1

Author: Le Thanh Tam, Pham Xuan Trang & Le Nhat Hanh

Determinants of Bank Profitability: The Case of Commercial Banks Listed on

the Vietnam’s Stock Exchange

Le Thanh TAM1, Pham Xuan TRANG2

National Economics University, Hanoi

Le Nhat HANH

University of Economics, Hochiminh City3

Abstract

Using panel data collected from 9 Vietnamese commercial banks listed in the HNX and HOSE in

period 2007-2013 data, this paper found out that: (1) profitability level of these banks is higher

than minimum requirements of CAMELS, that is, when compared to the average levels of

international standards - although some banks got low profitability in certain years; (2) the

profitability of these banks are determined by several internal and external factors. The key

significant internal factors are: bank size - the smaller banks have higher profit; total assets – a

total assets growth rate – the higher the profit; interest rates – higher interest rate level bring

better benefit to banks than deposit customers. To improve the bank profitability, management

should consider carefully the appropriate bank size, manage the cost structure well, implement

the rational interest rate policy, and manage the credit risks and other risks prudentially. Bank

returns are also strongly affected by macroeconomic variables, suggesting that macroeconomic

policies to promote low inflation and high GDP growth rate to have good impacts on bank

profitability and development.

Keywords: Capital, Commercial bank, Determinant, Macroeconomic factors, Profitability,

Risk.

Address Correspondence to: Le Thanh Tam, Ph.D. – School of Banking & Finance, National Economics

University, Hanoi, Vietnam. Email: tamlt@neu.edu.vn

Journal of Business Sciences (JBS)

Volume 1, Issue 2, pp. 1-12

(P-ISSN: 2521-5620; E-ISSN: 2521-5302)

December 2017

JOURNAL OF BUSINESS SCIENCES (JBS)

2

1. Introduction

Banks play a central function in the economy. They keep the savings of the public and finance the

development of business and trade (Casu et al, 2006; Rose et al, 2013). Several studies,

summarized and survey by Levine (1996) shown that the bank efficiency can also affect economic

growth via net return to savings and gross return for the investment. Several research studies on

factors affecting bank performance have been conducted extensively for US commercial banks

and, to a lesser extent, for financial institutions in Europe and in large emerging markets. The

respective empirical studies have focused their analysis either on cross-country evidence or on the

banking system of individual countries in Germany, France, United Kingdom, and the United

States. The studies by Molyneux and Thornton (1992), Demirguc-Kunt and Huizinga (1999),

Staikouras and Wood (2004), Goddard, Molyneux, and Wilson (2004), Pasiouras and Kosmidou

(2007), and Flamini et al. (2009) investigated the bank profitability using panel data set in one

country. However, relatively little is known about determinants of bank performance among banks

in other developing countries – particularly in Vietnam.

Thus, this paper attempts to empirically analyse the determinants of profitability of 9 commercial

banks listed on HOSE and HNX in the period from 2007 to 2013 by using panel data analysis.

2. Literature Review on determinants of bank profitability.

There are some mutual elements to categorise the determinants of banking profitability, focusing

on internal factors and external factors. Huizinga (1999) in his research used Regression Analysis

to determine the relationship between some bank ratios and measure of profitability. According

to the data of banks for 80 countries, they found that differences in interest margins and bank

profitability reflect various determinants: macroeconomic conditions, bank characteristics,

regulation for insurance of deposit and general financial system. Regarding their research, they

found that the bank ratio of concentration also influences to the bank profitability, as bigger banks

tend to have a bigger profit.

Aburime & Toni (2008) researched on 154 banks over the 1980-2006 period. However, it should

be noted that the majority of their work was focused on the analysis of external factors. According

to their results, they show that the inflation, real interest rates, the exchange rate and monetary

policy are significant macroeconomic indexes of bank performance in Nigeria. In this work, they

showed that there is a strong relationship between macroeconomic performance and profitability

of the bank. But they also reported that stock market development, banking sector development,

and financial structure are not statistically significant; and the relationship between bank

profitability and tax policy in Nigeria is inconclusive.

