A research report submitted to the Faculty of Commerce, Law and Management, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirements for the degree of Master of Management in Finance and Investment. Johannesburg, 2016 / The prediction of stock price and index level in a financial market is an interesting but highly complex and intricate topic. Advancements in prediction models leading to even a slight increase in performance can be very profitable. The number of studies investigating models in predicting actual levels of stocks and indices however, far exceed those predicting the direction of stocks and indices. This study evaluates the performance of ensemble prediction models in predicting the daily direction of the JSE All-Share index. The ensemble prediction models are benchmarked against three common prediction models in the domain of financial data prediction namely, support vector machines, logistic regression and k-nearest neighbour. The results indicate that the Boosted algorithm of the ensemble prediction model is able to predict the index direction the best, followed by k-nearest neighbour, logistic regression and support vector machines respectively. The study suggests that ensemble models be considered in all stock price and index prediction applications. / MT2017

This study has been prompted by the recent introduction of the JSE Alternative Exchange in South Africa, an alternative listing platform for smaller companies compared to the more established JSE Main Board Exchange. This new era has led to information asymmetry among current and prospective investors regarding the risk-return profile of the companies listed on the relatively new JSE Alternative Exchange and how this profile relates to the profile of firms listed on the long established JSE Main Board Exchange. In an attempt to fill the above information gap, this study sheds light on the short, medium and long-term performances of initial public offerings of companies listed on the JSE Alternative Exchange vis-a-vis that of JSE Main Board Exchange. This information is relevant for investment and financing decision making, principally for investors, venture capitalists and entrepreneurs. The findings of this research appear to be contrary to expectations and to corporate finance theory. The results indicate that on average, initial public offerings by larger JSE Main Board companies outperform the smaller JSE Alternative Exchange companies on a nominal and risk-adjusted bases in the short-medium and long-term. It must be noted however that the differences in performance are not statistically significant. On the other hand, in line with documented evidence in the literature, it was found that the risk of returns on the smaller capitalisation JSE Alternative Exchange companies was indeed higher than that of the JSE Main Board companies. A similarity identified between the average performances of the two listing platforms is that, the returns for companies decreased overtime between the short and long-term. This may be partly due to the impact of the 2007 economic recession.

Van Rensburg and Robertson (2003a) stated that the CAPM beta has little or no relationship with returns generated by size and price to earnings sorted portfolios. This study intends to demonstrate that a reformulated CAPM beta, estimated using return on equity as opposed to share returns, unravels the size and value premium. The study proves that the “cash-flow” generated beta partially explains the cross-sectional variation in share returns when measured over the long run, specifically when portfolios are sorted on book to market, however the cash flow beta is less successful when attempting to explain the small size premium. The premise of the study is that the cash flow dynamics of share returns eventually dominate the first and second moments and thus result in cash flow based measures of risk and return that should succeed in explaining the cross-sectional variation in share returns. The study makes use of vector autoregressive models in order to examine the short term effect of structural shocks to the cash flow fundamentals of a stock or portfolio through impulse response functions as well as quantifying a long-term relationship between cash flow fundamentals and share returns using a VECM specification. The study further uses fixed effects, random effects and GMM/dynamic panel data cross-sectional regressions in order to examine the ability of the cash flow beta explaining the value and size premium. The results of the study are mixed. The cash flow beta does well in explaining the returns of portfolios sorted on book to market, but fails to do the same with size sorted portfolios. In the cash flow betas favour, it performs far better than the conventionally measured CAPM beta throughout the study.

Thesis (M.M. (Finance & Investment))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Wits Business School, 2016 / Modeling and forecasting volatility is one of the crucial functions in various fields of financial engineering, especially in the quantitative risk management departments of banks and insurance companies. Forecasting volatility is a task of any analyst in the space of portfolio management, risk management and option pricing. In this study we examined different GARCH models in Johannesburg Stock Exchange (JSE) using univariate GARCH models (GARCH (1, 1), EGARCH (1, 1), GARCH-M (1, 1) GJR-GARCH (1, 1) and PGARCH (1, 1)). Daily log-returns were used on JSE ALSH, Resource 20, Industrial 25 and Top 40 indices over a period of 12 years. Both symmetric and asymmetric models were examined. The results showed that GARCH (1, 1) model dominate other models both in-sample and out-of-sample in modeling the volatility clustering and leptokurtosis in financial data of JSE sectoral indices. The results showed that the JSE All Share Index and all other indices studied here can be best modeled by GARCH (1, 1) and out-of-sample for JSE All Share index proved to be best for GARCH (1, 1). In forecasting out-of-sample EGARCH (1, 1) proved to outperformed other forecasting models based on different procedures for JSE All Share index and Top 40 but for Resource 20 RJR-GARCH (1, 1) is the best model and Industrial 25 data suggest PGARCH (1, 1) / DM2016

