Tác động của rủi ro tín dụng đến hiệu quả kinh doanh của các ngân hàng thương mại việt nam tiếng anh

MINISTRY OF EDUCATION AND TRAINING
UNIVERSITY OF ECONOMICS HO CHI MINH CITY
----------

NGUYEN QUOC ANH

IMPACTS OF CREDIT RISK ON
VIETNAMESE COMMERCIAL BANKS’
BUSINESS PERFORMANCE

SUMMARY OF ECONOMIC DOCTORATE THESIS

Faculty: Finance - Banking
Code: 62.34.02.01
Academic Instructor: Assoc. Prof. S.D. Nguyễn Đăng Dờn

Ho Chi Minh City – 2016


CHAPTER 1
INTRODUCTION
1.1 REASONS FOR RESEARCH
The empirical studies in Vietnam and other countries indicate that macroeconomic factors, such
as inflation, and GDP growth significantly impact nonperforming loans (NPL). Specific factors of the
banks were also tested, the previous research indicated that NPL ratio of the previous year and credit
growth ratehave the strongest impact on the NPL ratio of banks. Some studies show that there is a
relationship between credit risk and business performance of commercial banks through profitability
indicator ratios.NicolaePetria (2013), Hasan Ayaydin (2014) use the ROE (Profit after tax / Equity) as the
dependent variable and study the impact of credit risk on business performance of commercial banks. The
results indicate that credit risk has negative impact on business performance of commercial banks.
Credit risks appears objective in the market economy, especially in international integration trend

and financial crisis. The consequences of credit risk are the decline in bank profits and the destabilization
in the banking and economy system. From the above reasons, there is a need to study the impact of credit
risk on business performance of commercial banks in Vietnam.
Research gap
While there are several studies on the factors affecting credit risk of commercial banks in
Vietnam, there is no study on the effects of credit risk on business performance of commercial banks. The
authors propose that credit risk may affect business performanc ecommercial banks in Vietnam. The
authors examine factors affecting credit risk, such as macroeconomic factors and specific factors of
commercial banks, in the period of 2005 to 2015.
The findings also detect the factors affecting credit risk, indicating that NPL and credit risk
management affect the business performance of commercial banks in Vietnam.

1.2 RESEARCH PURPOSES
Particularly, the study aims to provide insights into the factors affecting credit risk, the impact of
credit risk on business performance of commercial banks in Vietnam, and suggest solutions to limit credit
risk, in order to enhance business performance of commercial banks in Vietnam. To be specified, this
research:
- Identify the factors affecting credit risk of commercial banks.
- The impact of credit risk on business performance of commercial banks.
- Measure factors influencing the level of credit risk, and the impact of credit risk on business
performance of commercial banks in Vietnam.
- Suggest solutions to limit credit risk, and enhance business performance of commercial banks in
Vietnam.

1.3 RESEARCH SUBJECTS AND SCOPE OF THE STUDY
- Research subject: the factors influencing the level of credit risk, and the impact of credit risk on
business performance of commercial banks in Vietnam.
- The study scope: focuses on factors influencing credit risk and business performance of
commercial banks in Vietnam. The major data sources were collected from 26 commercial banks in
Vietnam through Bankscope, macroeconomic data sources were collected from ADB indicators with

coverage from 2005- 2015.

1.4 RESEARCH METHODOLOGY
Quantitative research methods: using The Pooled OLS, Fixed effects method (FEM) & Random
effects model (REM) , and the GMM method to examine factors influencing credit risk; using Feasible
Generalized Least Squares (FGLS) model to study the impact of credit risk on business performance of


commercial banks in Vietnam. Furthermore, the authors also use several research methods such as
interpretation, synthesis, comparison and analysis to study the factors influencing credit risk of
commercial banks; current state of credit risk and business performance of commercial banks in Vietnam.

1.5 RESEARCH STRUCTURE
The content of this research consists of 5 chapters:
Chapter 1: Introduction
Chapter 2: Theoretical Foundations
Chapter 3: Research Methodology
Chapter 4: Research Results
Chapter 5: Conclusions and suggested solutions


CHAPTER 2
THEORETICAL FOUNDATIONS
2.1 CREDIT RISK
After examining multiple perspectives on credit risk, the authors define credit risk as risk that
arise during credit activities of banks, when customers do not pay their debts or repay debts on time for
the banks. This is the main risk in the banking business, so credit operations and credit risk will affect the
profitability and effectiveness of banks. Credit risk can lead to other risks that cause serious consequences
and disrupt the balance and stability of banks.

2.2 BUSINESS PERFORMANCE OF COMMERCIAL BANKS
Commercial banks use resources such as labor, infrastructure, financial resources for core
activities: taking deposits, lending and investments. This is the basis for determining the level of
efficiency and factors affecting the efficiency of commercial banks.
Similarly, in the study of banking activities, some authors use the production approach with a
view to banks as production units (Benston, 1965; Ferrier et al, 1990; Shaffnit et al, 1997 ; Zenios et al,
1999); some authors use intermediate approach, which views banks as financial intermediaries (Sealey
and Lindley, 1977; Maudos and Pastor, 2003;. Casu et al, 2003); and some other authors use modern
approach that banks play both roles (Frexias and Rochet, 1997; Denizer et al, 2000;.Athanassopoulos and
Giokas, 2000).

