




































109 

 

Finance, Accounting and Business Analysis 
Volume 6 Issue 2, 2024 

http://faba.bg/       
ISSN  2603-5324 

DOI: https://doi.org/10.37075/FABA.2024.2.02 

 

The Relationship between Macroeconomic Variables and South African 

Commercial Bank Performance 

 

Fabian Moodley1* , Babatunde Lawrence2 , Surendran Pillay3  
Department of Risk Management, North-West University, South Africa1 

School of Accounting, Economics and Finance, University of KwaZulu-Natal, South Africa2 

School of Accounting Economics and Finance, University of KwaZulu Natal, South Africa3 
* Corresponding author 

 

Info Articles   Abstract 
 

History Article: 

Submitted 26 April 2024 

Revised 28 August 2024 

Accepted 8 September 2024 
 

 Purpose: The study examined the short-run and long-run relationship between 

South African commercial bank performance and macroeconomic variables. 

Design/Methodology/Approach: Six South African commercial banks 

(ABSA, Standard bank, Nedbank, Capitec Bank, Investec Bank and FirstRand 

Bank), two macroeconomic variables (money supply and policy uncertainty) and 
two control variables (debt-to-equity ratio and the South African volatility index) 

were administered for the sample period, 2006-2022, using a panel autoregressive 
distributed lag (ARDL) model. 

Findings: The findings show that money supply and the debt-to-equity ratio has 

a positive long-run relationship with commercial bank performance. However, 
policy uncertainty and the South African volatility index has a negative long-run 

relationship with commercial bank performance. It is further evident that the 

error correction term exhibited negative and significant coefficients, which 
indicates a 76.08% imbalance between bank performance and independent 

variables.  

Practical Implications: Firstly, when the South African Reserve Bank (SARB) 

conducts policy adjustments, such policy changes should be in line with the 

findings of the study as it poses a significant effect on short-run and long-run 
commercial bank performance. Secondly, the Asset-Liability Committees 

(ALCO) of banks should consider the allocation of debt and the leverage position 
of their banks. That being, although debt increases bank performance, as found 

in the study, it also poses a significant effect on the liquidity position of banks. 
Hence, there should be added control of the banks’ liabilities as it will hamper 

the short-run and long-run performance of banks.  

Originality/Value: This study is the first to consider macroeconomic variables 
as a determinant of commercial bank performance in South Africa. Hence, the 

study provides insight into the relationship between macroeconomic variables 

and commercial bank performance. Moreover, the study focused on commercial 
banks that are part of emerging markets, where the performance of these banks 

differs from that of developed markets’ commercial banks. Lastly, the study 
considered the short-run/long-run relationship between macroeconomic 

variables and commercial bank performance, while the majority of studies 
consider current effects.  

Paper Type: Research Paper. 
 

 

Keywords:  

South Africa, banking 

sector, performance, 

commercial banks, ARDL 
 

 

JEL: G01, G10, G11.  

* Address Correspondence:   

E-mail: 55232345@nwu.ac.za1 

  217081567@stu.ukzn.ac.za2 

  pillays18@ukzn.ac.za3 

 

 
 
 
 

http://faba.bg/
https://doi.org/10.37075/FABA.2024.2.02
mailto:55232345@nwu.ac.za
mailto:217081567@stu.ukzn.ac.za
mailto:pillays18@ukzn.ac.za
https://orcid.org/0000-0001-8954-4933
https://orcid.org/0000-0001-5385-6812
https://orcid.org/0000-0002-1476-8796


F. Moodley, B. Lawrence and S.Pillay / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024 

110 

 

INTRODUCTION 

 

The aim with the formation of the first commercial bank in 1971 was to establish a prospect of 

economic growth through the facilitation of capital distribution, payments, and an increase in the production 

of individuals and businesses (Cowen 2000). Since then, the establishment of commercial banks globally has 

drastically increased, with the functions now expanded to include the transfer of risk, management of 

complex deals associated with financial instruments and financial markets, market transparency, and 

mitigation/control of risk (Albertazzi and Gambacorta 2009). Due to an increase in the functions of 

commercial banks, they now face an elevated risk exposure. This exposure includes the fluctuations of 

macroeconomic instruments used in the implementation of macroeconomic policy in various countries 

(Ratnoysi 2013). It is expected that when these macroeconomic instruments fluctuate, the central bank of 

any country will control the risk exposure through the implementation of macroeconomic policy. However, 

from history, it is evident that macroeconomic policy implementation has drastically affected the 

profitability of commercial banks and the functionality of the banking sector globally.  

