The master’s thesis deals about portfolio securities optimalization on Prague stock exchange. After the theoretical part follows global fundamental analysis with the guess of market trends. Next part describes fundemantal analysis on the association´s level. On the basis of fundamental analysis results was created optimized securities portfolio.

This master`s thesis assess the financial health of the company in 2004-2007 with using suitable methods of the financial analysis. It also suggests convenient proposals for strengthen financial situation of the company.

My thesis deals with analyses of returns of IPO’s in the Czech capital market. The theoretical section of the thesis contains of circumscription of main terms and methodics for preparations and realizations of IPO. In the analytical section these theoretical findings are applied. This thesis should bring realistic view of returns of IPO’s for investors and evaluate present trends on IPO market.

This thesis is aimed to exploitation of fundamental analysis in automatic trading. Technical analysis uses historical prices and indicators derived from price for price prediction. On the opposite, fundamental analysis uses various information resources for price prediction. In this thesis, only quantitative data are used. These data sources are namely weather, Forex, Google Trends, WikiTrends, historical prices of futures and some fundamental data (birth rate, migration, \dots). These data are processed with LSTM neural network, which predicts stocks prices of selected companies. This prediction is basis for created trading system. Experiments show major improvement in results of the trading system; 8\% increase in success prediction accuracy thanks to involvement of fundamental analysis.

Computational Intelligence has found a challenging testbed for various paradigms in the financial sector. Extensive research has resulted in numerous financial applications using neural networks and evolutionary computation, mainly genetic algorithms and genetic programming. More recent advances in the field of computational intelligence have not yet been applied as extensively or have not become available in the public domain, due to the confidentiality requirements of financial institutions. This study investigates how co-evolution together with the combination of par- ticle swarm optimisation and neural networks could be used to discover competitive security trading agents that could enable the timing of buying and selling securities to maximise net profit and minimise risk over time. The investigated model attempts to identify security trend reversals with the help of technical analysis methodologies. Technical market indicators provide the necessary market data to the agents and reflect information such as supply, demand, momentum, volatility, trend, sentiment and retracement. All this is derived from the security price alone, which is one of the strengths of technical analysis and the reason for its use in this study. The model proposed in this thesis evolves trading strategies within a single pop- ulation of competing agents, where each agent is represented by a neural network. The population is governed by a competitive co-evolutionary particle swarm optimi- sation algorithm, with the objective of optimising the weights of the neural networks. A standard feed forward neural network architecture is used, which functions as a market trend reversal confidence. Ultimately, the neural network becomes an amal- gamation of the technical market indicators used as inputs, and hence is capable of detecting trend reversals. Timely trading actions are derived from the confidence output, by buying and short selling securities when the price is expected to rise or fall respectively. No expert trading knowledge is presented to the model, only the technical market indicator data. The co-evolutionary particle swarm optimisation model facilitates the discovery of favourable technical market indicator interpretations, starting with zero knowledge. A competitive fitness function is defined that allows the evaluation of each solution relative to other solutions, based on predefined performance metric objectives. The relative fitness function in this study considers net profit and the Sharpe ratio as a risk measure. For the purposes of this study, the stock prices of eight large market capitalisation companies were chosen. Two benchmarks were used to evaluate the discovered trading agents, consisting of a Bollinger Bands/Relative Strength Index rule-based strategy and the popular buy-and-hold strategy. The agents that were discovered from the proposed hybrid computational intelligence model outperformed both benchmarks by producing higher returns for in-sample and out-sample data at a low risk. This indicates that the introduced model is effective in finding favourable strategies, based on observed historical security price data. Transaction costs were considered in the evaluation of the computational intelligent agents, making this a feasible model for a real-world application. Copyright / Dissertation (MSc)--University of Pretoria, 2010. / Computer Science / unrestricted

Thesis (M.A. Commerce (Economics)) -- University of Limpopo, 2019 / South Africa had targeted the oil and gas sector for investment through the industrial action plan as a special economic zone. However, certain economic fundamentals might negate the anticipated sector financial development. This study investigate how economic risk exposure influence oil & gas sector stock market returns from 2007 to 2015 on a monthly basis. The four macroeconomic variables used to measure economic risk exposure are Brent crude oil prices, the USD/ZAR exchange rate, broad money supply and gold prices. The adopted techniques include the GARCH model to incorporate volatility, the Johansen cointegration and Granger causality techniques. The results of the study found that change in Brent crude oil prices and broad money supply had a positive and significant impact on changes in oil & gas sector stock returns. Changes in exchange rate and gold prices had a negative and significant impact on the sector returns. The long-run relationship established one cointegrating equation in the series. Only Brent crude oil prices indicated a bi-directional Granger causality on the sector returns. Based on the findings, it is recommended that government may use exchange rate as a policy tool to attract interest in the sector. Regarding money supply, the reserve bank should further preserve its effective regulatory infrastructure including the laws, regulations and standards towards the achievement and maintenance of a stable financial system. Portfolio managers, risk managers and investors should monitor the gold price to mitigate losses due to its strength as a safe haven asset.

This paper studies factors that influence the stock return on the Shanghai Stock Exchange (SSE) market. To achieve this goal, a stock-fixed effects model is estimated using a panel data sample comprising 100 companies listed on the SSE market during the 72-month period from January 2002 to December 2007. I find that number of trades and book-to-market value in both up and down markets have a significant and positive impact on stock returns during the studied period, whereas stock returns were negatively affected by systematic risk in both up and down markets although less so in up markets. Price to earnings ratio did not show any significant effect on stock returns on the SSE. My overall results indicate that SSE did not satisfy the efficient market hypothesis 1 during the studied period from January 2002 to December 2007.

