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Management Research Review
The internal determinants of bank profitability and stability: An insight from
banking sector of Pakistan
Muhammad Ali, Chin Hong Puah,
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Muhammad Ali, Chin Hong Puah, (2018) "The internal determinants of bank profitability and stability:
An insight from banking sector of Pakistan", Management Research Review, https://doi.org/10.1108/
MRR-04-2017-0103
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The internal determinants of bank
profitability and stability
An insight from banking sector of Pakistan
Muhammad Ali
Department of Business Administration, Iqra University, Karachi, Pakistan and
Department of Economics, Universiti Malaysia Sarawak Faculty of Economics and
Business, Kota Samarahan, Malaysia, and
Chin Hong Puah
Department of Economics,
Universiti Malaysia Sarawak Faculty of Economics and Business,
Kota Samarahan, Malaysia
Abstract
Purpose –The purpose of this study is to examine the internal determinants of bank profitability and
stability in Pakistan banking sector. Because of specific research objectives, this study excludes the external
factors of profitability and stability to find the role of bank internal determinants in achieving high
performance.
Design/methodology/approach –A panel regression analysis is built on a balanced panel data using
24 commercial banks over the sample period of 2007-2015. The authors performed a separate analysis of bank
profitability and stability. Both models used a comprehensive set of bank internal determinants.
Findings –The results that were obtained from profitability model indicated that bank size, credit risk,
funding risk and stability have statistically significant impacts on profitability, while liquidity risk showed
the statistically insignificant impact on profitability. Regression findings from stability model reveal that
bank size, liquidity risk, funding risk and profitability have statistically significant impacts on stability, while
credit risk had an insignificant effect on stability. However, the effect of the financial crisis is uniform and
showed statistically insignificant impact in both models.
Practical implications –Overall, the authors’findings bring some new but useful insights to the
banking literature. Some recommendations may be functional for the sustainable performance of banks.
Originality/value –In view of study results, the authors provide interesting insights into the practices and
characteristics of banks in Pakistan. This study also highlights significant bank internal determinants to
improve understanding in the existing literature.
Keywords Finance, Corporate finance, Credit risk, Bank profitability, Financial institutions,
Bank stability, Funding risk
Paper type Research paper
1. Introduction
It is argued that bank profitability and stability in financial institutions is a growing
concern for regulators and bank supervisors. This issue has gained significant attention
among the researchers after 2007/2008 financial crisis. The debate on global financial crisis
accounts large banks for the crisis, which influenced significantly to the many economies
(Adusei, 2015). Since the global economies have emerged from the crisis period, Viñals et al.
(2013) indicate that the discussion on organizational complexity, optimal bank size and
financial institutions’activities has heightened. According to Vickers Report (2011),
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Received 10 April2017
Revised 11 April 2018
26 June 2018
Accepted 5 July 2018
Management Research Review
© Emerald Publishing Limited
2040-8269
DOI 10.1108/MRR-04-2017-0103
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2040-8269.htm
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policymakers in the USA (US) are more concern about bank performance and demanding
more liquidity and capital. This strenuous effort by regulators follow Basel-III requirement
and imposing restrictions on banks to invest in risky projects. On the same token, de Haan
and Poghosyan (2012) highlight that return volatility in US banks decreases due to bank
size. They further report this relationship as non-linear (size effect positively on return
volatility, when bank size crosses some threshold level). This explanation opens a debate on
the optimal bank size and the regulatory restrictions to analyze the bank stability during the
crisis period. Adusei (2015) characterized this argument in two ways:
(1) the restrictions on larger banks under capital surcharges; and
(2) the reduction in too-big-to-fail subsidies by the policymakers.
The second viewpoint is also supported by Stein (2013) and Farhi and Tirole (2012) work.
Moreover, earlier studies have increased the interest of regulators to design macro-
prudential indicators and framework to understand the return volatility (Kanas et al., 2012).
This is due to the effect of the crisis period on many economies and categorized bank
profitability as a macro-prudential indicator (Caprio and Klingebiel, 2002,Adusei, 2015).
The reason is obvious because a high performing banking system has a greater ability to
safeguard financial adversities. Furthermore, financial system stability has a direct
relationship with the determinants of bank profitability (Ali, 2015;Mörttinen et al., 2005;
Borio, 2003). This ascertains that unexplored profitability determinants should be of interest
to academicians, financial market analysts, bank regulators and managers. This justifies the
reason that why the past literature of bank profitability is flooded with empirical
investigations (Dietrich and Wanzenried, 2014;Mirzaei et al., 2013;Ali, 2015;Sufian and
Noor, 2012;Adusei, 2015;Sufian and Habibullah, 2009;Flamini et al., 2009;Duygun et al.,
2013;Garcia and Guerreiro, 2016). However, the findings of these studies produce mixed
results due to the difference in the statistical significance of selected variables (Kanas et al.,
2012). It is also a noteworthy point that previous studies have inordinately targeted a panel
data of several countries, where the findings are hard to generalize. This fact is also
supported by Adusei (2015),Ali (2015) and Raza et al. (2013) empirical works. Thus, our
research joins the intellectual debate on the determinants of bank profitability and stability
in Pakistan.
