Do Quynh Anh, Nguyen Due Hung* THE DETERMINANTS OF NON-PERFORMING LOANS IN VIETNAM COMMERCIAL BANKS: AN ECONOMETRIC STUDY

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IN VIETNAM COMMERCIAL BANKS:

AN ECONOMETRIC STUDY

Do Quynh Anh, Nguyen Due Hung*

Abstract

The study examines the determinants of bad loans in Vietnamese commercial banks including both macroeconomic and bank-specific factors by using balanced panel data of ten commercial banks in Vietnam from 2005 to 2011. The Pooled least squares estimation technique is used to test all hypotheses. The empirical results support the view that macro-factors, such as.

the inflation and the growth in real GDP impact significantly on the level of non-performing loans (NPLs) over the period ofthe study. Moreover, the impact of growth in real GDP and Inflation on NPLs IS in.ftantaneous. The results also show the significant evidences for the effect of bank- specific variables such as the previous NPLs ratio, credit growth, bank size, inefficiency and the ratio of loans to assets on NPLs Among these variables, the previous NPLs ratio and the credit growth show the strongest impacts on current bad loans.

Key Words: Non-performing loans, Vietnam commercial bank, bank - .specific factors, macroeconomic determinants.

1. Introduction

In Vietnam, non-performing loans (NPLs) is currently rising as central Issue In banking system. Since the starting point of local banks is quite lower than that ofthe regional average, development and profit are given the highest priority. This leads to the situation that risk management of Vietnam's local banks is almost Ignored without adequate and professional investment. That's why bad loans and other problems due to uncontrollability have not been

• Do Quynh Anh, Faculty of Economics, National Economics University (NEU), Hanoi.

Nguyen Due Hung. The Vietnam-Netherlands Programme for Master in Development Economics (MDE). NEU, Hanoi.

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The Determinants of Non-performing Loans in Vietnamese Commercial Banks...

addressed-in almost all local banks. For instance, at the end of 2005, low levels of NPLs were reported by Viet Nam's state-owned commercial banks (SOCBs), however, the expanding of NPLs is coming back currently. One ofthe reasons Is the difficulties in Vietnam's recent economy when real GDP growth has decelerated from 6.8% In 2010 to 5.9% In 2011, and reach to 4% in the first quarter of 2Q.12. Therefore. NPLs has been an urgent problem for researchers and policymakers in Vietnam. It is an issue that Includes a whole range of topics, which should be considered seriously.

The focus of this study was finding the effects of macroeconomic variables and bank- specific variables on NPLs in Vietnamese commercial banks with data annually from 2005 to 20! I. Most of Vietnamese commercial banks started presenting the number of NPLs from 2005 due to the Decision No. 493/2005/0D-NHNN. Therefore, the authors only collected the data from ten commercial banks that operated during the 2005-2006 to 2010-2011 periods.

2. Review of literature

There are a many studies about NPLs, therefore, many distinct definitions of NPLs can be found. The difference is come from specific regulation across countries. Some countries allow bank managers establish the amount of problem loans when a definition of impaired assets was not given. In other countries, regulators provide a precise definldon of impaired assets; yet, even in these countries, asset classification criteria also differ (Ci^el al., 2001). NPLs normally including various types of loan are classified by quantitative method such as number of days of overdue scheduled payment) or qualitadve method such as availability of Information about the customer's financial status, management judgment about future payments

For quantitative criteria, any loan, which Is ninety days overdue. Is qualified as a nonperforming loan. According to Greuning and Bratanovic (2003)and Fofack (2005), when principal and interest of the loans are overdue ninety days or more, the loans are Indeed nonperforming (this period may vary by Jurisdiction). However, depending on the credit product, some countries use different overdue dates {L\set al., 2001).

For qualitative criteria, loans may be classified into non-performing if there are reasons to doubt the customer's ability to continue to service such loans. The "qualitative" method can be based on the information about the customer's financial status as well as the credit Institution's internal credit ranking system of bank supervisor.

