IIUM Institute of Islamic Banking and Finance ISSN 2289-2117 (O) / 2289-2109 (P)
Behavior Investigation of Islamic Bank Deposit Return Utilizing Artificial Neural Networks Model
Saiful Anwar
a*, Rifki Ismal
b, Kenji Watanabe
caAhmad Dahlan School of Finance and Banking, Jakarta 15149, SEBI School of Islamic Economics, Depok 16517, and Center for Islamic Economics and Business, Faculty of Economics, University of Indonesia, Depok 16424, Indonesia
bFaculty of Economics, University of Indonesia, Salemba Raya 4, Jakarta 10430, Indonesia
cDepartment of Disaster Management and Safety, Nagoya Institute of Technology, Japan Abstract
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. This investigation utilizes artificial neural networks (ANN) model to examine 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 (RR) is used as dependent variable. As a result, the N(9-4-1), a chosen neural networks architecture, found that BIRT and INTR as proxy of interest rate, dominantly affect RR volatility with almost 98% of importance rate. It shows the very high dominance of interest rate in determining RR volatility, as indication of mimicking behavior, rather than remaining variables as proxy of real economic condition.
© 2012 The IIUM Institute of Islamic Banking and Finance.
Keywords: Islamic bank, depositor return, artificial neural networks, macroeconomic variables, Indonesia.
1.Introduction
Islamic banks commonly have two sources of funds. The first source is the public funds in bank deposits that comprises of Wadiah demand deposit, Mudharabah saving deposit, and Mudharabah time deposit. The second one is the public funds in non-bank deposits such as received financing, securities issued by banks, interbank liabilities, liabilities to the central bank; and other payables (Ismal, 2009).
Particularly, with Wadiah demand deposit, Islamic banks may obtain an explicit or implicit authorization 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 projects and share the profit with depositors as deposit return. Furthermore, 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 (El-Din, 2004). The Banks are not authorized to mix 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. On the contrary, the latter allows the banks to actively occupy the funds and share the risks with depositors without any voting rights (Grais and Pellegrini, 2006).
In practice, when depositor opens the Mudharabah time 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 bank’s profitability. The monthly return received by depositor is represented by rate of
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return (RR) 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 bank’s profit and predetermined loss and profit ratio offered to depositor (Zoubi and Olson, 2008). Consequently, in the macroeconomic turmoil, the bank’s profitability should be fluctuating, and afterwards the rate of return on time deposit will be fluctuating as well. However, El-Gamal (1997) noticed that the transactions in Islamic bank such as time deposit and 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 on Mudharabah time deposits due to following reasons:
1. Based on the nature of the product, time deposit account is the product, which only uses mudharabah contract. It means that this product is principally created for investment purpose. Depositor who opens this account is required to give notice to 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 example, when they withdraw the money through ATM machine. This product is more flexible than time deposit account and it can be developed either using mudharabah or wadiah principle.
2. The composition of Mudharabah time deposit product on total third party fund is 57.9% meanwhile the other product such as saving account and current account are 29.7% and 12.4%, 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 1.
Table 1. The percentage of time deposit account and saving account on total deposit
Simply saying, there is indication that the intention of depositor to patronize with Islamic banking is more likely to find a better return rather than as a mean of daily payment. Specifically, 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 bank’s operational strategy. For doing so, the research examines the importance rate of the past ten years of macroeconomic variables in affecting the volatility of Mudharabah time deposit return provided by ANN model. The importance rate indicates the level of domination or contribution of each independent variable in determining changes of dependent variable.
2.Indonesian Islamic bank industry
The Indonesian Islamic banking industry has been growing very well since the establishment of the first Islamic bank namely Bank Muamalat Indonesia (BMI) in 1992. Until the latest data of January 2010, there are six Islamic Commercial Banks (BUS) followed by twenty five Islamic Banking Windows/Unit (UUS) and one hundred forty Islamic Rural Banks (BPRS) integrating 1083 offices around the country as shown in table 2.
