The objective of the study was to comparatively assess the impact of credit risk on the performance of big and small banks in South Africa. Data from audited financial reports of 14 commercial banks were obtained and divided into two panel data sets and analysed using the R-Studio software version 3.5.1 to assess the impact of capital adequacy ratio (CAR), non-performing loan to gross loan (NPLGL), loan-to-deposit ratio (LTDR), leverage ratio (LR), board gender diversity (BGD), with bank size (total asset) and AGE as control variables, on performance, (return on asset [ROA] and return on equity [ROE]). The findings of the study revealed that non-performing loan (NPL), CAR, LR, LTDR and age of banks all have significant and greater impact on performance, as measured by ROA, of small banks when compared with big banks. Surprisingly, NPL was revealed to have a lesser impact on the ROE of small banks as compared to the ROE of big banks but showed no impact on the ROA of big banks during the period of 2008–2017.

Political risk is prevalent in Nigeria and tends to influence business outcomes and the stability of the banking system. As a result of this study, it was determined whether political risk matters to the performance of the banking sector in Nigeria. The effect of political risk on different banks’ performance measures, such as return on assets, return on invested capital, credit risk and stock price, were examined in a panel of 12 selected commercial banks for the period 2006–2018. Data was analyzed using a two-stage system of generalized method of moments. The results provided evidence that the effect of political risk on bank performance depends on the performance proxies. Specifically, political risk was found to be negatively related to banks’ returns on invested capital and positively related to deteriorating credit risk. Hence, it can be concluded that political risk induces poor banking system performance in Nigeria. The study provides a critical insight into the management of a country’s political systems in terms of their potential to create unfavorable conditions for banking systems to thrive.

This study examined the connectedness and propagation of risk in the South African equity sectors during the Global Financial Crisis (GFC), the European Debt Crisis (EDC), the US–China trade war, and the COVID-19 pandemic. Daily returns of nine Johannesburg Stock Exchange (JSE) super sectors were examined from 3 January 2006 to 31 December 2021. Applying the connectedness matrix and time-varying parameter vector autoregressive (TVP-VAR) model, in full sample and sub-periods, the study showed that dynamic total connectedness of the super sectors is high in absolute form (62%). Furthermore, it was found that the highest volatility connectedness was during the EDC (68.83%) and during the COVID-19 pandemic (68.57%), followed by the GFC (63.16%) and lastly the US–China trade war (42.09%), respectively. This suggests that the tendency for a systemic risk is highest during the EDC, COVID-19, and GFC periods, and lowest during the US–China trade war. The financial sector was the primary net-transmitter of shocks during the COVID-19 period, while the automobile and parts sector was the strongest net-transmitter of shocks during the GFC, EDC, and US–China trade war. Similarly, the strongest net recipient of shocks during GFC, EDC, and COVID-19 is the chemical super sector. The study concludes that there is a significant volatility connectedness among JSE super sectors. In addition, the JSE super sectors exhibit time-varying connectedness during extreme events. Moreover, the net-transmitter and net-receiver of shock do not change significantly during different crisis periods. The policy implications of the findings are highlighted in the concluding section.

Purpose: The study examined the effect of environmental, social and governance (ESG) on the commercial bank returns in South Africa. Design/Methodology/Approach: The study made use of a cross sectional panel model for the sample period 2015-2024. The dependent variable included five South African commercial banks (ABSA, Standard bank, Nedbank, Capitec Bank and Investec Bank) and the independent variable comprised of ESG ratings for each bank. The study also introduced control variables in the form of macroeconomic variables, namely, inflation, money supply, short-term interest rate, long-term interest rate, gross domestic product and real effective exchange rate. Findings: The findings demonstrate that commercial bank returns in South African is negatively affected by ESG compliance. Moreover, gross domestic product, short-term interest rate, long-term interest rate and real effective exchange rate has a positive effect on commercial bank returns. Practical Implications: Firstly, the prudent authority which governs the financial market must re-examine policies requiring South African commercial banks to be ESG compliant as it reduces the return perspective of each bank. Secondly, the Asset-Liability Committees (ALCO) of commercial banks should develop strategies that alters the mix of assets and liabilities to better manage the costs associated with ESG compliance. This way they can better manage the negative effect of ESG compliance on banks returns. Originality/Value: This study is the first to consider ESG compliance as a determinant of commercial bank returns in South Africa. Hence, the study provides insight into the effect between ESG compliance and commercial bank returns. It, therefore, contributes to emerging market literature which is centred on bank performance as appose to bank returns.

The paper aims to explore the determinants of total volatility connectedness of nine super sectors on the Johannesburg Stock Exchange (JSE) market from 3rd January 2006 to 31st December 2021. These sectors are Automobile and Parts, Chemical, Telecommunication, Technology, Energy, Health, Finance, Insurance, and General Industrials. The paper applied Diebold and Yilmaz connectedness matrix and the time-varying parameter – vector autoregressive (TVP-VAR) model to determine the sectorial total volatility connectedness index (STVCI). After that, the nonlinear autoregressive distributed lag model (NARDL) was used to determine the asymmetric effects and the drivers of STVCI. It was found that the partial sum decomposition of the South African volatility index (SAVI) and Economic Policy Uncertainty Index (EPU) are the key determinants of the STVCI both in the long and short run. However, domestic market return (DMR) shows no significant asymmetric effect on STVCI. The study concluded that SAVI and EPU are the key determinants of volatility connectedness among the JSE super-sectors. The results unveil important implications for sectorial investors and policymakers on potential regulations and stability of the significant determinants of spillover risk.

Purpose: The aim of the study was to examine the effect of macroeconomic variables on Brazil, Russia, India, China and South Africa’s (BRICS) property market returns under changing market conditions. Design/methodology/approach: The study utilised a Markov regime-switching model for the period February 2011 to June 2025. The dependent variables comprised BRICS’s property market returns, whereas the independent variables consisted of domestic inflation growth rate, gross domestic product growth rate and interest growth rate of each BRICS nation. Findings: The findings revealed that macroeconomic variables have a regime-specific effect on BRICS’s property market returns in bull and bear market conditions. That being said, inflation growth rate had a negative significant effect on Brazil’s, India’s and South Africa’s property returns in a bull market condition, whereas in the bear market condition, only India’s returns were negatively affected. Similarly, gross domestic product (GDP) had a negative significant effect on Russia’s and China’s property returns in a bull market condition, whereas in the bear market condition, the effect was insignificant. In contrast, interest growth rate had only a negative significant effect on South Africa’s and Brazil’s returns in bull and bear market conditions, respectively. The transition probabilities revealed that the bear market condition dominated BRICS property market returns, illustrating that the returns were decreasing across the sample period. Practical implications: Collectively, the study presents important implications for policymakers and investors, such that monetary policy adjustments and investment decisions must factor in the state of the property market, as bull and bear periods dictate the return perspective that influences investors and policymakers’ decisions. Originality/value: The study contributes to the literature by providing a comprehensive asymmetrical analysis of macroeconomic fundamentals’ influence on BRICS’s property market return. Unlike previous studies that focus predominantly on the linear effect, this study highlights the nonlinear perspective between macroeconomic and property market returns, offering nuanced insights for policymakers and investors.