這篇文章主要是利用三種交易系統測試 2003 年台灣股價加權指數期 貨的日內資料:移動平均穿越法、賽塔支撐壓力策略、K-D 隨機指標。 站在當沖者的觀點測試歷史資料的表現，並分別建立停損與停利點控 制交易中所發生的損失與利得。研究結果發現，在調整交易成本後， 順勢系統的表現的確可以獲得顯著的利潤且多頭的利潤多於空頭；而 逆勢系統則無法獲得顯著的利潤。 / This paper tests three kinds of trading strategies: two of them are momentum strategies－MA, Support and Resistance and the other is contrarian strategy － Stochastic Indicator by utilizing the futures contracts on Taiwan Stock Exchange Capitalization Weighted Stock Index in 2003. We test their historical performances of these three strategies in view of the day traders who must close out their positions before the closing in every single trading day. In addition, we combine each of these rules with the so called stop-loss-point and take-profit-point to control our gains and loss on the positions. For the momentum strategies, the results suggest that the returns following buy signals are higher than following sell signals. For contrarian strategy, there is no evidence that the returns are positive across all rules. In sum, our results reveal that it still has the possible to gain significant profits in the futures market for the day traders, even after adjusting the transaction costs.

This thesis focuses on training and testing neural networks for use within stockmarket trading systems. It creates and follows a well defined methodology for developing and benchmarking trading systems which contain neural networks.Four neural networks and consequently four trading systems are presented within this thesis. The neural networks are trained using all fundamental or all technical variables, and are trained on different segments of the Australian stockmarket, namely all ordinary shares, and the S&P/ASX200 constituents.Three of the four trading systems containing neural networks significantly outperform the respective buy-and-hold returns for their segments of the market, demonstrating that neural networks are suitable for inclusion in stockmarket trading systems.The fourth trading system performs poorly, and a number of reasons are proposed to explain the poor performance. It is significant, however, that the trading system development methodology defined in this thesis clearly exposes the potential failure when testing in-sample, long before the neural network would be used in real trading.Overall, this thesis concludes that neural networks are suitable for use within trading systems, and that trading systems developed using neural networks can be used to provide economically significant profits.

I explore the effects of the implementation of the two U.S. cap-and-trade policies on carbon dioxide (CO2) emissions for the two regions in which the policies are active as well as the regions on their respective borders. The cap-and-trade policy is a market-based approach to reduce emissions by capping the total amount of emissions produced and allowing emission producing entities to trade emission allowances on an open market. The two active cap-and-trade policies in the United States are the Regional Greenhouse Gas Initiative (RGGI), located in the northeastern United States, and in California, which differ in design, scope, and duration. I use a difference in differences design to analyze the change in CO2 emissions in the states effected by the policies relative to the rest of the country. I find that the RGGI policy has not reduced CO2 emissions in comparison to the rest of the non-policy states but only the California emission trading system has yielded a statistically significant decline. The results also find that the there is no evidence to suggest different changes in CO2 emissions in the states bordering both regions in which the policies are in place.

This thesis deals with the implementation of the software for automated stock trading based on trend indicators and oscillators. It describes the various signals that are provided by formations in charts and technical indicators, but also the possibility of using advanced artificial intelligence methods. This document describes entire development process of the software from individual parts to the folding of these parts into one system. It focuses on the optimization processes of individual parts, as well as a complete system. This thesis also deals with the testing of the system on historical data and its application on the latest data. It introduces the future plans, deployment options to the real market and its further improvement in order to develop ideal business system capable of autonomous thinking and trading.

