determinants of capital structure of commercial banks in … · using the data of 19 banks in ghana...

ASA University Review, Vol. 12 No. 1, January–June, 2018

Determinants of Capital Structure of Commercial Banks in

Bangladesh Listed in the Dhaka Stock Exchange Limited

Rima Pervin*

Reshma Nowreen**

Abstract

Capital structure refers to permanent financing of the firm that includes mix of company’s long-term debt,

specific short term debt, common equity and preferred equity. Capital through the form of optimum capital

structure performs the function as providing a cushion of protection against risk of failure and promoting

public confidence in the long run viability of financial firms like banks by absorbing financial and

operating losses until management can address the institutions’ problems and restore its profitability. The

study attempts to indentify the determinants and analyze their impact on capital structure of commercial

banks in Bangladesh. For the study purpose, we have used data of 30 private commercial banks in

Bangladesh listed in the Dhaka Stock Exchange (DSE) Limited over a period of 10 years from 2007 to

2016. The research is done through econometric procedure in estimating the relationship between bank’s

capital structure and its key determinants. The ordinary least square (OLS) technique is used to obtain the

numerical estimates of the coefficient that measures the extent of impact of the determinants on the capital structure of banks. Analysis reveals that profitability and size of the bank have significant negative relation

whereas risk has significant positive relation with capital structure of banking sector. Liquidity, age,

tangibility and asset growth demonstrate no significant impact on the capital structure of banking industry

in Bangladesh.

Keywords: capital structure, leverage, liquidity, profitability, asset growth, ROA, size, risk.

Introduction The capital structure of financial institutions is one of the crucial issues in corporate finance

literature. Determining an optimal financing mix is an important decision for it could lead to the

maximization of the market value of the firm. A large number of capital structure theories have emerged to explain why firms choose different mixes of debt and equity to finance their

operations. Due to some unique features, like extensive regulation and safety issue, banks

represent a special case. Through efficient mobilization of funds, banks ensure sustainable growth of the economy. There is no doubt that capital is the keystone of a bank’s financial strength. It

works as a safeguard for the bank at the time of unexpected losses from its activities and enables

the bank to run its operations in a sound and viable manner. So, banks should make strategic

choice regarding capital structure with proper care and attention by making a balance between solvency and liquidity. A number of theories have been developed regarding the determination of

the optimal capital structure; such as, Modigliani and Miller Theory (1958), Agency Cost Theory

(1976), Trade-Off Theory (1977) and Pecking Order Theory (1984). However, even after decades

*Assistant Professor, Dept. of Business Administration, Stamford University Bangladesh **Assistant Professor, Dept. of Finance, Bangladesh University of Business and Technology (BUBT)

86 ASA University Review, Vol. 12 No. 1, January–June, 2018

of remarkable researches, there is no universal theory of capital structure. So, the topic is open for further research.

Obtaining an optimal capital structure can lead a firm to maximize its value. There might be some

factors that influence the capital structure of a firm. If it is possible to identify the factors accurately, it will help the practitioners to meet the challenge of finding the right portion of debt

and equity in capital structure. According to the circular of Banking Regulation & Policy

Department of Bangladesh Bank, minimum paid up capital and reserve & surplus of a bank must be Tk. 400 crore of which paid up capital must be at least Tk. 200 crore. With a view to

strengthening the capital base of banks, it has been decided that henceforth banks will be required

to maintain Capital to Risk-Weighted Assets Ratio 10% at the minimum with core capital not less than 5%.The study attempts to identify the factors that influence the capital structure of banks

operating in Bangladesh. The result of the study may help the banks and policy planners to make

better policy regarding capital structure.

The remainder of the study proceeds as follows. Section two presents a brief review of previous

studies and related literature. Objectives of the study are described in section three followed by

data collection and study methodology in section four. Empirical results are discussed in the fifth section and section six concludes the study by providing limitations and the future directions.

