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International Journal of Innovative Research in Management Studies (IJIRMS)

Volume 4, Issue 2, March 2019. pp.12-23.

IMPACT OF NPAs ON FINANCIAL SOUNDNESS OF AXIS BANK IN

INDIA: BASED ON CAMEL MODEL

Nisha Khan

Research Scholar, Department of Commerce, Aligarh Muslim University, Aligarh, India – 202 002

Email: [email protected]

Abstract—Today’s the burning topic for Indian banking sector is their increased level of Non-performing assets (NPAs).

Since it measures the assets quality of banks and also have an impact on their financial soundness. The study analyses

the impact of NPAs on the financial soundness of Axis bank in India from 2009 to 2018. Multiple regression analysis has

been applied by taking CAMEL Model parameters as a dependent variable and NPAs ratios as independent variables.

From the result it has been found that NPAs have a significant impact on earning capacity and management efficiency

of Axis bank and concludes that for reducing their impact Axis bank must take corrective action to trim down their NPAs.

Keywords—CAMEL Model, Cost of Capital, Indian Economy, Indian Banking Sector, NPAs.

INTRODUCTION

Indian banking sector plays a major role for the overall development of the Indian economy but certain drastic issues

hamper the success of Indian banking sector one of the issues is related to their increased level of Non-performing assets

(NPAs). NPAs are basically the assets which fall short to create any periodical income. The concept of NPAs came in

force after the recommendations made on Narasimham Committee Report I 1991 for treating bad loan as NPAs. In 1992-

93 RBI first time issue certain guidelines for banks and financial institutions for treatment of bad debts as NPAs and

defines the assets has been treated as NPAs if the borrower fails to repay their principal amount along with its interest

with a period of 180 days the time period was reduced to 90 days on March 2004 (Lalitha, 2013; Jain, 2007). RBI gives

a proper definition for NPAs and as per RBI Master Circular No DBR. No. BP. BC.2/21.04.048/2015-16 dated July 1,

2015, paragraph 2.1.1 NPA is defined as “An asset, including a leased asset becomes non performing when it ceases to

generate income for the banks”. An advance is classified as NPA as per the certain guidelines laid down by RBI. These

are:

Table 1: Guidelines for NPAs in respect of various advances

Type of Advance Terms/Conditions Period

Term loan interest and/ or instalment of principal

remains overdue

more than 90 days

Overdraft/Cash Credit the account remains ‘out of order’ for 90 days

Bills Purchased and discounted the bill remains overdue more than 90 days

Short duration crops the instalment of principal or interest

thereon remains overdue

two crop seasons

Long duration crops the instalment of principal or interest

thereon remains overdue

one crop season

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Impact of NPAs on Financial Soundness of Axis Bank in India: Based on CAMEL Model

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Securitization transactions (in

accordance with directions

provided on securitization dated

February 1, 2006)

the amount of liquidity facility remains

outstanding

more than 90 days

Derivative transactions the overdue receivables representing

positive mark-to- market value of a

derivative contract, if remain unpaid

for a period of 90 days

from the specified due

date for payment

Source: RBI. (2015e). Master Circular- Prudential Norms on Income Recognition, Asset

Classification and Provisioning pertaining to Advances.

The assets classification of NPAs is substandard assets, doubtful assets, and loss assets and each category have different

provisioning norms and are of two types: Gross NPAs and Net NPAs. There are various factors responsible for their

occurrence studied by various researchers some of the factors are: Diversion of funds, failure of business, willful

defaulters, improper selection of borrowers, defective lending policies, lack of proper appraisal and follow up, recession

in market, mismatch of funds, failure to recognize EWS, ineffective recovery tribunals etc.(Sagar, 2016; Rathi and Kalani,

2014; Devi and Reddy, ̀ 2014; Kapoor, 2014; Parmar, 2014; Goel and Rekhi, 2013; Jain and Sheikh, 2012; Panery, 2012).

