Thesis (M.B.A.)--Boston University / PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / 2031-01-01

Abstract Due to the fast improvement of information technology ,the application of internet is spreading in our society ,so is electronic commerce(EC).In hence ,many companies are starting to provide all kinds of services on the internet .In the stock market , stock brokers provide online stock trading service ,that enable the stock trading will not limited by time¡Bregions and people any more .However ,the internet has became a new way of investment. In Taiwan, there are more than 70 online stock brokers and one million investors that use online stock trading, and the volume of online stock trading is 5.87% to the total stock market. According to relative documents and some professional assessment mechanism, this study generalize 15 indexes of satisfaction of online stock trading questionnaires to query online stock brokers and investors, answers that we want to know are: what are the features of online trading investors? What are their responses of online stock trading? What are the factors that influence the indexes of satisfaction of online stock trading? What are the differences of satisfaction between online stock brokers and investors? The research outcome shows that investors which use online stock trading service is more younger and educated¡Bfrequency of trade is fewer¡Bthe amount of money is not much and the investors prefer self-service .And security of transaction is the primary factors that make the investors does not like online stock trading. Furthermore ,this study has generalized five primary factors that influence the satisfaction of online stock brokers and investors ,they are :¡uweb design¡v¡B¡unet speed ¡v¡B¡ufinancial specialty¡v¡B¡ucustomer services¡vand¡ufees¡v. And still other ,in the comparison of satisfaction between online stock brokers and investors , there is a gap in the aspect of ¡ufinancial specialty¡v,and by the further analysis ,there are gaps in the following index:¡urichness and quality of research reports¡v¡B¡usupply of other financial instruments¡v¡B¡uquery of customers information¡vand ¡usecurity of transaction¡v

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

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 compares stock exchanges in Central and Eastern Europe. It covers exchanges of Prague, Budapest, Warsaw, Bucharest, Ljubljana, Zagreb, Vienna, and Istanbul. At first, all the exchanges are briefly introduced including naming five most liquid stocks. Selected market specifics are then compared including supported order types, tick sizes, fees policy, trading hours, safety breaks, taxes, market capitalization, and weights in global stock indices. Lastly, a comparison of trading activity is provided and analysis of important feature of trading -- liquidity is performed calculating selected liquidity measure for each market.

The most important factor that affects the decision making process in finance is the risk which is usually measured by variance (total risk) or systematic risk (beta). Since investors' sentiment (whether she is an optimist or pessimist) plays a very important role in the choice of beta measure, any decision made for the same asset within the same time horizon will be different for different individuals. In other words, there will neither be homogeneity of beliefs nor the rational expectation prevalent in the market due to behavioral traits. This dissertation consists of three essays. In the first essay, Investor Sentiment and Intrinsic Stock Prices, a new technical trading strategy is developed using a firm specific individual sentiment measure. This behavioral based trading strategy forecasts a range within which a stock price moves in a particular period and can be used for stock trading. Results show that sample firms trade within a range and show signals as to when to buy or sell. The second essay, Managerial Sentiment and the Value of the Firm, examines the effect of managerial sentiment on the project selection process using net present value criterion and also effect of managerial sentiment on the value of firm. Findings show that high sentiment and low sentiment managers obtain different values for the same firm before and after the acceptance of a project. The last essay, Investor Sentiment and Optimal Portfolio Selection, analyzes how the investor sentiment affects the nature and composition of the optimal portfolio as well as the performance measures. Results suggest that the choice of the investor sentiment completely changes the portfolio composition, i.e., the high sentiment investor will have a completely different choice of assets in the portfolio in comparison with the low sentiment investor. The results indicate the practical application of behavioral model based technical indicators for stock trading. Additional insights developed include the valuation of firms with a behavioral component and the importance of distinguishing portfolio performance based on sentiment factors.

We show that with the implementation presented in this paper, the Random Forest Classification model was able to predict whether or not a stock was going to increase in value during the coming day with an accuracy higher than 50\% for all stocks included in this study. Furthermore, we show that the active trading strategy presented in this paper generated higher returns and higher risk-adjusted returns than the passive investment in the stocks underlying the strategy. Therefore, we conclude \textit{(i)} that a Random Forest Classification model can be used to provide valuable insight on publicly traded stocks, and \textit{(ii)} that it is probably possible to create a profitable trading strategy based on a Random Forest Classifier, but that this requires a more sophisticated implementation than the one presented in this paper.

Ursprungligen fungerade aktier som ett medel för företag att säkerställa finansiering för nya satsningar och investeringar.    Företag ställde ut aktiebrev som investerare köpte och till skillnad mot ett vanligt banklån behövde inte företagen betala tillbaka dessa aktier. Detta säkerställde att de investerar som köpte aktier var tvungna att vara långsiktiga för ett aktieköp kunde vara för livet. Aktiemarknaden är en marknad där dessa aktier kan handlas mellan investerare. Fördelen med detta är att en investerare kan avbryta sin investering och växla in den i förtid. Nackdelen med aktiemarknaden är att detta innebar att det långsiktiga perspektivet inte längre var nödvändigt för en investerare. För många investerare blev det viktigare hur aktiemarknaden kommer utvecklas ”imorgon” snarare än om företaget hen investerare i gör en lönsam investering på tio års sikt. Koppling till företagens egentliga värde riskerar därmed brytas. Konsekvens av detta är att spekulativa bubblar byggs upp på aktiemarknaden i cykler med efterföljande krascher som medför stora förmögenhetsförluster för vanliga privatpersoner och stora omvälvningar i samhället i stort. Denna uppsats utforskar möjligheten att använda maskininlärning som ett verktyg för att kunna värdera aktier och förutspå kommande kursrörelser med syfte att hjälp investerare på aktiemarknaden att fatta bättre investeringsbeslut. Den tar avstamp i de datakällor som aktiemarknadsanalytiker använder för att studera denna marknad – det vill säga med hjälp av tekniska och fundamentala data.  Ett system har konstruerats för att dels klassificera bolag med hjälp av algoritmen ”artificiella neurala nätverk” och fundamentala data och dels för att förutsäga kommande dagskurser med hjälp av algoritmen ”Long Short Term Memory network” och tekniska data. Algoritmerna har utvärderats var för sig och som ett gemensamt system genom att simulerad handel utförs på en given test och valideringsperiod. Den hypotes som prövats är att ”att processa fundamentala data genom ett ANN och tekniska data genom ett LSTM kommer genera bra investeringsrekommendationer”. Resultaten som studien genererat har givet som konsekvens att denna hypotes inte har kunnat motbevisas.

This master's thesis is focused on algorithmic trading on the forex market using artificial neural networks. In the introduction, there are generally described terms concerning the trading. Subsequently, in the following chapters, the thesis describes the theory of neural networks and their possible use. The practical part contains designed business strategies with neural networks. Inputs used in the network are indicators of technical analysis or directly price level. Business strategies have been implemented and tested. In the conclusion, there are summarized findings of individual business models.

This work deals with the usage of neural network for the purpose of stock market prediction. A basic stock market theory and trading approaches are mentioned at the beginning of this work. Then neural networks and their application are discussed with their deeper description. Similar approaches are referred and finally two new prediction systems are designed. These systems are utilized by proposed trading model and tested on selected data. The results are compared to human and random trading models and new development steps are devised at the end of this work.