The failed implementation of macroeconomic policies in the 1970s, which compelled United States 

commercial banks to reduce their credit requirements for low-income households, gave rise to a market for 

subprime mortgages (Bordo 2008). This allowed for excess capital on hand, which created a subprime 

lending failure and a liquidity issue in the banking sector. Accordingly, the reduction in the liquidity of 

commercial banks reduced the function and profitability of the banking sector as the issue infiltrated other 

countries and gave rise to the dot-com bubble and global financial crises (Schularick and Taylor 2012). In 

an attempt to mitigate the liquidity contagion in the South African banking sector, the South African Reserve 

Bank (SARB) altered various macroeconomic rates (repo rate, prime rate and interest rates) to control 

inflation (Maredza and Ikhide 2013). The actions of the SARB did increase commercial banking 

profitability, but it was not enough to mitigate the low liquidity levels of commercial banks. 

Since then, there has been an increase in empirical literature to understand if bank profitability is 

macroeconomic driven or bank specific. The large number of literature studies point to the former, but the 

findings are largely centred around international studies with developed economies as opposed to emerging 

markets (Sangeetha and Moorarka 2019; Alfadli and Rioub 2020). It is evident that emerging market 

economies such as South Africa (SA) are more prone to shocks caused by macroeconomic variables, which 

affect each sector including the banking sector (Moodley 2020). Thus, it is important to determine how 

macroeconomic variables affect SA commercial bank profitably, as no study of this nature has been 

conducted in SA. Hence, this study examined the effect of macroeconomic variables (unemployment rate, 

policy uncertainty, money supply, inflation and gross domestic product (GDP)) on the profitability of eight 

SA listed commercial banks (ABSA, First Rand, Capitec, Investec, Sasfin, Nedbank, Standard Bank and 

Rand Merchant Bank). 

 

 

LITERATURE REVIEW 

 
The review of empirical literature suggests that there is common consensus among research scholars 

that determinants of bank profitability are macroeconomic driven (Kiganda 2014; Amzal 2016; Bhattarai 

2018). However, given the mixed and inconclusive findings among empirical evidence, there is no agreement 

on whether the effect is positive, negative, or significant in the short run and long run. Moreover, there is a 

lack of empirical literature demonstrating the effect of macroeconomic variables on SA commercial banks. 

Accordingly, to the best of the authors’ knowledge, no study in the SA context exists. Despite the limited 

empirical evidence in SA, many studies advocate for the use of return on assets (ROA), net interest margin 

(NIM), or return on equity (ROE) as proxies for banking sector profitability (See Lall 2014: Duraj and Moci 

2015; Sheefeeni 2015). In particular, a combination of all three proxies has been used in previous studies, as 

it accounts for both the bank’s profitability and the principal interest of a bank owner (see, amongst others, 

Ebenezer, Omar and Kamil 2017; Al-Homaidi, Tabash, Farhan and Almagtari 2018; Sangeetha and 

Moorarka 2019; Alfadli and Rioub 2020). 

Previous studies have examined the effect of macroeconomic variables on bank profitability. Some 

studies showed a positive relationship. Sheefeeni (2015) used the fixed effect model to examine the effect of 

inflation, GDP, exchange rate and interest rate on Namibia listed commercial bank profitability. The 

findings showed a positive significant relationship between macroeconomic factors and bank profitability. 

This was in line with a study conducted by Borio, Gambacorta and Hofmann (2017). Ebenezer, Omar and 

Kamil (2017) also examined the effect of macroeconomic factors on commercial bank profitability. Using 

the panel regression model for seven years, they found a significant positive relationship between GDP, 

inflation, and Nigerian listed banks profitability. Similarly, Zampara, Giannopoulos and Koufopoulos 

(2017) used the ordinary least squares (OLS) method to estimate the results and proved that GDP and 



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exchange rate had a positive significant effect on Pakistan listed banks profitability. Finding a positive 

relationship was in line with a study conducted by Hirindu and Kawshala (2017). In a more recent study, 

Alfadli and Rioub (2020) aimed at examining the effect of inflation on bank profitability of Gulf cooperation 

council countries. Using the OLS regression for a period of seven years, the findings illustrated a significant 

positive relationship between inflation and commercial banks profitability. The findings are supported by 

Chen and Lu (2021). 

