This paper develops a framework that analyzes how mergers and acquisitions in Alberta’s oil and gas industry affect stock prices. In this experiment, a multivariate regression is applied to several industry-specific variables to determine if they have impacts on the abnormal stock returns of acquirers. The results show that abnormal returns 5 days prior to the public announcement of the transaction are, in fact, driven by several industry-specific variables. However, the returns immediately after the M & A announcements are similar to previous research done in other industries. Acquirers’ gains 2 days after the announcement are essentially unaffected by the transaction. After a 90-day period, the share performances of acquiring firms tend to beat the index by 7% on average, but this is not thoroughly explained by the variables in the regression analysis.

This thesis addresses two main issues: first, forecasting short-term electricity market prices; and second, the application of short-term electricity market price forecasts to operation planning of demand-side Bulk Electricity Market Customers (BEMCs). The Ontario electricity market is selected as the primary case market and its structure is studied in detail. A set of explanatory variable candidates is then selected accordingly, which may explain price behavior in this market. In the process of selecting the explanatory variable candidates, some important issues, such as direct or indirect effects of the variables on price behavior, availability of the variables before real-time, choice of appropriate forecasting horizon and market time-line, are taken into account. Price and demand in three neighboring electricity markets, namely, the New York, New England, and PJM electricity markets, are also considered among the explanatory variable candidates. Electricity market clearing prices in Ontario are calculated every five minutes. However, the hourly average of these 5-minute prices, referred to as the Hourly Ontario Energy Price (HOEP), applies to most Ontario market participants for financial settlements. Therefore, this thesis concentrates on forecasting the HOEP by employing various linear and non-linear modeling approaches. The multivariate Transfer Function (TF), the multivariate Dynamic Regression (DR), and the univariate Auto Regressive Integrated Moving Average (ARIMA) are the linear time series models examined. The non-linear approaches comprise the Multivariate Adaptive Regression Splines (MARS), and the Multi-Layer Perceptron (MLP) neural networks. Multivariate HOEP models are developed considering two forecasting horizons, i.e. 3 hours and 24 hours, taking into account the case market time-line and the ability of market participants to react to the generated forecasts. Univariate ARIMA models are also developed for day-ahead market prices in the three neighboring electricity markets. The developed models are used to generate price forecasts for low-demand, summer peak-demand, and winter peak-demand periods. The HOEP forecasts generated in this work are significantly more accurate than any other available forecast. However, the accuracy of the generated HOEP forecasts is relatively lower than those of the price forecasts for Ontario's neighboring electricity markets. The low accuracy of the HOEP forecasts is explained by conducting a price volatility analysis across the studied electricity markets. This volatility analysis reveals that the Ontario electricity market has the most volatile prices compared to the neighboring electricity markets. The high price volatility of the Ontario electricity market is argued to be the direct result of the real-time nature of this market. It is further observed that the inclusion of the just-in-time publicly available data in multivariate HOEP models does not improve the HOEP forecast accuracy significantly. This lack of significant improvement is attributed to the information content of the market data which are available just-in-time. The generated HOEP forecasts are used to plan the short-term operation of two typical demand-side case-study BEMCs. The first case-study BEMC is a process industry load with access to on-site generation facilities, and the second one is a municipal water plant with controllable electric demand. Optimization models are developed for the next-day operation of these BEMCs in order to minimize their total energy costs. The optimization problems are solved when considering market price forecasts as the expected future prices for electricity. The economic impact of price forecast inaccuracy on both the case study is analyzed by introducing the novel Forecast Inaccuracy Economic Impact (FIEI) index. The findings of this analysis show that electricity market price forecasts can effectively be used for short-term scheduling of demand-side BEMCs. However, sensitivity to price forecast inaccuracy significantly varies across market participants. In other words, a set of price forecasts may be considered ``accurate enough'' for a customer, while leading to significant economic losses for another.

This thesis addresses two main issues: first, forecasting short-term electricity market prices; and second, the application of short-term electricity market price forecasts to operation planning of demand-side Bulk Electricity Market Customers (BEMCs). The Ontario electricity market is selected as the primary case market and its structure is studied in detail. A set of explanatory variable candidates is then selected accordingly, which may explain price behavior in this market. In the process of selecting the explanatory variable candidates, some important issues, such as direct or indirect effects of the variables on price behavior, availability of the variables before real-time, choice of appropriate forecasting horizon and market time-line, are taken into account. Price and demand in three neighboring electricity markets, namely, the New York, New England, and PJM electricity markets, are also considered among the explanatory variable candidates. Electricity market clearing prices in Ontario are calculated every five minutes. However, the hourly average of these 5-minute prices, referred to as the Hourly Ontario Energy Price (HOEP), applies to most Ontario market participants for financial settlements. Therefore, this thesis concentrates on forecasting the HOEP by employing various linear and non-linear modeling approaches. The multivariate Transfer Function (TF), the multivariate Dynamic Regression (DR), and the univariate Auto Regressive Integrated Moving Average (ARIMA) are the linear time series models examined. The non-linear approaches comprise the Multivariate Adaptive Regression Splines (MARS), and the Multi-Layer Perceptron (MLP) neural networks. Multivariate HOEP models are developed considering two forecasting horizons, i.e. 3 hours and 24 hours, taking into account the case market time-line and the ability of market participants to react to the generated forecasts. Univariate ARIMA models are also developed for day-ahead market prices in the three neighboring electricity markets. The developed models are used to generate price forecasts for low-demand, summer peak-demand, and winter peak-demand periods. The HOEP forecasts generated in this work are significantly more accurate than any other available forecast. However, the accuracy of the generated HOEP forecasts is relatively lower than those of the price forecasts for Ontario's neighboring electricity markets. The low accuracy of the HOEP forecasts is explained by conducting a price volatility analysis across the studied electricity markets. This volatility analysis reveals that the Ontario electricity market has the most volatile prices compared to the neighboring electricity markets. The high price volatility of the Ontario electricity market is argued to be the direct result of the real-time nature of this market. It is further observed that the inclusion of the just-in-time publicly available data in multivariate HOEP models does not improve the HOEP forecast accuracy significantly. This lack of significant improvement is attributed to the information content of the market data which are available just-in-time. The generated HOEP forecasts are used to plan the short-term operation of two typical demand-side case-study BEMCs. The first case-study BEMC is a process industry load with access to on-site generation facilities, and the second one is a municipal water plant with controllable electric demand. Optimization models are developed for the next-day operation of these BEMCs in order to minimize their total energy costs. The optimization problems are solved when considering market price forecasts as the expected future prices for electricity. The economic impact of price forecast inaccuracy on both the case study is analyzed by introducing the novel Forecast Inaccuracy Economic Impact (FIEI) index. The findings of this analysis show that electricity market price forecasts can effectively be used for short-term scheduling of demand-side BEMCs. However, sensitivity to price forecast inaccuracy significantly varies across market participants. In other words, a set of price forecasts may be considered ``accurate enough'' for a customer, while leading to significant economic losses for another.

