Risk and profitability of Islamic banks: A religious deception or an alternative solution?

Abstract

The aim of this paper is to examine whether Islamic finance could be an alternative to the traditional financial system and could guarantee stability in times of crisis. To this end, 78 Islamic banks in 12 countries have been studied over the 2004–2013 period. A series of bank-specific and other country-specific indicators are combined to explain the soundness of Islamic banking in terms of profitability as measured by ROA and ROE, and risk divided into credit risk measured by IMLGL and EQL, and insolvency risk measured by Z-SCORE. The aim is to estimate five regressions using dynamic panel data econometrics (GMM system). The results indicate that bank size and capital are the main factors responsible for increasing profitability and stability of Islamic banks and reducing their credit risk. However, the ratios forming the variable liquidity and asset quality often lead to inconclusive results. It is also found that macroeconomic variables, except inflation, are able to improve Islamic banks’ stability. This is not the case for credit risk where the ratio is still unfavorable.
The conclusion is that there are no major differences between IBs and CBs in terms of their profitability and risk features.

Full text

Please
cite
this
article
in
press
as:
Trad,
N.,
et
al.
Risk
and
profitability
of
Islamic
banks:
A
religious
deception
or
an
alternative
solution?
European
Research
on
Management
and
Business
Economics
(2016),
http://dx.doi.org/10.1016/j.iedeen.2016.09.001
ARTICLE IN PRESS
G Model
IEDEEN-9;
No.
of
Pages
6
European
Research
on
Management
and
Business
Economics
xxx
(2016)
xxx–xxx
www.elsevier.es/ermbe
Risk
and
profitability
of
Islamic
banks:
A
religious
deception
or
an
alternative
solution?
Naama
Trada,
Mohamed
Ali
Trabelsib,∗,
Jean
Franc¸
ois
Gouxc
aGroupe
d’Analyse
et
de
Théorie
Economique
Lyon
St.Etienne
(Gate-LSE).
93
Chemin
des
Mouilles,
69130
Écully,
France
/
University
of
Tunis
El
Manar,
Tunisia
bFaculty
of
Economic
Sciences
and
Management
of
Tunis,
University
of
Tunis
El
Manar,
Campus
Universitaire
Farhat
Hached
-
B.P.
248,
El
Manar
II,
2092,
Tunisia
cUniversité
Lumière
Lyon
II,
18
Claude
Bernard,
69365,
Lyon
Cedex
07,
France
a
r
t
i
c
l
e
i
n
f
o
Article
history:
Received
12
April
2016
Accepted
23
September
2016
Available
online
xxx
JEL
classification:
G01
G21
G24
G31
Keywords:
Islamic
banks
Credit
strength
Risk
and
profitability
GMM
system
a
b
s
t
r
a
c
t
The
aim
of
this
paper
is
to
examine
whether
Islamic
finance
could
be
an
alternative
to
the
traditional
financial
system
and
could
guarantee
stability
in
times
of
crisis.
To
this
end,
78
Islamic
banks
in
12
countries
have
been
studied
over
the
2004–2013
period.
A
series
of
bank-specific
and
other
country-
specific
indicators
are
combined
to
explain
the
soundness
of
Islamic
banking
in
terms
of
profitability
as
measured
by
ROA
and
ROE,
and
risk
divided
into
credit
risk
measured
by
IMLGL
and
EQL,
and
insolvency
risk
measured
by
Z-SCORE.
The
aim
is
to
estimate
five
regressions
using
dynamic
panel
data
economet-
rics
(GMM
system).
The
results
indicate
that
bank
size
and
capital
are
the
main
factors
responsible
for
increasing
profitability
and
stability
of
Islamic
banks
and
reducing
their
credit
risk.
However,
the
ratios
forming
the
variable
liquidity
and
asset
quality
often
lead
to
inconclusive
results.
It
is
also
found
that
macroeconomic
variables,
except
inflation,
are
able
to
improve
Islamic
banks’
stability.
This
is
not
the
case
for
credit
risk
where
the
ratio
is
still
unfavorable.
The
conclusion
is
that
there
are
no
major
differences
between
IBs
and
CBs
in
terms
of
their
profitability
and
risk
features.
©
2016
AEDEM.
Published
by
Elsevier
Espa˜
na,
S.L.U.
This
is
an
open
access
article
under
the
CC
BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
1.
Introduction
The
subprime
lending
crisis
that
shook
the
world
in
2007
showed
the
limits
of
the
traditional
financial
system
(Fakhfekh,
Hachicha,
Jawadi,
Selmi,
&
Idi
Cheffou,
2016;
Trabelsi,
2011).
All
financial
institutions
have
been
destabilized
and
the
economy
was
crippled
while
the
Islamic
financial
system
kept
its
stability
and
sustainability
(Ftiti,
Nafti,
&
Srairi,
2013;
Mat
Rahim
&
Zakaria,
2013).
The
emergence
of
this
crisis
and
the
economic
recession
that
followed
have
raised
several
questions
about
the
role
of
banks
in
such
an
incident
and
led
various
stakeholders
to
seek
solutions
to
financial
failures
(Bourkhis
&
Nabi,
2013;
Rosman,
Abd
Wahab,
&
Zainol,
2014).
Therefore,
special
attention
has
been
given
to
Islamic
finance
as
a
remedy
for
a
system
that
continues
to
present
difficul-
ties
by
questioning
its
strength
and
ability
to
absorb
the
turmoil
dominating
the
financial
landscape
(Hasan
&
Dridi,
2010;
Said,
2012;
Zarrouk,
2012).
