The thesis offers a framework for trading algorithm optimization and tests statistical and economical significance of its performance on American, Swedish and Russian futures markets. The results provide strong support for proposed method, as using the presented ideas one can build an intraday trading algorithm that outperforms the market in long term.

This dissertation consists of 3 essays that examine the impact of electronic screen trading in futures markets. The research provides empirical evidence on increasingly significant issues given the rapid global advances in technology used in securities markets. Each essay addresses the scarcity of conclusive research in order to aid researchers, regulators, exchange policy makers and systems builders as they confront issues related to electronic trading systems.

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 256-262). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 256-262). Also available on the Internet.

Pair trading is a well-known strategy based on statistical arbitrage. This strategy uses a short-term deviation from the mean value of the price ratio of two highly correlated stocks from the same sector as the opportunity to open a position. When ratio returns to its mean value again, the position closes. This strategy has been used for many years and the main outcome of this thesis was to test whether this strategy can be profitable even in current market conditions. For that purpose, data ranging from 2010 to April 2017 on all stocks included in the S&P 500 index were used. It was subsequently found that a pair trading strategy generated 25x higher absolute profit in comparison to random agent. Thus, it can still be considered as a profitable strategy.

Abstract This thesis investigates a set of equity market phenomena associated with investors' trading activity, using a comprehensive Finnish Central Securities Depository (FCSD) database that records practically all trades by Finnish investors. This database enables us to classify a large number of heterogeneous investors using both economic and institutional characteristics. The first essay classifies investors by trading activity. It analyzes trading styles of active and passive investors during the boom in technology stocks 1997–2000. We find that the herding tendency of active investors grew monotonically, year by year. Particularly large active investors used momentum and growth strategies. Moreover, buy pressures of active investors were positively related to contemporaneous daily returns. Passive investors, on the other hand, herd very strongly and their trading exhibited a contrarian style throughout the sample period. The second essay focuses on the relation between day trading of individual investors and intraday stock price volatility. I find a strong positive relation between the individual investors' day trades and volatility for actively day traded stocks. This finding suggests that day trading tends to increase volatility and/or day traders tend to become more active on the days of high volatility. The third essay tests the theoretical proposition of Amihud and Mendelson (1986) that investors hold assets with higher bid-ask spreads for longer periods. We measure holding periods of individual investors directly and find that they are positively related to spreads. The models control for a variety of other stock characteristics (e.g. value vs. growth orientation) and investors' attributes (e.g. gender) affecting holding periods. The fourth essay studies how both individual and institutional investors with different levels of capital gains and losses react to earnings announcements. I find that both sign and magnitude of capital gains affect individual investors' abnormal trading volumes. Individual investors are less prone to sell when they are carrying loses rather than gains. Furthermore, they react less to earnings announcements when capital gains or losses are large (over 20%). Taken together these findings provide support for prospect theory. Institutional investors appear to be less affected by psychological factors underlying prospect theory.

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.

The main aim of this diploma thesis is to analyze the options of trading Forex by combining fundamental and technical analysis in connection to intraday trading. One of the goals is to identify pros and cons of these analysis. Based on the gained information, design trading portfolio consisting of different strategies. Each strategy will be programmed as automated trading system and optimized and tested on historical price data.

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