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Active investors are always trying to find new ways of systematically beating the market. Since the advent of social media, this has become one of the latest areas where investors are trying to find untapped information to exploit through a technique called sentiment analysis, which is the act of using automatic text processing to discern the opinions of social media users. The purpose of this study is to investigate the possibility of using the sentiment of tweets directed at specific companies to construct portfolios which generate abnormal returns by investing in companies based on the sentiment. To meet this purpose, we have collected company specific tweets for 40 companies from the Nasdaq 100 list. These 40 companies were selected using a simple random selection. To measure the sentiment tweets were downloaded from 2014 to 2016, giving us three years of data. From these tweets we extracted the sentiment using a sentiment program called SentiStrength. The sentiment score for every company was then calculated to a weekly average which we then used for our portfolio construction. The starting point for this study to try and explain the relationship between sentiment and stock returns was the following theories: The Efficient Market Hypothesis, Investor Attention and the Signaling Theory. Tweets act as signals which direct the attention of the investors to which stocks to purchase and, if our hypothesis is correct, this can be exploited to generate abnormal returns. To evaluate the performance of our portfolios the cumulative non-risk adjusted return for all of portfolios was initially calculated followed by calculations of the risk adjusted return by regressing both the Fama-French Three-Factor model and Carhart’s Four-Factor model with the returns for our different portfolios being the dependent variables. The results we obtained from these tests suggests that it might be possible to obtain abnormal returns by constructing portfolios based on the sentiment of tweets, using a few of the strategies tested in this study as no statistically significant negative results were found and a few significant positive results were found. Our conclusion is that the results seems to contradict the strong form of the Efficient Market Hypothesis on the Nasdaq 100 as the information contained in the sentiment of tweets seems to not be fully integrated within the share price. However, we cannot say this with confidence as the EMH is not a testable hypothesis and any test of the EMH is also a test of the models used to measure the efficiency of the market.

The objective of this thesis is to: 1) explore and evaluate the trading platform MetaTrader, the programing language MetaQuotes and the IDE MetaEditor; 2) create a mechanical trading system for the platform MetaTrader; 3) test the system on the historical data. Contributions for this thesis consist of available theoretical information and observation from my own experiences with creation and testing of the mechanical trading strategy.

In this thesis, we propose two genetic-programming-based models that improve the trading strategies for mutual funds. These two models can help investors get returns and reduce risks. The first model increases the return by selecting funds with high Sortino ratios and allocates the capital equally, achieving the best annualized return. The second model also selects funds with high Sortino ratios, but reduces the risk by allocating the capital with the mean variance model. Most importantly, our model utilizes the genetic programming to generate feasible trading strategies to gain return, which is suitable for the market that changes anytime. To verify our models, we simulate the investment for mutual funds from January 1999 to December 2009 (11 years in total). The experimental results show that our first model can gain return from 2004/1/1 to 2008/12/31, achieving the best annualized return 9.11%, which is better than the annualized return 6.89% of previous approaches. In addition, our second model with smaller downside volatility can achieve almost the same return as previous results.

In recent years technical analysis has been used more and more frequently. The original concept of technical analysis is built on history will be continue to repeat itself. Therefore, analysts and investors could predict the market price by observing the historical data. The idea of pattern recognition technology comes from face recognition systems. In the system, the analyst captures the facial features from the entrant and then quantifies the features as codes. Through the process of recognition, the analyst can confirm the identity of the entrant. Pattern recognition applies the idea to extract information encoded in the stock market characteristics and recognize the market with historical data. In the application, pattern recognition can be regarded as a pre-operation of the technical analysis. Users analyze the current information through pattern recognition and can further build the strategy. This model has 19 codes captured from two dimensions; the first is price, and the second is the trend of ups and downs. The empirical results for the decade in the weekly frequency trading strategy are an annual return of 31.57% and annual risk of 26.66%. After the deduction of trading fees, the strategy has an annual return of 14.94% and annual risk of 26.72%.

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This report presents TSS - a Trading Strategy System developed to let traders define arbitrarily complex trading strategies in the Java programming language and evaluate them using historical stock information. In addition, TSS provides access to Google Trends data for use in meta-strategy definition, and has the ability to return the best strategy from a family of strategies using data mining algorithms. Finally, TSS is highly extensible - we can integrate new data feeds by simply extending the interface and database. / text

This master thesis is focused on technical analysis, which is used for forecasting the future trends of stocks. In the first part of master’s thesis are described theoretical basis, which are a base of practical part of this thesis. Next part of this thesis describes the design of application designed to support the technical analysis. Main part of this master thesis deals with building own trading rules and trading strategy.

The thesis is concerned with the design and optimization of a trading strategy on currency markets in order to maximize profit on the EURUSD currency pair. The strategy is based on standard technical indicators and is tested in demo account environment. The thesis describes the whole development from initial design to an optimized version of the draft.

This dissertation implements an optimal trading strategy based on the machine learning method and extreme value theory (EVT) to obtain an excess return on investments in the capital market. The trading strategy outperforms the benchmark S&P 500 index with higher returns and lower volatility through effective market timing. In addition, this dissertation starts by modeling the market tail risk using the EVT and reinforcement learning methods, distinguishing from the traditional value at risk method. In this dissertation, I used EVT to extract the characteristics of the tail risk, which are inputs for reinforcement learning. This process is proved to be effective in market timing, and the trading strategy could avoid market crash and achieve a long-term excess return. In sum, this study has several contributions. First, this study takes a new method to analyze stock price (in this dissertation, I use the S&P 500 index as a stock). I combined the EVT and reinforcement learning to study the price tail risk and predict stock crash efficiently, which is a new method for tail risk research. Thus, I can predict the stock crash or provide the probability of risk, and then, the trading strategy can be built. The second contribution is that this dissertation provides a dynamic market timing trading strategy, which can significantly outperform the market index with a lower volatility and a higher Sharpe ratio. Moreover, the dynamic trading process can provide investors an intuitive sense on the stock market and help in decision-making. Third, the success of the strategy shows that the combination of EVT and reinforcement learning can predict the stock crash very well, which is a great improvement on the extreme event study and deserves further study. / Business Administration/Finance