Proceedings of 3rd Asia-Pacific Business Research Conference
25 - 26 February 2013, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-19-1
A Panel Co integration Analysis of Bank Profitability and
Bank-Specific Variables in Islamic Banks
Noraina Mazuin Sapuan*, Afdzal Aizat Ramli** and Mohammad Rahmdzey
Roly***
In line with the increasing demand for Islamic banking products in
Malaysia, this study intended to measure the long run and short run
relationship of Islamic banking profitability with bank-specific variables
and financial structure. The analysis applied recently developed panel
unit root and panel cointegration techniques for dynamic panel
estimations. The data are utilized from 12 Islamic banks in Malaysia from
the period of 1996 to 2010. The result from Pedroni’s heterogenous
panel of cointegration revealed that bank profitability, bank specific
variables and financial structure are cointegrated in the long run.
JEL Codes: G21, C23
1. Introduction
Currently, Malaysia is in the position to become the international hub for Islamic finance
as indicated in the Economic Transformation Plan (ETP) since 2010. With the
increasing number of foreign players in this industry plus the increasing demand from
domestic and foreign customers would increase the possibility for Malaysia to achieve
this ambition. According to Economic Transformation Programme (2012) Malaysia is the
world's third largest market for Shariah assets that cover Islamic banks, takaful and
sukuk. Malaysia is also one of the main contributors to the global Islamic financial
assets with the Islamic assets grew by 23.8% in 2011 from RM350.8bil to RM434.6bil.
The Islamic finance industry in Malaysia continues to grow rapidly supported by a
conducive environment that establish from continuous product innovation, a diversity of
financial institutions from across the world, a broad range of innovative Islamic
investment instruments, a comprehensive financial infrastructure and adoption of global
regulatory and legal best practices has encouraged foreign financial institutions to make
Malaysia their destination of choice especially to conduct Islamic banking business
(Economic Transformation Plan). This has created a diverse and growing community of
local and international financial institutions.
In addition to the continuous growth of Islamic banks' market in Malaysia, this study
intended to measure the long run and short run relationship of Islamic banking
*
Noraina Mazuin Sapuan, Department of Finance and Economics, UNITEN, Malaysia. Email:
Noraina@uniten.edu.my
**
Afdzal Aizat Ramli, College of Foundation and General Studies, UNITEN, Malaysia. Email:
Aaizat@uniten.edu.my
***
Mohammad Rahmdzey bin Roly, Postgraduate Student, Universiti Putra Malaysia. Email:
rahmdzey@yahoo.com
1
Proceedings of 3rd Asia-Pacific Business Research Conference
25 - 26 February 2013, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-19-1
profitability and bank specific variables in Malaysia. This study will utilize panel data
analysis and applied recently developed panel unit root and panel cointegration
techniques.
The remainder of the paper proceeds as follows: Section 2 briefly reviews the previous
literature on bank profitability. Section 3 outlines the methodology and data used in the
study. Section 4 presents the empirical results from the analysis. Finally section 5
concluded the study.
2. Literature Review
Generally, bank profitability is determined by internal and external factors. Internal
determinants can be viewed as a bank-specific causal factor of profitability (e.g., bank
size, leverage, assets and liability portfolio mix, overhead expenses, liquidity ratio,
capital ratio, ownership and capital adequacy). According to Harun (2004) internal
factors that affected bank performance, are within the control of bank management and
it can be classified into two categories, i.e. financial statement variables and nonfinancial statement variables. Financial statement variables related to the decisions
which directly involve items in the balance sheet and income statement. Meanwhile,
non-financial statement variables involve factors that have no direct relation to the
financial statements such as number of branches and bank size. However, on the other
hand, external determinants could be termed as variables which are not associated with
bank management, however, affects the manner of functioning of the bank. Basically,
external factors can be divided into two type i.e. industry specific determinants and
macroeconomic variables.
Various studies have been forwarded to examine the conventional bank profitability, but
some similar studies also has been carried out for an Islamic bank case for example.
Abdul Samad and Hassan (1999), How et al. (2005) and Rosly and Abu Bakar (2003)
on the Malaysian banking industry, Turen (1995) on Bahrain Islamic Bank and Bashir
(2000) on Middle Eastern countries.