The financial sector of Pakistan has created some important progress toward local and
global businesses. The fact is also endorsed by the report of “Doing Business 2017: Equal
Opportunity for All”, published by the World Bank. The government of Pakistan has
announced a three-year plan to improve its global ranking toward business. Earlier,
Pakistan has completed three major reforms namely, getting credit, trading across borders
and registering property. As a result, Pakistan’s ranking toward “Doing Business Globally”
has improved from 148 to 144 out of 190 countries. These improvements conclude more
profitable and efficient business environment, particularly for the banking system. This
implies that a stable banking system in Pakistan provides more opportunities for businesses
in global and domestic markets. On the other side, the local entrepreneurs are still facing
some difficulties toward their financial solutions such as creditissues, funding opportunities
and other banking-related problems. Moreover, a recent agreement between China and
Pakistan on China-Pakistan Economic Corridor (CPEC) has emerged the importance of
stable and efficient banking system for Pakistan. The banks in Pakistan are likely to have a
significant share in the projects that come under CPEC agreement. The bank regulators are
expecting an important role of the banking sector to improve their profit margins. Since the
geographical position of Pakistan is considered as a strategic one, therefore, the growing
phenomenon of regional connectivity and globalization has increased the importance of
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Pakistan banking sector around the globe. Based on these arguments, the banking system
of Pakistan become more important to the investors, regulators and financial institutions of
other countries. It is a well-known fact that banks play their role as a financial intermediary
between borrowers and savers. Therefore, this required more explanation toward bank-
specific factors such as liquidity, credit, funding, bank size and other important factors
particularly a case of Pakistan.
The contributions of our investigation are largely twofold. First, our paper presents a
joint analysis of bank profitability and stability. Mirzaei et al. (2013) argue that the global
banking industry has experienced substantial changes and structural reforms, specifically
after the financial crisis of 2007/2008. This signifies that behavior of banks adopts
fundamental changes with emphasis on profitability and stability in recent periods. For
emerging countries, the stable and profitable banking system is an important feature to
project better economic conditions. Such type of banking environment, increase the
confidence of borrowers to finance their future projects. In this context, Albertazzi and
Gambacorta (2009) highlight the phenomenon, namely reforms in technological
advancement, the growth of financial markets, determinants of bank performance and
globalization are the significant predictor of a weak economic environment. Similarly,
Athanasoglou et al. (2008) work suggest that stable and profitable banking system
safeguard economic conditions from negative shocks. Furthermore, our first contribution
supports the fact provided by Mirzaei et al. (2013) work, suggest that the joint
investigation of bank stability and profitability is quite limited in existing literature.
Therefore, in the wider interest of bank regulators, our research findings may suggest
useful policy implications.
The second contribution of our analysis is the behavior of coefficients in a developing
country like Pakistan. Past studies well-explored bank internal determinants of
profitability and stability using a panel of different countries. This restricts policy and its
generalizability to a specific country due to change in dynamics of the financial sector of
a country. However, past studies also showed mixed results and produce vague
understanding about the determinants of profitability and stability(Sufian, 2010;Ali,
2015;Adusei, 2015;Ramlall, 2009;Goddard et al., 2004;Naceur and Omran, 2011;Anbar
and Alper, 2011). Thus, this research widening the scope and fills the gap by using
internal determinants of bank profitability and stability in Pakistan.
2. Literature review and “hypotheses”development
2.1 Bank profitability literature
Following previous work by Short (1979) and Bourke (1989), a substantial amount of work
has been attempted to determine the predictors of bank profitability. The respective
investigations have targeted their work either individual banking system or on the cross-
country analyses (Dietrich and Wanzenried, 2011). In this context, Abreu and Mendes (2002),
Molyneux and Thornton (1992),Goddard et al. (2004), Demirgüç-Kunt and Huizinga (1999)
Pasiouras and Kosmidou (2007),Micco et al. (2007)Staikouras and Wood (2004) and Mirzaei
et al. (2013) examine on a panel data set. Similarly, Berger (1995),Athanasoglou et al. (2008),
Mamatzakis and Remoundos (2003),Berger et al. (1987), Ben Naceur and Goaied (2008),
Neely and Wheelock (1997),García-Herrero et al. (2009), Abreu and Mendes (2002) and
Adusei (2015) presented their work on individual countries. These studies produce mixed
results, which is obvious, given the differences in investigating environments, their data
sets, countries and time periods (Abreu and Mendes, 2002). However, some of the elements
we use are similar to further examine the determinants of bank profitability.
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The relationship between bank size and profitability posit that advantage of scale
economy in transactions are likely exploited by large banks, which ultimately increase
profitability (Adusei, 2015). In addition, larger banks control market forces through
regulatory protection (too-big-to-fail) or strong brand image (Košak and
Cok (2008)). This
signifies that a positive relationship is expected between size-profitability relationship
(Pervan et al., 2010;Kosmidou, 2008;Adusei, 2015). Bertay et al. (2013) corroborates this
postulation, suggest that smaller banks are less profitable than larger banks. Similarly,
Flamini et al. (2009) use a large sample of 389 banks related to 41 Sub-Saharan African
countries (SSAC). They find a positive relationship between bank returns and bank size. In
contrast with Flamini et al. (2009), one study of de Haan and Poghosyan (2012) also provides
consensus on this relationship, argued that bank size secure return stability of banks. In the
same year, de Haan and Poghosyan (2012) extend their analysis on USA (USA), find that
return volatility decrease due to increase in bank size. Their investigation further report that
bank size and return volatility has a non-linear relationship (bank size effects positive on
return volatility, when the size exceeds the threshold limit). In recent times, Adusei (2015)
suggest that bank size has a positive effect on bank stability. On the other side, Košak and
Cok (2008) suggest a negative relationship between bank profitability and size, because
large banks are associated with diseconomies of scale. On the same token, Stiroh and
Rumble (2006) and Pasiouras and Kosmidou (2007) works attribute negative relationship of
bank size and profitability to agency costs, other expenses of large firms and the overhead
expenses of bureaucratic processes. The negative relationship between size-profitability is
also supported by Ben Naceur and Goaied (2008) and Sufian and Habibullah (2009).
