School of Economics and Finance (ETDs)

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    The effect of banking sector competition on firm creation in Sub-Saharan Africa
    (University of the Witwatersrand, Johannesburg, 2025) Ramahuta, Katlego; Gwatidzo, Tendai
    This study determines the effect of banking sector competition on firm creation in Sub- Saharan Africa (SSA). The study uses country-level data from multiple World Bank databases covering the period 2006-2021. While the analysis initially included all 48 SSA countries, only 40 countries were part of the final sample due to missing data in 8 countries. For a comprehensive view of the banking sector competition in the region, we use two non- structural measures of competition (Boone Indicator and the Lerner Index) and one structural measure (The concentration ratio calculated based on the top five banks, CR5). The Fixed Effects (FE) model and the two-step system Generalized Method of Moment (system-GMM) estimators are used to estimate the results of the analysis. Employing two estimators enhances robustness of the findings and ensures their consistency across different estimation techniques. Our findings support the market power hypothesis for SSA countries, indicating a positive relationship between banking sector competition and firm creation. This supports the view that increased competition in the banking sector will lead to an increase in the rate of firm creation. Based on the results, economic growth and higher levels of education lead to opportunities for firm creation. Our findings also show that new firm registration rates are lower in upper-income countries compared to lower-income countries in the region. The findings from this paper can inform policy decisions around banking sector competition regulation, and support initiatives that leverage firm creation as a tool to address unemployment and poverty in the region.
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    The Bias ratio: An effective fraud identification tool
    (University of the Witwatersrand, Johannesburg, 2025) Haddad, Remon; van Vuuren, Gary
    Financial fraud poses significant risks with far-reaching consequences, particularly in the context of growing assets under management and expanding equity markets. This thesis underscores the urgent need for robust measures to safeguard investors from fraudulent activities by exploring the consequences of notorious fraud cases such as Bernie Madoff’s Ponzi scheme. Through analyses of hedge fund, index fund and stock price return data in the US and SA, over various periods starting in 1997 to 2024, it becomes evident that tools such as the Bias ratio, kurtosis, and skewness can serve as effective mechanisms for detecting fraudulent behaviour. The Bias ratio emerges as a dual-purpose tool. Beyond its fraud detection capabilities, it functions as a performance measurement metric akin to the Sharpe ratio, offering additional value during security analysis. By highlighting suspicious historical outperformance and signalling securities with unusual performance patterns, the Bias ratio enriches the evaluation process, enabling investors to make informed decisions and avoid fraudulent investments. This thesis demonstrates the efficacy of the Bias ratio by examining its application in the notorious Madoff case, where it successfully flagged fraudulent activity that was overlooked by traditional measures like the Sharpe ratio. The findings emphasize the critical role of the Bias ratio in validating the legitimacy of returns and enhancing investor protection.
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    The interplay of energy access and labor market outcomes in South Africa
    (University of the Witwatersrand, Johannesburg, 2025) Moyo, Nomathamsanqa; Adetutu, Morakinyo
    This study investigates the relationship between energy access and employment opportunities in South Africa using the Autoregressive Distributed Lag (ARDL) model, analyzing macroeconomic data spanning 1990 to 2022. Unlike previous research focused on rural areas, this study adopts a macro-level approach, encompassing both urban and rural regions. The findings reveal a positive short- and long-run association between energy access and employment, indicating that increased electricity access correlates with higher employment levels. Inflation negatively affects employment, while foreign direct investment (FDI) and gross domestic product (GDP) have positive associations. Trade is significant in the short run but becomes insignificant in the long run. These results highlight key policy implications: expanding access to electricity, particularly in underserved areas, and investing in renewable energy infrastructure can enhance employment opportunities. Attracting FDI through improved infrastructure and incentives, implementing sound monetary policies to control inflation, and supporting economic growth through small and medium enterprises and innovation are critical for sustaining employment. Additionally, addressing South Africa’s high dependence on imports and promoting export-oriented industries can amplify the long-term benefits of trade on employment, emphasizing the need for targeted, structural policy interventions.
