This dissertation reconciles ostensibly conflicting evidence from prior research about the association between information uncertainty and post-earnings-announcement drift (PEAD). According to traditional PEAD studies there should be a positive association between PEAD and uncertainty about the implication of an earnings announcement for future earnings, referred to in this dissertation as "information uncertainty." Empirical studies have documented both positive and negative associations, however. In particular, studies that use analyst forecast dispersion as a proxy for information uncertainty report a negative association between information uncertainty and PEAD. Although the authors of those studies argue that their results are consistent with behavioral finance theories, a negative association between information uncertainty and PEAD is troubling because it is not consistent with the notion that more reliable information improves market efficiency. In fact, previous empirical studies that use proxies for information uncertainty other than analyst forecast dispersion find a positive association between information uncertainty and PEAD. This study argues that the negative association between analyst forecast dispersion and PEAD can be explained by "herding" behavior immediately after earnings announcements. I introduce an analyst-based proxy for information uncertainty that mitigates the effects of herding on forecast dispersion. I find that, after controlling for the effect of herding, there is a positive association between information uncertainty and PEAD even when analyst forecasts are used to measure information uncertainty.

In partial fulfillment of the Degree: Master of Commerce (Accountancy), University of the Witwatersrand, October 2017 / This research examines through the use of survey data which key factors around a companies‟ industry positioning, strategic decisions and internal qualitative capabilities, are considered by financial analysts when preparing their financial forecasts. The research covered buy-side and sell-side analysts in South Africa. The results were however found to be non-conclusive and did not align to previous research on this matter. Comparisons between analysts covering the same company were performed with consistencies found on average across all variables. It is interesting to note that when a detailed analysis and comparison was performed by individual variable for analysts covering the same company, different views on some of the variables were identified between buy-side and sell-side analysts, therefore supporting the research obtained during the literature review. It was found based on the tests performed that the factors which have an impact on forecasted financials relate to superior product/service strategy, innovation and ability to execute strategy. These variables were however noted not to be consistent across all the financial forecast factors and are contradictory to the research highlighted in the literature review as well as the outcomes of the original study, ie. There are additional factors which are considered important. Further research is recommended on analyst behaviour in South Africa. / GR2018

No description available.

by Li, Man-kong, Vincent, Yee, Yat-pui, Yvonne. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 92-94. / Library's copy:Disk for circulation (3.5 in.) / TABLE OF CONTENTS / ABSTRACT --- p.ii / TABLE OF CONTENTS --- p.iii / LIST OF TABLES --- p.v / ACKNOWLEDGEMENTS --- p.vi / CHAPTER / Chapter I. --- INTRODUCTION --- p.1 / Importance of Earnings Forecast --- p.1 / Previous Research Findings --- p.3 / Objective --- p.5 / Chapter II. --- AN OVERVIEW OF EARNING FORECASTING --- p.6 / Methods of Forecasting --- p.6 / Financial Analyst's Model --- p.7 / Time-series Model --- p.9 / Common Belief Regarding Forecast Superiority --- p.10 / Chapter III. --- RESEARCH DESIGN AND METHOD OF ANALYSIS --- p.12 / General Framework --- p.12 / Choice of Companies and Time Period --- p.13 / Data Preparation --- p.16 / Actual Earning Data --- p.16 / Forecast Data by Financial Analyst --- p.17 / Adjustment of Data --- p.18 / Time Series Model Data --- p.20 / Generation of Time-series Data --- p.20 / The Time-series Model --- p.20 / Data Analysis --- p.25 / Definition of Forecast Error --- p.25 / Comparison of Forecast Accuracy --- p.26 / Grouping of Companies --- p.26 / Method of Comparison --- p.27 / Chapter IV. --- RESULTS OF ANALYSIS AND IMPLICATIONS --- p.30 / General Data Manipulation --- p.30 / Data For Analysis --- p.30 / Input Data --- p.30 / Computed Forecast Errors --- p.31 / Comparison of Forecast Accuracy --- p.31 / First Time Friedman Test Results --- p.31 / Full Cross-Sectional Dependence --- p.32 / Sectorial Dependence --- p.34 / Cross-Sectional Independence --- p.37 / Wilcoxon Signed Rank Test and Second Time Friedman Test --- p.41 / Full Cross-Sectional Dependence. --- p.42 / Sectorial Dependence --- p.43 / Cross-Sectional Independence --- p.44 / Chapter V. --- CONCLUSION AND DISCUSSION --- p.45 / Findings From Results of Study --- p.45 / Limitation of Study --- p.48 / Cost of Forecast --- p.48 / Sample Size and Length of Study Period --- p.48 / Limited Past Earning Data --- p.49 / Accounting Income Manipulation --- p.50 / Suggested Further Study --- p.50 / APPENDIX A --- p.52 / APPENDIX B --- p.53 / APPENDIX C --- p.56 / BIBLIOGRAPHY --- p.92

