Determinants of Bank Profitability in Bangladesh

Determinants of Bank
Profitability in Bangladesh
2019
BANK SPECIFIC AND MACROECONOMIC DETERMINATS OF BANK
PROFITABILITY BASED ON THE EMPIRICAL EVIDENCE FROM
BANGLADESH BANKING SECTOR
Team EnvyUs
INDEPENDENT UNIVERSITY, BANGLADESH.

Group Name: Team EnvyUs
Group Members:
Name ID
Najmus Sakib 1530721
Md. Ali Ridwan 1510188
Md. Mehedi Hasan 1530260
Md. Tanvir Karim 1530553
Md. Tamim Abdullah 1530153
Course Name: Business Research Methods
Course ID: BUS485
Section: 4
Submitted To: Dr. Samiul Parvez Ahmed
Submission Date: April 1, 2019

i
Letter of Transmittal
April 1, 2019.
Dr. Samiul Parvez Ahmed,
Associate Professor,
Department of Finance,
School of Business,
Independent University, Bangladesh.
Plot-16, Block- B, Aftabuddin Ahmed Road,
Bashundhara R/A, Dhaka-1229, Bangladesh.
Subject: Submission of the research paper on the determinants of bank profitability
Dear Sir,
We would like to report the submission of a research paper on the determinants of the profitability
of banking sector of Bangladesh as you have ordered and gave us the permission to work on. This
research is an important part of our course, and we have tried our best to work on it carefully and
sincerely so that we can create an informative outcome.
From this, we got a remarkable amount of knowledge experience that may prove to be useful in
the future.
We thank you for all the support that made our paper a success and managed in accordance with
our common hope.
Sincerely yours,
Team EnvyUs.

ii
Acknowledgement
The final outcome of this project required a lot of guidance and assistance from many people.
Whatever we have done is only because of such guidance and assistance. We thank the School of
Business of Independent University, Bangladesh for giving us the opportunity to do this project
and providing us all the support and guidance we needed to complete the project.
We are also grateful to our respective faculty, Dr. Samiul Parvez Ahmed, Associate Professor and
Head of Department of Finance, School of Business, Independent University, Bangladesh. We
express deep and sincere gratitude to him whose guidance, encouragement, suggestion and
constructive criticism have contributed immensely to this report.
To all family members, relatives, friends and everyone who shared their support either morally or
physically, thank you.
Sincerely,
Team EnvyUs.

iii
Table of Contents
Letter of Transmittal ........................................................................................................................ i
Acknowledgement ..........................................................................................................................ii
Table of Contents...........................................................................................................................iii
Table Of Figures ............................................................................................................................ iv
Abstract........................................................................................................................................... 1
1. Introduction................................................................................................................................. 2
2. Problem Statement...................................................................................................................... 4
3. Literature Review........................................................................................................................ 5
3.1 Performance Measure: Profitability:..................................................................................... 5
3.2 Determinants of Bank Profitability:...................................................................................... 5
3.2.1 Internal Determinant: Liquidity..................................................................................... 5
3.2.2 Internal Determinant: Credit Risk.................................................................................. 9
3.2.3. Internal Determinant: Size.......................................................................................... 12
3.2.4 Internal Determinant: Capitalization............................................................................ 15
3.2.5 External Determinant: Inflation ................................................................................... 18
4. Methodology............................................................................................................................. 21
4.1 Descriptors of Research Design.......................................................................................... 21
4.2 Research Approach ............................................................................................................. 23
4.3 Research Objectives............................................................................................................ 23
4.4 Conceptual Framework....................................................................................................... 23
4.5 Hypotheses.......................................................................................................................... 26
4.5.1 Credit Risk and Profitability........................................................................................ 26
4.5.2 Liquidity and Profitability............................................................................................ 26
4.5.3 Capitalization and Profitability.................................................................................... 27
4.5.4 Size and Profitability.................................................................................................... 27
4.5.5 Inflation Rate and Profitability .................................................................................... 27
4.6 Sampling and Data Source.................................................................................................. 27
4.7 Data Analysis...................................................................................................................... 28
4.7.1 Descriptive Statistics.................................................................................................... 28
4.7.1.1 ROA (Return on Asset)......................................................................................... 29
4.7.1.2 Capitalization........................................................................................................ 29

iv
4.7.1.3 Credit Risk ............................................................................................................ 29
4.7.1.4 Inflation................................................................................................................. 29
4.7.1.5 Liquidity................................................................................................................ 30
4.7.1.6 Size........................................................................................................................ 30
4.7.2 Correlation Analysis .................................................................................................... 30
4.7.3 Regression Analysis..................................................................................................... 32
5. Findings and Analysis............................................................................................................... 34
5.1 Credit Risk and Bank Profitability...................................................................................... 34
5.2 Liquidity and Bank Profitability......................................................................................... 34
5.3 Capitalization and Bank Profitability.................................................................................. 34
5.4 Size and Bank Profitability................................................................................................. 35
5.5 Inflation and Bank Profitability .......................................................................................... 35
6. Recommendation ...................................................................................................................... 35
7. Conclusion ................................................................................................................................ 36
REFERENCES ............................................................................................................................. 37
Table Of Figures
Table 1: ROA according to Annual Report 2016-2017 of Bangladesh Bank................................. 2
Table 2: ROE according to Annual Report 2016-2017 of Bangladesh Bank................................. 3
Table 3: Descriptive Statistics ...................................................................................................... 28
Table 4: Correlation Analysis ....................................................................................................... 30
Table 5: Correlation ...................................................................................................................... 31
Table 6: Regression Analysis (a) ................................................................................................. 32
Table 7: Regression Analysis (b).................................................................................................. 32
Table 8: Ruling on Rejecting Null Hypothesis............................................................................. 33
Figure 1:Conceptual Framework .................................................................................................. 24
Figure 2: Explanations of Variables and proposed relation.......................................................... 25

Determinants of Bank Profitability
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Abstract
This paper intends to investigate the various bank-specific and macroeconomic forces that play a
major role behind the profitability of banks of Bangladesh. The paper considers liquidity,
capitalization, size, credit risk and inflation as forces behind bank profitability and examines to
what extent do these things effect the profitability of a bank. Data of 5 private commercial banks
during the period of 2013-2017 was used and analyzed in order to generate results.
This paper also acknowledges the fact that the study has limitations. Since, it is only conducted on
5 Private Commercial Banks, the study may not represent the most accurate picture of the banking
industry.

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1. Introduction
Bangladesh, as a country, has come a long way when the topic is economic growth. From 1972, in
just 40 years, the Gross Domestic Product (GDP) has increased from US $5.70 Billion to US
$285.82 Billion. The economy is the 42nd
largest in nominal terms and 31st
in terms of Purchase
Power Parity. From a least developed country to meeting the requirements of a becoming a
‘developing’ country and expected to become a developed country by 2041, it’s safe to say,
Bangladesh, as an economy, is growing rapidly.
In every country, banking sector is plays a significant role in the development of the financial
system and the whole economy in general. Bangladesh is not an exception. According to the report
of Bangladesh Bank, there are 59 scheduled banks. Among those, 41 are Private Commercial
Banks (PCBs), 6 are State Owned Commercial Banks (SOCBs), 3 are Specialized Banks (SDBs)
and 9 are Foreign Commercial Banks (FCBs). The number of total branches increased from 6,886
in 2008 to 10,114 by 2018.
The aforementioned information sounds good and promising, no doubt. But, the bigger picture is
a bit different. An unparalleled increase in bad debts, financial scams and liquidity shortage has
caused a severe crisis in the financial sector. As of now, more than 10 of the country’s strongest
banks are suffering from capital deficit. 13 of the banks were listed under regulators’ list of banks
with bad financial state in 2017.
According to the Annual Report 2016-2017 of Bangladesh Bank, profitability (measured by Return
on Asset and Return on Equity) of the banks from 2009 is as follows:
Table 1: ROA according to Annual Report 2016-2017 of Bangladesh Bank
ROA
Bank
Type
2009 2010 2011 2012 2013 2014 2015 2016 2017
(June)
SOCBs 1.0 1.1 1.3 -0.56 0.59 -0.55 -0.04 -0.16 -0.63
PCBs 1.6 2.1 1.6 0.92 0.95 0.99 1.00 1.03 0.68
FCBs 3.2 2.9 3.2 3.27 2.98 3.38 2.92 2.56 2.15