Turen (1995) investigated the profitability of Bahrain Islamic Bank (BIB) from three
perspectives which are financial ratio analysis of various profitability ratios and their risk
levels, stock analysis and portfolio analysis and found that Islamic banking offers high
performance and stability. In details, the findings showed that the financial ratio analysis
and stock analysis both revealed that BIB offers a higher return and a lower coefficient
of variation than the other commercial banks. Portfolio analysis indicated that BIB’s
stock is the best for the purpose of portfolio diversification. Based on his findings, he
concluded that bankers may achieve an above average performance at a moderate
level of risk by using the profit sharing concept as compared to the interest based
banking. However, the findings are contrary with Bhatti and Misman (2010) which found
that Islamic banks in Malaysia actually are underperforming compared next to
conventional banks.
2
Proceedings of 3rd Asia-Pacific Business Research Conference
25 - 26 February 2013, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-19-1
Bashir (2000) analyzed how bank characteristics and the overall financial environment
affect the performance of Islamic banks. His study examined the relationship between
profitability and the banking characteristics after controlling for economic and financial
structure indicators. The study used regression analysis to determine the underlying
determinant of Islamic bank performance. The factors analyze include bank size,
leverage, loans, short term funding, overhead and ownership. Among the controlled
factors are the external factors such as foreign ownership, taxes, and the market
capitalization. The data used in this study are the cross-country bank-level data,
compiled from income statements and balance sheets of 14 Islamic banks each year in
the year 1993 – 1998 in eight Middle Eastern countries. The analysis of determinants of
Islamic bank profitability confirms previous findings whereby the results indicate that
high capital and loan-to-asset ratios lead to higher profitability. The regression results
also show that implicit and explicit taxes affect the bank performance measure of some
financial and policy indicators that impact the overall performance of Islamic banks. The
findings show that Islamic banks’ profitability measures respond positively to the
increase in capital and negatively to loan ratios. The findings also seem to suggest that
a reserve requirement does not have a strong impact on profitability measures and that
favorable macroeconomic environment does stimulate a higher profit. However bank
size has negative impact on profitability.
Hakim and Neamie (2001) investigated a comparative study of the performance and
credit risk of banks in Egypt and Lebanon. The study covers the 1990’s which that time
the development of the banks showed a tremendous transformation. In addition to that,
also investigated the impact of liquidity, credit and capital on bank profitability in each
country’s banking sector. They conducted a regression analysis with three independent
variables of liquidity, credit and capital and profitability as the only dependent variable.
The study indicated that the return on the equity particularly proxy for profitability is a
direct and increasing function of the banking lending activities irrespective of these two
countries. Moreover, it depicted a strong link between capital adequacy and commercial
bank return with high capitalization acting as an impending to return.
How et al. (2005) examined the relationship between Islamic financing and the three
bank risks of credit risk, liquidity risk and interest rate risk, in a dual banking system.
The study was conducted on Malaysian commercial banks based on annual data
collected from 1988-1996. Univariate tests were conducted to determine the relationship
between Islamic financing and bank risks. At the same time, multiple ordinary least
squares (OLS) regressions between each of the three bank risks and their determinants
were run to measure the existence of interactions between the independent variables.
Their main finding was that commercial banks with Islamic financing facilities have
significantly lower credit risk and liquidity risks but significantly higher interest rate risk
compared to banks without Islamic financing facilities. They also concluded that bank
size is the significant determinant of credit risk while the significant determinants of
liquidity risk are off-balance sheet financing, the extent of securitization, loan volatility,
bank capital and bank size. As for the interest rate risk, the differences in interest rate
3
Proceedings of 3rd Asia-Pacific Business Research Conference
25 - 26 February 2013, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-19-1
risk across banks are explained by the proportion of loan sales to total liabilities and
bank size.
In terms of external factors, Bourke (1989) found that concentration has moderate and
positive related to pre-tax returns on assets. Molyneux and Thornton (1992) further
extended Bourke’s methodology and examine bank profitability in eighteen European
countries between 1986 and 1989. There is a significant positive relationship between
return on capital and concentration and a positive relationship for nominal interest rates.
