Thesis (Ph.D.) - Carleton University, 2002. / Includes bibliographical references (p. 253-260). Also available in electronic format on the Internet.

Thesis (M. Phil.)--University of Hong Kong, 1994. / Includes bibliographical references (leaves 138-142).

No description available.

No description available.

Thesis (Ph. D.)--University of Missouri-Columbia, 2004. / Typescript. Vita. Includes bibliographical references (leaves 116-121). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 2004. / Typescript. Vita. Includes bibliographical references (leaves 116-121). Also available on the Internet.

Thesis (Ph. D.)--West Virginia University, 2000. / Title from document title page. Document formatted into pages; contains vii, 160 p. : ill. (some col.) Includes abstract. Includes bibliographical references (p. 154-160).

Thesis (Ph. D.)--UCLA, 2008. / Vita. Description based on print version record. Includes bibliographical references (leaves 130-137).

This research project examines the link between social media and its effect on stock exchanges and movement of stocks. The study uses Twitter as its primary social media platform and focuses on its effect on the Johannesburg Stock Exchange. The study examines various forms of social media and micro-blogging sites in its attempt to provide a thorough understanding of the role of social media within the market. In line with its exploration of social media, the study analyses User-Generated Content, Sentiment Analysis and the impact of Word-of-Mouth. A brief explanation of Algorithmic Trading, the Efficient Market Hypothesis, and the Adaptive Market Hypothesis is also provided. The information used to show the relationship between Twitter and the JSE was extracted using a quantitative survey answered by registered traders on the JSE. The survey aimed to ascertain the level of information pertaining to stock movement posted to the platform by these traders, and how these traders used that same information to make trading decisions. The results of the study show that Twitter and other micro-blogging sites have a level of determination in stock exchanges. This study shows that traders make some use of online information to inform their trading decisions on the Stock Market. The validity of this online information stems from the fact that traders place trust in other people and other users’ experience, as proven by Word of Mouth. The findings of this study were contrary to the researchers’ expectation that Twitter was widely used as an informant for trade decisions. What is deduced from the available findings is that while Twitter and other social media platforms do to some extent provide information for traders on the JSE in making trade decisions, it is not a wide-spread basis for movement of shares. / Dissertation (MBA)--University of Pretoria, 2013. / zkgibs2014 / Gordon Institute of Business Science (GIBS) / MBA / Unrestricted

This PhD dissertation research primarily aims to empirically investigate into two financial topics using annual and monthly data sets of market-capitalization based size portfolio returns from the US stock market for the period 1925 to 2012. Using size-based portfolio returns is a pioneering effort for both topics. The first empirical research using annual data is on short and long horizon stock return predictability using three widely selected ratios in terms of price-output, price-earnings and price-dividend. Using univariate and multivariate predictive regressions for horizons from one year to fifteen years for the full sample and three different sub-samples for comparison reasons with the previous research using aggregate stock market data, it is reported that both short and long horizon return predictability exists albeit with different predictive ability for different horizons. Among the three selected ratios, overall the price-output ratio is empirically favoured as a superior predictor of stock returns. The empirical findings refer to that this is robust across the three sub-samples investigated. It is empirically shown that size significantly matters in terms of return predictability. The second empirical research using monthly data is on the analysis of impact of macroeconomic volatility in terms of inflation and industrial production growth on asymmetric time-varying volatility of stock returns. Using a two-stage econometric methodology, first, based on estimation of asymmetric conditional volatilities of stock returns and macroeconomic variables, and then employing a vector autoregression methodology; it is reported that volatility of size-based portfolio returns are, in general, not significantly dependent on macroeconomic volatility. It is also shown that stock return volatility is more responsive to its own previous shocks as shown by the variance decomposition. It is also found that size does not matter in this specific case.