Dissertation submitted in partial fulfillment of the requirements for the degree of Masters of Management in Finance and Investments (MMFI) in the Graduate School of Business Administration University of the Witwatersrand 2017. / The Western Cape Fishing industry has been a subject of discussion in numerous papers, in which the thrust has been to seek ways of sustaining the significantly fluctuating business. Common risk factors have been identified and strategies for managing the fishing business in turbulent periods have been proposed over the years. A closer examination of previous literature as well as empirical evidence indicate that the business has less to do to control or minimize the impact of most of its external factors, which include the Government imposed Total Allowable Catch (TAC) limit, the variability in natural marine populations, environmental factors and fuel price oscillations. In the interest of curbing the variability component which is borne by the internal factors, this study brings on board a quantitative dimension to the evaluation of the four commonly cited internal factors, namely; Earnings Per Share (EPS), Margin of Safety (MOS), Free Cash-Flow (FCF) and the Net-Worth (NW) on volatility of the fishing business. The performance of five large JSE-listed fishing firms: Brimstone, Oceana, Premier Fishing, Sea Harvest and Irvin & Johnson, is investigated with the view of modelling and forecasting their volatilities. Initially, the comparison of volatility forecasts from symmetric and asymmetric GARCH-family models is employed. The results of competing models are tested using cross-validation of mean error measures and the Superior Predictive Ability (SPA) and Model Confidence Set (MCS) tests. Later, a Vector Autoregressive (VAR) model is applied to assess the impact of the four commonly cited internal factors on volatility. The research analysis results reveal a generally high volatility of the Western Cape fishing sector stocks. When univariate GARCH models are applied, the asymmetric GARCH-family models (EGARCH and GJR), with fat tails, appear dominant in the sets of competing models for all stocks, which highlights evidence of the leverage effect in the sector. However, GARCH (1,1), outperformed its counterparts in modelling and forecasting Irvin & Johnson (AVI) and Oceana (OCE) stocks. In the VAR modelling process, the Granger-causality tests indicate limited causal-relationship between EPS, MOS, FCF and the company Net-worth with the companies’ volatility measures. The variance decomposition of the 10-year ahead forecast of volatility indicates that volatility lag, free cash flow and networth have the largest contribution on volatility in the long-run, followed by margin of safety. In view of the above observations, the research discusses recommendations to the Western Cape fishing business to improve business returns and sustainability. / MT2017

This write-up is submitted in partial fulfilment of the Master of Management Degree in Finance and Investments Degree. / Growth in commodity exporting economies, such as South Africa, is highly dependent on the revenue generated from exports. It is thus evident that as commodity prices fluctuate, income and the balance of payments will be accordingly impacted. This is further exacerbated by strong dependence on the imports of certain commodities. Oil is one such commodity on whose imports South Africa is highly dependent. Although natural gas is also imported, it is in lower quantities and is as such expected to impact South Africa to a lower extent. Coal, on the other hand, is among the main commodity exports and was expected to have an impact on (and be impacted by) South African macroeconomic fundamentals. In this study, we use a VECM and MGARCH model to test the interaction between South African macroeconomic variables and these three commodities. Our VECM findings indicate that oil and exchange rates are inflationary. This implies that an increase in oil prices and/or exchange rates (indicating a depreciation of the Rand against the U.S. Dollar) results in an increase in inflation. Inflation, on the other hand, propagates higher coal prices and to a lesser extent, higher interest rates. We account the latter to South Africa’s inflation targeting regime and the former to demand and supply dynamics which occur at RBCT as production costs increase (short-term coal export contracts and spot market sales). Natural gas is found to have weak impacts on interest rates and exchange rates. Our MGARCH model shows that only the innovations in natural gas and oil prices spillover into interest rates and exchange rate. There is no direct spillover captured. However, there is strong direct spillover from oil to inflation. Lastly, interest rates are found to have a strong direct volatility spillover to both oil and natural gas. We attribute this to the exchange rate impact that interest rates have and is supported by the exchange rate impact on commodity price volatility. We conclude that an in-depth understanding of triggers is pertinent for monetary and fiscal policy decisions in South Africa. Although the South African economy is relatively diversified compared to other developing countries, commodity price fluctuations do have a significant impact on economic performance. / MT2017

Thesis (M.Com.(Finance)--University of the Witwatersrand, Faculty of Commerce, Law and Management, School of Economic and Business Sciences, 2016. / This study sets out to investigate whether the Taylor Rule provides better the forecasts of the future short-term interest rates than the yield curve in the South African market. For the Taylor Rule we use OLS and use the open-market forward-looking Taylor Rule to forecast interest rates. For the yield curve, simple linear interpolation is used to derive forecast. We find that in the short term, forecasted one-month ahead interest rates closely track the actuals interest rates for both models. At longer horizons, there are larger deviations of forecasts from the actual. The RMSE analyses support the Taylor Rule as a superior forecasting model in all forecasting horizons. / MT2017

by Lo Yat-keung & Ma Kwok-wa. / Bibliography: leaves 46-47 / Thesis (M.B.A.)--Chinese University of Hong Kong, 1986

