This report shows how various determinants of profitability affects the performance of a private commercial bank based on Bangladesh.

1. An Assignment
On
“The Determinants of Profitability of Dutch Bangla Bank Limited”
Course Name- E-Commerce & E-Banking
Course Code- FIN - 5208
Submitted To:
Md. Shahidullah Kayser
Lecturer,
Department of Finance
Jagannath University
Dhaka.
Date of Submission: August 27, 2017
Submitted By:
On behalf of Group No: 08
Sohel Rana
Id No: M150203039
Session: 2015-16
6th
Batch
Department Of Finance
Jagannath University,
Dhaka

2. GROUP NO-08
SL NO NAME ID NO
1 SOHEL RANA M150203039
2 RAKIBUL HAQUE M150203040
3 MD.KAMRUL HASAN M150203041
4 MD.ARMAN KHAN M150203042
5 MD. ALAMIN KHAN M150203043

3. Letter of Transmittal
August 27, 2017
To
Md. Shahidullah Kayser
Lecturer,
Department of Finance
Jagannath University, Dhaka.
Subject: Submission of an assignment on “The Determinants of Profitability of Dutch Bangla
Bank Limited.’’
Dear Sir,
This is our pleasure to present our assignment entitled on “The Determinants of
Profitability of Dutch Bangla Bank Limited.’’. We tried our level best to merge all the
necessary and current information gathered from sources and knowledge acquired during
making the assignment to represent this assignment as a unique outcome of our efforts firmly
believe that knowledge and experience we gathered during making the report will be helpful
in our future professional life.
We respectfully request you to accept this report for further Analysis. Your support in this
regard will be highly appreciated.
Thanking you,
Sincerely yours
-----------------------------
Sohel Rana
On behalf of Group No: 08
Id No: M150203039
Session: 2015-16
6th
Batch
Department Of Finance
Jagannath University, Dhaka
.

4. Abstract
A profitable banking sector is better able to withstand negative shocks and contribute to the
stability of the financial system. For resisting negative shocks and maintaining financial
stability, it is important to understand the determinants that mostly affect the profitability of
the banking sector of Bangladesh.The study identifies bank specific characteristics and
macroeconomic determinants of profitability in the Bangladesh’s banking sector over the
years 2010 to 2016. The study uses relevant data from the Annual Reports of Dutch Bangla
Bank Ltd (DBBL) and other macroeconomic data that influence the overall economic
performance. The bank specific determinants that are important in influencing profitability:
Return on Equity (ROE), Return on Assets (ROA) and Capital Adequacy Ratio (CAR),Bank
size, Liquidity ratio, Loan to deposit ratio. Besides, three macroeconomic determinants
significantly influence profitability including growth in GDP, inflation and Interest Rates.
Keywords: Determinants of Profitability of Banking Sector of Bangladesh,Bank specific
characteristics, Macroeconomic determinants, Dutch Bangla Bank Ltd (DBBL),

5. Introduction
The links between financial intermediation and economic growth focus on the key functions
of financial systems in the saving investment growth nexus. The efficiency of financial
intermediation affects country’s economic growth and, at the same time, the bank (financial
intermediation) insolvencies could result in systemic crises which have negative
consequences for the economy as a whole. The diversification and techniques of risk sharing
and pooling affect to the reduction of risks. The banking sector in Bangladesh is one of the
most important mechanisms of its financial system. In maintaining the stability of the
banking system its sustainable profitability is very important. The financial services include
short and long1term credit, mortgages, pensions, savings, payments, leasing and factoring.
All these services that are offered by the banking sector could reduce the incidence of poverty
in Bangladesh. The profitability of the banks could ensure the sustainability of economic
growth in this country.
This study has investigated the performance of the Dutch Bangla Bank Ltd. using the recent
financial data from 2010 to 2016. The period covered include a time of significant reforms in
the country’s banking sector. Since the National Commission of Money, Credit and Banking
recommendations (1986) for broad structural changes in Bangladesh’s financial
intermediation system, a series of actions was taken by the Bangladesh Bank to improve
performance of the banks. These measures included actions such as deregulate interest rates,
improve transparency, strengthening loan classification standards, improve transparency and
reducing Bangladesh Bank’s controlover financial transaction and loan recovery. All the
measures resulted in theimprovement in nonperforming loan ratios and significant rise in
interest related income for all Bangladeshis. Although a series of actions have been taken by
the Bangladesh Bank to improve the performance of the banking system, overall profitability
has remained unstable.
This study tries to examine the determinants that influence theprofitability of the Bangladeshi
banking sector. The bank efficiency or profitability could be influenced by the internal and
external determinants (Sufian and Chong 2008, Athanasoglou, Brissimis and Delis 2008).The
internal determinants focus on bank specific features and are mainly influenced by abank’s
management decisions and policy objectives. While the external determinants, the
macroeconomic characteristics, are not related to bank management but reflect the economic
and legal environment that affect the operation and performance of financial institutions. The
results of the study are likely to be useful to the concerned stakeholders such as
policymakers, investors and also to the banking itself.
.