2.3 IMPACT OF CREDIT RISK ON BUSINESS PERFORMANCE OF
COMMERCIAL BANKS
2.3.1. Credit risk impacts profitability and risks of commercial banks
When credit risks arise, NPL increases, which results in lower sales and leads to a loss.
Furthermore, when NPLs incur, the costs will increase significantly, for example: the interest payment
costs, NPL management cost, provisioning costs and other credit-related expenses. The increase in costs
results in lower profit than the initial estimate. NicolaePetria (2013) indicates credit risk negatively affects
business performance of banks (as measured by ROE, ROA), which have a direct impact on and degrade
business performance of banks (Hasan Ayaydin, 2014).
2.3.2 Credit risk lead to bank instability, thereby affect the business performance of commercial
banks
NPL results in losses on bank assets.When high level of NPL is not limited, it will lead to a series of
serious effects. Normal losses occur in the lending amount, increase in operating costs, decrease in profit,
decrease in the value of assets, etc. which lead to bank reputation loss.
2.3.3. Credit risk affects macroeconomic factors
Highlevels ofcredit risk may impose systemic risk in the banking system, which then damage
general economic conditions of a country, the macroeconomic factors (Vania Andriani1,
SudarsoKaderiWiryono, 2015)

2.4 RESEARCH OVERVIEW ABOUT IMPACT OF CREDIT RISK ON
BUSINESS PERFORMANCE OF COMMERCIAL BANKS
2.4.1 The impact factors of credit risk
Table 2.1: Summary of the impact factors of credit risk.
Author(s)
Rajan and Dhal (2003)

Research
subject(s)
Non performing
loans

Variables

Model

Findings

Dependent variable: Panel data, Bank size has
the NPL ratio.
FEM, REM
negative impact
on NPLs. GDP


ofcommercial
Independent
banks in India variables:
loan
(2003 -2008).

growth, loan loss
reserve,
GDP
growth
rate,
unemployment rate,
interest rate.

growth rate has
positive effect on
NPLs. In good
business
environment,
NPLs decrease.

Berge andBoye (2007)

Problem loans of Dependent variable: GMM
Northern Europe problem loans
banks
Independent
(1993 –2005)
variables:
GDP
growth
rate,
unemployment, real
interest
rates,
inflation.

Problem
loans
impact nominal
interest rates and
unemployment
rates.

Salas andSaurina (2002)

Macroeconomic
and
microeconomic
variables impact
NPLs of Spanish
banks
(19851997)

Dependent variable: Panel
problem loans
data,FEM,
REM. NPL
Independent
ratio
variables:
GDP
growth rate, bank
size,
efficiency,
marginal

income
ratio, leverage ratio,
market power.

Bank size has
negative impact
on credit risk.
GDP
growth
rates has positive
impact
on
problem loans.

ZribiandBoujelbène
(2011)

Examine the
macroeconomic
and
microeconomic
variables that
impact credit risk
of ten
commercial
banks in Tunisia

Dependent variable: Panel data, Ownership
credit risk.
REM, FEM

structure,
profitability and
Independent
the
variables:
the
macroeconomic
ownership
indicators (GDP
structure,
the
growth
rates,
prudential
inflation,
regulation
of
exchange rates,
capital,
profits,
and interest rates
GDP,
inflation,
affect credit risk.
exchange rates, and
interest rates

Louzis et al. (2012)

The

macroeconomic
factors
and
bank’s variables
that affect NPL
in Greece bank
system.

Dependent variable: Dynamic
the NPL ratio
Panel Data,
GMM.
Independent
variables: real GDP
growth
rate,
unemployment
rates, interest rates
and public debt,
ROE,
liquidity
ratio,
noneffectiveness ratio,
bank size.

Problem
loans
due
to
macroeconomic

variables
(real
GDP
growth,
unemployment,
interest rates and
public debt).

Messai Study
factors Dependent variable: Panel data,
affecting NPLs the NPL ratio
FEM, REM.
of 85 banks in
three countries Independent

The GDP growth
rate, and ROA
has
negative
impactson NPLs;

(2003-2009).

Ahlem
(2013)

Selma


(Italy,

Greece variables:
GDP,
and
Spain) unemployment,
(2004-2008).
interest
rates,
growth
in
outstanding loans,
loan loss reserve.
MarijanaCurak, Sandra Examine factors
PepurandKlimePoposki
impacting NPLs
(2013)
of the banking
system
in
Southeast Europe
(2003-2010).

Bucuret al, (2014)

The influence of
macroeconomic
conditions
on
credit risk in
Romanian
(2008-2013)

Tehuluet al. (2014)

Dependent variable: Panel data of
the NPL ratio
69 banks in
10 countries,
Independent
GMM.
variable:
GDP,
unemployment
rates, interest rates,
growth
in
outstanding loans,
ROA, inflation.

There
is
a
negative
relationship
between
bank
size and NPL
ratio.

Dependent variable: Multivariate
credit scores

regression,
SPSS.
Independent
variable:
GDP,
inflation,
money
supply,
unemployment rate.

The growth rate
of money supply
and
exchange
rate
have
a
negative
relationship with
credit risk. The
unemployment
rate
has
a
positive
relationship with
credit risk.

Examine
the Dependent variable: Panel

bank-specific
loan loss reserve.
GLS.
determinants of
credit risk in Independent
variable:
credit
Ethiopian
growth,
bank
size,
commercial
ownership,
banks
operating
(2007 - 2011).
inefficiency, bank
liquidity,
profitability.

HasnaChaibiandZiedFtiti Credit
risk
(2015)
determinants:
Evidence from a
cross-country
study
(2005-2011).

unemployment

and interest rates
has a positive
impact on NPLs.

data, Credit
growth
and bank size
have
negative
impact on credit
risk.

Dependent variable: Dynamic
loan loss reserve.
Panel Data.
Independent
variable: inflation
rate, GDP, interest
rates,
unemployment,
exchange
rate,
efficiency,
leverage,
size,
profitability, loan
loss reserve.

Operating
inefficiency and

ownership have
positive impact
on credit risk.

All
examined
macroeconomic
variables affect
the NPL ratio.

Đào Thị Thanh Bình and Factorsaffecting
Dependent variable: Panel data of Bank size has
NPLs
of
14
positive impact


Đỗ VânAnh (2013)

commercial
banks in Vietnam
(2008-2012)

the NPL ratio

commercial
on NPLs. ROE
banks
in has

negative
Independent
Vietnam,
impact on NPLs.
variable: bank size, FEM, REM.
ROE,
GDP,
inflation.

