This thesis investigates how the U.S President’s Twitter mentions of individual companies’ investment announcements influence the short-term price of their stock. By assuming that the President’s comments on a company’s plans should be incorporated by markets as new information, testing the Efficient Market Hypothesis assumption that the markets incorporate all new information, the thesis seeks to contribute to a new, unexplored and growing, research field. This thesis utilizes a qualitative analysis method, studying Twitter mentions on the topic of Trump’s Tax Reform. The data in this thesis is derived from the President’s personal Twitter-account, company announcements, stock price charts, and the Standard & Poor’s S&P500 Index. To conclude, this study finds that although the President’s Twitter comments may signal his public approval of a company and its plans, it appears that any market reaction is only short-term, and that as the market incorporates additional information it returns to an informed state in terms of stock valuations. This study suggests that there are few observable indicators that Trump’s positive mentions on Twitter causes any significant market reaction.

This thesis develops a new Bayesian approach to structural break modeling. The focuses of the approach are the modeling of in-sample structural breaks and forecasting time series allowing out-of-sample breaks. Our model has some desirable features. First, the number of regimes is not fixed and is treated as a random variable in our model. Second, our model adopts a hierarchical prior for regime coefficients, which allows for the regime coefficients of one regime to contain information about regime coefficients of other regimes. However, the regime coefficients can be analytically integrated out of the posterior distribution and therefore we only need to deal with one level of the hierarchy. Third, the implementation of our model is simple and the computational cost is low. Our model is applied to two different time series: S&P 500 monthly returns and U.S. real GDP quarterly growth rates. We linked breaks detected by our model to certain historical events.

Abstract Introduction: The effect of Environmental, Social and Governance issues on stock prices is highly debated in literature. Different authors state that ESG has an influence on stock price and company value while others state that it has no or limited effect. Purpose: The purpose of this research is to explain the effect of ESG changes on stock prices and add information to the debate between both sides if there is, or if there is not an effect from ESG on stock prices. Research questions: 1. What is the effect of changes in Environmental concerns in stock prices? 2. What is the effect of changes in Social concerns in stock prices? 3.What is the effect of changes in Governance concerns in stock prices? Methodology: Event study method with a sample size of 484 companies from the S&P 500 which will be analyzed for the period of 2015-2017, which gave 1.420 different events. These companies got ratings for Environmental Pillar, Social Pillar, Governance Pillar, ESG Controversies, ESG Score and ESG Combined Score. For each event the abnormal stock returns were compared with the rating changes. The data is taken from EikonThomsonReuters. Conclusion: The results showed no correlation between Environmental, Social and Governance rating changes and abnormal returns. Also, the combined ratings did not show any correlations. Therefore, our study will support and contribute to the side of researcher Friedman (1970), Jacobs et al. (2010), Walley and Whitehead (1994), Drobetz et al. (2004) and other researchers which state there is no correlation between ESG and stock prices. Limitations: The study is based on ratings provided by EIKON, we assumed they are a clear and correct reflection of the actual ESG within companies. The second limitation is the anticipation effect, the response of the stock market is based on unawareness from investors. If the bases where the rating changes on is already known than there is no effect from investors because they already anticipated the decreased rating. There are also a few companies excluded from the research because of missing ratings. Also, these results are based on the S&P500 and therefore do not have to be true for other financial markets. Keywords: ESG, Stock Price, Environmental, Social, Governance, ESG-ratings, S&P 500, Event Study, EIKON

The intent of this thesis is to investigate how US equity funds performance differ due to their standard deviation. In order to accomplish this study, we collected daily data for 99 US equity funds for the period 2011-2020 and divided the funds into three risk classification groups based on their standard deviation for the year 2011. The collected data was used to perform an CAPM regression and to calculate returns on a three-, five- and ten-year basis. The results for the regression and the returns for the funds was later presented as average values for the different risk classification groups. We then compared the average outcomes for the three risk classifications with each other and the index S&P 500. Our result showed that the index S&P 500 outperformed the three risk classification groups average returns for every time period. We also noticed that the difference between the average returns and the index got greater by time. We did not find any big differences between our risk classifications when it comes to their performance. Our regression analysis resulted in many negative alpha values indicating that S&P 500, as many previous studies claims, outperforms actively mutual funds. The conclusion is therefore that we could not show any evidence that the there is a major different in performance between our risk groups but also that it is difficult for fund managers to outperform index.