Concentration shows a positive, statistically significant correlation with pre-tax return on
assets. Athanasoglou et al. (2008) found that industry-specific i.e. ownership and
concentration is insignificant in explaining profitability.
Meanwhile, the macroeconomic indicators such as interest rate, inflation, financial
structure and real growth output growth can influence bank interest margins and bank
profitability. Bourke (1989), Molyneux and Thornton (1992) and Ben and Goaied (2003)
discovered that interest rate positively affected the performance of the banking sector.
Bashir (2000). Ben and Omran (2008) and Sufian (2009) discovered that higher inflation
rate has a positive impact on Malaysian banks’ profitability. Bashir (2000) revealed GDP
can stimulate higher bank’s profit. Hassan and Bashir (2003) and Ben and Omran
(2008) found financial structure has a positive effect on bank profitability. This reflects
the complementarities between bank and stock market growth. The stock market
development variable is always positive and significant in all specifications, suggesting
that banks that operate in a well-developed stock market environment tend to have
greater profit opportunities.
3. Data and Methodology
3.1
Source of Data
The data were compiled from 12 banks annual reports for the period of 1996 to 2010.
All the selected banks were Islamic commercial banks and the commercial banks that
have been classified under the Islamic banking scheme (SPI). The sample data
represent 66 percent of the total population of Islamic banks in Malaysia.
3.2
Determinants of Variables
The bank profitability measures through the balance sheet ratios that represented by
Return on Assets (ROA) as a measurement of asset utilization. We defined ROA as the
ratio of income after tax or zakah over total assets.
Based on the past literature, we identified three categories of variable that can affect
bank performance, first, bank-specific factors and second, financial structure as a
control variable. The list of these variables is as below:
4
Proceedings of 3rd Asia-Pacific Business Research Conference
25 - 26 February 2013, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-19-1
In terms of size (SIZE), most of previous studies showed that size negatively affected
bank profitability (Ben Naceur and Goaied, 2003; Athanasoglou et al., 2008). The
negative relationship indicated that an increase in bank size represents the ability of
bank to diversify their products that may lead to higher risk and lower the return.
However, the study by Bashir (1999) and Goddard et al (2004) shown a positive and
significant relationship between bank performance and bank size when there is a
significant economic of scale.
The ratio of non-interest income over total assets (NIE). NIE is used as a proxy for fund
use management and the opportunity cost of reserves. The opportunity cost of reserves
is the average return on earning assets forgone by holding deposits in cash. This would
increase the cost of funds beyond the observed rate. To compensate for this, banks
would increase the return to both shareholders and depositors.
The ratio of total equity capital divided over total assets (STA). STA is used as a proxy
for the risk of solvency. Substituting equity for debt would reduce the risk of insolvency,
and therefore would lower the cost of borrowed funds. But since the equity is more
expensive funding source, an increase in equity capital may increase the average cost
of capital. Therefore, the higher return could be required ex-ante.
The ratio of total loans over total assets (FIN). This variable was used to estimate the
component of the income that is attributable to manage quality. As management
decisions affect the composition of assets that would bring higher return, those changes
would be reflected in higher return to depositors and shareholders. Higher loans with
less NPL would bring higher return to shareholders and depositors or bank financing
(loans) and are expected to be the main source of revenue, which would be expected to
impact profit positively.
The ratio between the total assets of financial institutions over the GDP (FM).
Competition from other financial services may affect bank operations. Therefore, FM is
used to measure the importance of other financial competitors in the economy.
According to Demirguc-Kunt and Huizinga (1998), Malaysia has relatively high ratios of
FM. Therefore the size of the banking system assets is expected to influence the bank
returns positively. A positive development of the banking sector would increase the
bank profits.
3.3 Methodology
3.3.1 Panel Unit Roots
The panel data technique has appealed to the researchers as it captures panel (banks)
specific effects and allows for heterogeneity in the direction and magnitude of the
parameters across the panel. In addition, it provides a great degree of flexibility in model
selection. The first step of this study is to investigate whether the variables contain a
panel unit root. A panel unit root tests in this study is examined based on Levin et al.