Review of Literature

Finance theorists conducted extensive research on determinants of capital structure of a firm over

time so that they could help a company choose its capital structure. Consequently, they developed a number of theories regarding this issue. The theories of capital structure mainly deal with the

influence of use of leverage on the value of a firm. Among a number of theories, Static Trade off

Theory, Pecking Order Theory and Agency Cost Theory are most influential and play a significant role in making decision concerning leverage preference.

Trade off Theory suggested by Modigliani and Miller (1963) considers taxes, cost of bankruptcy

and agency conflicts as three main factors. As per this theory, uses of leverage decline the tax liability and at the same time increase the after tax cash flows. The main advantage of debt

financing is tax deductibility of interest and the two costs of debt financing are bankruptcy cost

(Kim, 1978) and agency cost (Jesen and Meckling, 1976; Myers, 1977). Again, excess use of leverage may lead to bankruptcy. Finally, the theory urges to make a balance between these

three factors to form an optimal capital structure.

Pecking Order Theory (POT) states that asymmetric information may lead to increase in cost of financing. This theory demonstrates that firms should pursue a ladder of financing. Pecking Order

Theory (POT) ranks using retained earnings at the top of the order. Consequently, the firm will

arrange funds for its projects with internal equity if possible (Myers, 1984). In case of insufficiency of retained earnings, the firm will manage fund using leverage. According to

Determinants of Capital Structure of Commercial Banks in Bangladesh 87

Pecking Order Theory, corporate managers prefer retained earnings as the prime source of funds to debt and debt is a better option than equity financing (Myers and Majluf, 1984).

Agency Cost Theory (ACT) mainly focuses on obtaining an optimal capital structure through the

minimization of costs arising from agency conflict (Jensen and Meckling, 1976). Conflicts may exist between managers, owners and creditors. When a firm approaches to insolvency,

shareholders pursue management to take such decision that will transfer funds from debt holders

to equity holders. Therefore, debt holders demand higher return considering the possibility of any decision making against their interest. Debt and its interest payment decline the conflicts between

owners and managers. If management fails to meet up debt obligation in due time, creditors have

the right to take legal actions that may lead to loss of job of management. Again, managers are likely to misuse the free cash flows on bad investment sometimes. Hence, the benefit of using

debt is that it reduces the owner-manager agency conflict by forcing managers to work as

efficiently as possible to meet up debt obligations that will lead to share holders’ wealth

maximization (Jensen and Meckling, 1976).

There are numerous researches regarding the determinants of capital structure of financial

institutions covering different countries.

Chowdhury (2000) conducted a study based on agency cost model using cross section data of

Japanese and Bangladeshi firms to determine the impact of agency cost variables on the capital structure of the firms. In this study, mainly three agency cost variables have been used. Those are-

agency debt, agency equity and bankruptcy risk. The study concluded that the relationship of

agency debt variables and debt ratios is different between Japanese and Bangladeshi firms.

According to statistical result, agency debt variable was not a significant determinant of capital structure in case of Japan, while for Bangladesh it was an important influencing factor of capital

structure. Bankruptcy risk variable was observed to be statistically significant for the firms of

both countries. The result of this variable was as per the theoretical expectation indicating that the higher the bankruptcy risk, the lower the attractiveness of debt.

A research conducted by Mahbuba and Rahman (2017) explored the influence of several factors

on capital structure of seven listed insurance companies of Bangladesh. Considering the data set for the period of 2010 to 2014, it was found that profitability (ROA), growth (GR), liquidity

(LIQ) and dividend payout (DVP) have significant impact on the capital structure choice of life

insurance companies in Bangladesh. However, size (LnGRP), tangibility (ASTG), business risk (RISK) and non-debt tax shield (NDTS) have no significant impact.

