados obtem um desempenho um pouco pior do que os procedimentos baseados em janelas rolantes. No terceiro ensaio, usamos aprendizado supervisionado para gerar índices prospectivos baseados em tweets e notícias para inflação acumulada e investigamos se esses índices podem melhorar o desempenho da previsão de inflação. Nossos resultados indicam que os índices baseados em notícias fornecem ganhos preditivos significativos, principalmente para os horizontes de 3 e 12 meses à frente. Esses achados sugerem que a incorporação de mais fontes de informação do que apenas expectativas baseadas em opiniões de especialistas pode levar a previsões mais precisas. / [en] This dissertation consists of three essays concerning inflation forecasting, taking the Brazilian case as an application. In the first essay, we examine the effectiveness of several forecasting methods for predicting inflation, focusing on aggregating disaggregated forecasts. We consider different disaggregation levels for inflation and employ a range of traditional time series techniques, as well as linear and nonlinear machine learning (ML) models that deal with a larger number of predictors. For many forecast horizons, aggregation of disaggregated forecasts performs just as well as survey-based expectations and models generating forecasts directly from the aggregate. Overall, ML methods outperform traditional time series models in predictive accuracy, with outstanding performance in forecasting disaggregates. In our second essay, we investigate the potential benefits of combining individual inflation forecasts by proposing a time-varying bias correction for the average forecast. Our analysis includes estimations using both rolling windows and state-space models that use the recursiveness of the Kalman filter. We achieve good forecast performance for models based on small rolling windows for shorter and intermediate forecast horizons, while a state-space model performs slightly worse than procedures based on rolling windows. In the third essay, we use supervised learning to generate forward-looking indexes based on tweets and news articles for accumulated inflation and investigate whether these indexes can improve inflation forecasting performance. Our results indicate that news-based indexes provide significant predictive gains, particularly for 3- and 12-month-ahead horizons. These findings suggest that incorporating more information sources than just expectations based on experts opinions can lead to more accurate forecasts.

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Portfolio optimisation has a number of constraints resulting from some practical matters and regulations. The closed-form mathematical solution of portfolio optimisation problems usually cannot include these constraints. Exhaustive search to reach the exact solution can take prohibitive amount of computational time. Portfolio optimisation models are also usually impaired by the estimation error problem caused by lack of ability to predict the future accurately. A number of Multi-Objective Genetic Algorithms are proposed to solve the problem with two objectives subject to cardinality constraints, floor constraints and round-lot constraints. Fuzzy logic is incorporated into the Vector Evaluated Genetic Algorithm (VEGA) to but solutions tend to cluster around a few points. Strength Pareto Evolutionary Algorithm 2 (SPEA2) gives solutions which are evenly distributed portfolio along the effective front while MOGA is more time efficient. An Evolutionary Artificial Neural Network (EANN) is proposed. It automatically evolves the ANN¿s initial values and structures hidden nodes and layers. The EANN gives a better performance in stock return forecasts in comparison with those of Ordinary Least Square Estimation and of Back Propagation and Elman Recurrent ANNs. Adaptation algorithms for selecting a pair of forecasting models, which are based on fuzzy logic-like rules, are proposed to select best models given an economic scenario. Their predictive performances are better than those of the comparing forecasting models. MOGA and SPEA2 are modified to include a third objective to handle model risk and are evaluated and tested for their performances. The result shows that they perform better than those without the third objective.

