Impact of Non-Performing Loans on Profitability of Bank: The Case of Bank of Bhutan Limited

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Impact of Non-Performing Loans on Profitability of Bank: The Case of Bank of Bhutan Limited

Dr. Kriti Bhaswar Singh

Central University of Jharkhand, Ranchi

(Formerly Colombo Plan Faculty (MEA, GoI) to Royal University of Bhutan)

Abstract

Non-performing loan (NPL) or non-performing asset (NPA) is one of the indicators of the quality of the loan book managed by banks. Magnitude of NPL reflect health of a bank. Growing NPLs is a serious problem for banks in any economy. For a developing economy, such as Bhutan, it can pose a threat to its economic development. As the banking industry is one of the major contributors to the economic growth of a nation. NPL affects the profitability as well as capital of the lending institutions (RMA, 2015). An analysis of Bank of Bhutan Limited (BoBL), the oldest and largest bank of Bhutan, is presented in this paper. Gross and Net NPLs over a period of six years, from year 2013 to 2018, has been analyzed and its impact on bank's profitability, measured in terms of return on assets (ROA), return on equity (ROE) and net interest margin (NIM), has been studied. Regression analysis has been used to test hypothesis. It was found that both gross and net NPLs impact profitability of BoBL.

Key words: non-performing loans, non-performing assets, Bank of Bhutan, return on equity, net interest margin.

Introduction

NPLs are that proportion of a bank’s loan portfolio which the bank has classified as substandard, doubtful or loss assets, in accordance with the guidelines relating to asset classification issued by the central bank of the country, which is Royal Monetary Authority of Bhutan (RMA) in Bhutan. A high NPLs have a damaging impact on capital, liquidity and profitability of a bank.

Banks play a major role in the Bhutanese economy. In the absence of robust capital market, it is the bank which channels two-third of household savings of Bhutan into commercial credit as well as personal credit.

The economy of Bhutan is largely dependent on its banking system and a sustained impairment of balance sheets of banking industry can drag the economic growth of Bhutan.

Banking Sector in Bhutan

Banking sector in Bhutan is still in a developing phase. Bhutan’s banking sector has been playing a fundamental role in promoting economic growth and developing since last decades. According to the RMA annual report, 2017, Finance & insurance contributes 6,901.34 (5.23%) million to the GDP. The banking sector in Bhutan comprises of five banks namely: Bank of Bhutan Limited, Bhutan National Bank, Bhutan Development Banks, Druk Punjab National Banks, T Bank. The central bank of Bhutan is Royal Monetary Authority (RMA).

Bank of Bhutan Limited (BOBL), established on May 28, 1968, is Bhutan’s oldest and largest financial institution. In order to encourage monetization of the economy and improve banking services in the country, the Royal Government of Bhutan (RGOB) collaborated with the State Bank of India on October 7, 1971, to establish the BOBL, with SBI participating in the capital and management of the bank. SBI holds 40 percent of the bank's shares, while 60 percent were held by the RGOB. In 2002, the management of the bank was handed over to the RGOB with a corresponding reduction in SBI’s shareholdings to 20 percent.

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The RGOB, represented by Druk Holding and Investments, holds 80 percent of the bank's shares (RMA, 2016).

Loans and advances are also major source of income for banks. But failure to manage loans has negative effect on financial performance of banks and in turn on the economy at large. In general, banks classify their existing loan and advances into different broad groups: standard assets, watch assets, sub-standard assets, doubtful assets and loss assets. Accordingly, banks are required to provide adequate provisioning for these assets as mandated by Royal Monetary Authority of Bhutan in its prudential guidelines. As a result, all the good loans are considered regular loans and overdue loans have been classified from substandard to loss as shown in the table below:

Category Duration (Days)

Provisioning Requirement Provisioning

Standard 1-30 1 per cent General

Watch 31-90 1.5 per cent General

Sub- standard

91-180 20%; 30% for sector with highest exposure Specific

Doubtful 181-365 50%; 60% for sector with highest exposure Specific

Mor than 365 100 Spe ific Tab

e – 1 (Source: RMA PR 2017 under section 4.8: provisioning requirements) Fur

her, in case of classified loans banks must make provision according to the above scale as stated under asset classification and provision table. General provisions must be reserved for loans classified under standard and Watch. Specific Provisions must be allocated for loans classified under Sub-standard, Doubtful and Loss.

