The number of corporate failures in South Africa fluctuates greatly over time, and the characteristics of these fluctuations have not been investigated sufficiently. This paper examines the trends in corporate failures, specifically for small medium and micro enterprise (SMMEs) and private companies in South Africa, with a particular focus on identifying the dynamic features of the series and associated macroeconomic variables movements. We examine the interactions between corporate failures and macroeconomic aggregates, and specifically the accounts of policy-induced changes in the macroeconomy for the observed fluctuations of South African business failures in the period 1994–2009. This research investigates both the short-run and long-run dynamic linkages between corporate failures in South Africa and selected macroeconomic variables by employing the Autoregressive Distributed Lag (ARDL) bound test. Time series Error Correction Model (ECM) estimates suggest that macroeconomic risk factors are related to firm failures in the same direction both in the short run and the long run, and that adjustment to steady state path is quite quick. A regression model is also estimated with a dummy variable included to decipher the corporate failure rates during the 2007-2009 global financial crisis. The results demonstrate that macroeconomic aggregates exert differential impacts on corporate failures both in the short run and in the long run. The study also reveals that corporate failure rates in South Africa are significantly and positively associated with the average lending rate, inflation rate, new corporation, exchange rate, 2007-2009 financial crisis, and inversely related to gross domestic product (GDP) and money supply both in the short run and long-run. In general, the results show expected and consistent relationships between shocks on economic variables and corporate failures.

Magister Legum - LLM / In recent times, South Africa (‘SA’) has seen many corporate failures due to poor corporate governance. It spans across Johannesburg Stock Exchange (‘JSE’) listed companies, State Owned Enterprises (’SOE’s’)1 as well as non-listed companies,2 ranging from business such as mutual banks and companies that specialise in agricultural products to companies who deal in furniture and household goods. The ramifications of such failures were felt across all corners of SA and beyond.3 Reflecting on the above failures, one will find a common thread of poor corporate governance having played a hand in their catastrophic downfall.4 One such corporate failure was that of Steinhoff International, the once darling stock of investors in SA and abroad.

This study investigates the prediction of firms’ health in terms of bankruptcy and non-bankruptcy based on the sentiments extracted from the earnings calls. Bankruptcy prediction has long been a critical topic in the world of accounting and finance. A firm's economic health is the current financial condition of the firm and is crucial to its stakeholders such as creditors, investors, shareholders, partners, and even customers and suppliers. Various methodologies and strategies have been proposed in research domain for predicting company bankruptcy more promptly and accurately. Conventionally, financial risk prediction has solely been based on historic financial data. However, an increasing number of finance papers also analyze textual data during the last few years. Company’s earnings calls are the key source of information to investigate the current financial condition and how the businesses are doing and what the expectations are for the next quarters. During the call, management offers an overview of recent performance and provide a guidance for the next quarter expectations. The earnings calls summary is provided by the management and can extract the CEO’s sentiments using sentiment analysis. In the last decade, Machine Learnings based techniques have been proposed to achieve accurate predictions of firms’ economic health. Even though most of these techniques work well in a limited context, on a broader perspective these techniques are unable to retrieve the true semantic from the earnings calls, which result in the lower accuracy in predicting the actual condition of firms’ economic health. Thus, state-of-the-art Machine Learnings and Deep Learnings techniques have been used in this thesis to improve accuracy in predicting the firms’ health from the earnings calls. Various machine learnings and deep learnings method have been applied on web-scraped earnings calls data-set, and the results show that LONG SHORT-TERM MEMORY (LSTM) is the best machine learnings technique as compared to the comparison set of models.