Teses / dissertações sobre o tema "Stocks Rate of return Mathematical models"

"August 1998" Bibliography: leaves 74-78. The relationship between the returns to a stock, and ratio of book equity to market equity of the firm, are tested for the Australian stock market, and statistically significant evidence is found in support if the :book to market effect". Several tests are performed to determine whether this return premium is the result of additional risk or market inefficiency. No evidence is found to suggest that high book-to-market stocks are associated with additional risk, and only weak evidence is found to suggest that return premium is a result of investor over-reaction. An alternative explanation IS offered, relying on the dynamic behavior of firms and the process by which investors value the stocks of these firms.

Assignment (MAcc )--University of Stellenbosch, 2002.<br>ENGLISH ABSTRACT: The price-earnings ratio is one of a series of benchmarks developed after the Great Depression, to measure the fair value of shares on a relative basis. It originated from the idea that investors buy the earnings of a company and that the price-earnings ratio provides a consensus indication of the future growth potential of a company. Therefore, the price-earnings ratio is a rating of a company's future profitability. The price-earnings ratio developed, over the years, firstly, into an indicator of the relative risk associated with a company as the market anomalies associated with the ratio were investigated and clarified, and the theoretical background of the ratio integrated with the portfolio theory. It is now clear that the price-earnings ratio can be a useful indicator of the risk associated with an investment and the uncertainty associated with the duration of the growth phase of a company. Secondly, the price-earnings ratio is also a growth and valuation model with a theoretical background that can be linked to popular dividend discount models and the growth opportunities approach to investment valuation. With the use of the price-earnings ratio it is easy to visualise the relative profitability and the total investment required to raise a company's rating of future profitability. This simplicity allows one the opportunity to evaluate the reasonableness and likelihood of the investment reaching its projected potential profit targets. Lastly, as a result of accounting changes and the different accounting rules in force today, the price-earnings ratio also assists in the identification and elimination of the effects of accounting on investment decisions. It is apparent that the price-earnings ratio possesses the capabilities to assist investors significantly with the analysis of investment opportunities.<br>AFRIKAANSE OPSOMMING: Die prys-verdienste verhouding is een van 'n reeks relatiewe maatstawwe ontwikkel na die Groot Depressie om die redelike waarde van aandele te bepaal. Dit is gebaseer op die idee dat beleggers die winste van 'n maatskappy koop en dat die prys-verdienste verhouding 'n konsensus aanduiding verskaf van die toekomstige groeipotensiaal van 'n maatskappy. As gevolg hiervan is die prys-verdienste verhouding 'n aanduiding van die relatiewe toekomstige winsgewendheid van 'n maatskappy. Die prys-verdienste verhouding het oor die jare ontwikkel, eerstens as 'n aanwyser van die relatiewe risiko verbonde aan 'n maatskappy soos abnormaliteite wat daaraan verwant is ondersoek en verklaar is, en die teorieë onderliggend aan die verhouding ontwikkel het saam met die portefeulje teorie. Dit is nou duidelik dat die prys-verdienste verhouding 'n bruikbare aanduider is van die risiko wat geassosieer word met 'n belegging en die onsekerheid wat gepaard gaan met die duur van die groeifase van 'n maatskappy. Tweedens is die prys-verdienste verhouding ook 'n waardasie- en groeimodel met 'n teoretiese agtergrond wat verband hou met die populêre dividend verdiskonteringsmodelle en die groeigeleenthede-benadering tot waardasie. Met die gebruik van die prys-verdienste verhouding is dit maklik om die relatiewe winsgewendheid en die totale belegging wat benodig word om die waarde van die relatiewe winsgewendheid van 'n maatskappy te verhoog, tevisualiseer. Hierdie eenvoud verskaf die geleentheid om die redelikheid en die waarskynlikheid van 'n belegging om sy voorsiene winsgewendheidsdoelwitte te bereik, te evalueer. Laastens, as 'n resultaat van die rekeningkundige veranderinge, en die verskillende rekeningundige reëls huidiglik van toepassing in die wêreld, help die prys-verdienste verhouding ook met die identifikasie en die eliminasie van rekeningkundige komplikasies op beleggingsbesluite. Dit is duidelik dat die prys-verdienste verhouding die vermoë het om die belegger by te staan met die ontleding van beleggingsgeleenthede.

Stock return volatility has been shown to occasionally exhibit discrete structural shifts. These shifts are particularly evident in the transition from ‘normal’ to crisis periods, and tend to be more pronounced in developing markets. This study aims to establish whether accounting for structural changes in the conditional variance process, through the use of Markov-switching models, improves estimates and forecasts of stock return volatility over those of the more conventional single-state (G)ARCH models, within and across selected African markets for the period 2002-2012. In the univariate portion of the study, the performances of various Markov-switching models are tested against a single-state benchmark model through the use of in-sample goodness-of-fit and predictive ability measures. In the multivariate context, the single-state and Markov-switching models are comparatively assessed according to their usefulness in constructing optimal stock portfolios. It is found that, even after accounting for structural breaks in the conditional variance process, conventional GARCH effects remain important to capturing the heteroscedasticity evident in the data. However, those univariate models which include a GARCH term are shown to perform comparatively poorly when used for forecasting purposes. Additionally, in the multivariate study, the use of Markov-switching variance-covariance estimates improves risk-adjusted portfolio returns when compared to portfolios that are constructed using the more conventional single-state models. While there is evidence that the use of some Markov-switching models can result in better forecasts and higher risk-adjusted returns than those models which include GARCH effects, the inability of the simpler Markov-switching models to fully capture the heteroscedasticity in the data remains problematic.

by Wong Hiu Ming.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.<br>Includes bibliographical references (leaves 74-79).<br>Abstracts in English and Chinese.<br>ACKNOWLEDGMENTS --- p.iii<br>LIST OF TABLES --- p.iv<br>LIST OF ILLUSTRATIONS --- p.v<br>CHAPTER<br>Chapter ONE --- INTRODUCTION --- p.1<br>Chapter TWO --- THE LITERATURE REVIEW --- p.5<br>ARCH/GARCH Models<br>Nonparametric Method<br>Chapter THREE --- METHODOLOGY --- p.14<br>ARCH Modeling<br>Semiparametric GARCH Modeling<br>Causality Test<br>Local Polynomial Model<br>Chapter FOUR --- DATA AND EMPIRICAL RESULTS --- p.37<br>Data<br>GARCH Modeling<br>Semiparametric GARCH Modeling<br>Causality Test<br>Local Polynomial Model<br>Chapter FIVE --- CONCLUSION --- p.52<br>TABLES --- p.56<br>ILLUSTRATIONS --- p.62<br>APPENDIX --- p.71<br>BIBLIOGRAPHY --- p.74