Đỗ Quỳnh Anh, Nguyễn Factors affecting Dependent variable: Panel data, Inflation
and
Đức Hùng (2013)
NPLs
of the NPL ratio.
REM, FEM, GDP
growth
commercial
GMM.
have
impact
banks in Vietnam Independent
onNPLs.
variable:
GDP,
(2005 -2011).
inflation,
credit
NPLs
growth, bank size.
affectfollowing

year’s
NPLs.
Bank size has
positive
relationships
with NPLs.
Võ Thị Quý and Bùi Examine factors
Ngọc Toản (2014)
affecting credit
risk
of
commercial
banks.
(2009 – 2012).
Nguyễn Thị Ngọc Diệp Define
bank
and Nguyễn Minh Kiều characteristics
(2015),
affecting credit
risk
in
commercial
banks in Vietnam
(2010- 2013).

Dependent variable: GMM
loan loss reserve.
model,
panel data
Independent

of
26
variable:
credit commercial
growth, bank size, banks.
GDP growth.

Credit risk, credit
growth,
GDP
growth rate, the
impact
of
negative
loss
ratio in the year
on credit risk.

Dependent variable: Data panel
loan loss reserve
of
32
commercial
Independent
banks
in
variable:
credit Vietnam
growth, loan size, with
and operational cost regression

/ income loans least squares
ratio.
(OLS).

Credit growth,
loan size, and the
operational cost /
income
loans
ratio
impact
credit risk.

2.4.2 The impact of credit risk on business performance of commercial banks.
Table 2.2: Summary of studies about the impact of credit risk on business performance.
Author(s)

Research
subjects

Athanasolouet
al. (2006)

The
factors
affecting
the
profitability
of
Greek

banks
(1985 – 2001)

Hassan
Sanchez
(2007)

and The
factors
determining the
efficiency
of
commercial
banks in Latin
America

Variable

Model

Findings

Dependent variable: Panel
data, The bank-specific factors,
ROE, ROA,
FEM, REM, such as: loan loss reserve,
3GLS.
Equity-to-Asset
ratio,
Independent variable:

operating costs have an
loan loss reserve,
impact on bank profits.
Equity-to-Asset ratio,
operating costs.
Dependent variable: DEA model
bank efficiency.
Independent
variables:
capitalization
profitability,

level,
loan

Loan loss reserve has
negative relationship with
business
performance.
Capitalization level, and
profitability have positive
relationship with business


(1996-2003)
Aremu
Mukaila
Ayanda
(2013)

loss reserve, labor,
credit balance.

performance.

The effectiveness Dependent variable: ECM Model
of the bank in ROE, ROA, NIM
Nigeria
Independent variable:
(1980-2010)
loan loss reserve,
total Loans-to-Total
Assets Ratio, Equityto-Total Asset Ratio,
bank size, GDP.

Nicolae Petria Examine
(2013)
determinants of
banks'
profitability
of
EU 27 banking
systems

Loan loss reserve, total
Loans-to-Total
Assets
Ratio,
Equity-to-Total
Asset Ratio have negative

effect
on
business
performance.
Bank size has positive
effect
on
business
performance.

Dependent variable: Panel
data, Credit risk has negative
ROE, ROA
REM, FEM.
impact
on
business
performance (ROE) of
Independent variable:
commercial banks.
size, credit risk, cost
efficiency, liquidity,
HHI, GDP, inflation.

(2004-2011).
Hasan
Ayaydin
(2014)

Zou et

(2014)

Factors affecting Dependent variable: Panel
the capital and NIM, ROE, ROA,
GMM.
profits of Turkish
Independent variable:
banks
loan loss reserve,
(2003-2011).
capital ratio, foreign
ownership,
HHI,
liquidity, inflation,
GDP.
al. Examine
the
relationship
between
credit
risk
and
profitability
of
commercial
banks in Europe
(2007-2012)

Alshatti
(2015)

Examine
the
impact of credit
risk on financial
efficiency
of
commercial
banks in Jordan.

data, Loan
loss
reserve
negatively impact bank
performance, measured
through ROE variable.

Dependent variable: OLS
ROE and ROA
Independent variable:
the NPL ratio, CAR,
bank size

Dependent variable: Panel data.
ROA and ROE
Independent variable:
CAR, leverage ratio,
NPL ratio, loan loss
reserve.

Credit risk has no positive
impact on the profitability
of commercial banks.
NPL
ratio
has
a
significant impact on
ROE and ROA.

Credit
risk
impacts
business performance of
commercial banks.

(2005-2013)
Samuel (2015)

The influence of Dependent variable: OLS
credit risk on the ROA
profitability
of
banks in Nigeria. Independent
variable :

Lending rate has negative
relationship
with
profitability.

NPL / credit balance,
outstanding loans /
total deposits.
Gizawet

al. The influence of Dependent variable: Panel data and NPL

ratio,

loan

loss


(2015)

credit risk on the ROE, ROA
multivariate
business
regression
performance of Independent variable: analysis.
banks
in CAR, NPL ratio,
loan loss reserve.
Ethiopia.

reserve impact business
performance of banks in
Ethiopia.

(2003-2004).
Kodithuwakku The influence of
(2015)
credit risk on
business
performance of
commercial
banks in Sri
Lanka.

Dependent variable: Multivariate
ROA
regression,
Eview.
Independent variable:
reserve / total loans
ratio;
loan
loss
reserve / NPLs ratio;
loan loss reserve /
total asset ratio,
NPLs / loans ratio.

Nguyễn Việt Analysis factors The
inputs
and DEA
Hùng (2008)
affecting business outputs cost factors.

performance of
32 commercial
banks in Vietnam
(2001– 2005).

Loans and regulations
affect the profitability of
banks.