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Table 2: ROE according to Annual Report 2016-2017 of Bangladesh Bank
While all three types of bank experience inconsistent performance, FCBs remain in a very strong
position. SCBs suffer the most as the ROA and ROE both are negative from 2014-2017 which is
also under industry average. The PCBs experience a downfall for the years 2011-2012 in terms of
ROA and 2011-2013 in terms of ROE. PCBs experience a growth both in terms of ROA and ROE
from the aforementioned years, but the growth isn’t significant.
To understand the underlying reasons behind the performance of the bank, identifying,
understanding and evaluating the factors effecting the performance is necessary. This paper seeks
to examine the determinants of bank profitability over the period 2013-2017 for 5 Private
Commercial Banks. This paper investigates two different types of determinants of bank
profitability. The first type includes liquidity, credit risk, capitalization and size which are
considered as bank-specific or internal determinants. The second type includes inflation which is
considered as macroeconomic determinant or external determinant.
The remainder of the paper is structured as follows. Section 2 states the problem, Section 3 surveys
the relevant literature on bank profitability, Section 4 describes the methodology, Section 5
illustrates the findings and analyzation of the findings, Section 6 provides recommendation and
Section 7 concludes.
ROE
Bank
Type
2009 2010 2011 2012 2013 2014 2015 2016 2017
(June)
SOCBs 26.2 18.4 19.7 -11.87 10.93 -13.46 -1.47 -6.02 -19.38
PCBs 21.0 20.9 15.7 10.17 9.76 10.26 10.75 11.09 7.50
FCBs 22.4 17.0 16.6 17.29 16.93 17.67 14.59 13.08 10.81

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2. Problem Statement
As mentioned above, Bangladesh is a growing economy and banks are one of the most important
part behind it. The banking sector of Bangladesh is recently going through a very tough time. In
order to understand the situation and find the reasons behind the profit of the banks, we went
through five PCBs data for the period 2013-2017.
While going through the data, we found that the profit (mostly measured by ROA/ROE) of these
banks are not constant. In some years, the ROA and ROE was negative too. Even when the banks
came out of the situation, the growth rate wasn’t very high.
So, the questions started rising. What causes a bank to make profit? Are the determinants of profit
only bank specific? How significant is the role of different macroeconomic factors? This paper
seeks to investigate the issue of inconsistent performance of banks of Bangladesh and the things
are accountable for the bank’s profitability.

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3. Literature Review
3.1 Performance Measure: Profitability:
Profitability is the dependent variable used in this study. Profit is as a tool to measure the
performance of the company, which provides information relating to the management
responsibilities in the management of the resources entrusted to them. (Munawir, 2007: 68) We
will be using Return on Asset (ROA) as a measure of profitability. According to Masood and
Ashraf (2012), Return on assets (ROA) is a common measure for banks profitability to analyze
and evaluate the ability of banks to generate return from its sources of funds to produce profit.
Hassan and Bashir (2003) stated that ROA shows the profit earned per dollar of assets and most
importantly, reflects the management ability to utilize the bank’s financial and real investment
resources to generate profits. Based on a research, Rivard and Thomas (1997) suggest that bank
profitability is best measured by ROA in that ROA is not distorted by high equity multipliers and
ROA represents a better measure of the ability of the firm to generate returns on its portfolio of
assets. ROE on the other hand, reflects how effectively a bank management is in utilizing its
shareholder’s funds.
3.2 Determinants of Bank Profitability:
3.2.1 Internal Determinant: Liquidity
Banks face two main issues in regards to liquidity. Banks are in charge of managing liquidity
creation and liquidity risk. Liquidity creation enables investors and organizations to remain fluid
for organizations particularly when different types of financing move toward becoming difficult.
Managing liquidity risk is to guarantee the banks claim liquidity with the goal that the bank can
keep on serving its capacity.
In this study, loans to total asset ratio is used.
Nwaezeaku (2006) defined liquidity as the degree of convertibility to cash or the ease with which
any assets can be converted to cash. Liquidity risk is defined as a situation when a bank can’t meet
all the request of depositors either totally or partially for a given period (Jenkinson, 2008) An
asset’s liquidity can be used to describe how quickly, easily and costly it is to convert that asset
into cash (Berger & Bouwman, 2008).

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In the past, better practices for liquidity risk measures focused on the use of liquidity ratios. The
ratios previous studies used include liquid assets to total assets ratio (e.g. Bourke, 1989; Molyneux
and Thornton, 1992; Barth et al., 2003; Demirgüç-Kunt et al., 2003)
Besides, some studies use loans to total assets ratio (e.g. Demirgüç-Kunt and Huizinga, 1999;
Athanasoglou et al., 2006).
Liquidity risk measures can be calculated from balance sheet positions. In the past, better practices
for liquidity risk measures focused on the use of liquidity ratios. However, Poorman and Blake
(2005) indicated that it was not enough to measure liquidity just using liquidity ratios and it was
not the solution.
To measure the liquidity of banks, liquid assets to total assets ratio is used. The higher percentage
of ratio signifies that banks are more liquid. The one major cause of bank failure is inadequate
liquidity. On the other hand, more liquid assets in hand have an opportunity cost of higher return.
(Masood and Ashraf, 2012)
Financial ratios that measure liquidity are referred to as a company’s liquidity ratios. One such
ratio is the current ratio which determines a company’s ability to pay short term debts as they come
due (Van Ness, 2009).
Liquidity risk on bank profitability is mixed. Some studies found out the positive effect (e.g.
Molyneux and Thornton, 1992; Barth et al., 2003); others found out the negative effect (e.g.
Bourke, 1989; Demirgüç-Kunt and Huizinga, 1999; Kosmidou, 2005; Kosmidou, 2008).
Besides, previous studies found that banks with high liquidity have lower net interest margins.
(e.g. Demirgüç-Kunt and Huizinga, 1999; Shen et al., 2001; Demirgüç-Kunt et al., 2003; Naceur
and Kandil, 2009).
When a bank is not able to settle its obligations as they arise, then it is regarded illiquid. In cases
of illiquidity, he shareholders possibly depositors’ losses which as a result of bank default
(Odunayo & Oluwafeyisayo, 2015)
Liquidity and profitability are key variables, which provide information concerning the
performance and survival of many businesses. In order to obtain a long-term survival and healthy

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growth of any business venture, both profitability and liquidity should go hand in hand (ahmad,
2016)
Different researchers find that there is a relationship between liquidity and profitability of a bank.
They discovered return on asset dependent and liquidity independent variables. They inferred that
they were a causal connection between these two factors. In here we discuss their literature review
and their findings on currency and interest rate.
The tool for sustainable growth and profitable operations as well as the substance of the depositor’s
confidence and for the banks in meeting their short term obligations is adequate liquidity. (Ibe,
2013)
According to Eljelly (2004, p.2) the management of working capital becomes even more important
during crises periods, “liquidity management is important in good times and it takes further
importance in troubled times.” Also according to him, the efficient management of the liquidity
levels of a company is of extreme relevance for the firm’s profitability and well-being.
Hirigoyen (1985) argues that over the medium and long run the relationship between liquidity and
profitability could become positive, in the sense that a low liquidity would result in a lower
profitability due to greater need loans, and low profitability would not generate sufficient cash
flow, thus forming a vicious cycle.
Hirigoyen (1985) concludes his study showing that profitability and liquidity are determinants of
the company’s equilibrated survival. These two factors are at the same time, the results
(consequences) and restrictions (constraints). Therefore, the integration of both should lead to the
goal of flexibility.
According to Chandra (2001, p.72), normally a high liquidity is considered to be a sign of financial
strength, however according to some authors as AssafNeto (2003, p.22), a high liquidity can be as
undesirable as a low.
However Arnold (2008, p.537) points that holding cash also provides some advantages, such as
(1) provides the payment for daily expenses, such as salaries, materials and taxes. (2) Due to the
fact that future cash flows are uncertain, holding cash gives a safety margin for eventual