Survival
and
sustainability
of
these
banks
attracted
the
attention
of
everyone.
Several
studies
claim
that
the
current
financial
crisis
could
have
been
avoided
if
Islamic
finance
∗Corresponding
author.
E-mail
address:
daly1704@yahoo.fr
(M.A.
Trabelsi).
was
introduced
instead
of
conventional
finance
because
it
pro-
vided
alternatives
and
promised
a
better
future
for
humanity
(Beck,
Demirgüc¸
-Kunt,
&
Merrouche,
2013;
Choong,
Thim,
&
Kyzy,
2012).
According
to
them,
to
ensure
the
effective
functioning
of
the
global
financial
system,
the
shortcomings
of
conventional
finance
need
to
be
addressed.
Hence,
valuing
Islamic
finance
appears
to
be
a
cure
to
various
problems.
Experts
and
ethical
finance
supporters
have
always
claimed
that
an
Islamic
bank
(IB)
free
of
interest
is
not
only
fair,
but
is
also
more
stable
with
a
higher
capacity
for
shock
absorption
than
a
conven-
tional
bank
(CB)
(Ftiti
et
al.,
2013;
Mat
Rahim
&
Zakaria,
2013;
Zehri
&
Al-Herch,
2013).
However,
some
studies
have
questioned
the
effectiveness
of
Islamic
finance
by
suggesting
that
shock
absorption
capacity
and
prevention
of
crises
is
limited
(Ariff,
Bader,
Shamsher,
&
Hassan,
2008;
Said,
2012).
With
the
trust
crisis
that
currently
prevails
the
world
of
finance,
better
risk
management
has
become
a
need.
Since
IBs
are
now
part
of
the
global
banking
landscape,
they
are
concerned
by
this
need.
In
light
of
these
events,
banking
crisis
and
Islamic
finance
are
more
than
ever
at
the
heart
of
the
debate.
The
former
is
an
adverse
event
because
of
poorly
mastered
risk-taking
and
deterioration
of
solvency
while
the
latter
presents
itself
as
a
possible
alternative
for
funding
national
and
international
projects.
Lack
of
consensus
on
the
strength
of
these
banks
calls
for
http://dx.doi.org/10.1016/j.iedeen.2016.09.001
2444-8834/©
2016
AEDEM.
Published
by
Elsevier
Espa˜
na,
S.L.U.
This
is
an
open
access
article
under
the
CC
BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-
nd/4.0/).

Please
cite
this
article
in
press
as:
Trad,
N.,
et
al.
Risk
and
profitability
of
Islamic
banks:
A
religious
deception
or
an
alternative
solution?
European
Research
on
Management
and
Business
Economics
(2016),
http://dx.doi.org/10.1016/j.iedeen.2016.09.001
ARTICLE IN PRESS
G Model
IEDEEN-9;
No.
of
Pages
6
2
N.
Trad
et
al.
/
European
Research
on
Management
and
Business
Economics
xxx
(2016)
xxx–xxx
more
specific
attention.
This
is
one
of
the
issues
behind
the
moti-
vation
of
this
study
to
examine
specifically
the
strength
of
IBs
in
times
of
crisis
and
also,
to
determine
whether
Islamic
finance
could
be
a
true
growth
vector
that
deserves
to
be
an
alternative
or
just
a
financial
system
at
its
preliminary
stages.
The
methodology
consists
of
combining
a
series
of
micro
and
macro
variables
and
testing
their
effects
on
the
profitability
and
risk
of
78
IBs
in
12
countries
of
the
MENA
region
and
Pakistan,
known
by
a
strong
presence
of
IBs
over
the
2004–2013
period.
The
selected
period
takes
into
account
the
effects
record
before
and
after
the
2007
subprime
crisis.
Indeed,
since
the
aftermath
of
the
credit
crunch
and
the
global
financial
crisis
(2007–2009),
CBs
have
been
severely
criticized,
while
IBs
became
increasingly
considered
as
an
alternative
form
of
banking.
The
parameters
are
estimated
by
the
GMM
system
method.
The
second
section
consists
of
a
review
of
the
literature
deal-
ing
with
the
strength
of
IBs
during
the
global
financial
crisis.
The
description
of
data
and
methodology
are
discussed
in
the
third
section.
Results
are
analyzed
in
the
fourth
section,
followed
by
conclusion
and
implications.
2.
Banking
crises:
a
literature
review
Several
researchers
have
studied
the
profitability
of
IBs
(Choong
et
al.,
2012;
El
Khamlichi,
Sarkar,
Arouri,
&
Teulon,
2014;
Fun
Ho,
AbdRahman,
Muhamad
Yusuf,
&
Zamzamin,
2014;
Hasan
&
Dridi,
2010;
Jawadi,
Jawadi,
&
Louhichi,
2014;
Mat
Rahim
&
Zakaria,
2013;
Onakoya
et
al.,
2013)
and
their
level
of
risk
(Bourkhis
&
Nabi,
2013;
Rajhi
&
Hassairi,
2013)
and
this
is
by
combining
micro-
and
macro-
economic
indicators
and
making
a
comparative
analysis
with
the
conventional
financial
system.