Mak Wing Hei.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.<br>Includes bibliographical references (leaves 50-52).<br>Abstracts in English and Chinese.<br>ABSTRACT --- p.1<br>摘要 --- p.2<br>ACKNOWLEDGEMENTS --- p.3<br>TABLE OF CONTENTS --- p.4<br>Chapter CHAPTER 1 --- INTRODUCTION & LITERATURE REVIEW --- p.6<br>Chapter CHAPTER 2 --- DATA DESCRIPTION --- p.12<br>Chapter 2.1 - --- Coverage and Sources --- p.12<br>Chapter 2.2 - --- Match Accounting Data with Stock Returns --- p.12<br>Chapter 2.3 - --- Selection Rule --- p.13<br>Chapter 2.4 - --- Choice of the Threshold Variables Z --- p.14<br>Chapter CHAPTER 3 --- THE MODEL --- p.15<br>Chapter 3.1 - --- Estimating excess returns & Betas --- p.15<br>Chapter 3.2- --- Estimating Threshold Effects --- p.17<br>Chapter 3.3 - --- Testing the Number of Threshold Variables --- p.19<br>Chapter 3.4 - --- Estimating Threshold values --- p.21<br>Chapter CHAPTER 4 --- PRELIMINARY OBSERVATIONS --- p.21<br>Chapter 4.1 - --- Excess Returns --- p.21<br>Chapter 4.2 - --- "Relationship between Beta, Market Size and Book-to-Market Equity" --- p.24<br>Chapter CHAPTER 5 --- ESTIMATION RESULTS OF THE THRESHOLD MODEL --- p.35<br>Chapter 5.1 - --- Number of Threshold Variables --- p.35<br>Chapter 5.2- --- Threshold Value Estimates --- p.39<br>Chapter 5.3- --- The “and´ح case and “or´حcase --- p.40<br>Chapter 5.4 - --- Comparison with OLS --- p.45<br>Chapter CHAPTER 6 --- CONCLUSION --- p.48<br>REFERENCES --- p.50

Wong, Kin Lung Keith.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.<br>Includes bibliographical references (p. 127-131).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Thesis/Assessment Committee --- p.iii<br>Acknowledgement --- p.iv<br>Chapter 1 --- Introduction --- p.1<br>Chapter 2 --- Background Studies --- p.5<br>Chapter 2.1 --- Ultra-high Frequency Data Handling with Database Server --- p.5<br>Chapter 2.1.1 --- Use of Database Server --- p.5<br>Chapter 2.2 --- Ultra-high Frequency Data Treatments --- p.7<br>Chapter 2.2.1 --- Cleaning of Data --- p.7<br>Chapter 2.2.2 --- Matching of a Trade and Its Standing Quote --- p.13<br>Chapter 2.3 --- Tick-by-tick Price Modeling --- p.15<br>Chapter 2.3.1 --- Multivariate Linear Models --- p.15<br>Chapter 2.3.2 --- Duration and Volume Handling --- p.16<br>Chapter 2.3.3 --- VAR Model Selection Techniques --- p.20<br>Chapter 2.3.4 --- Seasonality Handling --- p.24<br>Chapter 3 --- Problem Definition and Framework --- p.27<br>Chapter 3.1 --- Engle and Patton's Model --- p.27<br>Chapter 3.2 --- Preparation of data --- p.31<br>Chapter 3.3 --- Methods to Estimate Diurnal Adjustment Param- eters --- p.38<br>Chapter 3.4 --- Transformation of the Model to Fit in VARX soft- wares --- p.40<br>Chapter 3.5 --- Modification of the Model --- p.47<br>Chapter 3.6 --- Estimating and Forecasting the Exogenous Vari- ables --- p.52<br>Chapter 3.6.1 --- Modelling BUYt and SELLt --- p.52<br>Chapter 3.6.2 --- Modelling DURt and VOLt --- p.53<br>Chapter 3.6.3 --- Modelling k(t) --- p.56<br>Chapter 3.6.4 --- Forecasting the Cross Terms and the Sum of Buys and Sells --- p.62<br>Chapter 3.7 --- Forecasting with the Main Model --- p.64<br>Chapter 4 --- Experimental Evaluation --- p.67<br>Chapter 5 --- Conclusion --- p.73<br>Chapter A --- Source and Data Information --- p.76<br>Chapter B --- Model Estimation Results for (3.13) --- p.80<br>Chapter C --- Model Forecasting Results for (3.13) and (3.2) --- p.102<br>Bibliography --- p.127

Li, Nasha.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.<br>Includes bibliographical references (leaves 28-30).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>摘要 --- p.ii<br>ACKNOWLEDGEMENTS --- p.iii<br>Tables and Figures --- p.v<br>Chapter 1. --- Introduction --- p.1<br>Chapter 2. --- Literature Review --- p.2<br>Chapter 3. --- Factor-Augmented Regression Framework --- p.6<br>Chapter 3.1 --- Estimation of latent factors --- p.8<br>Chapter 3.2 --- Number of factors --- p.9<br>Chapter 3.3 --- Interpretation of the factors --- p.11<br>Chapter 4. --- Data --- p.12<br>Chapter 5. --- Empirical Results --- p.13<br>Chapter 5.1 --- Common factors --- p.13<br>Chapter 5.2 --- Descriptive analysis --- p.16<br>Chapter 5.3 --- Macroeconomic factors and excess returns predictability --- p.18<br>Chapter 5.3.1 --- In-sample specifications --- p.18<br>Chapter 5.3.2 --- Out-of-sample prediction performance --- p.24<br>Chapter 6. --- Conclusion --- p.26<br>Reference --- p.28<br>Appendixes --- p.31<br>Appendix I: Tables and Figures --- p.31<br>Appendix II: Data --- p.52<br>Appendix III: Calculation of the Fama-French three factors --- p.59

Chung Kwong-leung.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.<br>Includes bibliographical references (leaves 80-84).<br>Abstracts in English and Chinese.<br>ACKNOWOLEDGMENTS --- p.iii<br>LIST OF TABLES --- p.iv<br>LIST OF ILLUSTRATIONS --- p.vi<br>CHAPTER<br>Chapter ONE --- INTORDUCTION --- p.1<br>Chapter TWO --- LITERATURE REVIEW --- p.5<br>Volatility<br>ARCH Models<br>The Accuracy of ARCH Volatility Estimates<br>Chapter THREE --- METHODOLOGY --- p.11<br>Testing and Estimation<br>Simulation<br>Chapter FOUR --- DATA DESCRIPTION AND EMPIRICAL RESULTS --- p.29<br>Data Description<br>Testing and Estimation Results<br>Simulation Results<br>Chapter FIVE --- CONCLUSION --- p.45<br>TABLES --- p.49<br>ILLUSTRATIONS --- p.58<br>APPENDICES --- p.77<br>BIBOGRAPHY --- p.80

