Thesis (Ph. D.)--Cornell University, 1988. / Vita. Includes bibliographical references (leaves 179-186).

Background: The day-of-the-week effect has been a widely studied field ever since the concept was introduced in the early 1970s. Historically, negative returns on Mondays have been the most common finding. In line with improved market efficiency, researchers have started to question the existence of this anomaly. Purpose: The purpose of this study is to examine the weak-form efficiency level within the Swedish stock market by using sophisticated statistical approaches. The authors aim to investigate if the day-of-the-week effect was demonstrated between 2000 and 2017. Method: To properly provide answers to this investigation, a quantitative study has been conducted on the OMXS30. The data has been analysed by using different kind of sophisticated statistical methods such as GARCH and TGARCH. Conclusion: The results show that the day-of-the-week effect was not demonstrated within the OMXS30 during this time period, providing evidence for improved market efficiency.

In this work we made an attempt to predict the upwards/downwards movement of the S&P 500 index from the news articles published by Bloomberg and Reuters. We employed the SVM classifier and conducted multiple experiments aiming at understanding the shape of the data and the specifics of the task better. As a result, we established the common evaluation settings for all our subsequent experiments. After that we tried incorporating various features into the model and also replicated several approaches previously suggested in the literature. We were able to identify some non-trivial dependencies in the data which helped us achieve a high accuracy on the development set. However, none of the models that we built showed comparable performance on the test set. We have come to the conclusion that whereas some trends or patterns can be identified in a particular dataset, such findings are usually barely transferable to other data. The experiments that we conducted support the idea that the stock market is changing at random and a high quality of prediction may only be achieved on particular sets of data and under very special settings, but not for the task of stock market prediction in general. 1

In recent decades, the rapid development of information technology in the big data field has introduced new opportunities to explore a large amount of data available online. The Global Database of Events, Location (Language), and Tone (GDELT) is the largest, most comprehensive, and highest resolution open source database of human society that includes more than 440 million entries capturing information about events that have been covered by local, national, and international news sources since 1979 in over 100 languages. GDELT constructs a catalog of human societal-scale behavior and beliefs across all countries of the world, connecting every person, organization, location, count, theme, news source, and event across the planet into a single massive network that captures what is happening around the world, what its context is and who is involved, and how the world is feeling about it, every single day. On the other hand, the stock market prediction has also been a long-time attractive topic and is extensively studied by researchers in different fields with numerous studies of the correlation between stock market fluctuations and different data sources derived from the historical data of world major stock indices or external information from social media and news. Support Vector Machine (SVM) and Logistic Regression are two of the most widely used machine learning techniques in recent studies. The main objective of this research project is to investigate the worthiness of information derived from GDELT project in improving the accuracy of stock market trend prediction specifically for the next days' price changes. This research is based on data sets of events from GDELT database and daily prices of Bitcoin and some other stock market companies and indices from Yahoo Finance, all from March 2015 to May 2017. Then multiple different machine learning and specifically classification algorithms are applied to data sets generated, first using only features derived from historical market prices and then including more features derived from external sources, in this case, GDELT. Then the performance is evaluated for each model over a range of parameters. Finally, experimental results show that using information gained from GDELT has a direct positive impact on improving the prediction accuracy. Keywords: Machine Learning, Stock Market, GDELT, Big Data, Data Mining

abstract: In this study I investigate the organizational strategies that Chinese power generation companies may use to reduce the impact of coal price increases on their profits. Organizations are open systems in that no organization possesses all the resources that it needs and all organizations must obtain resources from their external environments in order to survive. Resource dependent theory suggests that the most important goal of an organization is to find effective mechanisms to cope with its dependence on the external environments for resources that are critical to its survival. Chinese power generation companies traditionally rely heavily on coal as their raw materials, and an increase in coal price can have a significant negative impact on their profits. To address this issue, I first provide a systematic review of the resource dependence theory and research, with a focus on the strategies such as vertical integration, diversification, and hedging that organizations can undertake to reduce their dependence on the external environment as well as their respective benefits and costs. Next, I conduct a qualitative case analysis of the primary strategies the largest Chinese power generation companies have used to reduce their dependence on coal. I then explore a new approach that Chinese power generation companies may use to cope with increases in coal price, namely, by investing in an index of coal companies in the stock market. My regression analysis shows that coal price has a strong positive relation with the price of the coal company index. This finding suggests that it is possible for firms to reduce the negative impact of raw material price increase on their profits by investing in a stock market index of the companies that supply the raw materials that they depend on. / Dissertation/Thesis / Doctoral Dissertation Business Administration 2015