All banks need a loan classification or grading system to facilitate the monitoring and management of credit risk in their loan portfolios. With no international standard, the regulations on classifying loan depend on the national authorities and bank supervisors. For example. In the United Kingdom and Netherlands, the banks have not been adopted any particular form of loan cjassificatlon; however, banks still have been expected to have a prudent appraisal of loans. On the other hand, the U.S, Brazil, German systems opted for a more prescriptive approach. In more

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details. In U.S. loans have classified into five categories based on a set of criteria ranging from payment experience to the environment while German classifies loans into four categories and Brazil applies a nine-category system (Laurin and Majnoni, 2003).

Banks can draw up their own classification systems: yet still obey common regulating standards of authorities. The standard rules for loan classification that are currently used In most developed countries as the following (Greuning and Bratanovic. 2003):

Standard/pass. When debt service capacity is considered to be beyond any doubt. In general, loans and other assets that are fully secured (Including principal and Interest) by cash or cash-substitutes (e.g.. bank certificates of deposit and treasur> bills and notes) are usually classified as standard regardless of arrears or other adverse credit factors.

Specially mentioned/watch. Assets with potential weaknesses that may. if not checked or corrected, weaken the asset as a whole or potentially jeopardize a borrower's repayment capacity In the future. This, for example, includes credit given through an Inadequate loan agreement, a lack of control over collateral, or with but proper documentation. Loans to borrowers operating under economic or market conditions that may negatively affect the borrower In the future should receive this classification. This also applies to borrowers with an adverse trend In their operations or an unbalanced position in the balance sheet, but which have not reached a point where repayment is jeopardized. Credit officers should pay more attention to this category.

Substandard. This classification Indicates well-defined credit weaknesses thai jeopardize debt ser\icc capacity. In particular when the primary' sources of repayment are insufficient and the bank must look to secondary sources for repayment, such as collateral, the sale of a fixed asset, refinancing, or fresh capital. Substandard assets typically take the form of term credits to borrowers whose cash flow may not be sufficient to meet currently maturing debts or loans, and advances to borrowers that are significantly undercapitalized. They may also include short-term loans and ad\ances to borrowers for which the inventory-to-cash cycle is insufficient to repay the debt at maturity Nonperforming assets that are at least 90 days overdue are normally classified as substandard, as are renegotiated loans and advances for which delinquent interest has been paid by the borrower from his own funds prior to renegotiations and until sustained performance under a realistic repayment program has been achieved.

Doubtful. Such assets have the same weaknesses as substandard assets, but their collection in full is questionable on the basis of existing facts. The possibility of loss is present, but certain factors that may strengthen the asset defer its classification as a loss until a more exact status may be detennined. Nonperforming assets that are at least 180 days past due are also classified as doubtful, unless they are sufficiently secured.

Loss. Certain assets are considered uncollectible and of such little value that the continued definition as bankable assets Is not warranted. This classification does not mean that an asset has

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absolutely no recovery or salvage value, but rather that it is neither pracdcal nor desirable to defer the process of writing it off, even though partial recovery may be possible m the future.

Nonperforming assets that are at least one year past due are also classified as losses, unless such assets are very well secured.

In addition, classification methods for multiple loans to the same customer differ across country (Laurin and Majnoni, 2003). Some countries such as France, Italy, Brazil, Australia, India and China, once a loan Is classified as Impaired, the whole amount of loan should be classified in that same category. On the other hand, other countries (for example, Korea, Mexico, and Saudi Arabia) take a more flexible approach. Banks' decisions are depended on their reviews of each loan's performance, regardless of how the customer's other loans are rated.

3. Factors affecting Non-performing loans

Recently, various authors have reconsidered the interest on the determinants factors of bad loans. In such studies, the aggregate level of NPLs Is taken into account. And both the macroeconomic and bank-specific determinants are used as explanatory factors. The study seeks to contribute to the determinant of NPLs' literature in both macroeconomic factors and bank- specific factors.

3.1. Macroeconomic factors

Many empirical studies examine the Impact ofthe macroeconomic factors on bad loans (e.g.,Rinaldl and Sanchis-Arellano, 2006; Segovianoet al., 2006; Berge and Boye, 2007; Cifteret al.. 2009; and Nkusu, 2011). According to Dash and Kabra (2010), macroeconomic factors which are commonly emphasized: GDP growth, credit expansion, real interest rates, infiation, real effective exchange rate, unemployment rate, broad money supply (M2). In this study, the authors employ the GDP growth and the annual inflation rate as the primary macroeconomic determinant factors of bad loans.

