Abstract check the stationraity. The result of


The study examines the relationship between risk parameters and
financial performance. The study involved twenty one selected public sector
banks of India. Data were obtained from the reliable data source of selected
public sector banks. The data were subjected to statistical analysis. The cause and effect relationship was checked by regression model using
E-Views 9. Since, the time series data was employed, stationarity of the data
was checked in order to avoid spurious regression. The Augmented Dickey –
Fuller test was used for unit root testing to check the stationraity. The
result of the study revealed that non-interest earning and risk parameters have a significant effect on
financial performance.

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Key Words: Non-Interest Earning, Risk Parameters, Financial Performance,
Regression, Unit root, Public Sector bank, India.








and Conceptual

interest income is the income generating from the non-traditional activities of
banks. Revenue base of Indian Banking Industry is shifting from traditional
activities like loan making to non-traditional activities that generate service charges, trading revenue,
fee income, and other types of noninterest income. The financial crisis caused
by trading practices of investment banks in the U S in year 2007-08 has
revealed the weakness of business models of many banks. Due to their heavy
reliance on non-interest income, Investment banks were hit by the crisis that
exposed them to more income fluctuations than retail oriented banks that use
customer deposit as primary source of funding and provide traditional banking
services like lending.


Higher operating leverage is the major
difference between interest income and non-interest income from non-traditional
activities as banks are exposed to higher fixed income. But non-interest income
is usually more volatile than interest income because due to information costs
it is more difficult for borrowers to switch their lending relationship.


The most important issues in
banking industry are performance and risk issues. When future is unknown, there
is risk. Hence, one who can secure a future for themselves and their
organizations are those who can increase their knowledge with proper planning
and analysis. So today when risk management is studied, the goal is not to
eliminate the risk, but is to identify and determine its due costs.


The proposed study is based on the
phenomenon of risk. As risk management is focused to not only the eliminating
the risk but it is actually based on identification of risk at any level.
Financial organizations can face both type of risk, systematic and


Where the systematic risk are
categorized on the basis of three system-wide factors; market risk, interest
risk and purchasing power risk. On the other hand unsystematic risk covers
business risk and financial risk. Here capital adequacy ratio, non interest
earning ratio, net interest margin are taken to represent the systematic risk
as independent variables and cost to income ratio representing the unsystematic


Financial performance is a
subjective measure of how well a organization can use assets from its primary
mode of business and generate revenues. This term is also used as a general
measure of a organization’s overall financial health over a given period of
time, and can be used to compare similar organizations across the same industry
or to compare industries or sectors in aggregation.

There are many different ways to
measure financial performance, but all measures should be taken in aggregation.
Line items such as revenue from operations, operating income or cash flow from
operations can be used, as well as total unit sales. Furthermore, the analyst
or investor may wish to look deeper into financial statements and seek out the
return on assets specifically.


Description of Variables

Non-Interest Earning
Ratio: Non-interest
earning ratio can be measured as non-interest earning divided by total
earnings. It is calculated as:

NIER = NIE / Total Earnings

Capital Adequacy Ratio:  Capital Adequacy Ratio (CAR) is a measure of
the amount of bank’s capital expressed as a percentage of its risk weighted
credit exposure.

Cost to Income Ratio: It shows a company’s costs in relation to its
income. To get the ratio, divide the operating costs (administrative and fixed costs,
such as salaries and property expenses, but not bad debts that have been
written off) by operating income.

Net Interest Margin: Net interest
margin (NIM) is a measure of the difference
between the interest income
generated by banks or other financial institutions and the amount of interest paid out to their lenders
(for example, deposits), relative to the amount of their (interest-earning) assets.