Period Time deposit account Saving account
Dec-2006 52.25% 30.21%
Dec-2007 49.88% 33.75%
Dec-2008 52.30% 33.74%
Dec-2009 54.49% 31.33%
Dec-2010 55.51% 29.84%
Jan-2011 56.15% 29.75%
Table 2. Selected Islamic Banking Performance Indicators
Banking Indicator 2000 2002 2004 2005 2006 2007 2008 2009 2010
Islamic banks (unit) 2 2 3 3 3 3 5 6 6
Islamic banking Units (unit) 3 6 15 19 20 25 27 25 25
Islamic rural banks (unit) 79 83 88 92 105 114 131 139 140
Total offices (unit) 146 229 443 550 567 683 951 998 1083
Total asset (trillion Rp) 1.79 4.05 15.33 20.88 26.72 36.53 49.55 66.09 67.43 Total financing (trillion Rp) 1.27 3.28 11.49 15.23 19.53 27.94 38.19 46.88 47.14 Total deposit (trillion Rp) 1.03 2.92 11.86 15.58 20.67 25.65 36.85 52.27 53.16 Source: Bank of Indonesia , data until January 2010
Moreover, the industry has a healthy financial intermediary function and prudential banking operations.
The Financing to Deposit Ratio (FDR) has been lying on 106.76% on average from December 2000 to January 2010 and the Non Performing Financing (NPF) stands between 2%-5% of the total financings.
Other measures, like total assets, financings and deposits grow annually for more than 50%-60% on average. Lately, the total assets have reached Rp67.43 trillion with total financings of Rp47.14 trillion, very close with the total deposits of Rp53.16 trillion. Currently, the industry is able to deliver a competitive return to depositors compared with interest rate due to following reasons. First, 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 1. Secondly, the spread between return on financing and return sharing on deposits is mostly positive as depicted in figure 2.
Figure 1. Indonesian Islamic Bank’s profit Figure 2. Spread of Indonesian Islamic Bank’s Return 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 3.
3.Literature review
3.1. Mimicking interest rate issue
According to Islamic law called sharia, Islamic banks deliver the return to depositors based on the pre- determined profit and loss sharing (PLS) ratio. Under 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. Otherwise, it will be similar with the conventional banks, which pay interest
-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
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 bank’s performance.
Since the last two decades, the difference opinions among Islamic scholars regarding the mimicking interest rate of Islamic bank have became interesting topic. In the beginning, Nienhaus (1983) reported that Islamic banks use interest rate as a benchmark rate to calculate their profit and loss sharing ratio.
Furthermore, Ahmad (1992) disagrees with such condition and wishes to transform the economic system instantaneously to be an Islamic economic. On the other hand, Khan (1995) 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.
Figure 3. Comparison between return (RR) and interest rate (INTR) of one-month time deposit
However, some current researches reported interesting findings. Haron (2004), using linear regression, found that Islamic banks still benchmark interest rate to fixing their charges to the creditors as well as the rewards given to depositors. Moreover, Chong and Liu (2009) used linear regression to examine the relation between rate of return on deposits and interest rate of the conventional bank. They found that changes in conventional bank deposit rate causes changes in return on deposit. When return on deposit deviates far above (below) the interest rate on deposit, it will be adjusted towards the long-term equilibrium level and vice versa. In Indonesia, some researchers confirmed the same condition. Kasri and Kassim (2009), using Vector Auto Regressive (VAR) and Impulse Response Functions (IRF), found that shock in interest rate of conventional bank negatively affects the number of deposits in the Indonesian Islamic bank. Consequently, Islamic banks somehow maintain their return in order to compete with the conventional banks. Lately, Ismal (2010) reported that the movement of the return on deposits still tends to mimic the interest rate.
This study realizes that it is important to reconfirm the indication of mimicking interest rate in delivering return on deposits using ANN which has been proven that the model performs better than traditional statistical technique in making prediction and pattern recognition of Islamic banking return such as multiple linear regression and generalized autoregressive conditional heteroscedastic (GARCH) (Anwar and Mikami, 2011). This is to maintain the good image and performance of Indonesian Islamic banks. In contrast, if Islamic banks are found to mimic the interest rate convincingly in this research, it will strengthen previous findings that the banking operations are not purely sharia compliance while this is the ultimate reason for depositors to deal with Indonesian Islamic banks (Mars, 2008). Principally, the sharia requires Islamic banks to generate and share the return on investment depending on the real business return.
