International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 04, April 2019, pp. 818–829, Article ID: IJMET_10_04_081
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=4
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
Scopus Indexed
AN EXAMINATION OF THE RELATIONSHIP
BETWEEN SPREAD AND BURDEN IN
DETERMINING THE FINANCIAL EFFICIENCY:
A STUDY OF NEW GENERATION PRIVATE
BANKS IN INDIA
Dr. S. Sathyakala
Assistant Professor, Sona College of Technology, Salem
Prof. Umaya Salma Shajahan
Assistant Professor, Sona College of Technology, Salem
Dr. P. Kamalakannan
Assistant Professor, Sona College of Technology, Salem
ABSTRACT
The aim of this paper is to find out the financial efficiency of new generation
private banks operating in India during the period 2007-08 to 2016 -17. A Regression
analysis is used to find out how the independent variables are supporting dependent
variables. The study also aims in predicting how spread and burden of banks are
influencing its financial decisions. It is observed that The variables like Spread to
working fund, Spread to total income, Burden to total income, burden to working fund,
Non-interest income to working fund, Interest expended to total income are positively
correlated with net interest margin.
Key words: financial efficiency, india, spread, burden, regression, net interest income
Cite this Article: Dr. S. Sathyakala, Prof. Umaya Salma Shajahan and
Dr. P. Kamalakannan, An Examination of the Relationship Between Spread and
Burden in Determining the Financial Efficiency: A Study of New Generation Private
Banks in India, International Journal of Mechanical Engineering and Technology
10(4), 2019, pp. 818–829.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=4
1. INTRODUCTION
The banks play a major role in economic growth of any country (Richa Verma Bajaj 2016).
Banks were considered to be the backbone for any developing economy (Thangasamy 2014).
Today no country in the world can progress without a well-organized system of banking.
Stronger financial performance indicates that the banks were more stable and ascertain the
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Dr. S. Sathyakala, Prof. Umaya Salma Shajahan and Dr. P. Kamalakannan
safe position that forms a base for long term survival, better utilization of resources and
earnings and ensure optimum capital for absorbing risk and financial crisis (Krishna and
Kavitha 2017). Banks need to be efficient in all its activities. Efficiency describes the distance
exists between the inputs and outputs used by the concerned bank and the quantity of inputs
and outputs used by the efficient bank. (Aparana Bhatia and Megha Mahendru 2017). There
are three main efficiency concepts for analyzing the bank’s financial performance i.e.
Revenue efficiency, cost efficiency and profit efficiency.(Aparana Bhatia and Megha
Mahendru 2017).Finally these three efficiencies are determining the financial efficiency of the
banks. This research paper is divided into six sections. The first section is introduction and
banking in India. The second section deals with reviews and variables used in this study. The
third section describes data and methodology employed in this work. Fourth section describes
the statistical tools and the findings from the analysis are sectioned in five. Finally the
conclusion and scope for further research has been exhibited in section six.
1.1. Banking in India
The Indian banking industry is one of the largest in the world. Banking in India dates back to
the Vedic age. Initially in India Desi banking was much popular and the banking was done
with hundies. Earlier studies reveals that various kinds of banking instruments including loans
existed during Buddhist, Mauryan and the Mughal periods. But the formal banking system in
India can be traced to 1770 where the first bank “Bank of Hindustan” was established. Then
in the year 1786 “The General Bank of India” commenced its banking operations, but
regrettably these two banks are now redundant. Till the end of 17th century there were no
formal system of banking operations in India. Modern banking has its foundation during the
British period. During the early nineteenth century there were three main presidencies –
Bombay, Calcutta and Madras. Each of these presidencies had their own banks with respect to
their presidencies known as Bank of Calcutta, Bank of Bombay and Bank of Madras. Later
these three presidency banks merged in 1921 to form Imperial Bank of India and it becomes
State Bank of India in the year 1955.
The Reserve bank of India was established in the year 1935. After Independence in 1947
banking system was given a different direction and efforts were made to link the banking
system with the economic development of the nation as a whole. Despite of all this efforts the
real banking take place in India after July 1969 where the major banks were nationalized. The
major objective behind this nationalization is to develop backward areas, prevention of money
lenders, focus on priority sectors, faster the banking process and encouraging the savings
habit of the people. By considering this fourteen banks were nationalized by late prime
minister of India Mrs. Indira Gandhi for uplifting the low economic strata’s in the society.
