Indexation serves as a cornerstone of the asset management field. As such, asset managers across the globe are constantly testing different methodologies to find one which provides consistent superior performance against the rest. While previously, market capitalization weighted indexes have been the popular and simpler method to implement, the search of outperformance has evolved from only focusing on picking securities from larger institutions and has expanded to trying out various weighting methods so as to maximize on the best performing instruments. As yet, there is no definite winner, with the success of most methods being largely influenced by the type of market for which the index is intended as well as the macro-economic environment prevailing during the period. However, the fundamental indexation method has recently gained popularity, particularly in the global equity markets. This research paper explores the method of fundamental indexation and applies it to the corporate fixed income section of the South African market. The main aim is to determine whether the significant outperformance, which has been found in global fixed income markets as well as global and domestic equity markets, will hold true when the method is implemented on domestic bonds. This investigation uses the current domestic market corporate bond index, the OTHI, as a benchmark against two alternative bond indexes created using the fundamental indexing methodology. The first alternative index is a direct replication of the OTHI and has identical constituents to those of the original. This is called the OTHI_ALT. However, finding that the OTHI is heavily influenced by the debt issues of the government and other parastatal companies, a second more diverse index is created. This is named the SAFI_ALT, which maintains the same number of constituents in each period as the OTHI, but uses different universe selection methods and thus has different constituents. The study creates four sub-indexes for both the OTHI_ALT and the SAFI_ALT, using the fundamental metrics of the companies whose securities are included in the index. The fundamentals used are Sales, Cash Flow and Book Value, and in addition a Composite of all three fundamentals.

This dissertation investigates the impact of macroeconomic variables on the performance of African stock markets, focusing on Egypt, Mauritius, and South Africa during the period 2009– 2019. This dissertation employed the multiple linear regression model and the Granger causality test to ascertain the impact of these factors. For each country, the stock market index was used as a dependant variable while interest rates, inflation rate, money supply, exchange rate, gold price, and oil price were used as independent variables. The results from the country-specific models varied widely from country to country. The heterogeneity of the results may be explained by differences in economic fundamentals between the countries, for example, market depth, market size, and liquidity. The model showed that interest rate, which is inversely related to stock prices, isthe only significant variable in explaining stock prices in Egypt. In Mauritius, it wasfound that only three factors significantly affect stock prices, namely, exchange rate, gold price, and inflation. A depreciation of the Mauritius Rupee to the USD and an increase of the gold prices decrease stock prices in Mauritius, whilst the effect of inflation was found to be positive. In South Africa, results showed that inflation, money supply, and oil prices significantly affect stock prices in the Johannesburg Stock Exchange (JSE). However, unlike for Mauritius (where inflation has a positive impact), in South Africa, its effect is negative. By contrast, money supply and oil prices were found to impact the JSE stock prices positively. Against the backdrop of these findings, this dissertation encourages the governments and policy makers in emerging markets to consider stabilising the macroeconomy to create a conducive environment for stock market development. The Granger causality test reveals that stock prices can be used to predict oil prices in Egypt. In contrast, the South African data suggests no causal relationship between macroeconomic factors and stock prices. Finally, the same test in Mauritius shows that money supply can be used to predict stock prices, and stock prices can be used to forecast gold prices and exchange rates.

Nürtingen, FH, Diplomarb., 2004.

Master-Arbeit Univ. St. Gallen, 2008.

Master-Arbeit Univ. St. Gallen, 2007.

Yes / A portfolio optimisation problem involves allocation of investment to a number of different assets to maximize return and minimize risk in a given investment period. The selected assets in a portfolio not only collectively contribute to its return but also interactively define its risk as usually measured by a portfolio variance. This presents a combinatorial optimisation problem that involves selection of both a number of assets as well as its quantity (weight or proportion or units). The problem is extremely complex due to a large number of selectable assets. Furthermore, the problem is dynamic and stochastic in nature with a number of constraints presenting a complex model which is difficult to solve for exact solution. In the last decade research publications have reported the applications of metaheuristic-based optimisation methods with some success., This paper presents a review of these reported models, optimisation problem formulations and metaheuristic approaches for portfolio optimisation.