Sun Yuekang. / Thesis submitted in: October 1999. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 40-44). / Abstracts in English and Chinese.

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Wong Man Kit. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 118-121). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Table of Contents --- p.v / List of Tables --- p.viii / List of Figures --- p.x / List of Appendices --- p.x / Chapter Chapter 1 --- Introduction --- p.1 / Chapter Chapter 2 --- Literature Review --- p.6 / Chapter 2.1 --- Performance of Value Strategy in Stock Markets over The World --- p.7 / Chapter 2.2 --- Possible Explanations for Superior Return of Value Stocks --- p.11 / Chapter 2.2.1 --- Sampling Biases --- p.11 / Chapter 2.2.2 --- Risk Factors --- p.13 / Chapter 2.2.3 --- Expectation Error Hypothesis --- p.15 / Chapter 2.3 --- Studies for Value Strategy in Hong Kong --- p.20 / Chapter Chapter 3 --- Data and Methodology --- p.23 / Chapter 3.1 --- Methodology of Expectation Error Hypothesis --- p.23 / Chapter 3.1.1 --- Earnings Announcement Returns --- p.23 / Chapter 3.1.2 --- Past and Future Earnings Growth Rates of Stocks --- p.26 / Chapter 3.2 --- Data Source --- p.29 / Chapter 3.3 --- Portfolio Formation --- p.30 / Chapter 3.4 --- Variable Calculation Method --- p.31 / Chapter 3.4.1 --- Annual Buy and Hold Returns --- p.31 / Chapter 3.4.2 --- Earnings Announcement Returns --- p.32 / Chapter 3.4.3 --- Earnings Growth Rate of Portfolios --- p.33 / Chapter Chapter 4 --- Interpretation of Results --- p.34 / Chapter 4.1 --- Annual Buy and Hold Returns of Portfolios --- p.36 / Chapter 4.1.1 --- Annual Returns of Portfolios Sorted by B/M Ratio --- p.36 / Chapter 4.1.2 --- Annual Returns of Portfolios Sorted by E/P Ratio --- p.37 / Chapter 4.1.3 --- Analysis of Performance on Return Differences between Two Ratios --- p.38 / Chapter 4.2 --- Earnings Announcement Returns for Value and Glamour Portfolios --- p.41 / Chapter 4.2.1 --- 3-day Event Returns --- p.41 / Chapter 4.2.2 --- "B/M Ratio: 5,7,9 & 11 Days Event Returns" --- p.43 / Chapter 4.2.3 --- "E/P Ratio: 5,7,9 & 11 Days Event Returns" --- p.46 / Chapter 4.3 --- Past and Future Earnings Growths of Portfolios --- p.49 / Chapter 4.3.1 --- "Fundamental Variables, Prior and Post Returns of Portfolios" --- p.50 / Chapter 4.3.2 --- Earnings Performance of Portfolios --- p.51 / Chapter 4.3.3 --- Factors Affect Investor Expectation --- p.56 / Chapter Chapter 5 --- Conclusion --- p.59 / Tables --- p.64 / Figures --- p.76 / Appendices --- p.82 / References --- p.118