6. Literature Review
Article Research Objective Main Findings
Molyneux and Seth (1998) Analyzed the performance of
foreign banks in
Australia over the period
1989 to 1993.
The main finding of this
study is that foreign banks
with a full Australian license
have a significantly lower
market share with a return on
asset (ROA) as dependent
variable. The coefficients that
are significantly positive
include a foreign banks’
home country GDP growth,
and the Australian net
interest margin and non-
interest income.
Demirguc-Kunt and Huizinga
(1999)
Used the bank level data for
the period of 1988 to 1995
for 80 countries to examine
how bank characteristics and
the overall banking
environment affect both
interest rate margins and
bank returns.
Results suggest that
macroeconomic and
regulatory conditions have a
significant impact on interest
rate margins and profitability.
Lower market concentration
ratios lead to lower margins
and profits, The foreign
banks have higher margins
and profits than domestic
banks in developing
countries, while the opposite
holds in developed countries.
Saunders and Schumacher
(2000)
Investigated the determinants
of interest margins in six
countries of the European
Union and the US during the
years 1988 to 1995.
They suggest that
macroeconomic volatility and
regulations have a significant
impact on bank interest rate
margins. Besides, the results
also find an important trade-
off between ensuring bank
solvency, as defined by high
capital to asset ratios, and
lowering the cost of financial
services to consumers, as
measured by low interest rate
margins.

7. Guru, Staunton and
Balashanmugam (2002)
Examined the determinants
of bank profitability in
Malaysia. The study used a
sample of 17 commercial
banks during the 1986 to
1995 period.
The determinants of the
profitability consist of
internal and external
determinants. Among the
macroeconomic (external
determinants) indicators,
high interest ratio was
associated with low bank
profitability and inflation was
found to have a positive
effect on bank performance.
Neceur (2003) Used a sample of 10
Tunisian banks from 1980 to
2000 and a panel linear
regression model to
determine the profitability of
bank.
Reported a strong positive
impact of Capitalization to
ROA.
Goddard et al. (2004) Supports the assumptions of
positive relationship between
capital/asset ratio and bank’s
earnings.
Capital adequacy refers to the
sufficiency of the amount of
equity to absorb any shocks
that the bank may
experience. Higher capital
reduces the risk of bank
failure.
Goddard, Molyneux and
Wilson (2004)
Examined the performance
of European banks across six
countries.
They reported a relatively
firm relationship between
size of bank and profitability
that measured by return on
equity (ROE).
Kosmidou (2008) Examined the determinants
of performance of Greek
banks during the years 1990
to 2002—the period of EU
financial integration.
The results suggested that the
high return on average assets
(ROA) was found to be
associated with well
capitalized banks and lower
cost to income ratios.
Sufian and Habibullah
(2009a)
Studied the determinants of
the Chinese bank
profitability.
They assert all the internal
and external variables have
statistically Significant
impact on Chinese banks’
profitability. Nevertheless,
the impacts are not uniform
across bank types.