However, some studies also report an insignificant relationship between bank profitability
and bank size (Goddard et al., 2004 and Athanasoglou et al., 2008). In sum, our literature
analysis suggests that bank size and bank profitability relationship remain inconclusive and
requires further investigations.
Liquidity risk refers to the failure of a firm to fulfill its short-term liabilities. Kosmidou
(2008) and Adusei (2015) use bank liquidity risk as a measure of loan-to-deposit ratio. To
avoid insolvency risk, Curak et al., (2012) suggest that commercial banks keep a substantial
amount of liquid assets, which can easily transform into cash. However, higher liquidity
decreases bank profitability because of lower rate of return on liquid assets. Similarly,
Adusie (2015) and Rose and Hudgins (2008) also suggest a ratio between cash and due from
balances to total assets as a measure of liquidity risk. Thus, our study follows Rose and
Hudgins (2008) and Adusie(2015) guidelines to measure bank liquidity risk.
Credit risk is an important factor in the banking industry. Past literature usually
measures credit risk through loanloss provisions.These studies report that bank faces lower
profitability due to higher loan loss provision (Kosmidou, 2008;Athanasoglou et al., 2008).
Likewise, Tan and Floros (2012) found a negative relationship between credit risk and bank
profitability. On the other side, loan-to-asset ratio refers to credit risk (Adusei, 2015). In this
context, risk and return hypothesis posits that bank exposed to higher credit risk, when
loan-to-asset ratio is high. This situation requires efficient management of funds to earn
higher returns, which in turn improved profitability (Curak et al., 2012). Based on the above
discussion, the risk-return assumption suggests that credit risk effect negative on bank
profitability. Past empirical evidence also reports a positive relationship between bank
profitability and credit risk (Flamini et al., 2009). However, Curak et al. (2012) investigation
suggest that bank profitability is negatively associated with credit risk because higher loan-
to-asset ratio some time indicate higher credit risk. This situation increases borrowers’
default and declines overall bank profitability. In 1997, Berger and DeYoung (1997) also
proposed a hypothesis, namely “skimping hypothesis”between profitability and credit risk.
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This hypothesis suggests that bank credit risk effect negative on profitability because
banks usually seek greater profits in the long-run by enhancing cost-efficiency. In recent
literature, Afriyie and Akotey (2013) and Adusei (2015) gain our attention by using non-
performing loans as a measure of bank credit risk. Their study indicates that bank credit
risk has a positive impact on bank profitability. Hence, consistent with recent studies, our
research measures bank credit risk through loans-to-assets ratio.
The solvency risk plays an important role to predict bank profitability (Adusei, 2015).
The capital strength of a bank is measured through solvency risk (equity to total asset ratio),
while strong bank equity allows a bank to absorb external/internal shocks (Curak et al.,
2012). It is a noteworthy point that the bank considers its capital as a safety cushion through
which a bank enables to mitigate insolvency risk by maintaining a higher amount of
capitalization. In this way. risk-return hypothesis state that such type of a bank observes
low profitability. However, well-capitalized banks with credit-worthiness enhance the
confidence of customer deposits, which results, lower interest rates, interest expenses and
external financing. Furthermore, lower risk (greater equity to asset ratio) would increase
bank profitability. Hence, bank profitability and solvency risk may have a positive
relationship (Curak et al., 2012).
2.2 Bank stability literature
The possible explanation between size-stability relationships can be explained by the
agency theory of the firm. Jensen and Meckling (1976) argued that managers and owners of
the firm, consider incompatible goals to run the organization or firm. In other words, the
agency theory submits that manager’s actions and decisions become inordinately skewed
toward personal gain. In this sense, the size of a firm is increased because of the managerial
empire-building, hence, bad governance associates with large firms. Jensen (1986),Gabaix
and Landier (2008) and Murphy (1985) report that managers intend to enjoy private benefits
or outsized compensation from the large firm. Based on the above discussion, this study
expects a negative relationship between bank stability and bank size.
Similarly, the size-stability relationship is also explained by the stewardship theory.
Donaldson and Davis (1991) and Davis et al. (1997) suggest that managers of the firm fairly
use the resources of the firm and they are considered as inherently trustworthy employees.
This theory further argues that corporate managers pursue their duties without considering
additional rewards. One study of Etzioni (1975) relates such type of duty as normally
induced compliance, when managers ignore personal rewards. In addition, when corporate
managers anticipate their personal and future benefits (employment, promotions, pension,
medical, etc.) with the firm, their views become aligned with the firm goals. The crux of the
stewardship theory suggests that corporate managers are the least concern with their inner
motivation and they are more focus on firm’s good corporate performance. One question
arises from this discussion, whether or not the corporate manager’s implementation of
planning to achieve high corporate performance is supported by the organizational
structure. In this context, Donaldson and Davis (1991) argued that corporate managers are
only supported by the organizational structure, when they empower senior management
and consistent expectations to achieve firm goals. In sum, unlike the agency theory,
stewardship theory submits that increase in the size of a firm may enhance its stability. By
extraction, this theory posits a positive relationship between bank stability andbank size.