NPL
ratio,
Total
loans/Total assets ratio,
deposits / total loans ratio,
total costs / total revenues
ratio,
income
from
interest rate / income
from operations ratio
have negative impact on
the business performance
of banks.
Market shares, equity to
total assets ratio has
positive impact on the
business performance of
banks.

Trịnh

Quốc
Trung (2013)
và
Nguyễn
Văn Sang.

Analysis factors
affecting business
performance of
39 commercial
banks in Vietnam

(2005-2013)

Dependent variable: Tobit
ROE, ROA
regression
model.
Independent variable:
cost / revenue ratio,
deposit / loan ratio,
capital / total assets
ratio, market share,
loan / total assets,
ratio of overdue
debts
and
total
residual debt.

The higher the NPL ratio,
the lower the operating
efficiency. The higher
theratio of loans / total
assets,the
higher
operational
efficiency.
Total operating expenses /
revenue ratio negatively
correlated with ROE; The
higher theSelf-financing
ratio, the lower the ROE.

Source: Compiled from relevant research
SUMMARY OF CHAPTER 2
In this chapter, the authors introduce the theoretical foundations, and measurement method of
credit risk in commercial banks. The authors also analyze the causes and effects of credit risk on general
economy, business performance of commercial banks. In general, previous researches in Vietnam and
abroad on the factors influencing credit risk and the impact of credit risk on business performance were
also reviewed by the authors. It's also the foundation for research in the following chapters.


CHAPTER 3
METHODOLOGY
3.1 RESEARCH METHODOLOGY
Based on the theoretical foundation of credit risk and business performance of commercial banks,
the authors select and identify key research issues, using quantitative research methods. To be specific,
the authors use multivariate regression models through Pooled regression model, Fixed effect, Random
effect and use the GMM method to solve the endogenous regression on panel data. In addition, the

authors also use the methodological interpretation, synthesis, comparison and analysis method to achieve
research objectives.

3.2 FACTORS AFFECTING CREDIT RISK MODEL
Recent studies on this issueuse dynamic tabular data (Dynamic Panel Data), for example, Cheng
and Kwan (2000); Calderon and Chong (2001); Salas and Saurina(2002); Beck and Levine ( 2004),
Santos-Paulino and Thirlwall (2004); Carstensen and Toubal (2004); Athanasoglou et al. (2009); and
Merkl and Stolz (2009).The authorsselect multivariate regression model which is consistent with previous
studies, our model, which is based on the model of Hasna Chaibi and Zied Ftiti (2015), identifies factors
affecting the commercial banks RRTD:
NPLit = α+ γNPLi,t-1 + βjXi,t + vi + εi,t (1)
Where:
α: is the intercept
NPLi,t-1: the NPL ratio of bank iin year t. NPL ratio was used to measure the degree credit risk (Vania
Andriani, Sudarso Kaderi Wiryono, 2015)
γ : is the impact of negative loss ratio in the year t
Xi,t : the vector of independent variables, including macroeconomic variables and bank specific
variables. Bank specific variables:ETAi,t , LEVi,t , SIZEi,t , EFFi,t , ROEi,t , NIIi,t , PLLi,t;
macroeconomic variables: GGDPt , INRt , INFt , UNRt , EXRt
βj: the impact of independent variables on NPL ratio
vi: unobserved characteristic among banks.
εi,t: is the accumulation of the structure.
The lagged variable of the dependent variable - NPL – has a correlation with v. Therefore, if we apply the
smallest quadratic OLS methodology, an unbalanced and unstable estimation will be likely to occur. The
(1) regression equation will be stabilized if it is estimated by GMM (Generalized Method of Moments)
introduced by Arellano and Bond (1991).
* Bank internal variables:
(1) Loan loss reserve (LLR ) = Loan loss reserve/Total loans
Hypothesis 1: There is a positive correlation between Loan loss reserve and the NPL ratio.
(2) EFF- Operating inefficiency = Operating expenses/Operating income

Hypothesis 2: There is a positive correlation between operating inefficiency and NPL ratio.
(3) Leverage (LEV) = Total liabilities/Total assets
Hypothesis 3: There is a positive correlation between leverage and NPL ratio.
(4) Non-interest income (NII ) = Non-interest income/Total income
Hypothesis 4: There is an inverse correlation between non-interest income and NPL ratio
(5) Bank size (SIZE ) = Natural log of total assets


Hypothesis 5: There is a positive correlation between the bank size and NPL ratio.
(6) Return on Equity (ROE) = Net income/Total equity.
Hypothesis 6: There is an inverse correlation between ROE and the NPL ratio.
*

Macroeconomic variables

(7) Inflation (INF) = Inflation rate
Hypothesis 7: There is a positive correlation between the inflation rate and NPL ratio.
(8) GDP Growth (GGDP) = GDP Growth rate
Hypothesis 8: There is an inverse correlation between GGDP and NPL ratio.
(9) Nominal interest rate (INR) = Real interest rate
Hypothesis 9: There is a positive correlation between the real interest rate and NPL ratio.
(10) Unemployment rate (UNR)
Hypothesis 9: There is a positive correlation between the unemployment rate and NPL ratio.
(11) Exchange rate (EXR).
Hypothesis 11: There is an inverse correlation between the exchange rates and NPL ratio.
Table 3.1: Description of Model 1’s variables
Variable

Calculation

Expectation

Dependent
variable Problem loan / Total loan
measuring credit risk: NPL
ratio (NPL)
Independent variable
Bank’s internal variables
Loan loss reserve (LLR)
EFFinefficiency

Loan loss reserve/Total loans

Operating Operating expenses/Operating income

+
+

Leverage (LEV)

Total liabilities/Total assets

+

Non-interest income (NII)

Non-interest income/Total income

-

Bank size (SIZE)

Natural log of total assets

+

Return on Equity (ROE)

Net income/Total equity

-

Inflation (INF)

Inflation rate

+

GDP Growth (GGDP)

GDP Growth rate

-

Nominal interest rate (INR)