Using
ordinary
least
squares
(OLS),
Wasiuzzaman
and
Tarmizi
(2010)
examined
the
impact
of
internal
and
external
factors
on
the
profitability
of
16
Malaysian
IBs.
The
study
concluded
that,
unlike
the
sign
of
the
liquidity
variable,
assets
quality
and
capi-
tal
negatively
affect
bank
profitability,
which
is
inconsistent
with
the
results
of
Kosmidau,
Tanna,
and
Pasioures
(2005).
Choong
et
al.
(2012)
found
a
positive
effect
of
credit
risk,
concentration
and
liquidity
on
the
performance
of
13
Malaysian
Islamic
com-
mercial
banks.
Similarly,
using
multivariate
regression
models,
Akhtar,
Ali,
and
Sadaqat
(2011)
found
that
capital
ratios
have
a
significant
positive
impact
on
the
performance
of
IBs
in
Pak-
istan
during
the
2006–2009
period,
unlike
the
variable
bank
size
which
acts
negatively
on
the
performance
of
these
institutions.
However,
despite
inflation
and
the
official
exchange
rates
that
have
led
to
financial
instability,
Rajhi
and
Hassairi
(2013)
found
that
bank
size,
its
liquidity
and
GDP
growth
have
contributed
to
banking
stability.
However,
Asharaf,
Rizwan,
and
L’Huillier
(2016)
found
that
GDP
growth
has
no
significant
effect
on
the
financial
stability
of
136
IB
over
the
2000–2013
period.
Likewise,
using
a
GLS
regression
and
the
CAMELS
model,
Rashid
and
Jabeen
(2016)
studied
the
performance
of
a
group
of
IBs
and
CBs
during
the
2006–2012
period.
The
results
indicate
that
the
impact
of
GDP
and
credit
interest
rate
on
performance
is
negative
for
the
groups
of
banks.
However,
bank
size
positively
yet
insignificantly
affects
their
performance.
After
an
inter-period
comparison
(before
and
after
the
crisis)
of
20
IBs
of
the
GCC
countries,
Zarrouk
(2012)
showed
that
bank-
specific
factors
have
a
negative
impact
on
banking
performance
in
2008.
However,
when
real
economic
activity
was
affected
by
the
crisis
in
2009,
a
sharp
decline
in
profitability
and
liquidity
was
recorded
for
IBs
in
Bahreïn,
UAE
and
Kuwait.
However,
excessive
risk-taking
was
observed
for
IBs
in
UAE
during
and
after
the
crisis
compared
to
other
countries.
To
reach
more
robust
results
on
the
financial
stability
of
Islamic
banking,
some
researchers
have
conducted
comparative
studies
with
conventional
banking.
Indeed,
Beck
et
al.
(2013)
compared
88
IBs
to
422
conventional
banks
(CBs)
in
22
countries
where
both
groups
of
banks
coexist
over
the
period
1995–2009.
The
results
of
this
study
show
that
IBs
are
better
capitalized
and
have
bet-
ter
asset
quality
and
an
ability
to
take
risks.
Moreover,
Mat
Rahim
and
Zakaria
(2013)
compared
the
stability
of
a
group
of
Malaysian
IBs
and
CBs
during
the
period
2005–2010
using
the
Z-score
and
NPL
as
proxies
for
financial
stability.
These
authors
found
that
IBs
are
more
resistant
in
times
of
crisis
compared
to
CBs.
These
find-
ings
are
in
line
with
the
work
of
Onakoya
et
al.
(2013)
and
Zehri
and
Al-Herch
(2013)
who
found
that
IBs
are
more
profitable
and
stable
during
the
2007–2008
crisis
because
of
Shariah
require-
ments.
However,
these
conclusions
are
not
always
checked
like
in
a
comparative
analysis
of
the
performance
of
3
IBs
and
6
CBs
in
Egypt
over
the
period
2008–2010.
Indeed,
Fayed
(2013)
showed
the
superiority
of
CBs
in
terms
of
liquidity,
credit
risk
management,
solvency
and
profitability.
Similarly,
Miah
and
Sharmeen
(2015)
showed
that
CBs
are
more
efficient
in
managing
cost
than
IBs.
In
terms
of
financial
risk,
Jawadi,
Chaffou,
and
Jawadi
(2016)
showed
that
there
are
only
a
few
significant
differences
between
IBs
and
CBs.
Bearing
the
above
assumptions
in
mind,
the
following
three
hypotheses
can
be
formulated
and
tested,
using
econometric
regressions.
H1.
There
is
significant
relationship
between
profitability
of
IBs
and
micro
and
macro-economic
indicators.
H2.
There
is
significant
relationship
between
insolvency
risk
of
IBs
and
micro
and
macro-economic
indicators.
H3.
There
is
significant
relationship
between
credit
risk
of
IBs
and
micro
and
macro-economic
indicators.
3.
Data
and
methodology
Unlike
previous
studies,
this
is
a
study
on
the
strength
of
IBs
in
terms
of
both
risk
and
profitability.
The
sample
consists
of
78
IBs
in
12
countries
of
the
MENA
region
with
the
addition
of
Pakistan
noted
by
MENAP
(Table
1)
over
the
2004–2013
period.
The
sample
is
large
enough
to
provide
reliable
conclusions.
Data
are
taken
from
the
Bankscope
base.
3.1.
Definition
and
selection
of
variables
To
evaluate
the
financial
and
banking
system,
taking
profitabil-
ity
and
risk
indicators
as
dependent
variables
seems
useful.