8. Methodology
Data Collection
The required data for the analysis has been collected from the annual reports published during
the period 2010-2016. The data used for analysis are panel in nature. The data were selected
from the Annual Report of Dutch Bangla Bank Ltd. (DBBL) and from the website of
Bangladesh Bank. We have collected data regarding Dependent and Independent variables
from the above sources. The Dependent variable is the most accepted one the Return on
Equity (ROE) and the independent variables are Return on Assets (ROA), Capital Adequacy
Ratio (CAR), Bank Size, Liquidity Ratio, Loan Deposit Ratio, Non-Performing Loan to Total
Loan Ratio, GDP, Inflation Rate and Deposit Interest Rate. Here the Microeconomic
variables are Return on Equity (ROE) and the independent variables are Return on Assets
(ROA), Capital Adequacy Ratio (CAR), Bank Size, Liquidity Ratio, Loan Deposit Ratio, and
Non-Performing Loan to Total Loan Ratio and the Macroeconomic variables are GDP,
Inflation Rate and Deposit Interest Rate.
Research Method
The overall research is done by taking considerations of dependent and independent variables
which can also be further classified into Microeconomic and Macroeconomic variables.
Dependent Variable (Microeconomic Variable) -
Return on Equity (ROE)-Return on equity (ROE) is the amount of net income returned as a
percentage of shareholders equity.
ROE is expressed as a percentage and calculated as:
Return on Equity = Net Income/Shareholder's Equity
Independent Variables (Macroeconomic Variables) -
Return on Assets (ROA)-Return on assets (ROA) is an indicator of how profitable a
company is relative to its total assets.
The formula for return on assets is:ROA= Net Income/Total Assets
Capital Adequacy Ratio (CAR)-The capital adequacy ratio (CAR) is an international
standard that measures a bank’s risk of insolvency from excessive losses. Currently, the
minimum acceptable ratio is 8%. Maintaining an acceptable CAR protects bank depositors
and the financial system as a whole.
Expressed as a formula, the CAR equals the sum of the bank’s tier one capital plus tier two
capital, divided by its risk-weighted assets.
Bank Size-The log of the total assets of the bank is termed as the Bank size.
Liquidity Ratio-The ratio between the liquid assets and the liabilities of a bank or other institution.

9. Loan-to-Deposit Ratio-The loan-to-deposit ratio (LTD) is a commonly used statistic for
assessing a bank's liquidity by dividing the bank's total loans by its total deposits.
Nonperforming loan to loan ratio-A nonperforming loan (NPL) is the sum of borrowed
money upon which the debtor has not made his scheduled payments for at least 90 days.
Independent Variables (Macroeconomic Variables)
Gross domestic product (GDP)- is a monetary measure of the market value of all final
goods and services produced in a period (quarterly or yearly) or income.
Inflation Rate-Inflation is the rate at which the general level of prices for goods and services
is rising and, consequently, the purchasing power of currency is falling.
Deposit Interest Rate- The interest rate paid by financial institutions to deposit account
holders. Deposit accounts include certificates of deposit, savings accounts and self-directed
deposit retirement accounts.
Our collected data are presented below-
Microeconomic Variables of DBBL
YEAR ROE ROA CAR Bank Size Liquidity Ratio Loan Deposit Ratio NPLTL
2010 35.30% 2.20% 9.60% 11 66.87% 81.30% 2.40%
2011 27% 1.90% 11.80% 11.09 64.62% 79.10% 2.70%
2012 23.40% 1.70% 12% 11.19 58.78% 73.10% 3.00%
2013 17% 1.20% 13.70% 11.27 57.36% 73.30% 3.90%
2014 16.20% 1.10% 13.80% 11.33 57.61% 74.60% 4.40%
2015 19.30% 1.30% 13.70% 11.39 62.39% 81.50% 3.70%
2016 10.20% 0.70% 13.10% 11.44 62.64% 83.70% 5.20%
Source: Annual Reports of DBBL- 2010-2016
Macroeconomic Variables
YEAR GDP INFLATION Interets Rate
2010 5.57% 8.15% 7.14%
2011 6.46% 10.33% 10.02%
2012 6.52% 8.78% 11.69%
2013 6.01% 7.54% 11.19%
2014 6.06% 7.00% 9.08%
2015 6.55% 6.19% 8.20%
2016 7.05% 5.52% 6.20%
Source: Bangladesh Bank Website
We will exhibit here, how the dependent variable is influenced by independent variables
through various analyses and will also see the relation between the Microeconomic and
Macroeconomic variables.