Nominal interest rate

+

Unemployment rate (UNR)

Unemployment rate

+

Exchange rate (EXR)

VND/USD rate

-

Macroeconomic Variables

Source: Compiled from relevant studies


3.3 IMPACT OF CREDIT RISK ON BUSINESS PERFORMANCE OF
COMMERCIAL BANK MODEL
Nicolae Petria (2013), Hasan Ayaydin (2014), Aremu Mukaila Ayanda (2013) study the factors
influencing business performance of commercial banks, their studies conclude: the NPL ratio and loan
loss reserve’s impact on business performance of commercial banks. The authors use ROE and ROA as
dependent variables; credit risk is represented by NPL ratio (NPL) ratio and provision for loan losses as
PLL; other control variables were included in the model through vector X. The authors use the
multivariate regression model, used in the studies of Athanasolou et al (2006), Aremu Mukaila Ayanda
(2013), Hasan Ayaydin (2014), Alshatti (2015). Our model is as followed:
(ROEit, ROAit) = α+ β1NPLi,t + β2PLLi,t + βjXi,t + vi + εi,t (2)
Where:
α: the intercept
β1 and β2: the impact of NPL and PLL on ROE, ROA.

Xi,t : variable vectors: bank internal variables includes: EFFi,t, LEVi,t, NIIi,t, SIZEi,t , and
macroeconomic variables include: GGDPt , INRt , INFt , UNRt , EXRt
βj: the impact of independent variable i on ROE, ROA
vi : Unobserved characteristics among commercial banks.
εi,t : is the accumulation of the structure.
The authors use four models - Pooed OLS, Fixed Effects, Random Effect and FGLS - on panel data to
estimate and examine the impact of credit risk on business performance of commercial banks.
Hypothesis 12: There is an inverse correlation between NPL, LLP and ROE, ROA
Table 3.2: Description of Model 2’s variables
Variable

Calculation

Return on Equity (ROE)

Net income/Total equity

Return On Assets (ROA)

Net income/Total asset

Expectation

Non-performing loan ratio (NPL) Non-performing loan /Total loan

-

Loan loss reserve (LLR)

Loan loss reserve /Total loan

-

Leverage (LEV)

Total liabilities/Total asset

-

Non-interest income (NII )

Non-interest income/Total income

+

Bank size (SIZE)

Natural log of total assets

+

Operation inefficiency (EFF)

Operating expenses/Operating income

-

Macroeconomic Variables
Inflation (INF)

Inflation rate

GDP Growth (GGDP)

GDP Growth rate

+

Nominal interest rate (INR)

Nominal interest rate

-

Unemployment rate (UNR)

Unemployment rate

-

Exchange rate (EXR)

VND/USD rate

Source: Compiled from relevant researches

+/-

+/-

3.3 DATA SOURCES
Internal bank data sources were obtained from Bank-scope and audited financial statements of 26
Vietnamese commercial banks with coverage from 2005 to 2015. The authors used major data sources
from 26 commercial banks, whose total assets account for over 75% total assets of Vietnamese
commercial banks. Therefore, the data sources are representative of Vietnamese commercial banks.
Macroeconomic data was extracted from ADB Indicators with coverage from 2005 to 2015.
CHAPTER 3: SUMMARY
This chapter analyzes and decides which regression model to be appropriate for our research goal.
The research measures the factors affecting credit risk of commercial banks in Vietnam, using dynamic
panel data; the dependent variable as NPL ratio representing credit risk. The authors study the impact of
credit risk on business performance. Macroeconomic variables and bank internal variables inherent in the
bank were analyzed and selected. The hypotheses are presented in details in order to determine the impact
direction of variables.


CHAPTER 4
RESULTS AND DISCUSSION
4.1. MACROECONOMIC CONDITIONS IMPACT CREDIT RISK AND
BUSINESS PERFORMANCE OF COMMERCIAL BANKS.
According to the empirical studies, the macroeconomic factors affect the credit risk and business
performance of commercial banks. In recent years, Vietnamese economy is unstable because of the global
financial crisis. The changes in GDP growth, inflation, interest rates, exchange rates will change the
macroeconomic conditions. The changes in monetary policy, and interest rates will affect the bank
lending channel, NPLs, and impact business performance of commercial banks.

4.2 CREDIT RISK AND BUSINESS PERFORMANCE OF COMMERCIAL
BANKS IN VIETNAM
4.2.1 Credit risk
Credit outstanding balance accounts for large proportion of bank portfolio: Credit activities still

account for about 60-80% of the total assets of commercial banks, thus the income from credit activities
accounts for a large proportion of the total income of banks. Credit average growth rate reaches 19.15%
during 2008 – 2015.
NPL ratio and loan loss reserve expenses increase: credit activities of commercial banks in
Vietnam increase in the direction of increasing the size and growth rate, but does not focus on improving
credit quality. Furthermore, there is unfavourable change in the economy. Thus, the credit quality
decreases significantly. In 2012, the NPL ratio was 4,08%; in 2013 and 2014, the NPL ratio decreased
and it was reduced to 2.55% in 2015.
4.2.2 Business Performance
ROA and ROE increased in 2008-2010. However, in the period of 2008 – 2015, both ROA and
ROE indicators reduced sharply in 2012 (ROA: 43.12%, ROE: 46.8%). In 2013 and 2014, the
profitability of commercial banks increased compared to 2012, but it was only equal of 50% of the
average during 2009-2011.
Table 4.1: Profitability of commercial banks in Vietnam
Year

2008

2009

2010

2011

2012

2013

2014

2015

ROA (%)

1,29

1,01

1,29

1,09

0,62

0,49

0,51

0,4

ROE (%)

14,56

10,42

14,56

11,88

6,31

5,56

5,49

5,7

Source: Annual Report of the State Bank of Vietnam from 2008-2015

4.3 IMPACT OF CREDIT RISKS ON BUSINESS PERFORMANCE OF
COMMERCIAL BANKS IN VIETNAM
4.3.1 Credit risk decreases profits
4.3.2 The increase in loan loss reserve lead to the decrease in profits.
4.3.3 Bank restructuring to limit credit risk and improve business performance

4.4 FINDINGS
4.4.1 Factors affecting the credit risk
Table 4.2. Descriptive statistics of model 1
Variable

Obs

Mean

Std. Dev.