A
bank
is
said
to
be
stronger
than
another
if
it
is
stable
with
a
higher
capac-
ity
to
absorb
risks,
on
the
one
hand,
and
increased
performance
on
the
other
hand,
during
a
crisis.
Table
1
Country
included
in
the
sample.
List
of
country
Number
of
IB
1
Yemen
3
2
Iraq
5
3
Bahreïn
19
4
UAE
10
5
Kuwait
7
6
Saudi
Arabia
3
7
Qatar
4
8
Pakistan
4
9
Jordan
3
10
Iran
12
11
Sudan
4
12
Turkey
4
Total
78

Please
cite
this
article
in
press
as:
Trad,
N.,
et
al.
Risk
and
profitability
of
Islamic
banks:
A
religious
deception
or
an
alternative
solution?
European
Research
on
Management
and
Business
Economics
(2016),
http://dx.doi.org/10.1016/j.iedeen.2016.09.001
ARTICLE IN PRESS
G Model
IEDEEN-9;
No.
of
Pages
6
N.
Trad
et
al.
/
European
Research
on
Management
and
Business
Economics
xxx
(2016)
xxx–xxx
3
Table
2
Financial
strength
indicators.
Risk-based
indicators
Retained
measures
Insolvency
risk
Z-SCORE
(Returns
on
assets
+
capital
Ratio)/returns
on
assets
standard
deviation
Credit
risk
EQL
Total
equity/Net
loans
IMLGL
Impaired
loans/Gross
loans
Returns-based
indicators Retained
measures
ROA
Net
returns/Total
assets
ROE
Equity/Total
assets
3.1.1.
Profitability
In
this
study,
to
determine
profitability
of
banks,
two
financial
ratios
that
have
already
been
adopted
in
previous
studies
(Fayed,
2013;
Jawadi
et
al.,
2014)
are
used
as
reliable
measures
of
banking
performance,
namely
return
on
assets
(ROA)
and
return
on
equity
(ROE).
3.1.2.
Risk
Other
than
specific
risks,
IBs
are
subject
to
the
same
risk
category
as
CBs
such
as
credit
risk
and
insolvency
risk.
Insolvency
risk,
which
is
the
inability
of
the
bank
to
repay
its
debts
and
financial
obliga-
tions
because
of
bankruptcy
is
measured
by
Z-SCORE.
To
measure
credit
risk,
the
EQL
or
IMLGL
ratio
is
used.
These
three
steps
are
defined
in
Table
2.
These
financial
ratios
are
considered
the
main
strength
pillars
of
banks
to
identify
signs
of
increased
financial
vulnerability
and
to
assess
their
resilience
to
financial
shocks.
3.2.
The
Control
variables
In
this
study,
bank-specific
internal
indicators
are
combined,
including
bank
size,
capitalization,
liquidity
and
asset
quality
and
as
well
as
country-specific
external
indicators,
namely,
real
gross
domestic
product,
inflation
rate
and
official
exchange
rates
as
inde-
pendent
variables.
The
choice
of
these
ratios
aims
at
determining
an
instrument
to
provide
information
on
the
strength
of
IBs.
Table
3
shows
all
of
these
indicators.
3.3.
The
models
for
estimation
Panel
data
are
used
to
measure
the
strength
of
IBs.
Two
evalua-
tion
levels
are
possible:
the
first
gives
direct
insight
into
the
bank’s
ability
to
generate
profits
and
the
second
determines
the
ability
of
a
bank
to
manage
and
mitigate
incurred
risks.
The
robustness
of
Table
4
The
different
models
explaining
strength
in
terms
of
profitability-risk.
Profitability
equation
Panel.
ARENTABILITEj,i,t =
˛
+
ˇ1ˇjit +
ˇ2Mjit +
εjit
Panel.
a.1
ROAj,i,t =
˛
+
ˇ1ˇjit +
ˇ2Mjit +
εjit
Panel.
a.2
ROEj,i,t =
˛
+
ˇ1ˇjit +
ˇ2Mjit +
εjit
Risk
equation
Panel.
B
RISQUEj,i,t =
˛
+
ˇ1ˇjit +
ˇ2Mjit +
εjit
Insolvency
risk
Panel.
b.1 ZSCOREj,i,t =
˛
+
ˇ1ˇjit +
ˇ2Mjit +
εjit
Credit
risk
Panel.
b.2
EQLj,i,t =
˛
+
ˇ1ˇjit +
ˇ2Mjit +
εjit
Panel.
b.3
IMLGLj,i,t =
˛
+
ˇ1ˇjit +
ˇ2Mjit +
εjit
where
“i”,
“j”
and
“t”
indicate
successively
banks
(i
=
1,
2,
3,
.
.
.,
78),
countries
(j
=
1,
2,
3,
.
.
.,
12),
and
period
(t
=
2004,
2005,
.
.
.,
2013).
ˇ,
denotes
the
to-be-estimated
model’s
parameters; ˇjit,
a
vector
of
microeconomic
variables; Mjit,
a
vector
of
macroeconomic
variables;
εjit,
random
or
error
term.
results
is
ensured
by
using
a
set
of
financial
indicators
to
measure
profitability
(ROA
and
ROE)
of
IBs
and
their
risk
(IMLGL,
EQL
and
Z-score).
Applying
each
ratio
on
profitability
and
risk,
five
multiple
linear
models
are
estimated.
These
regressions
are
summarized
in
Table
4.