Under deregulated electric market, electricity price is no longer set by the monopoly utility company rather it responds to the market and operating conditions. Offering the right amount of electricity at the right time with the right bidding price has become the key for utility companies pursuing maximum profits under deregulated electricity market. Therefore, electricity market clearing price (MCP) forecasting became essential for decision making, scheduling and bidding strategy planning purposes. However, forecasting electricity MCP is a very difficult problem due to uncertainties associated with input variables.<p> Neural network based approach promises to be an effective forecasting tool in an environment with high degree of non-linearity and uncertainty. Although there are several techniques available for short-term MCP forecasting, very little has been done to do mid-term MCP forecasting. Two new artificial neural networks have been proposed and reported in this thesis that can be utilized to forecast mid-term daily peak and mid-term hourly electricity MCP. The proposed neural networks can simulate the electricity MCP with electricity hourly demand, electricity daily peak demand, natural gas price and precipitation as input variables. Two situations have been considered; electricity MCP forecasting under real deregulated electric market and electricity MCP forecasting under deregulated electric market with perfect competition. The PJM interconnect system has been utilized for numerical results. Techniques have been developed to overcome difficulties in training the neural network and improve the training results.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 192-198). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2000. / Typescript. Vita. Includes bibliographical references (leaves 192-198). Also available on the Internet.

published_or_final_version / Statistics / Master / Master of Philosophy

Over the past several decades, many techniques and approaches have been proposed and implemented for load and price forecasting. The objective of all of these methods was load and price forecasting with minimal error. However, researchers face several challenges in achieving this goal. For price forecasting, the main challenge is to forecast electricity prices accurately in a deregulated electric power market with volatile aspects. Decentralized or deregulated markets are very volatile systems. Hence, pattern following and accurate forecasting of electricity prices are difficult tasks using ordinary methods. In this thesis, a novel approach is introduced and implemented to overcome the challenges inherent in accurate price forecasting. This novel approach involves innovations in forecasting to improve the spot power price forecasting accuracy in a competitive market. To investigate the applicability and effectiveness of this technique, Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN), two well-known forecasting techniques, are developed.

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Electricity price forecasting is an important task for electricity market participants since the very beginning of the deregulation. Accurate forecasting is essential for designing bidding strategy, risk management, and market operation. However, due to the compli-cated factors affecting electricity prices, there are more uncertainties in electricity price forecasting and hence more complex than demand forecasting. This makes accurate price forecasting very difficult. In the last decade, several methods have been developed in order to fully capture the peculiarities of electricity price dynamics, from classic econometric time series models, e.g., autoregressive moving average (ARMA) model, generalized autoregressive conditional heteroscedasticity (GARCH) model to modern machine learning based techniques such as artificial neural networks (ANN) and sup-port vector machine (SVM). In spite of all models proposed in the literature, there is still no clear consensus about which model is substantively outperforming others. Therefore, when a single method is used, decision-makers are facing the risk of not choosing the best one. On the other hand, the prediction of electricity market prices still involves large errors. If decision-makers take the prediction result on faith, prediction errors could exposure them to serious financial risks. Based on these findings, it can conclude that (1) systematic methodologies and implementations which can efficiently address model selection uncertainty in price forecasting require an investigation; (2) more powerful and robust price forecasting models are still needed to reduce the fore-cast errors; and (3) In addition, the emphasis of price forecasting should shift away from point forecast to uncertainty around the forecast. Unfortunately, most researches in this area have been devoted to finding the single “best” estimates rather than dealing with the uncertainty in model selection and quantifying the predictive uncertainty. In this thesis the research focus is on: (1) finding methodologies and efficient imple-mentations to deal with the uncertainty in model selection; (2) developing more power-ful machine learning based approaches to model electricity spot prices and further im-proving the accuracy of electricity market price forecast; and (3) incorporating uncer-tainty estimation into the application of price forecasting. The thesis makes three main contributions to the study of this topic. Firstly, it proposes linear, nonlinear forecast combination frameworks to deal with model selection prob-lem; secondly, it introduces two novel models: support vector machine based nonlinear generalized autoregressive conditional heteroscedasticity model (SVM-GARCH) and extreme learning machine (ELM) to the price forecasting and furthermore gives a series of bootstrap-based interval construction procedures to quantify the prediction uncer-tainty. Finally, it proposes a more robust interval forecasting approach which is based on quantile regression to electricity price forecasting literature. The effectiveness and efficiency of the proposed approaches have been tested based on real market data of Australian National Electricity Market (NEM).