A 50% research report to be submitted in partial fulfilment for the degree of: MASTER OF COMMERCE (FINANCE) UNIVERSITY OF THE WITWATERSRAND / Several studies have assessed the forward-looking characteristic of share prices and confirmed their resultant capability as leading indicators of economic activity, especially in advanced economies. Contention however exists when evaluating the role of stock markets as leading indicators for less developed countries. This study examines the validity of the stock market as a leading indicator of economic activity in South Africa using quarterly time-series data for the period January 1992 to June 2014. Causality and cointegration between the JSE All Share Index against Real GDP and Real Industrial Production is evaluated by employing Granger-causality tests and the Johansen cointegration procedure. The empirical investigation indicates that unidirectional causality exists between the nominal and real stock indices and economic activity in South Africa, and confirms a long-run relationship between the JSE and GDP and Industrial Production. Therefore, similar to the study by Auret and Golding (2012), in a South African context, the stock market is in fact a leading indicator of economic activity. / MT2017

A thesis submitted to the Faculty of Commerce, Law and Management, University of the Witwatersrand, in fulfilment of the requirements for the degree of Doctor of Philosophy (Ph.D), September 2016 / This study considers momentum in share prices, per Jegadeesh and Titman (1993, 2001), on the cross-section of shares listed on the JSE. The key research objective is to define whether momentum is significant, independent and priced. ‘Significant’ implies that momentum produces significantly positive nominal and risk-adjusted profits, ‘independent’ means that momentum is independent of other non-momentum stylistic factor premiums and finally, ‘priced’ suggests that momentum is a priced factor on the JSE and thereby contributes to the cross-sectional variation in share returns. In order to determine the significance of the momentum premium on the JSE, univariate momentum sorts are conducted that consider variation in portfolio estimation and holding periods, weighting methodologies as well as liquidity constraints, price impact and microstructure effects. The results of the univariate sorts clearly indicate that momentum on the JSE is both significant and profitable assuming estimation and holding periods between three and twelve months. Furthermore, consistent with international and local literature, momentum profits reverse assuming holding periods in excess of 24 months. In order to determine whether momentum is independent, bivariate sorts and time-series attribution regressions are conducted using momentum and six non-momentum factors, namely: Size, Value, Liquidity, Market Beta, Idiosyncratic Risk and Currency Risk. The results of the bivariate sorts and time-series attribution regressions clearly indicate that momentum on the JSE is largely independent of the nonmomentum stylistic factors considered. Lastly, cross-sectional panel regressions are conducted where momentum is applied, in conjunction with the considered non-momentum factors, as an independent variable in order assess the relationship between the factors and expected returns on a share-by-share basis. The results of the panel data cross-sectional regressions clearly indicate that momentum produces a consistently significant and independent premium, conclusively proving that momentum is a priced factor that contributes to the cross-sectional variation in share returns listed on the JSE. / XL2018

Thesis (M.M. (Finance & Investment))--University of the Witwatersrand, Faculty of Commerce, Law and Management, Graduate School of Business Administration, 2013. / This study examined stock market reaction to annual earnings announcements using the most recent data from the Nairobi Securities Exchange (Kenya) and JSE Securities exchange (South Africa). The period of study is 1 January 2005, to 31 December, 2011. Using the event study methodology, the magnitude of market reaction to the earnings announcements for a sample of 261 listed firms on NSE and JSE is tested. Abnormal returns (ARs) were computed for each firm and tested how announcements impact a firms’ share price. The results show positive and significant returns on the announcement month for JSE, whereas the returns for NSE are negative and significant on the second month after announcement. In our study, JSE and NSE observed mean CAR of (+1.64%) and (-1.8606) respectively, suggesting that earnings contain important information for the market. We find that there is no post earnings announcement drift observed over the next six months after the announcement. The results are consistent with the efficient market hypothesis, thus suggesting that the Johannesburg securities exchange and Nairobi securities exchange are informationally efficient to earnings announcements by the sample of listed firms. Furthermore, our results show NSE firms performed better than JSE firms during the economic boom and meltdown, whereas JSE firms observed a good performance during the economic recession compared to NSE firms.