In modern organisations a large portion of senior management's time is now being consumed in finding ways to measure the contribution of their organisations' IS/IT investments on business performance. It has been shown that IS/IT investments in many organisations are huge and increasing rapidly every year and yet there is still a lack of understanding of the impact of the proper IS/IT investment evaluation processes and practices in these organisations. At the same time, the issue of expected and actual benefits realised from IS/IT investments has generated a significant amount of debate in the IS/IT literature amongst the researchers and practitioners. This is as true in Australia as it is in the rest of the developed world. Thus, one can argue that a detailed study of current practice in IS/IT investment evaluation in Australia is warranted. In this research study, an attempt was made to: (1) establish current Australian industry and government practices and norms in managing IS/IT benefits and evaluation; and (2) develop a framework based on the fit between theory and practice of IS/IT investment evaluation by large Australian organisations, particularly in an environment where much of the IS/IT is outsourced. Research objective one utilised an existing questionnaire based on Ward et al. (1996). This questionnaire was sent to the IS/IT managers of the largest 500 Australian organisations. The aim of this objective was to investigate IS/IT investment evaluation and benefits management and realisation in these Australian organisations, so as to shed light on the current practices and norms in this area. / The second research objective employed two case studies utilising semi-structured interviews, observation and document review. The aim of this objective was to develop a framework based on the fit between theory and practice of IS/IT investment evaluation by large Australian organisations. Results from the survey and two case studies were analysed and a framework for benefits realisation and investment evaluation was developed. The major contribution of this research include first, the creation of several useful guidelines for large outsourcing organisations undertaking IS/IT investment evaluation and benefits realisation processes and second, the development of a benefits realisation and investment evaluation framework which offers a practical tool to help the large organisations to determine when and how the IS/IT investment evaluation and benefits realisation should be adopted.

The performance of cash protection strategies implemented in the South African market are investigated in order to establish if investors are able to add value through the use of dynamic portfolio insurance methods. The analysis is performed, using monthly data, from January 1961 to August 2014 using six alternative methodologies including both a Fixed Rate and Rolling Average Stop-Loss approach, a Lock-In approach, a Constant Mix strategy, a Constant Proportion Portfolio Insurance ("CPPI") approach and an alternative CPPI approach using a Ratchet mechanism. The results indicate that the use of such cash protection strategies can markedly improve portfolio performance from a risk return perspective compared to a pure diversified investment strategy. Notably, the use of older, simpler trading strategies such as the Stop-Loss and Lock-In approaches at optimum threshold levels can still offer investors higher risk to reward benefits with less commitment required. These strategies, though, lack the flexibility observed with the more recently developed dynamic trading strategies in terms of providing for varying risk appetites.

Big data has become a rapidly growing field amongst firms in the financial sector and thus many companies and researchers have begun implementing machine learning methods to sift through large portions of data. From this data, investment management firms have attempted to automate investment strategies, some successful and some unsuccessful. This paper will investigate an investment strategy by using a deep neural network to see whether the stocks picked from the network will out or underperform the Russell 2000.

A experimentação e testes com o uso de protótipos físicos ou simulação em computadores é uma prática utilizada para o desenvolvimento de diversos produtos industriais. As indústrias aeronáutica, automobilística, mecânica, naval, petrolífera e microeletrônica, entre outras, há muito empregam uma ou ambas as técnicas citadas. No entanto, inexistem referências ou estudos sobre a aplicação prática e conceitual desse assunto dentro do ambiente bancário mundial. O propósito desta pesquisa é descrever e interpretar os modos de experimentação empregados atualmente para a formação de fundos de investimento no mercado financeiro brasileiro e propor novos procedimentos para o seu desenvolvimento em analogia ao processo industrial. A metodologia proposta foi demonstrada com a utilização de dados reais levantados durante quatro anos no mercado financeiro para o desenvolvimento de um fundo aberto referenciado DI fictício simulados com o auxílio de técnicas de Dinâmica de Sistemas e de Inteligência Artificial. Os resultados reforçam as possibilidades de analogia entre o desenvolvimento de produtos industriais tangíveis e serviços intangíveis, quebrando paradigmas e propondo novos conceitos sob uma ótica renovadora para o desenvolvimento de produtos financeiros. O problema de otimização apresentado representa um novo campo de aplicação para o uso integrado de Dinâmica de Sistemas com algoritmos inteligentes, sendo que os algoritmos genéticos provaram ser uma ferramenta de suporte à decisão adequada para um ambiente competitivo e globalizado. / Experimentation and tests using physical prototypes or computer simulation are practices used in the development of various industrial products. The aeronautical, automobile, mechanical, naval, petroleum and micro-electronics industries have been using one or both of the mentioned techniques for a very long time. However, this issue has not been studied in detail in the banking area. The aim of this research is to describe and interpret the experimentation modes currently used in the Brazilian financial market in order to propose new procedures to develop investment funds akin to the industrial process. The proposed methodology was tested using 4 years of real data of the Brazilian financial market in order to develop a fictional investment fund based on Dynamic Systems and Artificial Intelligence (genetic algorithms) simulation principles. The results strengthen the possible analogy between the development of tangible industrial products and intangible services and propose new concepts according to an innovative development methodology for financial products. The related optimization problem represents a new field of application for Dynamic Systems combined with intelligent algorithms and, particularly, the Genetic Algorithms (GA) based model provided an efficient decision-making tool to be used in a competitive and globalized environment.