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8/13/2019 Revised Version-final (3)
1/20
BEHAVIOR INVESTIGATION OF ISLAMIC BANK DEPOSIT RETURN
UTILIZING ARTIFICIAL NEURAL NETWORKS MODEL
AbstractPurps! " According to Islamic law, the Islamic banks deliver return to depositors based on
profit and loss sharing principle. Consequently, the return will be uncertain following real
business and economic condition. However, this research investigates the phenomenon that
Indonesian Islamic banking industry seems to mimic interest rate in generating return to
depositors.
M!t#$%&' " This investigation utilies artificial neural networks !A""# model to e$amine
the importance rate of each macroeconomic variables used. The rate indicates the level of
domination or contribution of each variable in determining the volatility of Islamic time
deposit return. The research uses ten years of monthly macroeconomic data as independent
variables. Additionally, the average rate of return from one%month time deposit of all
Indonesian Islamic banks !& is used as dependent variable.
F()$()&s " As a result, the "!'%(%)#, a chosen neural networks architecture, found that *I&T and
I"T& as pro$y of interest rate, dominantly affect && volatility with almost '+ of
importance rate. It shows the very high dominance of interest rate in determining &&
volatility, as indication of mimicking behavior, rather than remaining variables as pro$y of
real economic condition.
Or(&()a%(t' " According to our knowledge, this e$periment is the first study in Islamic
banking and finance in investigating the mimicking interest rate behaviour of Islamic bank
using A"" model.
K!'*r$s- Islamic bank, depositor return, artificial neural networks, macroeconomic
variables, Indonesia.
Pap!r t'p!- &esearch paper
1
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+, I)tr$uct()
Islamic banks commonly have two sources of funds. The first source is the public funds in
bank deposits that comprises of Wadiahdemand deposit, Mudharabahsaving deposit, and
Mudharabah time deposit. The second one is the public funds in non%bank deposits such asreceived financing, securities issued by banks, interbank liabilities, liabilities to the central
bank and other payables !Ismal, /00'#.
1articularly, with Wadiahdemand deposit, Islamic banks may obtain an e$plicit or
implicit authoriation to use the deposit money but they do not pay return or share the profit
to investors. In contrast, with Mudharabah saving deposit, bank may finance the Islamic
pro2ects and share the profit with depositors as deposit return. 3urthermore, Mudharabah
time deposit has two modes called restricted time deposit and unrestricted time deposit. In the
former, Islamic banks may only act as the fund manager, agent, or non%participating
Mudharib !4l%5in, /00(#. The *anks are not authoried to mi$ their own funds with this
account unless permitted by the account holders. Therefore, this account is not considered as
fund providers and is treated as an off balance sheet account. 6n the contrary, the latter
allows the banks to actively occupy the funds and share the risks with depositors without any
voting rights !7rais and 1ellegrini, /008#.
In practice, when depositor opens theMudharabahtime deposit account, both depositor
and the Islamic bank will make an agreement regarding the percentage of profit and loss that
will be shared over the period of deposit. As a result, depositor will receive uncertain amount
of money as return every month that depends on Islamic bank9s profitability. The monthly
return received by depositor is represented by rate of return !& which is calculated by
dividing return received with amount of money deposited. The Islamic banks publish the rate
of return monthly to assist depositors compare it with time deposit interest rate.
The theory says that the monthly rate is fluctuating because it depends on the individual
bank9s profit and predetermined loss and profit ratio offered to depositor !:oubi and 6lson,
/00+#. Consequently, in the macroeconomic turmoil, the bank9s profitability should be
fluctuating, and afterwards the rate of return on time deposit will be fluctuating as well.
However, 4l%7amal !)'';# noticed that the transactions in Islamic bank such as time depositand saving deposit, are themselves not purely Islamic. This is because the return on bank
deposits is seemly rewarded with a high correlation with the market interest rate or in other
word, it is called mimicking interest rate.
The paper discusses particularly about the operational behavior of Islamic bank in
generating return onMudharabahtime deposits due to following reasons-
). *ased on the nature of the product, time deposit account is the product, which only
uses mudharabahcontract. It means that this product is principally created for
investment purpose. 5epositor who opens this account is required to give notice to
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the bank when they want to withdraw the money. In contrast, the saving account
product is principally provided for day%to%day financial needs, which can be
withdrawn without giving any prior notice to the bank, for e$ample, when they
withdraw the money through AT< machine. This product is more fle$ible thantime deposit account and it can be developed either using mudharabahor wadiah
principle.