Benchmarking or mimicking the interest rate might violate such principle as interest is predetermined, fixed and not always representing the real business return.
3. 2 Macroeconomic variables and Islamic bank profitability
Some scholars have found the essential roles of macroeconomic variables in determining the performance of Islamic banks. Subsequently, it influences the return paid to depositor. Interestingly, amongst all macroeconomic variables, interest rate is the most significant variables affecting the Islamic
bank profitability rather than real macroeconomic variables such as money supply, exchange rate, stock indices, inflation rate, etc. These findings are reported by Bashir (2000); Haron (2004); and Hall et al., (2008).
Specifically, the real macroeconomic variables determine bank’s profitability in following ways.
Bourke (1989) mentioned that market expansion would enable banks to increase profits as represented by strong relationship between money supply and profit. Meanwhile, exchange rate does not affect Islamic bank profits from foreign exchange trading as it does to conventional bank since it is prohibited, but through its impact on the fluctuation 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, 2008). This will give more profit to Islamic banks from financing activities. Furthermore, Bashir (2000) found that inflation positively affect the banks’ profits if the revenue accrues from business is larger than the arising of overhead cost due to inflation. On the other hand, interest rate has been found to affecting majority 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 Artificial neural networks model
Actually, ANN 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. ANN consists of a group of artificial neurons, which are interconnected. Every single neuron processes information (receiving input and delivering output) using a special algorithm function.
Figure 4. A model of neuron
ANN model is the advance technique, which is commonly used in making prediction related with business failure and performance (Liou and Yang, 2008). Serju (2002) mentioned two advantages of ANN technique compared with other techniques to modeling the relationship between independent and dependent variables. First, they are universal estimators of function so that they can approximate any functional form to represent the actual data accurately. It means that ANN is considered as a data-driven rather than model-driven (Argyrou, 2006). Accordingly, ANN is suited for problems, which data are available but the underlying theoretical model is unknown (Zhang et al., 1998). The ANN is also superior to other statistical methods because ANN is able to deal with non-linear data and multi dimensional aspects. Second, ANN method is suited for the purpose of long-term forecast horizons. However, it is also as good as traditional statistical forecasting methods to estimate the shorter time horizons.
Technically, the ANN process can be seen in figure 4. In the figure, there is a neuron j, which has a certain number of inputs (x1,x2, x3…xj) and single output (yj). Each input has a weight (w1j, w2j, w3j…wij) as an indicator of the importance of the incoming signal to the neuron. The net value (uj) of the neuron is then calculated based on the sum of all inputs multiplied by their specific weight.
Further, the output value (yj) of a neuron refers to the threshold value (tj) and activation function. Each neuron has its own unique threshold value. If the net value (uj) is greater than the threshold value (tj), the neuron (j) will send the output (yj) 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. For this purpose, this research employs multi layer feed-forward network with back propagation as a learning rule. In figure 5, there is a 4-3-1 network architecture (in abbreviated form, N(4-3-1)) which consists of one input layer with 4 neurodes, one hidden layer with 3 neurodes and one output layer with 1 neurode. Every neuron in the layer works with the way as explained previously.
Figure 5. A multi layer feed-forward network
Specifically, the input layer is a layer that directly connects with the external information. All data in the input layer will be feed-forward to the hidden layer as the next layer. This layer functions as a feature detector of the signals and then delivers them to the output layer. Finally, 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 combination of hidden unit’s activation; the hidden unit’s activation function is a non-linear function of the weighted sum of inputs. Azadeh et al., (2007) explained the mathematical model of ANN as in the following.