Regarding studies from the Middle East, a few studies have shed some light on the bank

profitability. Khrawish (2011) accessed the Jordanian commercial bank profitability from 2000

through 2010 and categorised the factors affecting profitability into internal and external factors.

The author found that there is significant and positive relationship between return on assets (ROA)

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Author: Le Thanh Tam, Pham Xuan Trang & Le Nhat Hanh

and the bank size, total liabilities/ total assets, total equity/ total assets, net interest margin and

exchange rate of the commercial banks and that there is significant and negative relationship

between ROA of the commercial banks and annual growth rate of gross domestic product and

inflation rate.

In the South Eastern European Region, the impact of many variables on bank profitability

have been reported, according to nature and the purpose of each study. Athanasoglou et al. (2008)

point out that the determinants of bank profitability are usually discussed as a function of internal

and external variables. The internal variables are a proxy of microeconomics or bank-specific

determinants such as size, asset quality, capital adequacy, liquidity ratios, operation ratios, and

leverage, while the external determinants reflect macroeconomic indicators such as inflation, GDP,

and interest rates.

Flamini et al. (2009) investigated the bank profitability using panel data set of 389 banks

in 44 Sub-Saharan African countries. The findings were: these banks had high profit compared to

other regions; apart from credit, higher ROA was associated with larger bank size, activity

diversification, and private ownership. Macroeconomic variables such as low inflation and stable

economic growth also affect bank returns. However, higher returns do not immediately result in

the higher capital requirements. It implies that to strengthen financial stability, policymakers

should impose higher capital requirements.

The bulk of literature shows that: the profitability of banks can be assessed by either three

variables: return on assets (ROA), return on equity (ROE), and net interest margin (NIM). The

internal factors affecting bank profitability could be bank size (total asset could be the proxy for

this factor), the growth of total assets, credit risk (with a proxy of provision for loan losses), risk

(ratio of loans to bank’s equity), management expenses, the capital. The external factors which

have been assessed in several countries are inflation, GDP growth rate, population growth, interest

rate.

3. Data analysis for the case of Vietnam.

3.1. Variables and models

Our panel data covers the period of 7 years (2007 – 2013) of 9 commercial banks operating

in Vietnam4. All the data has been extracted from the financial statements of the commercial banks

listed on HOSE or HNX. The data on internal factors of commercial banks collected from their

financial statements. Regarding data on external factors, we collected from official websites of

General Statistics Office of Vietnam (GSO) and The State Bank of Vietnam (SBV).

To facilitate the analysis, the symbols of the variables in the research model are presented

in the following table:

Table 1: Variables and Abbreviations

Variable Abbreviations Variable type

Return on average assets ROA Dependent

Return on average equity ROE Dependent

Net interest margin NIM Dependent

4 These banks are: ACB (HNX), BIDV (HOSE), Vietinbank (CTG – HOSE), Eximbank (EIB – HOSE), Military

Bank (MBB-HOSE), National Citizen Bank (NVB-HNX), SHB (HNX), Sacombank (STB – HOSE), Vietcombank

(VCB – HOSE). Source: National Securities Commission (2014).

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Bank Size BS Independent and internal

Growth of total assets GTA Independent and internal

Credit risk CR Independent and internal

Risks R Independent and internal

Management Expenses ME Independent and internal

Capital C Independent and internal

Inflation I Independent and external

GDP Growth GDP Independent and external

Population growth PG Independent and external

Interest rate IR Independent and external

Based on the literature review above, the three econometric models will be used for

analyzing the profile presented in the following sections:

Model 1: ROAit = α0 + α1BSit + α2GTAit + α3CRit + α4Rit + α5EMit + α6Cit + α7Iit + α8GDPit +

α9PGit + α10IRit + εit

Model 2: ROEit = β0 + β1BSit + β2GTAit + β3CRit + β4Rit + β5EMit + β6Cit + β7Iit + β8GDPit + β9PGit

+ β10IRit + εit

Model 3: NIMit = λ0 + λ1BSit + λ2GTAit + λ3CRit + λ4Rit + λ5EMit + λ6Cit + λ7Iit + λ8GDPit + λ9PGit

+ λ10IRit + εit

Subscripts i and t index banks and time in years, respectively; α, β, and λ are coefficients while εit

is the error term.