/. The composition ofMudharabahtime deposit product on total third party fund is
=;.' meanwhile the other product such as saving account and current account are
/'.; and )/.(, respectively. Additionally, the trend of Mudharabah time
deposit products tends to increase otherwise, the Mudharabah saving account
tends to decrease as depicted in the table ).
Tab%! +, T#! p!rc!)ta&! - t(.! $!ps(t accu)t a)$ sa/()& accu)t ) tta% $!ps(t
P!r($ T(.! $!ps(t accu)t Sa/()& accu)t
5ec%/008 =/./= >0./)
5ec%/00; ('.++ >>.;=
5ec%/00+ =/.>0 >>.;(
5ec%/00' =(.(' >).>>
5ec%/0)0 ==.=) /'.+(
?an%/0)) =8.)= /'.;=
@imply saying, there is indication that the intention of depositor to patronie with Islamic
banking is more likely to find a better return rather than as a mean of daily payment.
@pecifically, this research investigates empirically the evidence that the Indonesian
Islamic banking industry mimics interest rate in generating return. This phenomenon cannot
be confirmed directly to the management of Islamic bank due to its confidentiality that
related with the bank9s operational strategy. 3or doing so, the research e$amines the
importance rate of the past ten years of macroeconomic variables in affecting the volatility of
Mudharabahtime deposit return provided by A"" model. The importance rate indicates the
level of domination or contribution of each independent variable in determining changes of
dependent variable.
0, I)$)!s(a) Is%a.(c ba)1 ()$ustr'
The Indonesian Islamic banking industry has been growing very well since the establishment
of the first Islamic bank namely *ank
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integrating )0+> offices around the country as shown in table /.
Tab%! 0, S!%!ct!$ Is%a.(c Ba)1()& P!r-r.a)c! I)$(catrs
Ba)1()& I)$(catr 022
2
022
+
022
0
022
3
0224 0225 0226 0227 0228 0229 02+2
Islamic banks !unit# / / / / > > > > = 8 8
Islamic banking nits !unit# > > 8 + )= )' /0 /= /; /= /=
Islamic rural banks !unit# ;' +) +> +( ++ '/ )0= ))( )>) )>' )(0
Total offices !unit# )(8 )+/ //' >>; ((> ==0 =8; 8+> '=) ''+ )0+>
Total asset !trillion &p# ).;' /.;/ (.0= ;.+8 )=.>
>
/0.+
+
/8.;
/
>8.=
>
('.=
=
88.0
'
8;.(
>
Total financing !trillion &p# )./; /.0= >./+ =.=> )).(
'
)=./
>
)'.=
>
/;.'
(
>+.)
'
(8.+
+
(;.)
(
Total deposit !trillion &p# ).0> ).+) /.'/ =.;/ )).+
8
)=.=
+
/0.8
;
/=.8
=
>8.+
=
=/./
;
=>.)
8
@ource- *ank of Indonesia , data until ?anuary /0)0
trillion with total financings of &p(;.)( trillion, very close with the total
deposits of &p=>.)8 trillion.
Currently, the industry is able to deliver a competitive return to depositors compared
with interest rate due to following reasons. 3irst, the profit grows positively showing the
progressive development of the industry. This also indicates the ability of Islamic banks to
provide a continuous and positive payment of return to their depositors as shown in figure ).
@econdly, the spread between return on financing and return sharing on deposits is mostly
positive as depicted in figure /. Therefore, those reasons enable the Islamic banks to provide
better return on deposits in some particular periods rather than the interest rate of
conventional bank as shown in figure >.