Min

Max

NPL
LLR
EFF
LEV
NII
SIZE
ROE
GGDP
INF
UNR
EXR
INR

233
271
262
276
264
276
275
286
286
286
286
286

0.022471
0.015822

0.01115
0.006623
0.487958
0.190311
0.869828
0.11084
0.160688
0.271395
17.34343
1.619804
0.114088
0.074759
6.246387
0.742069
9.280675
6.03656
2.206564
0.262572
18932.05
2319.773
9.820909
2.178888
Source: Computation using STATA 13

0.001
0.000129
0.079532
0.015271
-2.00369
11.88353

0.000749
5.247367
0.63
1.8
15916
7.62

0.1246
0.037018
2.0527
1.129474
0.785564
20.56153
0.444905
7.547248
23.11632
2.6
22380.54
13.46

The authors tested the correlation and multicollinearity model. The authors estimates all 3
models: Pooled model, FEM model and REM model. However, as discussed above, due to the
endogenous phenomenon in the model, the authors use GMM regression ((Ahmad and Ariff (2007),
Podpiera and Weill (2008), Louzis et al. (2012), Hasna Chaibi and Zied Ftiti (2015)) using panel data.
The final analytical results are based on the results of GMM regression.
Table 4.3. Regression result of model 1
Variable
L.NPL
LLR
EFF

LEV
NII
SIZE
ROE
GGDP
INF
UNR
EXR
INR
_cons

Pooled
NPL

FEM
NPL

REM
NPL

GMM
NPL

0.172***
[2.69]
1.269***
[7.90]
0.01
[1.31]
-0.00281

[-0.31]
0.0140**
[2.57]
-0.00332***
[-3.28]
-0.0192
[-1.16]
0.000113
[0.06]
-0.000289
[-0.93]
-0.00458
[-1.09]
8.57E-07
[1.11]
0.00194**
[2.07]
0.0347
[1.24]

0.0773
[1.15]
1.829***
[9.06]
0.00754
[0.84]
-0.00789
[-0.74]
0.0138**
[2.24]

-0.00652**
[-2.17]
-0.00874
[-0.45]
0.000943
[0.51]
-0.000264
[-0.86]
-0.00604
[-1.48]
1.23E-06
[1.07]
0.00127
[1.35]
0.0890**
[2.17]

0.172***
[2.69]
1.269***
[7.90]
0.01
[1.31]
-0.00281
[-0.31]
0.0140**
[2.57]
-0.00332***
[-3.28]
-0.0192

[-1.16]
0.000113
[0.06]
-0.000289
[-0.93]
-0.00458
[-1.09]
8.57E-07
[1.11]
0.00194**
[2.07]
0.0347
[1.24]

0.0868*
[1.79]
2.151***
[8.60]
0.00175
[0.27]
-0.00302
[-0.53]
0.0120***
[3.75]
-0.00774***
[-4.27]
-0.000479
[-0.03]
-0.000662
[-0.75]

-0.000146
[-1.28]
-0.00421**
[-2.37]
0.00000246***
[4.63]
0.000844*
[1.88]
0.0886***
[3.95]


Chow test (p-value)
Hausman test (p-value)
Bresh-Pagan test (pvalue)
Sargan test (p-value)
TTQ test (p-value)
N
R-sq
T
*

0.0108
0.00
1

204
204
204
0.452

0.469
statistics in brackets
p<0.1, ** p<0.05, *** p<0.01
Source: Author’s computation using STATA 13

1
0.0508
176

Therefore, using GMM model together with NPL variable has become an effective tool in answeering the
inner phenomenon arisen from the model.
The results are stable and reliable.
4.4.2 The impact of credit risk on business performance of commercial banks
The dependent variable ROE, representing business performance, has the maximum value at 0.444905,
minimum value at 0.000749, average value of ROE: 0.114088. Business performance of commercial
banks in the sample vary with the standard deviation 0.074759
Table 4.4. Descriptive statistics of model 2
Variable
ROE
NPL
LLR
EFF
LEV
NII
SIZE
GGDP
INF
UNR
EXR
INR

Obs
275
233
271
262
276
264
276
286
286
286
286
286

Mean
Std. Dev.
Min
0.114088
0.074759
0.000749
0.022471
0.015822
0.001
0.01115
0.006623
0.000129
0.487958
0.190311
0.079532

0.869828
0.11084
0.015271
0.160688
0.271395
-2.00369
17.34343
1.619804
11.88353
6.246387
0.742069
5.247367
9.280675
6.03656
0.63
2.206564
0.262572
1.8
18932.05
2319.773
15916
9.820909
2.178888
6.5
Source: Author’s computation using STATA 13

Max
0.444905
0.1246
0.037018

2.0527
1.129474
0.785564
20.56153
7.547248
23.11632
2.6
22380.54
13.46

NPL represents credit risk, has maximum value 0.1246, minimum value 0.001, average value
0.0228159, the fluctuation compared to the average value is 0.0165691. LLR also represents the credit
risk with the maximum value: 0.0370178, minimum value 0.0001286, average values 0.0111129, the
standard deviation 0.006813. These values indicate that there is slight variation in LLR values of banks in
the model.
Table 4.5. Regression result of model 2 with ROE
Variable
NPL
LLR
EFF

Pooled
ROE
-0.26
[-0.91]
-1.064
[-1.38]
-0.118***
[-4.67]

FEM
ROE
-0.0721
[-0.26]
-0.763
[-0.80]
-0.128***
[-4.46]