Some studies show a negative relationship. Saeed (2014) examined the effect of macroeconomic 

variables on United Kingdom listed commercial bank profitability. The data ranged from 2006 to 2012 and 

was used to regress the fixed effect panel model. The results suggested that inflation had a significant negative 

effect on bank profitability. Finding a negative relationship was in line with a study that was conducted by 

Amzal (2016) as the authors used the linear regression model for eight years and found a significant negative 

relationship between inflation and Islamic banks profitability. In a similar study, Abate and Mesfin (2019) 

used the fixed effect model to determine the effect of inflation, interest rates and GDP on Ethiopian listed 

commercial banks’ profitability. The findings for the data sample of nine years, illustrated a significant 

negative relationship between macroeconomic factors and bank profitability. Similarly, Salee and AshFaque 

(2020) also used the fixed effect model. However, the study was conducted on the Malaysian listed 

commercial banks profitability for a period of six years. The findings showed a significant negative 

relationship between GDP and commercial banks profitability. Studies conducted by Neupane (2020), Saif-

Alyousfi (2020), Rahman, Yousaf and Tabassum (2020) yielded identical conclusions. 

Some studies show no relationship. For example, Kanwal and Nadeem (2013) used the pooled OLS 

method to investigate the relationship between macroeconomic variables and the Pakistan listed commercial 

banks’ profitability. The results indicated no significant relationship between inflation, GDP, interest rates 

and bank profitability. Evans and Kiganda (2014) had similar findings when using the OLS technique. The 

findings show no significant relationship between GDP, exchange rate and Kenyan listed commercial banks 

profitability. Similarly, Simiyu (2015) used the fixed effect model to determine if macroeconomic variables 

affect bank profitability. The findings showed that there is no significant relationship between GDP, interest 

rates, inflation, and Nigerian listed banks profitability. The findings are in line with a study conducted by 

Akani, Nwanna, and Mbachu (2016).  

The review of empirical literature has supported the notion of mixed and inconclusive findings 

pertaining to the effect of macroeconomic variables and bank profitability. It is clear from the above 

mentioned that there is no consensus on what effect each macroeconomic variable has on bank profitability. 

Where one macroeconomic variable is said to positively affect bank profitability, there is also evidence that 

it has a negative or insignificant effect on bank profitability. Moreover, more international studies exist that 

have captured the effect of macroeconomic variables on bank profitability than SA studies. Accordingly, to 

the best of the authors’ knowledge, no study exists in the SA context. Thus, it is justifiable for a study of this 

context to be carried out, as it will not only add to the debate surrounding the effect of macroeconomic 

variables and bank profitability but will also be the first done in SA. 

 

DATA SETS AND METHODS 
 

Data sets 

The study examined the effect of macroeconomic variables on the performance of banks in South 

Africa. Yearly data for the top six commercial banks in South Africa (ABSA Bank, Standard Bank, Nedbank, 

First Rand Bank, Capitec Bank and Investec Bank) was collected with a sample period from 2006 to 2022. 

These banks comprise more than 85 percent of all banking assets in South Africa (Du Toit and Cuba 2018) 

and were thus deemed sufficient for the study. The sample period was dictated by the availability of data on 

these banks and as such was limited to the prescribed period.  The banks’ data and the control variables were 

extracted from the audited financial reports of the bank. However, the macroeconomic data was extracted 

from Mc Gregor BFA and the South African Reserve Bank’s (SARB) data bank. The construction of the 

variables used in the study is given below: 

 

Macroeconomic variables 
Return on equity:  

Return on equity estimates how the management of a bank or a firm optimizes the utilization of the invested 

shareholder’s fund to yield profit (Athanasoglou, Brissimis, and Delis 2008). It is also a measure of how 

much rand of profit are generated for each rand of shareholder’s equity. This study employs return on equity 

as the dependent variable to measure the impact of the response variables or macroeconomic variables on 

it, as the bank’s financial performance measure. 