The size-stability relationship can also establish in the perspective of “concentration-
fragility”and “concentration-stability”hypotheses (Uhde and Heimeshoff, 2009). Based on
concentration-fragility hypothesis assumptions, larger bank stability inflates in a
concentrated market via three main channels. First, the issues related to moral hazards are
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due to the fact that those banks, which are larger in size can be seen as “too big to fail”
institutions, but they are supported by state guarantees. Mishkin (1999) work submits that
managers face problems related to moral hazard due to increase in bank size and their risk-
taking behavior depreciate against their confidence, which is protected by state safety (i.e.
central bank intervene occurs to provide the bail-out program to financially distressed
banks). Similarly, Laeven et al. (2014) argued that the bail-out subsidies rescued larger
banks during financially distressed periods. Additionally, to better utilization of unstable
funding, larger banks receive a lower cost of debt in risky markets, where the activities are
highly volatile. Second, owing to the assumption that larger banks charge higher interest
rates on deposits due to their market power, the borrowers left no option to invest in risky
projects to return bank liabilities, which likely increase default risk. Third, managerial
efficiency (includes, risk diversification in assets and liabilities of concentrated bank
markets) may be declined (Mirzaei et al., 2013). In sum, the concentration-fragility
hypothesis suggests that bank size has a negative influence on stability.
Moreover, concentration-stability hypothesis submits that financial fragility of large
banks declines in concentrated markets through five channels:
(1) Sufficient amount of profits and capital buffer provide safety to large banks
toward liquidity and macroeconomic shocks.
(2) Managers risk-taking behavior may dissuade as soon as the charter value of large
banks improve. Additionally, better quality of credit investment enhances financial
stability of larger banks, but the amount of investment is smaller in size (Boot and
Thakor, 2000).
(3) Larger banks supervisory bodies focus on efficient management to minimize the
risk in highly volatile financial markets.
(4) The improved credit monitoring services are provided by larger banks.
(5) Larger banks are efficient in utilization of loan portfolios and they achieve greater
economies of scale, which further allows them to perform well in cross-border
activities.
Mirzaei et al. (2013) work explain this argument in two ways. First, the size of a bank
significantly influences portfolio diversification. In this situation, large banks are capable to
manage their operations under less capital funding and less-stable environment, which reduce
the risk of a bank. Second, large banks perform well in market-based activities because of
their competitive advantage of greater economies of scale. This fact is also supported by
Laeven et al. (2014) investigation. Therefore, the central part of the concentration-stability
hypothesis submits that bank size has a positive impact on bank stability.
Adusei (2015) study indicates that recent studies have ignored the size-stability
relationship. So far, our literature analysis also finds less empirical evidence on the size-
stability relationship. However, past literature has explored the relationship between bank
competition and bank size (Beck et al., 2013;Amidu and Wolfe, 2013;Fiordelisi and Mare,
2014). One study of Laeven et al. (2014) gain our attention by analyzing the effect of bank
size on stability. The study report that smaller banks are less risky than larger banks.
Similarly, Adusei (2015) supports a positive relationship between bank size and stability. In
contrast, Köhler (2015) investigates the effect of bank business and stability model using a
sample data from European Union (EU) banks. This research indicates that bank size shows
a negative and significant effect on stability. Altaee et al. (2013) found no statistically
significant relationship between bank size and stability of Gulf Cooperation Council
Countries (GCC). Thus, we draw an obvious conclusion that the size-stability relationship is
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inconclusive and requires further empirical support. Based on this argument, we investigate
the impact of bank size on stability in the banking sector of Pakistan.
The relationship between bank funding risk and stability receiving a considerable
amount of attention among the researchers (Adusei, 2015). In this context, Calomiris and
Kahn (1991) suggest that bank wholesale funding reduces risk through efficient utilization
of bank resources and capital diversification. Huang and Ratnovski (2011) argued that
wholesale funding price is less stable, while these funds are capable of repriced
instantaneously to show bank’s riskiness. In contrast, Shleifer and Vishny (2010) state that
customer deposits are more stable, however, the repriced process of customer deposit is too
slow. Demirgüç-Kunt and Huizinga (2010) suggest that bank instability is mainly associated
with the larger portion of non-deposit funding. However, Köhler (2015) associate non-deposit
funding risk with a different type of banks. This signifies that share of non-deposit funding
has a negative impact on the stability of retail-oriented banks, while this relationship is
positive for investment banks. Moreover, Adusei (2015) report a positive relationship
between bank stability and funding risk. Hence, we examine the relationship between bank
funding risk and bank stability of the Pakistan banking sector.
In general, bank stability and profitability are generally associated with the growth of a
country’s economy. Based on this argument, present study finds some gap that exists in the
previous studies. For instance, previous empirical work focused on profitability and
efficiency analysis whereas banking stability analysis is quite limited from the scope of past
studies. It is obvious that the existing literature differentiates past studies according to their
sample data, different methodological approach and objectives. This study also tried to
follow some methods and theoretical assumptions that are used in previous work. This
shows consistency with the existing literature but differentiated itself by highlighting some
new but relevant determinants of bank profitability and stability in Pakistan. In this way,
our investigation is an attempt to explain the relationship between key internal
determinants of bank profitability and stability. In sum, we conclude that past studies found
mix response that how internal factors influence the profitability and stability of banks.