NPAs does not only hamper the profitability position of banking sector but also have an important impact on banking

solvency, management efficiency, and liquidity position (Mohani and Deshmukh, 2013).

For measuring the financial performance of banking sector, a ratio-based model was first implemented by federal financial

institution examination council on November 13, 1979 which is called as ‘CAMEL Model’. The acronym of CAMEL is

capital adequacy, assets quality, management efficiency, earning capacity, and liquidity. As per the guidelines issued by

RBI all commercial banks follow the CAMEL parameters for accessing their financial position based on composite rating

given from best to worst so that the bank which ranked worst shall be given more concentration towards improving its

financial health. (Suba, 2015). Same has been used in the present study. Over the past ten years Indian banking sector has

been recorded a sharp increase in their NPAs level which causes the economic instability in a country. Different resolution

techniques have been implemented by Reserve Bank of India (RBI) from time to time but they hardly affect the increased

level of NPAs. Recently an Insolvency and Bankruptcy Code, 2016 has been passed by the parliament on 28 May 2016

for resolving stressed assets within a stipulated time period and in effective manner. For making it more effective first

amendment has been made in bill on 23 Nov, 2017 and furthermore second amendment taken place on 17 Aug, 2018 and

it came in force on 6 June, 2018.

LITERATURE REVIEW

The Indian banking sector has faced a burning issue of NPAs which hamper the growth of Indian economy in all sectors

as high level of NPAs indirectly means high range of credit defaulters that have a negative impact on net worth of the

banks (Aggarwal and Malik, 2016). Income diversification is also an important factor affected the assets quality of banks

the banks which follows income diversification have lower quality of assets and vice-versa (Ahamed, 2017). Several

studies have been conducted in India for determining the trend of NPAs in different sectors of banks and also for

examining their causes and their impact on profitability of banking sector (Sukul, 2017; Sinha, 2016; Gandhi, 2015;

Malini, 2015; Choudhary and Bhatnagar, 2014; Narula and Singla, 2014; Ahmad and Jegadeeshwaran, 2013; Khanna,

2012). A variety of statistical techniques has been used in these studied and found an overall increasing trend of NPAs in

India and have a negative correlation with the profitability of Indian banks. In India, previous researches provide a strong

evidence that among public and private sector banks level of NPAs is high in public sector bank as compared to private

sector banks despite of taking various measures by government for tackling the increased level of NPAs and find an

insignificant correlation between public and private sector banks with respect to their net profit and NPAs (Garg, 2016;

Subhamathi, 2016). While comparing the NPAs of public and private sector banks researcher drawn a conclusion that

year 2016 was recognized as a black mark for public sector banks as its NPAs become more than doubled from previous

year (Ahamed and Panwar, 2016) The impact of NPAs on profitability has been assessed by a researcher in a study by

checking the impact of NPAs on Return on assets(ROA), Return on equity (ROE), Capital adequacy ratio, and Cost of

capital among overseas banks and found they have impacted the growth of profitability of banks (Malini, 2015) so, it is

economically sound to examined the relationship between NIM (Net Interest Margin) and NPAs among public and private

sector banks and also to predict the impact of NPAs on operating profit of Indian banks. From the study it was found that

IJIRMS — Volume 4, Issue 2, March 2019

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among top two private sector banks of India ICICI Bank have negative correlation whereas Axis bank has weak

correlation between NPAs and NIM, and NPAs have a significant impact on the operating profit of Indian banks (Rathi

and Kalani, 2015; Laveena and Malhotra,2014). Sukul (2017) analyzes the trend of NPA among three private sector

banks in India i.e. ICICI Bank, HDFC Bank, and Axis Bank and find out their correlation with advance and found that

only Axis Bank NPAs have a negative relationship with its advances. For the purpose of judging the financial performance

of Indian banking sector CAMEL rating system has been adopted which is used by many researchers (Sonaje and

Nerlekar, 2017; Singh, 2016; Srinivasan and Saminathan, 2016; Shukla, 2015; Sharma, 2014; Karthikeyan and Shangari,

