The relationship between political risk, credit risk and profitability in the South African banking sector
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As the cornerstone of every economic structure, the financial system is one of the most important key elements in the economic development and economic growth of every country. The structure of the financial system comprises various financial markets and financial institutions, including banks. Due to their critical role in promoting economic growth, financial stability and capital formation, banks are viewed as among the largest and most vital types of financial institutions. However, due to their nature and functionality, banks are exposed to a number of risks. Studies have indicated that political risk and credit risk are the two oldest and most perilous risks faced by banks globally, as they influence banks’ capital, investment and profitability structure. This study employed quantitative research to analyse the relationship between political risk, credit risk and profitability in the South African banking sector, which is the study’s primary objective. The secondary data of four large banks, namely Absa, FirstRand, Nedbank and Standard Bank from 2001 to 2015 was collected. Data included return on equity (ROE), return on assets (ROA), net interest margin (NIM) and earnings per share (EPS) as the proxies for profitability. Two independent variables, credit risk, denoted by non-performing loans ratio (NPLR), and political risk denoted by political risk index (PRI) were used in the study. Lastly, bank size; operating expenses; economic activity; gross domestic product; and inflation and interest rate, were used as control variables. The profitability variables were obtained from the INET BFA dataset and the respective banks’ official websites. Political risk data was provided by ICRG, while South African macroeconomic variables were obtained from the South African Reserve Bank (SARB) and Statistics South Africa (Stats SA). The statistical tests and econometric models used to analyse the data included trend analysis, descriptive statistics, a correlation (multicollinearity) test and a unit root test. The panel pooled mean group (PMG) model, based on the Autoregressive Distributed Lag (ARDL) approach, was employed to test the cointegration among variables, and the error correction model (ECM) was used to determine the adjustment of the system to the equilibrium. The findings of the study revealed that both political and credit risk has a significant relationship with profitability. Moreover, the analysis of other variables indicated that bank size has a negative effect on South African banks’ profitability, while operating expenses indicate a positive and significant effect. The analysis of GDP growth and inflation exhibited a positive effect on profitability. These findings are an indication that bank profitability is not only influenced by political and credit risk alone, but by bank size, operating expenses, GDP growth and inflation among other factors. Therefore, in an attempt to provide a meaningful explanation of the movements in profitability, banks’ management should combine political risk, credit risk and bank size, operating expenses, GDP growth and inflation, in order to improve profitability management.