ε θ +
= f (x , )
Y
(1)Where x is the vector of explanatory variables, θ is weights vector (parameters) and ε is the random error component. Then, equation (2) is the unknown function for estimation and prediction from the available data. As such, the model can be formulated as:
+
+
= ∑ ∑
= =
m
j
j n
i ij i
j
x w v
h v
f Y
1 1
0
λ
(2)where:
Y = network output
f = output layer activation function v0= output bias
m= number of hidden units h= hidden layer activation function λj= hidden unit biases (j = 1,. . . ,m) n= number of input units
xi= inputs vector (i = 1,. . . ,n)
wij= weight from input unit i to hidden unit j
vj= weights from hidden unit j to output (j = 1,. . . ,m)
4.Methodology
Moody (1995) has reported that macroeconomic variables are categorized as non-linear time series data so that using traditional statistical techniques will need extra effort. This is because one should utilize other sophisticated technique to conform on strict assumptions of linear separability, multivariate normality and independence of the predictive variables (Ravi et al., 2008). Otherwise, just regressing of such data using traditional statistical technique may lead to false understanding and conclusion, indicated by high R2 but there is no meaningful relationship among the variables, (Kasri and Kassim, 2009).
Therefore, this research employs ANN model using Alyuda Neuro Intelligent software version 2.2. It is expected that, the application with its easiness and simplicity could develop robust ANN model to generate output, which is able to give significant contribution to existing literatures. The utilization of ANN will benefit this topic since the model is known as state of the art in learning complex relationships among independent variables. Unlike traditional statistical technique, the model does not need preliminary assumption between dependent and independent variable. In this research, particularly, the ANN model is used to investigate how the Islamic bank responses on macroeconomic fluctuation, which is reflected on the financial performance. Actually, this paper extends the previous research conducted by Hall et al., (2008) which also employed ANN model to assess the credit risk of Indonesian Islamic bank. Nonetheless, unlike Hall’s work, this research changes GDP variable as used in his research with the average of one- month time deposit interest rate (INTR). This is because the research intends to check INTR, as proxy 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 (INTR); (ii) narrow money (M1); (iii) exchange rate (EXCH); (iv) Jakarta stock indices (STIN), and; (v) inflation rate (INFR). Among those variables, INTR functions as proxy of interest rate, while the remaining variables functions as proxy of real macroeconomic variables expressing real business condition. It is expected that the model will provide strong evidence as interest rate will significantly dominate the RR volatility instead of real macroeconomic variables. This will be reflected on very high importance rate of interest rate. Otherwise, the second ANN model will be constructed. If so, the research will use two additional variables such as (a) the central bank interest rate (BIRT) and, (b) broad money (M2). It is expected that BIRT will enhance the domination of interest rate as well as in the first model. Meanwhile, the addition of M2 as used by Haron (2004) is pre-assumed would enhance dominance of real macroeconomic variables. This methodology is summarized on research framework as depicted in figure 6.
Additionally, the first model uses M1 rather than M2 due to the interest free consideration on money supply variables. Specifically, M1 is the money supply, which only consists of currency on hands of the public, demand deposits, other checkable deposits and traveler checks. Meanwhile, M2 is the sum of M1, saving deposits, time deposits and, overnight repurchase agreement issued by all commercial banks. It is believed that money supply plays a key role to linking the banking sector with the real sector, (Qin et al., 2005).
Figure 6. Research framework
At the end, the research uses average of rate of return of one-month mudharabah time deposits (RR) as independent variables in both models. For doing so, all data are collected from the central bank website (Bank of Indonesia) for 120 periods from January 2000 to December 2009. The statistical description of data, normality test and linearity test can be found in table 3 and figure 7.
5.Results of employing Alyuda neurointelligence software
The followings are the step-by-step explanation of the utilization of Alyuda Neurointelligence software based on the work of Argyrou (2006) which comprise of:
1. Data preparation 2. ANN learning process 3. Importance rate information 5. 1 First ANN model
5. 1. 1 Data Preparation
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: (1) missing values and (2) outliers. In particular, missing values are values that are not known. On the other hand, outliers are extreme values that differ from the most data. Then, the RR column was set up as the output or target variables while the macroeconomic variables are categorized as input variables. Furthermore, all data are designated as numerical data. Afterwards, the data are partitioned into three categorized: training data set, validation data set, and testing data set. This model uses 120 time series data which 117 data are accepted for network processing. Additionally, for data partitioning, this paper uses random method to determine 81 data for training set, 8 data for validation set, and 18 data for test set.