Prior to nationalization, majority of the bank transactions has been done by the richer section
and bank doors remained virtually closed for the weaker sections in the economy. The
regional rural banks were promoted in 1975 for providing financial assistance to agriculture.
The second phase of nationalization took place in the year 1980 with six banks. The
banking operations started to diversify from 1985 into mutual funds, investment banking,
venture capital, corporate counselling, etc.., The Indian banking reforms were reframed in the
year 1991 based on the Narashiman Committee report. Based on the committee
recommendation the late Prime Minister P.V.Narashima Rao announced deregulation in the
banking industry. It means relaxing the norms for entering into banking industry. After the
economy was open up before two decades ago, exactly in the year 1993, RBI received 113
applicants from large industrial houses for starting a banking business in India. Finally after
screening, ten applicants were selected on various grounds and they commenced their banking
business successfully in the year 1994. Currently the banking sector in India is fragmented
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An Examination of the Relationship Between Spread and Burden in Determining the Financial
Efficiency: A Study of New Generation Private Banks in India
and comprises of commercial banks, scheduled banks, non-scheduled banks, foreign banks,
regional rural banks, cooperative banks, nationalized banks, post office banks, SBI and its
associates and now with the emergency of payment banks. (Rani S.Ladha 2017). Banking in
India is regulated by RBI, which is the central bank of the country. India to become the third
largest domestic banking sector by 2050 after China and United States – PWC Survey. The
banking sector is expected to grow at 2.5 to three times the country’s GDP growth
rate(Transforming the way banking is done – Dec 9, 2012, Business Today) . The industry is
progressing but they need to go long.
1.2. New generation private banks in India
For many years public banks dominated the Indian banking industry and few private banks
expanded its prominence after nationalization. New generation banks started its operations
after liberalizing the economy. They are Axis bank, Development credit bank, Industrial
Credit and Investment Corporation of India, Indusind, Kodak Mahindra bank, Yes bank,
Housing Development and Finance Corporation of India. Initially ten banks were started but
four of the new generation private banks are not survive at present due to various reasons.
Centurion bank and Times bank were merged with Bank of Punjab which later merged with
HDFC bank. Eventually Global trust bank was also taken over by Oriental Bank of
Commerce. In spite of this failure, few private banks have inching up their profits and
position in the national market and they have also crushed few public and old generation
banks.
When the new generation banks started its operations in 1993, it had to compete with the
existing players in the market. Among them, few of them had been doing banking business
for over a century .At that time, state owned banks with extensive branch network dominated
the market. These banks faced the tough competition with the established players and
remarkably today these new banks are having a market share of 20% in deposits and
advances. In general it is observed that new private sector banks catalyzed country’s
economic growth. Despite of this, deregulation of interest rates, disinvestment policies created
a tough competition in the market. It necessitated the banks to improve their financial efficacy
by pulling more customers for its products and services. At this juncture, it is felt that to what
extent the new private banks are managing their financial soundness.
Technology in banks arise after the emergence of these new banks. The banks are also
known as Techno Savy banks created a digital revolution in the banking industry. These
banks developed the concept of direct selling by taking the loans to the customer’s door step.
They developed a strong distribution network and ensure that their products and services
reached the target customers in the market. The transformation of conventional banking to
convenient banking happened after the entry of new generation banks.
2. LITERATURE REVIEW
The present work has been attempted to study the impact of Spread and Burden in
determining the financial efficiency through Regression Model. The study includes new
generation private banks for the period ranges from 2006-2007 to 2016 -2017. All seven new
generation private banks were found operating during the stated study period. Generally the
efficiency of the banks has been measured on the basis of their productivity and profitability
(Richa Verma and B.S.Bodla (2011). The productivity of the bank comes through spread
which arrived from investments, loans and advances and profitability through reduction of
operating and non-operating expenses. After considering this the model for each bank has
been constructed for determining the major factor supporting the dependent variables from the
independent variables. A detailed reviews has been collected and compiled in table 1
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Dr. S. Sathyakala, Prof. Umaya Salma Shajahan and Dr. P. Kamalakannan
2.2. Variables commonly used in financial efficiency
Financial efficiency determines the organization’s ability in coordinating its resources. A
sound banking system is the result of effective utilization of its own resources. Of course an
efficient banking system is a good sign for maintaining economic stability in the country.