There empirical evidences for negative relationship between the GDP growth and bad loans are often mentioned in many previous studies such as Salas and Suarina (2002); Rajanand Dhal (2003); Jimenez and Saurina (2005); Fofack (2005); and Quagliarello (2007), As discussed by LIset al. (2009), during recessions, bad loans expand because of firms' and households' financial distress. When the economy expands strongly, the income of firms and households broaden which can Improve capacity of repayment more easily, contributing to lower NPLs.

Conversely, when the economy slows as low or negative GDP growth. It should contribute to a higher NPLs ratio.-T^s, the following hypothesis is formulated:

Hypothesis 1. The relationship between the GDP growth and NPLs is negative.

Inflationary pressures, which are considered in many studies, may have relationship with NPLs Fofack (2005) showed that the infiation rate contributes to a higher number of NPLs in Sub-

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S'aharan African countries. The author believes that high inflation rate leads to a rapid depletion of commercial banks' equity and greater level of NPLs in the Indian banks. By using consumer price Index, Chase et al. (2005) also found the significant correlation between Inflation and NPLs.

However, more recendy. Dash and Kabra (2010) did not find evidence for this relationship. Given more recent concerns, the authors posit the following hypothesis:

Hypothesis 2. The high inflation rate leads to the expanding of NPLs.

3.2. Bank-specific factors

Another strand of the literature reports the effect of bank-specific factors on NPLs.

Indeed, maximum efficiency and minimum credit risk are absolutely bank's desire, thus, the policy choices of each particular bank could engender the influence on the NPLs' evolution.

Basically, empirical evidences suggest that the effects of bank-specific factors on NPLs such as bad management, moral hazard, bank size, profit margins, inefficiency, and credit growth. The study investigates the influences of six bank specific variables on NPLs based on the available of data Including: the previous NPLs ratio,inefficiency, bank size, credit expansion, worse performance (return on equity) and the ratio of loans to total asset.

First of all, theemplrical evidences suggestthat the level of previous NPLs can affect the current level significantly. Dash and Kabra (2010) and Das and Gosh (2007) find the positive effect of previous bad loans ratio on NPLs The problem of bad loans has a sizeable legacy component arising from infirmities in the existing processes of debt recovery, inadequate legal provisions on foreclosure and bankruptcy and difficulties in the execution of court decrees. This means that the ratio of problem loans of previous period Is closely related to that ofthe current period, since the problem loans are not immediately written down but are. In fact, carried forward In the balance sheet. Therefore, the authors raise the following hypothesis:

Hypothesis 3. The previous NPLs haspositlve influenceon the current NPLs.

In banking sector, the presence of inefficiency is widely accepted starting from both theoretical reasoning and casual empiricism (TuratI, 2003). Inefficiency Is defined as the bad management with poor skills in credit scoring, appraisal of pledged collaterals and monitoring borrowers. In the previous studies. Inefficiency can be estimated differently. For example, according to Das and Ghosh (2007). bank-level inefficiency Is captured as

fxjcp _Operating ExpenseSj t Total Asset it Besides, Louzlse/ al. (2011) calculates Inefficiency of bank I at t

jj^gp^ _^Operating Expensesj t Operating Income ,,t

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The Determinants of Non-performing Loans in Vietnamese Commercial Banks..

Operating expenses include wage expenditure as well as non-wage expenses such as rent, taxes, directors' fees, lighting, advertisement and publicity expenses, etc. Operating income capture net Interest income, net fee and commission income, net gain from trading foreign currencies, net gain from trading of trading securities, net gain from sales of investment securifies, net other income and Income from investments in other entities.

One of typical studies, which found the impact of inefficiency on NPLs, is Berger and Young (1997). The authors invesdgate the relationship between loan quality, cost efficiency and bank capital in U.S. commercial banks from 1985 to 1994, and find that a significant negative relationship between cost efficiency and the level of NPLs in failed banks. It Implies that low cost efficiency is positively associated with bad loans' increase. Besides, the positive relationship between bad loans and cost efficiency are frequently discussed in literature (e.g,,Podpiera and Weill, 2008; Louzlst?/ al, 2011). The author then posits the following hypothesis:

Hypothesis 4. Low cost efficiency increases the level of NPLs.