Based on the theoretical framework, empirical researches and Vietnam conditions, the

research hypotheses of the paper are presented as below:

Table 2: The expected impact of independent variables on bank profitability

Dependent variable: ROA Dependent variable: ROE Dependent variable: NIM

Independent Variables Expected Signal

Independent

Variables

Expected

Signal

Independent

Variables

Expected

Signal

BS - BS - BS -

GTA + GTA + GTA +

CR + CR + CR +

R + R + R +

ME + ME + ME +

C + C + C +

I + I + I +

GDP + GDP + GDP +

PG + PG + PG +

IR + IR + IR +

These expected signals will be rejected or accepted according to regression results of three models

which presented above.

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Author: Le Thanh Tam, Pham Xuan Trang & Le Nhat Hanh

3.2.Descriptive statistics result of the variables

Following are the descriptive analysis of the 9 commercial banks listed on the stock exchange of

Vietnam.

Table 3: Descriptive statistics of the variables

Variable Obs Mean Std. Dev. Min Max Median

ME 63 1.45 0.53 0.53 3.01 1.37

BS 63 18.58 1.08 16.11 20.17 18.90

GTA 54 26.60 26.15 -37.26 100.33 20.47

R 63 8.64 4.07 1.66 19.13 7.68

NIM 63 11.13 3.67 0.00 20.02 10.79

ROE 63 15.21 7.75 0.07 44.49 14.47

ROA 63 1.26 0.61 0.01 3.13 1.26

CR 62 0.01 0.01 0.00 0.03 0.01

I 63 12.51 6.51 4.80 22.70 10.90

GDP 63 7.22 0.24 6.82 7.56 7.20

PG 63 1.06 0.05 1.00 1.10 1.10

IR 63 2.87 0.57 1.90 3.70 3.00

C 63 7.47 4.09 2.88 25.96 6.82

Source: Stata outputs based on financial statement reports of 9 banks listed on stock exchange

Most of the mean and median value of variables are in stable range, with low standard

deviation. This implies that there is very low variation in the data set and the distribution of data

is fairly 1symmetric. Two exceptional cases of the BS (bank size) and GTA (growth of total assets)

due to the fact that we have taken the various size of banks, and there was a large variation in the

GTA between banks during that period.

The mean levels of ROA (1.26%) and ROE (15.21%) are higher than minimum levels as

required by CAMELS and with bank scope database5 (ROA = 1%, ROE = 15%), showing that on

average, these banks had the acceptable profitability levels compared to international standards.

These banks are also having better performance among 39 commercial banks in Vietnam, as they

have the capacity to be officially listed on the stock exchange. However, the high standard

deviation and low in value represent the fact that not all of these 9 banks reach the standard

profitability.

The Correlation analysis was carried out to show connections between all the variables.

Table 2 below presents the results of the correlation analysis for the study in order to determine

the level of association among the variables.

Table 4: Correlation between variables

ME BS GTA R NIM ROE ROA CR I GDP PG IR C

ME 1

BS 0.13 1

GTA -0.52 -0.23 1

R 0.11 0.56 -0.22 1

NIM -0.48 0.05 0.33 0.06 1

5 http://www.cbs.dk/en/library/databases/bankscope

JOURNAL OF BUSINESS SCIENCES (JBS)

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ROE -0.30 0.29 0.22 0.29 -0.13 1

ROA -0.42 -0.12 0.41 0.27 -0.26 0.72 1

CR -0.08 0.43 -0.04 0.37 0.06 0.01 -0.19 1

I 0.03 -0.08 -0.07 -0.06 0.59 0.12 0.10 0.04 1

GDP 0.55 0.42 -0.41 0.02 0.25 0.35 0.51 0.08 -0.16 1

PG -0.17 -0.29 0.00 -0.08 0.03 0.00 0.14 0.08 -0.03 -0.52 1

IR -0.05 -0.12 -0.54 -0.06 0.01 0.07 0.00 0.19 0.11 -0.12 0.26 1

C -0.01 -0.55 0.08 -0.74 0.04 -0.45 0.16 -0.23 0.08 -0.12 0.18 0.11 1

Source: Stata outputs based on financial statement reports of 9 banks listed on stock exchange

Overall, with the correlation relationships between the independent variables in the range

below 0.6, it would indicate that multicollinearity was not an issue in these estimations, as no two

variables were highly correlated.

