No abstract available. / Thesis (MBA)-University of Natal, Durban, 2002.

No description available.

No description available.

The goal of this dissertation research is to model risk in delivery, operation and maintenance phases of infrastructure asset management. More specifically, the two main objectives of this research are to quantify and measure financial risk in privatizing and operational risks in maintenance and rehabilitation of infrastructure facilities. To this end, a valuation procedure for valuing large-scale risky projects is proposed. This valuation approach is based on mean-risk portfolio optimization in which a risk-averse decision-maker seeks to maximize the expected return subject to downside risk. We show that, in complete markets, the value obtained from this approach is equal to the value obtained from the standard option pricing approach. Furthermore, we introduce Coherent Valuation Procedure (CVP) for valuing risky projects in partially complete markets. This approach leads to a lower degree of subjectivity as it only requires one parameter to incorporate user's risk preferences. Compared to the traditional discounted cash flow analysis, CVP displays a reasonable degree of sensitivity to the discount rate since only the risk-free rate is used to discount future cash flows. The application of this procedure on valuing a transportation public-private partnership is presented. %and demonstrate that the breakeven buying price of a risky project is equal to the value obtained from this valuation procedure. Secondly, a risk-based framework for prescribing optimal risk-based maintenance and rehabilitation (M&R) policies for transportation infrastructure is presented. These policies guarantee a certain performance level across the network under a predefined level of risk. The long-term model is formulated in the Markov Decision Process framework with risk-averse actions and transitional probabilities describing the uncertainty in the deterioration process. Conditional Value at Risk (CVaR) is used as the measure of risk. The steady-state risk-averse M&R policies are modeled assuming no budget restriction. To address the short-term resource allocation problem, two linear programming models are presented to generate network-level polices with different objectives. In the first model, decision-maker minimizes the total risk across the network, and in the second model, the highest risk to the network performance is minimized.

In this study I investigate the potential improvement in multiple-based valuations from using composite valuations based on price to earnings and price to book ratios against their respective individual ratios and actual price in terms of their predictive accuracy against future price. It is motivated by the popularity of accounting-based market multiples used by practitioners in valuation activities with little published research documenting the absolute and relative performance of composite multiples and its vulnerability to manipulation by biased analysts. First, I generate benchmark multiples using a multiple regression approach and in turn these benchmark multiples are used in the generation of composite valuations. Second, I incorporate firm characteristics such as anticipated growth and financial positions in the development of these composite valuations. Third, I investigate any further improvement in predictive accuracy from enterprise value to sales ratio which is less subjective to accounting policy choices and conservative accounting. The main results support the hypothesis that composite benchmark multiples lead to improved valuations over single multiples and further improvement is achieved by incorporating the potential growth rate and financial condition in the composite benchmark multiples. In particular, the three ratio regression-based composite multiples with the growth and the financial condition factor has the smallest mean and median absolute valuation errors. Findings remain unchanged when the analysis is based on December fiscal year end firms and using a parsimonious model in the estimation regression. However, the analysis of mispricing reveals that the valuation model might be useful in settings where market price is not available, such as initial public offerings and court valuation of private firms where a valuation is needed due to strong evidence that high positive pricing errors identify subsequent high returns.

In this study I investigate the potential improvement in multiple-based valuations from using composite valuations based on price to earnings and price to book ratios against their respective individual ratios and actual price in terms of their predictive accuracy against future price. It is motivated by the popularity of accounting-based market multiples used by practitioners in valuation activities with little published research documenting the absolute and relative performance of composite multiples and its vulnerability to manipulation by biased analysts. First, I generate benchmark multiples using a multiple regression approach and in turn these benchmark multiples are used in the generation of composite valuations. Second, I incorporate firm characteristics such as anticipated growth and financial positions in the development of these composite valuations. Third, I investigate any further improvement in predictive accuracy from enterprise value to sales ratio which is less subjective to accounting policy choices and conservative accounting. The main results support the hypothesis that composite benchmark multiples lead to improved valuations over single multiples and further improvement is achieved by incorporating the potential growth rate and financial condition in the composite benchmark multiples. In particular, the three ratio regression-based composite multiples with the growth and the financial condition factor has the smallest mean and median absolute valuation errors. Findings remain unchanged when the analysis is based on December fiscal year end firms and using a parsimonious model in the estimation regression. However, the analysis of mispricing reveals that the valuation model might be useful in settings where market price is not available, such as initial public offerings and court valuation of private firms where a valuation is needed due to strong evidence that high positive pricing errors identify subsequent high returns.

Thesis (M.S.)--Rochester Institute of Technology, 2007. / Typescript. Includes bibliographical references (leaves 86-90).

Thesis (B.Sc. (Honours) in Economics)--Singapore Management University, 2005. / Senior thesis in part fulfillment for the BSc (Honours) in Economics degree presented to the School of Economics and Social Sciences, Singapore Management University 2004-2005. Includes bibliographical references (p. 73-74).

Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains xii, 173 p.; also includes graphics (some col.). Includes bibliographical references (p. 148-154).

Thesis (Ph. D.)--Ohio State University, 2004. / Title from first page of PDF file. Document formatted into pages; contains xv, 180 p.; also includes graphics (some col.). Includes bibliographical references (p. 115-121).