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Yes / There is no consensus in the literature as to which stock characteristic best explains returns. In this study, we employ a novel econometric approach better suited than the traditional characteristic sorting method to answer this question for the UK market. We evaluate the relative explanatory power of market, size, momentum, volatility, liquidity and book-to-market factors in a semiparametric characteristic-based factor model which does not require constructing characteristic portfolios. We find that momentum is the most important factor and liquidity is the least important based on their relative contribution to the fit of the model and the proportion of sample months for which factor returns are significant. Our evidence supports the view that irrational investor behaviour may drive stock returns.

The question for this thesis is whether the four major stock markets--the United States, Great Britain, West Germany, and Japan are interdependent or segmented. The study period runs from February 1979 to June 1987, with the Wall Street Journal as a source of data. The Granger causality test is used to test for relationships among the four major stock markets. The thesis is divided into five chapters-- 1) statement of the problem; 2) survey of literature; 3) methodology; 4) results and 5) conclusions. The overall findings of this thesis indicate that there are few or no comovement similarities among all the four stock markets. However, the findings do point out the significant influence of the United States stock market on the other three stock markets.

by Chan Kin Man, Eric, Ng Man Leung, Alfred, Poon Man Ching, Daniel. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1989. / Bibliography: leaves [113]-[115].

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

Financial Derivatives are one of the most popular and emerging innovations in the field of financial engineering. Since their inception, there has been a phenomenal growth in the volumes of derivatives traded all over the world. Financial markets are known to be extremely volatile and derivatives provide a way of eliminating or reducing the risks involved in these markets. Since these instruments derive their value from some underlying asset, trading in these instruments is bound to affect the underlying assets. Thus it becomes important to examine what these effects are and whether they have been favourable or detrimental to the underlying stock markets specially when there has been an explosive growth of these financial derivatives all over the world. This issue gains more importance in the case of emerging markets like India as they try to be more competitive and efficient as the developed Western markets. This thesis mainly deals with looking at this impact on the Indian stock markets. The Indian markets still being very new in this area, not many studies have been reported here related to this issue. The main focus of this thesis is to provide some more evidence on the impact of one kind of derivative instrument, namely options on different characteristics of underlying stocks in the Indian stock market. The thesis has the following objectives: • To examine the impact of option introduction on the price of underlying stocks in National Stock Exchange (NSE). • To examine the impact of option introduction on the volatility of underlying stocks in NSE • To examine the impact of option introduction on liquidity of underlying stocks in NSE NSE introduced derivatives beginning with index futures on June 12, 2000, followed by index options on June 4, 2001, options on individual securities on July 2, 2001 and finally futures on individual securities on November 9, 2001. Due to the temporal proximity of the introduction of index options and individual options, there exists a possibility of an interaction of these two effects. This problem is solved by a judiciously chosen sampling design. In particular, three groups of stocks are considered. The first group consists of stocks on which options were first introduced on 2nd July 2001 and thus would exhibit a combined effect of the two events if any. The second group consists of stocks on which options were introduced much later and therefore would show effects of individual option introduction if any. The third group comprises of nonoptioned stocks whose returns are considered around the date of index option introduction and thus would show effects of index option alone if any. To separate the two effects an ANOVA/ Logistic Regression model is used. An objective selection of the event and estimation windows is done using a Bayesian Change Point Analysis. The first part of the thesis looks at the effect of option introduction on the price of underlying stocks. A standard event study methodology as has been used in the literature is employed for this purpose. The study does not find any significant effect of option introduction on the prices. The second part of the thesis deals with the effect on volatility. Volatility is measured as the risk of a stock and as is done in the literature, three kinds of risk are looked at: total risk, systematic risk and the unsystematic risk. In case of the total risk, an F-test and an Ansari Bradley test is used to check for changes in the variance and scale parameters of market-adjusted continuously compounded returns of the stocks before and after option introduction. The results of these tests are recorded as a categorical variable taking on the value 0 for no change and 1 for a change and a Binomial Logistic Regression is used to separate the effects of the two events. Furthermore, after recording the results of the above mentioned tests as a categorical variable with three categories (0, 1, -1), a Multinomial Logistic Regression is also used in order to estimate the direction of the change (increase, decrease or no change). The ratios of after to before total risks are also analyzed using an ANOVA model. The systematic risk is measured using three kinds of betas – OLS betas, Scholes-Williams betas and Fowler-Rorke betas. The differences in the before and after betas of every stock are modelled using an ANOVA model in order to separate the two effects as well as the interaction effect. The unsystematic risk is estimated by the conditional variances and the unconditional variances of ARMA and ARMA-GARCH models fitted to market model excess returns. The ANOVA model is used here as well. In addition to this, the before and after ARCH and GARCH coefficients of GARCH (1, 1) models fitted to the excess returns are also compared using the ANOVA model. The results indicate that individual options are leading to a decline in total risk however index options are causing an increase in total risk. The interaction effect is significant in this case thereby causing an increase in total risk in the Group I stocks. The OLS betas indicate that individual option introduction seems to have increased the systematic risk. The Scholes-Williams betas indicate that index option introduction seems to have increased the systematic risk. The Fowler Rorke betas on the other hand, do not show any significant impact of individual option or index option introduction. For all the three betas index options introduction seems to have no effect on the systematic risk. Though the interaction effect seems to be significant in all the three cases, it however does not significantly affect the systematic risk in Group I stocks. As regards the unsystematic risk, both the conditional and unconditional variances of ARMA models show a significant reduction for individual option introduction but index options do not have any significant impact on either one of these measures. In case of unconditional variances of ARMA-GARCH models, none of the effects come out as significant. While comparing the news and persistence coefficients of GARCH (1, 1) models, the news coefficients indicate that the due to index option introduction, stocks are becoming more efficient in terms of absorbing the news more rapidly. No significant effect of either event is found on the persistence coefficients. The last part of the thesis deals with the liquidity issue. Liquidity has been measured using two measures – relative volume (based on daily data) and implicit bid-ask spread given by Roll (1984) (calculated from intra-day data). In case of the liquidity measures, the Logistic Regression models are used i.e. a categorical variable with two or three categories obtained from the results of a Wilcoxon Rank Sum test for comparing the median volume and spread before and after option introduction, is used. It is found that for the relative volume, individual option introduction has led to a favourable effect in terms of increasing the volume post introduction of options; however index options seem to have had a negative effect. As for the spread, index options seem to have had a stabilizing influence on the underlying stocks than the individual options.