 

 



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Unemployment rate:  

According to the Organization for Economic Co-operation and development (OECD), unemployed 

individuals are people above a specific age usually (15) not being in paid employment or self-employment 

but are currently available for work during the reference period (OECD 2020). The broader definition 

includes discouraged workers (Altman 2022). Studies such as Zampara et al. (2017) and Horobet, 

Radulescu, Belascu and Dita (2021) discovered a negative impact between of unemployment rate on bank 

performance. The study expects a non-significant impact on unemployment rate on banks’ return on equity. 

Unemployment rate is included as a macroeconomic measure to determine its effect on banks’ return on 

equity in this study. 

 
Policy uncertainty:  

This is a class of economic risk where the future path of government policy is uncertain, raising risk premia 

and leading businesses and individuals to delay spending and investment until uncertainty has been resolved 

(Baker, Bloom and Davis 2013). Policy uncertainty could refer to risk or uncertainty about government 

monetary or fiscal policy, electoral outcomes and taxation policies in any particular nation. This study 

employs the South African policy uncertainty index as one of the macroeconomic explanatory variables 

which could possibly influence bank performance, owing to the fact that government policies as mentioned 

above are factored into a policy uncertainty index. Iqbal, Gan and Nadeem (2020) used policy uncertainty 

in their study, they found policy uncertainty has a negative effect on bank performance (ROE). Similarly, 

Nguyen, Nghiem and Tripe (2021) found out that the US and Indian economic policy affects Indian banks’ 

profitability. Therefore, it is believed that the policy uncertainty index could impact bank performance 

whether positively or otherwise.  

 
Money supply (M2):  

Money supply of any nation is the total volume of money held by the public at a given time. Hence it refers 

to the currency in circulation i.e. (physical currency) and demand deposit i.e. (depositor’s easily accessed 

assets on the books of financial institutions. Uruakpa (2019) establish that monetary policy including money 

supply when utilized effectively can have a positive effect on the banks’ performance in Nigeria. The study 

envisages a positive impact of money supply on return on equity. 

 
Inflation:  

The South African inflation rate is the annual percentage change in the cost of a basket of goods and services 

for the average consumer. It is calculated using the consumer price index which is the average spending or 

living costs of a South African (Statistic SA 2024). This inflation rate is relevant to this study as its one of 

the macroeconomic instruments used by central banks to stabilize the economy. Studies such as Almansour, 

Alzoubi, Almansour and Almansour (2021) found that there exists a negative significant relationship with 

inflation and banks’ performance in Jordan. As Maria and Hussain (2023) equally found a negative impact 

of inflation on marketing-based performance measure such as Tobin’s Q. While it has a positive impact on 

accounting-based measures of banking performance such as return on equity. Hence, this study includes 

inflation as macroeconomic factor to ascertain its effect on return on equity. 

 
Gross domestic product:  

Gross domestic product is a monetary measure of the market value of all the final goods and services 

produced and sold in a specific time period in a country (Duignan 2017). It’s also the measure of the size of 

an economy (Callen 2012). Jaouad and Lahsen (2018) established that there exists no significant impact of 

gross domestic product on banks’ performance in Morocco. However, since banks directly fund or provide 

money to produce goods and services within an economy, this study includes the gross domestic product to 

examine its impact on banks performance. However, this study hypothesises a positive impact between 

banks’ return on equity and gross domestic product.  

 

Control Variables 
Debt-to equity ratio:  

Debt-to-equity ratio is a financial metric that measures a company’s financial leverage. It shows how much 

debt is used to finance its operations compared to its available equity (OECD 2013). Yuan, Gazi, 

Harymawan, Dhar and Hossain (2022) employed the debt-to-equity ratio as a control variable alongside 

other bank variables and demonstrates that it has a significant impact on performance, which should be 

controlled for.  

 
Net asset value:  

Net asset value of a bank is the value of a mutual fund obtained by subtracting liabilities of the bank from its 



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assets. In the aim of determining the effect of bank specific and macroeconomic determinants of bank 

profitability, O’ Connell (2022) employed net asset value as a control variable. In a similar way, this study 

employs net assets value as a control variable to determine the macroeconomic factors that impact bank’s 

performance. 

 
Retention rate:  

This refers to the number of retained customers in a bank to the number at risk. No study has employed 

retention rate as a control variable to the knowledge of the authors. 