Pan, Qi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 70-73). / Abstract also in Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Main Contributions --- p.5 / Chapter 1.2 --- Structure of Thesis --- p.6 / Chapter 2 --- Related Works --- p.7 / Chapter 2.1 --- Literature Review --- p.7 / Chapter 2.1.1 --- Existing Works on Bursty Feature Idenfication --- p.9 / Chapter 2.2 --- Classification --- p.9 / Chapter 2.2.1 --- Support Vector Machine --- p.9 / Chapter 2.2.2 --- Decision Tree and C4.5 Algorithm --- p.10 / Chapter 2.3 --- PageRank and HITS Algorithm --- p.10 / Chapter 2.3.1 --- PageRank --- p.11 / Chapter 2.3.2 --- HITS --- p.11 / Chapter 2.4 --- Efficient Market Hypothesis --- p.12 / Chapter 3 --- Problem Statement --- p.14 / Chapter 3.1 --- Volatility --- p.14 / Chapter 3.2 --- Financial Model --- p.15 / Chapter 3.3 --- Problem Statement --- p.16 / Chapter 4 --- Volatility V.S. Trend Prediction --- p.18 / Chapter 5 --- Bursty Volatility Features --- p.22 / Chapter 5.1 --- ADFIDF Measure --- p.24 / Chapter 5.2 --- Bursty Volatility Features --- p.28 / Chapter 5.3 --- Bursty Volatility Features Selection --- p.29 / Chapter 6 --- Volatility Ranking --- p.32 / Chapter 6.1 --- Graph Construction --- p.32 / Chapter 6.2 --- Volatility Ranking By News --- p.35 / Chapter 7 --- Volatility Index for Stock Volatility --- p.37 / Chapter 8 --- Experiments --- p.41 / Chapter 8.1 --- Experiments for Volatility Index --- p.41 / Chapter 8.1.1 --- Effectiveness of Volatility Index --- p.42 / Chapter 8.1.2 --- Information from News --- p.42 / Chapter 8.1.3 --- Information from Market --- p.45 / Chapter 8.1.4 --- Correlation Value --- p.46 / Chapter 8.1.5 --- Bursty Feature selection --- p.47 / Chapter 8.2 --- Experiments for Ranking --- p.48 / Chapter 8.2.1 --- Ranking Quality Comparison --- p.49 / Chapter 8.2.2 --- Capturing Bursty Features --- p.51 / Chapter 8.2.3 --- The Effectiveness of Feature Rank --- p.52 / Chapter 8.2.4 --- The Effectiveness of Random Walk --- p.53 / Chapter 8.2.5 --- Combination of VbN and GARCH --- p.54 / Chapter 8.2.6 --- Ranking Result Sample --- p.56 / Chapter 9 --- Conclusion --- p.58 / Chapter A --- Most Important Features for Stocks --- p.60 / Chapter B --- Correlation Matrix of Stocks --- p.63 / Chapter C --- News Index Evaluation Result Table --- p.65 / Chapter D --- Stock Data in Experiments --- p.67 / Chapter E --- Constructed Graph --- p.68 / Bibliograph --- p.70

Baker and Wurgler (2002) define a new theory of capital structure. In this theory capital structure evolves as the cumulative outcome of past attempts to time the equity market. Baker and Wurgler extend market timing theory to long-term capital structure, but their results do not clearly distinguish between the two versions of market timing: perceived mispricing and adverse selection. The main purpose of this dissertation is to empirically identify the relative importance of these two explanations. First, I retest Baker and Wurgler's theory by using insider trading as an alternative to market-to-book ratio to measure equity market timing. I also formally test the adverse selection model of the equity market timing: first by using post-issuance performance, and then by using three measures of adverse selection. The first two measures use estimates of adverse information costs based on the bid and ask prices, and the third measure is based on the close-to-offer returns. Based on received theory, a dynamic adverse selection model implies that higher adverse information costs lead to higher leverage. On the other hand, a naïve adverse selection model implies that negative inside information leads to lower leverage. The results are consistent with the equity market timing theory of capital structure. The results also indicate that a naïve, as opposed to a dynamic, adverse selection model seems to be the best explanation as to why managers time equity issues.

Thesis (Ph. D.)--University of California, San Diego, 2003. / Vita. Includes bibliographical references (leaves 106-110).

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: comprehensiblity; technical analysis; genetic programming; overfitting; cooperative coevolution. Includes bibliographical references (p. 82-87).

published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy

Not available / text