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    Predicting Wind Energy Production in South Africa Using Machine Learning
    (University of the Witwatersrand, Johannesburg, 2025) Reddy, Sivasha; Kutela, Dambala
    South Africa faces an urgent and escalating energy crisis driven by ageing coal infrastructure, frequent load shedding, and rising electricity demand. Wind energy presents a viable renewable energy alternative with significant potential to alleviate these challenges; however, its inherent variability complicates grid stability and energy planning. Accurate wind energy forecasting is essential for optimising power dispatch, minimising curtailment, and enhancing energy security. Despite advancements, traditional forecasting methods, such as physical models and statistical techniques, struggle to capture the complex and nonlinear nature of wind patterns, particularly in data-scarce environments like South Africa. This study investigates the application of machine learning models to improve wind power forecasting in South Africa, where data constraints and fluctuating meteorological conditions pose unique challenges. The research examines the effectiveness of machine learning in predicting wind energy production and assesses the role of explainable artificial intelligence techniques, such as SHapley Additive exPlanations, in enhancing model transparency and interpretability. Using historical meteorological data and turbine performance records from a South African independent power producer, the study evaluates multiple machine learning approaches to determine their predictive performance. A comparative analysis of different machine learning models highlights the most reliable techniques for wind energy prediction. The findings demonstrate that XGBoost outperforms Random Forest, Decision Tree, and K-Nearest Neighbour. Furthermore, the machine learning methods show a significant improvement over traditional statistical techniques, offering improved predictive accuracy while providing insights into key meteorological and operational factors influencing wind power generation. The integration of explainable artificial intelligence further ensures interpretability, fostering trust and practical usability among stakeholders. This research contributes to the renewable energy forecasting literature by adapting machine learning solutions to a data-scarce environment and emphasising the role of interpretability in real-world adoption. The results provide valuable insights for policymakers, energy planners, and grid operators, supporting South Africa’s transition to a more sustainable and resilient energy future. However, the study's findings may be limited in generalisability and accuracy due to the analysis focusing on a single wind turbine chosen based on data availability rather than representativeness. Consequently, the results may not extend to other wind farms or turbines operating under different geographic or climatic conditions.
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    Forecasting Exchange Rate Dynamics: A Comparative Study of Traditional Econometric Models and Machine Learning Models
    (University of the Witwatersrand, Johannesburg, 2025) Ndlovu, Teyven; Farrell, Gregory
    Accurate exchange rate forecasting is crucial for economic policy, risk management, and financial decision-making. This study compares traditional hybrid econometric models, specifically the Autoregressive Integrated Moving Average with Exogenous Variables and Generalized Autoregressive Conditional Heteroskedasticity (ARIMAX-GARCH), with machine learning approaches, including Random Forest, Multi-Layer Perceptron, and Long Short-Term Memory networks, to predict the South African Rand (ZAR) against major global currencies: the United States Dollar (USD), Euro (EUR), British Pound (GBP), Japanese Yen (JPY), and Chinese Yuan (CNY). Using daily exchange rate data from 2000 to 2024, model performance is evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (sMAPE), and Mean Directional Accuracy (MDA). The results indicate that no single model consistently outperforms across all currency pairs. The ARIMAX-GARCH model excels in trend prediction, the Random Forest model balances predictive accuracy and adaptability, the Multi- Layer Perceptron model minimizes absolute errors but struggles with directional accuracy, and the Long Short-Term Memory model captures long-term dependencies but underperforms in volatile markets. These findings highlight the need for hybrid forecasting models that integrate machine learning and econometric techniques while incorporating macroeconomic indicators to enhance predictive reliability.
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    Stock return co-movements in ESG investing: An African context
    (University of the Witwatersrand, Johannesburg, 2025) Mpelane, Chuma; Britten, James
    This study investigates the relationship between Environmental, Social, and Governance (ESG) focused investments, driven by the growing emphasis on ESG within financial markets. The study considers three African markets, which are the only African markets that currently offer dedicated ESG indices. The markets considered are: South Africa, Egypt, and Morocco. The analysis is conducted by examining co-movements between the ESG indices of the selected markets – the FTSE/JSE Top 30 Responsible (J110EP) Index for South Africa, the S&P/EGX ESG (SPEGESGE) Index for Egypt, and lastly the ESG 10 (ESGI) Index (recently named “MASI ESG”) for Morocco. The ESG 10 Index was formed in September 2018 and for that reason, this study considers a sample period ranging from January 2019 to December 2023. Total index return data is extracted from Bloomberg and the Moroccan Stock Exchange. The study tests co-movement through Principal Components Analysis (PCA), using the whole sample, and 12-month rolling window periods to uncover the largest proportion of variation in returns. Secondly, the study examines instances of structural breaks, using the Bai and Perron (2003a) multiple structural breaks tests, aiming to analyse the dynamics in co-movements in these markets. Lastly, the study tests for dynamic co-movement by pairing the indices and estimating the product of their z-scores, with the aim of identifying index pairs with a high number of positive estimates. The results of this study suggest no significant co-movement between the indices, with the exception of critical global crises such as the COVID-19 pandemic and the Ukraine-Russia war. During these events, the indices exhibited extreme co-movement, as expected, due to market downturns. Outside of these crises, no notable co-movement was observed, suggesting that ESG investors are not disadvantaged by focussing on ESG stocks.