 
National deficit:  

This is also known as fiscal deficit. It occurs when government expenditure is more than its revenue. A 

country’s primary deficit is the difference between the spending on goods and services and the revenues the 

country earns from taxes, minus transfer payments (IMF 2014). There is no prior study that has employed 

national deficit as a control variable on bank performance prior to this study. Hence, we employs national 

deficit as a control variable to examine its control effect on bank performance. 

 

The summary of variables used in the study is presented in Table 1 bellow.  

 

Table 1. Description of variables 

Variables employed in study 

Variables Abbreviation Statement Data range Data Source 

Return on equity ROE 
Performance 

 measure 
2006-2022 

Bank's Audited 

Report 

Unemployment rate UNEMP 
Macroeconomic 

 measure 
2006-2022 Mcgregor BFA 

Policy uncertainty LPU 
Macroeconomic 

 measure 
2006-2022 Mcgregor BFA 

Money supply M2 
Macroeconomic 

 measure 
2006-2022 Mcgregor BFA 

Inflation CPI 
Macroeconomic 

 measure 
2006-2022 Mcgregor BFA 

Gross domestic product GDP 
Macroeconomic 

 measure 
2006-2022 Mcgregor BFA 

Volatility index SAVI Control 2006-2022 Mcgregor BFA 

Debt-to-equity ratio D-E Control 2006-2022 
Bank's Audited 

Report 

Net asset value NAV Control 2006-2022 SARB 

Retention rate RE Control 2006-2022 SARB 

National deficit NE Control 2006-2022 SARB 

Source: Authors’ compilation (2024) 

 

Methodology 
To test the effect of macroeconomic variables, specifically money supply and the South African Policy 

uncertainty index, on banks’ performance, we utilised the autoregressive distributed lag (ARDL) model. In 

the model we employed the log of PU and M2 as macroeconomic variables that could affect the performance 

of banks while controlling for other variables that could affect bank performance. 

 

𝐴𝑖𝑡 = 𝑓(𝑀𝐸𝑖,𝑡 , 𝑀𝐸𝑖,𝑡−1) (1) 

 

Where 𝐴𝑖𝑡  denotes the response variable, 𝑀𝐸𝑖,𝑡 is the growth rate in loan provided by 𝑖𝑡ℎ bank in year 

t, and 𝑀𝐸𝑖,𝑡−1 is one year of lagged macroeconomic variables of the 𝑖𝑡ℎ bank. The ARDL model was applied 

for this estimation. In the estimation of the ARDL it is important that the variables are stationary at level or 

at first difference. Hence, we employed the unit root test established by Levin et al. (2002). 

 

∆𝐴𝑖,𝑡 =  𝑐0𝑖 + 𝑢𝐴𝑖𝑡−1 +  ∑ 𝑐𝑖∆𝐴𝑖,𝑡−𝑗
𝑝𝑖
𝑡=0 + 𝑤𝑖,𝑡 (2) 

 

Where 𝑐 is the constant term, which is supposed to differ across cross-sectional entities, and while 𝑢 

is the identical autoregressive coefficient, 𝑐𝑖 denotes the lag order, and 𝑤𝑖,𝑡 represents the disturbance term. 



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114 

 

The generalised ARDL (𝑝, 𝑞, 𝑞, … , 𝑞) model employed in the study is specified as follows: 

𝐴𝑖,𝑡 = ∑ 𝜋𝑖𝑗𝐴𝑖,𝑡−𝑗
𝑝
𝑡=1  + = ∑ Ω𝑖𝑗  𝑋𝑖,𝑡−𝑗

′ + 𝛼𝑖 +  𝑒𝑖𝑡
𝑞
𝑡=0  (3) 

 

Where 𝐴𝑖,𝑡 is the response variable, 𝐾𝑖𝑡
′ is a K × 1vector that is allowed to be purely I (0) or I (1) or 

cointegrated, 𝜋𝑖𝑗  is the coefficient of the lagged dependent variable called scalars, Ω𝑖𝑗  is 𝐾 × 1 coefficient 

vector, 𝛼𝑖 is the units-specific fixed effects. Number of cross-sections 𝑖 = 1, 2, … . , 𝑁 and time 𝑡 = 1, 2, … , 𝑇.  
𝑒𝑖𝑡 is the error term. 

