Thesis (M. Sc.)--University of Hong Kong, 2003. / Title proper from title frame. Also available in printed format.

Thesis (Ph.D.)--Drexel University, 2010. / Includes abstract and vita. Includes bibliographical references (leaves 91-92).

Thesis (doctoral)--University of Passau, 2007. / Includes bibliographical references (p. [127]-133). Also available in print.

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: fixed-income; database design; architecture. Includes bibliographical references (p. 39).

Thesis (Ph. D.) University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 10, 2007) Vita. Includes bibliographical references.

This thesis gives the formal derivations of the so-called Rubinstein's measures of risk aversion and their multivariate generalizations. The applications of these measures in portfolio selection models are also presented. Assuming that a decision maker's preferences can be represented by a unidimensional von Neumann and Morgenstern utility function, we consider a model with an uninsurable initial random wealth and an insurable risk. Under the assumption that the two random variables have a bivariate normal distribution, the second-order co-variance operator is developed from Stein/Rubinstein first-order covariance operator and is used to derive Rubinstein's measures of risk aversion from the approximations of risk premiums. Rubinstein's measures of risk aversion are proved to be the appropriate generalizations of the Arrow-Pratt measures of risk aversion. In a portfolio selection model with two risky investments having a bivariate normal distribution, we show that Rubinstein's measures of risk aversion can yield the desirable characterizations of risk aversion and wealth effects on the optimal portfolio. These properties of Rubinstein's measures of risk aversion are analogous to those of the Arrow-Pratt measures of risk aversion in the portfolio selection model with one riskless and one risky investment. In multi-dimensional decision problems, we assume that a decision maker's preferences can be represented by a multivariate utility function. From the model with an uninsurable initial wealth vector and insurable risk vector having a joint normal distribution in the wealth space, we derived the matrix measures of risk aversion which are the multivariate extension of Rubinstein's measures of risk aversion. The derivations are based on the multivariate version of Stein/Rubinstein covariance operator developed by Gassmann and its second-order generalization to be developed in this thesis. We finally present an application of the matrix measures of risk aversion in a portfolio selection model with a multivariate utility function and two risky investments. In this model, if we assume that the random returns on the two investments and other random variables have a joint normal distribution, the optimal portfolio can be characterized by the matrix measures of risk aversion. / Business, Sauder School of / Graduate

M.Comm. / ffntroduction Measuring and evaluating risks are essential in a dynamic derivative market to minimize risks. The management of risks in the derivative market is complex due to the non-linear properties of option pricing Method of study The a first step of the study analyzed the "greek" derivatives of a single option contract (e.g. delta, gamma, vega, theta). The next step was to combine and analyze the derivatives of various option contracts. The study pointed out that the risk profile can be amended by combining option contracts. A risk measurement and evaluation model was constructed by creating a table that will simulate option prices at different time horizons and at different market prices. The model will also simulate all the derivatives of options in a table form at different time horizons and at different market prices. The model finally used the tables to reflect the results graphically. Findings The last section of the study was devoted to scenario simulation to identify risks. Firstly the management of the delta was analyzed, and the use of the gamma to identify delta sensitivity was illustrated. The management of the vega was addressed next. The study showed that a combination of options can minimize the risk of vega. The effect of theta or the time value of a option was illustrated and linked to both gamma and vega. The study demonstrated that the results of volatile movements in the market can be simulated by combining the derivatives of options (e.g. add the deltas of options together), and to stress test the strategy. "What if' scenarios can be simulated to illustrate the effect on a current position combined with some amendments.