All profitability indicators (ROE, ROA, and NIM) of banks is inversely correlated with

ME. It means that management expenses ratio will decrease with changes in profitability ratios. It

seems that the increase in costs leads a decrease in profitability.

However, GTA of banks is positively correlated to ROE, NIM, and ROA which is

consistent with Pasiouras & Kosmidou (2007) and Smirlock (1985). Besides, risk (R) is positively

related with the NIM, which is consistent with Abreu and Mendens (2002). This implies that

positive relationships between risk, a growth of total assets and profitability indicators can be

observed in regression results.

3.3. Regression results and Hausman test for selecting the suitable model

In this part, we explore how the changes in explanatory variables influence the dependent

variables. Estimation was firstly done by applying the Fixed Effect Model (FEM) and Random

Effects model (REM). Because our panel data is unbalanced, econometricians recommend the

REM method as an efficient estimator for unbalanced panel data (Baltagi, 2005). This was

confirmed by the Hausman specification test which evaluated the efficiency between the REM and

FEM estimators for the panel regressions. The results of the Hausman test performed to select

between FEM or REM indicate that the REM is more suitable than the FEM because all values of

Prob > Chi2 are larger than 5%.6 These results of Hausman test are consistent with the theory that

random effects estimator is expected to generate more efficient results after controlling for possible

endogeneity and autocorrelation effects associated with dynamic models (Blundell and Bond

(1998). The REM coefficients of the regressions indicate how much ROA, ROE, and NIM

(dependent variables) change when there is a change in internal and external variables

(independent variables):

6 The Hausman test for models: Test HO: Difference in coefficients not systematic

Model 1: Chi2 (10) = 1.58. Prob>chi2 = 0.9987

Model 2: Chi2 (10) = 1.08. Prob>chi2 = 0.9998

Model 3: Chi2 (9) = 3.55. Prob>chi2 = 0.9387

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Author: Le Thanh Tam, Pham Xuan Trang & Le Nhat Hanh

Table 5: REM regression for ROA, ROE, and NIM

Model 1 (ROA) Model 2 (ROE) Model 3 (NIM)

VARIABLES FEM REM FEM REM FEM REM

ME -0.0727** -0.0968* -1.788 -1.302* -3.170*** -3.089***

(0.192) (0.164) (2.519) (2.135) (0.791) (0.697)

BS -0.135** -0.343** -7.703* -5.386*** -1.655** -0.237*

(0.299) (0.141) (3.913) (1.806) (1.229) (0.594)

GTA 0.00483* 0.00390* 0.0364* 0.0351* 0.0350** 0.0282**

(0.00314) (0.00281) (0.0411) (0.0366) (0.0129) (0.0119)

R -0.0715* 0.0826** 0.652** 0.785* 0.292* 0.288**

(0.0428) (0.0343) (0.560) (0.444) (0.176) (0.145)

CR -16.08 -10.44* -179.9* -105.8** 98.98* 74.16

(14.20) (12.16) (185.9) (157.8) (58.36) (51.53)

I -0.00925* -0.00561 0.166 0.189* 0.301*** 0.324***

(0.0132) (0.0117) (0.173) (0.153) (0.0542) (0.0498)

GDP 1.261 1.898** 26.22* 20.47** -0.623 3.482

(1.104) (0.745) (14.45) (9.662) (4.538) (3.157)

PG -1.154 -1.276 10.96* 12.48 7.761* 8.249

(1.431) (1.333) (18.73) (17.39) (5.880) (5.661)