Kam Ying Chuen.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.<br>Includes bibliographical references (leaves 81-83).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 2 --- Overview of Hong Kong Stock Market --- p.3<br>Chapter 2.1 --- Stock Exchange of Hong Kong --- p.3<br>Chapter 2.2 --- Hang Seng Index --- p.4<br>Chapter 2.3 --- Influences of the United States --- p.5<br>Chapter 2.4 --- Hong Kong Government's Intervention --- p.6<br>Chapter 3 --- Literature Review --- p.8<br>Chapter 3.1 --- Stable and Student t Distributions --- p.8<br>Chapter 3.2 --- Generalized Distribution --- p.10<br>Chapter 3.3 --- Socio-economic Model --- p.11<br>Chapter 3.4 --- Extreme Value Analysis --- p.11<br>Chapter 4 --- Methodology --- p.14<br>Chapter 4.1 --- Homogeneous Model --- p.15<br>Chapter 4.2 --- Inhomogeneous Model --- p.15<br>Chapter 4.3 --- Model Validity --- p.16<br>Chapter 4.3.1 --- Exceedance Rate --- p.17<br>Chapter 4.3.2 --- Distribution of Excesses --- p.17<br>Chapter 4.3.3 --- Independence --- p.18<br>Chapter 5 --- Data --- p.19<br>Chapter 5.1 --- Minute-by-minute Returns --- p.20<br>Chapter 5.2 --- Daily returns --- p.21<br>Chapter 5.3 --- Explanatory Variables for the Inhomogeneous Model --- p.21<br>Chapter 6 --- Empirical Results: Minute-by-minute Returns --- p.24<br>Chapter 6.1 --- Shape Parameter k --- p.24<br>Chapter 6.2 --- Location Parameter μ --- p.25<br>Chapter 6.3 --- Scale Parameter σ --- p.26<br>Chapter 6.4 --- Conditional Scale Parameter ψ --- p.27<br>Chapter 6.5 --- Specification Test --- p.29<br>Chapter 7 --- Empirical Results: Daily Returns --- p.29<br>Chapter 7.1 --- Homogeneous Model --- p.30<br>Chapter 7.2 --- Inhomogeneous Model --- p.31<br>Chapter 7.2.1 --- Constant Term --- p.32<br>Chapter 7.2.2 --- Dow Jones Industrial Average Returns --- p.33<br>Chapter 7.2.3 --- Volatility Indicators --- p.34<br>Chapter 7.2.4 --- Monday Dummy --- p.35<br>Chapter 7.2.5 --- Time Trend --- p.36<br>Chapter 7.2.6 --- Duration Dummy --- p.37<br>Chapter 7.2.7 --- Indicator for the Behavior of the Previous Trading Day --- p.38<br>Chapter 8 --- Conclusion --- p.39

Wu Haijun.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.<br>Includes bibliographical references (leaves 45-48).<br>Abstracts in English and Chinese.<br>Abstract --- p.ii<br>Acknowledgement --- p.iv<br>Chapter Chapter 1. --- Introduction --- p.1<br>Chapter Chapter 2. --- Literature Review --- p.4<br>Chapter 2.1. --- The Fisher Hypothesis --- p.4<br>Chapter 2.2. --- International Fisher Equation --- p.11<br>Chapter Chapter 3. --- Theoretical Basis on The Link Between Stock Return Differential and Inflation Rate Differential --- p.15<br>Chapter Chapter 4. --- Data Description --- p.19<br>Chapter Chapter 5. --- Results --- p.23<br>Chapter 5.1. --- Does The Generalized Fisher Hypothesis Hold In The Long Horizons --- p.24<br>Chapter 5.2. --- Does International Fisher Equation Hold --- p.29<br>Chapter 5.3. --- Can International Elements Account For The Failure of Fisher Hypothesis --- p.36<br>Chapter Chapter 6. --- Conclusion --- p.43<br>Bibliography --- p.45<br>Appendix A --- p.49<br>Chapter A.1. --- The link between interest rate differential and inflation rate differential --- p.49<br>Chapter A.2. --- Instrumental Variable Estimation --- p.53<br>Appendix B --- p.59<br>Chapter B.1. --- Hong Kong CPI(A) Source --- p.59<br>Chapter B.2. --- Taiwan CPI Source --- p.61<br>LIST OF TABLES<br>Table 4.1: Data Description --- p.21<br>Table 4.2: Means and Standard Deviations of Inflation and Stock Returns --- p.22<br>Table 5.1: Short-term (One Year) Test on Fisher Hypothesis on Stock Returns --- p.26<br>Table 5.2: Long-term (Five Years) Test on Fisher Hypothesis on Stock Returns --- p.27<br>Table 5.3: Long-term (Ten Years) Test on Fisher Hypothesis on Stock Returns --- p.30<br>Table 5.4: Short-term (One Year) Test For International Fisher Equation on Stock Returns --- p.33<br>Table 5.5: Long-term (Five Years) Test For International Fisher Equation on Stock Returns --- p.34<br>Table 5.6: Long-term (Ten Years) Test For International Fisher Equation on Stock Returns --- p.35<br>Table 5.7: Testing Effects of International Elements on The Fisher Hypothesis --- p.39<br>Table 5.8: Regression Results For The Coefficients of Domestic Inflation With and Without International Elements --- p.40