3.4.
Estimation
method
Unlike
a
dynamic
panel
GMM,
traditional
econometric
methods
(OLS,
fixed
effect
and
generalized
effect)
do
not
avoid
the
endo-
geneity
problem
arising
from
a
causal
relationship
between
the
independent
and
dependent
variables
due
to
lagged
dependent
variables.
To
solve
this
problem,
the
generalized
moment
method
(GMM)
is
used
as
a
generic
tool
to
estimate
a
statistical
model’s
parameters.
GMM
was
proposed
by
Arellano
and
Bond
(1991)
and
developed
by
Arellano
and
Bover
(1995)
and
Blundell
and
Bond
(1998)
to
solve
the
endogeneity
problem
in
the
independent
vari-
ables
using
a
series
of
instrumental
variables
generated
by
lagged
variables
(simultaneity
bias
problem
of
reverse
causality
and
pos-
sible
omitted
variables).
4.
The
results
and
interpretations
4.1.
Descriptive
statistics
A
descriptive
analysis
of
the
data
is
presented
in
Table
5.
The
results
indicate
that
during
the
study
period,
the
mean
values
of
IBs’
profitability
ratios
are
important.
These
institutions
also
have
low
credit
and
insolvency
risks.
On
the
micro
level,
IBs
possess
important
levels
of
liquidity,
capital
and
quality
of
major
assets.
Table
3
Micro
and
Macro-economic
Indicators.a
Bank-specific
variablesb(micro-economic)
(Bourkhis
&
Nabi,
2013;
Rosman
et
al.,
2014)
Country-specific
variables
(macro-economic)
(Ftiti
et
al.,
2013)
Bank
size-based
indicators
Capitalization-based
indicators
Assets-based
indicators
Liquidity-based
indicators
GDP
growth
(GGDP)
Napierian
logarithm
of
total
assets
for
each
bank
(SIZEBQ)
Capital/T
assets
(CTA)
Loan
loss
reserves/Gross
loans
(LLRGL)
Liquid
assets/Total
assets
(LQATA)
Inflation
rate
(in
%)
(INF)
Loan
loss
Provisions/Net
loans
(LLPNL)
Net
loans/Total
assets
(NetLTA)Liquid
assets/Deposits
and
short-term
financing
rate
(LQADstF)
Official
exchange
rate
(OEXCHRATE)
Loan
loss
reserves/Impaired
loans
(LLRIML)
Loan
loss
provision/Net
interest
income
(LLPNII)
aSource:
Bank-specific
data
are
taken
from
Bankscope
and
macroeconomic
data
are
taken
from
the
World
Bank’s
website.
bAll
bank-specific
data
are
converted
into
US
million
dollars.

Please
cite
this
article
in
press
as:
Trad,
N.,
et
al.
Risk
and
profitability
of
Islamic
banks:
A
religious
deception
or
an
alternative
solution?
European
Research
on
Management
and
Business
Economics
(2016),
http://dx.doi.org/10.1016/j.iedeen.2016.09.001
ARTICLE IN PRESS
G Model
IEDEEN-9;
No.
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Pages
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4
N.
Trad
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/
European
Research
on
Management
and
Business
Economics
xxx
(2016)
xxx–xxx
Table
5
Descriptive
statistics.
Variables
Mean
Std.
deviation
Minimum
Maximum
Observations
Bank
profitability
ROA
.015896
.0710761
−.6972
.3825
780
ROE
.3260669
.5650117
−.0946
10.2783
780
Bank
risks
Insolvency
risk
Z-score
1.842895
5.601636
−63.4594
91.1906
780
Credit
risk
EQL
167.6262
3086.199
−.0912
72,707.75
780
Bank-specific
indicators
Bank
size SIZEBQ
7.589844
2.290722
−.6086
17.8211
780
Capitalization
CTA
.2696777
.3805924
0
4.2667
780
Liquidity LQATA
.2485979
.2673401
.0002
4.7161
780
LQADstF
.7252938
1.177022
.0016
9.9772
780
Asset
quality
LLRGL
.1194953
.7904995
−.0031
19.555
780
NetLTA
.4651729
.4107012
0
6.4848
780
LLPNII
.293647
3.076064
−67.3777
13.7692
780
LLPNL
.2313598
1.566874
−3.3725
31.0272
780
Countries-specific
indicators
GDP
growth GGDP
.0515551
.0607457
−.1509
.5416
780
Official
exchange
rate
OEXCHRTE
.5479767
.9286388
.0001
3.7202
780
Inflation
INF
.0887636
.0979125
−.0487
.6483
780
As
macro-economic
variables,
GGDP,
OEXCHRATE
and
INF
respec-
tively
have
average
values
of
0.0515551,
0.5479767
and
0.0887636.
The
OEXCHRATE
has
a
higher
standard
deviation
than
INF
and
GGDP.
The
estimation
of
the
multiple
regression
models
requires
the
absence
of
multicollinearity
between
the
variables.
A
mul-
ticollinearity
problem
arises
when
two
independent
variables
are
highly
correlated.
Kervin
(1992)
states
that
a
serious
mul-
ticollinearity
problem
arises
when
exceeding
the
limit
of
0.7.
Referring
to
Kervin
(1992),
the
results
show
that
all
correlation
coefficients
are
below
0.7.
The
absence
of
multicollinearity
in
all
the
models
defined
above
is
concluded.
4.2.