(2002), Breitung (2000) and Im et al. (2003)). The Levin et al. (2002) and Breitung
5
Proceedings of 3rd Asia-Pacific Business Research Conference
25 - 26 February 2013, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-19-1
(2000) panel unit root tests assume a homogenous autoregressive unit root under the
alternative hypothesis. However, Monte Carlo experiments study by Hlouskova and
Wagner (2006) revealed that Breitung (2000) panel unit root test generally had the
highest power and smallest size distortions compared to Levin et al. (2002) and other
first generation panel unit root test. In the case of dynamic panel data model, the
recognition of parameter heterogeneity is important in order to avoid potential biases
which could emerge due to improper specification. Im et al (2003) panel unit root test is
utilized in the case of which allows for heterogeneity autoregressive coefficients.
3.3.2. Panel Cointegration
At the second step of our estimation, we look for a long run relationship using the panel
cointegration technique developed by Pedroni (1999). This technique is a significant
improvement over conventional cointegration tests applied in a time series data. While
pooling data is to determine the common long run relationship, it allows the
cointegrating vectors to vary across the members of the panel. With a null of no
cointegration, the panel cointegration test is essentially a test of unit roots in the
estimated residuals of the panel. The cointegration relationship is estimated as follows:
ROAit= αit + δit + β1i SIZEit + β2i NIEit + β3i STAit + β4i FINit + β5i FMit + εit (1)
Where i= 1, ….,N for each bank in the panel and t=1,…,T refers to the time period. The
parameter αit and βi allow for the possibility of bank-specific fixed effects and
deterministic trends, respectively. εit is estimated residuals which represent deviations
from the long run relationship. ROA is return on assets, SIZE is bank size, NIE is a ratio
non-interest earning over total assets, STA is the ratio of total equity capital divided over
total assets, FIN is the ratio of total loans over total assets and FM is the ratio between
the total assets of financial institutions over the GDP as a control variable.
Pedroni (1999, 2004) recommended two sets of tests for cointegration. First, the panel
tests are based on the within dimension approach (i.e. panel cointegration statistics)
which includes four statistics: panel v-statistic, panel ρ-statistic, panel PP-statistic, and
panel ADF-statistic. These statistics essentially pool the autoregressive coefficients
across different countries for the unit root tests on the estimated residuals. These
statistics take into account common time factors and heterogeneity across banks.
Second, the group tests are based on the between dimension approach (i.e. group
mean panel cointegration statistics) which includes three statistics: group ρ-statistic,
group PP-statistic, and group ADF-statistic. These statistics are based on averages of
the individual autoregressive coefficients associated with the unit root tests of the
residuals for each bank in the panel. All seven tests are distributed asymptotically as
standard normal. Of the seven tests, the panel v-statistic is a one-sided test where large
positive values reject the null hypothesis of no cointegration whereas large negative
values for the remaining test statistics reject the null hypothesis of no cointegration.
6
Proceedings of 3rd Asia-Pacific Business Research Conference
25 - 26 February 2013, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-19-1
4. Findings And Discussions
The results of unit root tests for a series of ROA, bank specific variables and control
variable in each bank using the Levin et al. (2002), Breitung (2000), Im et al. (2003) and
Fisher (1932) is reported in Table 1. For all variables, the null hypothesis of a unit root
cannot be rejected at their levels. However, upon taking first differences, the null of unit
roots is rejected by 10%, 5% and 1% levels. Therefore, it is concluded that all the series
are stationary and integrated of order one.
Table 1: Panel Unit Root Test Results
Series
ΔROA
ΔFIN
ΔSTA
ΔNIE
ΔSIZE
ΔFM
Null hypothesis: Unit root (assumes common unit root process)
Levin, Lin and Chu t-stat
Breitung t-stat
-8.26983***
-1.88290**
-10.4891***
-5.92377***
-1.17086
-8.54551***
-3.87214***
-1.90668**
-1.44516*
-2.94646***
-4.30131***
-12.8423***
Null hypothesis: Unit root (assumes individual unit root process)
Series
Im, Pesaran and Shin W stat
ADF - Fisher Chi-square
PP - Fisher Chi-square
ΔROA
-3.79010***
66.8240***
159.869***
ΔFIN
-1.52468*
40.0133**
101.965***
ΔSTA
-3.36699***
72.3841***
89.3409***
ΔNIE
-3.07241***
56.1211***
103.300***
ΔSIZE
-3.33517***
53.0569***
138.854***
ΔFM
-4.43188***
62.6372***
221.048***
Note: *,** and *** denote statistical significance at the 10%, 5% and 1% levels, respectively.