Evidently, few studies have attempted to establish a consensus among the researchers, but
the support is insufficient. This problem is exacerbated by the lack of empirical support and
demands clarity in the existing body of knowledge particularly in developing countries. We,
therefore, analyze the impact of internal determinants of bank profitability and stability in
Pakistan. Based on above discussion, we proposed following hypotheses:
H1. There is a significant impact of bank size on bank profitability.
H2. There is a significant impact of liquidity risk on bank profitability.
H3. There is a significant impact of credit risk on bank profitability.
H4. There is a significant impact of funding risk on bank profitability.
H5. There is a significant impact of bank stability on bank profitability.
H6. There is a significant impact of bank size on bank stability.
H7. There is a significant impact of liquidity risk on bank stability.
H8. There is a significant impact of credit risk on bank stability.
H9. There is a significant impact of funding risk on bank stability.
H10. There is a significant impact of bank profitability on bank stability.
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3. Methodology
3.1 Variables selection
This section explains the selection of variables to analyze bank stability and profitability in
Pakistan. Table I shows a summary of selected variables and their measurement in our
research.
3.2 Dependent variables
Our analysis considers two separate models, namely, bank stability and profitability. First,
Z-score (BSTAB) is used as a measure of bank stability or insolvency risk. This signifies
that lower risk of insolvency or instability is associated with a higher z-score value. Second,
return on equity (ROE) as a measure of bank profitability, which investigate the ability of
bank management to generate profits.
3.3 Independent variables
Consistent with past studies, we consider natural log of total assets as a measure of bank
size (Ali, 2015;Amidu and Wolfe, 2013;Adusei, 2015). Bank funding risk (FRISK) is the
second independent variable which is also computed by Zscores. This variable is newly
introduced by Adusei (2015) in banking literature, argued that funding risk is an important
factor to analyze because bank activities are dependent on customer deposits. This fact is
also supported by Köhler (2015). Hence, we expect a positive influence of funding risk on
bank stability and profitability. Liquidity risk (LRISK) is the ratio between total assets and
cash and due balances held at other depository institutions (Rose and Hudgins, 2008;
Adusei, 2015;Fiordelisi and Mare, 2014), whereas credit risk (CRISK) is measured by loans-
to-assets ratio (Curak et al., 2012;Adusei, 2015). We deliberately used loans-to-assets ratio as
a measure of credit risk because it indicates the vulnerability of a bank to variations in the
attitudes of its borrowers toward repayment. This means that an increase in borrower
default leads to close the bank insolvent. However, the measure of credit risk via loans-to-
assets ratio is not a novel. Past studies such as Adusei (2015) and Curak et al. (2012) also
used loans-to-assets ratio as a measure of credit risk. Additionally, return on asset (ROA)
and bank stability (BSTAB) is also included in our empirical model as explanatory
variables. This approach is consistent with Adusei (2015), when bank stability and
Table I.
Description of
variables
Variables Description Symbol
Dependent variables
Bank
profitability
Return on equity ROE
Bank stability Z-score computed from ROA, capital ratio and standard deviation of
ROA
BSTAB
Independent variables
Bank size Natural log of total asset BSIZE
Funding risk Z-score computed from DEP/TA ratio plus E/TA divided by the
standard deviation of DEP/TA
FRISK
Credit risk Loans-to-asset ratio CRISK
Return on asset Profit before tax divided by total assets ROA
Liquidity risk Due balances and cash held at the other depository bank to asset ratio LRISK
Financial crisis Dummy variable used for financial crisis, i.e. 1 = crisis period and
0 = other than crisis period
FC
(DUMM)
Source: Author's estimation
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profitability models are estimated distinctly. To neutralize the impact of financial crisis, we
created a dummy variable FC(DUMM) as an explanatory variable in the study models.
Overall, the variables symbolic representation and computation are reported in Table I.
3.4 Empirical models
Based on the past empirical studies, we develop our panel data models to examine the
impact of bank internal variables on its profitability and stability:
ROE ¼
a
þ
b
1BSIZE þ
b
2LRISK þ
b
3CRISK þ
b
4FRISK þ
b
5BSTAB
þFC DUMM
ðÞ
þ
«
(1)
BSTAB ¼
a
þ
b
1BSIZE þ
b
2LRISK þ
b
3CRISK þ
b
4FRISK þ
b
5ROA
þFC DUMM
ðÞ
þ
«
(2)
According to equations (1) and (2), BSTAB shows bank stability, whereas ROE and ROA
represents profitability; BSIZE highlight bank size; FRISK denotes fund risk; LRISK indicates
liquidity risk; CRISK is symbolized as credit risk and FC(DUMM) is used as a dummy variable
for financial crisis. Additionally,
b
and
«
are the regression parameters and error term.
Consistent with past studies, we calculate our bank stability variable using Z-score as a
measure of bank solvency risk or bank stability (Adusei, 2015;Stiroh,2004a, 2004b;Kasman
and Kirbas-Kasman, 2013;Demirgüç-Kunt and Huizinga, 2010 and others). The
computation of bank stability is as follows:
Z-score BSTAB
ðÞ
i;t¼ROAi;tþEi;t=Ai;t
s
ROAi;t
ðÞ
(3)
According to equation (3), BSTAB represents bank stability (computed by z-score), while
ROA denotes return on asset. The equity-to-total asset ratio refers (E/TA) and
s
ROA
indicates the standard deviation of ROA (Köhler, 2015). Additionally, iis an individual bank
and tindicates a time period. Hence, a lower instability or insolvency risk is expected
against higher z-scores.