Litrature Review

Sevral authors have studied the issue of NPLs and its impact on the health of banks. According to Chaudhuri and Sensarma (2008), a high NPAs impacts banks credit flow in the market. Their study concludes that NPAs shrinks a bank's resources due to non-receipt of agreed instalments and so it can now lend less. Further, prudential guidelines forces banks to create additional provision which in turn put pressure on available funds. The result of this study also matches with the study by Fernandez de Lis et al (2000). In nother study, Rajaraman and Vasishtha (2002) find that the rise in banks NPA is because of weak operating efficiency. The conclude that lack of proper procedure of recovery of loans results into high NPAs. Sen and Ghosh (2005), have found that the stringent regulations prescribed under the Basel norms have resulted in decrease in NPAs. Dhar and Bakshi (2015) have analyzed different factors which contribute to rising NPAs. They find that a high percentage of lending to sensitive sectors results into higher NPAs. Whereas, higher capital adequacy maintained by banks result into better net interest margin and decreases NPAs. Anjm & Karim (2016) in their study have found that high interest rate charged by the banks is also one of the factors that lead to non-performing loans. When the lending interest rates are high, borrowers are not able to service the loans and as a result non-performance loan increases. A similar study was done by Wangai David, Nemwel, & George (2012), which says that interest margin and bank size also affect non-performing loans. Stuy on banks in Bhutan, Singh (2017), finds that macroeconomic factors like GDP, inflation rate, fiscal policy, and monetary policy influence non-performing loans of Bhutan Development Bank Limited significantly. Further, bank specific factors like ROE and ROA were affected by NPLs.

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Resarch Methodology and Hypothesis of the Study

Review of existing literature indicates the impact of NPLs on the profitability of banks. Most of the studies reviewed are either from developed economies or from developing economies like India and China. There is dearth of literature in the context of banks in Bhutan. Therefore, the purpose of this study is to find out the impact of NPLs on the profitability of banks in Bhutan. More specifically, we analyze the effect of gross and net NPLs on the profitability. Return of assets (ROA), return on equity (ROE) and net interest margin (NIM) have been taken as the measure of profitability. At present there are five commercial banks in Bhutan. Bank of Bhutan Limited (BoBL) is taken as sample for the study, representing twenty percent of the population. BoBL is the oldest and largest bank in term of assets and loan portfolio. Dat

for the study has been obtained from secondary sources, namely: the annual reports of BoBL and RMA and National Statistics Bureau (NSB) of Bhutan. Six years data, from the year 2013 to 2018, have been taken for analysis. Regression analysis was used to analyze the relationship between gross and net NPLs of the bank as independent variables and return on asset, return on equity and net interest margin as dependent variables. To est the objectives mentioned above, the following hypotheses were formulated, these are tested using regression analysis: H01

Non performing loans of BoBL does not impact return on equity of the bank. H02

Non performing loans of BoBL does not impact return on assets of the bank. H02

Non performing loans of BoBL does not impact net interest margin of the bank. Dat Analysis and Interpretation

In his section data obtained from annual reports of the bank and other sources have been analyzed. In the first section, data of gross and net NPLs of BoBL and various profitability measures have been presented in table 2 and 3, followed by graphs of these measures. Profitability measures such as return on equity (RoE), return on assets (RoA) and net interest margin (NIM) for BoBL has been calculated. In the next section, regression analysis taking these profitability measures as dependent variable and non-performing loans, both gross and net, as independent variable has been presented followed by the analysis of results.

The following tables shows gross and net NPLs and different profitability measures of Bank of Bhutan Limited (BoBL) for the years 2013-18: Yea

Gro s Loans and Advances (in Nu* million) Net

Loans and Advances (in Nu million) Gro

s

NPLNet

NPLGro s NPL ratio Net

NPL ratio

11,118.18 10,944.12 582

59 233 48 5.2 % 2.1 % 201

15,598.63 15,274.18 389

97 65. 1 2.5 % 0.4 % 201

17,433.04 16,990.24 679

89 237 09 3.9 % 1.3 % 201

16,927.54 16,013.45 1,1

4.61 250 53 6.8 % 1.4 % 201

18,474.29 17,831.38 916

32 273 42 4.9 % 1.4 % 201

19,151.46 18,489.46 862

00 200 00 3.2 % 1.0 % Tab

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e 2: Source: Author’s calculation from Annual reports of the bank (*Nu – Ngultrum is the currency of

re 1: Gross and Net NPLs of BoBL during 2013-19 Year

Net Profit (in Nu million) Ret

rn on Assets (ROA)Ret

rn on

Equity(ROE)Net

Interest Margin Rate

510 78 1.8 % 22. 0% 127 92% 201

644 39 2.2 % 22. 0% 150 38% 201

660 38 2.3 % 20. 0% 190 76% 201

672 96 2.2 % 18. 4% 171 58% 201

864 96 2.4 % 17. 3% 137 65% 201

795 61 2.0 % 16. 2% 150 51% Tab

e 3 Source: Auth

re 2: ROA and ROE of BoBL during 2013-18

Anysis of data reveals that ROA of BoBL shows an increasing trend from 2013-2015 followed by decrease in subsequent years. However, ROE is observed to be falling from 2014-18. Gross and net NPLs are observed to fall in recent years during 2016-18 after rise during 2014-16. Gross NPLs touches high of

0 200 400 600 800 1000 1200 1400

2012 2013 2014 2015 2016 2017 2018 2019

Gross and Net NPLs

Gross NPLs Net NPLs

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

2013 2014 2015 2016 2017 2018

ROA and ROE of BoBL

ROA ROE

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about 7% percent in 2016 because of credit freeze due to balance of payment issues that the Bhutanese economy encountered. Regession analysis In he following paragraph regression result using non- performing loans as independent variable and profitability measures such as ROA, ROE and NIM as dependent variable has been presented. Results of regression analysis has been explained below the tables.