REM
ROE
-0.124
[-0.46]
-0.926
[-1.10]
-0.123***
[-4.72]

FGLS
ROE
-0.211
[-1.04]
-1.644***
[-3.03]
-0.191***
[-12.47]


LEV
NII

SIZE
GGDP
INF
UNR
EXR
INR
_CONS

-0.0614
[-1.60]
-0.0139
[-0.65]
0.0297***
[7.61]
0.0249***
[3.57]
-0.00379***
[-2.80]
-0.0241
[-1.50]
-0.0000172***
[-6.90]
0.0159***
[3.93]
-0.172
[-1.60]
230
0.565

-0.0715*

[-1.66]
-0.00625
[-0.28]
0.0242**
[2.37]
0.0234***
[3.54]
-0.00359***
[-2.90]
-0.0174
[-1.19]
-0.0000147***
[-3.79]
0.0151***
[4.02]
-0.119
[-0.84]
230
0.485
0.0000

-0.0712*
[-1.86]
-0.0102
[-0.49]
0.0288***
[5.45]
0.0240***
[3.74]
-0.00363***

[-2.97]
-0.0197
[-1.36]
-0.0000163***
[-6.34]
0.0153***
[4.16]
-0.167
[-1.57]
230

N
R-sq
Chow test (p-value)
Hausman test (p-value)
0.9382
Breusch-Pagan
test
(p-value)
0.0000
t statistics in brackets
* p<0.1, ** p<0.05, *** p<0.01
Source: Author’s computation using STATA 13

-0.0542**
[-2.08]
-0.0129
[-1.47]
0.0319***
[11.17]

0.0126***
[3.56]
-0.00150**
[-2.16]
-0.0184**
[-2.39]
-0.0000122***
[-9.41]
0.00633***
[3.09]
-0.140***
[-2.60]
230

The model used to examine the impact of credit risk on banks’ business performance controls the
multicollinearity and autocorrelation problems. However, while testing the residuals’ variance, the
authors detected that the model’s residual variance had heteroskedasticity. Therefore, the authors used
FGLS regression model to prevent heteroskedasticity.
Table 4.6. Regression result of model 2 with ROA
Variable
NPL
LLR
EFF
LEV
NII
SIZE
GGDP
INF

Pooled

ROA
-0.0858***
[-2.67]
0.00831
[0.10]
-0.0200***
[-7.02]
-0.0193***
[-4.45]
-0.000167
[-0.07]
-0.00145***
[-3.29]
0.00166**
[2.10]
-0.000481***

FEM
ROA
-0.0653**
[-2.01]
0.071
[0.65]
-0.0197***
[-5.96]
-0.0112**
[-2.25]
0.00426*
[1.66]
-0.00487***

[-4.13]
0.00155**
[2.04]
-0.000505***

REM
ROA
-0.0757**
[-2.38]
0.0286
[0.31]
-0.0197***
[-6.68]
-0.0181***
[-4.13]
0.000908
[0.37]
-0.00173***
[-3.39]
0.00168**
[2.20]
-0.000475***

FGLS
ROA
-0.0601**
[-2.57]
-0.0804
[-1.58]
-0.0327***

[-14.84]
-0.0127***
[-3.20]
0.00309*
[1.83]
-0.000613**
[-2.10]
-0.000142
[-0.29]
-0.000217**


UNR
EXR
INR
_CONS
N
R-sq
Chow test (p-value)
Hausman test (p-value)
Breusch-Pagan test (pvalue)
t statistics in brackets
* p<0.1, ** p<0.05, ***
p<0.01

[-3.15]
-0.0018
[-0.99]
-0.000000724**
[-2.58]

0.00168***
[3.67]
0.0615***
[5.08]
230
0.535

[-3.55]
-0.000888
[-0.53]
0.000000387
[0.87]
0.00181***
[4.18]
0.0887***
[5.43]
230
0.557
0.0000

[-3.25]
-0.00152
[-0.87]
-0.000000617**
[-2.17]
0.00168***
[3.82]
0.0616***
[5.11]
230

[-2.48]
-0.00019
[-0.19]
-0.000000215
[-1.24]
0.000989***
[3.71]
0.0478***
[6.40]
230

0.0000
0.0011

Source: Computation using STATA 13

CHAPTER 4 SUMMARY
In chapter 4, the authors analyzed and chose a suitable model to study which factors influencing
credit risk and the impact of credit risk on business performance of commercial banks in Vietnam from
2005 to 2015. The results from the regression model as well as the impact direction and the significance
level were analysed.


CHAPTER 5
CONCLUSIONS AND SOLUTIONS
5.1 CONCLUSIONS
Using GMM (Generalized method of moments) regression on dynamic panel data to explore
factors influencing credit risk, the authors found:
NPL ratio of current year is really affected by the NPL ratio of the previous year. Variable L.NPL

has positive impact on credit risk with 1% level of significance. There is a positive relationship between
loan loss reserve and NPL ratio at 1% level of significance. Therefore, the NPL ratio and loan loss reserve
have a positive relationship with credit risk commercial banks in Vietnam.
The authors found there is significantly statistical negative relationship between inefficiency and
NPL ratio, according to the regression results. Banks with ineffective cost management may have lending
problems, which leads to an increase in credit risk.
Leverage ratio and NPL ratio were found to have an inverse relationship with the 10% level of
significance. When banks leverage external funds and raise more capital, credit risk does not increase.
For macroeconomic variables, the authors only to find a statistically significant relationship
between the nominal interest rate and the NPL ratio at 5% level of significance. This implies that when
interest rates increase, the customer have more difficulties in making payment, credit risk increases.
Credit risk absolutely affects the business performance of commercial banks. The authors found a
reverse relationship between NPL ratio and ROE at 1% level of significance.
Table 5.1. Summary results of regression model 1
Expectation Result
Level
Significance
Bank internal variables
NPL ratio of previous year
+
+
1%
(L.NPL)
Loan loss reserve (LLR)
+
+
1%
Operating inefficiency (EFF)
+
+