Nguyen and Kayani (2013) conducted an analysis on the determinants of banks’ capital structure of Asian countries. They examined the influence of the determinants on the capital structure of

the banks in developed and developing countries. The paper mainly highlighted on the importance

of firm-specific determinants comparing to macroeconomic factors on capital structure decision

of banks. The results illustrated that the macroeconomic determinants depicted more variations in influencing capital structures of banks in different countries. Tax seemed to be the only factor that


explained the capital ratios of banks in emerging countries, while the remaining factors are insignificant.

Using the data of 19 banks in Ghana during the period 1998-2003, Amidu (2007) investigated

factors influencing the capital structure of banks. From the study he found a negative relationship between banks' short-term debt and their profitability. Risk and assets structure are positively

related to bank size, growth and tax. He also found banks' long-term debt is positively related to

profitability and inversely related to their risk, growth, size and tax. Gatsi and Akoto (2010) observed that profitable banks in Ghana use less debt or they depend more on internally generated

funds rather than external funds supporting the pecking order theory of capital structure.

Iwarereand and Akinleye (2010) conducted a questionnaire survey on Nigerian banks to

determine factors that influence banks to choose optimal capital structure. They suggested that

banks should reduce debt level and invest in current assets. Aremu et al. (2013) analyzed the

relationship between the level of leverage ratios and seven potential factors. They found significant relationship between banks capital and growth, size, dividend payout, profitability,

tangible assets, business risk and tax charges.

Analyzing the data of 22 commercial banks in Pakistan during the period 2006-2010, Ali et al.

(2011a; 2011b) found a significant positive relationship between banks' leverage and its

tangibility and size. They also found a significant negative relationship between banks' leverage and both its liquidity and profitability.

Brewer et al. (2008) studied on 78 largest private banks headquartered in 12 industrial countries

over the period from 1992 to 2005. They made an effort to clarify why capital ratios of banks vary across developed countries. They found that the differences may be explained by public

policy and regulatory regimes of each country. Above all, most of bank specific variables still

have the strongest explanatory power.

Siddiqui, S.S., (2012) conducted an analysis on determinants of capital structure of non-bank

financial institutions of Bangladesh. In their study, data of 24 firms have been used for the period

of 2006 to 2008. Long term, short term and total debt ratio were considered as dependent variables. Eight factors were considered as independent variables. Those are firm size, age of the

firm, liquidity ratio, profitability ratio, operating leverage, growth rate, tangibility ratio and debt

service coverage. It was concluded that except tangibility ratio and profitability ratio, all other factors significantly affect the capital structure decision of NBFIs of Bangladesh. Growth rate and

firm size are positively related with all the three leverage ratios. On the other hand, operating

leverage, debt service coverage and age of the firms have inverse relationship with the debt ratios. Vallascas and Hagendorff (2013) examined the sensitivity of risk to regulatory capital

requirements of banks. They investigated whether minimum capital requirements reflected the

risk of banks’ portfolio accurately. They used a cross-country sample of 246 large listed banks

from 41 countries for the period from 2000 to 2010. It was concluded that there was a positive relationship between asset volatility and risk-weighted assets. But due to a significant increase in


the market measure of banks’ portfolio risk, there might be a small increase in the regulatory capital requirements.

Another vigorous study was conducted regarding this issue by Greenlaw et al. (2008). They

concluded that the larger banks did not manage their capital structure based on regulatory requirements but more focused on internal value at risk. It was found that the leverage for banks

is high in high growth period due to lower perceived risk and vice versa.

Objective of the study

According to regulatory requirement, a new bank has to have Tk. 400 crore as capital to start its business. But this amount of capital requirement varies over time period to ensure that the bank

has the ability to absorb the reasonable amount of losses arising from their business activities. In

this study, we have tried to find out the factors that affect and have impact on the capital structure decision of banking sector. The main objective of the study is to assess the extent to which the

variables namely liquidity, profitability, age, tangibility, risk, size and asset growth of banking

firm affect the capital structure of commercial banks in Bangladesh.