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downturns. And finally (3) the ownership of cash guarantees the undertaken of highly profitable
investments that demands immediate payment.
Liquidity and profitability are two important aspects of a company’s health. The short – term
prospect of a company is judged by its liquidity, because it defines the company’s capability to
pay its short – term debts (Siegel & Shim, 2000).
According to Fraser (1998) liquidity and cash management may be financial discipline that are
often misunderstood and overlooked. That’s why it is important to understand the nature of
liquidity and how it actually affects a company’s profitability. A financial manager must find the
right balance between liquidity to ensure the survival of a company and also keep profitability
maintained in order to give the optimal return for its shareholder (Shin & Soenen, 1998).
Hong et al. (2014) revealed that systematic liquidity risk was the main cause of bank failures
occurring over the 2009 to 2010 period in the aftermath of the 2007-2008 GFC. Liquidity risk
could lead to bank failures through systematic and idiosyncratic channels. In addition, Acharya
and Naqvi (2012) and Wagner (2007) have shown that short-term liquidity have implications for
bank risk-taking and bank stability.
Purbaningsih and Ekuitas (2013) note that liquidity risk could arise in two forms; (1) there are
excess funds lying idle in the bank, which leads to the sacrifice of the high interest rates, (2) lack
of funds, which could result in incapability by the banks to meet their short-term financial
obligations (Purbaningsih & Ekuitas, 2013, p.58) According to Banafa (2015), if liquidity risk is
not mitigated, banks are likely to make losses and even collapse.
Dang (2011) affirms that a positive association exists amid commercial banks’ financial
performance and liquidity.
He and Xiong, 2012c, Hieider et al., 2009, and Acharya and Viswanathan (2011) have already
showed that credit and liquidity risks simultaneously interact and influence the stability of banks.
According to Dermine (1986), liquidity risk is seen as a profit-lowering cost. A loan default
augments liquidity risk because of the lowered cash inflow and depreciations it triggers.

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3.2.2 Internal Determinant: Credit Risk
Sound credit risk management system (which include risk identification, measurement,
assessment, monitoring and control) are policies and strategies (guidelines) which clearly outline
the purview and allocation of a bank credit facilities and the way in which credit portfolio is
managed; that is, how loans were originated, appraised, supervised and collected (Basel, 1999;
Greuning and Bratanovic 2003, Pricewaterhouse, 1994)
The key principles in credit risk management are; firstly, establishment of a clear structure,
allocation of responsibility and accountability, processes have to be prioritized and disciplined,
responsibilities should be clearly communicated and accountability assigned thereto (Lindgren,
1987). Heffernan (1996) stressed that credit risk is the risk that an asset or loan becomes
irrecoverable, in the case of outright default or the risk of delay in servicing of loans and advances.
Commercial banks may have a keen awareness of the need to identify, measure, monitor and
control credit risk as well as to determine that they hold adequate capital against these risks and
that they are adequately compensated for risks incurred (Bhattarai, 2016).
It has also been identified that high-quality credit risk management staff are critical to ensuring
that the depth of knowledge and judgment needed is always available, thus ensuring the
successfully management of credit risk in banks (Koford and Tschoegl, 1997 and Wyman, 1999).
The ratio of loan loss provisions to total loans (LLP/TL) is incorporated as an independent variable
in the regression analysis as a proxy of credit risk. (Sufian & Habibullah, 2009). Anderson (2013)
defines credit risk as “the probability that a legally enforceable contract may become worthless (or
at least substantially reduced in value) because the counterparty defaults and goes out of business.”
In the words of Saunders and Cornett (2011), it is the “risk that the promised cash flows from loans
and securities held by financial institutions may not be paid in full.” Thus, credit risk emerges due
to default by debt issuers and counterparties in derivatives transactions (Hull, 2012). A critical
assessment of banking trends in the past has suggested that credit risks associated with asset
portfolio causes most banks to worry (Basel Committee on Banking Supervision, 2006).
The ability of a bank to make profit and stay in operation depends on how it can make enough
room to avoid risks, but most importantly, be able to react positively to lose from non-performing
loans (Bobakovia, 2003)

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According to the European Central Bank (2016), “a bank loan is considered non-performing when
more than 90 days pass without the borrower paying the agreed installments or interest. Non-
performing loans are also called bad debt.”
A study conducted by Hosna, et al. (2009) on four commercial banks in Sweden, for the 2000 to
2008 financial year, revealed a positive correlation between credit risk and profitability.
Kithinji (2010), conducted a study to assess the impact of credit risk on profitability using
commercial banks in Kanya. The study resulted in a neutral impact from credit risk on profitability.
Four years later, another researcher in Kenya, Akonga'a (2014), conducted a study using data
gathered from 2008 to 2013 on 44 commercial banks in Kenya to find the effect of financial risk
management on the financial performance of banks in the country. Her study used return on assets
as a measure of financial performance and non-performing loans as a measure of financial risk.
Findings revealed that financial risk had significant impact on the financial performance of
commercial banks.
Chijoriga (1997) argues that the size and the level of loss caused by credit risk as compared to
others were severe to collapse a bank.
In order to minimize loan losses as well as credit risk, it is crucial for banks to have an effective
credit risk management system in place (Santomera 1997, Basel 1999). Credit risk management is
very vital to measuring and optimizing the profitability of banks. The long term success of any
banking institution depended on effective system that ensures repayments of loans by borrowers
which was critical in dealing with asymmetric information problems, thus, reduced the level of
loan losses, Basel (1999).
Effective credit risk management system involved establishing a suitable credit risk environment;
operating under a sound credit granting process, maintaining an appropriate credit administration
that involves monitoring, processing as well as enough controls over credit risk (Greuning and
Bratanovic 2003)
Most credit risk officers in the banking industry analyse factors such as; inflation, the level of
interest rates, the GDP rate, market value of collaterals among others, for banks in mortgage
financing. Also, traditional financial management texts posit that credit manager would take note

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of the five Cs of credit – character, capacity, capital, collateral and conditions to evaluate the
probability of default (Casu et al, 2006 and; Zech, 2003)
Cooper et al (2003) add that variations in credit risks would lead to variations in the health of
banks’ loan portfolio which in turn affect bank performance.
Ducas and McLaughlin (1990) had earlier argued that volatility of bank profitability is largely due
to credit risk. Specifically, they claim that the change in bank performance or profitability are
mainly due to changes in credit risk because increased exposure to credit risk leads to fall in bank
performance and profitability.
Kargi (2011) argues that credit risk management maximizes banks’ risk adjusted rate of return by
maintaining credit risk exposure within acceptable limit in order to provide framework for
understanding the impact of credit risk management on banks’ profitability.
Golden and Walker (1993) explained that contingencies are important for bankers in order to
reduce incidence of bad loans.
Noman et al. (2015) conducted an empirical study with the aims to find the effect of credit risk on
profitability of the banking sectors of Bangladesh. The study used an unbalanced panel data and
172 observations from 18 private commercial banks from 2003 to 2013. The study found a negative
and significant effect of credit risk on profitability.
Kaaya and Pastory (2013) examined the impact of credit risk on profitability and revealed that the
indicator of credit risk has the significant negative impact on profitability.
Felix and Claudine (2008) analyzed the relationship between bank performance and credit risk
management. The results confirmed that credit risk (a ratio of non-performing loan to total loan)
has a significant negative impact on return on equity and return on assets both measuring
profitability. The bank theory identifies six popular categories of risk which are related with credit
guidelines of banks. They include credit risk (risk of repayment), interest risk, portfolio risk,
operating risk, credit deficiency risk, and trade union risk (Muhammad, 2014). Analyses have
shown that, credit risk is the main risk that causes the collapse of a bank. (Sinkey, 1992, p.279)

