by Chan Po-ki, Annie. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaf 107). / ABSTRACT --- p.ii / TABLE OF CONTENTS --- p.iii / LIST OF TABLES --- p.v / LIST OF ILLUSTRATIONS --- p.vi / LIST OF APPENDICES --- p.viii / ACKNOWLEDGEMENTS --- p.ix / Chapter / Chapter I. --- INTRODUCTION / Prologue --- p.1 / Purpose of Study --- p.2 / Scope of Study --- p.2 / Organization of Paper --- p.3 / Limitations of Study --- p.3 / Chapter II. --- THE HONG KONG STOCK MARKET / Development of the Stock Market --- p.5 / The Hang Seng Index --- p.9 / Role and Importance of the Stock Market --- p.10 / Characteristics of the Stock Market --- p.10 / Chapter III. --- THEORETICAL CONCEPTS AND FRAMEWORK OF ANALYSIS / Efficient Markets Theory --- p.12 / Random Walk Theory --- p.13 / Investment Strategies --- p.14 / Passive Strategy --- p.14 / Active Strategy --- p.14 / Market Analysis --- p.15 / The Fundamental School --- p.15 / The Technical School --- p.16 / Implications of Random Walk for Technical and Fundamental Analysis --- p.18 / Seasonality --- p.19 / Chapter IV. --- SEASONALITY IN THE HONG KONG STOCK MARKET / Introduction --- p.21 / Research Design --- p.21 / The Hang Seng Index --- p.21 / The Sub-Index --- p.22 / Individual Stocks in Sub-Index --- p.25 / Data Analysis and Findings --- p.26 / Discussions --- p.36 / Chapter V. --- IMPLICATIONS ON TRADING STRATEGIES / Introduction --- p.39 / Research Design --- p.39 / Trading Strategies Hypothesized --- p.39 / Interest on Cash Deposit --- p.40 / Dimensions of Comparison --- p.42 / Data Analysis and Findings --- p.44 / "Comparison One - One Stock, One Year" --- p.44 / "Comparison Two - One Stock, Ten Years" --- p.57 / "Comparison Three - Portfolio, One Year" --- p.60 / "Comparison Four - Portfolio, Ten Year" --- p.65 / Chapter VI. --- SUMMARY AND CONCLUSION --- p.68 / APPENDIX --- p.70 / BIBLIOGRAPHY --- p.107

This research is based on a detailed description of model building for multivariate time series models. Under the assumption of stationarity, identification, estimation of the parameters and diagnostic checking for the Vector Auto regressive (p) (VAR(p)), Vector Moving Average (q) (VMA(q)) and Vector Auto regressive Moving Average (VARMA(p, q) ) models are described in detail. With reference to the non-stationary case, the concept of cointegration is explained. Procedures for testing for cointegration, determining the cointegrating rank and estimation of the cointegrated model in the VAR(p) and VARMA(p, q) cases are discussed. The utility of multivariate time series models in the field of economics is discussed and its use is demonstrated by analysing quarterly South African inflation and wage data from April 1996 to December 2008. A review of the literature shows that multivariate time series analysis allows the researcher to: (i) understand phenomenon which occur regularly over a period of time (ii) determine interdependencies between series (iii) establish causal relationships between series and (iv) forecast future variables in a time series based on current and past values of that variable. South African wage and inflation data was analysed using SAS version 9.2. Stationary VAR and VARMA models were run. The model with the best fit was the VAR model as the forecasts were reliable, and the small values of the Portmanteau statistic indicated that the model had a good fit. The VARMA models by contrast, had large values of the Portmanteau statistic as well as unreliable forecasts and thus were found not to fit the data well. There is therefore good evidence to suggest that wage increases occur independently of inflation, and while inflation can be predicted from its past values, it is dependent on wages. / Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2012.

Three Essays on Stock Market Seasonality Hyung-Suk Choi 136 pages Directed by Dr. Cheol S. Eun In chapter 1, we examine seasonality in returns to style portfolios, which serve as important benchmarks for asset allocation, and investigate its implications for investment. In doing so, we consider monthly returns on the style portfolios classified by six size/book-to-market sorting and six size/prior-return sorting over the sample period 1927 - 2006. The key findings are: first, as is well documented in the literature, small-cap oriented portfolios are subject to the January effect, but also to the 'negative' September and October effects. Second, cross-style return dispersion exhibits a seasonal pattern of its own (it is largest in January and smallest in August), suggesting possibly profitable trading strategies. Third, our seasonal strategies indeed yield significant profits, as high as about 18.7 % per annum. This profit is mostly attributable to the seasonal autocorrelation in style returns. Lastly, we find substantial seasonal patterns in style returns not only in the U.S. but also in other major stock markets Germany, Japan, and the U.K. Our seasonal style rotation strategy yields economically and statistically significant profits in all of these stock markets. In chapter 2, we examine the abnormal, negative stock returns in September which have received little attention from academic researchers. We find that in most of the 18 developed stock markets the mean return in September is negative and in 15 countries it is significantly lower than the unconditional monthly mean return. This September effect has not weakened in the recent period. Further, the examinations of the various style portfolios in the US market show that the September effect is the most pervasive anomalous phenomenon that is not affected by size, book-to-market ratio, past performance, or industry. Our finding suggests that the forward looking nature of stock prices combined with the negative economic growth in the last quarter causes the September effect. Especially in the fall season when most investors become more risk averse, the stock prices reflect the future economic growth more than the rest of the year. Our investment strategy based on the September effect yields a higher mean return and a lower standard deviation than the buy-and-hold strategy. In chapter 3, we establish the presence of seasonality in the cash flows to the U.S. domestic mutual funds. January is the month with the highest net cash flows to equity funds and December is the month with the lowest net cash flows. The large net flows in January are attributed to the increased purchases, and the small net flows in December are due to the increased redemptions. Thus, the turn-of-the-year period is the time when most mutual fund investors make their investment decisions. We offer the possible sources for the seasonality in mutual funds flows.