Research Design, Methodology and Measurement Issue

Data Sources and Data Collection Method

The study is based on secondary cross sectional data. The main source of data is the Dhaka Stock

Exchange (DSE) Limited. Information was collected from annual reports of the concerned listed companies from DSE library and also from the website of Bangladesh Bank.

Sampling Design

Though there are 57 scheduled banks and 6 nonscheduled banks operating in Bangladesh, we have selected only 30 scheduled banks for our study purpose as they are listed on DSE and

information is publicly available. For the convenience of our study, three commercial banks

namely Rupali Bank Ltd., Pubali Bank Ltd. and Uttara Bank Ltd. have been considered to start their business in the years when they became public limited though they have started their journey

in East Pakistan before the liberation war.

Methods of Data Analysis and Model Specification The study employs descriptive, correlation and inferential statistics to analyze the collected data

using STATA (Version 14) software. Descriptive statistical tools such as mean, standard

deviations, minimum and maximum were applied to describe relevant information about each variable. Correlation statistics is also used to identify directions of relationships and associations

among variables. Inferential statistics is used to test the model formulated below. The data were

analyzed using correlation coefficient and regression analysis. The regression model (Abdullah and Kamal, 2015) is as follows:


Levt= 0+ 1Liqt + 2ROAt+ 3 Aget+ 4 Tant + 5Riskt + 6 AGRt + 7Sizet + ε

Where,

0= The constant term,

1- 7= The coefficients of the independent variables, ε = The error term,

t=Year or time.

Table 1: Variables Specification

Variables Definitions of variables Authors used the variables in their

research and year

Lev (Leverage) The proportion of total liabilities to total

assets.

Amidu (2007), Gasti and Akoto (2010),

Ali et al. (2011a,2011b) ,Siddiqui,S.S.

(2012), Aremu et. al. (2013), Abdullah

and Kamal (2015), Mahbuba and Rahman (2017)

Liq (Liquidity) Current assets divided by total deposit Ali et al. (2011a,2011b), Siddiqui,S.S.

(2012), Abdullah and Kamal (2015),

Mahbuba and Rahman (2017)

ROA

(Profitability)

The ratio of net income to total assets. Amidu (2007), Gasti and Akoto (2010),

Ali et al.(2011a,2011b), Siddiqui,S.S.

(2012), Aremu et.at. (2013), Abdullah

and Kamal (2015), Mahbuba and

Rahman (2017)

Age No of years the bank is working Siddiqui,S.S. (2012), Abdullah and

Kamal (2015)

Tan(Tangibility) Fixed asset divided by total asset Ali et al.(2011a,2011b) , Siddiqui,S.S.

(2012),Aremu et. al. (2013), Abdullah

and Kamal (2015), Mahbuba and

Rahman (2017)

Risk The percentage change in net income Amidu (2007), Siddiqui,S.S. (2012),

Aremu et. al. (2013), Abdullah and

Kamal (2015), Mahbuba and Rahman

(2017)

AGR (Asset

Growth)

The percentage change in total asset. Amidu (2007Siddiqui,S.S. (2012),

Aremu et. al. (2013), Abdullah and

Kamal (2015), Mahbuba and Rahman (2017)

Size (Total

Asset)

Size measured by natural logarithm of

total asset

Amidu (2007), Ali et al.(2011a,2011b) ,

Siddiqui,S.S. (2012), Aremu et. al.

(2013), Abdullah and Kamal (2015),

Mahbuba and Rahman (2017)


Results and Discussions

Regression Results

Using OLS regression in our model, we get-

Table 2: Model summary and coefficient values

After applying OLS we can rewrite our model as follows:

Levt= 1.642209+(-.0959997)Liqt+ (-7.313913)ROAt+ .001268Aget+ 1.110345Tant + .018046

Riskt +(-.0512483)AGRt +( -.0510877)Sizet + ε

Now we would like to interpret the outcomes we got from our model using OLS estimation:

i. 0 = 1.642209: The value of intercept term is more than 1. It means that if the values of all

independent variables change by one, the dependent variable leverage will change by the value of

the intercept (1.642209) in the same direction which is statistically significant at usual level

supported by the P-value (.000).