 

Equation 3 can be deduced into the ARDL correction model as: 

𝐴𝑖,𝑡 = 𝜃𝑖 [𝐴𝑖,𝑡−1 − 𝛽𝑖  𝑋𝑡,𝑗] + ∑ 𝜋𝑖𝑗 
𝑝−1
𝑗=1  ∆𝐴𝑖,𝑡−𝑗 +  ∑ Ω𝑖𝑗

𝑞−1
𝑗=0  ∆𝑋𝑖,𝑡−𝑗 + 𝛼𝑖 + 𝑒𝑖𝑡 (4) 

 

Where 𝜃𝑖  = group-specific speed of adjustment coefficient, 𝛽𝑖 is the vector of long-run relationships, 

[𝐴𝑖,𝑡−1 − 𝛽𝑖  𝑋𝑡,𝑗] is the error correlation term or ECT, 𝜋𝑖𝑗 and Ω𝑖𝑗  are short-run dynamic coefficients. This 

model was selected based on the Akaike Information Criterion (AIC), which employs the smallest possible 

lag length. Therefore, to evaluate the effect of macroeconomic variables on the profitability (return on equity) 

of banks, we captured both the long run and the short run of dynamics in the ARDL model. 

 
RESULT 

 

Multicollinearity test  

Table 2 provides the variance inflation factor (VIF) test for the selected variables. The VIF test 

indicates whether multicollinearity exists among the selected independent variables and control variables. If 

the centred VIF value is between 1 and 2, this indicates no form of collinearity. However, if the centred VIF 

value lies above 2 then levels of collinearity exist among the selected variables. It is evident from Table 2 

that the centred VIF value for the South African volatility index, money supply, policy uncertainty and debt-

to-equity ratio is between 1 and 2. Hence, the study concludes only these variables exhibit no collinearity. 

Thus, the study omitted the rest of the variables and continued with the South African volatility index, 

money supply, policy uncertainty index and debt-to-equity ratio.  

 

Table 2. Variance inflation factor test 

 Coefficient Uncentred Centred 

Variable Variance VIF VIF 

UNEMP  25.77864  11737.31  14.49389 

SAVI  0.633176  18.8596  1.167066 

RE  0.059444  38.46258  2.303105 

NAV  0.047148  1.771517  3.764579 

NE  0.072277  418.1951  5.528234 

M2  0.104579  17.00404  1.519117 

LPU  0.240001  19.735774  1.487552 

GDP  9.555847  50979.39  25.65843 

D_E  0.192685  41.77135  1.804528 
CPI  1.324846  130.1524  2.648710 

C  71.98643  16086.25  NA 

    
Source: Author’s estimation (2024) 

 

Descriptive statistics  

The descriptive statistics for the variables used in this study are shown in Table 3. This study used 

one of the main measures of banking performance which is return on equity as the response variable. The 

return on equity ranges from minimum to maximum values of -1.0 percent to 1.55 percent with a mean of 

0.739. Moreover, return on equity attained the highest volatility value of 0.759 as compared to the South 

African market (SAVI) of 0.0884. The South African policy uncertainty index is the only variable with a 

negative mean of -0.281, giving rise to a negative skewness of -0.296, which is also evident with median and 

maximum values of -0.3056 and -0.8303 respectively. The volatilities of money supply, policy uncertainty 

and debt-to-equity ratio appear to fall in the same range of 0.2424, 0.2738 and 0.2375 respectively. Return 

on equity, policy uncertainty, money supply and debt-to-equity ratio show a negative skewness, illustrating 

that the tail of these variables is more pronounced on the left rather than the right with the exception of the 

South African volatility index.  



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Table 3. Descriptive statistics 

  ROE LPU M2 SAVI D_E 

Mean  0.7390 -0.2818  0.8594  1.3283  0.9686 

Median  1.1676 -0.3057  0.8561  1.3159  1.0386 

Maximum  1.5494  0.1280  1.3159  1.5246  1.2352 

Minimum -1 -0.8303  0.2355  1.1886  0.0453 

Std. Dev.  0.7594  0.2424  0.2738  0.0884  0.2376 

Skewness -0.8027 -0.2965 -0.265  0.4614 -1.9728 

Kurtosis  1.9809  2.9236  2.9039  2.6184  6.6678 

Jarque-Bera  15.2176  1.5047  1.2212  4.1964  122.1275 

Probability  0.000496  0.4712  0.5430  0.1227  0.000000 

Sum  74.6435 -28.463  86.79606  134.1563  97.8326 

Sum Sq. Dev.  57.6688  5.8771  7.4997  0.7821  5.6443 

Observation  101  101  101  101  101 

Source: Author’s estimation (2024) 