There is a strong evidence from the earlier studies that the most common determinant of
financial efficiency are return on equity, return on asset, bank and branch size, level of
capitalization, spread, asset quality, liquidity of the bank, burden, etc.., Like wise there are
various factors which helps the bank to determine its financial soundness, however the
common factors in majority of the studies are return on equity, return on asset, interest
received, interest earned, total income. Based on these works the study considers two
important variable includes spread and burden.
2.3. Modelling Techniques of Financial Efficiency
The most common statistical method employed in this study is Multiple Regression analysis,
Spread and Burden analysis. The Regression analysis has been used to estimate the impact of
selected number of factors on the profitability of the selected new generation private banks
operating in India. Moreover this analysis is performed to estimate the effect of the
independent variables (Spread and Burden) on dependent variable (Net Interest Income).
According to Ongore and Kusa (2013) multiple regression model helps in identifying the
specific factors which determines the financial efficiency of the banks. Also Rahman and
Bukair (2013) indicated multiple regression analysis specify a significant positive influence
on banks efficiency and CSR disclosures. Klimberg et al (2009) suggested that the forecasting
is an important technique used by the business organizations especially banks to plan and
evaluate their operations and one of the commonly used such techniques for forecasting is
regression analysis. Aggarwal and Priyanka (2016) stated in their research stated that the most
significant factors influencing ROA of public sector banks are Spread, Non-interest income,
Credit Deposit ratio and Non-performing assets. Some of the research studies on financial
efficiency of banking sector are briefly reviewed.
Table 1 Consolidation of Literature review on financial efficiency of banks
Author
Method
Goyal and Kaur (2008) CAMEL
Determinants
Capital adequacy,
asset quality,
employee efficiency,
earning quality,
liquidity.
Turkey, HSD Net profit
test
Result
It was concluded that the performance
of few banks were good during the study
period.
Prasad and Ravinder
(2011)
It was concluded that HDFC and ICICI
out performed in terms of profit when
compared with other two banks.
The empirical results had found that
there was a strong evidence between
internal and external factors on
profitability.
Sehrish Gul et.al (2011) POLS
Ganga Naidu (2012)
Return on asset,
return on equity,
return on capital
employed, net
interest margin
Compound Total expenditure,
annual growth total assets and
rate,
liabilities, interest
Coefficient of earned to total fund,
variation
interest expended to
total assets, spread as
percentage of total
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It was concluded that the ratios like
interest earned, total expenditure, net
profit to total funds have recorded low
which leads to decrease in profitability
ratios.
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An Examination of the Relationship Between Spread and Burden in Determining the Financial
Efficiency: A Study of New Generation Private Banks in India
Gurusamy (2012)
Vijay Kumar (2012)
Vincent Okoth Ongore
Gemechu Berhanu Kusa
(2013)
Iveta Repkova (2015)
Abdul Kaium Magud
(2016)
Kokobe Seyoum Alemu
Birhanv Diriba Negasa
(2016)
Serhat Yuksel
Sinemis Zengin (2017)
fund, interest earned,
non interest
expenditure, net
profit to total funds
percentage spread
Compound Working fund, total It was concluded that the selected
annual growth income, total deposit, profitability ratios are positively
rate and
total assets and net correlated with net profit
Analysis of worth
variance
CAMEL
Capital adequacy,
It was determined that State bank of
rating system asset quality,
India had higher level of capital
management,
adequacy, improvement in asset
earnings and liquidity position, management efficiency and the
ratios.
bank also excels in its liquidity position.
Regression Return on assets,
It was determined that gross domestic
return on equity, net product had a negative correlation with
interest income gross return on assets and net interest margin
domestic product and and positive correlation with return on
inflation
equity. The study also reveals that
inflation affects negatively the
profitability of commercial banks in
Kenya.
Data
Bank size, level of It was found that level of capitalization,
Envelopment capitalization, return liquid risk and portfolio risk had a
analysis (CCR on assets, credit risk positive impact on banking efficiency
& BCR
and liquid risk,
but return on assets, interest rates, gross
Model)
interest rate, number domestic product had a negative impact
of branches, gross
on CCR model. Likewise the liquidity
domestic product and risk and portfolio risk had a positive
market concentration impact on efficiency and gross domestic
product had a negative efficiency.
