Wang, Xiaolei. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 34-39). / Abstracts in English and Chinese. / Chapter Chapter 1. --- Introduction --- p.1 / Chapter Chapter 2. --- Identification of Stock Market Crashes --- p.5 / Chapter 2.1 --- Identification Criteria --- p.7 / Chapter 2.2 --- Identification Results --- p.8 / Chapter Chapter 3. --- Data --- p.10 / Chapter 3.1 --- Data Issue for Chapter 4 --- p.10 / Chapter 3.2 --- Data Issue for Chapter 5 --- p.12 / Chapter 3.3 --- Data Issue for Chapter 6 --- p.12 / Chapter Chapter 4. --- Examination of AFE --- p.13 / Chapter 4.1 --- Definition of AFE and MAAFE --- p.13 / Chapter 4.2 --- Examination of MAAFE --- p.14 / Chapter 4.3 --- Examination of AFE by Grouping Duration --- p.15 / Chapter Chapter 5. --- Examination of AFD --- p.18 / Chapter Chapter 6. --- Examination of the Relationship between AFD and ESR --- p.22 / Chapter 6.1 --- Portfolio Strategy - Sorting by Size and Dispersion --- p.23 / Chapter 6.2 --- Portfolio Strategy - Sorting by Size and Book to Market Ratio --- p.26 / Chapter 6.3 --- Fama-French Time Series Regression Test (Three-Factor Model) --- p.28 / Chapter 6.4 --- Fama-French Time Series Regression Test (Three-Factor Model with Dispersion on the Right Hand Side) --- p.30 / Chapter 6.5 --- Introduction of a Nonlinear Form of AFD to the Fama-French Model --- p.31 / Chapter Chapter 7. --- Conclusions --- p.32 / References --- p.34 / Appendix Table I to Table XVI --- p.40-55 / Figure I to Figure VI --- p.56-61

published_or_final_version / Statistics / Master / Master of Social Sciences

Thesis (MBA)--Stellenbosch University, 2008. / ENGLISH ABSTRACT: Empirical research is done to determine whether trading strategies based on technical analysis can outperform a naive buy-and-hold strategy. A study is made of classical and contemporary academic literature. The central investigation is threefold. Firstly, the degree of randomness of a chosen basket of securities is determined vis-a-vis the Random Walk Hypothesis. Secondly, the effectiveness of stop loss orders is assessed. Lastly, a collection of chosen trading strategies is back-tested on security data ranging over 20 years. Performance of these systems is measured on net average and risk-adjusted gains in the absence of transaction and taxation costs. The finding of this report is that, in the absence of these costs, certain technical trading strategies can indeed outperform a buy-and-hold strategy. Although end-of-day data is used throughout the study, the techniques can also be applied to intra-day trading. / AFRIKAANSE OPSOMMING: Empiriese navorsing is gedoen om te bepaal of handelstrategiee wat op tegniese ontleding gebaseer is, beter kan presteer as 'n klassieke konserwatiewe koop-en-hou-strategie. Omvattende literatuurstudie is gedoen van klassieke en kontemporere literatuur, en die kruks van die navorsing is drieledig. Eerstens word die toevalligheidsgraad van 'n gekose aandelepakket ten opsigte van die hipotese van ewekansigce koersbeweging bepaal. Tweedens word die effektiwiteit van "stop-verlies" opdragte ontleed en laastens word 'n versameling historiese handelstrategiee getoets met die laaste 20 jaar se aandeledata. Die prestasie van die onledingstelsels word gemeet aan die hand van die netto gemiddelde en risiko aangepaste opbrengste met uitsluiting van die transaksie en belasting kostes. Die bevinding van die studie is dat met uitsluiting van die transakie en belasting kostes, die gebruik van tegniese ontledings inderdaad hoer opbrengste lewer as die klassieke koop-en-hou strategie. Alhoewel dag sluitingsdata deurlopend vir die studie gebruik was, kan die tegnieke ook op intradag data toegepas word.

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

This project aims to make a comparison between the Swedish and Indianelectricity market, the design of new improvements will achieve a betteroperation for both markets as well as the price forecasting for markets. Thisresults will give a clear idea about the electricity prices, different energy uses andpeak hours and also the carbon dioxide emissions.Also the main organizations of the market and their roles has been characterized,discussing about the functions of the Market Operator and the System Operator.And also the different markets, the trading products and the price formation havebeen explained and giving an idea about the market structure with enough details.Moreover, Time Series Analysis explained in a detail manner and some of themost used methods in Time Series Analysis are also explained in a very goodmanner. Mainly the results section includes the description of the market situationin Swedish and Indian electricity markets comparison, which includes Powerinstalled capacity, electricity generation, main renewable technologies andpolicies to increase the renewable energy share in total electricity generated.After this analysis, the strengths and weakness of both markets are presented andthe main problems of Swedish electricity system like dependency for nuclearpower, uncertainty for solar electricity generation and the Indian electricitysystem problems like high losses in power system, power quality issues, and veryless focus on energy mix with renewable systems.Finally, due to the quick development of the energy sector in the last few yearsto reach a new design for the electricity market, different kinds ofrecommendations for the future have been considered.

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

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

This paper aims to investigate the apparent existence of two anomalies in the South African stock market based on regular strike action, namely the month of the year effect and seasonality across specific sectors of the Johannesburg Stock Exchange.