Return on Assets:
The return on assets (ROA) is a ratio that measures company earnings before
interest & taxes (EBIT) against its total net assets. The ratio is
considered an indicator of how efficient a company is using its assets to
generate before contractual obligation must be paid.

on assets gives a sign of the capital strength of the banking industry, which
will depend on the industry; banks that require large initial investment will
generally have lower return on assets.


et.al. (2017), investigated the relationship between noninterest income ratio
and the performance of banks and the influence of noninterest income ratio on
its performance. This paper analyzed that the operating expenses emanating from
noninterest income business are much higher than the interest income business
and interest income rises from loan business. NIR can be negatively correlated
with bank performance and suggested that the increase of noninterest income may
improve the performance. They concluded that the higher the noninterest rate
is, the lower the performance of commercial bank will be.

et.al. (2016), aimed to understand the contribution of non-interest income and
the risks associated with it. This study indicates that both interest and
non-interest income have consistent growth while growth of non-interest income
is more than the growth of interest income and the risks associated with
non-interest income can be summed up by the increasing contribution towards
Revenue. The Profitability Ratios suggested that, volatility of non-interest
income has not affected Public Sector Banks but affected the profitability
ratios of Private Banks and Foreign Banks. The study revealed that private
banks can have more risk appetite than the public sector banks and foreign
banks have some part of their income as profit from exchange. The results
indicated that non-interest income is positively influenced by return on
equity, profit per employee, loan quality, and personalized customer service
offered to bank customers.

(2015), investigated impact
of noninterest income on bank performance in Tanzanian. The study indicated
that interest income found to have positive impact on risk adjusted return to
equity with the same intensity to that of non-interest income as there exited
perfect negative correlation between these two income sources. He concluded
that increase in noninterest income has negative impact on bank performance
across all banks and result supported that diversification is better for the
bank performance than giving focus to the non-interest income.

Damankah, Anku-Tsede and Amankwaa (2014), showed a
positive relationship between prime rate and inflation. The outcome showed a
negative relationship between NII and bank size, is indicated that smaller
banks are generating more non-interest revenue. This study suggested that banks
involved in higher levels of non-traditional activities have higher risk
exposures from their conventional banking business and the relationship between
liquidity and NII was positive and significant. It was found that interest
income (INI), exposure to risk (ExpR), and liquidity (LIQ) are main driving
factors in non-interest earning activities and banks with higher anticipated
loan losses and high liquidity, smaller banks with lower levels of deposits are
mostly engaged in non-interest earning activities.

Karakaya (2012), examined bank profitability determinants
and relationship between non-interest income and bank performance in Turkey.
The paper studied that small banks have higher capital adequacy, adopted
tighter loan policy and their expenses is less. It shows that a positive
correlation existed between banks’ overheads and their sizes. They found that
non-interest income margin of banks are increasing and bank performance is
affected by non-interest income.  The
study revealed that banks have larger size gained higher profits, increased
equity capital profitability and non-interest margin. The study also
established that non-interest income is the main factor having effect on equity
capital profitability.

Li (2014), investigated the impact of non-interest income
on efficiency of banks in china. The study observed that technical and pure
technical efficiency increases due to inclusion of non-interest income. The
proportion of non-interest income to operating revenue resulted in U-shaped
relationship between bank efficiency and non-interest income. He concluded that
inclusion of non-interest income output showed increase during the sample
period but does not result in significant increase of bank efficiency with the

Trivedi (2015), analyzed the impact of new business lines
and income streams on banks’ stability and profitability. They studied that
banks have been active in generating a certain amount of income from fee-based
services and observed that variability in diversification between banks is
higher but lower in risk adjust performance. Control variables are introduced
which can have impact on performance and impact of diversification is positive
on profitability but negative on risk adjust measures. On the other hand profitability
may not be a driving force behind strategic shifts in banks.

Williams and Prather (2010), examined the impact on bank
risk between margin income and fee-based income in Australia. This paper
revealed that fee-based income is riskier than marginal income and suggested
that banks’ shareholders will be benefited from increased non-interest income
through diversification but shareholders should monitor exposer of non-interest
income to certify they do not over exposed. Diversification of banks reduces
the systematic risk possibility but increased disclosure of banks non-interest
income resulted in understanding of bank risk determinants.

Muriithi, Waweru and Muturi (2016), reviewed the effect
of credit risk on financial performance of commercial banks in Kenya and
observed that credit risk components are significant in clarifying variations
in return on equity. Both in short run and long run bank increased credit risk
have negative impact on banks’ financial performance and reduce profits. The
study concluded that banks with high asset quality and low non-performing loan
are more profitable and reducing capital by increasing loan loss provision that
affects the profitability.

