Volatility is an important component of asset pricing; an increase in volatility on markets can trigger changes in the risk distribution of financial assets. In conventional financial theory, investors are considered to be rational and any changes in relevant risk are assumed to be a result of the movement in fundamental factors. However, herein this study, it is hypothesized that there are movements in risk that are driven by volatility linked to sentiment-driven noise trader activity whose patterns are irreconcilable with changes in fundamental factors. This assertion is tested using a daily sentiment composite index constructed from a set of proxies and Generalised Autoregressive Conditional Heteroscedasticity models on the South African market over a period spanning July 2002 to June 2018. The results show that there is a significant connection between investor sentiment and stock return volatility which shows that behavioural finance can significantly explain the behaviour of stock returns on the Johannesburg Stock Exchange. It is, thus, recommended that due to the inadequacies of popular asset pricing models such as the Capital Asset Pricing Model, consideration should be made towards augmenting these asset pricing models with a sentiment risk factor.

This study explored the role of artificial intelligence (AI) in predicting companies’ financial distress. We used Artificial Neural Networks (ANN) to develop and test financial distress prediction models for the financial services and manufacturing companies listed on the Johannesburg Stock Exchange (JSE) for the period 2000–2019. Our constructed ANN Models achieved classification accuracy rates of 81.03 and 96.6 percent for the financial services and manufacturing industries, respectively. Both models could also predict financial distress up to five years prior to the firm being classified as distressed. This study provided key theoretical and practical contributions to the current literature by highlighting the potential role of AI models in solving financial problems. Creditors can use the models built in this study as a default prediction tool, investors as an investment decision-making tool, and for business managers a performance guidance tool to ensure long term financial sustainability.

Through globalization and financial market liberalization, the opening up of markets has increased cross-border investments as investors search for higher risk-adjusted returns. This ability to invest internationally has raised the attention given to emerging markets that offer higher risk-adjusted returns relative to developed markets. However, despite the growing importance of emerging markets, the literature on the nature of volatility in global markets is typified by generalizations of findings from developed markets. To fill this gap, this study comparatively examined the nature of stock return volatility in developed G7 and emerging BRICS markets. Broad market index data and GARCH models over the period 2003:01–2020:08 were employed. The study found evidence of volatility persistence, asymmetry, mean reversion and weak evidence of a risk premium in both emerging and developed markets. There was also evidence of significant differences in the nature of volatility within the two sets of markets. These volatility patterns in both groups cast doubt on the assertion that developed markets are more informationally efficient than emerging markets. Thus, markets in the same group may not always have the same nature of volatility, especially in the wake of structural events such as the COVID-19 global pandemic.

This study investigated the impact of investor sentiment impact on sectoral returns and their volatility on the Johannesburg Stock Exchange using a proxy-based composite investor sentiment index and generalised autoregressive conditional heteroscedasticity models. Overall, findings showed a negative relationship between prevailing sentiment and subsequent returns and a positive relationship between investor sentiment and sector returns volatilities. Additionally, there was evidence of variability of sentiment effects on the sector returns and volatilities. Accordingly, firms that raise financing through the stock market, portfolio managers with investments thereon and policymakers seeking to ensure that markets operate efficiently need to consider the impact of market-wide investor sentiment on volatility and returns.

During periods of economic uncertainty, investors often display irrational behaviour that amplifies market volatility. To examine the validity of this growing assertion, this study investigated the Johannesburg Stock Exchange (JSE) from January 2000 to end of December 2023, focusing on how investor sentiment affects market volatility, with consideration of differing effects based on firm size. Utilizing a GARCH-MIDAS framework which allows for the decomposition of volatility into short-run and long-run components, the research examined whether investor sentiment significantly influences stock market volatility in South Africa. The analysis revealed that small- and mid-cap indices are particularly sensitive to shifts in investor sentiment in the short run. In contrast, the long-run volatility of mid-cap stocks is significantly influenced by macroeconomic factors, particularly inflation and exchange rate fluctuations. The findings also demonstrate that firm size has a significant impact on volatility dynamics, highlighting the diverse ways that small-, mid-, and large-cap stocks react to economic and behavioural factors. These results show that the JSE’s volatility patterns are shaped by the size-dependent roles of macroeconomic data and investor sentiment, and contributes to literature by providing novel evidence from a major emerging market, highlighting the importance of firm size in understanding the temporal pattern of volatility and its determinants.

The article reports on the interactions and possibility of coordination between macroprudential and monetary policies in South Africa, based on business and financial cycles synchronisation. To this end, relying on financial and economic time series indicators spanning the period 2000M01–2018M12, a two-step Markov switching dynamic factor model was adopted for the measurement of composite indices, while both the dynamic conditional correlations and asymmetric generalised dynamic conditional correlations models were adopted for synchronisation analysis, together with the Metcalfe scale of coordination. The empirical evidence obtained is such that, under conditions of financial and real economic stress in South Africa, when there is crisis management rather than crisis prevention, macroprudential policy and monetary policy decisions are complementary. Therefore, there will be limited/no need for coordination between the two policy decisions. However, under normal times when there is crisis prevention rather than management, macroprudential policy and monetary policy decisions are noncomplementary; hence, the greatest degree of coordination is warranted, even though it might not be easy. Therefore, we conclude that it is possible to the coordinate the conduct of macroprudential, and monetary policies based on the synchronicity of business and financial cycles.