2014; Desai, 2013; Prasad and Ravinder, 2012; Kaur, 2010; Dash and Das, 2009). All studies examined the different

parameters of banking sector through CAMEL Model and determine the financial performance of Indian Banking sector

by assigning the ranks to them based on various ratios calculated for each CAMEL parameter. As it evaluated the financial

soundness the author studies the financial performance based on this model among 20 public sector and private sector

banks and recognize the factors which highly affects their performance are profit per employee, debt equity ratio, total

advance to total deposit ratio, and net NPAs to total advance ratio (Mouneswari et.al, 2016). Based on different ratios of

each CAMEL parameters ranks are assigned to the commercial banks in which it was found that among public sector

banks Andhra bank is at the top, among private sector banks HDFC bank was on the top and Bank of Bahrain and Kuwait

stood first with respect to foreign banks and an author make a comparative study of financial performance between public

sector banks and private/foreign sector banks among which the study concluded that private/foreign sector banks has

better capital adequacy, management soundness, and assets quality whereas the earning and liquidity position was good

in public sector banks (Srinivasan and Saminathan, 2016; Dash and Das, 2009). Bansal and Mohanty (2013) selected the

top most banks on the basis of their market capitalization for judging their financial performance under which it was

found that the financial performance of Axis bank was not effective as in overall ranking it stood last. Therefore, this

paper aims to enhance the existing literature in Indian context and also provide an empirical support for Indian banking

sector with respect to the impact of NPAs on financial health of banking sector in India.

OBJECTIVES OF THE STUDY

➢ To analyze the impact of NPAs parameters on the financial soundness of Axis bank based on CAMEL Model.

HYPOTHESIS OF THE STUDY

Based on objectives the following hypothesis has been framed:

H0: Testing the significant impact of NPAs parameter on the financial soundness of Axis bank based on CAMEL Model.

• H01: There is no significant impact of gross and net NPA ratio on Capital Adequacy (Solvency) of Axis bank.

• H02: There is no significant impact of gross and net NPA ratio on Management Efficiency of Axis bank.

• H03: There is no significant impact of gross and net NPA ratio on Earning Capacity of Axis bank.

• H04: There is no significant impact of gross and net NPA ratio on Liquidity of Axis bank.

RESEARCH METHODOLOGY

The present study is analytical in nature conducted on secondary data collected from Annual Reports and Reserve Bank

of India website for the period of ten financial years from 2008-09 to 2017-18. The statistical tool used for analyzing the

impact is multiple regression analysis on Eviews7. Based on the above literature reviews various proxy variables taken

for the analysis are as follows:

Table 2: List of Dependent and Independent Variables

Variables Abbreviations Proxy Measures

Dependent Variables

C- Capital Adequacy (Solvency) CRDR Credit Deposit Ratio

M- Management Efficiency PPE Profit Per Employee

E- Earning capacity ROE Return on Equity

L- Liquidity CDR Cash Deposit Ratio

Independent Variables

Non Performing Assets (NPAs)

GNPAR Gross NPA to Gross Advance Ratio

NNPAR Net NPA to Net Advance Ratio


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Source: Generated by the researcher

Before conducting the multiple regression analysis, the assumptions of Normality (Histogram and Jarque bera Test),

Linearity (scatter plots), Autocorrelation (Breusch Godfrey LM Test), and Heteroskedasticity (Breusch-Pagan-Godfrey