Having established that all variables are stationary at I(1), we proceed to test whether a
long-run relationship exists between them. We report the result from Pedroni’s
heterogeneous panel test in Table 2. The results reject the null of no cointegration when
they have large negative values and significant at 1 percent significant level except for
the panel v-stat and panel ρ-stat as well as group ρ-stat. which rejects the null of
cointegration. According to Pedroni (2004), ρ-stat and Phillip Peron tests tend to underreject the null in the case of small samples. We therefore concluded that there is a
cointegration in long run between bank profit and the explanatory variables.
7
Proceedings of 3rd Asia-Pacific Business Research Conference
25 - 26 February 2013, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-19-1
Table 2: Pedroni’s heterogeneous panel cointegration results
Statistics
-2.802634
2.627366
-6.579653***
-2.866186***
Panel v-statistics
Panel Philips-Perron (1988) type ρ-statistics
Panel Philips-Perron (1988) type t-statistics
Panel Dickey-Fuller (1979) ADF type t-statistics
Group Philips-Perron (1988) type ρ-statistics
Group Philips-Perron (1988) type t-statistics
Group Dickey-Fuller (1979) ADF type t-statistics
3.850653
-12.15417***
-3.155816***
Note: *** denote statistical significance at the 1% levels, respectively.
The results for short run relationship and error correction model are shown in Table 3.
Based on the result, there is no short run relationship between all independent variables
except for previous bank profit. The error correction equation (ecm t-1) coefficient can be
used to determine how fast deviations from long run equilibrium are eliminated following
changes in each variable. The negative sign are statistically significant at 1% level, thus
confirmed a long run relationship existence among the variables. The error correction is
-0.0009, it implies that a growth in bank profit is slow to adjust to disequilibrium in the
error term.
Table 3: Error Correction Equation Estimation Results
Variables
Speed of adjustment
coefficient
D(ROA(-1))
D(FIN(-1))
D(NIE(-1))
D(SIZE(-1))
D(STA(-1))
D(FM(-1))
c
Coefficient
-0.009079
Std. Error
0.00339
t-statistic
-2.67852***
-0.302130
0.006015
0.016388
0.125599
0.005584
-0.141752
0.000740
0.08405
0.00861
0.02459
0.25811
0.01604
0.24381
0.15209
-3.59460***
0.69883
0.66644
0.48660
0.34814
-0.58141
0.00487
R-Squared
F-statistics
Akaike AIC
S.D.
Dependent
Variables
0.181141
3.981801
3.579700
1.514185
Note: *** denote statistical significance at the 1% levels, respectively.
8
Proceedings of 3rd Asia-Pacific Business Research Conference
25 - 26 February 2013, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-19-1
5. Conclusion
Over the last few years, a number of important changes made by Bank Negara
Malaysia strike the Islamic banking industry in Malaysia, leading to increase the
competition and pressure on Islamic bank profitability. The Central Bank (BNM) has
successfully established a well developed and sophisticated Islamic financial system for
over 30 years. Rapid liberalisation coupled with facilitative business environment has
encouraged foreign financial institutions to make Malaysia their destination of choice to
conduct Islamic banking business. This has created a diverse and growing community
of local and international financial institutions.
By using balance bank level panel data, this study seeks to investigate the existence of
long-run and short-run relationship between Malaysia Islamic banks’ profitability and
bank-specific variables during the period of 1996-2010. The empirical findings using
Pedroni’s heterogenous panel of cointegration disclosed that bank profitability, bank
specific variables and financial structure are cointegrated in the long run. However there
is no short run relationship between these variables. This indicated that any policies
aiming at improving the bank specific variables will have a delayed effect of bank
profitability, but the effect is significant.
References
Abdul Samad and Hassan, M K 1999, The performance of Malaysian Islamic bank
during 1984-1997: an exploratory study. International Journal of Islamic Financial
Services, vol. 1(3), pp. 1-14.
Athanasoglou, P P, Brissimis, S N, and Delis, M D 2008, Bank-specific, industry-specific
and macroeconomic determinants of bank profitability. International, Financial Markets,
Institution and Money, vol. 18, 21–136.
