This paper examines the effect of Quantitative Easing (QE) on listed companies and sectors in South Africa. The unconventional monetary policy carried out by the developed markets had spill over effects in emerging market economies. We focus on the policies performed by the United States. Our interest is to find out whether the QE announcements had any impact on the returns of listed companies and sectors in South Africa. An exploratory analysis is done on the macroeconomic and financial indicators in SA to provide grounds for doing the analysis on the listed companies. This analysis shows that the exchange rate and portfolio inflows were impacted by QE. However, other local factors were in play in affecting the exchange rate. The shrinkage in the global economic activity affected the Gross Domestic Product (GDP) growth rate. The changes in inflation cannot be attributed to QE. Most of the portfolio inflows were in the bond market and since some were directed to the equity market we proceed to check whether stocks and sectors had abnormal returns as a result. Our empirical analysis shows that only three companies had significant Cumulative Abnormal Returns (CARs) in the three phases of QE. On the sector front, nine out of the 34 sectors had significant CARs every time QE was announced. A broader classification of these sectors into industries shows that the industries represented are industrials, consumer goods, consumer services and financials. In QE1, the industrials industry and the consumer services industry had negative CARs but in QE2 and QE3, they had positive CARs. The consumer goods industry had positive CARs during the three phases of QE. This research concludes that QE1 had the greatest impact on the Johannesburg Stock Exchange (JSE) and its impact was negative. QE2 had a positive impact on the JSE since most companies and sectors had significant positive CARs. The impact of QE3 on sector abnormal returns was almost neutral. We also provide an investment strategy on the JSE using various indices for the periods following QE2 and QE3. Out of the 14 indices used, the small caps index is given a higher weighting in both portfolios due to its low risk.

The cost of equity is used extensively for capital allocation decisions, and the various methods used to estimate it often result in materially different outcomes. A model of the impact of known risk factors on the implied cost of equity used by equity analysts, who are seen as informed market participants, could be a guideline and sense check for other professionals estimating cost of equity for capital allocation decisions. This study, an implementation of Arbitrage Pricing Theory, attempts to create a parsimonious model of factors that are associated with the implied cost of equity premium utilised by equity analysts on the New York Stock Exchange ("NYSE"). After limiting the sample to NYSE-listed companies that were primarily exposed to US macro-economic conditions and were likely to be valued overwhelmingly on a going-concern basis, the test sample consisted of 5,343 company quarters covering the period 2006 to 2015. In the first part of the methodology, sixteen factors identified from previous literature as possibly influencing the cost of equity were tested for their association with the implied equity risk premium, as calculated from analysts' two-year earnings forecasts and target share prices using the Easton-method. Only those factors that were statistically significantly associated with the implied cost of equity were retained for the second part of the methodology, in which mixed effects modelling and optimisation using the Akaike information criterion was used to find a parsimonious model linking the statistically most significant factors to the implied cost of equity. The final model could explain 40% of the variation in implied risk premium by the fixed effects (specified variables), and 62% when the random effects (observable effects of unspecified variables) were included. The study found that the risk free rate was most strongly (and negatively) associated with the size of the implied equity risk premium. Other factors that are statistically significantly associated with the implied equity risk premium are the two-year beta (+), the profitability dummy variable (-), return on equity (-), two-year share price volatility (+), long-term growth (+), Market momentum (+), and the debt to equity ratio (+). It was further found that not all factors which have historically been shown to influence returns are significantly associated with implied cost of equity estimates, which is contrary to expectations in a fully efficient market, where the only difference in the two would result from the information that changes cash flow expectations or the risk profile of the cash flows. This study contributes to the current body of literature on cost of equity in the following ways: • To the author's knowledge, this study combines a far wider array of factors of all types than any of the previous studies on the topic, and uses target prices rather than market prices to calculate the implied cost of equity premium. • The study uses the adaptive and recursive option valuation model to eliminate companies for which the testing would not be relevant. • The study used mixed effects modelling to measure the impact of the various factors on the cost of equity premium.