Asfaw and Veni (2015), examined the link between the
banks specific factors in Ethiopian private commercial banks and indicated the
effectiveness of credit risk management system based on the level of risk
factors associated with borrowers. Variables have negative correlation with
credit risk ratio but deposit rate has positive correlation with credit risk.
Study revealed that due to credit risk culture credit growth had negative
impact on loan problems and bank profitability indicator had negative
relationship with credit problem while bank size also has negative correlation
with credit risk.

Poudel (2012) studied various
parameters pertinent to credit risk management that affect banks’ financial
performance. It has been analyzed that all the risk management indicators have
direct relationship with performance and there is no any relationship between
cost per loan assets and performance. It suggested that In order to reduce risk
on loans and achieve maximum performance the banks need to allocate more funds
to default rate management and try to maintain just optimum level of capital
adequacy. He concluded that success of bank performance depends on risk management
and default rate management is the single most important predictor of the bank
performance among the risk management indicators.


Haque and Wani (2015) studied the
relationship between financial risk and financial performance of Commercial
banks in India and also measure the impact of financial risks on the financial
performance of commercial banks in India. It has been analyzed that both public
and private sector banks are exposed to the vagaries of financial risk and
solvency risk from all the variables, have positive relationship with the
profitability of commercial banks. They suggested that to enhance operational
efficiency and profitability commercial banks should install the latest
advances in their systems, processes, strategies, internal controls and
transparency in services and operations and banks should also rebuild the
conventional risk management system. It has been found by the study, interest
rate risk, liquidity risk, credit risk, capital risk and solvency risk possess
the power of bringing change of 84 percent in profitability of the banks, out
of which solvency risk alone has the power to change about 52.4 percent in profitability.


Kohler (2013) analyzed the impact
of banks’ non-interest income share on risk in the German banking sector. He
suggested that banks are more stable if they have a more diversified income
structure and depend neither heavily on interest nor on non-interest income. He
concluded that trading income which is significantly more volatile than fee and
commission income, in contrast, has no significant effect on bank stability and
he also indicate that the impact of non-interest income on risk significantly
depends on the activities used to generate non-interest income.

Sun and Chang (2010) investigate
the role of risk in determining the cost efficiency of international banks in
eight emerging Asian countries. It has been found that banks operating in a
high exchange rate volatility environment are more efficient than those
operating in low exchange rate volatility, the exchange rate volatility has
negative effect on the inefficiency effect and they found an optimal level of
interest rate volatility for making decisions. They concluded that each risk
measure presents a dissimilar effect on banks’ efficiency and more detailed
facts about how these risk measures influence both the level and variability of
the inefficiency effect across countries and over time.


Hoseininassab, et.al. (2013)  study recognized the importance of efficiency
and risk as two fundamental important categories in banking industry and also identifies
the impact of credit, operational, market and liquidity risks on banking system
efficiency. It has been found that impact of risk factor on financial status of
banks and financial institutes is undeniable and for this reason it potentially
can affect on financial decisions. It has been suggested that  more number of input and output are used and
the impact of different risk parameters on efficiency in a more expanded time
period are studied  for more accurate
efficiency evaluation.  They concluded
that financial security costs, facing liquidity risk make banks to receive
higher costs than common market rates to provide financial security and Debt to
other banks can be mentioned as one of the variable that affects the liquidity
of banks.


Altunbas, Manganelli and
Marques-Ibanez (2011) study was designed to evaluate macro-financial models
linking financial stability and the performance of the economy and early
warning systems and systemic risk indicators and also assessing contagion risks.
It has been considered that higher level of Tier I capital ex-ante generally decreases
the likelihood of bank distress during the crisis and relying on a more solid
funding structure reduces bank risk during times of crisis. It has found that
in terms of the asset structure, both bank size and the ratio of loans to total
assets are positively related to our measures of bank risk, while
securitization is negatively related. 
They have suggested that regulators would require to intensify
supervisory interference. The study 
recommend a better understanding of the risk-taking incentives, in
particular by those banks experiencing rapid increases in their stock market


of the study

To check the impact of risk parameters on financial performance.

To calculate non-interest
earning ratio.

To open new avenues for
further researches.



