The effect of non-Performing loans on profitability of commercial banks: Case of Vietnam

al Joint Stock Bank during the period of 2008 to 2013. Some banks only keep the LDR below 100% and 
increase it slowly to gain more profit and to avoid a shock in the liquidity pool. For instance, Ho Chi Minh Development 
Commercial Joint Stock Bank increased its LDR slowly from 63,99% in 2014 to 86,69% in 2016. Kien Long Joint Stock 
Commercial Bank during 2011 to 2017 dropped its LDR from 103.26% to 80.76% in 2015. As the result, its ROA decreased 
from 2.21% to 0.65%. After this, the bank pushed back its LDR rate and the ROA slowly recovered. 
 382
Fig. 1. Histogram of LDR frequency 
Annual GDP growth rate (GGDP): Regression coefficients of GGDP = -0.0867 is statistically significant. This means that the 
GDP growth of Vietnam has negative impact on the bank’s performance. Therefore, it can be concluded that with in the growing 
economy, the banks of Vietnam are more likely to perform worse. 
Fig. 2. GDP growth and average bank's ROA from 2008 to 2017 
Source: Author’s compilation 
The high GDP growth indicates that the economic is stable and well developing. With the developing economy, demand for 
credit would rise as the needs to expand investment and productivity also rise. Such economies will let banks offer deals more 
confidently, at the same, time trigger the liquidity risk. If the lending activities are made, so are the default risk and the 
profitability. The moral hazard can cause banks to make riskier trades; risk management can be loosened. As can be seen in Fig. 
2, at the beginning of the crisis that happened in 2008, the GDP fell from 5.662% to 5.398% in 2009. At the same time the ROA 
of commercial banks slightly increased from 1.16% to 1.21% in 2009. This could come from the demand for fund during the 
downfall of the economic; risky assets are retreated from commercial banks and more secure loans were made. The same pattern 
applied up to 2017. The period from 2012 to 2015 was when Vietnam’s economy has recovered and begin to develop again, 
which is shown by the rapid increase in GDP from 5.247% in 2012 to 6.68% in 2015 and followed by the fall in average ROA 
of commercial banks. 
5. Conclusion and recommendation 
On the basis of inheriting the results of previous studies, the author uses the fixed and random effects model, as well as the 
feasible general least square method to construct the test with panel data. The test results have shown that when the rate of non-
performing loans increases, the bank’s ROA will decrease, meaning that the bank profitability will be lowered. Furthermore, 
the research results have pointed out that in the case of Vietnam, the loans to deposits rate and the growth of GDP both have an 
0
10
20
30
40
Fr
eq
ue
nc
y
0.0000 0.5000 1.0000 1.5000
LDR 
5.662%
5.398%
6.423% 6.240%
5.247% 5.422%
5.984%6.679% 6.211%
6.812%
1.162% 1.218%1.107%1.111%0.927%0.816%0.684%0.598%0.609%
0.734%
0.000%
1.000%
2.000%
3.000%
4.000%
5.000%
6.000%
7.000%
8.000%
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
GDP Average ROA
H. L. Do et al. /Accounting 6 (2020) 383
impact on the bank’s performance; while the bank size does not matter. Therefore, the authors suggest the following actions in 
order to prevent the impact of NPLs on bank’s profitability: 
Raising equity: Raising equity contributes to the ability of a bank responding to liquidity crisis and to cover for the bad debt, 
which is what the Modern Portfolio Theory recommended. Improving financial capacity such as capital and asset quality will 
provide the bank with back up for the unrecovered loans. However, this action needs to be proceeded with caution to avoid 
further volatility. In order to increase equity, banks need to develop a balanced policy in the distribution of profit for dividend 
payments and retain earnings. Banks need to retain some profits in addition to shareholders’ equity to increase the scale of 
capital for future investment. Increasing equity often leads to dilution of the shareholdings. Therefore, bank owners need to 
accept this situation in order to expand pool of shareholders, thereby increasing equity to reduce the risk and increase stability. 
In addition, diluting the shareholding rate and limiting the concentration of large capital ownership in a small group of 
shareholders also promote the development of corporate governance, avoiding bank manipulation and acquisition by dominating 
group of shareholders. This unfortunate circumstance can cause great losses to other shareholders and distort the financial 
situation of banks. 
Appropriate risk managing system: Commercial banks need to complete the Basel II credit risk management framework. A 
good credit risk management system must be placed in an appropriate risk environment. The risk management strategy should 
clearly identify the level of general risk and the level of credit risk tolerance to be the guideline for the risk management 
operation. The bank's credit risk strategy must be developed based on comprehensive assessments, thoroughly understanding of 
the bank's business situation and macroeconomic situation. The Board of Directors should be responsible for the final approval 
of the credit risk strategy. Credit risk management procedures must also be appropriate. In order to obtain a reasonable credit 
risk management process, the bank needs to establish reasonable credit risk criteria, appropriate authority hierarchy, and risk 
appetite of the bank. In addition, credit risk management policies coupled with credit growth, high-risk lending such as securities 
investments, and real estates need to be regularly reviewed to ensure the compliance with risk management strategies during 
each period. Furthermore, the system has to be carefully monitored and frequently evaluated to ensure the effectiveness and to 
avoid asymmetric information. This work is required to be carried out regularly by risk management departments and other 
independent monitoring departments. 