1%
Variable

Leverage (LEV)
Non-interest income (NII)
Bank size (SIZE)
Macroeconomic variables
Inflation (INF)
GDP Growth (GGDP)
Nominal interest rate (INR)
Unemployment rate (UNR)
Exchange rate (EXR)

+
+

+
-

+
+
+
+
+
Source: Compiled from research results

of

10%

5%

Leverage (LEV) and business performance have a positive relationship with 1% level of
significance. Commercial banks balance and control of capital resource well with appropriate lending
options in order to maximize profits with low cost.
There is an inverse relationship between non-interest income and business performance with 1%
level of significance. This is an reverse effect because banks cannot always manage non-interest income
efficiently.
There is a positive relationship between bank size and business performance with 1% level of
significance. Larger size could create economies of scale, increasing efficiency and business performance
of banks.
There is an inverse relationship between EFF and business performance with 1% level of
significance. Better cost management will bring more income to banks, increasing profit and business
performance.
For macroeconomic variables, these is a positive relationship between GDP Growth and ROE
with 1% level of significance. When the economic grows at a healthy rate, banks’ business performance
will be better with an operational efficiency increase, and vice versa.


There is an inverse relationship between the exchange rates and business performance of bank
with 1% level of significance. The increase in exchange rate will reduce business performance of
commercial banks.
Table 5.2. Summary results of regression model 2
Variable
Expectation Result
Significance level
Non-performing loan (NPL) 1%
Loan loss reserve (LLR)

-

-

Leverage (LEV)

-

+

1%

Non-interest income (NII)

+

-

1%

Bank size (SIZE)

+

+

1%

Operating inefficiency (EFF)

-

-

1%

Inflation (INF)

+/-

-

GDP Growth (GGDP)

+

+

Nominal interest rate (INR)

-

+

Unemployment rate (UNR)

-

Exchange rate (EXR)

+/-

Macroeconomic variables

5%

-

1%

Source: Compiled from research results

5.2 SOLUTIONS FROM MODEL RESULTS
First: the NPL ratio in the previous year will increase credit risk in the following year.
Commercial banks should have solutions to reduce next year’s NPL ratio in order to limit credit risk
potential.
Second: There exists an inverse relationship between the NPL ratio and ROE. Commercial banks
should strengthen credit risk management solutions as well increase profit from interest income and credit
fees.
Third: Loan loss reserve and NPL have a positive relationship. Commercial banks need to
accurately determine the quality of bank credit, classify loan and introduce provisions against loan losses
objectively and honestly.
Fourth: There is an inverse relationship between operation inefficiency and NPL ratio.
Commercial banks need to balance between interest income and non-interest income in order to increase
business performance efficiently.
Fifth: Leverage indicates that capital structure has a negative impact on credit risk. High leverage
leads to high risk acceptance trend.
Sixth: The results of the model indicate that: when commercial banks do not manage non-interest
income effectively, business performance reduces. Therefore, commercial banks need solutions to balance
between interest income and non-interest income.
Seventh, the bank size factor has a positive impact on business performance of commercial banks.

Commercial banks should have plans to increase equity in order to create advantages of scale economies.
Eighth, there is a positive relationship between the nominal interest rate and credit risk.
Vietnamese State Bank needs to introduce solutions to reduce the nominal interest rate in order to limit
credit risk potential.

5.3 SOLUTIONS FOR ECONOMIC STABILITY
5.4 SOLUTIONS FOR CREDIT RISK LIMITATION OF COMMERCIAL BANKS
IN VIETNAM
5.4.1 Strengthening credit risk management
5.4.2 Building credit risk management system based on Basel
5.4.3 Controlling credit process and improve credit approval process


5.4.4 Monitoring, inspecting and remedialing credit risk

5.5 SOLUTIONS TO ENHANCE BUSINESS PERFORMANCE OF
COMMERCIAL BANKS
5.5.1 Improving the legal framework for credit activities
5.5.2 Improving the Banking Organizational Structure
5.5.3 Increasing bank size

5.6 RESEARCH CONTRIBUTIONS
-

The research presents factors affecting credit risk:
NPL ratio in the previous year with increases credit risk in the following year. High loan loss
reserve also increases credit risk in the commercial banks. Loan loss provision control is
necessary to limit the credit risk of commercial banks.
Operation cost management inefficiency can increase credit risk.
Credit risk reduces business performance of commercial banks through inverse relationship with

statistically significance.
Larger bank size will create banks’ higher efficiency and better business performance.
Better cost management will bring a higher income and bank profits, which, in turn, increases
business performance.
The inefficiency in non-interest income management will lead to lower business performance.
This is a reverse effect, which is statistically significant.
A healthy economic growth rate will contribute to the increase in business performance of bank.
Therefore, the authors recommend our government to control economy to stabilize banks’
operation.

5.7 LIMITATIONS AND FUTURE RESEARCHES
5.7.1
-

Limitations
The use of secondary data sources from the financial statements of commercial banks in Vietnam
with coverage from 2005 to 2015, so there is a deficiency in data collection that may affect
research results .
Some independent variables in model 1 and 2 were reversed, different from our expectations and
to other researches.The explanation is because of data sample and the actual conditions of the
commercial banks in Vietnam.
The authors only use the NPL ratio and loan loss reserve to represent credit risk.

5.7.2 Future research
The authors would like to suggest some research directions as follows:
- Using other independent variables representing credit risk and business performance.
- Conducting more regression tests to test the viability of the model.
- Expanding the research scope to regional banks in order to draw lessons for Vietnamese market.
CHAPTER 5 SUMMARY
In Chapter 5, the authors summarizes the key results of the study. After that, the authors proposes

some solutions to limit credit risk of commercial banks in Vietnam. The proposals and solutions are
derived from the results of the regression model.