F(&ur! +, I)$)!s(a) Is%a.(c Ba)1:s pr-(t F(&ur! 0, Spr!a$ - I)$)!s(a) Is%a.(c Ba)1:s R!tur)
4
-200,000
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
Dec-00
Jun-01
Dec-01
Jun-02
Dec-02
Jun-03
Dec-03
Jun-04
Dec-04
Jun-05
Dec-05
Jun-06
Dec-06
Jun-07
Dec-07
Jun-08
Dec-08
Jun-09
Dec-09
-25.00
-20.00
-15.00
-10.00
-5.00
0.00
5.00
10.00
15.00
20.00
25.00
Jan-00
Jul-00
Jan-01
Jul-01
Jan-02
Jul-02
Jan-03
Jul-03
Jan-04
Jul-04
Jan-05
Jul-05
Jan-06
Jul-06
Jan-07
Jul-07
Jan-08
Jul-08
Jan-09
Jul-09
8/13/2019 Revised Version-final (3)
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F(&ur! 3, C.par(s) b!t*!!) r!tur) ;RR< a)$ ()t!r!st rat! ;INTR< - )!".)t# t(.! $!ps(t
3, L(t!ratur! r!/(!*
3.1. Mimicking interest rate issue
According to Islamic law calledsharia, Islamic banks deliver the return to depositors based
on the pre%determined profit and loss sharing !1D@# ratio. nder this principle, the return on
deposits is uncertain. However, this uncertainty should be depending on the profit and
business condition, rather than imitating the volatility of interest rate. 6therwise, it will be
similar with the conventional banks, which pay interest on deposits according to the pre%
determined interest rate. Consequently, along the period of the time deposits, depositors
receive the revenue, which has no relation with bank9s performance.
@ince the last two decades, the difference opinions among Islamic scholars regardingthe mimicking interest rate of Islamic bank have became interesting topic. In the beginning,
"ienhaus !)'+># reported that Islamic banks use interest rate as a benchmark rate to calculate
their profit and loss sharing ratio. 3urthermore, Ahmad !)''/# disagrees with such condition
and wishes to transform the economic system instantaneously to be an Islamic economic. 6n
the other hand, Ehan !)''=# gave difference opinion that mimicking an interest rate based
system is the short%run alternative. This will be gradually replaced with a more Sharia
compliant banking system later on.
However, some current researches reported interesting findings. Haron !/00(#, using
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linear regression, found that Islamic banks still benchmark interest rate to fi$ing their charges
to the creditors as well as the rewards given to depositors.
8/13/2019 Revised Version-final (3)
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of the price of goods that affect business trading and market. The other variable such as stock
indices may lead to a higher growth at the firm, industry and country level !Aburime, /00+#.
This will give more profit to Islamic banks from financing activities. 3urthermore, *ashir
!/000# found that inflation positively affect the banks9 profits if the revenue accrues frombusiness is larger than the arising of overhead cost due to inflation. 6n the other hand,
interest rate has been found to affecting ma2ority of funding and financing activities of
Islamic bank and later on the depositor return. However, the channel of interest affects
depositor return will be investigated further in this research.
3. 3 !rtificial neural networks model
Actually, A"" model is a branch of artificial intelligence that is powerful to solve the
problem especially with regard to pattern classification and recognition. It is a computational
model where its structure and function imitate the biological neuron in the human brain.
A"" consists of a group of artificial neurons, which are interconnected. 4very single neuron
processes information !receiving input and delivering output# using a special algorithm
function.
A"" model is the advance technique, which is commonly used in making prediction
related with business failure and performance !Diou and Gang, /00+#. @er2u !/00/#
mentioned two advantages of A"" technique compared with other techniques to modeling
the relationship between independent and dependent variables. 3irst, they are universal
estimators of function so that they can appro$imate any functional form to represent the
actual data accurately. It means that A"" is considered as a data%driven rather than model%
driven !Argyrou, /008#. Accordingly, A"" is suited for problems, which data are available
but the underlying theoretical model is unknown !:hang et al.)''+#. The A"" is also
superior to other statistical methods because A"" is able to deal with non%linear data and
multi dimensional aspects. @econd, A"" method is suited for the purpose of long%term
forecast horions. However, it is also as good as traditional statistical forecasting methods to
estimate the shorter time horions.
Technically, the A"" process can be seen in figure (. In the figure, there is a neuron 2,
which has a certain number of inputs !$),$/, $>$2# and single output !y2#. 4ach input has aweight !w)2, w/2, w>2wi2# as an indicator of the importance of the incoming signal to the
neuron. The net value !u2# of the neuron is then calculated based on the sum of all inputs
multiplied by their specific weight.
F(&ur! 4, A .$!% - )!ur)
7
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3urther, the output value !y2# of a neuron refers to the threshold value !t2# and activation
function. 4ach neuron has its own unique threshold value. If the net value !u2# is greater than
the threshold value !t2#, the neuron !2# will send the output !y2# to the other neurons. In
addition, activation function is a function used to transform the activation level of a unit
!neuron# to an output signal. Currently, the most popular activation function is called sigmoid
and logistic.
A single neuron might not be useful enough to solve the problems. It needs a
combination of some neurons into multilayer structured neurons called as neural networks to
learn and answer the pattern classification and recognition problems. 3or this purpose, this
research employs multi layer feed%forward network with back propagation as a learning rule.