Trend
Deposits, loans and It was concluded that a bank with higher
analysis
advances, investment, deposits, loans and advances,
income, return on
investments, branches, employees did
assets and return on not always mean that had better
equity
profitability performance.
Regression Bank specific,
It was determined that all the selected
Industry Specific and variables affect performance of the
Macro economic
banks significantly and there is a
Variables
negative relationship between inflation
and bank financial performance.
Regression Return on assets,
There is a negative relationship between
return on equity, net non-interest income and net interest
interest margin
margin
3. DATA AND METHODOLOGY
3.1. Sample banks and data
There is strong evidence from the earlier studies there has been a significant transformation in
the structure of banking industry that too after deregulation. So it was certain to study the
banks which started its processes after deregulation. Seven new generation private banks were
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Dr. S. Sathyakala, Prof. Umaya Salma Shajahan and Dr. P. Kamalakannan
selected and for the period 2007-08 to 2016-17. The financial data were obtained from RBI
website and from its various publications.
3.2. Description of Variables
The variables used in this research for analyzing the financial performance have some
common characteristics with the variables Sensarma and Ghosh (2004), Sehrish et al (2011)
Hanumantha Rao (2011), Ganganaidu (2012). To analyze the determinants of net interest
margin in Indian private banks the ratio of net interest income to total assets is used as
dependent variable for all the periods. Likewise, for identifying the factor that affect net
interest margin, twelve variables were considered. The list of dependent and independent
variables are depicted in Table 2. The main variables are spread and burden where the former
is the difference between the average ratios of interest income to assets and the average ratios
of interest expended to liabilities. (P.R.Brahmananda, 2001) and burden is the difference
between non – interest expense and non- interest income of the bank. Generally, the higher
the Spread ratio, the higher is the profitability, other conditions being equal.
Table 2. List of Independent Variables
S.No
Independent variables
1
Spread to working fund
2
3
4
5
6
7
8
9
10
11
12
Description
This ratio enlightened the relationship between net interest
margin and the working fund of the banks during the stated
period.
Spread to total income
This ratio expresses the relationship between net interest
margin and the total income of the bank which includes
interest earnings, non-interest income and other income.
Interest earned to working fund It is defined as the relationship between interest earned and
working funds of the bank. Interest earned includes
interest and discount earned by the bank.
Interest earned to total income This ratio explains the relationship between interest earned
to total income which consists of interest income, noninterest income and other income.
Interest expended to total income This ratio shows a portion of total income used by the
bank for paying interest on deposits and interest on
advances
Interest expended to working
This ratio explains the percentage of working fund
fund
constitutes interest cost
Burden to working fund
This ratio shows the relationship between burden and
working funds of the bank.
Burden to total income
This reflects the relationship between burden and the
bank’s total income during the stated period.
Non-interest income to working This ratio expresses the relationship between non-interest
fund
income which comprises of earned commission,
brokerage, service charges and other income to working
funds of the banks.
Non-interest income to total
This ratio reveals that the percentage share of non-interest
income
income to the total income of the banks.
Non interest expenditure to
This ratio shows the relationship between the interests
working fund
spent to the working fund of the banks.
Non interest expended to total
This ratio shows the percentage of interest expended by
income
the banks from its total income.
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An Examination of the Relationship Between Spread and Burden in Determining the Financial
Efficiency: A Study of New Generation Private Banks in India
4.1.a. Regression equation
Two equations are designed to analyze the relationship of dependent variable on independent
variable. The developed regression equations for the study are:
Equation 1 is designed to analyze the relationship between spread and net interest margin.
Spread = Y = b0+b1X1+b2X2+b3X3+………..+b6X6
Where Y = Net interest margin
bo = Constant
b1, b2, b3….b6 are regression co efficient
X1,X2, X3……X6 are independent variables where X1 is spread to total income,
X2 is Spread to working fund, X3 is Interest earned to total income, X4 is Interest earned to
working fund, X5 is Interest expended to total income, X6 is Interest expended to working
fund.
Equation 2 is designed to analyze the relationship between burden and net interest margin
Burden = Y = b0+b7X7+b8X8+ b9X9 +………+b12X12
Where Y = Net interest margin
bo = Constant
b7, b8, b9….b12 are regression co efficients
X7,X8, X9……X12 are independent variables where X7 is Burden to working
fund, X8 is Burden to Total income, X9 Non-interest income to working fund, X10 is Noninterest income to total income, X11 is Non interest expenditure to working fund, X12 is Non
interest expenditure to total income.