Test), and has been checked and the results are shown in appendix. Based on above hypothesis four multiple regression

models have been run for checking the impact and these are as follows: `

Table 3: Multiple Regression Models

Model 1 (H01) (CRDR) t = α + β1 (GNPAR) t + β2 (NNPAR) t + εt

Model 2 (H02) (PPE) t = α + β1 (GNPAR) t + β2 (NNPAR) t + εt

Model 3 (H03) (ROE) t = α + β1 (GNPAR) t + β2 (NNPAR) t + εt

Model 4 (H04) (CDR) t = α + β1 (GNPAR) t + β2 (NNPAR) t + εt

Source: Generated by the researcher

ANALYSIS AND INTERPRETATION

Table 4 shows the result of multiple regression analysis for model 1 explained that both of the independent variables

shows insignificant prob.value i.e. 0.9216 and 0.6931 but GNPAR have a negative impact on CRDR as its coefficient

value is found to be negative i.e. -1.042. The R2 value explains that 51.56% variation in regressand (i.e. CRDR) is caused

due to regressors (i.e. GNPAR and NNPAR) and the value of adjusted R2 is 0.3771. Since the p-value of model is 0.079

which is more than 0.05 (0.07 ˃ 0.05) at 95% level of confidence meaning thereby GNPAR and NNPAR both have an

insignificant impact on CRDR (i.e. Solvency) of Axis bank. Hence, the null hypothesis (H01) states that there is no

significant impact of gross and net NPAs on capital adequacy (or Solvency) of Axis bank is accepted.

Table 4: Summary of Multiple Regression Analysis (Model 1)

Dependent Variable- CRDR

Independent

Variables

Unstandardized

Coefficients Std. Error t-statistics Prob. value

Constant 77.22303 5.881115 13.13068 0.0000

GNPAR -1.042514 10.21600 -0.102047 0.9216

NNPAR 8.255649 20.06851 0.411373 0.6931

R2

Adjusted R2

F-statistics

P-value (F)

Durbin- Watson

0.515591

0.377189

3.725302

0.079112**

0.775612

Note: GNPAR= Gross NPA to gross advance ratio, NNPAR= Net NPA to net advance ratio

**indicates insignificant value and acceptance of null hypothesis.

Source: Generated by the researcher using Eviews7.

Table 5 shows the result of multiple regression analysis for model 2 proposed that among both of the independent

variables NNPAR have a negative relationship with dependent variable i.e. PPE as its coefficient value is -8.029 implying

that if by keeping other factors constant NNPAR increases by 1% on an average then PPE declines by 8.029%. The value


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of R2 explained that 75.32% variation in dependent variable (i.e. PPE) has been jointly explained by GNPAR and NNPAR

as independent variable. The adjusted R2 shows addition of outside predictors in model having a value of 68.27%. The

p-value is 0.007 which is much less than 0.05 (0.05˃0.007) at 95% level of confidence which signify that GNPAR and

NNPAR has a significant impact on PPE (i.e. management efficiency) of Axis bank. Hence, the null hypothesis (H02)

states that there is no significant impact of gross and net NPAs on management efficiency of Axis bank is rejected.


Dependent Variable- PPE

Independent

Variables

Unstandardized


Constant 15.24537 2.399413 6.353791 0.0004

GNPAR 1.921232 4.167988 0.460950 0.6588

NNPAR -8.029056 8.187676 -0.980627 0.3594

R2

Adjusted R2

F-statistics

P-value (F)

Durbin- Watson

0.753208

0.682696

10.68199

0.007467*

0.796417


*indicates significant value and rejection of null hypothesis.


Table 6 shows the result of multiple regression analysis of model 3 indicates that among independent variables NNPAR

are statistically significant by having a prob. value less than 0.05 i.e. 0.01 but both the regressors (i.e. GNPAR and

NNPAR) affected the regressand (i.e. ROE) negatively as their coefficient value is found to be negative i.e. -0.215 and

-5.641. While keeping other factors stable if there is 1% increase in GNPAR on an average ROE decline by 0.215%

similarly if there is 1% increase in NNPAR on an average ROE decline by 5.641%. The explanatory power of R2

explained that 99.32% variation in ROE has been collectively elucidated by both GNPAR and NNPAR while 0.68% is

interpreted by factors outside the model. The adjusted R2 show a value of 0.991320. P-value stood at 0.000 much less

than 0.05 (0.05˃0.00) at 95% level of confidence which signify that both independent variables (i.e. GNPAR and

NNPAR) have high significant impact on dependent variable (i.e. ROE) or on earning capacity of Axis bank. Hence, the

null hypothesis (H03) states that there is no significant impact of gross and net NPAs on earning capacity of Axis bank is

rejected.