In figure =, there is a (%>%) network architecture !in abbreviated form, " !(%>%)## which consists
of one input layer with ( neurodes, one hidden layer with > neurodes and one output layer
with ) neurode. 4very neuron in the layer works with the way as e$plained previously.
F(&ur! 5, A .u%t( %a'!r -!!$"-r*ar$ )!t*r1
@pecifically, the input layer is a layer that directly connects with the e$ternal
information. All data in the input layer will be feed%forward to the hidden layer as the ne$t
layer. This layer functions as a feature detector of the signals and then delivers them to the
output layer. 3inally, the output layer functions as collector of the features detected and then
producing the output as a response. In the networks, output is the function of the linear
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combination of hidden unit9s activation the hidden unit9s activation function is a non%linear
function of the weighted sum of inputs. Aadeh et al., !/00;# e$plained the mathematical
model of A"" as in the following.
+= #,!"f# !)#
Bhere $ is the vector of e$planatory variables, is weights vector !parameters# and J is
the random error component. Then, equation !/# is the unknown function for estimation and
prediction from the available data. As such, the model can be formulated as-
++=
= =
m
$
$
n
i
i$i$ vw"hvf#) )
0 !/#
where-
G K network output
f K output layer activation function
v0K output bias
mK number of hidden units
hK hidden layer activation function
L2K hidden unit biases !2 K ),. . . ,m#
nK number of input units
$iK inputs vector !i K ),. . . ,n#
wi2K weight from input unit i to hidden unit 2
v2K weights from hidden unit 2 to output !2 K ),. . . ,m#
4, M!t#$%&'
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investigate how the Islamic bank responses on macroeconomic fluctuation, which is reflected
on the financial performance. Actually, this paper e$tends the previous research conducted by
Hall et al., !/00+# which also employed A"" model to assess the credit risk of Indonesian
Islamic bank. "onetheless, unlike Hall9s work, this research changes 751 variable as used inhis research with the average of one%month time deposit interest rate !I"T. This is because
the research intends to check I"T&, as pro$y of interest rate, affects the rate of return
volatility as the main purpose of the study.
In the beginning, the research uses five macroeconomic variables, which consist of- !i#
average of one%month time deposit interest rate !I"T !ii# narrow money !
8/13/2019 Revised Version-final (3)
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Additionally, the first model uses /.8; +=.)( %0.08 (./; E%@ dK.)(/>>, pO.0=
@TI" )/0 ),)=/.+= ==/,>+>.)+ ;(>.// 8;.+= 0.;) %0.+> E%@ dK.)='/+, pO.0)
;.(/ /0,)((,)0',>8(.00 )(),'/'.'
=
)/,'=8.>
;
%0.0) %0.+> E%@ dK.0'>+>, pP ./0
,>+'.)
(
/(/,==0,>=+,>'0.0
=
('/,('(.0/ ((,'=+.>= 0.)> %0.=) E%@ dK.0+;+', pP ./0
I"3& )/0 0.;) 0.++ 0.'( 0.0' =.( ((.)+ E%@ dK.)(+>8, pO.0=
I"T& )/0 '.8' ;.>; /.;) 0./= 0.(/ %0.'/ E%@ dK.)>(=', pO.0=
*I&T )/0 )0.;8 )0.= >./( 0.> 0.8= %0.;) E%@ dK.)>'(/, pO.0=
&& )/0 ;.'8 /.;; ).8; 0.)= %0.8+ >.)+ E%@ dK.0;80/, pP ./0
F(&ur! 7, T#! %()!ar(t' t!st
11
Scatterplot of multiple variale! a"ain!t ##
$ata %J&% 9v'120c
()*+
S,%
1
2
%/#
%,#
%#,-2 0 2 4 6 8 10 12
##
-2(5
0
2(5
4(5
6(5
8(5
1(6
1.2(6
1.4(6
1.6(6
1.8(6
2(6
2.2(6
2.4(6
()*+ 8069.5935160.8713'
S,% 2037.8358-111.1611'
1 3.6049(5-12554.3363'
2 1.3237(6-27671.7495'
%/# 0.8752-0.0213'
%,# 2.70630.8777'
%#, 4.0550.8421'
8/13/2019 Revised Version-final (3)
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5, R!su%ts - !.p%'()& A%'u$a )!ur()t!%%(&!)c! s-t*ar!