Research in this thesis deals with some unexplored, or only partially explored, issues relating to the information content of volatility of the idiosyncratic component of asset returns at the firm and industry-level, both in the context of developed and emerging stock markets. Specific issues we have investigated include potential role of idiosyncratic volatility of equity returns for the explanation of future stock market volatility, aggregate economic activity, cross-border information transmission, and fundamental efficiency of stock prices. Chapter 2 of the thesis presents research into the information content of firm and industry-level idiosyncratic volatility, estimated as cross-sectional volatility (CSV), for future market-level volatility in Australia. We find that CSV does contain information beyond what is already contained in the lagged market-level return shocks and has a significant positive relationship with conditional market volatility. Our analysis gives new empirical evidence that the effect of CSV is stronger in relatively stable market conditions than in more volatile market conditions. We also examine how the information content of stock turnover and aggregate company announcements compares with that of CSV, and take a novel data-driven approach to verify whether CSV captures any information about multiple common factor shocks in asset returns. The explanatory power of CSV for future market volatility remains robust even after controlling for the effects of stock turnover, company announcements and omitted factor shocks in returns. These results are in line with the theoretical models relating volatility to the flow of information to the market, and suggest that the amount of information as captured by the firm and industry-level CSV shares a common co-movement with the market-wide information flow. In Chapter 3, unlike most other studies investigating the role of macroeconomic aggregates in explaining the fluctuations in stock market returns, we consider the possibility of reverse causality, and that using idiosyncratic volatility of industry-level stock returns in the context of Australia. Both the theories of investment and consumption under uncertainty and the models of sectoral reallocation provide rationale for the analysis. By explicitly modeling the cyclical patterns of industry-level volatility and relating it to corresponding cyclical behaviour of macroeconomic variables, we show that industry-level volatility is a leading indicator of the cyclical movements in output growth and inflation in Australia. We find complementary evidence from the multi-step Granger causality test and the impulse response analysis based on a vector autoregression of industry-level volatility, GDP growth, inflation and changes in unemployment rate. However, the forecast error variance decompositions suggest that although the industry-level volatility accounts for a significant fraction of the forecast error of inflation, this explains only a small fraction of output and unemployment uncertainties. Further analysis indicates that industry-level volatility contains better information about the future state of the economy than does aggregate stock market volatility. In Chapter 4, we explore a new but potentially important channel of crossborder information transmission between international stock markets ���� idiosyncratic volatility of stock returns. Specifically, we analyze the role of US and Japanese idiosyncratic volatility in transmitting information across three smaller but advanced Asia-Pacific stock markets – Australia, Hong Kong and Singapore. We find that, similar to cross-market first and second moment return correlations, market-wide measures of IV are also highly correlated across countries. The effect of US and Japanese IV information is found to be much stronger on cross-market conditional volatility process than on the returns process. Further, we find significant contemporaneous and dynamic information transmission from IV of the US and Japan to the trading volume of other stock markets. Transmission of IV information, in general, seems to have gained momentum in the period since the Asian crisis of 1997. Overall evidence presented in this chapter is consistent with the interpretation that IV may contain information about some unobservable factors driving international stock market co-movement. In Chapter 5, we make the first attempt to understand the direct relationship between firm-specific variations in returns and firm fundamentals by analyzing firmlevel micro panel data in the context of each of a set of emerging Asian stock markets. After properly accounting for unobserved firm-specific effects, volatility persistence and potential endogeneity bias, we find that firm-specific variation of stock returns is highly correlated with, and is significantly explained by, alternative proxies of firm-specific variation of fundamentals in a majority of the emerging markets in Asia. Further analysis reveals that the observed effect of firm-specific fundamentals variation on returns variation is not indirectly driven by some other factors known to affect stock return volatility, viz., firm size, stock turnover, and leverage. Consistent with the rational approach, these results suggest that stock prices in majority of the Asian emerging markets are not devoid of fundamentals.<br>Doctor of Philosophy (PhD)

Lau Yuk Hoi.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.<br>Includes bibliographical references (leaves 109-110).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Acknowledgement --- p.iii<br>Table of Contents --- p.iv<br>List of Notations --- p.vi<br>List of Propositions --- p.vii<br>List of Figures --- p.viii<br>List of Appendices --- p.x<br>Chapter Chapter 1. --- Introduction - The Reaction Approach --- p.1<br>Chapter Chapter 2. --- Assumption for OLG Model --- p.7<br>Chapter 2.1 --- Assumption A --- p.7<br>Chapter Chapter 3. --- Equilibrium Conditions Without Fundamental Risk --- p.9<br>Chapter 3.1 --- Price as a Weighted Average --- p.9<br>Chapter 3.2 --- Determination of A and B --- p.11<br>Chapter 3.2.1 --- Assumption B --- p.12<br>Chapter 3.2.2 --- RE Line and NE Line --- p.13<br>Chapter 3.2.3 --- Equilibrium values of A and B --- p.14<br>Chapter 3.3 --- Rational Expectation on Price Variance (RV Line) --- p.16<br>Chapter 3.4 --- Noisy Expectation on Price Variance (NV Line) --- p.18<br>Chapter 3.4.1 --- DeLong's Model --- p.19<br>Chapter 3.4.2 --- Bhushan's Model --- p.21<br>Chapter 3.5 --- Change in Relative Perceived Variance --- p.23<br>Chapter 3.5.1 --- General Problem of OLG Model in Noisy Trading --- p.23<br>Chapter 3.5.2 --- Changes in Noise Traders' Beliefs --- p.24<br>Chapter 3.5.3 --- "Relative Perceived Price Variance of n, θ" --- p.25<br>Chapter 3.5.3.1 --- "Effect of Increasing θ on Price Variance, dC/dθ" --- p.26<br>Chapter 3.5.3.2 --- "Effect of Increasing θ on Expected Price Level, dp/dθ" --- p.27<br>Chapter Chapter 4. --- Equilibrium Conditions With Fundamental Risk --- p.31<br>Chapter 4.1 --- Price as a Weighted Average --- p.32<br>Chapter 4.2 --- Determination of A and B --- p.34<br>Chapter 4.2.1 --- Assumption C --- p.34<br>Chapter 4.2.2 --- RE Line and NE Line --- p.35<br>Chapter 4.2.3 --- Equilibrium values of A and B --- p.36<br>Chapter 4.3 --- Rational Expectation on return Variance (RV Line) --- p.37<br>Chapter 4.4 --- Noisy Expectation on Return Variance (NV Line) --- p.40<br>Chapter 4.4.1 --- De Long's Model --- p.41<br>Chapter 4.4.2 --- Bhushan's Model --- p.42<br>Chapter 4.5 --- Change in Relative Perceived Return Variance --- p.45<br>Chapter 4.5.1 --- Specification of Noisy Expectation --- p.46<br>Chapter 4.5.2 --- Relative Perceived Return Variance of n，Θ --- p.46<br>Chapter 4.5.2.1 --- "Effect of Increasing Θ on Price Variance, dC/dΘ" --- p.47<br>Chapter 4.5.2.2 --- "Effect of Increasing Θ on Expected Price Level, dp/dΘ" --- p.48<br>Chapter 4.6 --- Relative Perceived Price Risk versus Relative Perceived Dividend Risk --- p.52<br>Chapter Chapter 5. --- Conclusion and Discussion --- p.55<br>Figures --- p.58<br>Appendices --- p.86<br>References --- p.109