Given the extensive participation of mining stocks in the Peruvian stock market, the Lima Stock Exchange (BVL) provides an ideal setting for exploring both the impact of metal returns on mining stock returns and stock market volatility, and the comovements between mining stock returns and metal returns. This research is a first attempt to explore these issues using international metal prices and the prices of the most important mining stocks on the BVL and the IGBVL index. To achieve this, we use univariate GARCH models to model individual volatilities, and the Exponentially Weighted Moving Average (EWMA) method and multivariate GARCH models with time-varying correlations to model comovements in returns. We found that Peruvian mining stock volatilities mimic the behavior of metal volatilities and that there are important correlation levels between metals and mining stock returns. In addition, we found time-varying correlations with distinctive behavior in different periods, with rises potentially related to international and local historical events. / Dada la amplia participación de acciones mineras en el mercado de valores peruano, la Bolsa de Valores de Lima (BVL) resulta un escenario ideal para explorar tanto el impacto de los ren- dimientos de acciones de metales en los rendimientos de las acciones mineras y la volatilidad del Mercado de valores, así como los co-movimientos entre los rendimientos de las acciones mineras y los rendimientos de los metales. Este estudio es un primer intento en explorar estos temas usando precios internacionales de los metales y los precios de las acciones mineras más importantes de la BVL y del índice IGBVL. Para conseguir esto, hemos usado modelos GARCHunivariados para modelar las volatilidades individuales, y el método de Media Móvil Ponderada Exponencialmente (EWMA) y modelos GARCH multivariados con correlaciones de variantes en el tiempo a modelos de co-movimientos en rendimientos. Hemos encontrado que las volatilidades imitan el comportamiento de las volatilidades de los metales y que hay importantes niveles de correlación entre los metales y el retorno de las acciones mineras. Adicionalmente, encontramos correlaciones variantes en el tiempo con un comportamiento distintivo en periodos diferentes, el que aumenta potencialmente en relación con eventos históricos internacionales o nacionales.

Exchange rate risk, interest rate risk and oil price fluctuations are the most demonstrated risks in the GCC (Gulf Cooperation Council) countries (Arouri and Nguyen, 2010). Research, however, in this area is still underdeveloped. The importance of this study is to contribute to this research gap. This research aims to show how these three risks affect firms' market values by examining 473 listed firms in Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and United Arab Emirates for the period January 2007 to June 2012. The research further examines the determinants of these risks. The study uses the AR (1) EGARCH-M model. The results indicate that stock returns in GCC countries are influenced by the exchange rate risk, interest rate risk and oil price risk. However, the exposure was highest for exchange rate risk and lowest for interest rate risk. While the effects of these risks were mixed, overall, exchange rate risk and oil price risk showed more positive significance as compared to the interest rate risk that showed more negatively significant effect on firm values. The level of the effect of these risk also differed from country to country. However, firms in United Arab Emirates revealed the highest exposure to all the three risks while those in Saudi Arabia showed the least exposed to the three risks. Oman firms also showed high exposure to exchange rate and interest rate risks. The segregated results overall showed lower exposure of financial firms as compared to non-financial firms. However, the non-financial firms in Bahrain were more exposed to the risks than the financial firms. In Saudi Arabia, the financial firms revealed the least exposure to the risk suggesting effective risk management practices. In addition, foreign operations and firm size had a significant influence on the extent of the firms’ exposure to all the three risks. Leverage also influenced the level of exposure to interest rate risk. Profitability, growth and liquidity did not reveal a significant influence on the level of exposure. Further, increasing the risk does not lead to increased returns in most of the GCC countries. The risk-return parameters were largely negative. However, positive news increases return volatility more than negative news in most countries. Also, the current volatility of most GCC firms’ returns are time varying, are a function or past innovation and past volatility. The volatility of stock returns, which is affected by changes in the risk factors, could demonstrate the non-prioritisation of risk management by firms.

Thesis brings broader picture about the relationship between unconventional measures implemented by ECB and valuation of banking sector in the Eurozone. It also examines implementation of unconventional measures with regard to the three crises, which struggled European economy in the years thereafter the beginning of the last financial crises. Attention is also directed towards factors which have direct impact on the valuation of banks and are influenced by steps adopted by monetary authority. For this purpose two stage dividend discount model, model of economic value added, and price to book ratio model are used. Main tools necessary for the analysis are databases of the platform Bloomberg and statistics gathered by ECB. Hypothesis suggesting the positive relationship between unconventional measures implemented by ECB and valuation of banking sector in the Eurozone is proved for mid-term horizon. In case of immediate reaction of shares the results are divided based on the structure of specific measure and also on the expectations of financial market. Thesis as a whole confirms the positive effects of unconventional measures on the valuation of shares, therefore entice to additional research. In fact sometimes monetary policy might cause distortion in the prices on the financial markets and endanger main goals of central bank.

abstract: Recent research finds that there is significant variation in stock market participation by state and suggests that there might be state-specific factors that determine household stock market participation in the United States. Using household survey data, I examine how accounting quality of public companies at the state level affects households’ stock market participation decisions. I find that households residing in states where local public companies have better accounting quality are more likely to invest in stocks. Moreover, those households invest greater amounts of their wealth in the stock market. Cross-sectional tests find that the effect of accounting quality on stock market participation is more pronounced for less affluent and less educated households, consistent with prior findings that lacking familiarity with and trust in the stock market is an important factor deterring those types of households from stock investments. In state-level tests, I find that these household outcomes affect income inequality, which is less severe in states where high public-firm accounting quality spurs more stock market participation by poorer households. Conversely, in states where public firms have lower accounting quality, stock market participation among poorer households is less common, and a larger share of high equity returns accrues to richer households, exacerbating income inequality. / Dissertation/Thesis / Doctoral Dissertation Accountancy 2020

This research aims to examine if a negative relationship exists between the dividend yield and stock price volatility of firms listed on the Swedish Stock exchange market, which is of utter interest and intrinsic for investors and financial analyst in the process of valuing a security’s and a stock portfolio's risk and return. The data that was utilized for this study consists of 52 companies for the period of 2010 to 2019 which makes up for 520 observations. A pooled regression model and a multiple ordinary least squares model was applied to test the relationship. The results show a negative relationship between the dividend yield and stock price volatility. On the other hand, the results indicate that there is a significant positive relationship between earnings volatility and stock price volatility. However, there is a negative relationship for leverage, market value and asset growth with stock price volatility.