With the aim for higher economical efficiency, considerable and radical changes have occurred in the worldwide electricity sector since the beginning of 1980s. By that time, the electricity sector has been controlled by the state-owned vertically integrated monopolies which manage and control all generation, transmission, distribution and retail activities and the consumers buy electricity with a price set by these monopolies in that system. After the liberalization and restructuring of the electricity power sector, separation and privatization of these activities have been widely seen. The main purpose is to ensure competition in the market where suppliers and consumers compete with each other to sell or buy electricity from the market and the consumers buy the electricity with a price which is based on competition and determined according to sell and purchase bids given by producers and customers rather than a price set by the government. Due to increasing competition in the electricity market, accurate electricity price forecasts have become a very vital need for all market participants. Accurate forecast of electricity price can help suppliers to derive their bidding strategy and optimally design their bilateral agreements in order to maximize their profits and hedge against risks. Consumers need accurate price forecasts for deriving their electricity usage and bidding strategy for minimizing their utilization costs. This thesis presents the determination of system day ahead price (SGOF) at the day ahead market and system marginal price (SMF) at the balancing power market in detail and develops artificial neural network models together with multiple linear regression models to forecast these electricity prices in Turkish electricity market. Also the methods used for price forecasting in the literature are discussed and the comparisons between these methods are presented. A series of historical data from Turkish electricity market is used to understand the characteristics of the market and the necessary input factors which influence the electricity price is determined for creating ANN models for price forecasting in this market. Since the factors influencing SGOF and SMF are different, different ANN models are developed for forecasting these prices. For SGOF forecasting, historical price and load values are enough for accurate forecasting, however, for SMF forecasting the net instruction volume occurred due to real time system imbalances is needed in order to increase the forecasting accuracy.

The purpose of this research is to evaluate the performance of the emerging stock markets in three regions. The regions chosen as our testing targets are Europe, The Middle East, and Asia. Performance for 2002 to 2004 will be compared to the U.S. stock market.

Thesis (MBA (Business Management))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: The purpose of this report is to investigate whether the returns of five different trading systems applied is able to outperform the return of a Buy & Hold (B&H) strategy when applied to the Johannesburg Stock Exchange/Financial Times Stock Exchange (JSE/FTSE) Top 40 Index future contract (ALSI). The study starts with an overview of theoretical and empirical studies regarding technical trading systems as well as the application of these technical trading systems in various strategy formats. Five common trading systems were selected for the test. They include the Volatility Channel, the Bollinger Channel Breakout, the Donchian Channel, the Dual Moving Average and the Triple Moving Average systems. The trading systems were applied in three different types of strategies. In the first test the systems were employed using randomly selected parameters to generate trading signals. In the second test the systems were optimised to select the parameters that would yield the most profitable returns over the test period. Finally in the third test a stop loss was added to the systems to investigate whether it would improve returns. In virtually all tests the systems outperformed the B&H approach. This was primarily due to the collapse of world financial markets in 2008 that caused the systems, which are all trend following by nature, to generate large returns. If it had not been for this event, the trend-following systems would all have underperformed the total return generated by the B&H strategy over the duration of the test period. The tests revealed that the selection of the parameters that generate the trade signals for the trading systems can drastically influence the profitability of a trading system. Furthermore the implementation of stop-loss strategies does not necessarily improve the return or drawdown that a system displays, as several of the systems were negatively influenced by the implementation of the stop-loss strategy. / AFRIKAANSE OPSOMMING: Die doel van hierdie verslag is om te ondersoek of die opbrengs van vyf verskillende verhandelingstelsels die opbrengs van die Koop-en-Hou-strategie kan klop soos toegepas op die JSE/FTSE Top 40 Indeks termynkontrak (ALSI). Die studie begin met ’n oorsig oor teoretiese en empiriese studies oor tegniese verhandelingstelsels, asook die toepassing van hierdie tegniese stelsels in verskeie strategiese formate. Vyf algemene verhandelingstelsels is gekies vir die ondersoek, naamlik die Volatiliteitskanaal (Volatility Channel), die Bollinger Kanaal Uitbreek (Bollinger Channel Breakout), die Donchian Kanaal (Donchian Channel), die Tweeledige Bewegende Gemiddelde (Dual Moving Average) en die Drieledige Bewegende Gemiddelde (Triple Moving Average). Die stelsels is op drie verskillende tipes stategieë toegepas. In die eerste toets was die stelsels geïmplementeer deur lukraak gekose parameters te gebruik om verhandelingseine voort te bring. In die tweede toets was die stelsels geoptimaliseer deur die parameters te kies wat die mees winsgewende opbrengs oor die toetsperiode sou voortbring. In die derde toets was ’n staakverlies (stop loss) geïmplementeer om te ondersoek of dit die opbrengs sou verbeter. Feitlik al die toetse het getoon dat die verhandelingstelsels die Koop-en-Hou-benadering geklop het. Aangesien al die stelsels die algemene tendens in die mark volg, het hulle hoë opbrengste getoon hoofsaaklik as gevolg van die beermark wat die wêreld se finansiële markte in 2008 gekenmerk het. As hierdie gebeurtenis nie plaasgevind het nie, sou hierdie stelsels swakker gevaar het as die Koop-en-Hou-strategie gedurende die tydperk van die toetsperiode. Die toetse het aangedui dat die keuse van die parameters wat verhandelingseine vir die stelsels gegenereer het, die winsgewendheid van ’n verhandelingstelsel drasties kan beïnvloed. Die implementering van ’n staakverlies- (stop-loss) strategie verbeter nie noodwendig die opbrengs van ’n stelsel nie, aangesien verskeie stelsels negatief beïnvloed was deur die staakverlies-strategie.