Thesis (MBA)--Stellenbosch University, 2015. / ENGLISH ABSTRACT: This research report investigates the history and current status of the Johannesburg Stock Exchange Alternative Exchange and its performance over time. The focus is on comparing the AltX with the JSE Main Board, the JSE top 40, The JSE Small Cap Index and London’s Alternative Investments Market AIM. The different listing requirements and the JSE Main Board will be explored. It then goes further to compare the performance of the JSE with that of AltX and AIM over time. A comparison between listings and de-listings is drawn between the AltX and the JSE Main Board. Complete risk analysis is then conducted in an attempt to compare the risk of listing on AltX, JSE and AIM and determine whether the AltX holds more risk than the other exchanges given its relaxed listing requirements and market sentiment around AltX. In comparing risk analysis with market sentiment as well as actual results, it can be concluded that AltXwhen analysed using beta; standard deviation; maximum draw down; Value at Risk; and the Sharpe ratio, does not carry significantly more risk than the JSE Main Board or AIM. The AltXdoes meet its requirements and is doing what it is designed to do, namely offering an opportunity for small and medium sized companies to raise capital and providing investors with the opportunity to become shareholder and trade in those shares as well as being a spring board to the JSE Main Board, but that moving to the Main Board does not always create more value for shareholders or has a positive influence on share price or liquidity.This brings the conclusion that company performance is still based on the individual performance of the company and not dependant on where the company is listed.

LL.M. (Commercial Law) / Please refer to full text to view abstract

This work attempts to make a breakthrough in the empirical research of market inefficiency by introducing a new approach, the value frontier method, to estimate the magnitude of stock bubbles, which has been an interesting topic that has attracted a lot of research attention in the past. The theoretical framework stems from the basic argument of Blanchard & Watson’s (1982) rational expectation of asset value that should be equal to the fundamental value of the stock, and the argument of Scheinkman & Xiong (2003) and Hong, Scheinkman & Xiong (2006) that bubbles are formed by heterogeneous beliefs which can be refined as the optimism effect and the resale option effect. The applications of the value frontier methodology are demonstrated in this work at the market level and the firm level respectively. The estimated bubbles at the market level enable us to analyse bubble changes over time among 37 countries across the world, which helps further examine the relationship between economic factors (e.g. inflation) and bubbles. Firm-level bubbles are estimated in two developed markets, the US and the UK, as well as one emerging market, China. We found that the market-average bubble is less volatile than industry-level bubbles. This finding provides a compelling explanation to the failure of many existing studies in testing the existence of bubbles at the whole market level. In addition, the significant decreasing trend of Chinese bubbles and their co-moving tendency with the UK and the US markets offer us evidence in support of our argument that even in an immature market, investors can improve their investment perceptions towards rationality by learning not only from previous experience but also from other opened markets. Furthermore, following the arguments of “sustainable bubbles” from Binswanger (1999) and Scheinkman & Xiong (2003), we reinforce their claims at the end that a market with bubbles can also be labelled efficient; in particular, it has three forms of efficiency. First, a market without bubbles is completely efficient from the perspective of investors’ responsiveness to given information; secondly, a market with “sustainable bubbles” (bubbles that co-move with the economy), which results from rational responses to economic conditions, is in the strong form of information-responsive efficiency; thirdly, a market with “non-sustainable bubbles”, i.e. the bubble changes are not linked closely with economic foundations, is in the weak form of information-responsive efficiency.