10. Research Model
In this report we will perform six analyses to show how various determinants of profitability
works. Now we will give a breif overview of all the reseach models we have used in this task.
Descriptive statistics are used to describe the basic features of the data in a study. They
provide simple summaries about the sample and the measures. Together with simple graphics
analysis, they form the basis of virtually every quantitative analysis of data.
Normality tests are used to determine if a data set is well-modeled by a normal distribution
and to compute how likely it is for a random variable underlying the data set to be normally
distributed.
Stationary Test- In statistics, the Dickey–Fuller test tests the null hypothesis of whether a
unit root is present in an autoregressive model. The alternative hypothesis is different
depending on which version of the test is used, but is usually stationary or trend-stationary. It
is named after the statisticiansDavid Dickey and Wayne Fuller, who developed the test in
1979.
Regression analysis is a set of statistical processes for estimating the relationships among
variables. It includes many techniques for modeling and analyzing several variables, when
the focus is on the relationship between a dependent variable and one or more independent
variables (or 'predictors'). More specifically, regression analysis helps one understand how
the typical value of the dependent variable (or 'criterion variable') changes when any one of
the independent variables is varied, while the other independent variables are held fixed.
Multicollinearity (also co linearity) is a phenomenon in which one predictor variables in a
multiple regression model can be linearly predicted from the others with a substantial degree
of accuracy. In this situation the coefficient estimates of the multiple regressions may change
erratically in response to small changes in the model or the data.
Autocorrelation, also known as serial correlation, is the correlation of a signal with a
delayed copy of itself as a function of delay. Informally, it is the similarity between
observations as a function of the time lag between them. The analysis of autocorrelation is a
mathematical tool for finding repeating patterns, such as the presence of a periodic signal
obscured by noise, or identifying the missing fundamental frequency in a signal implied by
its harmonic frequencies.
The collected data will be used in these models to determine the profitability of the Bank and
how well it is interrelated with the macroeconomic factors.

11. 1. Descriptive Statistics
This section includes the descriptive statistics of the model variables, estimated results of the
model mentioned in the methodology and therobustness test of the model. To identify the
internal factors that contribute tothe profitability of the bank, a balanced panel data has been
used.In the model all the variables are tested for each cross section and each time period. The
descriptive statistics is presented in the table below-
ROE ROA CAR Bank
Size
Liquidit
y Ratio
Loan
to
Deposi
t Ratio
NPLT
L
GDP Inflatio
n
Interest
Rate
(Deposit
)
Mean 0.21
2
0.01
4
0.12
3
11.2
4
0.614 0.7809 0.036 0.06
3
0.076 0.091
Median 0.19
3
0.01
3
0.13
1
11.2
7
0.623 0.791 0.037 0.06
4
0.075 0.091
Std.
Deviation
0.08
3
0.00
5
.015 0.16
1
0.036 0.0437 0.009 0.00
4
0.016 0.020
Skewness 0.62
0
0.13
4
-1.31 -0.41 0.216 -0.069 0.408 -0.09 0.438 -0.09
Kurtosis 0.37
7
-0.8 1.42 -1.07 -1.46 -2.16 -0.72 0.12
3
-0.11 -1.35
Observation
s
7 7 7 7 7 7 7 7 7 7
Table- Descriptive Statistics of the major variables
Table shows the general characteristics of thedata. If we observe the descriptive statistics we
can see that the central (meanand median) values are very near to each other. The minimum
variation is found in case of ROA which indicates that the ratio remains stable across time
periods for the bank. Other variables variation are also very little which is a good sign for the
bank.
2. Regression Analysis
In order to perform Regression analysis, we need to assign dependent and independent
variables. For the purpose of the study we will take Return on Equity as Dependent variable
and GDP, Inflation Rate and Deposit interest rate as Independent variables. We can find out
how the rate of equity changes due to the changes in these macroeconomic factors.
Dependent Variable- Return On Equity (ROE)
Independent Variables- GDP, Inflation Rate, Interest Rate (Deposit)