The effect of bank size on bad loans Is also examined through some empirical studies. In order to capture bank size variable, some different methods are used. For instance. Dash and Kabra (2010) and Louzise/ al (2011) calculated bank size as the relative market share of bank I at time t

While Das and Ghosh (2007) estimated bank size by including the natural logarithm of total assets as a monotonic transformation of assets.

SIZEit = Log (bank asset)

Some studies argue that large banks have fewer bad loans andreport a negative relationship between NPLs and bank size that (Salas and Saurina, 2002; Hu et al, 2006). The bigger banks might be more efficient at managing NPLs due to their diversified loan portfolios.

However, in other cases, large banks could take excessive risks by increasing their leverage;

therefore have more bad loans. Indeed, bigger banks may resort to excessive risk taking because market discipline is not imposed by its creditors who expect government protection in case of a bank's failure (Stern and Feldman, 2004). Thus, a positive relationship between NPLs and bank size is also found in others (Rajan and Dhal, 2003; Dash and Kabra, 2010). Given more recent concerns, the author posits the following hypothesis:

Hypothesis 5. The level of NPLs is positive with bank size.

Evidently, a rapid credit growth is supposed one ofthe most critical roots of bad loans in many studies. The growth rate of credit is calculated as the percentage change in the loans of banklattlme t(Louzise;i3/.,2010; Dash and Kabra, 2010)

AI r , A MC LOANS, t - L O A N S i t - i

^^^^^h^= L O A N S , , . . •

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According to Liser a/.(2010), during economic expansions many banks compete strongly for market share in loans, thus bring about high credit growth rates. Redundant lending by commercial banks is often considered as a significant indicator of bad loans in many researches such as Keeton and Morris (1987),Sinkey and Greenwalt( 1991), Keeton (1999), Salas and Saurina(2002), and Jimenez and Saurina(2005). Indeed, Clair (1992) and Salas and Saurina (1999b) find evidence that past credit growth explains the current number of bad loans. Thus, with the increase of credit during the study period, the authors expect a positive relation between bad loans and credit expansion and the sixth formal hypothesis is stated as follows:

Hypothesis 6. The credit expansion has positive relationship with NPLs.

According to Sinkey and Greenwalt (1991) and Dash and Kabra (2010), the ratio of loans to assets also has a significandypositive effect on NPLs. It is captured as

LOANS, t '^'•^ ASSETS(,t

The ratio of loans to assets is proxy forthe risk appetite of banks to bad loans. Thisis because that banks having a high loan to asset ratio likely lead to a higher number of bad loans during economic downturn periods.

•Hypothesis 7. There is a positive association between NPLs and the ratio of loans to assets.

In addition,Louzlse/a/.(2010,2011) found the relationship between worse performance and NPLs. The worse performance in banks is defined in two variables as the return on equity (ROE) orthe retum on assets (ROA). The ROE and ROA variables can be measured

^' '^^^•-^"TotTE^qu'ity,, ^"^ ^^^^•^ "TotTIsJeL,/ "^^^'^^' '"^^''^^^^^ ^^e i'" baok at time t.

The authors arguethat worse performance may proxy for lower quality of skills with respect to lending activities. This means that past earnings correlated negatively with bad loans. Therefore, the following hypothesis is developed:

Hypothesis 8. The worse performance has a negative correlation with NPLs.