Models
estimation
and
interpretation
of
results
The
results
of
the
five
models
are
shown
in
Table
6.
The
null
hypothesis
H0on
the
validity
of
the
instruments
is
not
rejected
(the
probabilities
of
Hansan
statistic
are
greater
than
5%,
indicating
that
the
instruments
are
exogenous
together).
In
addition,
there
is
no
order
2
serial
autocorrelation
(the
probabilities
of
Arellano
&
Bond
test
AR
(2)
are
greater
than
5%).
This
indicates
that
the
GMM
system
model
is
consistent
and
has
a
good
specification
of
instruments
without
heteroscedasticity
or
autocorrelation
problems.
A
general
reading
of
the
results
of
Table
6
indicates
that
all
variables
are
statistically
significant,
except
for
the
LLRGL
variable
(Models
3
and
4),
SIZEBQ
(Model
5)
and
OEXCHRATE
(Model
4).
In
particular,
the
variable
size
(SIZEBQ)
affects
positively
and
very
significantly
the
profitability
of
IBs.
Increasing
bank
size
(higher
total
assets)
leads
to
higher
profitability.
Hasan
and
Dridi
(2010),
Zeitoun
(2012),
Muda,
Shaharuddin,
and
Embaya
(2013)
and
Rashid
and
Jabeen
(2016)
found
similar
results.
However,
credit
risk
and
its
effects
are
negative
and
highly
signifi-
cant
compared
to
the
results
obtained
by
Cihák
and
Hesse
(2008).
This
can
be
explained
by
the
fact
that
the
strong
presence
of
IBs
in
different
activities
facilitates
the
adjustment
of
their
credit
risk
monitoring
and
results
in
better
diversification
and
risk
absorption.
The
latter
is
illustrated
by
the
positive
yet
not
significant
relation-
ship
with
insolvency
risk.
This
reflects
a
low
insolvency
probability
and
therefore
high
stability
for
IBs.
Here
the
results
seem
to
be
consistent
with
the
results
of
Fayed
(2013)
and
Rajhi
and
Hassairi
(2013)
who
found
similar
correlation.
As
mentioned
by
Sufian
and
Mohamad
Noor
(2009),
Akhtar
et
al.
(2011),
Choong
et
al.
(2012),
Onakoya
and
Onakoya
(2013),
Beck
et
al.
(2013)
and
Ramlan
and
Adnan
(2016),
bank
capitalization
has
a
positive
and
a
very
significant
effect
on
profitability.
In
terms
of
risk,
capitalization
negatively
and
very
significantly
correlates
with
credit
risk.
This
implies
that
IBs
capitalization
decisions
are
primar-
ily
based
on
risk
reduction.
This
relationship
is
not
surprising
as
it
refers
to
the
principle
of
prohibition
of
interest
in
Islam.
IBs
are
not
allowed
to
borrow
money
from
other
banks
nor
from
a
last
resort
bank.
The
Z-score
is
positively
yet
not
significantly
affected
by
cap-
ital.
Thus,
a
sufficient
level
of
capital
makes
for
a
better
protection
against
banking
crises.
In
light
of
these
results,
it
seems
that
capital
adequacy
is
a
safety
valve
and
a
guarantee
of
bank
profitability
and
stability.
Therefore,
the
bank
should
maintain
a
minimum
capital
to
ensure
sufficient
funds
against
unexpected
losses
and
negative
shocks.
Except
for
the
correlation
between
LQADstF
and
ROA,
all
the
variables
explaining
the
profitability-liquidity
ratio
are
positively
and
significantly
related.
Thus,
a
better
liquidity
position
maxi-
mizes
the
gains
of
IBs.
This
is
similar
to
the
findings
of
Wasiuzzaman
and
Tarmizi
(2010),
Zeitoun
(2012)
and
Beck
et
al.
(2013).
At
the
level
of
credit
risk,
the
latter
is
very
significantly
and
negatively
affected
by
the
two
liquidity
measures,
except
for
the
relationship
LQATA
and
IMLGL.
The
result
in
this
study
indicates
that
the
more
fluid
the
bank,
the
lower
its
credit
risk
and
therefore
the
more
it
resists
a
liquidity
crisis
period.
However,
when
it
comes
to
insol-
vency
risk,
the
relationship
is
not
clear
since
the
LQADstF
ratio
affects
negatively
and
very
significantly
the
Z-score.
However,
the
relationship
is
positive
and
highly
significant
when
liquidity
is
mea-
sured
by
LQATA.
This
positive
finding
has
already
been
validated
by
numerous
studies
namely
that
of
Rajhi
and
Hassairi
(2013).
Asset
quality
of
the
bank
is
also
another
internal
indicator
that
determines
profitability
and
risk
of
IBs.
Profitability-wise,
assets
quality
is
in
good
standing
since
the
LLRGL,
LLRIML
and
LLPNL
variables
measuring
this
quality
act
positively
and
very
signifi-
cantly
on
ROA
and
ROE.
Similar
results
were
obtained
by
Kosmidou
et
al.
(2005),
Beck
et
al.
(2013)
and
Ftiti
et
al.
(2013).
However,
this
conclusion
is
not
always
correct
because
the
NetLTA
and
LLP-
NII
variables
act
negatively
and
very
significantly
on
profitability
except
for
LLPNII
and
ROE.
As
for
credit
risk,
it
positively
correlates
with
the
LLPNII,
LLRGL
and
NetLTA
ratios.