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3.2.3. Internal Determinant: Size
According to several resources done on this sector, researchers have found that bank performance
varies with the size (economies of scale). We are going to use the log of total asset as a proxy
variable for size as total asset has a close correlation with the size. This variable controls for the
cost differences and product and risk diversification that result from the size of a bank. The first
variable could lead to a positive relationship between size and bank profitability but requires
substantial economies of scale (Bourke, 1989; Molyneux & Thornton, 1992; Akhaven et al., 1997;
Bikker & Hu, 2002; Goddard et al., 2004). The second variable could have a negative impact if
increased diversification results in lesser credit risk and therefore lower returns.
In this study, the logarithm of total assets has been used to indicate the size.
Size is introduced to account for existing economies or diseconomies of scale in the market.
Smirlock (1985) finds a positive and significant relationship between size and bank profitability.
Demirguc-Kunt and Huizinga (2000) suggest that the extent to which various financial, legal and
other factors (e.g. corruption) affect bank profitability is closely linked to firm size. In addition, as
Short (1979) argues, size is closely related to the capital adequacy of a bank since relatively large
banks tend to raise less expensive capital and, hence, appear more profitable.
Using similar arguments, Bikker and Hu (2002) and Goddard et al. (2004), among others, link
bank size – especially in the case of small to medium-sized banks – to capital and in turn to
profitability. However, many other researchers suggest that little cost saving can be achieved by
increasing the size of a banking firm (Berger et al., 1987).
A study done on the banks of Croatia by Pervan, Pelivan and Arnerić (2015) found out that size is
considered to have a positive impact on bank profitability, i.e. larger banks should make larger
profits because they exploit the economies of scale. This would mean that, based on the economies
of scale, larger banks can obtain cost advantages, achieve greater operational efficiency and
consequently realise higher profits. For example, banks can adopt new methods and technologies
in their operations and/or may hire more qualified staff and therefore become more efficient and
competitive in comparison to their rivals. The study also showed that size of the bank also has a
positive impact on its reputation, thus facilitating the sales of high-quality products and services
at higher prices, which in turn lead to higher profits. However, for those banks that have become
extremely large, this impact could also be negative. The assumption that the largest banks usually

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make the largest profitability rates is associated with the market share (MSd) variable. Therefore,
many banks try to increase market share, i.e. their own sales, in order to make higher profits.
Reasons for that can be found in economies of scale and scope and the resulting cost advantages.
Furthermore, large firms may have more capital and may be more innovative than their smaller
competitors. In addition, high market shares are usually related to market power, i.e. firm’s ability
to raise the market price of a good or service and consequently achieve greater profitability.
Increasing banks’ asset size can also reduce risk by diversifying operations across product lines,
sectors, and regions (Mester 2010). Halkos and Salamouris (2004) investigated the effect of bank
assets on its efficiency, they concluded from their study about Greek banks that, the higher the
bank assets the higher the efficiency. Similar results were found by (limam, 1998) in gulf countries
that is, a positive relationship between asset size and efficiency, and the same results were found
in European banking sectors through a study of Bikker (1999). As margins and fees tended to
tighten in many domestic banking markets during the 1980s and 1990s, many banks responded by
implementing strategies of product diversification, merger and overseas expansion in an attempt
to defend their profitability (Santomero and Eckles, 2000; Hughes et al., 2002). Financial services
diversification allows managers to offer a wider range of services and spread the risks of lending
across a larger number of asset categories, reducing monitoring costs (Diamond, 1984). Weber
and Devaney (1998) indicated that local banks in rural areas usually make only local loans and
tend to be less technically efficient due to small-scale operations.
Gilbert (1997, 2000) asserts that large banks with more resources are likely to expand into rural
areas leading to greater competition and efficiency. However, there is another view by Gilbert and
Belongia (1988) that argues that large banks, being able to diversify, may be less inclined to invest
in agricultural loans. The entry of large banks into rural areas, therefore, may actually lower credit
availability in rural areas.
Goddard (2004) finds that a bank’s size could directly determine a bank’s profitability. According
to Goddard, a bank’s profitability initially increases with size due to the scale economy but declines
if the size exceeds a threshold level—the exhaustion of the scale economy and bureaucratic
managerial style could lead to performance inefficiency.
Berger (1995) and Humphrey (1997) find that, in general, large banks perform better than small
banks, but it is less clear whether large banks benefit from the scale economy. They state that better

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practice in terms of technology and management structure is more important than the scale
efficiency. Spathes (2002) had tested the financial markets through a study conducted to
investigate Greek banks, his study focused on the banks asset size effect, he aimed of his study to
investigate the effectiveness of large and small Greek banks through investigating (ROE) as a
profitability measure and its relationship with some factors classification such as assets volume,
liquidity and risk. Data from year1990 up to 1999 were used to discover the success factors of
these banks, the results of the study proved that, large banks are more efficient than small ones;
small banks are characterized by high capital yield (ROE) while large banks are characterized by
high asset yield (ROA).
Pasiouras and Kosmidou (2007) find a positive and significant relationship between the size and
the profitability of a bank. This is because larger banks are likely to have a higher degree of product
and loan diversification than smaller banks, and because they should benefit from economies of
scale. Other authors, such as Berger et al. (1987), provide evidence that costs are reduced only
slightly by increasing the size of a bank and that very large banks often encounter scale
inefficiencies. Micco et al. (2007) find no correlation between the relative bank size and the ROA
for banks, i.e. the coefficient is always positive but never statistically significant. A study from
Kaufman (1992) indicates that increasing size has a positive effect on profitability. However,
banks that become extremely large experience negative effects of size due to bureaucratic and
other reasons. Hence, the size-profitability relationship should be non-linear (Eichengreen and
Gibson, 2001). On another study done by Perven et al., (2015) on bank profitability in Croatia,
has illustrated a positive and significant association between bank size and profitability. Another
study done by Arellano and Bond, (1991) shows that banks should make use of their size to exploit
cost advantages whose realization together with improved management would contribute to further
increases in efficiency which would result into higher profitability. Some studies, on the other
hand, has generated opposite results. Haron (2004) proved that, size has no significant effect on
profitability measured by ROA. On the other hand, the results that reached by Hassan and Bashir
(2003) proved that, size has a negative relationship on profitability. The same result was found by
Alkassim (2005) study, whom results shown that, total assets have a negative effect on profitability
for Jordanian commercial banks. On the same issue Alrashdan (2002) also investigated the
determinants of Jordanian’s banks profitability covering the period of 1985-1999. The results of
his study revealed that, return on asset (ROA) is positively related to liquidity and total assets while

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it’s negatively related to financial leverage and cost of interest. Finally, the results showed
insignificant relation between interest rate risk and ROA (Al-Jarrah et al. 2010). Naceur and
Goaied, (2008) who studied the impact of macroeconomic environment, financial structure and
bank specific characteristics on the profitability of Tunisian bank’s from 1980-2000.The study
found out that bank size had a negative influence on profitability. According to Košak and Čok,
(2008), the negative relationship can be as a result of diseconomies of scale that are associated
with large banks especially after accelerated growth periods. Heffernan and Fu, (2008) has studied
and stated the profitability of different Chinese banks from 1999-2006 and found that bank size
had no statistically significant influence on bank performance. A study done on banks of Kuwait,
Darrat and Yousif (2002) also stated found a negative relationship. Furthermore, Leong and Dollar
(2002) when investigated the Singaporean banks have highlighted more inefficiencies in the
activities of larger and more complexes banks.
3.2.4 Internal Determinant: Capitalization
In finance literature, capitalization is a measurement of health of a bank’s balance sheet. In this
study, the equity ratio has been used to measure the capitalization. The equity ratio is used to
measure the amount of assets that are financed by owners’ investment. It compares the total equity
in the company to the total asset of the company. This ratio illustrates two important financial
concept of a bank. The first one is the amount of total asset owned by the investors. The second
one is the illustration of how leveraged the company is with debt. It indicates the bank’s capability
to manage losses and risk exposures. The cost of capital is reduced by higher levels of equity which
results in a better profitability. Moreover, banks with higher equity-to-assets ratio have a reduced
need for external funding. On the other side, lower capital ratio causes higher leverage risk which
results in higher borrowing costs.
The ratio is uses as a basic measures of capital strength (Golin, 2001). The ratio is also widely used
to assess the situation of financial power of a bank. In developing countries, a solid capital structure
is crucial to counter financial disasters and unstable macroeconomic conditions. Weak capital
structure cannot withstand dangerous and unstable situations; so the higher strength of capital
structure is needed to bear losses and to dismiss the risk of insolvency.
High capital ratio can be considered an indicator of low leverage and therefore low risk. In
addition, banks with higher equity-to-assets ratios are usually less dependent on external funding,