IR 0.0696** 0.0290*** 0.568 1.431* 1.123 1.424*

(0.221) (0.190) (2.886) (2.469) (0.906) (0.804)

C 0.00122 0.000792* 0.514** 0.684** 0.00401* 0.0496**

(0.0283) (0.0230) (0.371) (0.299) (0.116) (0.0976)

Constant 9.651 10.49* 79.70 87.17 -25.78 -28.71

(5.989) (5.477) (78.39) (71.34) (24.61) (23.24)

Observations 53 53 53 53 53 53

R-squared 0.568 0.548 0.826

Number of

code1

9 9 9 9 9 9

Standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

It is clear that the relationship between profitability indicators (ROA, ROE, and NIM) and

capital (C) is positive and statistically significant. The banks with larger capital are able to

diversify their business operations by strengthening their ability to assume the risk and get funds

at low cost, which will enhance their liquidity position. The overall effect will be an improvement

of their lending, with a positive effect on profitability. As Athanasoglou et al. (2005) pointed out,

a bank with a sound capital position is able to pursue business opportunities more effectively and

has more time and flexibility to deal with problems arising from unexpected losses, thus achieving

JOURNAL OF BUSINESS SCIENCES (JBS)

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increased profitability. These results are also confirmed by the studies by Bourke (1989), Flamini

et al. (2009).

Bank size (BS) has a negative and statistically significant effect on banks’ profitability

indicators (ROA, ROE, and NIM). Therefore, this result does not provide evidence for the

economies of scale theory. This conclusion is in line with Athanasoglou et al. (2008), Dietrich &

Wanzenried (2009) and Staikouras & Wood (2004). The negative relationship could be that, as the

banks are becoming extremely large, the bureaucratic procedures have negatively affected their

performances. For instance, the mergers and acquisitions of banks in Vietnam during that period,

where the number of banks was reduced, could have caused the returns of the banks to decline

(SBV, 2013).

In line with expectations, the management expenses (ME) variable has a negative and

significant effect on banks’ profitability indicators (ROA, ROE, and NIM). This result also implies

that efficient management of banks’ expenses, by reducing the cost of operations, improves the

performance of the banks. The most important policy lesson to the banks is that reducing the cost

of operations reduces the incidence of failure of the banks and hence strengthens the confidence

of the shareholders and the public through improved "financial performance of the banks. Thus,

as stated by Efficiency Structure (ES) hypothesis, an efficiently managed bank will earn higher

profits than the less efficient ones. This signal is consistent with research findings of Huizinga

(1999) and Pasiouras & Kosmidou (2007).

Interest rate variable (IR) has a positive and significant effect on banks’ profitability

indicators (ROA, ROE, and NIM) as expected. Most banks charge a higher rate of interest on loans

and advances because of their perceived risk of doing business than paying deposit interest rates

to depositors. Borrowers have no readily available alternative sources of borrowing to finance their

investments, the availability doctrine, rather than the cost doctrine, has been the only option. They

are ready, at whatever cost, to obtain loans from the banks as far the loans are available. The higher

interest rates benefit the banks in terms of higher profits but at the expense of the overall economic

development of the country.

Expectedly, the GDP variable has the positive and statistically significant relationship with

profitability. The positive effect is an indication that higher GDP represents improved business

opportunities, which ultimately leads to higher profitability for Vietnamese Commercial Banks

from 2007 to 2013. The result is consistent with theory as documented in the study by Dietrich &

Wanzenrid (2009).

With regard to the population growth rate (PG), the impact of this variable on the

profitability of commercial banks is not clear. In addition, all the regression coefficient of this

variable is not statistically significant. It may come from the fact that these banks do not cover

outreach to all the regions. They still focus on urban customers because of the huge market

potential here.