Financial analyst commonly advice individual investors with a long investment horizon to invest in portfolios comprised more of equities. This advice is usually coupled with the practice of shifting the investor's portfolio from risky asset holdings towards bonds and cash as the investor's target date gets closer. This view rests on the notion that equities tend to be less risky over the long horizon and that stock returns exhibit mean reversion overtime. The purpose of this dissertation is to find the optimal asset allocation over various investment horizons; and investigate how the optimal asset allocation changes over the long investment horizon. The study uses data from South Africa's financial market covering the period December 2001 to December 2014. The mean - variance framework generated the optimal asset allocation over 12 investment horizons. The study finds that, over 90 percent of the portfolio should be vested into fixed - income South African bonds, with little over 5 percent equities allocation, over longer investment periods. In addition, the study found evidence of time diversification on the JSE all shares index and the presence of mean reversion properties for the all s hares index. With these conclusions, implications and recommendations are suggested

This research report implements and tests the effectiveness of a trend following trading strategy on the South African Futures Exchange (SAFEX) through utilising Donchian Channels and modelled after the 'Turtle method' which was first popularized in the United States in the 1970s before the automation of trading models. Prior literature focused on the commodities and equity indices spectrum of futures contracts in North American and Asian markets while this report replicates the model and attempts to optimize it for use on the SAFEX. The objective of this research is to invigorate academic study of trading strategies in the South African market by employing what was a successful, albeit very simple, trend following strategy on a sparsely studied academic field in South Africa. The contrarian trading strategy comprises three systems that generate idiosyncratic entry and exit signals using Donchian Channel theory to identify a price breakout from an average true range (ATR) band in the attempt to profitably trade on a price trend. The three systems implemented include: The short term system (System 1) generating a 'long' position when an instrument price moves above the 20-day 'high' and exit when it moves below the 10-day 'low', and vice versa for short positions; the long term system (System 2) following the same logic with 55-day entries and 20-day exits, and a third system (Integrated system) integrating the short and long term systems. A 20-day average true range is used to determine position sizing, stop-losses and additional contract purchases when a price-trend is potentially identified, while fractional asset allocation theory is drawn upon to determine optimal capital allocation to position.

Includes bibliographical references. / This research report documents an example of evidence of investor overreaction in the marketplace, with overreaction to short-term information found to be exploitable via price corrections in order to generate market-beating returns. An efficient market should render any consistent abnormal returns unattainable. Hence any technical analysis allowing an investor to obtain such returns would indicate a degree of market inefficiency. Three signal generation strategies are employed to test for momentum and price corrections in the market, namely using a stock's price and moving average, ranking stocks based on prior returns, and allocating stocks as overbought and oversold. The strategies are employed on data comprising the top 60 stocks on the JSE as at August 2012. The period tested runs from January 1998 to August 2012. Signal generation by means of price and moving average encompasses trade signals being generated by a stock's price moving above or below a variable moving average. Returns to this strategy tend to be maximized when employing a short-term (20-day) moving average, with an annualised above market return of 14,9 achievable. Using the returns of a stock in an immediately preceding formation period as a ranking criterion to classify stocks into a portfolio is found to be a superior method to generate trading signals. A portfolio of the best performing stocks in a preceding period ("the winner portfolio") is found to be able to outperform the market. Given a minimum formation period of 50 days, price continuation is achieved after holding the portfolio for at least 30 days, with annualized market excess returns greater than 10 achieved at longer formation and holding periods. A portfolio of the worst performing stocks in the same period ("the loser portfolio") is able to outperform the winner portfolio, and is capable of achieving returns of 20 in excess of the market, given a formation period as low as 10 days, while closing the investment position after no more than 10 days.

Includes bibliographical references. / The aim of this paper is to find solutions to the asset allocation problem in the South African environment. These solutions look at a variety of different investor's preferences. These include an investor's age, risk aversion and required levels of returns. To do this, an analysis was done of prior research, so the most up to date mean-variance asset allocation model could be developed. Returns from 10 different indices, over different asset classes were gathered. The indices of importance were found to be: All Bond Index (ALBI), Inflation Linked All Maturities Index (ILB), Salient's Momentum Active Index Fund (MOME), Salient's Value Active Index Fund (VAL), South African Short Term Fixed Interest Index (STEFI) and South African Property Index (SAPY).