16 | P a g e
with a positive impact on their profits. Thus, well-capitalized banks are estimated to be less risky
and profits are likely to be lower as far, as these banks are supposed to be relatively safer in the
event of loss or liquidation. (Menicucci, Paolucci, 2016)
Berger (1995), on a study done on banks of 12 countries selected from Europe, North America and
Australia, said that higher capital level breeds higher profitability level since by having more
capital, a bank can easily adhere to regulatory capital standards so that excess capital can be
provided as loans. The capital ratio (TE/TA), which is measured by total equity over total asset,
reveals capital adequacy and should capture the general safety and soundness of the financial
institution (Gull, 2011).
In the recent years, the relationship between bank capital and risk (profitability) has become a
cause for concern because the level of capital may result in beneficial or adverse effects on bank
profitability.
Previous studies on the relationship between capital and risk (profitability) have provided mixed
results. A study done by Barth et al. (2008) find notably that some Asian countries, such as
Philippines, Singapore, and Indonesia, are strengthening capital requirements, while some are
easing their capital requirements, such as South Korea and Japan, in the aftermath of their crises.
Sufian & Habibullah (2009) stated that leverage (capitalization) has been demonstrated to be
important in explaining the performance of financial institutions, its impact on bank profitability
is ambiguous. Leverage is a fund from the depositors who deposit their money that the
management of bank will take to roll up the business (Qureshi, 2012). Leverage indicates how
much the asset has been financed by debt (Mwangi, 2014).
A study done by Abreu and Mendes (2002) on commercial banks of four different EU countries
illustrates that that well-capitalized banks have low bankruptcy costs and higher interest margins
on assets. Naceur (2003) also stated that high net interest margin and profitability are likely to be
associated with banks with high amount of capital and large overheads. Athanasoglou et al. (2006)
have done an empirical study to investigate the effect of bank specific, Industry-specific and
macroeconomic determinants on the profitability of Greek banks. The study shows that capital is
very important determinant in explaining bank profitability.

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A study done on the banks of Pakistan by Javaid et al. (2011) found that capital ratio shows a
significant impact on profitability and concluded by saying well-capitalized banks experience
higher returns. In another study done across 18 European countries, Molyneux and Thorton (1992)
also argued that the capital ratio impacts banks’ performance positively, in relation to state-owned
banks.
According to Berger (1995), a lower capital ratio suggests a relatively risky position, one might
expect a negative coefficient on this variable. The same research showed that an increase in capital
may raise expected earnings by reducing the expected costs of financial distress, including
bankruptcy. However, it could be the case that higher levels of equity would decrease the cost of
capital, leading to a positive impact on bank profitability (Molyneux, 1992). Nelson and Daniel
(1991) stated that the leverage will give an impact to the profitability of bank.
Sufian & Habibullah (2009) stated that strong capital structure is essential for financial institutions
in developing economies, since it provides additional strength to withstand financial crises and
increased safety for depositors during unstable macroeconomic conditions. Bourke (1989) states
TE/TA is expected to have a positive relation with performance because well capitalized banks are
less risky and more profitable.
According to a study done by Agusman et al. (2008) on Asian banks, it was found that equity-to-
total-assets are negatively related to risk, but do not reach any significance. Another study done
by Altunbas et al. (2007) on 15 European banks showed inefficient European banks appear to hold
more capital and take on less risk. Empirical evidence shows a negative relationship between risk
and the level of capital. Berger (1995) showed that there is a strong positive relationship between
capital and earnings (ROE), meaning well capitalized firms face lower expected bankruptcy costs,
which in turn reduce their cost of funding and increase their profitability by doing a study on U.S.
commercial banks.
Iannotta et al. (2007), upon doing a research on 181 European banks across 15 countries, stated
that capital is associated with positive profitability and risk, which can be attributed to the fact that
a different asset risk can be compensated by a different level of capitalization. Jacques and Nigro
(1997) also found that there is a positive relation between changes in profitability and capital.

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A study done by Bashir (2000) on eight Middle Eastern countries show that higher leverage and
large loans to asset ratios, lead to higher profitability. Alexiou and Sofoklis (2009) showed that
positive relationship between capital and profitability exists. A study done on banks of China by
Garcı´a-Herrero et al. (2009) produces same results. Staikouras & Wood (2003) claim that there
exists a positive link between a greater equity and profitability among EU banks.
But, some studies have showed that the relationship between capital strength and profitability is
not always positive. Studies done by Saona, 2011; Ali et al., 2011; Staikouras and Wood, 2004.
Showed that banks with higher capital ratio are less risky compared to others with lower capital
ratio which in line with the conventional risk-return hypothesis, illustrates that banks with lower
capital ratio have higher profits compared to well-capitalized financial institutions.
Angbazo (1997) concluded that the US banks, those which are well-capitalized, are more profitable
than the others. Same type of positive realtion between profitability and capital strength has also
been illustrated by Sufian and Chong (2008), Hassan and Bashir (2005) and Vong and Chan
(2009).
3.2.5 External Determinant: Inflation
Inflation is the sustained rate of growth in the in the average price level of the goods and services
of a given economy. It illustrates how the purchasing power of a certain currency decreases with
the increase in price. It is most commonly measured by the percentage rise in the Consumer Price
Index which is reported by Bangladesh Bureau of Statistics in the context of this country. The
annual inflation rate is used in this study.
Friedman defined inflation as a “steady and sustained increase in the general price level”.
Usually, high loan interest rates and high income are a result of high inflation rates. Perry (1992)
states that whether inflation is anticipated or unanticipated determines the effect of inflation on
banking performance. An anticipated inflation and adjusted interest rates will result in a positive
effect on profitability. In contrast to that, unanticipated inflation results in difficulties of cash flow
for borrowers. That can lead to premature termination of loan arrangements and loan losses. In
terms of adjusting the interest rates, if the banks act slow, the chances that the cost may increase
faster than the generated revenue. Hoggarth et al. (1998) stated that difficulties rise in terms of
difficulties in planning and negotiating loans because of high and variable inflation may cause.

19 | P a g e
Kessel & Alchian (1962) argued that inflation, whether or not it is anticipated, increases the cost
of holding money. However, inflation will decrease the money people are willing to hold in an
economy only if it is anticipated. The costs of holding money have increased in the past as a result
of unanticipated inflation has impacts on the future costs of holding money only insofar as past
inflation influences expectations about the future.
Previous studies done on the relationship between inflation and profitability illustrates mixed
findings. Guru et al. (2002) and Jiang et al. (2003) conducted research on Malaysia and Hong Kong
and stated that high inflation rates results in higher bank profitability. The study of Abreu and
Mendes (2001) nevertheless report a negative coefficient of inflation for European countries. In
addition, Demirguc-Kunt and Huizinga (1999) notice that banks operating in developing countries
make less profit when inflation happens. Because inn these countries bank cost actually increase
at a faster rate than bank revenue.
The effects of inflation can be extensive and can weaken the constancy of the financial system and
the ability of the regulator to regulate the solvency of financial intermediaries. Revell (1979) stated
that the level of inflation can be used in a strong extent to explain variations in bank profitability.
The impact of inflation on the customers and the consequent changes in the demand for different
kinds of financial services is considered as an important indirect influence on the bank.
Bourke (1989) have shown a positive relationship between inflation, central bank interest rates,
GDP growth, and bank profitability. Studies done by Molyneux and Thornton (1992), Demirguc-
Kunt and Huizinga (1999), Albertazzi and Gambacorta (2009) supports this argument. Driver and
Windram (2009) argued that upon the condition that expected inflation will be equal to actual
inflation, there will be no decrease in business activities and no negative effect on banks
performance.
Staikouras and Wood (2004) argued that unexpected rises of inflation cause cash flow difficulties
for borrowers that can result in premature termination of loan arrangements and precipitate loan
losses. It has been long recognized in finance literature that to affect the operations and margins
of banks through interest rates, inflation is a must-go route. Staikouras and Wood (2003) also
pointed out that inflation may have direct effects (e.g. rise in the price of labor) and indirect effects
(e.g. changes in interest rates and asset prices) on the profitability of the banks. Athanasoglou,
Delis and Staikouras (2006) found out that inflation positively and significantly influences