Structural changes in the knowledge economy have greatly affected the way business is conducted and the processes firms create value. The financial reporting system is inadequate as a result of such changes, and disclosure of intellectual capital (IC) information has gained importance for communicating with capital markets. Empirical research documents corporate governance (CG) factors influencing IC disclosure practices, as well as demonstrates the value-relevance and predictive power of IC information. The disclosure of IC information by listed firms is a topic that has attracted considerable attention from contemporary researchers, but scant empirical evidence exists. Much of the researchers has examined CG as a key determinant of IC (and nonfinancial) disclosure; in contrast, few provides evidence for explaining their controversial findings of board independence on disclosure. In addition, a lack of studies confirms the literature about the use of IC information by capital market participants. Therefore, this thesis aims to examine disclosure of IC information in relation to outside directors, auditors, and sell-side analysts respectively. The specific objectives of this thesis are to examine whether outside directors’ expertise is a determinant of IC disclosure; and the extent to which the disclosure of IC information impacts on audit effort and analysts’ forecasts. In order to address these research objectives, a content analysis of IC disclosure (a self-constructed index of 64 coded items) in strategic reports released by FTST 350 companies is used. The content analysis captures and measures IC disclosure by category (i.e., human, structural & relational capital), notion (i.e., static vs. dynamic), and connection (i.e., across categories vs. with strategies). Using multivariate regression models that were primarily developed upon information asymmetry arguments and agency theory, the specific objectives of this thesis are addressed in three empirical chapters. The findings in Chapter 3 showed that proportion of outside directors (NEDs) with cross-directorship, nonaccounting and academia expertise has a positive association with IC disclosures, whereas board independence itself has no effect on the disclosures. The findings indicates that the monitoring role of NEDs alone is inadequate in promoting IC disclosure. Rather, it supports the importance of the dual role (i.e., monitor and advisory) of a supervisory board. The results also respond to the UK CG Code in their recommendation that the combination of skills, experience and knowledge guarantees a sound information environment to the market. Nonetheless, findings raised a further concern about the quantity of IC disclosures when companies have more NEDs with accounting expertise. On whether and how disclosure of IC information impacts on audit effort, Chapter 4 found that firms with high levels of IC disclosure in the previous year pay more audit fees (proxied for audit effort) in the current year regardless of their earnings quality conditions. It was also found that firms greatly disclosing dynamic IC information are charged more than those of focusing on static IC disclosure. In addition, findings in Chapter 5 revealed that there is a negative relation between IC disclosure and analyst forecast errors, indicating that UK sell-side analysts appreciate the disclosure of IC information and thus confirming that IC information has predictive ability of explaining a firm’s future value. It was further identified that disclosed IC information absorbs the negative effect of concentrated executive ownership and opaque financial environment. Overall, the results of this thesis suggest that IC reporting process could be improved by having sufficient outside directors with certain types of expertise on the board. In doing so, improved IC disclosure helps to reduce information asymmetry (proxied by analyst forecast accuracy) between firms and outside investors, albeit firms bear a significant increase in audit fees. This study calls for guidelines for IC disclosure in the UK and the support of assurance services to enhance credibility of firm-provided IC information in a bid to promote the communication of IC information with the capital market.

Wirtschaftsprognosen sollen die Unsicherheit bezüglich der zukünftigen wirtschaftlichen Entwicklung mindern und Planungsprozesse von Regierungen und Unternehmen unterstützen. Empirische Studien bescheinigen ihnen jedoch in aller Regel ein unbefriedigendes Qualitätsniveau. Auf der Suche nach den Ursachen hat sich in Form der rationalen Erwartungsbildung eine zentrale Grundforderung an  die Prognostiker herausgebildet. So müssten offensichtliche und systematische Fehler, wie bspw. regelmäßige Überschätzungen, mit der Zeit erkannt und abgestellt werden. Die erste Studie der Dissertation übt Kritik am vorherrschenden Verständnis der Rationalität. Dieses ist zu weitreichend, weshalb den Prognostikern die Rationalität voreilig abgesprochen wird. Anhand einer neuen empirischen Herangehensweise wird deutlich, dass die Prognosen aus einem anderen Blickwinkel heraus durchaus als rational angesehen werden können. Der zweite Aufsatz zeigt auf, dass in Form von Befragungsergebnissen öffentlich verfügbare Informationen bestehen, die bei geeigneter Verwendung zu einer Verbesserung der Qualität von Konjunkturprognosen beitragen würden. Die Rationalität dieser Prognosen ist daher stark eingeschränkt. Im dritten Papier erfolgt eine Analyse von Prognoserevisionen und deren Ursachen. Dabei zeigt sich, dass es keinen Zusammenhang zwischen der Rationalität und der Qualität der untersuchten Prognosezeitreihen gibt. Die vierte Studie dient der Präsentation der Ergebnisse eines Prognoseplanspiels, welches den Vergleich der Prognosen von Amateuren und Experten zum Ziel hatte. Es stellt sich heraus, dass die Prognosefehler erhebliche Übereinstimmungen aufweisen.