This study investigates the possible diversification benefits of multiple cryptocurrencies (Bitcoin, Ethereum and Litecoin) in a diversified portfolio from the perspective of a South African investor over the period 30 July 2015 to 20 December 2017. Cryptocurrencies are mostly still in their infancy, and reliable information regarding their usefulness as an asset class in a diversified portfolio is scarce to come by. This study adopts a quantitative research methodology which incorporates the following statistical methods: i) mean-semivariance optimisation; ii) Kendall Tau-b correlations; and, iii) autocorrelation function for serial correlations. The JSE All Bond Index is used as bond investment proxy, a combination of the JSE Top 40, Resources Index and Financial-Industrials Index is used as an equity investment proxy, and the LBMA Gold PM is used as a gold investment proxy. The study found that all three cryptocurrencies under investigation yielded risk-return benefits for a diversified portfolio. The alternative cryptocurrencies (Ethereum and Litecoin) exhibited higher levels of downside risk (semideviation) than Bitcoin, but proportionately greater returns. Hence, the addition of these two cryptocurrencies to a portfolio that includes Bitcoin and traditional assets resulted in an expansion of the efficient frontier. Ethereum exhibited slightly lower correlations to Bitcoin than Litecoin, which is most likely attributed to its greater technological differences, but performed worse as a diversifier. All three cryptocurrencies yielded similar low to very low correlations to all traditional assets, including gold - representative of the potential diversification benefits. The autocorrelation function resulted in high positive serial correlations for all three cryptocurrencies, indicative of strong trending behaviour and high volatility.

This dissertation investigates the existence of time diversification on the Johannesburg Stock Exchange (JSE), with the goal of providing investor guidance toward their optimal investment horizon on the JSE Focusing on the Random Walk and Mean Reversion Models, a variety of tests were employed to identify serial correlation within the JSE logarithmic total returns. By assessing the possibility of mean reversion or trending behavior in returns, this study aims to determine if short-term variance (as a risk measure) calculation intervals accurately describe the long-term risk on the JSE when scaled. Additionally, the skewness of the logarithmic and arithmetic return distributions on the JSE, as the return interval lengthens, was investigated. The focus was on a composite JSE All Share Index (ALSI) resulting from the merger of the FTSE/JSE All Share Total Return Index (J203T), the JSE Actuaries Index (adjusted for dividends (AJ203)) and early JSE total return data (Firer & McLeod, 1999). The JSE All Bond Index (ALBI) was used in this study as an alternate asset class to JSE Equities. The dataset is comprised of 117 years (01/01/1900 to 31/12/2016) of ALSI and 18 years (31/12/1998 to 31/12/2016) of ALBI price and return series. The frequency of returns analyzed range from monthly to twenty-year total returns. The dataset was further analyzed, into a period before and after 1987 to observe the long and short-term serial correlation dynamics of the JSE, and to investigate how these change over time. This breakpoint (1987) was chosen due to the belief that structural change occurred on the JSE after 1986. Data analysis included; descriptive statistics and tests for normality, the Augmented Dickey Fuller and Phillips-Perron tests for stationarity, the Autocorrelation Function tests for serial correlation, the Quandt-Andrews and Bai-Perron tests for structural breaks, the Variance Ratio Test, and the Runs Test. These parametric and nonparametric methods were performed on both the nominal and real total returns of the ALSI and ALBI. This investigation uncovered significant short-term trending behavior in the ALSI returns, combined with evidence of medium-term mean reversion in this indexes returns. A lack of mean reversion and limited evidence of trending behavior in the ALBI returns were uncovered. ALSI returns have rejected the Random Walk Model over the short and medium-term, while ALBI returns have for the most part, failed to significantly reject the Random Walk Model. The short-term trending behavior in ALSI returns was observed at the monthly, quarterly and semi-annual return frequencies. This behavior suggests that if variances (as risk measures) calculated over these shorter trending periods, are scaled to represent the risk of longer periods, they will underestimate the true period risk on the JSE. Furthermore, the implications of the mean reversion evidence in three yearly returns, suggest that if the variances (as risk measures) are calculated over these three-year periods, and were to be scaled to represent the variance of longer periods, then these longer periods would have their period risk overstated. This paper has documented the change in the logarithmic return distribution of the ALSI, that exhibited negative skewness as the return holding period lengthens. Paradoxically positive skewness is observed as the return holding period increased was observed for the arithmetic distribution of ALSI returns. In the presence of autocorrelation in ALSI returns, portfolio and fund managers should employ the Lo and MacKinlay (1988) variance adjustment to unbias their risk estimates - if they scale short or medium term variances. The existence of Mean Reversion at the three-year frequency in South African Equities, provides evidence to support Time Diversification. As a direct result of this, this study proposes that a five to six-year holding period is optimal to take advantage of these mean reverting returns.

