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 Moodley
1
* , Babatunde Lawrence
2
, Surendran Pillay
3
Department of Risk Management, North-West University, South Africa
1
School of Accounting, Economics and Finance, University of KwaZulu-Natal, South Africa
2
School of Accounting Economics and Finance, University of KwaZulu Natal, South Africa
3
* 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.za
1
217081567@stu.ukzn.ac.za
2
pillays18@ukzn.ac.za
3
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
F. Moodley, B. Lawrence and S.Pillay / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024
111
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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112
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
F. Moodley, B. Lawrence and S.Pillay / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024
113
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
Statement
Data range
Data Source
Return on equity
Performance
measure
2006-2022
Bank's Audited
Report
Unemployment rate
Macroeconomic
measure
2006-2022
Mcgregor BFA
Policy uncertainty
Macroeconomic
measure
2006-2022
Mcgregor BFA
Money supply
Macroeconomic
measure
2006-2022
Mcgregor BFA
Inflation
Macroeconomic
measure
2006-2022
Mcgregor BFA
Gross domestic product
Macroeconomic
measure
2006-2022
Mcgregor BFA
Volatility index
Control
2006-2022
Mcgregor BFA
Debt-to-equity ratio
Control
2006-2022
Bank's Audited
Report
Net asset value
Control
2006-2022
SARB
Retention rate
Control
2006-2022
SARB
National deficit
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.





(1)
Where

denotes the response variable, 

is the growth rate in loan provided by

bank in year
t, and 

is one year of lagged macroeconomic variables of the

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).









(2)
Where is the constant term, which is supposed to differ across cross-sectional entities, and while
is the identical autoregressive coefficient,
denotes the lag order, and

represents the disturbance term.
F. Moodley, B. Lawrence and S.Pillay / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024
114
The generalised ARDL
󰇛

󰇜
model employed in the study is specified as follows:

=



+ =



󰆒


(3)
Where

is the response variable,

󰆒
is a K vector that is allowed to be purely I (0) or I (1) or
cointegrated,

is the coefficient of the lagged dependent variable called scalars,

is coefficient
vector,
is the units-specific fixed effects. Number of cross-sections   and time   

is the error term.
Equation 3 can be deduced into the ARDL correction model as:

=














(4)
Where
= group-specific speed of adjustment coefficient,
is the vector of long-run relationships,
[



] is the error correlation term or ECT,

and

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
F. Moodley, B. Lawrence and S.Pillay / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024
116
0.0891 p-value at 1
st
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.
F. Moodley, B. Lawrence and S.Pillay / Finance, Accounting and Business Analysis, Volume 6, Issue 2, 2024
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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