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profitability. They stated that with inflation bank income increases more than bank costs. This is
viewed as the result of the failure of bank customer’s ability to forecast future inflation compared
to the bank managers.
Athanasoglou et al., (2008), upon conducting a research on banks from South Eastern European
region, found that concentration has a positively correlation with bank profitability and inflation
has a strong effect on profitability, while banks’ profits are not significantly affected by the real
GDP per capita fluctuations.
Hoggarth et.al. (1998) also stated that bank earnings are majorly effected by high and variable
inflation. The reasons being, the assessment of loan decisions become quite difficult, since a loan
arrangement which performs at the anticipated rate of inflation may turn out to be much more
marginal if inflation is unexpectedly low and realized interest rates thus unexpectedly high. This
uncertainty of inflation may cause problems in planning and in negotiation of loans.
Finally, high and variable inflation encourages bank financing investment in property markets, an
investment strategy which may lead to market losses or great profitability according to the
implemented monetary policy. Alexiou, Sofoklis (2009) stated that inflation has a significant
effect on poriftability. Asutay & Izhar (2007) also showed that inflation positively effects the
profitability.
In contrast, Afanasieff et al. (2002), Ben Naceur and Kandil (2009) argued that the inflation rate
has negative impact on interest margins. Afanasieff et al. stated that inflation may be capturing the
effect of seignorage collection on interest margins.
Ben Naceur and Kandil explain the negative coefficient by illustrating that a higher inflation rate
increases uncertainty and reduces demand for credit. The other argument is that this negative
relationship may be linked to comparatively slower adjustment of revenues to the faster growing
costs for inflation (Wendell and Valderrama, 2006 and Abreu and Mendes, 2003). According to
Kosmidou (2008), increased competition and stability and low inflation has led to a decline of the
interest rate spread. The study of Kosmidou (2008) concluded by saying that inflation has a
significant negative on bank profitability.

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4. Methodology
4.1 Descriptors of Research Design
Category
The degree to which
the research question
has been crystallized
or structured
Type
Formal Study
Explanation
This research begins
with stating hypotheses;
it answers the questions
asked in research and it
follows a precise
procedure
Category
The method of data
collection
Type
Observation
Explanation
The research is carried
out by observing the
data and the effect of
the data on the
relationship of
independent variables
and dependent variables
over the time period of
5 years.
Category
The power of the
researcher to
produce effects in
the variables under
study
Type
Ex Post Facto
Explanation
Since the variables are
measured based on data
provided by the banks,
there's no control of the
researcher to have over
the study. This paper
will also provide a
after-the-fact report on
the measured variable.

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Category
The purpose of the
study
Type
Causal
Explanation
This research is
conducted in order to
identify the extent and
nature of cause-and-
effect relationships
Category
The Time Dimension Type Cross-sectional
Explanation
This research is going
to be carried out only
once and will represent
the scenario of that
particular time.
Category
The topical scope of
the study
Type
Statistical Study
Explanation
This research is
conducted in order to
identify the extent and
nature of cause-and-
effect relationships
Category
The topical scope of
the study
Type
Field Setting
Explanation
The data for the
research was collected
from Annual Financial
Reports, which occured
on actual conditions.

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4.2 Research Approach
There are three types of research approaches. They are: Qualitative, Quantitative and Mixed
Methods. Quantitative research is an approach for testing objective theories by examining the
relationship among variables which, in turn, can be. The final written report has a set structure
consisting of introduction, literature and theory, methods, results, and discussion. Since, this
research is aimed to test the relationship between the independent and dependent variables; the
significance of the relationship by analyzing the numbered data through statistical procedures, we
are taking the quantitative approach.
4.3 Research Objectives
The objective of this research is to examine the effects of several internal and external factors on
the profitability of banks of Bangladesh for the period of 2013-2017. This paper the will be able
to specify factors and their significance which effects the profitability of the banks and help the
banks to deal with the aforementioned factors to perform better.
4.4 Conceptual Framework
Credit risk, Liquidity Risk, Capitalization, Size and Inflation are the factors that have been used in
this research which affects the profitability of banks in Bangladesh. Here, Credit risk, Liquidity
Risk, Capitalization, Size and Inflation are the independent variables and the profitability is the
dependent variable. The relationship between these variables are illustrated by the multiple linear
regression equation. The multiple linear regression equation is:
Y = α + β1X1 + β2X2 +……… + βpXP + ε; where,
For our study, the equation will be:
Profitability = α + β1CR + β2LR + β3C + β4S + β5I + ε; where:
α = Constant; Y= Dependent variable
Β= Coefficient; X= Independent variable
ε= Error;
α = Constant; CR= Credit Risk (IV); Profitability= Dependent variable
β= Coefficient; LR= Liquidity Risk (IV);
ε= Error; C= Capitalization (IV);
I= Inflation Rate (IV); S= Size ((IV);

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Internal Determinants External Determinants
Loan Loss
Provision
Equity Total
Asset
Net
Income
Total
Loans
Credit
Risk
Liquidity
Risk
Capitalization Size Inflation
Profitability Return on Asset
= Factors used to
calculate Independent
Variables and to calculate
the factor that measures
Dependent Variable
= Factor used to
measure Dependent
Variable
= Independent
Variables
= Dependent Variable
Figure 1:Conceptual Framework

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In the aforementioned framework, we can see that the factors that affect the profitability of the
banks are Credit Risk, Liquidity, Capitalization, Size of the firm and Inflation Rate. These are the
independent variables. Profitability of the bank, which is the dependent variable, is measured and
indicated by Return on Asset. The variables have been calculated by using the following equations:
Factors Description Variables used as
determinant to measure
the factor
Equation
Profitability
Measures the profitability of a bank by
comparing the profit (net income) it’s
generating to the capital it’s invested in
assets in a given year.
ROA (Return on Asset)
𝑁𝑒𝑡 𝐼𝑛𝑐𝑜𝑚𝑒
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡
Credit Risk Risk of loss resulting from a borrower's
failure to repay a loan or meet
contractual obligations. Indicates how
much a bank is provisioning in a given
year relative to its total loans.
LLP/TL (Loan Loss
Provision/Total Loans) 𝐿𝑜𝑎𝑛 𝐿𝑜𝑠𝑠 𝑃𝑟𝑜𝑣𝑖𝑠𝑖𝑜𝑛
𝑇𝑜𝑡𝑎𝑙 𝐿𝑜𝑎𝑛𝑠
Liquidity Liquidity risk refers to how a bank’s
inability to meet its obligations (whether
real or perceived) threatens its financial
position or existence in a given year.
Indicates how much of the bank’s asset
is tied up in loans in a given year
TL/TA
𝑇𝑜𝑡𝑎𝑙 𝐿𝑜𝑎𝑛
Capitalization Measures bank’s capital strength in a
given year.
E/TA
𝐸𝑞𝑢𝑖𝑡𝑦
Size Indicates the possible competitive
advantages gained with size. Controls
the differentiation and diversification.
LNTA Log Of Total Asset
Inflation Rate Indicates the rate at which prices
increase over time, resulting in a fall in
the purchasing value of money
IR
Data directly collected
from the report of
Bangladesh Bank
Figure 2: Explanations of Variables and proposed relation