This study investigates whether earnings components are correctly priced by the Chinese investors. Under the Chinese GAAP, total earnings can be easily decomposed into core earnings and non-core earnings. Core earnings are more persistent than non-core earnings and cash flows from operations are more persistent than accruals, as expected. However, the market underestimates (overestimates) the value implications of current core (non-core) earnings for future earnings. Furthermore, the market overprices (underprices) accruals (cash flows from operations). Therefore, future returns adjusted for risk factors identified in this study are predictable by the information contained in the components of current earnings. Both the portfolio tests and regression analysis generate economically significant abnormal returns that are robust to sensitivity checks. Further analysis suggests that there is no significant difference in the extent of mispricing across firms with different characteristics such as transaction costs, arbitrage risks, investor sophistication, or firm size. This could be due to the measurement errors in the proxy variables for these characteristics.<br>Wu Donghui.<br>"July 2003."<br>Advisers: In-Mu Haw; James Xie.<br>Source: Dissertation Abstracts International, Volume: 64-07, Section: A, page: 2551.<br>Thesis (Ph.D.)--Chinese University of Hong Kong, 2003.<br>Includes bibliographical references (p. 121-130).<br>Available also through the Internet via Current research @ Chinese University of Hong Kong under title: Mispricings of earnings components empirical evidence from China.<br>Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Electronic reproduction. Ann Arbor, MI : ProQuest dissertations and theses, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.<br>Abstracts in English and Chinese.<br>School code: 1307.

Chen Chen.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.<br>Includes bibliographical references (leaves 122-129).<br>Abstracts in English and Chinese.<br>ABSTRACT --- p.ii<br>ACKNOWLEDGEMENT --- p.iv<br>TABLE OF CONTENTS --- p.v<br>LIST OF FIGURES --- p.vii<br>LIST OF TABLES --- p.viii<br>Chapter<br>Chapter I. --- INTRODUCTION --- p.1<br>Chapter 1.1 --- Introduction --- p.1<br>Chapter 1.2 --- Objectives and Motivation --- p.5<br>Chapter 1.3 --- The Asian Crisis --- p.9<br>Chapter 1.4 --- Procedures and Findings --- p.18<br>Chapter 1.5 --- Summary --- p.20<br>Chapter II. --- LITERATURE REVIEW --- p.21<br>Chapter 2.1 --- Definition and Determinants --- p.21<br>Chapter 2.2 --- Measurement Model --- p.25<br>Chapter 2.3 --- Exchange Rate Fluctuation and Market Value of the Firm --- p.28<br>Chapter 2.3.1 --- Exchange Rate Fluctuation and Stock Return --- p.28<br>Chapter 2.3.2 --- Some Problems of the Measurement Model --- p.31<br>Chapter 2.4 --- Exchange Rate Fluctuation and Market Risk of the Firm --- p.42<br>Chapter 2.5 --- Summary --- p.45<br>Chapter III. --- HYPOTHESES，METHODOLOGY & DATA --- p.47<br>Chapter 3.1 --- Hypotheses --- p.47<br>Chapter 3.2 --- Research Design --- p.50<br>Chapter 3.3 --- Sample Selection --- p.56<br>Chapter 3.3.1 --- Selection of Sample Group --- p.56<br>Chapter 3.3.2 --- Selection of Control Group --- p.61<br>Chapter 3.3.3 --- Comparison of Two Groups --- p.62<br>Chapter 3.4 --- Data and the Measurement of the Variables --- p.64<br>Chapter 3.5 --- Summary --- p.67<br>Chapter IV. --- EMPIRICAL RESULTS AND DISCUSSION --- p.68<br>Chapter 4.1 --- Exchange Rate Variability and Stock Return Volatility --- p.68<br>Chapter 4.2 --- Exchange Rate Variability and Market Risk --- p.81<br>Chapter 4.3 --- Interpretations --- p.87<br>Chapter 4.3.1 --- Phenomenon 1: Cost of Equity and Net Cash Flows --- p.89<br>Chapter 4.3.2 --- Phenomenon 2: Increased Return Variability and the US Stock Market Return --- p.92<br>Chapter 4.4 --- Alternative Explanation --- p.96<br>Chapter 4.5 --- Summary --- p.99<br>Chapter V. --- CONCLUDING REMARKS --- p.100<br>APPENDICES<br>APPENDIX 1. Firm Lists --- p.105<br>APPENDIX 2. Estimates of CAPM Betas --- p.115<br>BIBLIOGRAPHY --- p.122

by Cheung Chan-Wah.<br>Thesis (M.B.A.)--Chinese University of Hong Kong, 2001.<br>Includes bibliographical references (leaves 80-86).<br>ABSTRACT --- p.ii<br>TABLF OF CONTENTS --- p.iii<br>LIST OF TABLES --- p.iv<br>ACKNOWLEDGMENTS --- p.v<br>Chapter<br>Chapter I --- INTRODUCTION l --- p.1<br>Chapter II. --- REVIEW OF LITERATURE --- p.7<br>Chapter III. --- METHODOLOGY。 --- p.16<br>Summary Statistics --- p.16<br>Correlation --- p.21<br>GARCH Estimation --- p.22<br>Chapter IV. --- NATIONAL MARKET INDEX AND DATA --- p.31<br>National Stock Indices and Trading Mechanisms --- p.31<br>Stock Return Data and Data Transformation --- p.34<br>Chapter V. --- EMPIRICAL RESULTS --- p.37<br>Summary Statistics --- p.37<br>Cross-Correlation --- p.45<br>GARCH Estimation --- p.51<br>Chapter VI. --- SUMMARY AND CONCLUSION --- p.75<br>BIBLIOGRAPHY --- p.80

