My dissertation investigates the financial linkages and transmission of economic shocks between the US and the smallest emerging markets (frontier markets). The first chapter sets up an empirical model that examines the impact of US market returns and conditional volatility on the returns and conditional volatilities of twenty-one frontier markets. The model is estimated via maximum likelihood; utilizes the GARCH model of errors, and is applied to daily country data from the MSCI Barra. We find limited, but statistically significant exposure of Frontier markets to shocks from the US. Our results suggest that it is not the lagged US market returns that have impact; rather it is the expected US market returns that influence frontier market returns The second chapter sets up an empirical time-varying parameter (TVP) model to explore the time-variation in the impact of mean US returns on mean Frontier market returns. The model utilizes the Kalman filter algorithm as well as the GARCH model of errors and is applied to daily country data from the MSCI Barra. The TVP model detects statistically significant time-variation in the impact of US returns and low, but statistically and quantitatively important impact of US market conditional volatility. The third chapter studies the risk-return relationship in twenty Frontier country stock markets by setting up an international version of the intertemporal capital asset pricing model. The systematic risk in this model comes from covariance of Frontier market stock index returns with world returns. Both the systematic risk and risk premium are time-varying in our model. We also incorporate own country variances as additional determinants of Frontier country returns. Our results suggest statistically significant impact of both world and own country risk in explaining Frontier country returns. Time-variation in the world risk premium is also found to be statistically significant for most Frontier market returns. However, own country risk is found to be quantitatively more important.

The question for this thesis is whether the four major stock markets--the United States, Great Britain, West Germany, and Japan are interdependent or segmented. The study period runs from February 1979 to June 1987, with the Wall Street Journal as a source of data. The Granger causality test is used to test for relationships among the four major stock markets. The thesis is divided into five chapters-- 1) statement of the problem; 2) survey of literature; 3) methodology; 4) results and 5) conclusions. The overall findings of this thesis indicate that there are few or no comovement similarities among all the four stock markets. However, the findings do point out the significant influence of the United States stock market on the other three stock markets.

Avsikten med denna studie har varit att studera det nya fenomenet börsinfluencers och deras samexistens med professionella licensierade rådgivare samt beskriva på vilket sätt betydelsen av den finansiella rådgivarrollen kan ha förändrats, något som inte har behandlats av tidigare forskning. Studiens genomförande utgår från det teoretiska ramverkets nyckelbegrepp Det klassiska ekonomiska finansparadigmet, beteendevetenskaplig finans, den rationella investeraren, den irrationella investeraren, egna fundamentala analyser, flockbeteende & tillgänglighetsheuristik, professionella rådgivare, börsinfluencers och postmodernism som tillsammans bildar analysmodellen. Modellen visar på hur de två rådgivningstrenderna samexisterar och den är indelad i två delar, en “rationell” sida och en “irrationell” sida för att slutligen visa att existensen av båda sidorna tillåts i det postmoderna samhället. Denna studie hade ett postmodernistiskt synsätt vilket medförde att resultatet endast ska betraktas som en av flera versioner av den yttre verkligheten. För att på bästa möjliga sätt kunna uppfylla syftet och besvara frågeställningarna användes dels en innehållsanalys av en dokumentstudie och dels en tematisk analys av sex semistrukturerade intervjuer där kriteriet vid urval av respondenter var att ha ett intresse för aktiemarknaden samt följa minst en börsinfluencer på Instagram. Studien använde dessa två olika metoder för att både kunna beskriva börsinfluencers agerande på sociala plattformar samt för att kunna analysera deras följares uppfattningar och upplevelser av fenomenet. Studien visade att börsinfluencers fyller olika behov hos respondenterna, där vissa ser på fenomenet som en typ av rådgivare vars åsikter och indirekta råd värderas högt vid investeringsbeslut medan andra vänder sig till börsinfluencers för inspiration i sin investeringsprocess. De tidigare tydliga gränsdragningarna och definitionerna av vad en finansiell rådgivare är har luckrats upp och istället lämnat plats för börsinfluencers, fenomenet kan betraktas som produkten av individers pragmatiska behov av lättillgänglig finansiell rådgivning. Där resultatet är börsinfluencers, den hyperrealistiska rådgivaren. / The purpose of this study has been to examine the new phenomenon of stock market influencers and their coexistence with professionally licensed advisers and to describe in what way the significance of the financial adviser role may have changed, something that has not been addressed by previous research. The implementation of the study is based on the theoretical framework's key concepts The classic economic finance paradigm, behavioral finance, the rational investor, the irrational investor, own fundamental analyzes, herd behavior & accessibility heuristics, professional advisors, stock market influencers and postmodernism that together form the analysis model. The model shows how the two advisory trends coexist and it is divided into two parts, one “rational” side and one “irrational” side, to finally show that the existence of both sides is allowed in the postmodern society. This study had a postmodernist approach, which meant that the result should only be considered as one of several versions of the external reality. In order to best fulfill the purpose and answer the questions, a content analysis of a document study and a thematic analysis of six semi-structured interviews were used, where the criterion when selecting respondents was to have an interest in the stock market and follow at least one stock market influencer on Instagram. The study used these two different methods to both be able to describe the actions of stock market influencers on social platforms and to be able to analyze their followers' perceptions and experiences of the phenomenon. The study showed that stock market influencers meet different needs of the respondents, where some see the phenomenon as a type of advisor whose opinions and indirect advice are highly valued in investment decisions while others use stock market influencers for inspiration in their investment process. The previously clear boundaries and definitions of what a financial adviser is have loosened up and instead left room for stock market influencers, the phenomenon can be considered as the product of individuals' pragmatic need for easily accessible financial advice. Where the result is stock market influencers, the hyperrealistic advisor.

