Wits Business School (ETDs)

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    Voluntary and Involuntary Delisting – Implications for Shareholder Wealth on the Johannesburg Stock Exchange
    (University of the Witwatersrand, Johannesburg, 2024) Mekwa, Itumeleng; Alagidede,Imhotep Paul
    The delisting of stocks from major stock exchanges has been a focal point of academic and practitioner research due to its significant implications on market dynamics and investor wealth. Companies may opt to delist voluntarily to pursue private strategies or be involuntarily delisted for failing to meet regulatory requirements. The dichotomy between voluntary and involuntary delisting has generated extensive debate regarding the underlying drivers and the consequent impact on the value of assets traded. Despite a substantial body of literature on this subject, there is a notable scarcity of research focused on a major stock market such as the Johannesburg Stock Exchange (JSE). This thesis aims to fill this gap by examining the wealth effects of delisting events and identifying the determinants of delisting on the JSE. The event study methodology and logistic regression analysis is utilised for the study. The sample comprises 92 companies delisted from the JSE, encompassing voluntary and involuntary delistings. The findings reveal that delisting events generally result in significant negative impacts on shareholder wealth. Contrary to previous studies, voluntary delisting events do not demonstrate significant abnormal returns, suggesting market efficiency. Involuntary delisting events also fail to show significant abnormal returns, which may be attributed to informed investor behaviour. The sector-specific analysis highlights that the Consumer Non-Cyclical and Industrial sectors are particularly adversely affected by voluntary delistings, while the Technology sector experiences negative impacts from involuntary delistings. Regarding delisting determinants, cash flows emerge as a significant factor influencing overall delisting decisions, while growth prospects are particularly relevant for involuntary delistings. The study acknowledges limitations, including a relatively small sample size and the exclusion of specific contextual factors, and suggests avenues for further research. Based on the findings, policy recommendations are proposed to mitigate the negative impacts of delisting. These recommendations aim to benefit individual investors, companies, regulators, and financial advisors. Overall, this thesis contributes to a deeper understanding of the wealth effects of delisting events and the determinants of delisting decisions on the JSE, offering valuable insights for scholars and practitioners in financial markets.
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    The effect of investments in digital assets on the performance of the company
    (2021) Mofokeng, Senzekile
    The fourth industrial revolution (4IR) has been topical for South Africa as one of the strategies to gain a competitive advantage as a country. 4IR comprises artificial intelligence (AI), the internet of things (IoT), virtual and augmented reality, 3D manufacturing, and blockchain. Due to an increase in data volumes, the need for storage capacity and computational power, business intelligence, and cloud computing are also regarded as foundational technologies influencing trends. This study focuses on three categories of 4IR technologies namely, artificial intelligence, business intelligence (BI) & data sciences, nd cloud computing to investigate the share price performance of companies listed on the Johannesburg Stock Exchange (JSE). Using a quantitative design, an events study methodology is used based on the efficient market hypothesis. This determines whether public announcements made through the stock exchange news service (SENS) result in an increase in the share price and correspondingly market value measured through abnormal returns. The news tested was for a JSE listed company purchase of a target company to acquire digital fourth industrial revolution assets. Four hypotheses were tested, whether share price showed any response to 4IR technology investments. Second, whether share price increase within three days after an announcement. Third, increase in abnormal returns on the day of the news release, and fourth, enquiry into increased abnormal returns three days after the announcement date. Empirical evidence failed to support hypotheses 1, 2,and 4, whereas hypothesis 3 was supported. Significantly, BI data science related announcements could predict an increase in abnormal returns on the day of the announcement as well as the debt ratio. Whilst the adoption of 4IR technologies in South African corporates have increased, from the study one can infer that investors do not perceive a positive effect on wealth and value increase from these investments with the exception of business intelligence and data science investments