4. Methodology, data and variables

Based on the foregoing discussion, one can postulate the following equation for the ratio of problem loans of bank i In year t (NPL,.,):

InNPL,, = PolnNPL,,., + PiSIZE,,, + P2ALOANS,,, + P 3 A L 0 A N S , M + P4INEF,, + P5ROE,, ^ p6lnL_A,, + P7CPI, + psInCPl,., + pgAGDP, + pioAGDP,.i + e,,

i=],...N,t=l,...T

Where, LnNPL,, and InNPL,,., represents the natural log ofthe ratio of NPLs to total loans for bank 1 in year t and t-1; SIZE,, is the ratio ofthe relative market share of each bank's assets that capture the size ofthe Institution at time; ALOANS,, and ALOANS,,., represent the 3'8

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growth In loans for bank I in year t and t-1 respectively; INEF,, is the ratio of operating expenses to operating Income of bank i at time t; ROE,, is the ratio of profit to total asset of bank i at time t; lnL_A„ Is the natural log ofthe loans to total asset ratio for bank i in year t; CPI, and CPL-i is the annual inflation rate as measured by the consumer price index at time t and t-1; AGDPi and AGDPM represents the annual growth in real GDP at time t and t-1 respecdvely; and e„ is the error term which should be normally distributed with zero mean and constant variance.

For using the real GDP growth rate variable, the author includes the current rate as well as one lag to assess the timing. AGDPi and AGDPi-i represents the annual growth in real GDP at time t and t-1 respectively. Moreover, the negative coefficients are expected as the growing of economy lead to the increase In repayment capacity as well as the lower NPLs.

The inflation rate (CPI) is also included the current rate and one lag as GDP. It is expected that having a positive relationship with NPLs, since the Inflationary pressures contribute to the expanding of bad loans.

Macroeconomic variables such as GDP and CPI are included in both contemporaneously and with one-year lag because economic shocks could not effect Immediately on the bank's performance.

The five bank-specific factors are used including the previous NPLs ratio (NPLi-i), bank size (SIZE), annual expansion in loans (ALOAN), the retum on equity (ROE), Inefficiency or the ratio of operating expenses to operating income (fNEF), and the ratio of loans to total asset (L_A).

Table 1. Summary of variables used in regression model

Variables

LnNPLu

AGDP,

ACPI,

LnL_A,,

Size,,

Definition

The natural logarithm ofthe ratio of NPLs to total loans for bank i in y e a r t

NPLjt

' " " ' ^ ' • " T O T A L L O A N S , / ' ™ ' * ' The annual growth in real GDP at time t

The annual inflation rate as measured by the consumer price index at time t.

The natural logarithm of the ratio of loans to total asset of bank i at (-) time t.

LOANS,.

Is the relative market ^hare of bank i at time t.

S I Z E , , = „ f^ '•' X 1 0 0 % 2] Asset, t

Expected sifin

(-)

(+)

(-)

(+)

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ROE,, INEF,, ALoans,,

Retum on Equity ~ the rauo of profit to total equity of bank i at timet.

Profits,, ' " - " ' i , t T o t a l E q m t y i t

The ratio of operating expenses to operating income of bank i at dme t

_ operating Expenses-,, '' Operating Income i ,

The growth in loans of bank i at time t computed as follows:

LOANS,.-LOANS,t_i

(-)

(+) (+) The ratio of problem loans of previous period is closely related to that of the current period. It is expected having a positive relationship.

In order to capture bank-level inefficiency (INEFF), the ratio of operadng expenses to operadng income is collected. Operating expenses include wage expenditure as well as non-wage expenses such as rent, taxes, directors' fees, lighting, advertisement and publicity expenses, etc.

Operating income capture net interest income, net fee and commission income, net gain from trading foreign currencies, net gain from trading of trading securities, net gain from sales of investment securities, net other income and income from investments in other entities. Positive coefficient will be expected.

The Size variable Is constructed by computing the relative market share ofthe asset of each commercial bank. The authors expect positive signs.

The annual growth in loans Is used in current and one-year lag. The competition in market share can force commercial banks to compromise on the quality of borrowers, which, in turn, may lead to a higher level of NPLs. The positive correlation is expected.

The L_A variable, which is the ratio of loans to asset, Is expected as positive effect on bad loans. Finally, ROE variable is collected from the ratio of profit to total equity and expected as negative correlation with bad loans. All Size, L_A and ROE variables are included contemporaneously In addition, the bank specific variables vary with time and across institutions.

The methodology for calculating each variable and the a priori coefficient signs are given by Table I.