 

Pairwise correlation coefficients between variables 
Table 4 presents the Pearson correlation coefficients of the variables. The results show that only the 

debt-to-equity ratio is significantly negatively correlated with the return on equity at a 1 percent level of 

significance. Similarly, the policy uncertainty is significantly negatively correlated with the South African 

volatility index. However, the correlation between policy uncertainty and the remaining variables are 

insignificant.  

 

Table 4. Correlations analysis 

  ROE LPU M2 SAVI D-E 

ROE 1     

 -----     

      

LPU 0.022713 1    

 -0.8216 -----    

      

M2 0.031426 0.1116 1   

      

 -0.7551 0.2665 -----   

      

SAVI 0.011187 
 

-0.50594 
-0.01324 1  

 0.9116 0 0.8954 -----  

      

D-E -0.351674 -0.08825 0.023569 0.10307 1 

 0.0003 0.3802 0.815 0.3051 ----- 

Source: Author’s estimation (2024) 

 
Unit root and stationarity test 

The unit root test is presented in Table 5. Based on Levin et al. (2002), the individual test for 

stationarity reveals that return on equity, policy uncertainty, debt-to-equity ratio and money supply are all 

stationary at level I (0), while the South African volatility index shows a stationarity of first difference I (1). 

In addition, the augmented Dicky-Fuller (ADF) test was also carried out to support the Levin, Lin and Chu 

(2002) test. The ADF test reveals that return on equity, money supply and debt-to-equity ratio were 

stationary at levels, but at 1st difference all variables were stationary. Although debt-to-equity ratio has a 



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116 

 

0.0891 p-value at 1st difference, we accepted it as a stationary variable since it was stationary at levels, i.e. 

with (0.0056). 

 
Table 5. Unit root test 

Levin, Lin & Chu t-stat 

  Level 1st Difference   

Variable t-stat p-value t-stat p-value Order of Integration 

ROE -4.8142 0.0000 -6.3739 0.0000 I(0) 

LPU -3.239 0.0006 -8.7755 0 I(0) 

SAVI 0.6785 0.7513 -3.544 0.0002 I(0) 

D-E -4.733 0 -2.0126 0.0221 I(0) 

M2 -8.5372 0 -6.8148 0 I(0) 

ADF Test 

  Level 1st Difference   

Variable t-stat p-value t-stat p-value Order of Integration 

ROE -3.1169 0.0015 54.0086 0.0000 I(0) 

LPU 15.7715 0.2019 57.662 0 I(0) 

SAVI 9.1275 0.692 38.5949 0.0001 I(0) 

D-E 27.975 0.0056 18.976 0.0891 I(0) 

M2 56.2498 0 56.7176 0 I(0) 

 Source: Author’s estimation (2024) 

 

Panel ARDL result and discussion 
Table 6 shows the panel ARDL (1, 2, 2, 2, 2) regression result. The short-run estimates demonstrate 

the error correction term, which is vital for the consistency and validity of the model. The error correction 

term must be negative and statistically significant. The result show that error correction term (-0.7608) is 

negative and significant at a 1 percent level of significance. Therefore, 76.08 percent of the imbalance 

between return on equity, policy uncertainty, money supply, South African volatility index and debt-to 

equity ratio was eliminated. However, from the long-run results, it could be concluded that all variables are 

significant at a 5 percent level of significance, with exception to money supply being significant at a 10 

percent level of significance. While the coefficients of the policy uncertainty and the South African volatility 

index are negative, the coefficients of money supply and debt-to-equity ratio are positive. Therefore, it can 

be suggested that there is a short-term relationship between return on equity and the explanatory variables. 

In the long-run, it can be said that the South African volatility index (control variable) and policy uncertainty 

have a negative impact on the return of equity of banks in South Africa. However, it is suggested that the 

debt-to-equity ratio and money supply have a positive impact on the return on equity of banks. 