This
replicates
the
conclu-
sion
of
Fayed
(2013)
indicating
that
assets
quality
of
IBs
is
worse.
However,
we
found
a
negative
relationship
when
asset
quality
is
measured
by
the
LLRIML
and
LLPNL
ratios.
As
for
insolvency
risk,
the
determinants
of
asset
quality
significantly
and
positively
influ-
ence
insolvency
risk
except
for
the
LLPNL
ratio.
This
means
that
IBs
hold
a
better
asset
quality
that
contributes
to
their
stability.

Please
cite
this
article
in
press
as:
Trad,
N.,
et
al.
Risk
and
profitability
of
Islamic
banks:
A
religious
deception
or
an
alternative
solution?
European
Research
on
Management
and
Business
Economics
(2016),
http://dx.doi.org/10.1016/j.iedeen.2016.09.001
ARTICLE IN PRESS
G Model
IEDEEN-9;
No.
of
Pages
6
N.
Trad
et
al.
/
European
Research
on
Management
and
Business
Economics
xxx
(2016)
xxx–xxx
5
Table
6
The
GMM
method.
Independent
variables
Dependent
variables
Profitability
Risk
ROA
ROE
IMLGL
EQL
Z-score
Lag
of
dependent
variable
.2961881***
(0.000)
-.0557787***
(0.000)
−.1157699***
(0.000)
−.0023678
(0.241)
−.0643735***
(0.000)
SIZEBQ
.0014246***
(0.000)
−.0457217***
(0.000)
−11.66354***
(0.000)
−188.7318***
(0.000)
.0116077
(0.597)
CTA
.0527337***
(0.000)
.923109***
(0.000)
−56.42507***
(0.000)
−771.1078***
(0.000)
.3356698***
(0.004)
LQATA
.0667462***
(0.000)
.0199392**
(0.011)
40.38281***
(0.000)
−771.1078***
(0.000)
4.003787***
(0.000)
LQADstF
−.0084533***
(0.000)
.0827773***
(0.000)
−2.938507***
(0.000)
−35.99283***
(0.000)
−.8327935***
(0.000)
LLPNII
−.0005285***
(0.000)
.001532***
(0.006)
.5046256***
(0.001)
13.50544***
(0.000)
.0666788***
(0.000)
LLRGL
.0024043**
(0.033)
.0217778***
(0.000)
.034898
(0.804)
.3713076
(0.872)
.2879773***
(0.000)
NetLTA
−.0255029***
(0.000)
−.0668329***
(0.000)
31.30992***
(0.000)
.453.2474***
(0.000)
3.022795***
(0.000)
LLRIML
9.34e−06***
(0.000)
.0000435***
(0.000)
−.0034486***
(0.000)
−.0472671***
(0.000)
.0008352***
(0.000)
LLPTL
.0017584***
(0.000)
.0037648***
(0.000)
−3.63166***
(0.000)
−63.58212***
(0.000)
−.2008109***
(0.000)
GDPG
.2543156***
(0.000)
−.6695082***
(0.000)
73.97792***
(0.000)
1193.71***
(0.000)
4.041103***
(0.000)
OEXCHRATE
−.0173183***
(0.000)
.0175464***
(0.000)
1.650693**
(0.015)
15.44519
(0.278)
.2906751***
(0.000)
INF
.0620295***
(0.000)
−.1774357***
(0.000)
144.0045***
(0.000)
2375.19***
(0.000)
−2.013971***
(0.000)
Constant
−.0186164***
(0.000)
.4671898***
(0.000)
68.5012***
(0.000)
1132.795***
(0.000)
−.220385
(0.211)
Observations
780
780
780
780
780
Hansan
test
73.81
64.64
50.24
37.20
67.42
P-value
of
Hansan
test
1.000
1.000
1.000
1.000
1.000
Sargan
test
240.60
76.31
477.84
411.04
55.27
P-value
of
Sargan
test
0.000
1.000
0.000
0.000
1.000
Arrellano
&
Bond
test
AR
(1) −1.85
−1.28
0.77
−1.46
−1.78
P-value
d’AR
(1)
0.065
0.202
0.444
0.143
0.075
Arrellano
&
Bond
test
AR
(2)
−0.15
−1.41
−0.88
−0.85
0.17
P-value
of
AR
(2)
0.878
0.157
0.376
0.395
0.862
*
Significant
at
10%.
** Significant
at
5%.
*** Significant
at
1%.
The
results
obtained
on
the
relationship
between
profitability,
risk
(insolvency
and
credit
risks)
of
IBs
and
the
different
bank-
specific
variables
seem
to
validate
our
three
hypotheses.
On
the
macroeconomic
level,
the
official
exchange
rate,
infla-
tion
and
GDP
growth
tend
to
influence
positively
and
very
significantly
credit
and
insolvency
risks
with
the
exception
of
inflation-insolvency
risk.
Fayed
(2013),
Rajhi
and
Hassairi
(2013)
and
Mat
Rahim
and
Zakaria
(2013)
found
similar
results.
The
posi-
tive
relationship
between
OEXCHRATE
and
the
Z-score
is
different
from
that
found
by
Rajhi
and
Hassairi
(2013)
and
Bourkhis
and
Nabi
(2013).
Indeed,
the
INF
variable
should
have
a
negative
impact
on
credit
risk
as
uncertainty
makes
banks
more
conservative
and
cautious,
but
this
has
not
been
confirmed
by
the
positive
rela-
tionship
in
this
study.