The study is causal in
nature. It is aimed to find out the impact of risk parameters on ROA. The study
is done to analysis the relationship in Indian context. Data of capital
adequacy ratio, cost to income ratio, net interest marginand non-interest
earning ratio were taken to define the risk parameters, while the ROA was taken
as a parameter of financial performance. 
The data have taken for the previous 5 years (2012 to 2016). All the
Public Sector Banks were taken as the population of the study and sampling
frame was 21 Public Sector Banks in India. Sample elements were taken Capital
Adequacy Ratio, Cost to Income Ratio, Net-Interest Margin, Non-Interest Earning
Ratio and ROA.

Result &

Root Test

time series data was employed, it is important to test for the stationarity of
the variables in order to avoid spurious regression. The Augmented Dickey –
Fuller test was used for unit root testing. The results of the unit root test
for the variables are presented below:

Table 1: Unit Root Test



Critical value


Level of

Order of
































Unit Root tests showed that all variables stationary at level Order of integration. Augmented
Dickey- Fuller unit root test statistics are greater than their critical values
considered at 1% level of significance was considered.

Correlogram Residual Test of Stationarity:

Chart 1: Correlogram

Correlogram residual
test was applied on the variables, NIER, CAR, CIR, NIM (independent) &ROA (dependent)
of our proposed research. The assumption of this test is that all the spices
must be restricted within the fitted (regression / estimated or predicted) line
and actual line. Thus, there is no autocorrelation in the data and it explained
the stationarity of the data.

stationarity is checked by measuring the last P value of the Q-Statistics.the
assumption of this test is, the corresponding p value of Q- Statistics must be
greater than the standard value (0.05).Here, in the above table, last P value of the Q-Statistics (0.530) is more than the standard value
(0.05), hence these results recommend that the data is stationary.

Regression Analysis:

H0 – There
is no significant effect of risk parameters on ROA.



Table 2

































outcome of regression model has shown that the Prob.
value of t-statistic of independent variables; capital adequacy ratio (0.0005),
cost to income ratio (0.0000), non-interest margin (0.0000) and non-interest
earning ratio (0.0000) are less than 0.05 so, there is a significant effect of CAR,

y = a + b1x1
+b2x2 +b3x3+b4x4+

ROA = -0.395368 + 0.050854 (CAR) + (-0.074900) (CIR) + 0.691867 (NIM) +
0.097509 (NIER) + e

Table 3



Adjusted R-squared

Durbin-Watson statistic









The above table (Table-3) defines the results of regression
analysis. The coefficient of determination 0.896964 means that 89.69 % of the variation in ROA is being explained by
the independent variables capital adequacy ratio, cost to income ratio, net-interest
margin and non-interest earning ratio. Durbin-Watson statistic (1.575) is close
to idle value 2, thus there is no autocorrelation among the variables. Value of
F-statistic 238.2214 is significant at 0.0000%which
is less than 5% reveals, model is
good fit.

Assumption Tests:

Breusch-Godfrey serial correlation LM test:

H0 – residuals are not serially correlated.

Table 4

Model  Summery











the above table it is resulted that P-value (0.1211) of Observed
R-square is more than standard value (0.05) so, null hypotheses
is not rejected. It means the residuals
are not serially correlated.

Heteroskedasticity test

H0 – residuals are not Heteroskedastic.

Table 5










the above table it is resulted that P-value (0.0673) of Observed
R-square is more than standard value (0.05) so, null hypotheses
is not rejected. It means the residuals
are not Heteroskedastic.

ARCH LM test

H0 – there is no ARCH effect in the series.

Table 6










the above table it is resulted that P-value (0.8917) of Observed
R-square is more than standard value (0.05) so, null hypotheses
is not rejected. It means there is no
ARCH effect in the series.


and Suggestion

The purposed research is
focused on cause and effect relationship between risk parameters (non-interest
earning ratio, capital adequacy ratio, cost to income ratio, net interest
margin) and financial performance indicators i.e. return on assets (ROA). It is
suggested that the relationship can be tested by taking some other financial
performance indicator like ROE, Earning per share, Profit before Tax and return
on capital employed etc.

adequacy ratio, cost to income ratio, net interest margin, non-interest earning
ratio is considered as independent variable to investigate the impact on
dependent variables (ROA).  It is
suggested that same study can be carried by taking some other dominating
variables like Inflation, Interest rate and exchange rate etc.

This study is focused only
on the Public Sector banks so it is suggested that it may performed on private
sector banks. Further studies can be conducted on comparative basis between
Public sector banks and Private sector banks.