ii. 1 = -.0959997: It shows a negative relationship between dependent variable leverage and

explanatory variable liquidity implying that if liquidity increases by one unit, other things

remaining the same, the dependent variable leverage will decrease by.0959997 unit. But the big

p-value (.317) indicates that it is not statistically significant at usual significance level (1%, 5%

and 10%).

iii. 2= -7.313913:It shows a negative relationship between dependent variable leverage and

explanatory variable ROA representing the profitability of banks. It implies that if ROA increases

_ c o n s 1 . 6 4 2 2 0 9 . 2 6 4 2 9 7 1 6 . 2 1 0 . 0 0 0 1 . 0 9 4 0 9 2 . 1 9 0 3 2 7 S i z e - . 0 5 1 0 8 7 7 . 0 2 1 9 5 5 6 - 2 . 3 3 0 . 0 3 0 - . 0 9 6 6 2 1 - . 0 0 5 5 5 4 5 A G R - . 0 5 1 2 4 8 3 . 2 2 4 7 4 8 3 - 0 . 2 3 0 . 8 2 2 - . 5 1 7 3 4 7 9 . 4 1 4 8 5 1 2 R i s k . 0 1 8 0 4 6 . 0 0 6 2 3 7 9 2 . 8 9 0 . 0 0 8 . 0 0 5 1 0 9 4 . 0 3 0 9 8 2 6 T a n 1 . 1 1 0 3 4 5 . 9 9 1 7 5 2 7 1 . 1 2 0 . 2 7 5 - . 9 4 6 4 2 3 7 3 . 1 6 7 1 1 5 A g e . 0 0 1 2 6 8 . 0 0 1 3 6 9 2 0 . 9 3 0 . 3 6 4 - . 0 0 1 5 7 1 5 . 0 0 4 1 0 7 4 R O A - 7 . 3 1 3 9 1 3 1 . 2 6 6 9 0 9 - 5 . 7 7 0 . 0 0 0 - 9 . 9 4 1 3 2 2 - 4 . 6 8 6 5 0 3 L i q - . 0 9 5 9 9 9 7 . 0 9 3 7 0 3 8 - 1 . 0 2 0 . 3 1 7 - . 2 9 0 3 2 9 6 . 0 9 8 3 3 0 2 L e v C o e f . S t d . E r r . t P > | t | [ 9 5 % C o n f . I n t e r v a l ]

T o t a l . 4 2 3 6 9 4 1 7 1 2 9 . 0 1 4 6 1 0 1 4 4 R o o t M S E = . 0 3 8 5 1 A d j R - s q u a r e d = 0 . 8 9 8 5 R e s i d u a l . 0 3 2 6 2 8 9 6 7 2 2 . 0 0 1 4 8 3 1 3 5 R - s q u a r e d = 0 . 9 2 3 0 M o d e l . 3 9 1 0 6 5 2 0 4 7 . 0 5 5 8 6 6 4 5 8 P r o b > F = 0 . 0 0 0 0 F ( 7 , 2 2 ) = 3 7 . 6 7 S o u r c e S S d f M S N u m b e r o f o b s = 3 0

. r e g r e s s L e v L i q R O A A g e T a n R i s k A G R S i z e


by one unit, other things remaining the same, leverage will decrease by7.313913. The big p-value (P-value = 0.000) indicates that it is statistically significant at usual significance level (1%, 5%

and 10%). The negative sign of parameter indicates that if a bank earns more profit, it will use

less leverage to finance its assets rather it will use its profit by retaining more and converting that

profit into capital meaning that banks prefer internal financing to external financing.

iv. 3 =.001268: It indicates a positive relationship between dependent variable leverage and

explanatory variable age that represents the number of years the bank is working. It implies that

the more years a bank is working the more it will use leverage to finance its asset inferring that

over time period the management of banks become more optimistic thereby preferring more

leverage. But the p-value (.364) indicates that the age of banks exercises no significant impact on

bank’s capital structure decision at usual significance level (1%, 5% and 10%).