The followings are the step%by%step e$planation of the utiliation of Alyuda
"eurointelligence software based on the work of Argyrou !/008# which comprise of-
). 5ata preparation
/. A"" learning process
>. Importance rate information
%. 1 &irst !'' model
%. 1. 1 (ata )reparation
The input to Alyuda has similar form with a spreadsheet. At the beginning, all data must be
uncontaminated from data anomalies, because they will give negative impact to neural
network performance. There are two conditions of data anomalies namely- !)# missing values
and !/# outliers. In particular, missing values are values that are not known. 6n the other
hand, outliers are e$treme values that differ from the most data. Then, the && column was set
up as the output or target variables while the macroeconomic variables are categoried as
input variables. 3urthermore, all data are designated as numerical data. Afterwards, the data
are partitioned into three categoried- training data set, validation data set, and testing data
set. This model uses )/0 time series data which )); data are accepted for network
processing. Additionally, for data partitioning, this paper uses random method to determine
+) data for training set, + data for validation set, and )+ data for test set.
The ne$t step is to normaliing the input data to make it appropriate for networks
processing. The normaliation simply converts the input data into a new version before a
neural network is trained. *ishop !)''=# offers the following three reasons for this
normaliation- 3irst, to ensure that the sie of input data reflects their relative importance indetermining the required output. @econd, to facilitate the random initialiation of weights
before training the network and third, different variables possibly have different
measurements unit, therefore their typical values could be different significantly.
%. 1. 2 !'' *earning )rocess
Actually, A"" needs some important stages before go to learning process as following. !)#
finding the best architecture. !/# training the networks. !># evaluating the trained network
through validating and testing the knowledge gained from the training process.
The first stage is to find the best architecture of A"". In this stage, the Alyuda runs
12
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e$haustive searching to find the best possible architecture under following limitations to
avoid over fitting such as using r%squared as fitness criteria and trials are limited for /0.000
iterations. The process takes considerable time because it intends to search the best network
architecture among all alternatives in the specific range. Among two other top networksresulted from this process, Alyuda chooses "!;%>%)#as the networks architecture used according
to some specific parameters as can be seen in table (.
Tab%! 4, Sp!c(-(c para.!t!rs us!$ t c#s! t#! b!st )!t*r1s arc#(t!ctur!
ID Arc#(t!c"
tur!
= -
W!(ts
F(t)!ss Tra()
Errr
Va%($at()
Errr
T!st
Errr
AIC Crr!%at() R"S>uar!$
) Q;%/%)R )' 0.+;++/+ 0.=(==0) 0.=(+)(; 0.;'/;)8 %>8;.0( 0.+;' 0.;;)()
/ Q;%(%)R >; 0.+;0(+/ 0.=;0='> 0.(;;(+( 0.;/0++) %>/;.>'+ 0.+; 0.;==')
> Q;%>%)R /+ 0.++8(0/ 0.=(+808 0.(;/;(' 0.8;)/8> %>(+.=+) 0.++8 0.;+//(
3urthermore, the research trains the networks to generate the information to be
validated and tested for producing comprehensive knowledge regarding the determination of
macroeconomic variables on && volatility. However, there are three configurations needed
before training. 3irst, the logistic activation function is selected for all the neurodes
regardless of the layer on which they reside. @econd, the sum of squared errors is selected to
minimie the output error function. This is summation of squared differences between the
actual value and model9s output. 3or completeness, the neural network output falls in the
range from 0 to ), because of the logistic activation function used. 3urther, the networks are
trained with specific condition to avoid over fitting such as !)# choosing back propagation
algorithm as learning algorithm, !/# setting the learning and momentum rates at 0.), !>#
stopping the training process when the model9s mean squared error reduces by less than
0.00000) or the model completes /0,000 iterations, whichever condition occurs first.
"e$t stage is to evaluate the trained network through validation and testing. The result
of evaluation is e$pressed in two ways statistical indicator and graph e$amination. The
former is shown by value of correlation !r#, &/, mean of absolute error !A4# and mean of
absolute relative error !A&4# as shown in table =.
Tab%! 5, P!r-r.a)c! - )!t*r1s
Para.!t!r Va%u!
