Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 142-145). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 142-145). Also available on the Internet.

Thesis (MEngSc)--University of Melbourne, Faculty of Engineering, 2010. / Typescript. Includes bibliographical references (p. 70-74)

Thesis (Ph. D.)--University of Wisconsin--Madison, 1981. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 115-117).

Thesis (Ph. D.)--University of California, San Diego, 2006. / Title from first page of PDF file (viewed September 7, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 125-130).

Thesis (Ph. D.)--Dept. of Mathematics and Statistics and Dept. of Economics. University of Missouri--Kansas City, 2006. / "A dissertation in mathematics and economics." Advisor: Yong Zeng. Typescript. Vita. Title from "catalog record" of the print edition Description based on contents viewed Jan. 29, 2007. Includes bibliographical references (leaves 121-124). Online version of the print edition.

Thesis (M.Soc.Sc.)--University of Hong Kong. 1989. / Also available in print.

abstract: Private placement is an important financing tool for listed companies in China, and the lock-up arrangement is its supporting system. The Efficient Market Hypothesis suggests that, if investor expectations are unbiased, there will be no abnormal fluctuations in the stock prices of listed companies before and after the unlocking day. However, around the time of the unlocking of private placement shares, the stock prices generally show a V-shaped pattern. Through the empirical analysis of the Chinese A-share stocks from May 8th,2006 to December 31st, 2016, I found that from the 40th day before the unlocking day to the 90th day after, the stock price showed an evident first-downward-then upward trend. The lowest price appeared near the unlocking day. Meanwhile, the greater stocks fall before the unlocking day, the greater prices rise after that. The characteristics of the distinctive difference between the stock prices before and after the unlocking day can provide investment opportunities. By reviewing research on investor behavior, this paper suggests that the V-shaped pattern can be explained by the influence of investors’ psychological factors on their trading behavior. The general performance of the stocks before the unlocking day is negative due to the increasing uncertainty perceived by investors. After the unlocking day, the uncertainty gradually disappears and the stock rebounds. In addition, I found that stock returns during the lock-up period, shareholder background, and the length of lock-up period also had significant impacts on the V-shaped price trend. / Dissertation/Thesis / Doctoral Dissertation Business Administration 2018

M.Comm. / The availability of large amounts of information and increases in computing power have facilitated the use of more sophisticated and effective technologies to analyse financial markets. The use of neural networks for financial time series forecasting has recently received increased attention. Neural networks are good at pattern recognition, generalisation and trend prediction. They can learn to predict next week's Dow Jones or flaws in concrete. Traditional methods used to analyse financial markets include technical and fundamental analysis. These methods have inherent shortcomings, which include bad timing of trading signals generated, and non-continuous data on which analysis is based. The purpose of the study was to create a tool with which to forecast financial time series on the Johannesburg Stock Exchange (JSE). The forecasted time series information was used to generate trading signals. A study of the building blocks of neural networks was done before the neural network was designed. The design of the neural network included data choice, data collection, calculations, data pre-processing and the determination of neural network parameters. The neural network was trained and tested with information from the financial sector of the JSE. The neural network was trained to predict share prices 4 days in advance with a Multiple Layer Feedforward Network (MLFN). The mean square error on the test set was 0.000930, with all test data values scaled between 0.1 - 0.9 and a sample size of 160. The prediction results were tested with a trading system, which generated a trade yielding 20 % return in 22 days. The neural network generated excellent results by predicting prices in advance. This enables better timing of trades and efficient use of capital. However, it was found that the price movement on the test set within the 4-day prediction period seldom exceeded the cost of trades, resulting in only one trade over a 5-month period for one security. This should not be a problem if all securities on the JSE are analysed for profitable trades. An additional neural network could also be designed to predict price movements further ahead, say 8 days, to assist the 4-day prediction

Please read the abstract in the section 00front of this document / Dissertation (MSc (Mathematics of Finance))--University of Pretoria, 2006. / Mathematics and Applied Mathematics / unrestricted