12. Result of Regression Analysis
SUMMARY
OUTPUT
Regression Statistics
Multiple R 0.90412
R Square 0.817433
Adjusted R
Square
0.634867
Standard Error 0.049617
Observations 7
ANOVA
df SS MS F Significance
F
Regression 3 0.033068 0.011023 4.477449 0.124920347
Residual 3 0.007386 0.002462
Total 6 0.040454
Coefficients Standard
Error
t Stat P-value Lower 95% Upper
95%
Intercept 0.559717 0.329169 1.700393 0.187616 -
0.48784611
1.607279
GDP -7.82556 4.467959 -1.75148 0.178159 -
22.0445948
6.393484
INFLATION 4.134524 1.609524 2.568787 0.08258 -
0.98769976
9.256748
Interest Rate -1.86703 1.228894 -1.51928 0.226007 -
5.77792288
2.043858
Table- Regression Analysis of the dependent and independent variables
The fitted line is reasonably good. The goodness of fit, R2
shows that the independent
variables explain about 81.75% of the variations in the dependent variable. The value of
adjusted R2
is 0.6348 which states that 63.48% variation in ROE is explained by variations in
independent variables. The value of F statistics is 4.47 and it’s -value shows the overall
significance or explanatory power or the fitness of the model. The coefficient of the Inflation
is 4.134524, implying that a one percent increase in total Inflationincreases the Rate of Equity
by 4.13 percent. On the other hand a one percent increase in Deposit Interest Rate will
decrease the rate of equity by 1.86 percent.Again a one percent increase in GDP will decrease
Rate of Equity by 7.83 percent, if all other variables remaining constant. So, the estimated
regression equation is produced below-
In ROE= 0.559717-7.82556GDP+4.134524Inflation-1.86703Interest Rate.

13. 3. Unit Root Test (Stationarity Test)
We use Augmented Dickey-Fuller (DF) test to operate the unit root test. We consider
following equation;
ΔYt = β1+β2t+Yt-1+Ut
Where, t is trend variable in each case, both hypothesis is that,
H0: = 0 [Time series is non-stationary]
Ha: < 0 [Time series is stationary]
If the null hypothesis is rejected, it means that Yt is a stationary time series. If The computed
value is less than even the 10 percent critical value in absolute terms then null hypothesis is
accepted and conclusion is that the time series is non-stationary. The unit root test results are
shown in Table 1.
VARIABLE NULL
HYPOTHESIS
TEST
STATISTIC
ASY.
CRITICAL
VALUE
10%
DECESION PROBABILITY
ROE
A (1) =0, T-
TEST
-3.593236 -2.593 H0 rejected .4175
ROA
A (1) =0, T-
TEST
-6.9969 -2.593 H0 rejected
CAR
A (1) =0, T-
TEST
-3.372883 -2.593 H0 rejected .587
Bank Size
A (1) =0, T-
TEST
-4.690971 -2.593 H0 rejected .0208
Liquidity Ratio
A (1) =0, T-
TEST
-3.367925 -2.593 H0 rejected .682
Loan Deposit
Ratio
A (1) =0, T-
TEST
-2.054641 -2.593 H0 rejected .2619
NPLTL
A (1) =0, T-
TEST
-1.663665 -2.593 H0 accepted .7638
GDP
A (1) =0, T-
TEST
-3.016418 -2.593 H0 rejected .6518
INFLATION
A (1) =0, T-
TEST
-3.417740 -2.593 H0 rejected .8425
DEPOSIT
INTEREST
RATE
A (1) =0, T-
TEST
-5.669889 -2.593 H0 rejected .7620
Here dependent variable ROA and other independent variables such as ROE, ROA, CAR,
BANK SIZE, LIQUIDITY RATIO, LOAN DEPOSIT, GDP, INFLATION, DEPOSIT
INTEREST RATE all are individually H0 rejected that is they are stationary and does not
contain a unit root. Here the only non-stationary variable is NPLTL. So, the regression of a
stationary time series on other non-stationary time series will not produce a spurious
regression.