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    An exploratory study into sustainability strategies adopted by Spaza Shops in the face of challenges
    (University of the Witwatersrand, Johannesburg, 2025) Nkantsu, Busisiwe; Maisela, Sikhumbuzo
    This study was carried out to determine sustainability strategies used by the spaza shops in the townships of Diepsloot, Cosmo City, and Alexandra. The spaza shop market is saturated and most spaza shops fail because of external and internal challenges. A fight for survival is paramount for such businesses and there is a great need for effective sustainable strategies. With very few studies on the success stories of these informal retailers, it is unclear how spaza businesses remain viable in the face of obstacles caused by both internal and external factors. Other studies have examined the sustainability issues affecting spaza retailers, but the goal of this study is to discover the strategies they use to stand out in a sector that is already saturated with competitors. This study was qualitative and exploratory in nature. To get more insightful data, spaza shops that have been operating for more than two years were chosen using a purposive sampling technique. Thirteen spaza shop operators participated in semi-structured interviews to gather information. The findings highlight the following strategies as crucial to the sustainability of the spaza shops: capitalising on business resources: marketing stratagems; selling selected products; having a competitive advantage; and partnerships with other businesses. The neighborhood crime watch associations that the business community established have been instrumental in reducing crime in the townships, which has allowed operators of spaza stores to increase operating hours. The spaza shops reported a reduction in loss of resources due to burglary and theft. The researcher recommends that business management and entrepreneurial support be acquired by spaza shop operators, the government should outsource skills development training for these retailers, and more partnership initiatives should be encouraged to promote sustainability.
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    The role of shareholder activism in turning around poor performing target companies on the JSE: A case of Value Capital Partners
    (University of the Witwatersrand, Johannesburg, 2025) Mlamleli, Akona
    There is a rising trend of shareholder activism led by activist investors who acquire minority stakes in poorly managed companies with promising business models. The study explores the rising trend of shareholder activism, in which activist investors acquire minority stakes in poorly managed but promising companies to address governance and performance issues. This surge in investor activism, driven by environmental, social, and governance concerns and minority investor rights, aims to enhance the long-term competitiveness of target firms. Focusing on Value Capital Partners, South Africa's first hedge fund activist investment firm, the research evaluates the company’s impact on selected firms from 2014 to 2024. Specifically, it examines Value Capital Partners’ strategies, collaboration with management, and campaign outcomes, using financial reports and investor communications to assess the effectiveness and sustainability of its interventions. The study analyses changes in share prices, financial metrics, and strategic directions to determine the influence of hedge fund activism on corporate performance. Findings indicate that Value Capital Partners generally achieved positive outcomes during the study period, demonstrating the potential of hedge fund activism to improve corporate performance in South Africa. However, impact varied across companies, highlighting both successes and challenges. The research underscores the role of activist investors in promoting sustainable growth and sheds light on the elements that both enhance and hinder their success and lastly suggests future research directions to optimise activist investment strategies in the region.
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    Vehicle-to-grid technology in South Africa’s energy transition
    (University of the Witwatersrand, Johannesburg, 2023-01) Ramutla, Machwene Thelma; Saruchera, Fanny
    The energy sector in South Africa has been undergoing a transition to a low carbon economy and a shift in its energy structure. This transition emphasises the need for renewable energy sources and the advancement of new energy vehicles to meet the country's climate change mitigation commitments. Conversely, the growing use of renewable energy and electric vehicles (EVs) raises concerns about energy security and the need for increased power generating capacity. Intending to find a solution to the abovementioned concerns, the subject of this study was the role that vehicle-to-grid (V2G) technology could play in the energy transition. The theories underpinning this research were the diffusion of innovation and the multi-level perspective. Qualitative data were collected using semi-structured interviews. Thirteen (13) experts purposively selected from the automotive industry, national energy department, regulator and utility, research institution, independent power producers, charging infrastructure provider, and an EV owner participated in this research. According to the study, V2G technology could contribute to South Africa's energy transition in the long run. For the technology to drive a meaningful change, subsidies, local manufacturing of electric vehicles, and consumer-awareness campaigns will be required to encourage investments in EVs and change public perceptions. The study also emphasized the need for public charging infrastructure, affordable EV models, and supportive government policies. Several challenges were identified that may hinder the adoption of this technology. These challenges included the need for an increase in generation capacity to handle EV charging, smart meters for billing purposes, and the inability of the current electricity grid to support V2G. Although the potential of V2G technology is enormous, it requires substantial collaboration between the government and private sector, investment in charging infrastructure, and modernisation of the electricity grid.
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    The effects of renewable energy integration on profitability & employment- a case of South African mines
    (University of the Witwatersrand, Johannesburg, 2024) Sikoe, Oratile; Kutela, Dambala
    The mining industry has been seen as one of the most energy-intensive industries in the world. That is also responsible for being a source of critical raw materials for other sectors in a country, such as manufacturing, construction, transportation, and energy sectors. In the past years we have observed an increase in mining companies adopting renewable energy in order to introduce cleaner energy sources into their mining operations. However, the economic effects of renewable energy source adoption are understudied in South Africa. This research is set out to examine and understand the effects of renewable energy integration into the mining industry on profitability and employment, with the use of the Autoregressive Distributed Lag Stationarity (ARDL) model. Our results revealed that there is a positive long-run relationship between renewable energy integration, employment, and mining profitability in the South African mining industry, including in the short-run. More specifically, the results show that the adoption of renewable energy sources bolsters both profitability and employment in the mining industry of South Africa such that a unit integration of renewable energy will most likely result in a respective percent increase in employment and mining profitability. Our research is the first of its kind in providing this evidence compared to related literature which is not industry specific. Overall, our findings underscore the importance of the transition by industries to renewable energy to simultaneously promote economic growth and ameliorate environmental quality in the context of developing countries where extractive industries pervade.