Thesis (Ph. D.)--Florida State University, 2005. / Advisor: David Peterson, Florida State University, College of Business, Dept. of Finance. Title and description from dissertation home page (viewed Jan. 24, 2006). Document formatted into pages; contains viii, 131 pages. Includes bibliographical references.

Author's thesis (doctoral)--Universität Frankfurt am Main, 2009. / Includes bibliographic references (p. 175-217).

Huang, Zheng. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (p. 30-32). / Abstract also in Chinese. / Abstract (English) --- p.i / Abstract (Chinese) --- p.ii / Acknowledgement --- p.iii / Table of Contents --- p.iv / List of Tables and Figures --- p.v / Chapter Chapters: --- List of Tables and Figures / Chapter 1. --- Introduction --- p.1 / Chapter 2. --- Literature Review: Accrual Anomaly --- p.7 / Chapter 3. --- Data and Descriptive Statistics --- p.12 / Chapter 3.1 --- Sample Selection --- p.12 / Chapter 3.2 --- Variable Measurement --- p.12 / Chapter 3.3 --- Descriptive Statistics --- p.14 / Chapter 4. --- Basic Accrual Anomaly Evidence for Loss Firms --- p.15 / Chapter 5. --- Empirical Evidences of the Pseudo accrual Trading Strategy --- p.18 / Chapter 5.1 --- The Effectiveness of the Pseudo accrual Trading Strategy --- p.18 / Chapter 5.2 --- The Asset Growth Effect Tests of the Pseudo Accrual --- p.22 / Chapter 6. --- Sensitivity Tests --- p.25 / Chapter 7. --- Conclusion --- p.28 / References --- p.30

Includes bibliographical references.

Thesis (Ph. D.)--University of Oregon, 2005. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 69-71). Also available for download via the World Wide Web; free to University of Oregon users.

Thesis (Ph. D.)--University of Rhode Island, 2005. / Typescript. Includes bibliographical references (leaves 127-134).

This thesis studies the problem of experimentally evaluating the scaling behaviors of existing multicore real-time task scheduling algorithms on large-scale multicore platforms. As chip manufacturers rapidly increase the core count of processors, it becomes imperative that multicore real-time scheduling algorithms keep pace. Thus, it must be determined if existing algorithms can scale to these new high core-count platforms. Significant research exists on the theoretical performance of multicore real-time scheduling algorithms, but the vast majority of this research ignores the effects of scalability. It has been demonstrated that multicore real-time scheduling algorithms are feasible for small core-count systems (e.g. 8-core or less), but thus far the majority of the algorithmic research has never been tested on high core-count systems (e.g. 48-core or more). We present an experimental analysis of the scalability of 16 multicore real-time scheduling algorithms. These algorithms include global, clustered, and partitioned algorithms. We cover a broad range of algorithms, including deadline-based and utility accrual scheduling algorithms. These algorithms are compared under metrics including schedulability, tardiness, deadline satisfaction ratio, and utility accrual ratio. We consider multicore platforms ranging from 8 to 48 cores. The algorithms are implemented in a real-time Linux kernel we create called ChronOS. ChronOS is based on the Linux kernel's PREEMPT RT patch, which provides the underlying operating system kernel with real-time capabilities such as full kernel preemptibility and priority inheritance for kernel locking primitives. ChronOS extends these capabilities with a flexible, scalable real-time scheduling framework. Our study shows that it is possible to implement global fixed and dynamic priority and simple global utility accrual real-time scheduling algorithms which will scale to large-scale multicore platforms. Interestingly, and in contrast to the conclusion of prior research, our results reveal that some global scheduling algorithms (e.g. G-NP-EDF) is actually scalable on large core counts (e.g. 48). In our implementation, scalability is restricted by lock contention over the global schedule and the cost of inter-processor communication, rather than the global task queue implementation. We also demonstrate that certain classes of utility accrual algorithms such as the GUA class are inherently not scalable. We show that algorithms implemented with scalability as a first-order implementation goal are able to provide real-time guarantees on our 48-core platform. / Master of Science

published_or_final_version / Business / Doctoral / Doctor of Philosophy

This study investigates whether market learning explains the absence of the accrual anomaly in recent years by examining three conditions associated with the presence of the anomaly in prior research: (i) a differential relation between future earnings and cash flows versus accruals, (ii) incorrect weighting of cash flows and accruals by investors when predicting earnings, and (iii) association of earnings forecast errors with returns. All of these conditions are widely documented in the anomaly period. In the no-anomaly period, I continue to find a differential relation of cash flows and accruals with future earnings. However, investors appear to correctly weight accruals and cash flows in their earnings predictions implicit in beginning-of-year security prices, consistent with learning. This study also investigates whether improvements in analyst forecasts contribute to investor learning and the absence of the anomaly. The association between analyst optimism and accruals is weaker in the no-anomaly period, but is still statistically significant. Furthermore, the anomaly ended simultaneously for firms followed by analysts and for non-followed firms, suggesting that improvements in analyst forecasts alone cannot account for improved market efficiency with respect to accruals. The results suggest that the anomaly was similar for firms held by institutional investors and for firms with no institutional holdings before the discovery of the anomaly while the anomaly ended sooner for held firms than for non-held firms after the discovery of the anomaly, consistent with the conjecture that arbitrage by institutional investors reduce the anomaly. Overall, the findings are consistent with market learning and suggest that improvement in investors' interpretation of accruals after the discovery of the anomaly explains the end of the anomaly. This improvement in investor learning is not due to changes in analysts' forecasting behavior, however.

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