14. 4. Normality Test
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
ROE 7 100.0% 0 .0% 7 100.0%
CAR 7 100.0% 0 .0% 7 100.0%
SIZE 7 100.0% 0 .0% 7 100.0%
LIQUIDITY 7 100.0% 0 .0% 7 100.0%
LOANDEPOSITS 7 100.0% 0 .0% 7 100.0%
NPLTL 7 100.0% 0 .0% 7 100.0%
GDP 7 100.0% 0 .0% 7 100.0%
INFLATION 7 100.0% 0 .0% 7 100.0%
DEPOSITINTEREST 7 100.0% 0 .0% 7 100.0%
ROA 7 100.0% 0 .0% 7 100.0%
Tests of Normality
Kolmogorov-Smirnova
Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
ROE .163 7 .200*
.971 7 .903
CAR .217 7 .200*
.837 7 .094
SIZE .135 7 .200*
.962 7 .834
LIQUIDITY .197 7 .200*
.920 7 .466
LOANDEPOSITS .216 7 .200*
.880 7 .226
NPLTL .161 7 .200*
.964 7 .854
GDP .189 7 .200*
.965 7 .864
INFLATION .101 7 .200*
.984 7 .976
DEPOSITINTEREST .136 7 .200*
.963 7 .845
ROA .180 7 .200*
.975 7 .930
a. Lilliefors Significance Correction
*. This is a lower bound of the true significance.
Here we see in the first table the case processing summery of the variables for the normality
test. The table shows that there is no missing variable in the test and all the variable are Valid
at 100%

15. In the second table, we see the normality test of all variables. We take 5% level of
significance and take null hypothesis is that the variables are not normally distributed and
take alternative hypothesis that the variables are normally distributed. Here we use KS test
and SW test for normality test. If the value of Significance is less than .05 then we infer that
the null hypothesis accepted and conclude that the variable is not normally distributed. But if
the significance value is greater than .05 then we reject null hypothesis and accept alternative
hypothesis and conclude that the variable is normally distributed. First, we see the KS test.
Here the sig. of ROE is .20 which is greater than .05. so, we can say that the variable ROE is
normally distributed. In the same we see the significance of all the variable is .20 which is
greater than .05. so, we conclude that all the variables are normally distributed according KS
test. Now we see the SW test. Here we see the sig. of ROE is .903 which is greater than .05.
so, we can say that ROE is normally distributed. In the same way, the sig. of the all variables
are greater than .05. so, we can say that all the variables are normally distributed. The result
of SW TEST is more valid than KS TEST.
5. Auto Correlation Analysis
Auto Correlation Matrix
ROE NPLTL LR LDR INFLA GDP CAR DIR BS
ROE 1.00 0.95 0.61 0.04 0.69 0.64 0.88 0.08 0.94
NPLTL 0.95 1.00 0.46 0.13 0.81 0.54 0.76 0.34 0.91
LR 0.61 0.46 1.00 0.80 0.20 0.08 0.73 0.61 0.49
LDR 0.04 0.13 0.80 1.00 0.36 0.32 0.26 0.89 0.12
INFLA 0.69 0.81 0.20 0.36 1.00 0.31 0.53 0.59 0.82
GDP 0.64 0.54 0.08 0.32 0.31 1.00 0.45 0.15 0.65
CAR 0.88 0.76 0.73 0.26 0.53 0.45 1.00 0.18 0.87
DIR 0.08 0.34 0.61 0.89 0.59 0.15 0.18 1.00 0.23
BS 0.94 0.91 0.49 0.12 0.82 0.65 0.87 0.23 1.00
Here we can see that ROE is strongly negatively related with Non-Performing Loan to Total
Loan. It means that if Non-Performing Loan increase, the return of DBBL will decrease. The
relationship between ROE to Liquidity Ratio is 0.61. It means that liquidity ratio affects the
ROE. If liquidity ratio changes, ROE will positively change. LDR means loan to deposit ratio
and it is poorly related with ROE (0.04). It can affect ROE so much. Here ROE vs Inflation
0.69. It means inflation positively affects ROE. ROE vs GDP is (0.64), it expresses that if
GDP Increase, the return will decrease. The CAR vs ROE is opposite relation. DIR or
Deposit Interest Ratio is 0.04 and means it is poorly related. IF BS or Bank Size increases, it
will decrease return. Because it strong negatively related.