Dependent Variable- ROE

Independent

Variables

Unstandardized


Constant 21.24701 0.487120 43.61757 0.0000

GNPAR -0.215520 0.846170 -0.254701 0.8063

NNPAR -5.641360 1.662233 -3.393844 0.0115

R2 0.993249


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Adjusted R2

F-statistics

P-value (F)

Durbin- Watson

0.991320

514.9206

0.000000*

1.454503


*indicates significant value and rejection of null hypothesis.


Table 7 shows the result of multiple regression analysis of model 4 depicted that both independent variables (i.e. GNPAR

and NNPAR) are statistically insignificant as its prob. value is more than 0.05 (i.e. 0.8548 and 0.7315). But GNPAR have

a negative relationship with CDR as the coefficient value stood negative i.e. -0.254 indicating that if GNPAR increased

by 1% on an average while other factors remain uniform then CDR declined by 0.25%. The R2 value stood at 0.242132

which symbolizes that only 24.21% fluctuation in CDR has been mutually explained by GNPAR and NNPAR. The p-

value of F-statistics stood at 0.378946 much more than 0.05 (0.05˂ 0.37) at 95% level of confidence which evidence that

both independent variables (i.e. GNPAR and NNPAR) have highly insignificant impact on dependent variable (i.e. CDR)

or on liquidity of Axis bank Hence, the null hypothesis (H03) states that there is no significant impact of gross and net

NPAs on liquidity of Axis bank is accepted.


Dependent Variable- CDR

Independent

Variables

Unstandardized

Coefficients

Std. Error t-statistics Prob. value

Constant 6.351745 0.769836 8.250772 0.0001

GNPAR -0.253848 1.337272 -0.189825 0.8548

NNPAR 0.938283 2.626963 0.357174 0.7315

R2

Adjusted R2

F-statistics

P-value (F)

Durbin- Watson

0.242132

0.025598

1.118218

0.378946**

1.536455


**indicates insignificant value and acceptance of null hypothesis.


CONCLUSION AND SUGGESTIONS

The paper found a lot of empirical evidences in relation to Non-performing assets (NPAs) in different sectors of Indian

banking sector and also with respect to CAMEL model performance evaluation technique. The present study have

investigated the impact of NPAs on the financial soundness of Axis bank in India which was the first new private sector

bank established as UTI in 1994 when the government give approval on the entry of private banks in Indian banking

sector. We have explored whether NPAs have significant impact on the financial health of Axis bank where the financial

health is measured with five important parameters of CAMEL model. Our results shows that NPAs of Axis bank increased


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drastically from last three years as its GNPAR increased from 1.79% in 2016 to 6.77% in 2018 i.e. by 4.98% similarly,

the NNPAR increased from 0.75% in 2016 to 3.4% in 2018 i.e. by 2.65% and while running multiple regression for

finding out the impact of NPAs it has been found that NPAs have significant impact on earning capacity and management

efficiency of Axis bank but does not have a significant impact on solvency and liquidity of Axis bank. The results are

rationale with many studies which find out the impact of NPAs (Subhamathi, 2016; Wachasunder, 2016; Malini, 2015,

Narula and Singla, 2014; Narayan and Surya, 2014; Soni and Heda, 2014; Ganesan and Santhanakrishna, 2013) but made

an addition with regard to the use of CAMEL model for accessing the financial position of Axis bank so that it become

easy to find out how NPAs impacted the financial health of Axis bank.

