Faculty of Commerce, Law and Management (ETDs)

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Subject: search.filters.subject.Machine Learning

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    The factors influencing the adoption of Machine Learning for regulation by central banks in SADC
    (University of the Witwatersrand, Johannesburg, 2024) Kunene, Sibusiso; Totowa, Jacques
    The study investigates SADC central banks' readiness to adopt machine learning technologies with raw data collected through an online survey. Subsequently, the raw data was transformed into modellable data using principal component analysis and further fitted into the proposed logistic regression model design. The data underwent reliability and validity tests, which confirmed that the measurements of the constructs were consistent, reliable, and appropriately represented the intended constructions. Correlation analysis was employed to examine the hypotheses of the model, and multiple and stepwise regression were performed as additional tests of the model. The results show that IT infrastructure is instrumental in enabling SADC central banks to implement machine learning capabilities. Top management is crucial for implementing ML, but adequate IT infrastructure is also essential. The regulatory environment and IT infrastructure indirectly influence SADC central banks' readiness to adopt ML capabilities, despite top management's direct impact. The derivable policy implication from these results is that working groups among the sampled SADC central banks need to be formed to address the noted shortcomings within IT infrastructure and regulatory-related aspects of this adoption holistically
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    Business model innovation in South African companies under the changing post-COVID-19 world of work
    (University of the Witwatersrand, Johannesburg, 2021) Hlabathi, Katekani; Mzyece, Mjumo
    Businesses that have survived pandemics and other major global disruptions have demonstrated the importance of continually re-evaluating their business models. Implementing business model innovation has been shown to significantly enhance a business's chances of surviving major global disruptions. This study aims to determine how the application of business model innovation, particularly in South African enterprises, has enabled these businesses to survive and remain profitable in a changing work environment, especially during the COVID-19 pandemic. In this context, business model innovation refers to the creative introduction of new ways of the business providing value to their customers through the products they sell or services they provide. A qualitative study with ten (10) respondents from South African enterprises was conducted to test the proposition that businesses who apply business model innovation in pandemics, such as the COVID-19 pandemic, will survive and become even more profitable. The study was conducted in several enterprises from different industries, using interviews and questionnaires. The study aims to provide a possible framework to be used by businesses during pandemics and to provide a basis for further research on the subject. The study's key findings show that there are both internal and external factors that influence the implementation of an innovative business model. COVID-19 was rated highly as an influence that is top of mind, affecting how firms conducted their businesses today. The study also revealed that customers and stakeholders are key to developing an innovative business model. The limitations of the study relate to the number of respondents and their location. This was a direct effect of the qualitative nature of the study and the physical and other restrictions due to COVID-19; thus, the results may not be widely representative or fully replicable. Nevertheless, overall, the study indicates that business model innovation could give businesses the competitive advantage and the differentiation needed to succeed during times of uncertainty.
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    Mean-Variance Optimisation of A South African Index Based Portfolio Using Machine Learning
    (University of the Witwatersrand, Johannesburg, 2021) Makgoale, Katlego; Jakubose, Sibanda
    This study embarked on a comparison of the effectiveness of the Markowitz Mean- Variance Portfolio Optimisation against utilising a Machine Learning Technique to construct an optimal portfolio. The study aimed to: Construct an optimal portfolio using the Mean-Variance Analysis Framework, Construct an optimal portfolio using a Machine Learning Technique (Support Vector Regression), Contrast the results of the Minimum-Variance Portfolio and the Machine Learning Portfolio. The stocks of the FTSE JSE FIN15 index were chosen to construct the portfolio. The historical returns of the stocks in the index were used to trained (December 2014 to June 2019) and test the models(June 2019 to December 2020). The Mean-Variance Analysis and Minimum-Variance Portfolio were constructed using Python code that the author compiled. Similarly, the Support Vector Regression model was built in Python. The weights for the Machine Learning portfolio were calculated using the pseudo-inverse matrix and the predicted value of the Regression Model. It was found that the Minimum-Variance and Machine Learning portfolio produced different portfolios, but both containing fewer holdings than the original index. The performance of the Minimum-Variance Portfolio exceeded that of the index and the Machine Learning Portfolio with regards to relative(excess) returns and total returns in the out of sample period. It was found that the Machine Learning portfolio performs well at replicating the index returns but fails to exceed them and typically has a higher risk associated with it. It was concluded that the Minimum-Variance portfolio would be the most attractive to a risk-averse investor and the Machine Learning portfolio underperforms the Minimum variance and the index. Therefore confirming the effectiveness of Mean-variance Optimisation in a South African context against a Machine Learning Technique
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    Investigate the role of skills development hubs in equipping disadvantaged communities in South Africa to gain competencies required for the Fourth Industrial Revolution (4IR)
    (University of the Witwatersrand, Johannesburg, 2020) Desai, Mohsin; Sibanda, Tonderai
