REVIEW
of
FINANCE -
November, 2018
31
the loan-to-deposit ratio is the more likely NPL
increase. Therefore, Circular 13 issued by the State
Bank (Regulations on safety ratios in operation
of credit institutions) stipulates that loans should
not exceed 80% for banks and 85% for non-bank
financial institutions. These loans are usually
shown on the balance sheet of most banks in the
loan category. Therefore, it is necessary to limit the
LDR so that NPL is lowered accordingly.
CAR has a p-value of 0.8620 which is higher than
0.05, so the hypothesis “H1: CAR has no impact on
NPL” means CAR has a negative impact on NPL
due to the negative correlation coefficient. The
results of this study are consistent with previous
studies such as Sinkey and Greenawalt (1991);
Shrives and Dahl (1992). This is in line with Basel’s
requirement to require banks to maintain CAR at
8% (Basel II) to control credit risks and ensure better
asset quality. According to Basel III, the coefficient
ranges from 10.5% to 13% (Roy et al., 2013).
The size of the bank has a negative impact onNPL.
The larger the bank is, the higher the loan portfolio is.
This fact leads to the higher total assets of the bank
which results in NPL arising more and more in the
process of loan if the bank does not control and
manage loans effectively. Regarding the Liquidity
Ratio, this variable is not statistically significant, and
this result coincides with the Ozili (2017) study.
Conclusion and recommendation
Research results show that the ROE is significant
statistically with 95% confidence due to p-value
value lower than 5%. This result is consistent with
the results of Anjom and Karim (2016). When ROE
increases, the bank is operating effectively and
there are mechanisms to limit and handle NPL
in the process of granting credit to customers. At
the same time, all high-risk loans require banks
to make much provision, which leads to a drop
in profitability as well as ROE (Kupčinskas and
Paškevičius, 2017). The remaining variables are not
statistically significant at 95% confidence interval,
but the relationship between variables in this study
is consistent with previous studies. Only CAR is not
statistically significant, but has a negative impact
on NPL. As CAR increases, banks have high capital
buffers to deal with credit risks that occur in their
lending activities. This result is consistent with
previous studies Sinkey and Greenawalt (1991);
Shrives and Dahl (1992).
Although the estimation of the model is not
deviate and adequate and no wrong form of
function, the R2 coefficient is too low. Thus,
according to the authors’ recommendations based
on previous research, macro variables should be
included in the model such as inflation rate, GDP
growth rate, unemployment rate, short-term
interest rate... In addition, when reviewingprevious
authors’ own research on the impact of internal
controls on NPL, it is necessary to add variables
to models to improve coefficient of determination.
Components of internal control can beincluded in
the model: control environment, risk assessment,
control activity, information - communication and
monitoring.
There are many previous research studies that
were conducted about the macro and micro-factors
that affect NPL and all their results had significant
models. This model is quite insignificant therefore
we want to try to separate the micro-factors and
macro-factors, because the macro-factors could be
the exogenous. Therefore, in order to have better
results,weneed tocombineboth these two factors (as
recommended above) and use Generalized method
of moments (GMM) with instrument variables.
References:
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reducing non-performing loans;
3. Basel. (1999). Principles for themanagement of credit risk Consultive paper
issued by Basel Committee on Banking Supervision. Basel;
4. Berger, A. N. & DeYoung, R. (1997). Problem Loans and Cost Efficiency in
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