Table 3. The statistical description
Variable Valid N Mean Std.Dev. Std. Error Skewness Kurtosis Kolmogorov-Spirnov (Normality test)
EXCH 120 9,350.33 932.67 85.14 -0.06 4.27 K-S d=.14233, p<.05
STIN 120 1,152.85 743.22 67.85 0.71 -0.83 K-S d=.15928, p<.01
M1 120 260,537.42 141,929.95 12,956.37 -0.01 -0.83 K-S d=.09383, p> .20 M2 120 1,103,389.14 492,494.02 44,958.35 0.13 -0.51 K-S d=.08789, p> .20
INFR 120 0.71 0.94 0.09 5.4 44.18 K-S d=.14836, p<.05
INTR 120 9.69 2.71 0.25 0.42 -0.92 K-S d=.13459, p<.05
BIRT 120 10.76 3.24 0.3 0.65 -0.71 K-S d=.13942, p<.05
RR 120 7.96 1.67 0.15 -0.68 3.18 K-S d=.07602, p> .20
Figure 7. The linearity test
The next step is to normalizing the input data to make it appropriate for networks processing. The normalization simply converts the input data into a new version before a neural network is trained. Bishop (1995) offers the following three reasons for this normalization: First, to ensure that the size of input data reflects their relative importance in determining the required output. Second, to facilitate the random initialization of weights before training the network and third, different variables possibly have different measurements unit, therefore their typical values could be different significantly.
Scatterplot of multiple variables against RR data IJAI 9v*120c
EXCH STIN M1 M2 INFR INTR BIRT
-2 0 2 4 6 8 10 12
RR -2E5
0 2E5 4E5 6E5 8E5 1E6 1.2E6 1.4E6 1.6E6 1.8E6 2E6 2.2E6 2.4E6
EXCH = 8069.5935+160.8713*x STIN = 2037.8358-111.1611*x M1 = 3.6049E5-12554.3363*x M2 = 1.3237E6-27671.7495*x INFR = 0.8752-0.0213*x INTR = 2.7063+0.8777*x BIRT = 4.055+0.8421*x
5. 1. 2 ANN Learning Process
Actually, ANN needs some important stages before go to learning process as following. (1) finding the best architecture. (2) training the networks. (3) 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 ANN. In this stage, the Alyuda runs exhaustive 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 20.000 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 networks resulted from this process, Alyuda chooses N(7-3-1) as the networks architecture used according to some specific parameters as can be seen in table 4.
Table 4. Specific parameters used to choose the best networks architecture ID Architecture # of Weights Fitness Train
Error
Validation Error
Test Error AIC Corre- lation
R-Sq
1 [7-2-1] 19 0.878828 0.545501 0.548147 0.792716 -367.04 0.879 0.77
2 [7-4-1] 37 0.870482 0.570593 0.477484 0.720881 -327.398 0.87 0.76
3 [7-3-1] 28 0.886402 0.548606 0.472749 0.671263 -348.581 0.886 0.78
Furthermore, 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 RR volatility. However, there are three configurations needed before training. First, the logistic activation function is selected for all the neurodes regardless of the layer on which they reside. Second, the sum of squared errors is selected to minimize the output error function. This is summation of squared differences between the actual value and model’s output. For completeness, the neural network output falls in the range from 0 to 1, because of the logistic activation function used. Further, the networks are trained with specific condition to avoid over fitting such as; (1) choosing back propagation algorithm as learning algorithm, (2) setting the learning and momentum rates at 0.1, (3) stopping the training process when the model’s mean squared error reduces by less than 0.000001 or the model completes 20,000 iterations, whichever condition occurs first.
Next stage is to evaluate the trained network through validation and testing. The result of evaluation is expressed in two ways; statistical indicator and graph examination. The former is shown by value of correlation (r), R2, mean of absolute error (AE) and mean of absolute relative error (ARE) as shown in table 5.