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So, now, the aforementioned multiple linear regression equation is rewritten as:
ROA = α + β1 LLP/TL + β2 TL/TA + β3 E/TA + β4 LNTA + β5IR + ε; where:
ROA= Dependent Variable (Illustrates Profitability)
α = Constant
LLP/TL= Independent Variable (Illustrates Credit Risk)
TL/TA= Independent Variable
e (Illustrates Liquidity Risk)
E/TA= Independent Variable (Illustrates Capitalization)
LNTA= Independent Variable (Illustrates Size)
IR= Independent Variable (Illustrates Inflation Rate)
ε= Error
4.5 Hypotheses
In this research, our prediction is referred to as the alternative hypothesis and opposite outcome is
referred to as null hypothesis. The null hypothesis proves the proposition to be false.
4.5.1 Credit Risk and Profitability
4.5.2 Liquidity and Profitability
H0= There is no relationship between LLP/TL (credit risk) and ROA
(profitability)
HA= There is a negative correlation between LLP/TL (credit risk) and ROA
(profitability)
H0= There is no relationship between TL/TA (liquidity) and ROA (profitability)
HA= There is a positive correlation between TL/TA (liquidity) and ROA
(profitability)

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4.5.3 Capitalization and Profitability
4.5.4 Size and Profitability
4.5.5 Inflation Rate and Profitability
4.6 Sampling and Data Source
The research was carried out on the five conventional PCBs (Private Commercial Banks). The
banks are:
 Prime Bank
 South East Bank Ltd.
 Bangladesh Commerce Bank Ltd.
 Mercantile Bank Ltd.
 Bank Asia
H0= There is no relationship between E/TA (capitalization) and ROA
(profitability)
HA= There is a positive correlation between E/TA (capitalization) and ROA
(profitability)
H0= There is no relationship between TA (size) and ROA (profitability)
HA= There is a positive correlation between TA (size) and ROA (profitability)
H0= There is no relationship between IR (inflation rate) and ROA (profitability)
HA= There is a positive correlation between IR (inflation rate) and ROA
(profitability)

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Data used in this study are secondary data. As the data related to the variables we are measuring
is usually done by the banks primarily, we used the annual reports from annual financial reports
that in the period 2013-2017 to gather the necessary data. Secondary data is data that has been
collected by the data collection agency and published to the community of data users (Kuncoro,
2009:148). Therefore, data collection techniques used in this study using the techniques of
documentation that is by collecting, recording, and reviewing secondary data in the form of audited
financial statements published by the banks.
4.7 Data Analysis
4.7.1 Descriptive Statistics
In the descriptive status, the mean value is probably the most commonly used method of describing
average value or the central tendency of a data group.
The Median is the score which is found at the exact middle of the set of values.
The minimum and the maximum value represents the smallest and the largest value in the dataset
respectively.
The Standard Deviation, represented as Std. Dev. in the Eviews or simply as SD, is a more accurate
and detailed estimate of dispersion. The Standard Deviation shows the relation that set of scores
has to the mean of the sample of the data set. The probability describes the likeliness of occurrence
based how close the value is to 0 or 1. Close to 0 indicates the less likeliness of occurrence and
close to 1 indicates more likeliness of occurrence.
ROA CAPITALIZATION CREDITRISK INFLATION LIQUIDITY SIZE
Mean 0.00774 0.09334 0.022664 0.0652 0.872584 25.7156
Median 0.0083 0.0908 0.026 0.062 0.9071 26.12
Maximum 0.0162 0.1336 0.0322 0.075 0.9324 26.55
Minimum 0.0001 0.0676 0.0007 0.057 0.5 23.92
Std. Dev 0.004709 0.015311 0.009207 0.006564 0.113026 0.861569
Probability 0.625318 0.384638 0.144064 0.329 0 0.048561
Table 3: Descriptive Statistics

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4.7.1.1 ROA (Return on Asset)
Here, for ROA (Return on Asset) the mean is 0.007740 which means the average value or the
central tendency of the data presented in ROA is 0.007740. The median for ROA is 0.008300
which indicates the exact middle point the data presented in ROA 0.008300. The Minimum of
ROA is 0.000100 and the Maximum for ROA is 0.016200 which illustrates that 0.000100 is the
smallest and 0.016200 is the largest value in the dataset of ROA. The Std. Dev. or SD for ROA is
0.004709. It indicates that the dispersion of the dataset of ROA is 0.004709 from its mean. The
probability is 0.625318. Since, it is closer to 1, it is more likely to occur this event.
4.7.1.2 Capitalization
Here, for Capitalization the mean is 0.093340 which means the average value or the central
tendency of the data presented in Capitalization is 0.093340. The median for Capitalization is
0.090800 which indicates the exact middle point the data presented in Capitalization 0.008300.
The Minimum of capitalization is 0.067600 and the Maximum for Capitalization is 0.133600
which illustrates that 0.067600 is the smallest and 0.133600 is the largest value in the dataset of
Capitalization. The Std. Dev. or SD for Capitalization is 0.015311. It indicates that the dispersion
of the dataset of Capitalization is 0.015311 from its mean. The probability is 0.384638. Since, it is
closer to 0, it is less likely to occur this event.
4.7.1.3 Credit Risk
Here, for credit risk the mean is 0.022664 which means the average value or the central tendency
of the data presented in credit risk is 0.022664. The median for credit risk is 0.026000 which
indicates the exact middle point the data presented in credit risk 0.026000. The Minimum of credit
risk is 0.000700 and the Maximum for credit risk is 0.032200 which illustrates that 0.000700 is
the smallest and 0.032200 is the largest value in the dataset of credit risk. The Std. Dev. or SD for
credit risk is 0.009207. It indicates that the dispersion of the dataset of credit risk is 0.009207 from
its mean. The probability is 0.144064. Since, it is closer to 0, it is less likely to occur this event.
4.7.1.4 Inflation
Here, for inflation the mean is 0.065200 which means the average value or the central tendency of
the data presented in inflation is 0.065200. The median for inflation is 0.062000 which indicates
the exact middle point the data presented in inflation 0.062000. The Minimum of inflation is
0.057000 and the Maximum for inflation is 0.075000 which illustrates that 0.057000 is the smallest

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and 0.075000 is the largest value in the dataset of inflation. The Std. Dev. or SD for inflation is
0.006564. It indicates that the dispersion of the dataset of inflation is 0.006564 from its mean. The
probability is 0.329000. Since, it is closer to 0, it is less likely to occur this event.
4.7.1.5 Liquidity
Here, for Liquidity the mean is 0.872584 which means the average value or the central tendency
of the data presented in Liquidity is 0.872584. The median for Liquidity is 0.907100 which
indicates the exact middle point the data presented in Liquidity 0.907100. The Minimum of
liquidity is 0.500000 and the Maximum for Liquidity is 0.932400 which illustrates that 0.500000
is the smallest and 0.932400 is the largest value in the dataset of Liquidity. The Std. Dev. or SD
for Liquidity is 0.113026. It indicates that the dispersion of the dataset of Liquidity is 0.113026
from its mean. The probability is 0.000000. Since, it is 0, it is not likely to occur this event.
4.7.1.6 Size
Here, for Size the mean is 25.71560 which means the average value or the central tendency of the
data presented in Size is 25.71560. The median for Size is 26.12000 which indicates the exact
middle point the data presented in Size is 26.12000. The Minimum of size is 23.92000 and the
Maximum for Size is 26.55000 which illustrates that 23.92000 is the smallest and 26.55000 is the
largest value in the dataset of Liquidity. The Std. Dev. or SD for Size is 0.861569. It indicates that
the dispersion of the dataset of Size is 0.861569 from its mean. The probability is 0.048561. Since,
it is closer to 0, it is less likely to occur this event.
4.7.2 Correlation Analysis
ROA CAP CREDITRISK INFLATION LIQUIDITY SIZE
ROA 1 -0.35928 -0.42091 0.259367 0.124607 0.668938
CAP -0.35928 1 0.233322 0.278454 0.260281 -0.70249
CREDITRISK -0.42091 0.233322 1 -0.125912 -0.298784 -0.17338
INFLATION 0.259367 0.278454 -0.125912 1 0.23468 -0.21174
LIQUIDITY 0.124607 0.260281 -0.298784 0.23468 1 -0.12704
SIZE 0.668938 -0.70249 -0.173382 -0.211739 -0.127038 1
Table 4: Correlation Analysis