One of the most popular investment and trading strategies over the last decade, has been the currency carry trade, which allows traders and investors to buy high-yielding currencies in the Foreign Exchange spot market by borrowing, low or zero interest rate currencies in the form of pairs, such as the Australian Dollar/Japanese Yen (AUD/JPY), with the purpose of investing the proceeds afterwards into fixed-income securities.To be able to determine the causality between the returns of equity markets and the foreign exchange market, we choose to observe the sensitivity and influence of two equity indexes on several pairs involved in carry trading. The reason for studying these relationships is to further explain the causes of the uncovered interest parity puzzle, thus adding our contribution to the academic field through this thesis.To accomplish our goals, data was gathered for daily quotes of 16 different currency pairs, grouped by interest differentials, and two equity indexes, the S&P 500 and FTSE All-World, along with data for the VIX volatility index, for the 2003-2012 period. The data was collected from Thomson Reuters Datastream and the selected ten year span was divided into three different periods. This was done in order to discover the differences on how equity indexes relate to typical carry trade currency pairs, depending on market developments before, during and after the world financial crisis.The tests conducted on the collected data measured the correlations, influences and sensitivity for the 16 different currency pairs with the S&P 500 Index, the FTSE All-World index, and the volatility index between the years of 2003-2012. For influences and sensitivity, we performed Maximum Likelihood (ML) regressions with Generalized Autoregressive Conditional Heteroscedasticity (GARCH) [1,1], in Eviews software.After analyzing the results, we found that, during our chosen time period, the majority of currency pair daily returns are positively correlated with the equity indexes and that the FX pairs show greater correlation with the FTSE All-World, than with the S&P 500. Factors such as the interest rate of a currency and the choice of funding currency played an important role in the foreign exchange markets, during the ten year time span, for every yield group of FX pairs.Regarding the influence and sensitivity between currency pairs and the S&P 500 with its VIX index, we found that our models explanatory power seems to be stronger when the interest rate differential between the currency pairs is smaller. Our regression analysis also uncovered that the characteristics of an individual currency can show noticeable effects for the relationship between its pair and the two indexes.

This paper attempts to assess the impact of price fluctuations in oil resulting from worldwide oil supply shocks on the real returns of the U.S. stock market, specifically the S&P 500, during the period of 1986 to 2011. While much past research has found an inverse relationship to exist between simply oil price increases and stock market returns, not many studies have been conducted that focus on the effects of shifts in oil supply. The model utilized, a variation of that used by Hamilton (2008), determines that changes in oil prices arising from oil supply shocks one quarter prior (t-1) and one year prior (t-4) have an effect on real stock returns. However, an F-test assessing the joint impact of the explanatory variables is unable to reject the null hypothesis that the joint effects of changes in oil prices arising from supply shocks have zero effect on the returns of the stock market.

A volatilidade é uma medida de incerteza quanto às variações dos preços de ativos. Este trabalho tem como objetivo analisar a volatilidade, através dos diversos modelos da família GARCH, de três índices de mercados financeiros: Dow Jones, IBovespa e S&P 500. Com este intuito, foram aqui utilizadas técnicas univariadas e multivariadas, bem como análises de Causalidade de Granger. Através das duas primeiras ferramentas, escolhemos o melhor modelo para cada um destes casos. Usando a terceira ferramenta, concluímos que o IBovespa é significativamente influenciado pela abertura do Dow Jones e do S&P500. Por outro lado, mostramos que a abertura do IBovespa não impacta, nem à 10% de significância, os índices Dow Jones e S&P 500. Também concluímos que a incorporação de um dos índices americanos ao modelo do IBovespa torna-o mais significativo, uma vez que o mercado acionário brasileiro é impactado pelos dois índices citados anteriormente. Desta forma, este trabalho mostra que os modelos GARCH multivariados aparentam ser mais eficazes na estimação da volatilidade de ativos financeiros do que os modelos GARCH univariados. / The volatility is a measure of the uncertainty of variations of asset prices. The main goal of this work is to analyze the volatility, by the use of several models of the GARCH family, of three financial market indexes: Dow Jones, IBovespa and S&P 500. With this purpose, we use univariate and multivariate techniques, as well as Granger Causality. Using these first two tools, we choose the best model for each one of these cases. Using the third tool, we conclude that the IBovespa is significatively influenced by the opening of the Dow Jones and the S&P 500 indexes. On the other hand, we show that the opening of the IBovespa does not impact, not even at 10% of significance, the Dow Jones and S&P 500 indexes. We also conclude that incorporation of one of these American indexes to the model involving IBovespa makes it more significant, once the Brazilian Stock Market is impacted by the two American indexes we mention before. This work shows that multivariate GARCH models seem to be more efficient in the volatility estimation of financial assets than univariate GARCH models.