v. 4 =1.110345: It indicates a positive relationship between dependent variable leverage and

explanatory variable tangibility. It implies that if tangibility increases by one unit, other things

remaining the same, the dependent variable leverage will increase by 1.110345. Alternatively, we

can say that if a bank invests more in fixed asset, it will require more capital for which the bank will heavily depend on leverage. But the big p-value (.275) indicates that it is not statistically

significant at usual significance level (1%, 5% and 10%).

vi. 5 =.018046: It indicates a positive relationship between dependent variable leverage and

explanatory variable risk defined as percentage change in net income what actually should occur.

It implies that if a bank faces more risk, it will use more leverage. The fact is that if the profit of banks fluctuates more, it will face more risk and will require more capital to absorb that risk for

which it will depend more on leverage. The value of big p-value (.008) indicates that it is

statistically significant at usual significance level (1%, 5% and 10%).

vii. 6= -.0512483: It indicates a negative relationship between dependent variable leverage and

explanatory variable asset growth which is defined as percentage change in total asset. It implies that if the asset growth is high, a bank will use less leverage. It can be explained in this way that if

a bank’s asset growth rate is high, it will depend less on leverage rather it will use more equity

capital to finance that asset to minimize the risk of expansion. The value of big p-value (.822)

indicates that it is not statistically significant at usual significance level (1%, 5% and 10%).

viii. 7= -.0510877:It indicates a negative relationship between dependent variable leverage and

explanatory variable size which has been measured by natural logarithm of total asset. It implies

that if the total asset increases, the bank will use less leverage. It can be explained in the same

way that we have done in case of asset growth, i.e., if a bank’s total asset increases over time, it will depend less on leverage rather it will use more equity capital to finance that asset to minimize

the risk of expansion. The value of big p-value (.030) indicates that it is statistically significant at

usual significance level ( 5% and 10%).


Interpretation R2

The value of R2 = 0.9230 implies that the model or explanatory variables explains 92.30%

movement of the dependent variable. The positive value of adjusted R2 = 89.85% indicates the

model has excellent power to explain the movement of dependent variable.

Comment on F-statistics

The null hypothesis of F-statistics is β1 =β2 =β3 = β4 =β5 =β6= β7= 0. It implies that none of the

explanatory variables explains the dependent variable. The P-value (0.00) indicates that we reject the null hypothesis that means all the explanatory variables have sufficient power to explain the

dependent variable leverage.

Tests for Heteroskedasticiy

Here we have done hettest by using every variable. To check for heteroskedasticity we used the

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity and the outcome is given below.

Table 3: Result of Heteroskedasticity

Breusch-Pegan /Cook Weisberg test for heteroskedasticity implies that here the problem of heteroskedasticity does not exist because the P-value = (0.8205) which is greater than (0.05) at a

5% significance level. Therefore, we should accept the null hypothesis of homoskedasticity as no

heteroskedasticity problem exits, means the OLS estimates are unbiased and efficient and t-test,

F-test, Confidence intervals are reliable.

Tests for Autocorrelation

The vce, correlation matrix will help us to find out whether any multicollinearity problem exist samong the variables represented by the following correlation matrix.

Prob > chi2 = 0.8205

chi2(1) = 0.05

Variables: fitted values of Lev

Ho: Constant variance

Breusch-Pagan / Cook-Weisberg test for heteroskedasticity

. estat hettest


Table 4: VCE correlations Matrix

From the above correlation matrix, we can see that multicollinearity problem exists between asset

growth which is the percentage change in total asset and ROA as well as between size as measured by natural logarithm and ROA but the problem is not severe. However, there are

various ways to resolve this problem. Among them the mostly used techniques are log

transformation, dropping variables and factoring. But in our study, we are not moving further

because for those sorts of tests time variable needs to be considered.