5. Results and discussion

The findings imply a mixed relationship between GDP growth and NPLs. The GDP growth related negatively on bad loans immediately, but positively in one-year lag. However, the negadve relationship Is significant at the 10 percent level while the positive relationship is Insignificant Therefore, the result still supports the Hypothesis 1 In case of Vietnam, the level of future NPLs should increasewhen the economy slows down. Thesefindingsis in line with prior theoretical studies (e.g., Salas and Suarina, 2002; Rajan and Dhal, 2003; Fofack, 2005; and Jimenez and Saurina, 2005).

The results also present a mixed reladonship between inflation and NPLs. The variable has a positive relationship with NPLs contemporaneously but a negative impact after one year.

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This result at time t is in line with the predicdons In the literature (e.g.,Fofack, 2005; Chase et al, 2005), and consistent with Hypothesis 2. The influence of Inflation at time t-1 is insignificant.

Itmay because the endogenous problem that does not control in the model.

The relationship between the previous NPLs and the current NPLs is positive and statistically significant at the 1% level. This result supports the Hypothesis 3 strongly that the previous NPLs Increase the level of current bad loans. This indicates that when a commercial bank had a high level of bad loans in last year, the current NPLs will increase aroundO.62 percent.

The relationship between inefficiency and bad loans is negadve and statistically significant at the 1% level. However, this result is not supporting the Hypothesis 4. This means that high measured efficiency causes increasing number of NPLs. According to this view, there exists a trade-off between allocating resources for underwriting and monitoring loans and measured cost efficiency. In other words, banks, which devote less effort to ensure higher loan quality will be more cost-efficient, however, there will be a burgeoning number of NPLs in the long-run.

The relationship between bad loans and bank size is posidve and statistically significant at the 5% level supporting the Hypothesis 5. The positive sign means that large banks could take more risks by increasing their leverage; therefore have more bad loans. In other words, bigger banks may resort to excessive risk taking because market discipline is not imposed by Its creditors who expect government protection in case of a bank's failure (Stern and Feldman, 2004). This positive association is also found in many previous studies, for example: Rajan and Dhal, 2003;

Dash and Kabra, 2010.

The results reported in Table 7 are mixed with regard to the association between NPLs and credit expansion. The variable has a negative effect on bad loans at time t but a positive reladonship at time t-l, The variable statistically is strongly significant at the 1% level in both timet and t-1. The result at time t is inconsistent with the effect of credit expansion and hence not supports Hypothesis 6. Based on this result, It suggests that commercial banks, which extend relatively greater number of credit, are likely to gain lower bad loans. However, there is a positive association between credit growth and bad loans at time t-1. That suggests that an increase in outstanding loans today will have a positive impact on problem loans one year hence. This result is consistent with the Hypothesis 6. In case of Vietnam, the high credit growth rate may not Impact on bad loans immediately.

The variable L_Ai,t which represents the risk appetite ofthe commercial banks is positive and significant at the 5 percent level supporting the Hypothesis 7. This follows that commercial banks, which are high-risk takers, are likely to gain a higher number of bad loans (Sinkey and Greenwalt, 1987).

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The coefficients for the worse performance are negative as expected. However, the variable is stadstically insignificant, therefore not support Hypothesis 8. It means that worse performance may not impact on the level of NPLs in Vietnam.

Table 2. Regression results

Variables Coefficient Std, error T - statistics Prob.

Macro-factors AGDP,

AGDP,., CPI, CPI,.,

-0,451097 0,0045608 0.0448533 -0,048013

0.2473074 0.1115226 0.0233597 0.0461662

-1.82 0.04 1.92 -1.04

0.074 0.968 0.060 0.303 Bank'Speciflc factors

LnNPL,,,., ALoanSi,, ALoans,,,., LnL A,., Size,,, INEF,,, ROE,,,

0,6157504 -1,808006 1.148698 1.471707 6.176109 -2.375142 -0.0077559

R-squ 0.0683619 0.3076856 0.3420488 0.6738011 2.400911 0.7849249 0.0062445 a r e d - 0 . 8 3 8 3

9.01 -5,88 3.36 2.18 2.57 -3.03 -1.24

0.000 0 000 0.001 0.033 0.013 0.004 0.219

6, Conclusions and policy implications 6.1. Conclusions

The empirical results suppoti the view that itiacro-factors, such as, the inflation and the growth in real GDP impact significantly on the level of NPLs over the period of the study. In particular, the findings revealed that there is a significant negative contemporaneous relationship between the GDP growth and bad loans. In case of Vietnam, when the economy slows as low, the level of future NPLs should increase. The result shows that the impact of growth in real GDP on NPLs is instantaneous.