Short-term electricity price forecasting is now crucial practice in deregulated electricity markets, as it forms the basis for maximizing the profits of the market participants. In this thesis, short-term electricity prices are forecast using three different predictor schemes, Artificial Neural Networks (ANNs), Support Vector Machine (SVM) and a hybrid scheme, respectively. ANNs are the very popular and successful tools for practical forecasting. In this thesis, a hidden-layered feed-forward neural network with back-propagation has been adopted for detailed comparison with other forecasting models. SVM is a newly developed technique that has many attractive features and good performance in terms of prediction. In order to overcome the limitations of individual forecasting models, a hybrid technique that combines Fuzzy-C-Means (FCM) clustering and SVM regression algorithms is proposed to forecast the half-hour electricity prices in the UK electricity markets. According to the value of their power prices, thousands of the training data are classified by the unsupervised learning method of FCM clustering. SVM regression model is then applied to each cluster by taking advantage of the aggregated data information, which reduces the noise for each training program. In order to demonstrate the predictive capability of the proposed model, ANNs and SVM models are presented and compared with the hybrid technique based on the same training and testing data sets in the case studies by using real electricity market data. The data was obtained upon request from APX Power UK for the year 2007. Mean Absolute Percentage Error (MAPE) is used to analyze the forecasting errors of different models and the results presented clearly show that the proposed hybrid technique considerably improves the electricity price forecasting.

Commodities primárias, tais como metais, petróleo e agricultura, constituem matérias-primas fundamentais para a economia mundial. Dentre os metais, destaca-se o alumínio, usado em uma ampla gama de indústrias, e que detém o maior volume de contratos na London Metal Exchange (LME). Como o preço não está diretamente relacionado aos custos de produção, em momentos de volatilidade ou choques econômicos, o impacto financeiro na indústria global de alumínio é significativo. Previsão de preços do alumínio é fundamental, portanto, para definição de política industrial, bem como para produtores e consumidores. Este trabalho propõe um modelo ótimo de previsões para preços de alumínio, por meio de combinações de previsões e de seleção de modelos através do Model Confidence Set (MCS), capaz de aumentar o poder preditivo em relação a métodos tradicionais. A abordagem adotada preenche uma lacuna na literatura para previsão de preços de alumínio. Foram ajustados 5 modelos individuais: AR(1), como benchmarking, ARIMA, dois modelos ARIMAX e um modelo estrutural, utilizando a base de dados mensais de janeiro de 1999 a setembro de 2014. Para cada modelo individual, foram geradas 142 previsões fora da amostra, 12 meses à frente, por meio de uma janela móvel de 36 meses. Nove combinações de modelos foram desenvolvidas para cada ajuste dos modelos individuais, resultando em 60 previsões fora da amostra, 12 meses à frente. A avaliação de desempenho preditivo dos modelos foi realizada por meio do MCS para os últimos 60, 48 e 36 meses. Um total de 1.250 estimações foram realizadas e 1.140 variáveis independentes e suas transformadas foram avaliadas. A combinação de previsões usando ARIMA e um ARMAX foi o único modelo que permaneceu no conjunto de modelos com melhor acuracidade de previsão para 36, 48 e 60 meses a um nível descritivo do MCS de 0,10. Para os últimos 36 meses, o modelo combinado proposto apresentou resultados superiores em relação a todos os demais modelos. Duas co-variáveis identificadas no modelo ARMAX, preço futuro de três meses e estoques mundiais, aumentaram a acuracidade de previsão. A combinação ótima apresentou um intervalo de confiança pequeno, equivalente a 5% da média global da amostra completa analisada, fornecendo subsídio importante para tomada de decisão na indústria global de alumínio. iii / Primary commodities, including metals, oil and agricultural products are key raw materials for the global economy. Among metals, aluminum stands out for its large use in several industrial applications and for holding the largest contract volume on the London Metal Exchange (LME). As the price is not directly related to production costs, during volatility periods or economic shocks, the financial impact on the global aluminum industry is significant. Aluminum price forecasting, therefore, is critical for industrial policy as well as for producers and consumers. This work has proposed an optimal forecast model for aluminum prices by using forecast combination and the Model Confidence Set for model selection, resulting in superior performance compared to tradicional methods. The proposed approach was not found in the literature for aluminum price forecasting. Five individual models were developed: AR(1) for benchmarking, ARIMA, two ARIMAX models and a structural model, using monthly data from January 1999 to September 2014. For each individual model, 142 out-of-sample, 12 month ahead, forecasts were generated through a 36 month rolling window. Nine foreast combinations were deveoped for each individual model estimation, resulting in 60 out-of-sample, 12 month ahead forecasts. Model predictive performace was assessed through the Model Confidence Set for the latest 36, 48, and 60 months, through 12-month ahead out-of-sample forecasts. A total of 1,250 estimations were performed and 1,140 independent variables and their transformations were assessed. The forecast combination using ARMA and ARIMAX was the only model among the best set of models presenting equivalent performance at 0.10 MCS p-value in all three periods. For the latest 36 months, the proposed combination was the best model at 0.1 MCS p-value. Two co-variantes, identified for the ARMAX model, namely, 3-month forward price and global inventories increased forecast accuracy. The optimal forecast combination has generated a small confidence interval, equivalent to 5% of average aluminum price for the entire sample, proving relevant support for global industry decision makers.

