This thesis examines the profitability of the oldest known form of technical analysis, candlestick trading strategies. Unlike traditional technical analysis which is based around close prices, these strategies generate buy and sell signals that are based on the relationship between open, high, low and close prices within a day and over consecutive days. Traditional technical analysis, which has been the focus of previous academic research, has a long-term focus with positions being held for months and years. In contrast, candlestick technical analysis has a short-term focus with positions being held for ten days or less. This difference is significant as surveys of market participants indicate that they place 50 per cent more importance on technical analysis for horizons of a week than they do for horizons of a year. Candlestick technical analysis was developed on rice data in Japan in the 1700s so the tests in this thesis, using Dow Jones Industrial Index (DJIA) component stock data for the 1992 - 2002 period, are clearly out of sample tests. These tests are more robust to criticisms of data snooping than is the existing technical analysis literature. Proponents of technical analysis in the Western world would have had the opportunity to have become aware of candlestick trading strategies by this study's timeframe and would also have had the opportunity to source the data and software necessary to implement these strategies. So, a direct test of market efficiency is possible. This was not achievable by authors of many previous papers, who used data starting in the early 1900s and techniques that could not have been implemented at that time. Using an innovative extension of the bootstrap methodology, which allows the generation of random open, high, low and close prices, to test the profitability of candlestick technical trading strategies showed that candlestick technical analysis does not have value. There is no evidence that a trader adhering to candlestick technical analysis would out-perform the market.

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).

Thesis (M.B.A.)--University of North Carolina Wilmington, 2008. / Title from PDF title page (viewed May 27, 2009) Includes bibliographical references (p. 42-44)

Thesis (Ph. D.)--University of California, San Diego, 1999. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of California, San Diego, 2000. / Vita. Includes bibliographical references.

Thesis (Ph.D.) - Carleton University, 2007. / Includes bibliographical references (p. 125-139). Also available in electronic format on the Internet.

Thesis (Ph.D.) - University of St Andrews, April 2010.

Thesis (Ph. D.)--Florida State University, 2006. / Advisor: Kai-Sheng Song, Florida State University,College of Arts and Sciences, Dept. of Statistics; David R. Peterson, Florida State University, College of Business, Dept. of Finance. Title and description from dissertation home page (viewed Sept. 27, 2006). Document formatted into pages; contains ix, 147 pages. Includes bibliographical references.

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2007. / Includes bibliographical references. Also available in electronic version.

Thesis (Ph. D.)--University of Washington, 2006. / Vita. Includes bibliographical references (leaves 133-139).