Thesis submitted in fulfilment of the requirements for the degree of Master of Management in Finance & Investment in the Faculty of Commerce, Law and Management Wits Business School at the University of the Witwatersrand 2017 / This thesis empirically examines the stock market reaction to mergers and acquisitions (M&A) announcements in South Africa, and also analyses the effects of the method payment. Data was collected from 34 acquisitions, consisting of acquirer and target companies in the same industry listed on the Johannesburg Stock Exchange, (JSE). The transactions were of mergers and acquisitions for the period 2003 – 2013. The event study methodology was used to calculate cumulative average abnormal returns for the acquiring companies over the total event window. Parametric t-tests were then applied to test the significance of the cumulative average abnormal returns, and a comparison of the pre and post-announcement returns was done over the event window. A comparison is also done for cash and share acquisitions over the entire event window (-10, +10). From the findings, it is clear that there were no significant abnormal returns or significant differences between the pre and post announcement returns. Comparing the two payment methods (cash and share payments), the results also show that there were no significant differences between these methods. The study therefore concluded that merger and acquisition announcements did not create any value for shareholders of acquiring companies during and around the announcement period. / MT2017

Thesis (M.Com. (Finance))--University of the Witwatersrand, Faculty of Commerce, Law and Management, School of Economic and Business Sciences, 2016 / The purpose of the study is to investigate the effects of relaxing the assumption of multivariate normality typically utilised within the traditional asset pricing framework. This is achieved in two ways. The first involves the introduction of higher moments into the linear Capital Asset Pricing Model while the second involves a Monte Carlo experiment to determine the impact of skewness and kurtosis on test statistics traditionally employed to assess the validity of asset pricing models. We commence by establishing non-normality for the majority of sample portfolios. A cross-sectional regression approach is employed to estimate factor risk premia and test higher moment Capital Asset Pricing Models. Unconditional coskewness and unconditional cokurtosis are found to be priced within the market equity (size) sorted and book equity/market equity (value) sorted portfolio sets over the period January 1993 to December 2013. Conditional coskewness and conditional cokurtosis are found to be priced for only the size sorted portfolios over the period January 1997 to December 2013. Factor risk premia estimated for coskewness are generally positive while risk premia estimated for cokurtosis are negative. This suggests a positive relationship between coskewness and expected return and a negative relationship between cokurtosis and expected return. The results of the asset pricing model tests are mixed. The pricing errors for higher moment Capital Asset Pricing Models are shown to be significantly different from zero for size sorted portfolios while pricing errors on the value sorted, dual size-value sorted and industry portfolios are found to be statistically insignificant. This suggest that none of the asset pricing models tested are the true model as it would explain variation in expected returns regardless of the data generating process. Finally we show that the Ordinary Least Square Wald test statistic has the most desirable size characteristics while the Generalised Least Squares J-test statistic has the most desirable power characteristics when dealing with non-normal data.

A thesis submitted to the School of Economic and Business Sciences, Faculty of Commerce, Law and Management, University of the Witwatersrand in fulfilment of the requirements for the degree of Doctor of Philosophy (Ph/D). Johannesburg, South Africa June 2016 / In recent years, the debate on market efficiency has shifted to providing alternate forms of the hypothesis, some of which are testable and can be proven false. This thesis examines one such alternative, the Adaptive Market Hypothesis (AMH), with a focus on providing a framework for testing the dynamic (cyclical) notion of market efficiency using South African equity data (44 shares and six indices) over the period 1997 to 2014. By application of this framework, stylised facts emerged. First, the examination of market efficiency is dependent on the frequency of data. If one were to only use a single frequency of data, one might obtain conflicting conclusions. Second, by binning data into smaller sub-samples, one can obtain a pattern of whether the equity market is efficient or not. In other words, one might get a conclusion of, say, randomess, over the entire sample period of daily data, but there may be pockets of non-randomness with the daily data. Third, by running a variety of tests, one provides robustness to the results. This is a somewhat debateable issue as one could either run a variety of tests (each being an improvement over the other) or argue the theoretical merits of each test befoe selecting the more appropriate one. Fourth, analysis according to industries also adds to the result of efficiency, if markets have high concentration sectors (such as the JSE), one might be tempted to conclude that the entire JSE exhibits, say, randomness, where it could be driven by the resources sector as opposed to any other sector. Last, the use of neural networks as approximators is of benefit when examining data with less than ideal sample sizes. Examining five frequencies of data, 86% of the shares and indices exhibited a random walk under daily data, 78% under weekly data, 56% under monthly data, 22% under quarterly data and 24% under semi-annual data. The results over the entire sample period and non-overlapping sub-samples showed that this model's accuracy varied over time. Coupled with the results of the trading strategies, one can conclude that the nature of market efficiency in South Africa can be seen as time dependent, in line with the implication of the AMH. / MT2017