Thesis (Ph. D.)--University of Missouri-Columbia, 2005. / 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 (May 15, 2007) Vita. Includes bibliographical references.

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: Robust estimation; portfolio optimization. Includes bibliographical references (leaf 24).

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

The two main questions arising from the problem of optimal bond portfolio management concern the formulation of an optimal trading rule and the specification of an appropriate dynamic risk measure in which to express portfolio objectives. We study these questions in two related essays: (l) a theoretical study of optimal trading policies in view of, as yet unspecified, portfolio objectives when trading is costly; and (2) an empirical, comparative study of several bond risk measures, proposed in the literature or in use by practitioners, for the government or default-free bond market. The theoretical study considers a delegated portfolio management setting, in which the manager optimizes a cumulative reward over a finite time period and where the reward rate increases with portfolio value and decreases with deviations from the given risk objectives. Trading is then often not worthwhile, as the possible gains from smaller objective deviations are offset by losses on account of transactions costs. This setting obviates the need for separate ex post performance evaluation. The trading problem is formulated as one of optimal impulse control in the framework of stochastic dynamic programming; this formulation improves upon prior results in the literature using continuous control theory. A myopic optimal trading rule is characterized, which is also applicable to time-homogeneous problems and more general preferences. An algorithm for its use in applications is derived. The empirical study applies the usual methods of stock market tests to the returns of constant risk bond portfolios. These portfolios are artificial constructs composed, at varying risk levels, of traded bonds on the basis of six different one or two dimensional risk measures. These risk measures are selected in order to obtain a cross-section of term structure variabilities; they include duration, short interest rate risk, long (13-year) interest rate risk, combined short and consol rate risks, duration combined with convexity, and average time-to-maturity. The sample period is the 1970s decade, for which parameter estimates for the risk measures— where necessary—are available from source papers. This period is known to be one with wide-ranging term structure movements and is therefore ideally suited for the tests of this paper. Portfolios are formed at two levels of diversification: bullet and ladder selection. We confirm that all of these risk measures are reasonably effective in capturing relevant bond market risk: the state space of bond returns has in all cases a low dimension (two or three), with only a single factor significantly priced. Best fit is found for portfolios selected by duration, the 13-year spot yield risk, and the two-dimensional short/consol rate risk, all of which consist predominantly of "long" rate risk. The short rate-based risk measure does not explain portfolio returns as well: it has difficulty discriminating between portfolios with long remaining times-to-maturity. Convexity, furthermore, adds nothing to the explanatory power of duration. Average time-to-maturity compares reasonably well with the above risk measures, provided the portfolios are well-diversified across the maturity spectrum; this lends some support to the use of yield curves. A strong diversification effect has also been found, to the extent that the returns on ladder portfolios are practically linear combinations of two or three of the portfolios, typically the lowest and highest risk portfolios in the one dimensional risk cases, with an intermediate portfolio added in the two-dimensional cases. Provided that diversified portfolios are used, the comparatively easy to implement duration measure is as good as any of the risk measures tested. / Business, Sauder School of / Finance, Division of / Graduate

Thesis (MBA)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: The academic view on project portfolio management is not as well formulated as in commercial literature (PMI / APM / IPMA). It is not understood if there is sufficient agreement in academia for a universally accepted definition of project portfolio management and its value contribution. A structured literature review was conducted to determine if there are commonly reoccurring project portfolio management principles identified in academic literature and to find the most suited definition of PPM. The research revealed a number of principles that are consistently referenced by the various articles. A suitable definition to satisfy the majority of the articles was not identified and the author proposes a definition to suit the academic content.

published_or_final_version / Business Administration / Master / Master of Business Administration

published_or_final_version / Mathematics / Master / Master of Philosophy

Student Number : 0204111E - M Com research report - School of Economic and Business Sciences - Faculty of Commerce / As organisations continuously attempt to do more with less, Chief Information Officers (CIOs) must manage their portfolio of IT investments more effectively and efficiently. In order to achieve this, CIOs can adopt a portfolio management approach; however, there are barriers to the adoption to IT portfolio management. The purpose of this research was to explore the barriers to adoption of IT portfolio management. The barriers were identified by respondents from various sectors and across various levels in their organisations and then ranked in order to determine the most critical factors that impede adoption of IT portfolio management. Data was collected using the Delphi ranking type method, and targeted at CIOs, IT executives, and project managers. The questionnaire was designed to identify perceptions of the most significant barriers to IT portfolio management adoption and strategies for mitigating the effects of these barriers were drawn from the literature. The rank order of 11 barriers was determined from the individual ratings and rank orders of 38 respondents in the final phase with ‘the lack of executive sponsorship, support, and understanding of IT portfolio management’ being ranked as the most critical barrier.

This study contributes to the debate on active and passive portfolio management by providing an alternate means of constructing an active portfolio. This “fusion strategy” has underpinnings in the realm of behavioural finance, namely the value-growth phenomenon and the momentum effect. The fusion strategy developed in this study was compared against two passive benchmarks and four active benchmarks. All returns are calculated net of transaction costs, initially set to 1% per month per share. Statistical testing, done via stochastic dominance, yielded inconclusive results in the majority of cases. The exception however, was that Fund B stochastically dominated the fusion strategy at second order. This implies that a risk-averse investor would prefer to invest in Fund B. By the use of Sharpe and Treynor ratios, the results were also inconclusive. However, the Sortino ratio shows that the fusion strategy outperforms all benchmarks chosen, except Fund A. The performance of the fusion strategy was also not induced by either a sector rotation strategy, the existence of the January effect or by the level of transaction costs.

The objective of this study is to answer the following research question: How large is a diversified portfolio? Although previous work is abundant, very little progress has been made in answering this question since the seminal work of Evans and Archer (1968). This study proposes two approaches to address the research question. The first approach is to measure the rate of risk reduction as diversification increases. For the first approach, I identify two kinds of risks: (1) risk that portfolio returns vary across time (Evans and Archer (1968), and Campbell et al. (2001)); and (2) risk that returns vary across portfolios of the same size (Elton and Gruber (1977), and O'Neil (1997)). I show that the times series risk reaches an asymptote as portfolio size increases. Cross sectional risk, on the other hand, does not appears to reach an asymptote as portfolio size increases. The second approach consists of comparing portfolios' performance to a benchmark portfolio that is assumed to be diversified (Statman (1987)). I develop a performance index. The performance index is calculated, for any given test portfolio, as the ratio of the Sharpe-like measure of the test portfolio to the Sharpe-like measure of the benchmark portfolio that is assumed to be diversified. The index is based on the intuition that an increase in portfolio size reduces times series risk and cross sectional risk, and increases transaction costs. Portfolio size is worth increasing as long as the marginal increase in the performance index from a decrease in risk is greater than the marginal decrease of the performance index from an increase in transaction costs. Diversification is attained when the value of the index reaches one. The results of my simulations indicate that the size of a well diversified portfolio is at the very least 30. This number can be substantially higher if, for example, the investment horizon length, the benchmark portfolio, and/or the cost of investing in the benchmark portfolio are changed. The active diversification strategy considered in this study, which consists of optimizing randomly selected portfolios, does not seem to produce smaller diversified portfolios. This result supports the market efficiency hypothesis.