Table 6: Conclusion on the determinants of Vietnamese commercial banks’ profitability

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Author: Le Thanh Tam, Pham Xuan Trang & Le Nhat Hanh

Independent Variables Expected Signal Actual signal Hypothesis

accepted/rejected

BS - - Accepted

GTA + + Accepted

CR +

+ on ROA/ROE

- on NIM

Accepted

R + + Accepted

ME + - Rejected

C + + Accepted

I +

+ on ROE/NIM

- on ROA (insignificant)

Accepted

GDP + + Accepted

PG + Unclear No conclusion

IR + + Accepted

Source: Author calculation basing on REM & FEM regressions result

The coefficient of the variable representing credit risk (CR) indicates that CR has a negative

and significant on ROE and ROA but not on NIM. The higher the interest rate margins, the higher

the profits and banks are able to shield themselves against hazards of credit risk resulting from

adverse selection and moral hazard. This is consistent with findings of Athanasoglou et al. (2008)

& Flamini et al. (2009). For the minus correlation with NIM, it shows that the other incomes are

higher than costs to cover the loss in the net interest margin, and the unused of provision for loan

losses which have been returned. These banks have saved provision for loan losses because of the

efficient credit risk solving.

For inflation variable (I), the coefficient is positive and significant, in NIM and ROE but

negative and insignificant in ROA. That bank management may anticipate the rate of inflation and

react accordingly. Consequently, banks in Vietnam tend to be more profitable in inflationary

environments. If predictions become correct, such adjustments in interest rates could be

incorporated in inflation expectations, to achieve higher profits. If the forecast is incorrect, the

effect of inflation on bank's profitability could be negative or less significant. This is consistent

with the finding by Goddard et al. (2004b) that the effect of inflation on bank profitability depends

on the ability of inflation forecast by the bank's management.

4. Major findings & conclusion

This research contributes to the basic ratiocinate the case of Vietnam, and to shows

concurrently what management should determine to develop their bank's profits effectively. It

points out that if the bank decides to expand bank size, they could face the reducing profits.

Besides, Management Expense (ME) also has a negative relationship with the profitability of

banks. Concurrently, the research has found out that IR variable, Capital, and GDP has a positive

impact on the index reflecting the bank's profits.

The differences in the expected signals of two independent variables (credit risk and growth

of population) in Vietnam case with literature reviews show that (i) these banks have reserved

more provision for loan losses than actual uses, which means they have good credit risks solutions;

and (ii) these banks still operate in urban areas, not yet reaching the rural areas.

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The policy implications for improving bank profitability are:

First, choosing the appropriate bank size level, as small size may bring better benefits.

Bank managers should be more careful when deciding to scale up development as if they want to

increase profits, it requires them to adjust banking activities in the most appropriate way. The

large-size banks or the banks who want to increase their size by M&A, by shares issued should

consider their decision carefully.

Second, the cost of management should be considered and checked carefully, as the more

expenses on them, the less the profitability. Banks should be cost-effective in selecting the good

management model.

Third, the interest rate policy should be rational and should be one of key competition

policies of banks. Customers pay very much attention to the borrowing and deposit prices in the

medium-rank market. In calculating the lending interest rates, risk premium should always be one

of the key factors.

Fourth, credit risk and other risks should be managed in a cost-effective way. Using

provision for loan losses and other prevention methods are prioritized. Banks may have to spend

more costs for risk prevention, but it is more efficient and spent fewer opportunity costs than to

solve the risks which are already occurred. Solving non-performing loans effectively by different

measures, including reviewing the lending process, applying the standard internal credit scoring

system, selling to the market or to asset management company (VAMC) to clear up their balance

sheets.

Fifth, market segments and financial services should be diversified more. The urban market

is good, but the rural market is also very potential + a huge customer base and less risky. Banks

should also enhance and improve quality of traditional and new services to bring more customers,

including retailer and wholesale customers in the emerging market of more than 90 million people

of Vietnam.

The limits of this research are: (i) relying mostly on secondary data; (ii) number of banks

in the sample are still small; (iii) A similar study should also be carried out on the relationship

between financial performance of commercial banks incorporating more financial and

accounting variables and also taking into account the prevailing macroeconomic situation

(such as monetary policies, tax structure) and the microeconomic situation (like capital structure)

in the country.

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