Based on the result some suggestions are required to be given to be deployed with respect to Axis bank and also for

overall banking sector in India which continuously facing the problem of increased level of NPAs. In India many

evidences are found with reference to the measures required for reducing the level of NPAs such as early recognition of

the problem, identification of genuine borrowers, effective lending policies, proper evaluation of loan, better technique

for management of credit risk, proper use of preventive techniques like DRT, SARAESI Act 2002, Lok Adalat etc

(Garg,2016; Kumar,2014; Rao,2014; Singh,2013; Bhuyan and Rath,2013; Manjule,2013;) but in context to the present

study for trimming down the level of NPAs, Axis bank must enhance their securitization while giving loan to different

sectors of the economy specially to non-priority sector comprises of industrial and service sector, as within last few years

NPAs in non-priority sector is more than the NPAs held in priority sector.

Overall, the study has some important implication for the Indian banking sector and also provides a valuable insight for

different future researches as by judging the impact of NPAs on financial performance of banks they can take timely

action for reducing the NPAs level and enhancing the quality of loan assets and should be more focused towards accessing

the credit risk of banks.

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APPENDIX

I. Normality: Histogram and Jarque Bera Test

Figure 1: Model 1

Figure 2: Model 2

Figure 3: Model 3

Figure 4: Model 4


0

1

2

3

4

5

6

-10 -5 0 5 10 15

Series: ResidualsSample 2009 2018Observations 10

Mean 1.99e-14Median -1.131587Maximum 13.09133Minimum -9.919377Std. Dev. 6.481801Skewness 0.652380Kurtosis 3.035368

Jarque-Bera 0.709854Probability 0.701225

0

1

2

3

4

5

6

-5.0 -2.5 0.0 2.5 5.0 7.5




0

1

2

3

-1.00 -0.75 -0.50 -0.25 0.00 0.25 0.50 0.75 1.00


Mean 3.39e-15Median 0.069128Maximum 0.851021Minimum -0.985292Std. Dev. 0.536874Skewness -0.297684Kurtosis 2.458977


0

1

2

3

4

5

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0





22

II.Linearity: Scatter Plot

Model 1

Figure 1: CRDR and GNPAR

Figure 2: CRDR and NNPAR


Model 2

Figure 3: PPE and GNPAR

Figure 4: PPE and NNPAR


Model 3

Figure 5: ROE and GNPAR

Figure 6: ROE and NNPAR

65

70

75

80

85

90

95

100

1 2 3 4 5 6 7

GNPAR

CRDR

65

70

75

80

85

90

95

100

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

NNPAR

CRDR

0

4

8

12

16

20

1 2 3 4 5 6 7

GNPAR

PPE

0

4

8

12

16

20

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

NNPAR

PPE

0

4

8

12

16

20

24

1 2 3 4 5 6 7

GNPAR

ROE

0

4

8

12

16

20

24

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

NNPAR

ROE


23


Model 4

Figure 7: CDR and GNPAR

Figure 8: CDR and NNPAR


III.Autocorrelation: Breusch Godfrey LM Test

Result of Breusch Godfrey LM Test of Autocorrelation

(H0): There is no Autocorrelation

Models Lags LM-Statistics Prob.Value

Model 1 1 3.6746 0.0513

2 1.5527 0.1472*

Model 2 1 3.3947 0.0573

2 1.4705 0.1570*

Model 3 1 0.3960 0.4313*

Model 4 1 0.0098 0.8984*

*indicates insignificant value and acceptance of null hypothesis.


IV.Heteroskedasticity: Breusch-Pagan-Godfrey Test

Result of Breusch-Pagan-Godfrey Test Heteroskedasticity Test

(H0): There is no Heteroskedasticity

Models F-Statistics Obs * R-Squared Prob. Value

Model 1 0.7508 1.7663 0.4135*

Model 2 0.8037 1.8675 0.3931*

Model 3 0.6772 1.6212 0.4446*

Model 4 0.6935 1.6537 0.4374*

*indicates insignificant value and acceptance of null hypothesis.


4.8

5.2

5.6

6.0

6.4

6.8

7.2

7.6

8.0

8.4

1 2 3 4 5 6 7

GNPAR

CDR

4.8

5.2

5.6

6.0

6.4

6.8

7.2

7.6

8.0

8.4

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

NNPAR

CDR

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