On
the
other
hand,
unlike
the
signs
of
the
relationship
between
the
OEXCHRTE
and
profitability
ratios,
GGDP
affects
positively
and
very
significantly
ROA,
which
means
that
an
increase
in
GDP
of
a
country
improves
performance
of
banks
oper-
ating
in
that
country.
This
is
consistent
with
the
work
of
Srairi
(2009),
Wasiuzzaman
and
Tarmizi
(2010),
Choong
et
al.
(2012),
Zeitoun
(2012)
and
Muda
et
al.
(2013).
However,
there
is
a
neg-
ative
and
a
highly
significant
relationship
when
profitability
is
measured
by
ROE.
The
same
interpretation
applies
when
we
con-
sider
the
inflation
variable.
The
positive
and
significant
effect
on
ROA
at
the
1%
level
confirms
the
results
of
Delis
and
Papanikolaou
(2009)
and
Wasiuzzaman
and
Tarmizi
(2010)
who
found
a
positive
correlation.
Their
results
indicate
that
with
inflation,
bank
profitability
increases
more
than
its
costs.
However,
it
has
had
a
negative
and
a
significant
effect
on
ROE
at
the
1%
level.
Significance
of
the
relationship
between
the
different
external
determinants
and
the
dependent
variables
confirm
once
more
the
initial
three
hypotheses.
5.
Conclusion
and
implications
The
purpose
of
this
study
is
to
examine
whether
an
interest-free
financial
system
could
be
an
alternative
to
the
traditional
final
sys-
tem
or
a
financial
supplement
with
some
limitations.
To
address
this
issue,
a
series
of
micro
and
macroeconomic
indicators
are
combined
to
explain
the
strength
of
IBs
in
terms
of
profitability
measured
by
the
two
ROA
and
ROE
ratios,
and
risk
measured
by
credit
risk
(IMLGL
and
EQL)
and
insolvency
risk
(Z-score).
Consistent
with
previous
results,
the
different
internal
and
external
determinants
significantly
affect
the
two
measures
of
pro-
fitability
of
IBs
at
the
5%
and
10%
levels.
The
same
is
true
for
credit
and
insolvency
risks.
The
results
indicate
that
bank
size
and
capital
are
key
indica-
tors
of
increased
profitability
and
stability
of
IBs
and
reduce
their
credit
risk.
It
also
seems
that
measures
of
liquidity
often
positively
affect
profitability
and
bank
stability,
yet
negatively
affect
credit
risk
except
for
a
few
ratios.
As
for
measures
of
asset
quality,
the

Please
cite
this
article
in
press
as:
Trad,
N.,
et
al.
Risk
and
profitability
of
Islamic
banks:
A
religious
deception
or
an
alternative
solution?
European
Research
on
Management
and
Business
Economics
(2016),
http://dx.doi.org/10.1016/j.iedeen.2016.09.001
ARTICLE IN PRESS
G Model
IEDEEN-9;
No.
of
Pages
6
6
N.
Trad
et
al.
/
European
Research
on
Management
and
Business
Economics
xxx
(2016)
xxx–xxx
results
are
inconclusive.
Moreover,
it
is
noted
that
the
macroeco-
nomic
variables,
except
for
inflation,
are
external
indicators
that
favor
the
stability
of
IBs.
This
is
not
the
case
for
credit
risk
where
the
ratio
is
still
unfavorable.
However,
a
clear
relationship
between
profitability
and
the
three
external
variables
has
not
been
found.
The
results
obtained
in
this
study
lead
to
the
conclusion
that
the
Islamic
financial
system
cannot
be
a
substitute
to
the
tradi-
tional
system,
but
rather
a
financial
supplement
to
the
conventional
system.
The
present
study
identified
several
factors
that
may
eventually
help
bank
managers
to
improve
the
financial
outlook
of
their
firms
by
controlling
profitability
and
risk.
It
also
helps
them
understand
how
macroeconomic
indicators
affect
this
pair
in
the
banking
sec-
tor.
Managers
of
IBs
can
focus
their
attention
on
assets
quality
to
improve
profitability
of
these
banks
and
minimize
their
risk
level.
Finally,
the
survival
and
sustainability
of
IBs
can
be
issues
of
con-
cern.
Indeed,
Islamic
finance
takes
its
strength
from
investments
coming
from
sovereign
funds
obtained
on
oil
earnings
because
of
the
exuberant
increase
in
oil
prices
that
has
reached
150
dollars
for
the
barrel
and
has
led
oil-producing
Islamic
countries
to
place
funds
in
IBs.
Nevertheless,
given
the
fall
in
oil
prices
and
the
wars
raged
by
some
Gulf
countries
in
addition
to
the
Saudi-Iran
conflict,
there
is
a
loss
in
deposits
growth
on
the
one
hand,
and
a
slack-
ening
of
the
public
finances
of
the
oil-producing
countries
on
the
other.
This
manifested
itself
in
a
massive
withdrawal
of
liquidity
from
the
banking
system
and
in
particular
from
IBs.
This
concern
stems
essentially
from
the
fact
that
in
2015,
in
the
GCC
countries,
conventional
bond
emissions
increased
by
140%
to
reach
58
billion
dollars,
while
the
sukuk
decreased
by
22%
reaching
18
billion
dollars
(according
to
Standard
&
Poor’s).
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