by Chan Tsun Kit.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.<br>Includes bibliographical references (leaves 64-67).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Acknowledgments --- p.iii<br>Table of Contents --- p.iv<br>List of Tables --- p.vi<br>List of Figures --- p.vii<br>List of Appendices --- p.viii<br>Chapter Chapter 1. --- Introduction --- p.1<br>Chapter Chapter 2. --- Background --- p.4<br>Chapter 2.1 --- The Importance of Real Estate Sector --- p.4<br>Chapter 2.1.1 --- Employment Sector --- p.5<br>Chapter 2.1.2 --- Investment Sector --- p.5<br>Chapter 2.1.3 --- Banking Sector --- p.6<br>Chapter 2.1.4 --- Government Sector --- p.6<br>Chapter 2.2 --- Characteristics of the Real Estate Market --- p.7<br>Chapter 2.3 --- Price Movement --- p.10<br>Chapter 2.4 --- Major Developer --- p.13<br>Chapter 2.4.1 --- Sun Hung Kai Properties --- p.15<br>Chapter 2.5 --- Contribution of Real Estate Sector on Stock Market --- p.16<br>Chapter 2.6 --- Connection between Real Estate and Stock Market --- p.17<br>Chapter Chapter 3. --- Literature Review --- p.19<br>Chapter Chapter 4. --- Methodology --- p.24<br>Chapter 4.1 --- The Model --- p.24<br>Chapter 4.2 --- Variables Used --- p.27<br>Chapter 4.3 --- Sources of Data --- p.29<br>Chapter Chapter 5. --- Empirical Findings --- p.30<br>Chapter Chapter 6. --- Implication --- p.33<br>Chapter Chapter 7. --- Limitation --- p.35<br>Chapter Chapter 8. --- Conclusion --- p.37<br>Tables --- p.39<br>Figures --- p.48<br>Appendices --- p.50<br>Bibliography --- p.64

by Sze Kin Wan.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 1998.<br>Includes bibliographical references (leaves 69-75).<br>Abstract also in Chinese.<br>ACKNOWLEDGMENTS --- p.iii<br>LIST OF TABLES --- p.iv<br>LIST OF ILLUSTRATIONS --- p.v<br>CHAPTER<br>Chapter ONE --- INTRODUCTION --- p.1<br>Chapter TWO --- REVIEW OF THE LITERATURE --- p.7<br>Stock Returns and Trading Volume<br>Volatility<br>Chapter THREE --- ECONOMETRIC ANALYSIS --- p.16<br>Unit Root Tests<br>Lag Length Tests<br>Causality Detection between Two Series<br>ARCH Modelling<br>Chapter FOUR --- DATA AND ESTIMATION RESULTS --- p.34<br>Data<br>Unit Root Test<br>Optimal Lag Length<br>Causality Detection<br>GARCH Modelling<br>Chapter FIVE --- CONCLUSION --- p.62<br>APPENDIX --- p.67<br>BIBLIOGRAPHY --- p.69<br>ILLUSTRATIONS --- p.76

Leung Lok-yee.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.<br>Includes bibliographical references (leaves 70-71).<br>Abstracts in English and Chinese.<br>Chapter 1. --- Introduction and Literature Review --- p.1<br>Chapter 2. --- Methodology --- p.4<br>Chapter A. --- Momentum Strategies<br>Chapter B. --- Relative Strength Indexes<br>Chapter 3. --- Data --- p.13<br>Chapter 4. --- Emirical Findings --- p.15<br>Chapter A. --- Momentum Strategies<br>Chapter B. --- Relative Strength Indexes<br>Chapter 5. --- Conclusion --- p.37<br>Chapter 6. --- Tables --- p.39<br>Chapter 7. --- Bibliograhy --- p.70

Thesis (M.M. (Finance & Investment)--University of the Witwatersrand, Faculty of Commerce, Law and Management, Wits Business School, 2016<br>Purpose - The paper provides an investigation of the relationship of macroeconomic risk factors and REITs. The study considers the conditional volatilities of macroeconomic variables on the excess returns and conditional variance of excess returns in developing and developed markets and provides a comparison thereof. Methodology approach - The study employs three-step approach estimation in the methodology (Principal Component Analysis, GARCH (1,1) and GMM) to estimate the asset pricing model. The preliminary study indicated that there are only two developing economies (Bulgaria and South Africa), as defined by National Association of Real Estate Investment Trust (NAREIT), with REIT indices. We additionally included the United States as the developed economy. Findings – Our results indicate that the real economy and business cycles (proxied by GDP growth rate and industrial production index), price stability (proxied by the GDP deflator), exchange rates and interest rates do not explain developing country REIT returns represented by Bulgaria and South Africa, as well as in developed markets, represented by the US. However unlike the developing markets, changes in industrial production and inflation are important variables that affect the conditional variance of REIT returns in the US.<br>GR2018

碩士<br>淡江大學<br>國際貿易學系<br>85<br>In Taiwan stock market,the newly listed stocks are the investors'''' favorite targets owing to exist the"honey moon" effect.However in the recent years,there were many newly listed stocks broken down its initial public offering(IPO)prices .So ,the main objective of this paper is tring to find some investing guideline through empirical study in order to handle the behavior of investing in the newly listed stocks. The reaserching period of this paper is divided into two periods.The establishment period of forecasting model is from 1987 to 1995,and the testing preiod is the whole year of 1996.In this study ,first we adopt "stepwise regression" to establish forecasting models of the breakdown of IPO prices of the newly listed stocks accroding to different attributions.Second, we test the performance of seven forecasting models over the newly listed stocks of 1996.Finally,we choice the best model to analyze the relations between the rate of premium(discount) of IPO prices and the different kinds of duration return.The main resurts of this paper include:1.The newly listed stocks IPO prices become more easy to breakdown duringthe bearish period of 1996.After the middle of march , the"honey moon"effect restarted under the stimulus of bullish market.2. We have contemplate four different attributions in the establishment of forecasting models.Those attributions can effectively capture the behavior of the breakdown of IPO prices in our testing period.3.Accroding to the testing results of seven forecasting models,the best modles are the modle 1 and modle 7.So, when investors want to invest in the newly listed stocks,we recommend that the key points should be the following two factors：a.If investors try to establish forecasting models of the breakdown of IPO prices with different duration,to extend the duration will increse the predictive power of the econometric model.b.Investors must to perceive the finacial statement about the formula in establishing IPO prices in public issue communigue .Investors can pierce the powerful stockholders attitude to IPO price which will affect the supportability of price of the newly listed stocks.4. Accroding to the result of correlation coefficient analysis,we recommendinvestors should invest in those newly listed stocks with higher degree of premium of IPO prices .