According to Alyuda manual, the value of correlation (r) and R2 are the indicators of multiple correlations between independent or predictor variables and dependent or predicted variable. The coefficients of r can range from -1 to +1. When the closer r is to 1, the stronger the positive linear relationship will be between both variables. In contrast, the closer r is to -1, the stronger the negative linear relationship will be. Besides, when r is near 0, it means that there is no linear relationship between both variables. Specifically, R2 is a statistical ratio that compares model forecasting accuracy with accuracy of the simplest model that just uses mean of all target values as the forecast for all records. The closer this ratio to 1 the better the model is. Moreover, small positive values near zero indicate poor model. On the other hand, negative values indicate models that are worse than the simple mean-based model.
Furthermore, the quality of the networks can also be examined through deviation value between the predicted output and the desired one.
This examination is presented on mean value of AE and ARE. Specifically, AE and ARE (in absolute value) are error values that show the quality of in-sample prediction of the model. It means that the smaller the error is the better quality of the networks will be. In accordance with table 3, Alyuda statistically indicates that the performance of N(7-3-1) in learning the relationship among independent and dependent variables is very good. Another way to evaluate the network is through graphic examination as can be seen in figure 8. According to this figure, the networks performance in making prediction of RR is very good. It is shown by the pattern of predicted line of RR is located very near with the actual line of RR.
Table 5. Performance of networks
Parameter Value
Correlation (r) 0.8851
R2 0.7316
Mean of AE 0.544927
Mean of ARE 0.069602
Figure 8. Actual vs. prediction graph for first ANN Model 5. 1. 3 Importance rate information
Finally, the N(7-3-1) reveals the importance rate information as a comprehensive knowledge resulted from learning process. The information are shown in table 6. In the table, we found that INTR and M1 are the most significant variables in determining RR fluctuation with 51.43% and 31.71% of importance rate, respectively. Meanwhile, STIN, EXCH, and INFR contribute with 12.76%, 3.62% and 0.46%, respectively. Since the networks do not provide strong evidence that the interest rate dominantly affects RR volatility (only 51.43%), therefore the research needs to conduct further examination.
Table 6. Importance rate of independent variables No Independent Variables Importance rate (%)
1 INTR 51.433336
2 M1 31.71058
3 STIN 12.763219
4 EXCH 3.625164
5 INFR 0.467701
5. 2 Second Model
Generally, 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 BIRT and M2. The first variable functions as
proxy of interest rate, while the later functions as real macroeconomic variable. Furthermore, Alyuda choose N(9-4-1) as network architecture according to table 7.
Table 7. Specific parameters used to choose the best networks architecture ID Architecture # of weights Fitness Train
Error
Validation Error
Test Error AIC Corre- lation
R-Sq
1 [9-2-1] 23 0.718717 0.832368 0.795799 0.983952 -324.812 0.719 0.51
2 [9-4-1] 45 0.871687 0.574935 0.720515 0.731708 -310.784 0.872 0.76
3 [9-3-1] 34 0.856346 0.62559 0.768716 0.639988 -325.944 0.856 0.73
5. 2. 1 Importance Rate Information
Again, before goes to the information, the research needs to investigate the quality of networks in learning process. Using the same methods as used in the first model, the statistical indicator shows the networks learn very well through the value of correlation (r), R2 , mean value of AE and mean value of ARE as shown in table 8.
Table 8. Performance of networks
Parameter Value
Correlation (r) 0.8585
R2 0.6431
Mean of AE 0.51796
Mean of ARE 0.07277
Furthermore, the actual vs. prediction graph using in-sample data supports the same performance as depicted in figure 9.
Figure 9. Actual vs. prediction graph for second ANN Model
Finally, as shown in table 9, the networks put both INTR and BIRT as the most significant variable in determining the RR volatility with almost 98% of importance rate. Accordingly, we have found that interest rate affects RR significantly
Table 9. Importance rate of independent variables No Independent Variables Importance rate (%)
1 INTR 71.70672
2 BIRT 25.56138
3 M1 1.380907
4 M2 0.704832
5 EXCH 0.513852
6 STIN 0.103915
7 INFR 0.028393
6. Interpretation of the results
There are two ways for Islamic banks to smooth the payment of return on deposits. First, the banks grant some of their profit to adjust return on deposit referring to the market interest rate on deposits directly, (Chong and Liu, 2009). Second, the banks benchmark central bank interest rate in financing process to adjust their financing rate instantaneously to compete with market credit rate. This way helps Islamic banks to offer financing to the market competing with conventional bank. Subsequently, the bank will be able to deliver the competitive return on deposit following interest rate without sacrificing their profit, (Haron, 2004).
