The thesis deals with technical analysis and introduction to the possibilities of its use in deciding to invest in the capital market. The theoretical part describes the theoretical background needed for the initial introduction to technical analysis and its indicators. In the practical part are gradually elaborated various indicators of technical analysis applied to the development of the shares of Intel Corporation and in the end there is the comparison of their profitability.

This diploma thesis focuses on the general characteristic of the technical analysis and its use in deciding to invest in the stock market. Thesis includes theoretical data, which deal with problems concerning an investment in the capital market. In the practical part are some indicators of technical analysis applied to the selected shares, by means of them is formed investing strategy for the beginning investor. Furthermore, in practical part is described the design of application for technical analysis.

This thesis focused on general characteristics of technical analysis and the use of its instruments to support making decisions when trading in stock market. Within the theoretic part are processed theoretic solutions which are primarily relevant to technical analysis. At the specific stock item is by the help of proposed application consequently implemented the analysis of individual indicators of technical analysis. At the conclusion is compared the profitability and reliability of used indicators.

The thesis deals with the use of genetic algorithms in the process of creating automated trading systems. The emphasis is on testing the robustness of the developed strategies, their practical applicability in the financial markets and minimizing risk through diversification. The output of this work is a portfolio consisting of three strategies that achieved 31.3% return on capital during the fourth quarter of 2014.

This thesis is a summary describing the current housing situation in the city of Zlin. It deals with the comparison of above normal rents in different areas of the city and consider amendments to leases in terms of location, amenities and size of the apartment. Collected data are divided into apartments for the 1 +1, 1 + kk, 2 +1, 2 + kk, 3 +1, 3 + kk. Mapping the market and its prices in the lease is done both textually and graphically.

This master thesis focuses on shares of companies who pay out dividends therefore dividend stocks. Shares from Czech stock market RM-SYSTÉM are anaylzed from publicly available data and portfolio is subsequently created from them.

This master thesis is focused on designing and implementing a software system, that is able to trade autonomously at stock market. Neural networks are used to predict future price. Genetic algorithm was used to find good combination of input parameters.

Over a long period of time, researchers have investigated the efficiency of financial markets. The widely accepted theory of the subject is the Efficient Market Hypothesis, which states that prices of financial assets are set efficiently. A common way to test this hypothesis is to analyze the returns generated by technical trading rules which uses historical prices in an attempt to predict future price development. This is also what this study aims to do. Using adjusted daily closing prices ranging over 2007 to 2019 for 5120 stocks listed on the U.S stock market, this study tests a momentum trading strategy called the stochastic oscillator in an attempt to beat a buy and hold strategy of the Russel 3000 stock market index. The stochastic oscillator is constructed in three different ways, the Fast%K, the Fast%D and the Slow%D, the difference being that a smoothing parameter is used in the Fast%D and Slow%D in an attempt to reduce the number of whiplashes or false trading signals. The mean returns of the technical trading strategies are tested against the mean returns of the buy and hold strategy using a non-parametric bootstrap methodology and also, the risk adjusted returns in terms of Sharpe Ratios are compared for the different strategies. The results find no significance difference between the mean returns of the buy and hold strategy and any of the technical trading strategies. Further, the buy and hold strategy delivers a higher risk adjusted return compared to the technical trading strategies, although, only by a small margin. Regarding the smoothing parameter applied to the strategies, it seems to fulfill its purpose by reducing the number of trades and slightly increasing the mean returns of the technical trading strategies. Finally, for deeper insight in the subject, a reading of "The efficiency of financial markets: A dual momentum trading strategy on the Swedish stock market" by Netzén Örn (2018) is recommended.

Background. Stock market prediction is an active yet challenging research area. A lot of effort has been put in by both academia and practitioners to produce accurate stock market predictions models, in the attempt to maximize investment objectives. Tree-based ensemble machine learning methods such as XGBoost have proven successful in practice. At the same time, there is a growing trend to incorporate multiple data sources in prediction models, such as historical prices and text, in order to achieve superior forecasting performance. However, most applications and research have so far focused on the American or Asian stock markets, while the Swedish stock market has not been studied extensively from the perspective of hybrid models using both price and text derived features.  Objectives. The purpose of this thesis is to investigate whether augmenting a numerical dataset based on historical prices with sentiment features extracted from financial news improves classification performance when predicting the daily price trend of the Swedish stock market index, OMXS30. Methods. A dataset of 3,517 samples between 2006 - 2020 was collected from two sources, historical prices and financial news. XGBoost was used as classifier and four different metrics were employed for model performance comparison given three complementary datasets: the dataset which contains only the sentiment feature, the dataset with only price-derived features and finally, the dataset augmented with sentiment feature extracted from financial news.  Results. Results show that XGBoost has a good performance in classifying the daily trend of OMXS30 given historical price features, achieving an accuracy of 73% on the test set. A small improvement across all metrics is recorded on the test set when augmenting the numerical dataset with sentiment features extracted from financial news.  Conclusions. XGBoost is a powerful ensemble method for stock market prediction, reflected in a satisfactory classification performance of the daily movement direction of OMXS30. However, augmenting the numerical input set with sentiment features extracted from text did not have a powerful impact on classification performance in this case, as the improvements across all employed metrics were small.

Aim: This study aims to examine whether highly ranked companies in sustainability generate a higher return then companies operating in controversial industries in the swedish market during the years 2015-2019.  Method: This study examines return and if connections exist whether the companies apply sustainability thinking or controversial industry. To investigate correlations this study uses a quantitative method thru using regression analysis with data obtained from the years2015-2019 in the swedish market. The study has formed a null hypothesis and an alternative hypothesis to test collected secondary data. The study is based on a deductive approach.Totally 32 companies are included in this study and the collected secondary data comes från Nasdaq and the companies own annual reports. The return is the studies dependent variable and P/E ratios, the standard deviation and ROA are used as control variables in this study.  Result and conclusion: The studies conclusion is that the result is insignificant, thesustainable companies nor the controversial companies performs better according to this studies data and analysis. The study’s regressions show no difference in returns between the studied controversial companies and the sustainable companies. The result shows neither positive nor negative relationships and therefore the study’s alternative hypothesis is rejected. / Syfte: Syftet med denna studie är att undersöka ett urval av hållbara företag eller kontroversiella företag presterar en bättre avkastning på den svenska marknaden under perioden 2015-2019. Studien grundar sig på att reda ut om en hållbar investering kan ge en förklaring till investeringens avkastning.  Metod: Studien har utifrån litteraturgenomgången format en nollhypotes och alternativhypotes som har prövats med hjälp av tillämpad sekundär data. Insamlingen av den sekundära datan som studien tillämpat har byggts på en deduktiv ansats. Urvalet resulterade i att 32 företag stycken företag har undersökts. Sekundärdatan har hämtats från Nasdaq och företagens årsredovisningar. Avkastningen är använd som beroende variabeln, hållbara företag som den oberoende och P/E-tal, standardavvikelsen och ROA är studiens kontrollvariabler. Detta har testats med hjälp statiska regressionsanalyser. Resultat & slutsats: Resultatet från regressions analyserna visade att inget signifikant samband inte kunde påvisas. Vilket betyder inget signifikant samband mellan hållbarhet och avkastning. Vi kunde inte se någon skillnad i avkastning mellan de studerade kontroversiella företagen och de hållbara företagen. Resultatet visar inte att sambandet är negativt eller positivt och studiens alternativ hypotes förkastas.