The Global Financial Crisis underscored both the complexity and brittleness of the global financial system, especially for small to medium enterprises dependent on the current banking regime for credit. More than ever, we have also begun to see the disentanglement of small businesses from traditional banks at the local and regional level in the form of CDFIs, fintech alternative lending, and now complementary currencies. Through interviews with the management and members of the BizX complementary currency this study asks what the attitudes and motivations are behind its offering and use. In addition, it inquires into the economic and psychological benefits that arise from it. Often referred to as a barter network but more accurately described as a commercial trade exchange, BizX and its member's attitudes and motivations differ significantly from other complementary currencies in its apolitical stance to trade and large national and international membership. Its value proposition, the development of hyper-local economies, is real but its aspirational attempt at creating a robust community similar to its community currency siblings is questionable. Nonetheless, its value as an economic development tool is undeniable and the research concludes its implementation within a larger structure of economic development self-reliance strategies should be given serious consideration for future planning.

Thesis (MBA)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: South African businesses might be missing out on the benefits of commercial barter trading, which has seen considerable adoption in other parts of the world. Commercial barter is the non-monetary exchange of products and services between organisations specifically from the small to medium business sector. The research aim was to unpack and understand the level of awareness, engagement and perceptions of commercial barter in order to assess the viability, scope and scale of commercial barter as a trade instrument across small to medium businesses in South Africa. The research took on a quantitative approach surveying representatives of 68 firms primarily situated in Cape Town, South Africa. The research results suggested that commercial barter is a viable method of trade for small to medium businesses. Although viable, the scale to which it will propagate is dependent on the growth of awareness as well as the growth of knowledge on barter practices amongst firms that form part of the small to medium business sector. The study further found that commercial barter is favourable to organisations that offer either goods or services, more so to firms that are part of business services, information technology, hospitality and personal services industries. With commercial barter displaying viability, a Barter Exchange Network is suggested for mass introduction to South Africa. The generic business model was described using the Business Model Canvas by Alexander Osterwalder.

abstract: Stock market news and investing tips are popular topics in Twitter. In this dissertation, first I utilize a 5-year financial news corpus comprising over 50,000 articles collected from the NASDAQ website matching the 30 stock symbols in Dow Jones Index (DJI) to train a directional stock price prediction system based on news content. Next, I proceed to show that information in articles indicated by breaking Tweet volumes leads to a statistically significant boost in the hourly directional prediction accuracies for the DJI stock prices mentioned in these articles. Secondly, I show that using document-level sentiment extraction does not yield a statistically significant boost in the directional predictive accuracies in the presence of other 1-gram keyword features. Thirdly I test the performance of the system on several time-frames and identify the 4 hour time-frame for both the price charts and for Tweet breakout detection as the best time-frame combination. Finally, I develop a set of price momentum based trade exit rules to cut losing trades early and to allow the winning trades run longer. I show that the Tweet volume breakout based trading system with the price momentum based exit rules not only improves the winning accuracy and the return on investment, but it also lowers the maximum drawdown and achieves the highest overall return over maximum drawdown. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

This thesis is devoted to the technical analysis with the emphasis on design, testing and using of trading systems. Its objective is to find out whether it is possible for a trader to design and trade his own profitable trading system with widely accessible tools and methods. First part of the thesis is focused on the chart analysis and description of candlestick charts including their rate of profit success, all based on hands-on experience in a real market. It continues with a breakdown of most used methods based on moving averages. The second chapter fully describes main stage of trading system development and follows up with third chapter on practical application of the theoretical assumption on the real market conditions, i.e. to design a profitable trading system

A main subject of the presented master thesis is trading and investing in capital, commodities and foreign exchange markets over the world with support of technical analysis constructed by artificial intelligence. The thesis also produces step-by-step guide to stock and futures trading, building a successful trading system and gaining profits from invested capital.

This thesis focuses on automated trading systems for financial markets trading. It describes theoretical background of financial markets, different technical analysis approaches and theoretical knowledge about automated trading systems. The output of the present paper is a diversified portfolio comprising four different investment models aimed to trading futures contracts of cocoa and gold. The portfolio tested on market data from the first quarter 2013 achieved 46.74% increase on the initial equity. The systems have been designed in Adaptrade Builder software using genetic algorithms and subsequently tested in the MetaTrader trading platform. They have been finally optimized using sensitivity analysis.