Our review of past literature suggests that fewer studies predict bank profitability
using funding risk (Adusei, 2015;Köhler, 2015). According to Adusei (2015),bank
funding risk is associated with the loss occurs due to the fall in deposit mobilization
performance. In our research, we analyze the impact of funding risk on bank
profitability through Z-scores of funding risk. In line with past studies, we compute
funding risk as follows:
Z-score FRISK
ðÞ
i;t¼DEP=TAi;tþE=TAi;t
s
DEP=TAi;t
ðÞ
(4)
In above equation (4), the funding risk is denoted by Z-score (FRISK) and is calculated
through the sum of the deposit-to-total asset (DEP/TA) ratio and the E/TA ratio, which is
further divided by the standard deviation of DEP/TA ratio. Recent literature report that the
bank funding risk must be analyzed for bank profitability because retail banks mobilize
customer deposits for their funding-related activities (Köhler, 2015;Adusei, 2015). Thus, our
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study expects a positive relationship between bank funding risk and profitability (Adusei,
2015;Köhler, 2015).
Additionally, we used independent variables in t 1 while dependent variable in
time t. This implies that our independent variables are lagged variables to mitigate the
potential problem of endogeneity (Adusei, 2015;Hannan and Prager, 2009). The
argument behind this methodology is that bank stability and profitability are
considered as a function of the lagged values of all the exogenous variables. Finally, our
research uses the log transformation of the variables to avoid the problem of skewness
in the data. Thus, we report Table I for the explanation of dependent and independent
variables.
3.5 Estimation procedure
To check the model suitability, we performed two tests. First, Hausman test is used to assess
the null hypothesis, state that, fixed effect (FE) model and random effect (RE) model have no
systematic differences. This implies that the Hausman test suggests the most appropriate
method between FE and RE model. More simply, the Hausman test indicates the most
appropriate model, whether FE or RE model is appropriate for the analysis. Therefore, FE
model should be considered if the null hypothesis of Hausman test is rejected, otherwise, the
RE model should be analyzed if the null hypothesis of Hausman test is accepted. Moreover,
Park et al. (2010) suggests reasonable ways to adopt whether FE or RE model. Importantly,
the researcher should perform some basic tests to choose FE or RE models. However, the
selection of model is based on theoretical assumptions of the stated tests. It is also a
noteworthy point that panel data some time shows the significant presence of FE and RE
models. In this case, the researcher can use both FE and RE models. Theoretically, it is not
allowed to use both types of the model due to their contradictory assumptions. Park’set al.
(2010) study tried to explain the use of both FE and RE models but he strongly recommends
to use either of the models due to the loss of a degree of freedom and its parsimony.
Therefore, this study performs Hausman test to consider one of the models either FE or RE
model. Among others, Bleaney and Neaves (2013) research used Hausman test to select FE
or RE model. In past empirical studies, many investigations have been conducted to
distinguish whether FE or RE model should be analyzed by using Hausman test,
particularly when measuring banking performance.
We used Wald test to examine the joint significance of our independent variables to
predict the variance in the dependent variable. To mitigate the problem of multicollinearity in
the model, we used Variance inflation factor (VIF). If the mean VIF statistics is greater than
10, the regression model suffers from the problem of multicollinearity. In Tables III and IV,
the mean VIF value for profitability model is 1.95 and stability model is 2.66, respectively.
This implies that our both models are free from the problem of multicollinearity. Moreover, a
panel regression also demands to check likelihood chances of heteroscedasticity in the model.
Under the assumptions of classical linear regression, heteroscedasticity may influence the
reliability of ttest and Ftest which produce spurious conclusion about the significance of
regression coefficient. Therefore, our test for profitability and stability model accepts the null
hypothesis of homoscedasticity against the alternative hypothesis of heteroscedasticity.
Finally, we also used Durbin–Watson (DW) test to investigate the autocorrelation in the
regression models. For both models, the DW test value for profitability model is 1.89and 1.92
for stability model. This indicates that our study models are not influenced by the problem of
autocorrelation.
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3.6 Data source
We collect a sample data of 24 commercial banks, which includes 19 conventional and five
Islamic banks in Pakistan. Because of data constraints, our balanced panel data cover a
sample period of 2007 to 2015. In Pakistan, banking industry has faced some issues like
closure/opening, merger/acquisition of new and old banks. Over the past few years, some of
the domestic and foreign banks have dropped out from the market, while new banks
required more time to generate financial observations. These particular scenarios limit our
study sample; therefore, the selection of sample size is subject to the data availability. The
annual financial statements were used to gather the data of bank-specific variables.
4. Estimation results
4.1 Descriptive statistics
We report Table II for the descriptive statistics of our sample data. This shows minimum,
maximum, mean, total observations and standard deviation of the sample data. The average
value of the data is represented by the mean value and the deviation from the mean is
indicated by standard deviation. Overall, the average value of BSTAB is -0.42 and its
standard deviation is 1.02. Similarly, the mean value of ROE is 0.01 with a standard
deviation of 0.28. Furthermore, the mean value of bank size and funding risk is 18.04 and
201.56, while their standard deviation is 1.13 and 293.26, respectively. The total number of
observations is 216 for all the study variables. Therefore, Table II shows further descriptive
statistics of the remaining variables.