The first model shows that only 51% contribution of INTR affecting RR volatility. This finding is not enough to explain that Islamic banks in Indonesia rewarding some of their profit to adjust return on deposit referring to the market interest rate. In accordance with this, the figure 3 depicts that Indonesian Islamic banks have delivered better return rather than Interest rate for three times. In contrast with Chong and Liu (2009), this investigation does not support their finding, which says changes in deposit return of Islamic bank in Malaysia, tends to mimic deposit interest rate directly.
On the other hand, the second way helps us explaining why addition of BIRT increases the contribution of INTR about 20% to be 71.7% in the second model. ANN might learn the inter-relation of BIRT and INTR in determining RR volatility. Specifically, when the banks use BIRT as financing rate benchmarking to increase the profit and then adjust RR for mimicking INTR. Hence, the second model discovers that Islamic bank referring to central bank interest rate in pricing the financing rate for generating depositor return in order to mimicking deposit interest rate as noticed by Haron (2004). This also explains how Islamic banks maintain the return on deposit to response on interest shock that affects the number of Islamic bank deposit, (Kasri and Kassim, 2009)
Actually, the learning process of BIRT and INTR inter-relationship can be seen through calculation and adjustment of weights comes from input layer. Unfortunately, this research cannot provide empirical evidence to show the process since the software has no feature to describe internal process of networks where the networks learn inter-relationship among independent variables in hidden layer. This will be interesting to discover the process using other application in the future research.
7. Conclusion
In the operations of Islamic banks, there is an indication of return of Mudharabah time deposits mimicking the interest rate through adjustment of both financing and deposit rate. This research, however, has successfully investigated the empirical evidence of mimicking interest rate in the operational of Indonesian Islamic banking industry. Particularly, it is known thorough the way of Islamic banks
benchmarking central bank interest rate in pricing 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 extends, this is acceptable as the country adopts dual banking system and the share of the industry is still less than 3%. Nevertheless, for the future, Islamic banking industry is recommended to be more independent and reflecting the true Islamic banking operations. Besides 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.
Acknowledgements
The authors thank Prof. Dr. H. Fathurrahman Djamil from STIE Ahmad Dahlan, Jakarta and PT. Bank Syariah Mandiri, Jakarta. The authors gratefully acknowledged that this research is financed by Ministry of Education, Republic of Indonesia.
References
Aburime, T. (2008). "Determinants of Bank Profitability: Macroeconomic Evidence from Nigeria". Working
Ahmad, S. (1992), Towards Interest-Free Banking, International Islamic Publishers, New Delhi, India.
Anwar, S., and Mikami, Y. (2011). "Comparing Accuracy Performance of ANN. MLR and GARCH Model in Predicting Time Deposit Return of Islamic Bank". International Journal of Trade, Economics and Finance. 2(1): 44-51.
Argyrou, A. (2006). "Predicting Financial Distress Using Neural Networks: Another Episode to the Serial". Master Thesis. Swedish School of Economics and Business Administration.
Azadeh, S.F. Ghaderi, M. Anvari, M. Saberi, and H. Izadbakhsh. (2007). "An Integrated Artificial Neural Network and Fuzzy Clustering Algorithm for Performance Assessment of Decision Making Units". Applied Mathematics and computation. 187(2): 584-599.
Bashir, M. (2000). "Determinants of Profitability and Rate of Return Margins in Islamic Banks: Some Evidence from the Middle East". Paper presented at Economic Research Forum. 26-29 October. Amman. Jordan. Accessed December 20, 2011. www.erf.org.eg/CMS/getFile.php?id=753.
Bishop, M., C. (1995). Neural Networks for Pattern Recognition. 1st ed. Oxford University Press Inc. New York.
USA.