16. Autocorrelation
Autocorrelation can also be referred to as lagged correlation or serial correlation, as it
measures the relationship between a variable's current value and its past values. When
computing autocorrelation, the resulting output can range from 1 to negative 1 in line with the
traditional correlation statistic. An autocorrelation of 1 represents a perfect positive
correlation. I have used Microsoft Excel and the help of YouTube to measure the auto
correlation.
Auto Co-relation
for
Rxy(
0)
for
Rxy(1)
for
Rxy(
2)
For
Rxy(
3)
for
Rxy(
4)
for
Rxy(
5)
for
Rxy(
6)
for
Rxy(-
1)
for
Rxy(-
2)
For
Rxy(-
3)
for
Rxy(-
4)
for
Rxy(-
5)
for
Rxy(-
6)
12.4
6% 0.00%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
9.53
%
8.26
%
6.00
%
5.72
%
6.81
%
3.60
%
7.29
% 9.53%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
6.32
%
4.59
%
4.37
%
5.21
%
2.75
%
0.00
%
5.48
% 6.32%
8.26
%
0.00
%
0.00
%
0.00
%
0.00
%
3.98
%
3.79
%
4.52
%
2.39
%
0.00
%
0.00
%
2.89
% 3.98%
4.59
%
6.00
%
0.00
%
0.00
%
0.00
%
2.75
%
3.28
%
1.73
%
0.00
%
0.00
%
0.00
%
2.62
% 2.75%
3.79
%
4.37
%
5.72
%
0.00
%
0.00
%
3.13
%
1.65
%
0.00
%
0.00
%
0.00
%
0.00
%
3.72
% 3.13%
3.28
%
4.52
%
5.21
%
6.81
%
0.00
%
1.97
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
1.04
% 1.97%
1.65
%
1.73
%
2.39
%
2.75
%
3.60
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
0.00
%
35.5
1%
27.68
%
21.5
7%
16.6
3%
13.3
2%
9.57
%
3.60
%
27.68
%
21.57
%
16.63
%
13.32
%
9.57
%
3.60
%
Sum of
Data 1.181
Square
Root 1.087
Auto
Corelatio
n 0.087
If the returns do exhibit autocorrelation, the stock could be characterized as a momentum
stock; its past ROE seem to influence its future returns. The investor runs a regression with
two prior trading sessions' returns as the independent variables and the current return as the

17. dependent variable. We found that returns positive autocorrelation of 0.87, while past returns
seem to influence future returns, and she/he can adjust her/his portfolio to take advantage of
the autocorrelation and resulting momentum.
6. Multicollinearity
To analyze the Multicollinearity, I have used SPSS statistics tool, and doing so I have
gathered some knowledge on it.
Coefficientsa
Model
Collinearity Statistics
Tolerance VIF
1 CAR .027 36.556
Liquidity .017 58.600
NPLTL .026 39.111
GDP .071 14.114
Inflation .010 101.610
INT .006 168.000
a. Dependent Variable: ROE
Collinearity Diagnosticsa
Model Dimension Eigenvalue
Condition
Index
Variance Proportions
(Constant) CAR
Liqu
idity NPLTL GDP Infl. INT
1 1 6.858 1.000 .00 .00 .00 .00 .00 .00 .00
2 .097 8.397 .00 .00 .00 .01 .00 .00 .00
3 .033 14.410 .00 .00 .00 .00 .00 .00 .00
4 .007 31.410 .00 .00 .00 .04 .02 .02 .00
5 .004 41.694 .00 .04 .00 .04 .03 .00 .00
6 .000 148.954 .27 .26 .00 .08 .10 .05 .03
7 .000 488.558 .73 .70 1.00 .83 .85 .92 .97
Dependent Variable: ROE
Here in first table titled “Coefficientsa” we have VIF in the last column. It shows we have
Multicollinearity issues in all case. The moderate Multicollinearity issue is above 5 and
below 10 and in Liquidity and as all variable CAR, Liquidity, NPLTL GDP, Inflation and
INT, we have absolute Multicollinearity issue as they are above 10. To analyze this, I have
chosen the CAR, Liquidity Ratio, NPLTL, GDP, Inflation Loan Deposit, Interest Rate and as
independent variables and ROE as dependent variable.

