Abstract

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.

Key Words: Non-Interest Earning, Risk Parameters, Financial Performance,

Regression, Unit root, Public Sector bank, India.

Introduction

and Conceptual

Framework

Non-

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

unsystematic.

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

risk.

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.

Return

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.

Literature

Review

Limei

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.

Singh

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.

Mndeme

(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

time.

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

valuations.

Objective

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.

Research

Methodology

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 &

Discussion:

Unit

Root Test

Since

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

results

Variable

ADF-statistic

Critical value

Probability

value

Level of

significance

Order of

integration

NIER

-4.510687

-3.491345

-2.888157

-2.581041

0.0003

1%

5%

10%

Level

CAR

-8.032016

-3.491345

-2.888157

-2.581041

0.0000

1%

5%

10%

Level

CIR

-4.648011

-3.494378

-2.889474

-2.581741

0.0002

1%

5%

10%

Level

NIM

-4.257695

-3.491345

-2.888157

-2.581041

0.0009

1%

5%

10%

Level

ROA

-3.824871

-3.494378

-2.889474

-2.581741

0.0037

1%

5%

10%

Level

The

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

Test

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.

Statistically,

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

REGRESSION

ANALYSIS

VARIABLE

COEFFICIENT

STD.ERROR

T STATISTIC

PROB.

C

-0.395368

0.183262

-2.157395

0.0333

CAR

0.050854

0.014071

3.614095

0.0005

CIR

-0.074900

0.002996

-24.99869

0.0000

NIM

0.691867

0.036942

18.72859

0.0000

NIER

0.097509

0.009359

10.41837

0.0000

The

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,

CIR, NIM, NIER on ROA.

y = a + b1x1

+b2x2 +b3x3+b4x4+

e

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

0.097509 (NIER) + e

Table 3

MODEL

SUMMARY

R-squared

Adjusted R-squared

Durbin-Watson statistic

F-statistic

Prob.(F-statistic)

0.900745

0.896964

1.575539

238.2214

0.000000

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.

Regression’s

Assumption Tests:

Breusch-Godfrey serial correlation LM test:

H0 – residuals are not serially correlated.

Table 4

Model Summery

F-statistic

1.720551

Probability

0.1367

Obs*R-squared

8.713434

Probability

0.1211

From

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

F-statistic

1.753363

Probability

0.0576

Obs*R-squared

22.58674

Probability

0.0673

From

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

F-statistic

0.018195

Probability

0.8930

Obs*R-squared

0.018532

Probability

0.8917

From

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.

Limitations

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.

Capital

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.