With increasing globalisation and the need to expand into new markets quickly and efficiently, South African firms are more than ever relying on mergers and acquisitions (M&A). It is therefore important to revisit the debate on whether M&A is a beneficial long-term corporate strategy for shareholders, especially given that little South African literature exists on this issue. This study addresses this question by examining both the short- and long-run share return performances resulting from 204 mergers and acquisitions (M&A) over the period 2003-2014, involving companies listed on the Johannesburg Stock Exchange (JSE) as acquirers. The measurement of long-run performance of M&A and other corporate events such as share buy backs and seasoned offerings remains contentious primarily due to concerns on the appropriate benchmarks for abnormal share return performance as a result of these events and the methodology used to measure long-run realized returns from these events. With regard to benchmarks, a combination of four return factors deemed appropriate for the South African equity market is used to benchmark the abnormal returns related to M&A activities. These factors are the JSE’s Financial & Industrials Index (JSE index code J213 or colloquially known as the Findi), the JSE’s Resources Index (JSE index code J210 or colloquially known as the Resi), and the size and book-to-market factors. Two methods have been widely used to determine the long-run share return performance from corporate events: The Buy-and-Hold Abnormal Return (BHAR) approach and the more statistically robust Calendar Time Portfolio (CTP) approach. Using these two approaches, this study finds that, in the long term, there are no statistically significant abnormal returns associated with merger and acquisition transactions for the sample of South African acquirers tested. The correlation of a number of key transaction attributes with long-run M&A related share return performance is also examined in this study. The following characteristics are thus tested: the method of payment (cash, equity or cash and equity), the listing status of acquisition targets (private, public or subsidiary), the target’s geographical location (cross-border or non-cross border, i.e. South African), the relatedness of the target’s industry to the acquirer’s (i.e. conglomerate versus horizontal M&A) and the percentage of the target acquired (50% or more and less than 50%). The results indicate that cash acquirers outperform both equity and cash and equity acquirers, acquirers of subsidiaries outperform acquirers of private or public targets, cross-border acquirers outperform non cross-border acquirers, conglomerate M&A underperform horizontal or related M&A and gaining control, i.e. acquiring 50% or more of the target results in slightly higher return than not gaining control. In addition, the short-run share return performance of M&A is examined to investigate whether investors’ short-run expectations from M&A announcements manifest in the long-run. The findings indicate that a positive abnormal short-run return is on average achieved in the -5, 5 event window. However, the market corrects for this initial positive reaction to M&A announcements, as the positive return becomes insignificant within 10 days of the announcement. The results of this study indicate that South African companies’ merger and acquisition activities do not deliver any statistically significant short- or long-term value to shareholders, implying that great care should be taken when considering such actions.

This study investigates a new asset allocation technique termed Factor Adjusted Rolling Economic Drawdown (FAREDD), whereby resources are allocated to different assets by way of integrating Principle Component Analysis (PCA) with existing Rolling Economic Drawdown Methods (REDD). The primary purpose of this model is to create a portfolio with low drawdown levels, that can withstand turbulent market periods thus protecting portfolio value through providing stronger diversification benefits while still seeking to maximise risk adjusted and overall return. This will have strong implications for investors as it could provide an additional method and tool to be considered during the asset allocation decision stage if they have a strong drawdown aversion. The concept of FAREDD is developed in this study within a South African context and compares this method with several traditional allocation methods including mean-variance optimised models, risk parity as well as traditional rolling economic drawdown models. So far, at the point of writing this study, the author has been unable to find any previous studies documenting this type of application of PCA to REDD. In addition to this, all previous studies that has investigated rolling economic drawdown has been conducted exclusively on the United States of America. The literature finds that REDD provides a viable and superior alternative to traditional asset allocation in the long run. Thus, as part of this study, a second objective is to investigate whether REDD models provide sufficient protection and superior returns in a developing economy with a significantly lower number of available liquid assets and higher volatility due to increased political, economic and business risk, when compared to alternative more traditional allocation techniques. The key findings of this study are that the FAREDD model does outperform the traditional REDD model that it is compared to for the period and it also meets the objective of providing low drawdowns and volatility while achieving strong risk-adjusted returns. However, the model does not provide the strongest drawdown protection of all portfolios tested. The FAREDD model is surpassed by the minimum-variance portfolio in this regard but from a risk adjusted basis and an overall return perspective it far outperforms the minimum-variance portfolio. Therefore, the performance of the FAREDD model is mixed and its optimality would need to be assessed relative to an investor’s risk appetite and risk-return trade-off. In addition to this, the paper finds that the performance of traditional REDD models in the South African context are mixed when compared to traditional asset allocation techniques thereby indicating that REDD models may not be superior in the South African market place at all times. However, they can provide relevant and potential asset allocation alternatives for mangers to consider.