This thesis examines regional integration in Southern Africa and the evolution of SADC. Regional developments are evaluated with the yardsticks of integration theory, against the background of international regionalisation, and in terms of the region's practical record, its rhetoric and future agenda. The extent to which economic integration is progressing, is determined, after which the thesis focuses on political integration within SADC - both de Jure and de facto. Finally, developments within the region are evaluated in light of normative prerequisites for increased political integration. The thesis finds that the integration process in SADC does not fit into traditional integration theory, and concludes that successful economic integration in the region is not necessarily a prerequisite to political integration, but would facilitate it. The research finally concludes that there is evidence of embryonic political integration within SADC, which will wane or grow depending primarily on the political will of its constituents / Political Science / M.A. (Politics)

Exponential smoothing algorithms are very attractive for the practical world such as in industry. When considering bivariate exponential smoothing methods, in addition to the properties of univariate methods, additional properties give insight to relationships between the two components of a process, and also to the overall structure of the model. It is important to study these properties, but even with the merits the bivariate exponential smoothing algorithms have, exponential smoothing algorithms are nonstatistical/nonstochastic and to study the properties within exponential smoothing may be worthless. As an alternative approach, the (bivariate) ARIMA and the structural models which are classes of statistical models, are shown to generalize the exponential smoothing algorithms. We study these properties within these classes as they will have implications on exponential smoothing algorithms. Forecast properties are studied using the state space model and the Kalman filter. Comparison of ARIMA and structural model completes the study. / Mathematical Sciences / M. Sc. (Statistics)

This thesis explores various aspects of long memory behaviour in African stock markets (ASMs). First, we examine long memory in both equity returns and volatility using the weak-form version of the efficient market hypothesis (EMH) as a criterion. The results show that these markets (largely) display a predictable component in returns; while evidence of long memory in volatility is mixed. In general, these findings contradict the precepts of the EMH and a variety of remedial policies are suggested. Next, we re-examine evidence of volatility persistence and long memory in light of the potential existence of neglected breaks in the stock return volatility data. Our results indicate that a failure to account for time-variation in the unconditional mean variance can lead to spurious conclusions. Furthermore, a modification of the GARCH model to allow for mean variation is introduced, which, generates improved volatility forecasts for a selection of ASMs. To further evaluate the quality of volatility forecasts we compare the performance of a number of long memory models against a variety of alternatives. The results generally suggest that over short horizons simple statistical models and the short memory GARCH models provide superior forecasts of volatility; while, at longer horizons, we find some evidence in favour of long memory models. However, the various model rankings are shown to be sensitive to the choice of error statistic used to assess the accuracy of the forecasts. Finally, a wide range of volatility forecasting models are evaluated in order to ascertain which method delivers the most accurate value-at-risk (VaR) estimates in the context of Basle risk framework. The results show that both asymmetric and long memory attributes are important considerations in delivering accurate VaR measures.

協同規劃、預測與補貨(Collaborative Planning, Forecasting and Replenishment,CPFR)，在歐美經過一些企業的採用後已經有顯著的成效，目前國內已經有一些企業相繼採用或即將採用CPFR，期望能因此降低供應鏈作業成本及提升供應鏈作業績效，以提升企業競爭力。在CPFR流程與供應鏈協同作業環境下，一個供需雙方協同，且績效良好的的銷售預測具有關鍵的重要性，是管理決策與協同合作時的的重要依據；但是多數的企業並沒有一個結構化、系統化的預測流程及方法，而是各部門透過簡單時間序列方法、天真預測法或人為經驗法則估算需求，進行多點且不同方法之預測，這樣的銷售預測較無穩定的品質，亦較難提供管理者合理的數據解釋。本研究結合時間序列、多元回歸模型與基因演算法發展出一個CPFR流程下之三階段混合預測方法，以買賣方直接之銷售資料、銷售計畫等資訊進行以「週」為單位之個別商品銷售預測。同時本研究中，亦以國內某製造業公司與其顧客（一國際大型零售連鎖店通路商）之產品銷售資料進行方法的驗證；實驗顯示，本研究所提出之預測方法之預測結果較Jeong等人(2002)所提結合多元回歸模型與基因演算法之二階段預測系統之預測結果佳；亦較傳統使用普通最小平方法求解之一般統計回歸方法預測結果佳。 / It has been verified in pilot projects by many European and American Corporations that Collaborative Planning, Forecasting and Replenishment (CPFR) can improve supply chain performance. Enterprises nowadays in Taiwan are implementing or going to implement CPFR, with hopes to reduce their supply chain operation cost, enhance logistic performance and increase their competition capability consequently. Under CPFR process and supply chain collaboration environment, a supply and demand both sides promised identical sales forecast with well forecasting performance for order decision making and cooperation is very important. Due to the dynamic complexities of both internal and external co-operate environment, many firms resort to qualitative, navie forecasting or other simple quantitative forecasting techniques and have many forecasts in their organization. However, these forecasting techniques lack the structure and extrapolation capability of quantitative forecasting models or without stable performance, while multi-forecasts providing different views of demand. Forecasting inaccuracies exist and typically lead to dramatic disturbances in sales order and production planning. This paper presents a hybrid forecasting model for sales forecasting requirements in CPFR. A three stage model is proposed that integrate the time series model, regression model and use genetic algorithm to determine its coefficients efficiently. Direct sales information and related planned events in both collaborated sides is used for individual product’s “week” sales forecasting. To verify this model, we experiment on two different products and produce forecasts with datum from one manufacturer in Taiwan and its international retailer. The results shows that the hybrid sales forecasting model has better forecasting performance than not only the causal-genetic forecasting model proposed by Jeong et al. (2002), but also ordinary regression model with no genetic training process.