4.2 Regression analysis (bank profitability)
This study uses return on equity (ROE) to proxy bank profitability. We proceed our
profitability model using the fixed effect (FE) model. This is because the
x
2
probability
value of Hausman test is significant at the 1 per cent level of significance and the null
hypothesis is rejected (random RE model is suitable). According to Table III, the adjusted R
2
value is 0.64 and the D.W test value is 1.89. The F-statistic and
x
2
value of Wald test is also
significant at the 1 per cent level of significance. Hence, the diagnostic test results indicate
that our bank profitability model is useful for further analysis.
The size-profitability hypothesis suggests that large banks are associated with greater
economies of scale in transactions which in turn more profits. Košak and
Cok (2008) work
highlight that these banks hold market power due to the strong brand image to gain
regulatory protection (too-big-to-fail). This argument establishes a positive relationship
between bank size and profitability (Bertay et al., 2013;Adusei, 2015;Pervan et al., 2010;
Kosmidou, 2008). According to Table III, our findings indicate that bank size has a positive
significant impact on profitability. The possible implications could be that banks in
Pakistan are efficient to attain economies of scale, which in turn greater profitability.
Table II.
Descriptive statistics
Statistic ROE BSTAB BSIZE LRISK CRISK FRISK ROA
Mean 0.01 -0.42 18.04 12.04 39.95 201.56 1.02
Maximum 0.24 1.20 20.08 43.09 68.06 1396.95 0.13
Minimum 1.65 -3.25 12.35 0.23 17.25 -238.71 -1.20
Std. dev. 0.28 1.02 1.13 7.52 10.90 293.26 0.62
Observations 216 216 216 216 216 216 216
Source: Author's estimation
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Table III further reveals that funding risk (FRISK) has a negative effect on bank
profitability. The result implies that banks are aggressive to generate more customer
deposits. This situation increases operational cost such as, increasing promotional activities,
offering low and attractive interest rate and others. Thus, the bank faces lower profitability
due to a decrease in interest income from investment and lending operations. This argument
is also supported by Adusei (2015) investigation.
It is a general viewpoint that banks maintain a higher amount of liquid assets to
mitigate insolvency problems (Curak et al., 2012). However, a lower rate of return is
associated with liquid assets which reduce profitability. On the other hand, Adusei
(2015) suggest that liquidity risk has a positive effect on bank profitability. In our
analysis, Table 3 indicates a negative impact of liquidity risk on profitability, which is
the prior prediction of our research. This means that higher amount of liquid assets led
to decrease the profitability of banks in Pakistan. According to the risk-return
hypothesis, a higher amount of loan-to-asset ratio increases higher credit risk, which
results in commensurate compensation to overall profitability in the form of higher
return (Curak et al., 2012). This statement establishes a positive relationship between
bank credit risk and profitability. The results reported in Table3, confirm this
postulation and indicate that bank credit risk has a positive effect on bank profitability.
The findings are also consistent with Afriyie and Akotey (2013) work. Banks usually
raise more customer deposits for loaning and investment purpose. If a bank maintains
its higher amount of non-performing loans due to customer default, such type of banks
is exposed to insolvency risk or stability crisis. This required additional capital to
protect further losses (Adusei, 2015). Similarly, stability-profitability postulations
suggest a positive relationship between bank stability and profitability. According to
our findings, Table III suggest that stability has a positive influence on bank
profitability. However, the financial crisis has a negative impact on bank profitability
in Pakistan.
Table III.
Panel least square
regression: (fixed
effect model)
Variable
Dependent variable: ROE
Coefficient t-value p-value
Constant -3.626 -3.277 0.001
***
BSIZE (1) 0.157 3.030 0.002
***
LRISK (1) -0.064 -1.441 0.151
CRISK (1) 0.219 3.044 0.002
***
FRISK (1) -0.172 -2.441 0.015
**
BSTAB (1) 0.289 4.239 0.000
***
FC(DUMM) -0.029 -0.718 0.473
Adj R
2
= 0.64
D.W stat= 1.89
F-stats= 11.35
***
Wald test:
x
2
= 52.51
***
Hausman test:
x
2
= 37.11
***
Mean VIF = 1.95
Heteroscedasticity = 2.78
(p-value) = 0.92
Notes: BSTAB denotes bank stability; BSIZE is bank size; LRISK is liquidity; CRISK is credit risk; FRISK is fund
ris; FC(DUMM) is financial crisis dummy; and ROE is return on equity; All independent variables are lagged (-1)
variables.
***
significance at 1 per cent level;
**
significance at 5 per cent level
Source: Authors’calculations
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4.3 Regression analysis (bank stability)
The Hausman test has been applied to check whether the fixed effect (FE) or random effect
(RE) model is appropriate for bank stability model. According to Table IV, the p-value of
x
2
suggests that we should perform FE model to estimate bank stability model. Sufian and
Habibullah (2009) and Wooldridge (2002) argued that the FE model produce unbiased and
steady coefficients. The adjusted R
2
value is 0.97 with DW value of 1.92. The F-statistic is
also significant at 1 per cent significance level. Overall, our diagnostic test statistics confirm
the suitability of our bank stability model.
Past literature is limited to determine the size-stability relationship. Some studies report a
negative relationship between bank size and stability (Laeven et al., 2014;Köhler, 2015;
Altaee et al., 2013). On the other hand, Adusei (2015) found a positive effect of bank size on
stability. According to Table IV, our analysis suggests that bank size has a negative impact
on stability, which is supported by the assumptions of agency theory (bank size has an
adverse effect on stability). However, this negative relationship opposes the postulations of
steward theory and concentration stability hypothesis (increase in bank size improve bank
stability).