Sen Sui.<br>Thesis submitted in: August 2003.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.<br>Includes bibliographical references (leaves 88-92).<br>Abstracts in English and Chinese.<br>Abstract --- p.ii<br>Acknowledgement --- p.iv<br>Dedication --- p.v<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Motivation and Objective --- p.1<br>Chapter 1.2 --- Contributions --- p.4<br>Chapter 1.3 --- Thesis Organization --- p.5<br>Chapter 2 --- Background of Risk Management --- p.7<br>Chapter 2.1 --- Measuring Return --- p.8<br>Chapter 2.2 --- Objectives of Risk Measurement --- p.11<br>Chapter 2.3 --- Simple Statistics for Measurement of Risk --- p.15<br>Chapter 2.4 --- Methods for Value-at-Risk Measurement --- p.16<br>Chapter 2.5 --- Conditional VaR --- p.18<br>Chapter 2.6 --- Portfolio VaR Methods --- p.18<br>Chapter 2.7 --- Coherent Risk Measure --- p.20<br>Chapter 2.8 --- Summary --- p.22<br>Chapter 3 --- Selection of Independent Factors for VaR Computation --- p.23<br>Chapter 3.1 --- Mixture Convolution Approach Restated --- p.24<br>Chapter 3.2 --- Procedure for Selection and Evaluation --- p.26<br>Chapter 3.2.1 --- Data Preparation --- p.26<br>Chapter 3.2.2 --- ICA Using JADE --- p.27<br>Chapter 3.2.3 --- Factor Statistics --- p.28<br>Chapter 3.2.4 --- Factor Selection --- p.29<br>Chapter 3.2.5 --- Reconstruction and VaR Computation --- p.30<br>Chapter 3.3 --- Result and Comparison --- p.30<br>Chapter 3.4 --- Problem of Using Kurtosis and Skewness --- p.40<br>Chapter 3.5 --- Summary --- p.43<br>Chapter 4 --- Mixture of Gaussians and Value-at-Risk Computation --- p.45<br>Chapter 4.1 --- Complexity of VaR Computation --- p.45<br>Chapter 4.1.1 --- Factor Selection Criteria and Convolution Complexity --- p.46<br>Chapter 4.1.2 --- Sensitivity of VaR Estimation to Gaussian Components --- p.47<br>Chapter 4.2 --- Gaussian Mixture Model --- p.52<br>Chapter 4.2.1 --- Concept and Justification --- p.52<br>Chapter 4.2.2 --- Formulation and Method --- p.53<br>Chapter 4.2.3 --- Result and Evaluation of Fitness --- p.55<br>Chapter 4.2.4 --- Evaluation of Fitness using Z-Transform --- p.56<br>Chapter 4.2.5 --- Evaluation of Fitness using VaR --- p.58<br>Chapter 4.3 --- VaR Estimation using Convoluted Mixtures --- p.60<br>Chapter 4.3.1 --- Portfolio Returns by Convolution --- p.61<br>Chapter 4.3.2 --- VaR Estimation of Portfolio Returns --- p.64<br>Chapter 4.3.3 --- Result and Analysis --- p.64<br>Chapter 4.4 --- Summary --- p.68<br>Chapter 5 --- VaR for Portfolio Optimization and Management --- p.69<br>Chapter 5.1 --- Review of Concepts and Methods --- p.69<br>Chapter 5.2 --- Portfolio Optimization Using VaR --- p.72<br>Chapter 5.3 --- Contribution of the VaR by ICA/GMM --- p.76<br>Chapter 5.4 --- Summary --- p.79<br>Chapter 6 --- Conclusion --- p.80<br>Chapter 6.1 --- Future Work --- p.82<br>Chapter A --- Independent Component Analysis --- p.83<br>Chapter B --- Gaussian Mixture Model --- p.85<br>Bibliography --- p.88

This Paper empirically investigates the behavior of Africa’s stock price volatility over time in ten African equity markets. It also attempts to establish the existence of a relationship between volatility and expected returns in the chosen equity markets. The effect of volatility on the stock prices is also investigated, together with establishing variations in the stock return volatility risk premia. Lastly, an investigation of whether volatility is transmitted from international markets to African markets is also undertaken. The sample period starts from November 1998 until December 2016. The preliminary empirical results show a mixed finding in the mean-variance tradeoff theory. Based on the GARCH-type models, the empirical results show that volatility of stock returns show the characteristics of volatility clustering, leptokurtic distribution and leverage effects over time for all the Africa equity markets. A weak relationship between volatility and expected returns is also found in all the African equity markets studied. The results also showed that as volatility increases, the returns correspondingly decrease by a factor of the coefficient for most of the equity markets. These results negate the theory of a positive risk premium on stock indices. It was also observed that stock return volatility risk premia have variations over time. The study also established that there was volatility transmission from the international markets into Africa equity markets.<br>MT2017

Ng, Hon Yip.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.<br>Includes bibliographical references (p. 79-83).<br>Abstracts in English and Chinese.<br>Chapter 1 --- Introduction --- p.1<br>Chapter 2 --- Basic Concepts in Portfolio Theory --- p.8<br>Chapter 2.1 --- Statistical Model --- p.8<br>Chapter 2.2 --- Mean-Variance Optimization --- p.9<br>Chapter 2.3 --- The Efficient Frontier --- p.11<br>Chapter 2.4 --- The Tangency Portfolio and The Capital Market Line --- p.13<br>Chapter 2.5 --- Mathematical Formulation of Portfolio Optimization --- p.17<br>Chapter 3 --- Derivation of The Improved Estimator --- p.29<br>Chapter 4 --- Simulation Study --- p.40<br>Chapter 4.1 --- Procedure of Simulation --- p.40<br>Chapter 4.2 --- Simulation Results --- p.46<br>Chapter 4.2.1 --- Zero Correlation --- p.47<br>Chapter 4.2.2 --- Positive Correlations --- p.50<br>Chapter 4.2.3 --- Negative Correlations --- p.52<br>Chapter 5 --- Conclusion and Future Direction --- p.56<br>Chapter A --- Simulation results for p = 200 --- p.58<br>Chapter B --- Simulation results for p = 400 --- p.61<br>Chapter C --- Simulation results for p = 500 --- p.64<br>Chapter D --- Simulation results for p = 200 with negative correlations --- p.67<br>Chapter E --- Simulation results for p = 400 with negative correlations --- p.71<br>Chapter F --- Simulation results for p = 500 with negative correlations --- p.75<br>Bibliography --- p.79