31 | P a g e
As mentioned earlier, ROA is the dependent variable and Capitalization, Credit Risk, Inflation,
Liquidity and Size are independent variables. The scale which determines the correlations is as
follow:
Relation Between Correlation Value Correlation
ROA and Capitalization -0.35928 Moderate Negative Correlation
ROA and Credit Risk -0.42091 Moderate Negative Correlation
ROA and Inflation 0.259367 Weak Positive Correlation
ROA and Liquidity 0.124607 Weak Positive Correlation
ROA and Size 0.668938 Moderate Positive Correlation
Table 5: Correlation
Weak, Moderate and Strong
relationship will be labeled as
positive or negative based on if
their values are positive or
negative.
-1 •Perfect Negative
Correlation
0-0.3 •Weak
Relationship
0.31-0.7 •Moderate
Relationship
0.71-
0.99
•Strong
Relationship
+1
•Perfect
Positive
Relationship

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Dependent Variable: ROA
Method: Panel Least Squares
Date: 03/30/19 Time: 12:17
Sample: 2013 2017
Periods included: 5
Cross-sections included: 5
Total panel (balanced) observations: 25
4.7.3 Regression Analysis
Here, using the data from Table 6, we can rewrite our linear regression equation as:
ROA = -0.127322 + (- 0.13863) LLP/TL + 0.000836 TL/TA + 0.056327 E/TA + 0.004515 LNTA
+ 0.247111IR
Variable Coefficient Std. Error t-Statistic Prob.
C -0.127322 0.029652 -4.293927 0.0004
CAPITALIZATION 0.056327 0.05961 0.944926 0.3566
CREDITRISK -0.13863 0.073255 -1.892446 0.0738
INFLATION 0.247111 0.097662 2.530262 0.0204
LIQUIDITY 0.000836 0.005996 0.139388 0.8906
SIZE 0.004515 0.000981 4.603948 0.0002
R-squared 0.692722 Mean dependent var 0.00774
Adjusted R-squared 0.61186 S.D. dependent var 0.004709
S.E. of regression 0.002934 Akaike info criterion -8.61953
Sum squared resid 0.000164 Schwarz criterion -8.327
Log likelihood 113.7441 Hannan-Quinn criter. -8.5384
F-statistic 8.566671 Durbin-Watson stat 1.309605
Prob(F-statistic) 0.00022
Table 7: Regression Analysis (b)
Table 6: Regression Analysis (a)

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Or, ROA = -0.127322 + (- 0.13863) Credit Risk + 0.000836 Liquidity + 0.056327 Capitalization
+ 0.004515 Size + 0.247111 Inflation Rate
This implies the fact that in case of increased credit risk, it will result in the decrease of ROA. If
the other variables increase, ROA will increase too.
To reject null hypotheses, we have assumed the significance level of 10%. If the probability value
is higher than the significance level, the null hypothesis will not be rejected and if the probability
value is lower than the significance level, the null hypothesis will be rejected.
Hypothesis
Between
Prob. Value Prob. Value
(in
percentage)
Significance
Level
Comparison Ruling
Capitalization and
ROA
0.3566 35% 10% Prob. > Significance
Level
Null hypothesis will not
be rejected
Credit Risk and
ROA
0.0738 7% 10% Prob. < Significance
Level
Null hypothesis will be
rejected
Liquidity and
ROA
0.8906 89% 10% Prob. > Significance
Level
Null hypothesis will not
be rejected
Size and ROA 0.0002 0.02% 10% Prob. < Significance
Level
rejected
Inflation and ROA 0.0204 2% 10% Prob. < Significance
Level
rejected
Table 8: Ruling on Rejecting Null Hypothesis
Table 7 shows that the R-squared is 0.692722 which means that the percentage of the response
variable variation is 69%. The higher the R-squared, the better the model fits the presented data.
The R-squared of our study is 69% which indicates that the model used fits our presented data
well. Prob. (F- Statistic) is 0.022% (0.00022 in Table 7) which is less than our significance level
of 10% which also indicates that the model is a good fit.

34 | P a g e
5. Findings and Analysis
5.1 Credit Risk and Bank Profitability
Our study portrayed a moderate negative relation between credit risk and profitability as we
expected. This outcome supports the results showed by Chijoriga (1997) who states that loss
caused by credit risk as compared to other reasons were severe to collapse a bank. Cooper et al
(2003) stated that variations in credit risks would lead to variations in the health of banks’ loan
portfolio which in turn affect bank performance. Our results also supported the study done by Felix
and Claudine (2008). They stated that the relationship between bank performance and credit risk
management. The results confirmed that credit risk (a ratio of non-performing loan to total loan)
has a significant negative impact on return on equity and return on assets both measuring
profitability.
5.2 Liquidity and Bank Profitability
Our study showed that liquidity and bank profitability has a positive correlation. This supports the
arguments made by Molyneux and Thornton, (1992) and Barth et al., (2003) who also found a
positive relation between liquidity and profitability. Low liquidity can result in a lower profitability
because the banks will need greater amount of loans and the low profit will not generate sufficient
cash flow which will form a vicious circle (Hirigoyen, 1985). The reasons behind this can be the
ability to deal with uncertain crisis, provide the payment for daily expenses and immediate
investment. (Arnold, 2008)
5.3 Capitalization and Bank Profitability
Our study illustrated a moderate negative correlation between capitalization and bank profitability.
This is different from the expected outcome of the study. The negative relation supports some
previous studies done by Saona, 2011; Ali et al., 2011; Staikouras and Wood, 2004. The reason
behind this maybe that the banks with higher capital ratio are less risky compared to others with
lower capital ratio which in line with the conventional risk-return hypothesis, illustrates that banks
with lower capital ratio have higher profits compared to well-capitalized financial institutions.
However, study with bigger sample size in our case should be done to be clear about the impact of
capitalization on bank profitability.

35 | P a g e
5.4 Size and Bank Profitability
Our study showed a moderate positive correlation between size and bank profitability. This
relationship was expected on our studies. Our findings are similar with the findings of Bikker and
Hu (2002), Goddard et al. (2004), Pervan, Pelivan and Arnerić (2015). Halkos and Salamouris
(2004) investigated the effect of bank assets on its efficiency, they concluded from their study
about Greek banks that, the higher the bank assets the higher the efficiency. Using that line of
argument, it can be said that increasing banks’ asset size can also reduce risk by diversifying
operations across product lines, sectors, and regions and result in more profit (Mester 2010).
According to Goddard, a bank’s profitability initially increases with size due to the scale economy
but declines if the size exceeds a threshold level—the exhaustion of the scale economy and
bureaucratic managerial style could lead to performance inefficiency.
5.5 Inflation and Bank Profitability
We found a positive relation between inflation and bank profitability as expected. Guru et al.
(2002) and Jiang et al. (2003) showed the same relationship between these two. This happens
because bank income increases more than bank costs which is viewed as the result of the failure
of bank customer’s ability to forecast future inflation compared to the bank managers. (Delis and
Staikouras, 2006). Some other studies done by Molyneux and Thornton (1992), Demirguc-Kunt
and Huizinga (1999), Albertazzi and Gambacorta (2009) also supports our findings.
6. Recommendation
Upon observing the findings, it can be concluded that the banks should be careful about the credit
risk. As it is empirically proved that, credit risk can lower the amount of profit a bank makes,
banks should really take measures to minimize the credit risk they have. Since, our study and some
other previous studies have shown that capitalization can have a negative impact on profitability,
banks should maintain a fine balance capitalization and risk reward situation. Banks should also
try to maintain a healthy balance of asset in order to deal with crisis and investment more fficiently
and effectively. In terms of inflation, banks must make sure that they properly anticipate the
inflation to capitalize on it and increase the profit margin.

36 | P a g e
7. Conclusion
As a growing economy, it is a necessary headache for a country like Bangladesh to ensure that
banking sector is healthy and growing in a healthy rate too. It is necessary for any economy, in
order to grow and sustain the growth, make sure that the banking industry is doing well. The recent
problems that banks of Bangladesh are going through, have really put the industry in an
unfavorable situation. If the banks can’t overcome the current issues, they will be unable to survive
in the long run thus hurting the entire economy. The things that have been discussed in this study
should be taken care of as they effect the profit that a banks make.
Further study with a bigger sample size is desired won the road to get a better and clearer
understanding of the topic.

37 | P a g e
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