This thesis examines the relationship between stock market returns and systematic risk factors in twelve industrial countries. Using the APT framework, the thesis investigates the notion of international stock market integration versus segmentation in terms of pricing risk, international stock market efficiency in terms of eliminating arbitrage opportunities across domestic markets, and the validity of the international version of the APT according to a model that specifies purely domestic factors. Starting with ordinary least squares estimation the thesis investigates the responses of investors in their national stock markets to systematic shocks. By employing iterative non-linear multivariate seemingly unrelated regression estimation, this work avoids the statistical problems encountered in the second-pass test of the two-stage procedure. This study found that the international stock market was neither integrated nor efficient and that the IAPT was not supported by the results during the period investigated. It was demonstrated that partial and regional integration, regional efficiency, and regional IAPT validity cannot be ruled out. Moreover, the alternative model proved to be practically valid.

This dissertation examines how the stock market reacts to announcements of introduction of co-branded new products. Despite the apparent enthusiasm of practitioners towards co-branding--the practice of using two established brand names on the same product--, there is a dearth of research on if and how co-branding can be effectively leveraged to significantly increase the value added of new products. Whether greater financial rewards accrue to the manufacturer of the co-branded product (i.e. the primary brand parent) or to the partner firm that lends its brand to the co-branded product (i.e. the secondary brand parent), and how these rewards may differ depending on the characteristics of the co-branded product itself are yet unanswered questions. Using data from the consumer packaged goods industry, I empirically examine the extent to which co-branding increases the market value of the parent firms and analyze the determinants of the magnitude of increase in market value for both firms involved in the co-branding alliance. I present empirical evidence in support of a positive stock market reaction to the introduction of co-branded new products and find that this reaction is greater, on average, than the market reaction to the introduction of single-branded new products. I also show that the consistency between the brand images of the two products, the innovativeness of the product, and the exclusivity of the co-branding relationship significantly impact the market?s reaction to the announcement of new co-branded products. Moreover, these effects manifest both in the short term (i.e., at the time of the announcement) and over a longer time window (i.e., during the year following the announcement). Furthermore, I find that not all types of co-branding partnerships are equal. Composite co-branding (where both brands bring a substantive contribution to the formulation of the new product) results in higher financial rewards to the partners compared to ingredient and endorsement partnerships. The findings provide important managerial guidelines for increasing firm value through co-branding partnerships.

Mode of access: World Wide Web. / Thesis (Ph. D.)--University of Hawaii at Manoa, 2005. / Includes bibliographical references (leaves 92-95). / Electronic reproduction. / Also available by subscription via World Wide Web / vii, 95 leaves, bound 29 cm

This study examines the frequency of extreme trading days and investment behaviour in Sweden. We show that the frequency, as well as the magnitude of extreme trading days has increased over time. We also show that the frequency of extreme trading days in a year is positively correlated to the frequency the preceding year. Furthermore, we show that aggregate cash flows into equity and bond funds are unrelated to risk measured by standard deviation of return. Our findings show that investors, individuals as well as corporations, use simple passive investment strategies and hence, do not believe in market timing or wish to risk capital on capturing far tail or black swan type returns.

This thesis examines the return volatility of Hong Kong stock market on the firm-level, industry-level, and market-level during a fifteen year sample period between 1991 and 2005. The identified patterns of stock return volatilities contribute to the understanding of an important Asian market.

Thesis (Ph. D.)--University of Hawaii at Manoa, 2005. / Includes bibliographical references (leaves 92-95).

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