Description of Summary Table

The results of regression analysis have been summarized in the following table with a range of descriptive statistics. The descriptive statistics in the following table shows that on an average the

banking sector in Bangladesh tends to have around 94% debt and only 6% equity. Liquidity, on

average, is .7614 or 76% approximately. Similarly, the mean value of ROA is 0.01075 which

indicates that during the sample period the banking industry of Bangladesh earned only 1.08 % profit. The sample banks are working on an average 19 years as represented by age with a

minimum of 10.5 years and maximum of 30.5 years. The proxy of tangibility of assets which is

the proportion of fixed asset to total asset during the sample period for the sample banks has the mean value of 0.021. Similarly, risk of sample banks’ assets has the mean value of .03285 which

indicates that the sample banks are less risky implying the fluctuation in net income is low. The

asset growth has a mean value of around .2064 which indicates that during the sample period, the banking sector of Bangladesh has grown by 21% approximately with respect to their asset size.

The mean value of size measured by natural logarithm of total asset is 11.588.

_cons -0.3675 0.6902 0.2529 -0.1577 -0.3847 -0.1661 -0.9537 1.0000

Size 0.1670 -0.5997 -0.3863 0.1045 0.3878 0.0318 1.0000

AGR -0.2231 -0.5522 0.3661 0.1663 0.3923 1.0000

Risk -0.2319 -0.4462 -0.0203 0.3713 1.0000

Tan -0.0801 0.0562 -0.3366 1.0000

Age -0.0484 -0.0410 1.0000

ROA -0.2506 1.0000

Liq 1.0000

e(V) Liq ROA Age Tan Risk AGR Size _cons

Correlation matrix of coefficients of regress model

. estat vce, correlation


Table 5: Descriptive Statistics

Conclusion In this study, an effort is exerted to examine the factors affecting the capital structure of commercial banks in Bangladesh. The data of 30 commercial banks listed in the Dhaka Stock Exchange Limited have been used covering a time period of 2007-2016. The study attempts to analyze the relationship between the level of leverage and a set of explanatory variables of capital structure. Seven factors namely liquidity, tangibility, profitability, growth rate of asset, risk, size and age of the firm have been considered as independent variables. The study reveals that leverage is significantly and negatively associated with profitability and size of the banks. We also find a significant positive association of level of leverage with risk of banks. Tangibility, liquidity, age, and asset growth exhibit no significant influence on the capital structure of the banks in Bangladesh. Pragmatically, the results are in consistent with a number of international evidences on this issue. The positive relationship between leverage and profitability implies that profitable firms generally have less amount of leverage. Usually, profitable firms have the ability to finance their growth internally by using retained earnings. Moreover, less profitable firms may not have such alternatives that may force them to go for debt financing. The study also recommends significant negative relationship between debt level and size, demonstrating that with the increase in total assets, dependence on leverage will be lower. Again, it is found that there is a significant positive relationship between risk and level of leverage. Due to fluctuation in profitability, banks may face risk that may lead them to finance more with leverage. Coefficient of determination (R2) of 0.9230 indicates strong power of the co-relational degree among the independent variables and dependent variable.

Size 11.58886 .5207123 9.647029 12.94009

AGR .2063851 .0577749 -.027501 .2970672

Risk .0328521 1.618629 -8.44482 1.114936

Tan .0210006 .0098729 .0056659 .0451507

Age 18.61667 7.121705 10.5 30.5

ROA .0107506 .0123717 -.047186 .028779

Liq .7614013 .1008073 .5195388 .9565153

Lev .9353756 .1208724 .8465221 1.566823

Variable Mean Std. Dev. Min Max

Estimation sample regress Number of obs = 30

. estat summarize


For future research, there is a scope to study several macro-economic factors that may affect the capital structure decisions. Again, researchers may develop a stronger model by including additional firm specific factors affecting the capital structure decision.

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