Extensive empirical evidence shows that passively managed index-tracking mutual funds and exchange-traded funds (ETFs) outperform actively managed portfolios. On the other hand, there are abundant findings that stocks admitted to an index outperform those deleted from the index. This study tests an issue that has been largely ignored in academic studies but is highly related to the above two seemingly disparate areas of researches. The paper examines the long-term performance of non-index individual stocks and stock portfolios relative to the index. The study proposes that the inclusion and maintenance criteria for index component stocks are long-term performance indicators. Therefore, an index can be regarded as a passively managed and highly diversified portfolio of expected outperformers. Using a complete set of H-shares listed on HKEx for the period 2001 to 2017, the study finds that 44.25% (55.75%) of individual stocks have positive alphas (negative alphas) relative to the index. The average alpha for the family of all non-index stock is negative but statistically insignificant, i.e., 77 positive alphas and 97 negative alphas. Most alphas are statistically insignificant, but only 5 are positive, and 2 are negative at 5% significance level. From the risk and return perspective, the index dominates two-third of the non-index H-shares. Regression analyses show that H-index outperforms non-index H-shares in general and the market capitalization and turnover ratio play an important role in determining the long-term performance of H-shares, which are the major factors for the admission and maintenance criteria of H-index. The findings strongly support our conjecture that the index admission and maintenance criteria are the quality assurance of individual constituent stocks of an index. The paper provides incremental evidence on the widely documented result that index trackers outperform actively managed portfolios. Nevertheless, the study extends the recent literature on the long-term performance of stocks that are admitted to (or excluded from) an index. The findings of the study have significant implications for securities markets participants, including index providers and ETF issuers

Thesis (MBA)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: Portfolio managers and investors have developed numerous stock-picking strategies for managing stock market portfolios, many of which have been researched extensively in international markets. For example, research has shown that value stocks have higher returns than growth stocks in markets around the world (Fama & French 1998). A very popular value investing strategy is the ‘magic formula’ developed and published by Joel Greenblatt, in 2006, in his book The little book that beats the market. This strategy is based on constructing portfolios where return on capital and earnings yield are used as selection criteria. Greenblatt (2010) provided results that showed that the magic formula strategy was able to persistently outperform the United States stock market from 1988 to 2009. This study provides a back-test of the magic formula on stocks listed on the Johannesburg Stock Exchange for the period 1 January 1998 to 31 December 2013. The return was benchmarked against the FTSE/JSE J203 All Share Total Return Index and several other popular value investing strategies over the same period. It was found that, even after adjusting for risk, the magic formula was able to consistently outperform the market index. While the magic formula was able to outperform the market index, it was not the top performing value investing strategy evaluated in this study. The magic formula was outperformed by the combination of size and book-to-market, book-to-market alone, dividend yield, and earnings yield value investing strategies. While the magic formula, and the above mentioned value investing strategies, were able to outperform the market index in terms of overall geometric mean returns, there is not enough evidence to conclude that these value investing strategies outperformed the market index by a statistically significant margin.

Thesis (MBA)--Stellenbosch University, 2015. / ENGLISH ABSTRACT:The main aim of this study was to determine the effect of unanticipated information, or noise, on the returns of cap-weighted portfolios in various segments of the JSE for the period 1995 to 2014. Capital Market Theory states that the optimal ex ante portfolio comprises all shares in a market/segment weighted by ex ante market capitalisation. The optimal ex ante portfolio is however rarely the optimal ex post portfolio, because it is underweighted in shares that will unexpectedly become ‘winners’ during the investment period and overweighted in those that will become ‘losers’. According to Fuller, Han and Tung (2012), all investors in a segment would gain maximum alpha from a portfolio weighted by ex post market capitalisation – in other words, a ‘perfect foresight’ (PF) portfolio. The excess return of the PF portfolio over the benchmark portfolio therefore is an estimate of the negative effect of noise on the return of the benchmark portfolio. In this study, the returns of PF portfolios were compared with the All Share, Large Cap, Mid Cap, Small Cap, Financials, Industrials and Resources segments of the JSE. Intuitively, information to guide decisions on portfolio weighting would be more valuable and deliver more profit when the cross-sectional standard deviation of share returns is high. A secondary aim was therefore to investigate the correlation between cross-sectional standard deviation and PF excess return. It was found that a strong positive correlation (more than 88%) existed between cross-sectional standard deviation and PF excess return in all segments. In ascending order of cross-sectional standard deviation and PF excess return, the results for the segments were Financials (25% and 5%), Resources (28% and 6%), Large Cap (29% and 8%), Industrials (30% and 9%), All Share (32% and 9%), Mid Cap (36% and 13%) and Small Cap (43% and 17%).