Furthermore, Table IV indicates a positive relationship between bank funding risk and
stability. This submits that customer deposits are efficiently mobilized by the banks in
Pakistan to attain higher stability. These findings are the prior expectations of our research
and supported by previous research studies (Shleifer and Vishny, 2010;Köhler, 2015;
Adusei, 2015;Demirgüç-Kunt and Huizinga, 2010).
As expected, Table IV depicts that the relationship between credit risk andbank stability
is negative and establish a prior prediction of our research. Adusei (2015) argued that bank
credit risk should have a negative influence on bank stability due to poor standards in
lending rates. However, in Table IV, we found a positive effect of profitability on stability,
while liquidity risk andfinancial crisis has a negative impact on bank stability. The findings
are in line with the priorinvestigation of Adusei (2015).
Table IV.
Panel least square
regression: (fixed
effect model)
Variable
Dependent variable: BSTAB
Coefficient t-value p-value
Constant 6.392 4.463 0.000
***
BSIZE(-1) -0.266 -3.937 0.000
***
LRISK(-1) -0.240 -2.595 0.010
**
CRISK(-1) -0.018 -0.295 0.768
FRISK(-1) 0.509 6.912 0.000
***
ROA(-1) 0.066 1.729 0.086
*
FC (DUMM) -0.012 -0.260 0.795
Adj R
2
= 0.97
D.W stat = 1.92
F-stats = 159.18
***
Wald test:
x
2
= 64.31
***
Hausman test:
x
2
=87.23
***
Mean VIF = 2.66
Heteroscedasticity = 1.88
(p-value) = 0.25
Notes: BSTAB denotes bank stability; BSIZE is bank size; LRISK is liquidity; CRISK is credit risk; FRISK is fund
risk, FC(DUMM) is financial crisis dummy and ROA is return on asset.; All independent variables are lagged (-1)
variables;
***
significance at 1 per cent level;
**
significance at 5 per cent level;
*
significance at 10 per cent level
Source: Authors’calculations
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5. Conclusion and policy implications
This study has examined how bank internal determinants affect the stability and
profitability of banks in Pakistan during the year 2007 to 2015. We separately analyzed the
bank stability and profitability models. To date, very few empirical workshave assessed the
internal determinants of bank stability and profitability of banking sector of Pakistan. Our
findings clearly show that bank size has a negative effect on bank stability, while funding
risk has a positive impact on bank stability. We used the financial crisis as a dummy
variable and found a negative but insignificant impact on profitability and stability models.
On the other side, the relationship between bank size and profitability is positive. However,
funding risk has a negative insignificant effect on bank profitability. We also found
evidence that other bank-specific variables have some relevance to bank stability and
profitability in Pakistan.
Overall, our findings bring some new but useful insights to the literature that assess
stability and profitability of banks in Pakistan. We consider the relevance of our research
findings for several reasons. First, our results validate the findings from previous studies on
bank stability and profitability. Second, we estimate separate models for bank stability and
profitability with the inclusion of some new factors, which extends our understanding
related to bank-specific determinants. Third, our sample period starts from 2007 to 2015
which covers some important changes in the recent period of Pakistan banking industry.
According to our findings, we suggest that credit risk and size of the bank needs extra
attention to gain more profits. Similarly, bank managers may analyze the credit worthiness
of borrowers prior to lending them. This will require collateral security from the borrower
that provides a protecting shield for banks to avoid default risk. Additionally, the banks will
be able to protect credit amount along with additional profit on collateral security. On the
other side, results from bank stability analysis recommend that the banking operations
should be used efficiently to avoid negative shocks of bank size on stability under prudential
banking procedures. Furthermore, bank stability may also improve through funding risk.
This can be done by giving the extra efforts in channelizing bank deposits. Also, our
findings have useful insights for comparison with the banking system of other countries like
India, China, Malaysia, Iran and Turkey. This signifies that the Pakistan banking sector
have improved its internal factors in the past few years. However, the regulators are
required to pay more attention on risk and return indictors because their policies are still
behind as compared to other emerging countries like India, China and Malaysia. We also
suggest bank regulators to provide some innovative banking facilities in remote areas such
as, mobile banking and new branch outlets. This will help them to capture potential
customer deposits, which will result in a stable banking system.
As a regulatory authority of the banking sector, State Bank of Pakistan should take
initiatives to provide level playing field for local and foreign banks. The flexible policies for
new entrants will help banking system to be more inclusive. The financial markets like
China, India and other developing countries are focusing on more supportive banking
services, particularly in new banking technology, asset-based securities and hedge funds.
Therefore, the bank managers should also focus on new banking technology and other
financial solutions. Moreover, the policymakers should also target the trade policy of
Pakistan with its major partners. For this purpose, a stable banking system and investor-
friendly policies will be helpful to attract foreign players like Turkey, China, Malaysia, Iran
and Arab world countries.
Even though our study sample covers active commercial banks in Pakistan and used key
determinants of bank profitability and stability, it has some limitations. Forthcoming
research studies may include the effect of merger and acquisition to identify more
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determinants of profitability and stability. Additionally, our study is restricted to bank-
specific factors and omitted macroeconomic or external factors of bank profitability and
stability. Therefore, we suggest future researchers to analyze how macroeconomic or
external environment determines bank profitability and stability of commercial banks.
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Corresponding author
Muhammad Ali can be contacted at: alisaleem_01@yahoo.com
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