Over the past decade of accounting and finance research, the Ohlson (1995) model has been widely adopted as a framework for stock price prediction. While using the accounting data of 391 companies from SP500 in this paper, Bayesian statistical techniques are adopted to enhance both the estimative and predictive qualities of the Ohlson model comparing to the classical approaches. Specifically, the classical methods are used for the exploratory data analysis and then the Bayesian strategies are applied using Markov chain Monte Carlo method in three stages: individual analysis for each company, grouping analysis for each group and adaptive analysis by pooling information across companies. The base data, which consist of 20 quarters' observations starting from the first quarter of 1998, are used to make inferences for the regression coefficients (or parameters), evaluate the model adequacy and predict the stock price for the first quarter of 2004, when the real observations are set as the test data to evaluate the predictive ability of the Ohlson model. The results are averaged within each specified group categorized via the general industrial classification (GIC). The empirical results show that classical models result in larger stock price prediction errors, more positively-biased predictions and have much smaller explanatory powers than Bayesian models. A few transformations of both classical and Bayesian models are also performed in this paper, however, transformations of the classical models do not outweigh the usefulness of applying Bayesian statistics.

Power systems all over the world advance towards the direction of deregulation in the past few years. Introducing competition mechanism and the principle of market rules in deregulation. Utility companies will face unprecedented changes and challenges. Taiwan power company is also working on the deregulation direction with a competitive environment opened up, it will improve the scientific and technological levels and the service quality of electricity. Load management functions as the marginal price of electricity is predicted. Consumers can get Real-Time Pricing information determine their own buying strategy. One most representative deregulation example in U.S.A. is the PJM(Pennsylvania¡BNew Jersey¡BMaryland)system combining generating, transmitting, distribution and sales of electricity. It offers the information of real-time power supply and is one of the cases in the world. Historical data in the thesis comes from PJM. Artificial Neural Network was designed to the Locational Marginal Price(LMP), considering the factors such as temperature and other relevant data from deregulation with the introduction of various parameters in forecasting, and the use of week as a counting base. LMP will be forecasted. The forecasted results will be to check the accuracy and performance with initial data.

Herdecke, Privatuniv., Diss--Witten, 2006.

published_or_final_version / Statistics / Master / Master of Philosophy

This thesis is an investigation into the relationship that exists between macroeconomic variables and the pricing of common stock under trending market conditions. By introducing a dichotomous independent variable as a way of distinguishing between periods of rising and falling thereby attaching an additional expected premium to each of five accepted sources of macroeconomic risk for participation in ‘Bear’ markets. 228 observations of the fourteen industry sub-groupings of former TSE 300 were examined separately. The ultimate results were obtained using the Arbitrage Pricing Theory (APT) as the model to obtain factor exposures. The results show that there is no significant relationship between market trend and the pricing of common stock when the APT is applied. The final recommendation is that more research is needed.