Bashir, A 2000, ‘Assessing the performance of Islamic banks: Some evidence from the
Middle East’. Paper presented at the ERF 8th meeting in Jordan
Ben, NS and Goaied 2003, ‘The determinants of the Tunisian banking industry
profitability: Panel evidence’. Retrived from http://www.erf.org.eg/tent. Date of retrieved:
1 December 2010.
Ben, NS and Omran, M 2008, ‘The effect of bank regulations, competition and financial
reforms on MEAN banks’ profitability’. Economic Research Forum Working Paper, No.
449.
Bhatti, I and Misman, FN 2010, ‘Risks Exposure in Islamic Banks: A Case Study of
Bank Islam Malaysia Berhad (BIMB)’. Australian Centre for Financial Studies - Finsia
Banking and Finance Conference 2010.
9
Proceedings of 3rd Asia-Pacific Business Research Conference
25 - 26 February 2013, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-19-1
Bourke, P 1989, Concentration and other determinants of bank profitability in Europe,
North America and Australia, Journal of Banking and Finance, vol.13, pp.65–79.
Breitung, J 2000, The local power of some unit root tests for panel data. Advances in
Econometrics, vol. 15, pp.161–177.
Demirguc-Kunt, A, Huizinga, H 2000, ‘Financial structure and bank profitability’. Policy
ResearchWorking Paper Series 2430. The World Bank.
Guru BJ and Balashanmugam 2002, ‘Determinants of Commercial Banks Profitability in
Malaysia’, University Multimedia Working Papers.
Hakim S and Neamie S 2001, ‘Performance and credit risk in banking: a comparative
study of Egypt and Lebanon’. ERF Working Paper Series, Working Paper 0137.
Hassan M and Bashir A 2003, ‘Determinants of Islamic banking profitability’. Paper
presented at the ERP 10th Annual Conference in Morroco.
Haron, S 2004, Determinants of Islamic Bank Profitability. Global Journal of Finance
and Economics. USA, Vol 1, No 1.
How, J C, Melina, A K and Verhoeven, P 2005, Islamic financing and bank risks: the
case of Malaysia. Thunderbird International Business Review, 47(1), pp.75-94.
Im, KS, Pesaran, MH and Shin, Y 2003, Testing for unit roots in heterogeneous panels,
Journal of Econometrics, 115, pp. 53–74.
Levin, A, Lin, CF and Chu, C 2002. Unit root tests in panel data: asymptotic and finite
sample properties. Journal of Econometrics, vol. 108, pp. 1–24.
Molyneux, P and Thornton, J 1992, Determinants of European bank profitability: a note.
Journal of Banking and Finance, vol.16, pp.1173–1178.
Pedroni, P 1999, Critical values for cointegration tests in heterogeneous panels with
multiple regressors, Oxford Bulletin of Economics and Statistics, vol. 61 pp. 653–670.
Pedroni, P 2000, Fully modified OLS for heterogeneous cointegrated panels. Advanced
in Econometrics, vol. 15, pp. 93–130.
Pedroni, P 2004, Panel cointegration: asymptotic and finite sample properties of pooled
time series tests with an application to the PPP hypothesis: new results. Econometric
Theory, vol. 20, pp.597–627.
Rosly, SA and Abu Bakar MA 2003, Performance of Islamic and mainstream banks in
Malaysia. International Journal of Social Economics, 30(12), pp.1249-1265.
10
Proceedings of 3rd Asia-Pacific Business Research Conference
25 - 26 February 2013, Kuala Lumpur, Malaysia, ISBN: 978-1-922069-19-1
Suffian, F 2009, Determinants of bank efficiency during unstable macroeconomic
environment: empirical evidence from Malaysia. Research in International Business and
Finance, vol. 23, pp. 54-77.
Turen, S 1995, Performance and risk analysis of the Islamic Banks: The Case of
Bahrain Islamic Bank. Journal of Islamic Economics, vol. 7, pp.3 -13.
Williams, B 2003, Domestic and international determinants of bank profits: foreign
banks in Australia. Journal of Banking and Finance, vol. 9, pp. 115-136.
11