This dissertation consists of two essays, in which I examine the effects of incomplete or noisy information on expected risk premium in equity markets. In the first essay I provide empirical evidence demonstrating that an information-quality (IQ) factor, built on accrual-based information precision measure, is priced. This result still stands after controlling for factors, such as size, Book-to-Market (B/M) ratio, and liquidity. To explain this empirical observation, I derive a continuous-time model in the spirit of Merton’s (1973) Intertemporal Capital Asset Pricing Model (ICAPM) to examine how systematic IQ risk affects security returns. Unique to my model, imprecise information influences the pricing of an asset through its covariance with: (i) stock return; (ii) market return; and (iii) market-wide IQ. In equilibrium, the aggregate effect of these covariance terms (proportional to IQ-related betas) represents the systematic component of IQ risk and therefore requires a risk premium to compensate for it. My empirical test confirms that the aggregate effect of systematic IQ risk is significant and robust to the inclusion of other risk sources, such as liquidity risk. In the second essay I extend a recent complete information stock valuation model with incomplete information environment. In practice, mean earnings-per-share growth rate (MEGR) is random and unobservable. Therefore, asset prices should reflect how investors learn about the unobserved state variable. In my model investors learn about MEGR in continuous time. Firm characteristics, such as stronger mean reversion and lower volatility of MEGR, make learning faster and easier. As a result, the magnitude of risk premium due to uncertainty about MEGR declines over learning horizon and converges to a long-term steady level. Due to the stochastic nature of the unobserved state variable, complete learning is impossible (except for cases with perfect correlation between earnings and MEGR). As a result, the risk premium is non-zero at all times reflecting a persistent uncertainty that investors hold in an incomplete information environment.

No description available.

Diss. Stockholm : Handelshögskolan, 2007.

Zugl.: Duisburg, Essen, University, Diplomarbeit, 2004.

Zugl.: Köln, Fachhochsch., Diplomarbeit.

Thesis (Ph. D.)--University of Chicago, Dept. of Economics, June 1998. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Chicago Graduate School of Business, June 1999. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Chicago, Graduate School of Business, August 2000. / Includes bibliographical references. Also available on the Internet.

No description available.

The delivery of medium and mega project has been a challenge for a number of decades, with attempts made to reduce the associated issues around engineering projects implementation. Project delivery before 1950 mainly relates to cost, time, and scope, with a lack of documentation pertaining to methods, as well as inadequate techniques to achieve a quality final product. The concept of Asset Operational Readiness (AOR) emanates in the 1950s from the military as means of providing the “developmental state of weapons systems”. The concept gained momentum as it was associated with “system safety” in the 1980s for decision-making. AOR can be defined as an establishment of a state or configuration which, after completion of the project, “places the right people in the right places at a right time working with the right hardware according to the right procedures and management controls”. The research work covered in this thesis, aims to propose a best-practice AOR framework for mega-projects in the power generation industry. A thorough Literature Review provides an overview of best practices on the AOR requirements for various industrial fields. The survey shows that AOR implementation follows the Project Life Cycle Management (PLCM) principles, from conceptual and pre-feasibility phases to commissioning and operation phases. In addition, the survey considers methodologies and techniques, which aids to enhance AOR framework development such as Root Cause Analysis (RCA) exercises. The study has provided an opportunity to develop an AOR theoretical framework refinement methodology, inclusive of RCA, AOR assessment tools, qualitative survey tool, and scoring systems. The AOR best practice framework and refinement methodology application to a real mega project case study, with historical data, enables a stage wise assessment of each component for individualized performance rating. This provides an identification of the areas that require refinement to have an improved AOR framework as outcome. The research outcome shows that there are implications for inadequate development and implementation of items in the proposed framework. The implications range from rework during manufacturing and construction, poor product quality delivery, poor performance post commissioning, and overall cost overruns. In addition, the study provides evidence that implementation of the AOR framework aids a project to realize its potential and yield positive results, which ultimately benefits an organization in terms of quality product delivery, cost reduction, and optimal Operations and Maintenance of the established asset. / Dissertation (MEng)--University of Pretoria, 2019. / Civil Engineering / MEng / Unrestricted