18. Conclusion
The study was carried out with the main purpose of identifying the potential bank specific
and macroeconomic determinants of bank profitability in Bangladesh banking sector. To
recap, there are few literatures that examined the profitability of the bank in the developing
countries compared to the studies that were conducted in the context of developed countries.
This study examined the performance of a Bangladeshi commercial bank (Dutch Bangla
Bank Ltd) during the period 2010 to 2016.
The bank specific determinants that were examined consist of Return on Equity (ROE),
Return on Assets (ROA) and Capital Adequacy Ratio (CAR), Bank size, Liquidity ratio,
Loan to deposit ratio, NPLTL. Besides, three macroeconomic determinants significantly
influence profitability including growth in GDP, inflation and Interest Rates. To identify the
significant relationship between profitability and those potential determinants, the study used
the Multiple Regression Analysis (MRA. The study found that all bank specific determinants
influenced the profitability of the Bangladeshi banking sector. The empirical findings of this
study suggest that bank specific characteristics such as Capital Adequacy Ratio (CAR),
Liquidity ratio, Loan to deposit ratio have positive and significant impacts on bank
performance, while non-traditional activities exhibit negative relationship with bank
profitability. The empirical findings suggest that non-traditional activities and liquidity have a
mix (positive and negative) impact on bank profitability. As for the impact of macroeconomic
indicators, GDP and market deposit rates have positive and significant impacts on bank
performance. On the other hand, inflation shows negative relationship with the profitability of
the Bangladeshi banking sector.
The findings of this study offer considerable policy relevance. It could be argued that the
more profitable banks will be able to produce more products and services and directly
improve the economy of the country. In addition, to ensure the competitiveness of the
Bangladeshi banking sector, the capability to maximize risk adjusted returns on investment
and sustaining stable and competitive returns represent a significant element. Thus, from the
regulatory perspective, the performance of the banks should be considered based on their
efficiency and profitability.
Moreover, in view of the increasing competition attributed to the more liberalized banking
sector, bank management as well as the policymakers will be more inclined to identify the
effective and efficient ways to obtain the optimal utilization of capacities. Therefore, the
resources will be fully utilized and eliminate the wastage during the production of banking
products and services.

19. Reference
1. Aburime TU (2008). Determinants of Bank Profitability: Macroeconomics Evidence
from Nigeria. Lagos Journal of Banking, Finance and Economics, Available at
http://ssrn.com/abstract 1231064.
2. Bourke P (1989). Concentration and other determinants of bank profitability in
Europe, North America, and Australia. Journal of Banking and Finance, (13): 65-79.
http://dx.doi.org/10.1016/0378-4266(89)90020-4.
3. Demirguc-Kunt, A. and H. Huizinga (1999), “Determinants of Commercial Bank
Interest Margins and Profitability: Some International Evidence”, WorldBank
Economic Review, pp. 379-408.
4. Goddard, J., P. Molyneux and J. O. C. Wilson (2004), “The Profitability of European
Banks: A Crosssectional and Dynamic Panel Analysis”, The Manchester School, pp.
363 381.
5. Guru, B. K., J. Staunton and B. Balashanmugam (1999), “Determinants of
Commercial Bank Profitability in Malaysia”, Paper Presented at the Proceedings of
the 12th Annual Australian Finance and Banking Conference, Sydney, Australia,
December 16 -17.
6. Kosmidou, K. (2008), “The Determinants of Banks, Profits in Greece during the
period of EU Financial Integration”, Journal of Managerial Finance, Available at:
http://www.emeraldinsight.com, Accessed on 05/06/2010.
7. Molyneux, P. and R. Seth. 1998. “Foreign Banks, Profits, and Commercial Credit
Extensions in the United States.” Applied Financial Economics, 8(5):533-539.
8. Naceur, S. (2003), “The Determinants of the Tunisian Banking Industry Profitability:
Panel Evidence”, Available at: www.mafhoum.com/press6, Accessed in 29 May 2010.
9. Saunders, A. and L. Schumacher. 2000. “The Determinants of Bank Interest Rate
Margins: An International Study.” Journal of International Money and Finance
,19(6):813-832.
10. Sufian, Fadzlan and Muzafar Shah Habibullah (2009), “Bank Specific and
Macroeconomic Determinants of Bank Profitability: Empirical Evidence from the
China Banking Sector”, Frontiers of Economics in China, Vol. 4, No. 2.
11. tradingeconomics.com/bangladesh/deposit-interest-rate
12. tradingeconomics.com/bangladesh/gdp-growth.

20. 13. tradingeconomics.com/bangladesh/inflation-cpi
14. Vong& Chan (2008), 'Determinants of Bank Profitability in Macao',
University of Macau, 222-24.
15. www.dutchbanglabank.com/investor-relations/financial-statements.html