本論文分為兩部分，第一部份是探討全球航空產業的油料避險會不會對公司價值有所影響，以及油料避險的誘因。第二部份則是檢視全球航空公司的風險曝露會不會影響分析師的預測誤差，尤其是燃油價格變動的風險曝露。 / In the first essay, we examine whether jet fuel hedging increases the market value of airline companies around the world. Using a sample of 70 airline companies from 32 countries over the period 1995 to 2005, we find that jet fuel hedging is not significantly positively related to their firm value in the global airlines, but this positive relationship holds in the various sub-samples and is significant for US and non-alliance firms. Moreover, our results show that the risk-taking behavior of executives and the tendency to avoid financial distress are important determinants for the jet fuel hedging activities of non-US airline companies. Alleviating the problem of underinvestment is also an important factor to explain the jet fuel hedging activities of US and non-alliance firms. Our results add support to the growing body of literature which finds that hedging increases firm value for global airline companies. In the second essay, we examine the extent analysts revise their earnings forecasts in response to oil price, interest rate and foreign exchange rate shocks they have observed during the year, and whether these revisions contain additional information about how current and past price shocks affect reported earnings, using the sample of the global airline industry. Empirical results indicate that jet fuel hedging can increase analysts’ forecast revisions in the total sample, and in the sub-sample of the volatile fuel price period. These results can also be seen in US and non-US airlines, and airlines with both strong and weak governance. Overall, our results show that oil price shocks play an important role in investor and analyst information uncertainty with regard to the global airline industry. Consequently, corporate risk disclosures only provide limited information about firms’ financial risk exposures. Two essays are comprised in this dussertation to examine whether jet fuel hedging has effects on firm value and analysts’ forecast accuracy in the global airline industry. Using global data allows us to cmpare the differences of jet fuel hedging behavior and incentives for hedging across different sub-samples. Furthermore, we also examine how jet fuel hedging affects analysts’ forecast erros across different sub-samples and its implications for firm disclosures about their risk exposures in the financial reports. In the first essay, we examine whether jet fuel hedging increases the market value of airline companies around the world. Using a sample of 70 airline companies from 32 countries over the period 1995 to 2005, we find that jet fuel hedging is not significantly positively related to their firm value in the global airlines, but this positive relationship holds in the various sub-samples and is significant for US and non-alliance firms. Moreover, our results show that the risk-taking behavior of executives and the tendency to avoid financial distress are important determinants for the jet fuel hedging activities of non-US airline companies. Alleviating the problem of underinvestment is also an important factor to explain the jet fuel hedging activities of US and non-alliance firms. Our results add support to the growing body of literature which finds that hedging increases firm value for global airline companies. In the second essay, we examine the extent analysts revise their earnings forecasts in response to oil price, interest rate and foreign exchange rate shocks they have observed during the year, and whether these revisions contain additional information about how current and past price shocks affect reported earnings, using the sample of the global airline industry. Empirical results indicate that jet fuel hedging can increase analysts’ forecast revisions in the total sample, and in the sub-sample of the volatile fuel price period. These results can also be seen in US and non-US airlines, and airlines with both strong and weak governance. Overall, our results show that oil price shocks play an important role in investor and analyst information uncertainty with regard to the global airline industry. Consequently, corporate risk disclosures only provide limited information about firms’ financial risk exposures.