Background: Research shows that there are incompatible outcomes for companies who have completed an Initial Public Offering, which entails difficulties in ascertaining what acompany's financial performance will look like after the Initial Public Offering. Purpose: The purpose of the study is to investigate whether an Initial Public Offering resultsin an improvement or a deterioration in companies' financial performance in connection with a stock exchange listing on the Stockholm Stock Exchange. Furthermore, the study's sub-question aims to examine whether the financial performance differs based on the size ofthe company, in connection with an Initial Public Offering.  Theory: This study is based on three theories; Window Dressing Theory, Agency CostTheory and Window Of Opportunity.  Method: The study has applied a quantitative method in combination with a deductive approach. Two financial ratios have been applied to examine the financial performance, which are; profit growth and return on equity.  Conclusion: The empirical result shows that an Initial Public Offering leads to a deterioration in profit growth for the entire sample, before in relation to after the listing. Furthermore, the size of the company turns out to have no effect on how the financial performance is affected in relation to an Initial Public Offering.

Research on the relationship between the oil market and the stock market has been a frequently discussed topic. Regarding the connection between oil and the stock market, there are different opinions about whether there is a relationship or not, therefore there is still room left for further research on the subject matter. In addition, none of the studies we could identify researched the Swedish stock market with the effect on different sectors separately at the stock market. The purpose of this paper is to study the relationship between the return- and volatility of the oil and how it affects the Swedish stock market. We will partly analyze the relationship between oil return and the specific sectors on the Swedish stock market while also studying the relationship with the stock market as a whole. Furthermore, we will also look at the connection between the oil volatility index (OVX) with regards to how it affects both the sectors and the Swedish stock market.  The method used in the study is quantitative consisting of two linear regression models which will be redesigned into two multiple regression models containing our control variables. The data which were used in the study was compiled into time-series data and the estimates were performed with OLS-estimations.  The result of the study was that no statistically significant relationship could be found between the Swedish stock market and oil return- and volatility. Furthermore, in the sectoral analysis, five sectors became statistically significant given their relationship to oil return. When examining the relationship between the oil volatility and the sectors on the Swedish stock market the result gained was three statistically significant sectors. Thus, there is no evidence for a statistically significant relationship between the Swedish stock market and the oil return- and volatility. However, we conclude that the oil return- and volatility have a sectoral effect on the Swedish stock market. / Forskning om relationen mellan oljans pris och aktiemarknaden har varit ett väl diskuterat ämne. Beträffande sambandet mellan oljan och aktiemarknaden råder det skilda meningar om huruvida det finns ett samband eller inte, därav finns det fortfarande utrymme för vidare forskning. Dessutom undersöker ingen av studierna vi identifierat den svenska aktiemarknaden och hur olika sektorer på marknaden påverkas enskilt.  Syftet med denna studie är att studera sambandet mellan avkastningen- och volatiliteten i oljan och hur det påverkar avkastningen på den svenska aktiemarknaden. Dels kommer vi att undersöka förhållandet mellan oljans avkastning och enskilda sektorer på Stockholmsbörsen, såväl som vi undersöker börsen i helhet. Vi kommer också att studera hur oljevolatilitetsindex (OVX) påverkar avkastningen för dessa sektorer och Stockholmsbörsen som helhet.  Studien använder sig av en kvantitativ metod bestående av två initiala linjära regressionsmodeller som sedan omkonstrueras till två multipla regressionsmodeller innehållande kontrollvariabler. Studiens data har sammanställts till tidsseriedata och skattningarna utfördes med OLS-estimeringar.  Resultatet av studien blev att inget statistiskt säkerställt samband kunde hittas mellan Stockholmsbörsen och oljans avkastning respektive volatilitet. Vidare i den sektoriella analysen blev fem sektorer signifikanta vid undersökning av oljans avkastning. Fortsättningsvis undersöktes oljans volatilitet mot sektorerna vilket resulterade i tre signifikanta sektorer. Slutsatsen blir således att det inte finns ett signifikant samband mellan Stockholmsbörsen som helhet och oljans avkastning- samt volatilitet. Däremot kan vi konstatera att oljans avkastning såväl som volatilitet har en sektoriell påverkan.

A rational long-horizon stock investment decision is a complex process due to uncertainty in supply and demand, competitive advantage, macroeconomic parameters and various perspectives of investors. Today, the '€˜non-tangible assets'€™ (NTA) that include goodwill and intangible assets are a significant part of corporate assets, but their role in stock performance has not well studied. The purpose of this research is to empirically analyze the implications of NTA and of gross domestic product (GDP) of the United States on the stock price. According to the efficient market hypothesis, stock price reflects all relevant information. The research question focused on the extent to which NTA and the GDP reflected in the stock price. To determine the extent to which NTA and GDP reflected on the stock price, regression analysis and other statistical tests were used. The sample for the empirical study was 56 corporations listed on the New York Stock Exchange (NYSE) and National Association of Securities Dealers Automated Quotation (NASDAQ). The required data from October 2007 to September 2018 were collected from the United States Securities and Exchange Commission (SEC) and the United States Bureau of Economics (BEA). The key findings of the study are: the NTA and stock price of 45 corporations have a statistically significant correlation as opposed to 11 corporations. The combined NTA of these 11 corporations for the third quarter of 2018 was $531.64 billion. Furthermore, the GDP and stock price of 53 corporations have a statistically significant correlation, but no evidence for three corporations was found. The significance for positive social change is knowledge from this research about the implications of NTA and GDP on stock performance that the investors, policymakers, and other stakeholders could use for preserving the limited resources and creating wealth.