Given the volatility of the stock market and the multitude of financial variables at play, forecasting the value of stocks can be a challenging task. Nonetheless, such prediction task presents a fascinating problem to solve using machine learning. The stock market can be affected by news events, social media posts, political changes, investor emotions, and the general economy among other factors. Predicting the stock value of a company by simply using financial stock data of its price may be insufficient to give an accurate prediction. Investors often openly express their attitudes towards various stocks on social medial platforms. Hence, combining sentiment analysis from social media and the financial stock value of a company may yield more accurate predictions. This thesis proposes a method to predict the stock market using sentiment analysis and financial stock data. To estimate the sentiment in social media posts, we use an ensemble-based model that leverages Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models. We use an LSTM model for the financial stock prediction. The models are trained on the AAPL, CSCO, IBM, and MSFT stocks, utilizing a combination of the financial stock data and sentiment extracted from social media posts on Twitter between the years 2015-2019. Our experimental results show that the combination of the financial and sentiment information can improve the stock market prediction performance. The proposed solution has achieved a prediction performance of 74.3%.

In this thesis we compared a profitability of several investment strategies that could be used at the stock markets and we adjusted them for the need of Prague Stock Exchange. The strategies that we used are based on some different principles and they provide different results. We found that the strategies based on the technical approach gave us better results than the strategies based on the fundamental approach. The next finding is that those strategies for which the investors would go also into the short sell perform better than the strategies for which the investors would take just the long positions. The best results we got for the momentum strategy, which is based on the autocorrelation of returns.

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The diploma thesis focuses on the specification of suitability of legal entities to invest in securities "Exchange Traded Funds" in the current conditions of the Czech Republic. The results of the thesis serve to present alternative, modern ways to invest surplus money and get a new yield. Conclusions can be used by senior management of designated legal entities.

This work deals with stock market prediction via technical and psychological analysis. We introduce theoretical resources of technical and psychological analysis. We also introduce some methods of artificial intelligence, specially neural networks and genetic algorithms. We design a system for stock market prediction. We implement and test a part of system. In conclusion we discuss results.

Subject of this thesis is initial public offering and it’s main goal is describing of obtaining external funds for company’s funding. In the first part of the thesis is description of basic structure of financial markets, especially stock markets. The term IPO and it’s advantages and disadvantages are described. The last part is focused on individual steps of IPO realization. This thesis is focused on evaluation of chosen company’s entry on stock market. Especially macro-economic presumptions for IPO, readiness of chosen company and choice of suitable market are evaluated.

This master thesis deals with issue of trade on commodity market, especially the gold. It uses the artificial intelligence resources, more accurate non-linear auregressive neural network. The purpose is the prediction of the gold prices by indicators which has impact on the gold.