Bourke, P. (1989)."Concentration and Other Determinants of Bank Profitability in Europe. North America and Australia". Journal of Banking and Finance. 13:65-67.
Chong, B., and M, Liu. (2009). "Islamic Banking: Interest-Free or Interest-Based?" Pacific Basin finance Journal. 17 (1): 124-144.
El-Din, Seif Tag. (2004). Issues in Accounting Standard for Islamic Financial Institutions. Markfield Institute of Higher Education. September 2004.
El-Gamal, M., A. (2007). "Can Islamic Banking Survive? A Micro-evolutionary Perspective".Working Paper Department of Economics. University of Wisconsin. Madison. Wisconsin. 21 February.
Fen-May, Liou., and Chien-Hui, Yang, (2008). "Predicting Business Failure Under the Existence of Fraudulent Financial Reporting", International Journal of Accounting and Information Management. 16 (1):74 – 86. . Grais, Wafik., and Pellegrini, Matteo. (2006). "Corporate Governance and Stakeholders Financial Interest in
Institutions Offering Financial Services". World Bank Policy Research Working Paper. Series number 4053.
Washington DC. 1 November. Hall, Maximilian, J.B., Muljawan, D., Supray
Hall, Maximilian, J.B., Muljawan, D., Suprayogi, Moorena. L. (2008). Using the Artificial Neural Network (ANN) to Assess Bank Credit Risk: A Case Study of Indonesia. Applied Financial Economics. Vol 19. pp. 1825 – 1846.
Haron, S. (2004). Determinants of Islamic Bank Profitability. Global Journal of Finance and Economics. Vol 1 No.1.
pp.11-33.
Ismal, Rifki. (2009). Industrial Analysis of Liquidity Risk Management in Islamic Bank. Journal of Islamic Banking and Finance. Vol 26 No. 2.
Ismal, Rifki. (2010). How Do Islamic Banks Manage Liquidity Risk. Kyoto Bulletin of Islamic Area Studies. Vol 3 No.2. pp. 54-81. March 2010.
Kasri, R., and Kassim. S. (2009). Empirical Determinants of Saving in the Islamic Banks: Evidence from Indonesia.
JKAU: Islamic Econ. Vol.22 No.2. pp. 3-23.
Khan, M. (1995). Essay in Islamic Economics. Leicester. U.K: The Islamic Foundation.
Paper Department of Banking and Finance, University of Nigeria. Accessed 20 December, 2011. http://
papers.ssrn.com/sol3/papers.cfm?abstract_id=1231064.
Mars, Research Specialist. (2008). The Study of the Market and Islamic Banking Depositors Behaviors 2008. PT.
Mars Indonesia. Jl. Paus 89G Rawamangun. Jakarta.
Moody, J. (1995). Economic Forecasting: Challenges and Neural Network Solutions. paper presented at International Symposium on Artificial Neural Networks. 18-20 December. Hsinchu. Taiwan. available at: http://neuron- ai.tuke.sk/~daliman/Esej/2/moody95economic.pdf. [accessed 20 December 2011]
Nienhaus, V. (1983). Profitability of Islamic Banks Competing with Interest Banks. Journal of Research in Islamic Economics. Vol 1 No. 1. pp.37-47.
Qin, D., Quising, P., He, X. Liu, S. (2005). Modelling monetary transmission and policy in China. Journal of Policy Modelling. Vol 27 No.2. pp.157-175.
Ravi, V., Kurniawan, H., Kok Thai, P.N., Kumar R.V. (2008). Soft Computing System for Bank Performance Prediction. Applied Soft Computing. Vol.8. pp. 305-315.
Serju, P. (2002). Monetary Conditions & Core Inflation: An application of Neural Networks. working paper. Research Services Department. Research and Economic. Programming Division. Bank of Jamaica. July.
Zhang, G., Patuwo, B., Hu, M. (1998). Forecasting With Artificial Neural Networks: The State of the Art.
International Journal of Forecasting. Vol.14 No.1. pp.35 - 62.
Zoubi, T.A., and Olson, D. (2007). Financial Characteristics of Banking Industry in the GCC Region: Islamic vs.
Conventional banks. available at: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.116.5171. [20 December 2011].