The finding shows that there is a mixed relationship between inflation and bad loans. The variable has a positive relationship with NPLs immediately but a negative impact afler one year.

This means that high inflation in the current period could lead to the increasing in the level of NPLs in the banking sector. However, high inflation from the previous period causes commercial banks to incur lower nonperforming loans.

With respect to the bank-specific variables, the result shows that the credit expansion negatively relate to bad loans. It therefore follows that commercial banks, which extend relatively higher levels of credit, are likely to incur lower NPLs. However, the study also finds the positive

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relationship between the credit growth and NPLs after one year; thus, increase in the level of outstanding loans increases NPLs.

It is also evident that inefficiency is an important determinant of NPLs suggesdng that banks with high measured efficiency exhibit greater levels of NPLs. Indeed, banks, which devote less effort to ensure higher loan quality will be more cost-efficient, however, there will be a burgeoning number of NPLs in the long run.

The results show that bank size positively relate to bad loans, suggesting that large banks could take more risks by increasing their leverage; therefore have more NPLs. Furthermore, the authors find evidence for the positive relationship between the ratio of loans to assets and bad loans. This means that banks, which are high-risk takers, are likely to incur greater levels of NPLs.

Finally, with regard to the impact ofthe worse performance, the authors particularly find that the negative effect ofthe worse performance on bad loans is pronounced during the period ofthe study.

6.2. Policy implications

Depending on the regression model's results, the authors may suggest some policies for banks' supervisors and policy-makers in order to reduce the level of NPLs in Vietnam. The empirical results support that both macroeconomics and bank-specific factors impact significantly on bad loans.

First, in the study, two macro variables, which are the growth of GDP and inflation rate, have significant relationships with bad loans. In particular, the GDP growth related negatively on bad loans immediately. On the contrary, Infladon rate has a posidve relationship with NPLs contemporaneously. These results suggest that the level of NPLs in Vietnam is affected by macroeconomic environment such as GDP growth and Inflation. When the economy slows such as low or negative GDP growth, the number of bad loans should build up. Besides, the high inflation rate in Vietnam is responsible for the expanding of current NPLs. These results also show that the impact of GDP growth and Inflation on NPLs is instantaneous. Therefore, when the economy is not unstable as low GDP growth or high Inflation, the commercial banks' supervisors should pay more attention in credit risk management to keep NPLs under control.

Second, there is evidence that credit growth may serve as significant leading indicators of problem loans. Specifically, evidence suggests that a current rapid expansion of lending by banks often leads to the level of future NPLs. As mentioned by Honohan (1997, cited in Das and Ghosh, 2007), bank supervisors should use 'speed limits' to restrict the rate of growth of banks' loan portfolios. Such 'speed limits' need not necessarily be applied to the entire loan portfolio, but could be restricted to those types of lending which are perceived as posing significant risk to banks' loan portfolios and can therefore engender problem loans.

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Third, the previous NPLs ratio also has a strongly positive relationship with the current bad loans. This suggests that regulatory authorities could use this measure in order to warn banks of high potential NPLs.

Fourth, the authors found the evidence that bigger commercial banks tend to have higher problem loans It means that large banks could take excessive risks by increasing their leverage;

therefore have more bad loans. This finding may suggest regulatory authorities should monitor closely to lending activities of big commercial banks In Vietnam.

Finally, the bank supervisors can use cost efficiency and the ratio of loans to assets, are the indicators for fiiture problems loans. Specifically, the inefficiency of banks has a strong negative with NPLs. In other words, high cost efficiency increases the level of NPLs. it mendons that banks, which devote less effort to ensure higher loan quality will be more cost-efficient, however, there will be a burgeoning number of NPLs in the long run. In addition, the rado of loans to assets Is related positively with NPLs which suggests that banks having a high loan to asset ratio likely lead toahighernumber of bad loans during economic downturn periods./.

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