Mo l Depndent Variable:

Return on Assets U

andardized Coefficients Sta

dardized Coefficient T S

g B

. Error Bet 1

o stant) 1.2 7.58 4. 0.02 Gr

s Non-performing loans .08

.21 .21 .66 .04 R=.

15,R Square=.172, F Value= .340 at p value=.043Tab

e 4: Regression Results of gross NPLs and ROA The results of regression analysis, where the dependent variable is return on assets and independent variable is gross NPL is presented in table 4. p value is less than 0.05 (p=.043). Thus, null hypothesis is rejected and can be said that gross NPLs impact ROA. Thus, it can be said that there is a significant relationship between return on assets and Gross NPL. The value of R square is 0.172 which indicates that gross NPL explains the 17.2 percent of total variance in the value of return on assets.

Moel Depndent Variable: Return on Equity U

g B

. Error Bet 1

o stant) 7.6 23.8 43. 7.07 Gr

s Non-performing loans .38

1.1 .21 .36 .03 R=.

74,R Square=.454, F Value= .231 at p value=.034Tab e 5: Regression Results of gross NPLs and ROE Tab

e 5 shows the results of regression analysis, where the dependent variable is return on equity and independent variable is gross NPLs. p value of .034, which indicates significant relationship between return on equity and gross NPLs. Thus, the null hypothesis is rejected and it can be concluded that gross NPLs impacts change in return on equity. The value of R square is .454 indicates that gross NPL explains the 45.4 percent of total change in the value of return on equity. Mod

l Dep

ndent Variable: Net Interest Margin U

g B

. Error Bet 1

o stant) 127 46938. 55 3. 4.31 Gr

s Non-performing loans 2.5

77.3 3 .21 .12 .03 R=.

59, R Square=.434, F Value=.062 at p value=.031 Tab e 6: Regression Results of gross NPLs and NIM Tab

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e 6 shows the results of regression analysis, where the dependent variable is net interest margin and independent variable is gross NPLs. p value of 0.031 indicates that there is significant relationship between net interest margin and gross NPLs. Thus, we reject null hypothesis and conclude that gross NPLs impact net interest margin. R square is .434 which indicates that the ratio of gross NPLs explains 43.4 percent of total variance in the value of net interest margin. Mod

l Dep

ndent Variable: Return on Assets U

g B

. Error Bet 1

o stant) 2.7 2.09 27 42.00 Ne

non-performing loans -.3

4.07 -.7 7-3. 23.03 R=.

20, R Square=.672, F Value= 7.31 at p value=.038 Tab e 7: Regression Results of net NPLs and ROA Tab

e 7 shows the results of regression analysis, where the dependent variable is return on assets and independent variable is net NPLs. p value is .038 which indicate that there is a significant relationship between return on asset and net NPLs. Thus, null hypothesis is rejected and it can be said that return on asset is impacted by net NPLs. The value of R square is .672 which indicates that net NPLs, explains the 67.2 per cent of total variance in the value of return on assets. Further, the Coefficient β indicates that a unit increase in net NPLs leads to decrease in profitability of banks measured by ROA by 30.4%.Mod

l Dep

ndent Variable: Return on Equity U

g B

. Error

Bet

1

o stant) 13. 85.82 17 1.00 Ne

Non-performing loans -1.

67.53 -.6 1-3. 17.00 R=.

67,R Square=.588, F Value=5.235 at p value=.002 Tab e 8: Regression Results of net NPLs and ROE Tab

e 8 shows the results of regression analysis, where the dependent variable is return on equity and independent variable is net NPLs. It is observed that there is significant relationship between return on equity and net NPLs (p value .002). Thus, we reject null hypothesis and it can be said that net NPLs impact ROE of the bank. The value of R square is .588 which indicates that net NPLs explains the 58.8 per cent of total variance in the value of return on equity. Further, the negative sign of regression coefficient indicates that there is an inverse relationship between return on equity and net NPLs.Mod

l Dep

ndent Variable: Net Interest Margin U

g B

. Error

Bet

1

o stant) 164 71213. 4215 60.00 Ne

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Non-performing loans -14

869 11. 34-.6 4-3. 28.02 R=.

12, R Square=.659, F Value= 3.216 at p value=.021 Tab e 9: Regression Results of net NPLs and NIM Tab

e 9 shows the results of regression analysis, where the dependent variable is net interest margin and independent variable is net NPLs. It is observed that there is significant relationship between net interest margin and net NPLs (p value .021). Hence, we reject null hypothesis and conclude that NPLs impacts the net interest margin of BoBL. Further, the value of R square is .659 which indicates that net NPLs explains the 65.9 percent of total variance in the value of net interest margin. The negative sign of regression coefficient indicates that there is an inverse relationship hip between net interest margin and Net NPLs.

Conclusion

The study finds that both gross and net non-performing loans impacts the profitability of Bank of Bhutan Limited. All null hypotheses are rejected and can be concluded that NPLs of BoBL impacts the profitablity of the banks. The result obtained is in tune with the previous research study (Sharma & Rathore, 2016), Chaudhuri and Sensarma (2008) and Singh (2017).

References

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