The long-run coefficients in Table 6 show that money supply has a coefficient of 0.1456. This implies 

that approximately 15 percent of the increase in the return of equity of banks in South Africa is obtainable 

when there is an increase in money supply in the economy in the long run. The positive relationship between 

money supply and bank equity return is supported by Liu, Bashir, Abdalla, Salman Ramos-Meza, Jain and 

Shabbir (2024). Similarly, a positive long-run coefficient between the debt-to-equity ratio and return on 

equity suggest that return on equity increases with the debt-to-equity ratio of the banks in South Africa. This 

finding is in line with theory, which stipulates that the more debt a bank requires, it will increase net profits 

by an amount greater than the interest cost of the additional debt, which leads to banks delivering a higher 

return on equity to its investors. Moreover, the negative coefficient between return on equity and policy 

uncertainty implies that a 30 percent decrease in the rate of policy uncertainty will result in a decrease in the 

return on equity of banks in South Africa. Ozili and Arun (2022) established that high economic policy 

uncertainty has a positive effect on banks’ profitability in Asia and the region of the Americas. This indicates 

that improvement and stability in policies that stimulate economic growth directly impact and empower 

banks to make profits in such an economy. The South African volatility index as a control variable in this 

study shows that it has a negative long-run relationship with return on equity. This implies that about 87 

percent of the decrease in volatility results in a decrease in the return on equity of the banks in South Africa.  

 

 

 



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117 

 

Table 6. Panel ARDL  

Model: ARDL (1, 2, 2, 2, 2) 

LONG-RUN EQUATION 

Variable Coefficient Std. Error t-Statistic Prob.* 

LPU -0.3001 0.1384 -2.1686 0.0366 

M2 0.1456 0.0805 1.8097 0.0785 

SAVI -0.8655 0.3097 -2.7943 0.0082 

D-E 0.7102 0.2919 2.4324 0.02 

SHORT-RUN EQUATION 

ECT (-1) -0.7608 0.2358 -3.2261 0.0026 

D(LPU) 0.339998 0.110677 3.072 0.004 

D(LPU(-1)) 0.111767 0.087486 1.277538 0.2094 

D(M2) -0.242346 0.088432 -2.740466 0.0094 

D(M2(-1)) -0.128371 0.064416 -1.992833 0.0537 

D(SAVI) -0.048244 0.197751 -0.243963 0.8086 

D(SAVI(-1)) -0.434834 0.190286 -2.285168 0.0281 

D(D_E) 0.312825 0.498814 0.627138 0.5344 

D(D_E(-1)) 0.302316 0.573602 0.527048 0.6013 

C 0.978496 0.372772 2.624923 0.0125 

Source: Author’s estimation (2024) 

 

CONCLUSION 

 

At the inception of this study, the academics’ aim was to investigate the short-run and long-run 

relationship between macroeconomic variables and South African commercial bank performance. The 

choice of South African commercial banks was in line with data availability and comprised of ABSA Bank, 

Standard Bank, Nedbank, First Rand Bank, Capitec Bank and Investec Bank. The return on equity was used 

as a proxy for the bank performance whereas the macroeconomic variables comprised money supply and 

policy uncertainty. The macroeconomic variables were restricted due to the study finding multicollinearity 

among the omitted variables. The study further imposed control variables such as the debt-to-equity ratio 

and South African volatility index to isolate bank performance. The findings of the panel ARDL model 

illustrated that policy uncertainty and the South African volatility index have a negative long-run 

relationship with commercial bank performance. However, money supply and debt-to-equity ratio have a 

positive long-run relationship with commercial bank performance. It is further evident that the error 

correction term exhibited negative and significant coefficients, which indicates a 76.08% imbalance between 

bank performance and independent variables.  

The findings of the study are significant for the body of literature. Firstly, the findings illustrate that 

fluctuating macroeconomic variables have an influence on bank performance. This indicates that the SARB 

should consider this when conducting policy adjustments, which should be in line with the findings of the 

study as it would ensure a significant effect on short-run and long-run bank performance. Secondly, the 

ALCO committee of banks should consider the allocation of debt and the leverage position of their banks. 

That being, although debt increases bank performance, as found in the study, it also has a significant effect 

on the liquidity position of banks. Thus, if there is not enough control over the banks’ liabilities as generated 

from interest expenses, it will hamper the short-run and long-run performance of banks. Appropriate 

measures should therefore be implemented in line with the findings of this study.  

 

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