Acknowledgement 
This paper has been supported by National Economics University. 
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Appendix 
List of banks for research data 
 Name Code 
1 Asia Commercial Joint Stock Bank ACB 
2 Joint Stock Commercial Bank for Investment and Development of Vietnam BID 
3 Vietnam Export Import Commercial Joint Stock Bank EIB 
4 Vietnam Joint Stock Commercial Bank for Industry and Trade CTG 
5 Ho Chi Minh Development Commercial Joint Stock Bank HDB 
6 Kien Long Commercial Joint Stock Bank KLB 
7 National Joint Stock Commercial Bank NVB 
8 Joint Stock Commercial Bank of Saigon - Hanoi SHB 
9 Saigon Thuong Tin Commercial Joint Stock Bank STB 
10 Joint Stock Commercial Bank for Foreign Trade of Vietnam VCB 
11 Military Joint Stock Commercial Bank MBB 
12 Vietnam International Joint Stock Commercial Bank VIB 
13 Vietnam Prosperity Commercial Joint Stock Bank VPB 
14 Petrolimex Commercial Joint Stock Bank PGB 
15 Tien Phong Commercial Joint Stock Bank TPB 
H. L. Do et al. /Accounting 6 (2020) 385
Results from fixed effects model 
Results from random effects model 
Hausman test 
F test that all u_i=0: F(14, 131) = 3.77 Prob > F = 0.0000
 rho .36486287 (fraction of variance due to u_i)
 sigma_e .0047632
 sigma_u .00361019
 _cons .0307258 .0072862 4.22 0.000 .016312 .0451396
 BANKSIZE -.0018635 .0007079 -2.63 0.009 -.0032639 -.0004631
 GGDP -.1059966 .0899528 -1.18 0.241 -.2839448 .0719515
 LDR .0079103 .0027028 2.93 0.004 .0025635 .013257
 NPL -.0458482 .0318621 -1.44 0.153 -.1088791 .0171826
 ROA Coef. Std. Err. t P>|t| [95% Conf. Interval]
corr(u_i, Xb) = -0.4668 Prob > F = 0.0001
 F(4,131) = 6.76
 overall = 0.0540 max = 10
 between = 0.0030 avg = 10.0
 within = 0.1712 min = 10
R-sq: Obs per group:
Group variable: Banks Number of groups = 15
Fixed-effects (within) regression Number of obs = 150
 rho .21627246 (fraction of variance due to u_i)
 sigma_e .0047632
 sigma_u .00250217
 _cons .0248835 .006322 3.94 0.000 .0124927 .0372744
 BANKSIZE -.0008526 .0004943 -1.72 0.085 -.0018214 .0001162
 GGDP -.1781982 .0833829 -2.14 0.033 -.3416257 -.0147707
 LDR .0067517 .0025401 2.66 0.008 .0017732 .0117301
 NPL -.06552 .0307857 -2.13 0.033 -.1258589 -.005181
 ROA Coef. Std. Err. z P>|z| [95% Conf. Interval]
corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0002
 Wald chi2(4) = 22.25
 overall = 0.0883 max = 10
 between = 0.0000 avg = 10.0
 within = 0.1580 min = 10
R-sq: Obs per group:
Group variable: Banks Number of groups = 15
Random-effects GLS regression Number of obs = 150
 Prob>chi2 = 0.1765
 = 6.32
 chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
 Test: Ho: difference in coefficients not systematic
 B = inconsistent under Ha, efficient under Ho; obtained from xtreg
 b = consistent under Ho and Ha; obtained from xtreg
 BANKSIZE -.0018635 -.0008526 -.0010109 .0005068
 GGDP -.1059966 -.1781982 .0722016 .0337461
 LDR .0079103 .0067517 .0011586 .0009236
 NPL -.0458482 -.06552 .0196717 .0082117
 fe re Difference S.E.
 (b) (B) (b-B) sqrt(diag(V_b-V_B))
 Coefficients 
 386
Feasible General Least Square 
© 2020 by the authors; licensee Growing Science, Canada. This is an open access article distributed 
under the terms and conditions of the Creative Commons Attribution (CC-BY) license 
( 
 _cons .0233874 .0060569 3.86 0.000 .0115162 .0352587
 BANKSIZE -.0003036 .0003475 -0.87 0.382 -.0009848 .0003776
 GGDP -.2206235 .086561 -2.55 0.011 -.3902799 -.0509671
 LDR .0046976 .0023861 1.97 0.049 .0000209 .0093744
 NPL -.0867038 .0313899 -2.76 0.006 -.1482269 -.0251808
 ROA Coef. Std. Err. z P>|z| [95% Conf. Interval]
Log likelihood = 573.9505 Prob > chi2 = 0.0017
 Wald chi2(4) = 17.30
Estimated coefficients = 5 Time periods = 10
Estimated autocorrelations = 0 Number of groups = 15
Estimated covariances = 1 Number of obs = 150
Correlation: no autocorrelation
Panels: homoskedastic
Coefficients: generalized least squares
Cross-sectional time-series FGLS regression