by Poon Yeuk Wan, Tsang Fei.<br>Thesis (M.B.A.)--Chinese University of Hong Kong, 1998.<br>Includes bibliographical references (leaves 54-55).<br>Acknowledgements --- p.i<br>Abstract --- p.iii<br>Table of Contents --- p.iv<br>List of Tables --- p.vi<br>List of Appendix --- p.vii<br>Chapter Chapter1 --- Introduction --- p.1<br>Chapter 1.1 --- Project Objective --- p.1<br>Chapter 1.2 --- Project Structure --- p.2<br>Chapter 1.3 --- Data --- p.3<br>Chapter Chapter 2 --- Emerging Markets´ؤ-An Overview --- p.5<br>Chapter 2.1 --- Latin America --- p.5<br>Argentina --- p.5<br>Brazil --- p.7<br>Chile --- p.7<br>Colombia --- p.8<br>Mexico --- p.8<br>Peru --- p.9<br>Venezuela --- p.9<br>Chapter 2.2 --- Eastern Europe --- p.10<br>Czech Republic --- p.10<br>Poland --- p.10<br>Slovakia --- p.11<br>Hungary --- p.11<br>Russia --- p.11<br>Chapter 2.3 --- Middle East --- p.12<br>Israel --- p.12<br>Jordan --- p.12<br>Chapter 2.4 --- Implication For Further Analysis --- p.13<br>Chapter Chapter 3 --- Analysis and Findings I: Descriptive Statistics Analysis --- p.14<br>Chapter 3.1 --- Objective of Descriptive Statistic Analysis --- p.14<br>Chapter 3.2 --- Findings --- p.16<br>Eastern Europe --- p.16<br>Latin America --- p.16<br>Middle East --- p.17<br>Chapter 3.3 --- Conclusion --- p.18<br>Chapter Chapter 4 --- Analysis and Findings II: Day-of-the- Week (Monday effect) Test --- p.19<br>Chapter 4.1 --- Objective --- p.19<br>Chapter 4.2 --- Literature Review --- p.19<br>Chapter 4.3 --- Methodology --- p.21<br>Chapter 4.4 --- Data --- p.23<br>Chapter 4.5 --- Analysis --- p.24<br>Chapter 4.6 --- Empirical findings --- p.25<br>Chapter I. --- The equality of return test --- p.25<br>Eastern Europe --- p.26<br>Latin America --- p.26<br>Middle East --- p.26<br>Overall --- p.27<br>Local currency versus US currency --- p.27<br>Chapter II. --- Comparison of Monday return with returns of other days within the week --- p.27<br>Chapter l. --- Without exchange rate effect --- p.28<br>Chapter 4.7 --- Monday effect一-an overview --- p.31<br>Comparison by region --- p.31<br>Eastern Europe --- p.31<br>Latin America --- p.31<br>Middle East --- p.32<br>The effect of exchange rate --- p.32<br>Chapter Chapter 5 --- Analysis And Findings III: Correlation Analysis --- p.33<br>Chapter 5.1 --- Literature Review --- p.33<br>Chapter 5.2 --- Objective --- p.35<br>Chapter 5.3 --- Methodology --- p.35<br>Chapter 5.4 --- Findings --- p.38<br>Chapter I --- Correlations Within Regions --- p.38<br>Eastern Europe --- p.33<br>Latin America --- p.40<br>Middle East --- p.42<br>Chapter II. --- Correlation Among Regions --- p.43<br>Eastern Europe vs. Latin America --- p.43<br>Latin America vs. Middle East --- p.44<br>Eastern Europe vs. Middle East --- p.45<br>Chapter III. --- Correlations with the United States --- p.46<br>US vs. Eastern Europe --- p.46<br>US vs. Latin America --- p.46<br>US vs. Middle East --- p.47<br>Chapter 5.5 --- Conclusion --- p.43<br>Chapter Chapter 6 --- Conclusions and Implications --- p.49<br>Implications on market integration --- p.52<br>BIBLIOGRAPHY --- p.54<br>APPENDIX --- p.56

Lau, Chi Keung.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.<br>Includes bibliographical references (leaves 187-188).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Acknowledgements --- p.iii<br>Table of Contents --- p.iv<br>List of Chosen Samples Results --- p.v<br>List of Tables --- p.vi<br>List of Figures --- p.vii<br>Chapter Chapter 1. --- Introduction --- p.1<br>Chapter Chapter 2. --- Literature Review --- p.4<br>Chapter 2.1 --- Real Estate Literature --- p.4<br>Chapter 2.2 --- Financial Literature --- p.8<br>Chapter Chapter 3. --- Methodology --- p.15<br>Chapter 3.1 --- Augmented Dickey Fuller Test --- p.15<br>Chapter 3.2 --- Band-Pass Filter --- p.18<br>Chapter Chapter 4. --- Data Description --- p.20<br>Chapter Chapter 5. --- Empirical Results --- p.23<br>Chapter 5.1 --- Contemporaneous Correlation --- p.24<br>Chapter 5.2 --- Results after Band-Pass Filtering --- p.26<br>Chapter 5.3 --- Lead-lag Relationship Analysis --- p.30<br>Chapter Chapter 6. --- Conclusion --- p.35<br>Appendix 1. Variable Definition --- p.38<br>Appendix 2. Limitation --- p.41<br>Appendix 3. Results of Chosen Samples --- p.45<br>Appendix 4. Tables --- p.54<br>Appendix 5. Figures --- p.109<br>Bibliography --- p.187

Thesis (M.Com. (Marketing Management and Information Systems))--University of the Witwatersrand, Faculty of Commerce, Law and Management, School of Economic and Business Sciences, 2012.<br>In today‟s increasingly dynamic and competitive markets, organizations are continuously pressurized to meet financial targets in order to realise company goals in an efficient and effective manner. The banking sector in South Africa has, for a very long time, operated in a predominantly oligopoly market, however, due to increasing pressure from new entrants like Capitec Bank, there has been a shift of focus to ensuring long term profitability and competitiveness as the new entrants are constantly implementing strategies that cause customer switching. The literature reveals that long-term profitability requires that a firm implements sustainable development projects to ensure long-term profitability. However, many of the profitability models in use today do not have a variable for sustainable development and yet it is a key factor in drawing returns on investment. This study adopts the South African banking sector and investigates the applicability of Rust, Lemon and Zeithaml‟s (2004) return on investment model for the various initiatives that the banks have implemented. From qualitative research with the banks, it was found that sustainable development forms a large part of the annual budget with the aim of improving the brand perceptions and increasing the likelihood of customer retention and attraction. However, the results from the bank‟s customers reveal that the initiatives being sponsored by the banks have very little effect on their decision to switch or remain banking with a specified bank. Basic customer satisfaction techniques like clear communication and customer care still outweigh any corporate initiative like sponsoring the local soccer league thus although these initiatives do benefit to keep the brand name in customer‟s minds, they do less in realising returns. The model reveals a technique to quantify return on investment taking into account factors like sustainable development and it was found that the model is applicable and useful in a South African setting. Recommendations include applying the model to gauge not only the possibility of returns but also how much a company can expect to receive after investing a specified amount of money on any initiative. This model will be very useful for planning especially for capital intensive projects as the current economic environment cannot accommodate for misappropriation of funds.<br>MT2017