    South Africa’s participation in the global trend of the Fourth Industrial Revolution (4IR), has grown to include almost every business segment and is set to influence every conceivable aspect of all industries. This 4IR era, which is blurring the lines between the digital, physical, and biological spheres, began as an initiative to combat challenges faced by the manufacturing sector. Today, however, it is characterized by a blend of technologies and can be somewhat daunting to many organisations, not to mention individuals in general. South Africa’s National Development Plan (NDP) highlights the fact that together with social development, there is a dire need for bridging the gap of skills shortages, especially in disadvantaged communities (Kraak, 2004). This social entrepreneurship research investigates the extent that skills development hubs in disadvantaged communities can assist in the alleviation of poverty, by bridging the gap of skills in 4IR areas that will be essential for equipping Africans to be at the forefront of technological advancements. The research focused on the development of Africa 4IR training hubs, targeting initially, the main economic hubs of Gauteng province and then expanding throughout South Africa. Technological skills are deemed to be in short supply in South Africa and filling this skills gap could invariably alleviate unemployment and poverty, especially amongst disadvantaged communities. The projections and proposal for the need of training hubs through this research is based on findings drawn from existing literature and from interviewing young professionals, university students, corporate managers and entrepreneurs. Using institutional theory as a lens, this research aimed at investigating the role of skills development hubs in equipping disadvantaged communities in South Africa. Additionally, it provided a suitable collaborative framework that involved all relevant stakeholders from the context of social entrepreneurship. Also, to start low cost training hubs and develop competencies required in the era of the Fourth Industrial Revolution through public-private partnerships
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    The perceived impact of Emerging Technologies on Cybersecurity in the South African financial sector
    (University of the Witwatersrand, Johannesburg, 2022) Philips, Denzil; Pillay, Kilu
    This study is based on the investigation of what is the perceived impact of emerging technologies on cybersecurity in South African financial institutions. New and emerging technologies have made significant advancements in many industries that can be very disruptive in nature, and the majority of these technologies have changed the cyber threat landscape as well. These include, among other things, cloud computing, artificial intelligence, and machine learning. The study offers insight into how these emerging technologies affect the cybersecurity of financial institutions in South Africa. The study consisted of Information technology risk and cybersecurity individuals. The sample size of 11 individuals was seen as sufficient based on the spread across the financial sector and the experience within the various industries. The individuals were from banks, insurers and market infrastructures within the South African financial sector. The sample focused on key financial institutions specifically banks, insurers, and market infrastructures, based in different provinces in South Africa such as Johannesburg and Cape Town where the impact could be systemic in the country. A qualitative study was adopted by the researcher based on systems theory to determine the relationship between the adoption of emerging or new technologies and the impact it has on cybersecurity. There were various responses from the different institutions, focusing on the adoption of emerging technologies, the effects of this adoption on the cybersecurity environment, the risk and vulnerability management processes, and the ability to adapt and respond to new cybersecurity risks introduced by emerging technologies. The results of the study found that there is a clear link between the adoption of emerging technologies and the increase in cybersecurity requirements with emerging technologies significantly impacting the cybersecurity domain/functio
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    Use of Artificial Intelligence, Machine Learning and Autonomous Technologies in Mining Industry, South Africa
    (University of the Witwatersrand, Johannesburg, 2023) Nong, Setshaba; Sethibe, Tebogo
    The mining industry plays a convincing role globally in driving various industries and contributing to economic prosperity. Locally, South Africa is known for having some of the largest minerals reserves in the world, although it is burdened with challenges inhibiting its progress and competitiveness. It is, however, expected that with application of AI, ML and AT will be able to revolutionise the industry, changing its fortunes, which will increase its competitiveness globally in the process attract investment and contribute to its longevity. As a result of these benefits, this research sought to investigate implication of AI, ML, AT technologies implementation in the mining industry of South Africa. The technologies are considered novel, especially in the mining industry, making employing qualitative study appropriate to assess how the implementation is received by the industry including perceptions and its potential impacts. Key findings of the study indicate that these technologies have the capacity to change the trajectory of the South African mining industry by dealing with issues of safety, costs, labour and efficiency. There is also an opportunity to pursue additional resources locked in pillars, by depth and dangerous working conditions due to geological complexities. However, capital costs, the nature of narrow tabular ore bodies and variability of various conditions are found to be some of the inhibiting factors for implementations of these technologies. As a result, there is no mine that has implemented any of these technologies as a primary means of production. This research will measure current perceptions of industry stakeholders and insights, role of government, mining companies, and equipment manufacturing response. The research highlight areas of impact and challenges that will contribute to strategy development in the process contributing to its sustainability. It is important to consider application of theory of constraint which is a detailed analysis which can assist mining companies in identification of inherent challenges so as to be able to respond appropriately with solutions offered by AI, ML and AT
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    A Machine Learning Approach to Corporate Bankruptcy Prediction Using BERT-Based Sentiment Analysis
    (University of the Witwatersrand, Johannesburg, 2023-03) Mhlambi, Lwazi Lungile; Seetharam, Yudhvir
    The study of bankruptcy prediction has centred on whether firm level information is predictive. Seminal work by Altman (1968) articulates the failure of a business utilising its financial variables that are associated and classified in part to either the liquidity, profitability, solvency, leverage, or activity of a corporation. While this understanding is intuitive, recent studies have broadened the scope of financial ratios used in this prediction as well as incorporated exterior forces affecting the firm, either at an enterprise-wide or an economic-wide level to predict corporate bankruptcy. In the same breath, one cannot ignore the insider knowledge that the leaders and managers of firms would have leading to corporate bankruptcy. Therefore, this provides a curious opportunity in which we can incorporate the sentiment in the analysis provided by the leaders of such firms as an input in predicting the bankruptcy of a given firm. This study applies the Bidirectional Encoder Representations from Transformers (BERT) based sentiment analysis approach to import human sentiment as a variable from corporate disclosure data and apply it to existing corporate bankruptcy models over the period between 1995 to 2022 in South Africa, the United Kingdom and the United States of America