Correlation !r# 0.++=)
&/ 0.;>)8
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According to Alyuda manual, the value of correlation !r# and &/ are the indicators of
multiple correlations between independent or predictor variables and dependent or predicted
variable. The coefficients of r can range from %) to S). Bhen the closer r is to ), the stronger
the positive linear relationship will be between both variables. In contrast, the closer r is to %),the stronger the negative linear relationship will be. *esides, when r is near 0, it means that
there is no linear relationship between both variables. @pecifically, &/is a statistical ratio that
compares model forecasting accuracy with accuracy of the simplest model that 2ust uses
mean of all target values as the forecast for all records. The closer this ratio to ) the better the
model is. , Alyuda statistically
indicates that the performance of "!;%>%)# in learning the relationship among independent and
dependent variables is very good. Another way to evaluate the network is through graphic
e$amination as can be seen in figure +. According to this figure, the networks performance in
making prediction of && is very good. It is shown by the pattern of predicted line of && is
located very near with the actual line of &&.
F(&ur! 8, Actua% /s, pr!$(ct() &rap# -r -(rst ANN M$!%
%. 1. 3 Importance rate information
3inally, the "!;%>%)# reveals the importance rate information as a comprehensive knowledge
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resulted from learning process. The information are shown in table 8. In the table, we found
that I"T& and and >).;) of importance rate, respectively. .8/ and 0.(8, respectively. @ince the networks do not providestrong evidence that the interest rate dominantly affects && volatility !only =).(>#,
therefore the research needs to conduct further e$amination.
Tab%! 6, I.prta)c! rat! - ()$!p!)$!)t /ar(ab%!s
N I)$!p!)$!)t Var(ab%!s I.prta)c! rat! ;?/)'
( 4MCH >.8/=)8(
= I"3& 0.(8;;0)
%. 2 Second Model
7enerally, the second model is treated in the same way as in the previous model. This model
uses the same data as well with two additional variables namely *I&T and
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&/ 0.8(>)
')=
; I"3& 0.0/+>'>
6, I)t!rpr!tat() - t#! r!su%ts
16
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There are two ways for Islamic banks to smooth the payment of return on deposits. 3irst, the
banks grant some of their profit to ad2ust return on deposit referring to the market interest rate
on deposits directly, !Chong and Diu, /00'#. @econd, the banks benchmark central bank
interest rate in financing process to ad2ust their financing rate instantaneously to competewith market credit rate. This way helps Islamic banks to offer financing to the market
competing with conventional bank. @ubsequently, the bank will be able to deliver the
competitive return on deposit following interest rate without sacrificing their profit, !Haron,
/00(#.
The first model shows that only =) contribution of I"T& affecting && volatility. This
finding is not enough to e$plain that Islamic banks in Indonesia rewarding some of their
profit to ad2ust return on deposit referring to the market interest rate. In accordance with this,
the figure > depicts that Indonesian Islamic banks have delivered better return rather than
Interest rate for three times. In contrast with Chong and Diu !/00'#, this investigation does
not support their finding, which says changes in deposit return of Islamic bank in
8/13/2019 Revised Version-final (3)
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financing rate to generate competitive return for mimicking deposit interest rate.
These results reveal that Islamic banks are not independent in determining the return on
deposits to depositors. To some e$tends, this is acceptable as the country adopts dual banking
system and the share of the industry is still less than >. "evertheless, for the future, Islamicbanking industry is recommended to be more independent and reflecting the true Islamic
banking operations. *esides sharing the actual return from business activities and not being
affected by interest rate, they have to have their own benchmark rate to be used in pricing
their Islamic deposit and financing contracts.
Ac1)*%!$&!.!)t
The authors thank 1rof. 5r. H. 3athurrahman 52amil from @TI4 Ahmad 5ahlan, ?akarta,
2ournal9s reviewers and editor for valuable comments and guidance. The authors gratefully
acknowledged that this research is financed by papers.cfmVabstractWidK)/>)08(, !accessed /0
5ecember, /00'#.
Ahmad, @. !)''/#, ,owards Interest-&ree anking International Islamic 1ublishers, "ew
5elhi, India.
Anwar, @., and
8/13/2019 Revised Version-final (3)
19/20
"orth America and AustraliaU,/ournal of anking and &inance, Fol. )>, pp. 8=%8;.
Chong, *., and %/>.
Ehan, #, 1rofitability of Islamic *anks Competing with Interest *anksU,
/ournal of +esearch in Islamic 0conomics. Fol ) "o. ), pp.>;%(;.
Nin, 5., Nuising, 1., He, M. Diu, @. !/00=#,
8/13/2019 Revised Version-final (3)
20/20
*ank 1erformance 1redictionU,!pplied Soft 5omputing, Fol.+, pp. >0=%>)=.
@er2u, 1. !/00/#,
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