This thesis deals with the analysis of the profitability and risk of selected sectors on a stock exchange. For analysis of the industry period of 5 years was selected. This period begins in January 2010 and ends in December 2014. Data for the analysis were obtained from the New York Stock Exchange. Ratings industry is based on key indicators of profitability and risk. The profitability of the sector was calculated average and total. The risk was assessed by standard deviation, variance and coefficient of variation. The next step was to evaluate the sector by pricing model of capital asset. The coefficients alpha and beta were obtained by linear regression. MS Excel software was used for calculation. The first part describes the capital market, its subjects and the stock exchanges. For assessing the shares the basic formulas for calculating profitability, risk and CAPM are described in the theoretical part. Methodology paper describes the procedure for evaluating stocks and sectors. There is described a precise procedure of calculating individual indicators. In the third section the results of the analyzed sectors are evaluated. There is described the risk assessment of the industry and the future development of the sector. In conclusion the capital market and forecast of its development are evaluated.

A volatilidade é uma medida de incerteza quanto às variações dos preços de ativos. Este trabalho tem como objetivo analisar a volatilidade, através dos diversos modelos da família GARCH, de três índices de mercados financeiros: Dow Jones, IBovespa e S&P 500. Com este intuito, foram aqui utilizadas técnicas univariadas e multivariadas, bem como análises de Causalidade de Granger. Através das duas primeiras ferramentas, escolhemos o melhor modelo para cada um destes casos. Usando a terceira ferramenta, concluímos que o IBovespa é significativamente influenciado pela abertura do Dow Jones e do S&P500. Por outro lado, mostramos que a abertura do IBovespa não impacta, nem à 10% de significância, os índices Dow Jones e S&P 500. Também concluímos que a incorporação de um dos índices americanos ao modelo do IBovespa torna-o mais significativo, uma vez que o mercado acionário brasileiro é impactado pelos dois índices citados anteriormente. Desta forma, este trabalho mostra que os modelos GARCH multivariados aparentam ser mais eficazes na estimação da volatilidade de ativos financeiros do que os modelos GARCH univariados. / The volatility is a measure of the uncertainty of variations of asset prices. The main goal of this work is to analyze the volatility, by the use of several models of the GARCH family, of three financial market indexes: Dow Jones, IBovespa and S&P 500. With this purpose, we use univariate and multivariate techniques, as well as Granger Causality. Using these first two tools, we choose the best model for each one of these cases. Using the third tool, we conclude that the IBovespa is significatively influenced by the opening of the Dow Jones and the S&P 500 indexes. On the other hand, we show that the opening of the IBovespa does not impact, not even at 10% of significance, the Dow Jones and S&P 500 indexes. We also conclude that incorporation of one of these American indexes to the model involving IBovespa makes it more significant, once the Brazilian Stock Market is impacted by the two American indexes we mention before. This work shows that multivariate GARCH models seem to be more efficient in the volatility estimation of financial assets than univariate GARCH models.

A volatilidade é uma medida de incerteza quanto às variações dos preços de ativos. Este trabalho tem como objetivo analisar a volatilidade, através dos diversos modelos da família GARCH, de três índices de mercados financeiros: Dow Jones, IBovespa e S&P 500. Com este intuito, foram aqui utilizadas técnicas univariadas e multivariadas, bem como análises de Causalidade de Granger. Através das duas primeiras ferramentas, escolhemos o melhor modelo para cada um destes casos. Usando a terceira ferramenta, concluímos que o IBovespa é significativamente influenciado pela abertura do Dow Jones e do S&P500. Por outro lado, mostramos que a abertura do IBovespa não impacta, nem à 10% de significância, os índices Dow Jones e S&P 500. Também concluímos que a incorporação de um dos índices americanos ao modelo do IBovespa torna-o mais significativo, uma vez que o mercado acionário brasileiro é impactado pelos dois índices citados anteriormente. Desta forma, este trabalho mostra que os modelos GARCH multivariados aparentam ser mais eficazes na estimação da volatilidade de ativos financeiros do que os modelos GARCH univariados. / The volatility is a measure of the uncertainty of variations of asset prices. The main goal of this work is to analyze the volatility, by the use of several models of the GARCH family, of three financial market indexes: Dow Jones, IBovespa and S&P 500. With this purpose, we use univariate and multivariate techniques, as well as Granger Causality. Using these first two tools, we choose the best model for each one of these cases. Using the third tool, we conclude that the IBovespa is significatively influenced by the opening of the Dow Jones and the S&P 500 indexes. On the other hand, we show that the opening of the IBovespa does not impact, not even at 10% of significance, the Dow Jones and S&P 500 indexes. We also conclude that incorporation of one of these American indexes to the model involving IBovespa makes it more significant, once the Brazilian Stock Market is impacted by the two American indexes we mention before. This work shows that multivariate GARCH models seem to be more efficient in the volatility estimation of financial assets than univariate GARCH models.