The two equation are combined for the purpose of analysis
4. EMPIRICAL RESULTS
4.1. Results of Regression analysis
Table 1 Regression estimates of Spread on Net Interest Income
Name of the bank
Axis
DCB
ICICI
InduInd
Kotak Mahindhra
Yes
HDFC
Constant
-6.326
-7.781
9.837
14.434
6.688
-3.519
-55.430
R
0.961
0.923
0.873
0.923
0.899
0.956
0.959
R2
0.924
0.851
0.762
0.852
0.808
0.913
0.920
F– Ratio
97.263
45.782
25.595
45.982
33.742
36.848
40.112
Significance
0.00*
0.00*
0.001*
0.00*
0.00*
0.00*
0.00*
Table 1 Regression estimates of Burden on Net Interest Income
Name of the bank
Axis
DCB
ICICI
InduInd
Kotak Mahindhra
Yes
HDFC
Constant
-1.170
5.012
-1.087
-6.175
5.509
-2.996
15.375
R
0.932
0.883
0.762
0.904
0.899
0.962
0.966
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R2
0.868
0.780
0.581
0.818
0.809
0.926
0.932
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F– Ratio
23.059
28.414
11.089
35.880
33.843
43.872
48.161
Significance
0.001*
0.001*
0.010*
0.00*
0.00*
0.00*
0.00*
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Dr. S. Sathyakala, Prof. Umaya Salma Shajahan and Dr. P. Kamalakannan
5. FINDINGS
5.1. Axis bank – Spread
The resulted equation is Net Interest Income = - 6.326 +4.152* Spread to Working Fund. The
Multiple Linear Regression is found to be fit as R2 is 0.85 for Net Income. The independent
variables contribute 92 percent variation in the Net Interest Income and statistically
significant at 1 % level. It is found that Spread to Working Funds having positive association.
The resulted equation also shows that Net Interest Income is predicted by 4.152 increase of
spread to total income. Further Spread to Total Income, Interest Earned to Working Fund,
Interest Earned to Total Income, Interest Expended to Working Fund and Interest Expended
to Total Income are excluded.
5.2. Axis Bank – Burden
The resulted equation is Net Interest Income = -1.170 + 4.366 * Burden to Working Fund –
2.738 * Burden to Total Income. The Multiple Linear Regression is found to be fit as R2 is
0.86 for Net Income. The independent variables contribute 87 percent variation in the Net
Interest Income and statistically significant at 1 % level. It is found that Burden to Working
Fund and Burden to Total Income are having positive association. The resulted equation also
shows that Interest Income is predicted by 4.366 increase of Burden to Working Fund and
2.738 decrease of Burden to Total Income. Further Non-Interest Income to Working Fund,
Non-Interest Income to Total Income, Non-Interest Expenditure to Working Fund and NonInterest Expenditure to Total Income are excluded.
5.3 Development Credit Bank – Spread
The resulted equation is Net Interest Income = - 7.781+4.846* Spread to Total income. The
Multiple Linear Regression is found to be fit as R2 is 0.85 for Net Income. The independent
variables contribute 85 percent variation in the Net Interest Income and statistically
significant at 1 % level. It is found that spread to total income is having positive association.
The resulted equation also shows that Net Interest Income is predicted by 4.846 of spread to
total income. Further Spread to Working Fund, Interest Earned to Working Fund, Interest
Earned to Total Income, Interest Expended to Working Fund, and Interest Expended to Total
Income are excluded.
5.4. Development Credit Bank – Burden
The resulted equation is Net Interest Income = 5.012 – 1.567* Non-Interest Income to
Working Fund. The Multiple Linear Regression is found to be fit as R2 is 0.780 for Net
Income. The independent variables contribute 78 percent variation in the Net Interest Income
and statistically significant at 1 % level. It is found that Non-Interest Income to Working Fund
is having positive association. The resulted equation also shows that Interest Income is
predicted by 1.567 decrease of Non-Interest Income to Working Fund. Further Burden to
Working Fund, Burden to Total Income, Non-Interest Income to Total Income, Non-Interest
Expenditure to Working Fund and Non-Interest Expenditure to Total Income are excluded.
5.5. Industrial Credit and Investment Corporation of India – Spread
The resulted equation is Net Interest Income = 9.837 – 3.911* Interest Expended to Total
Income. The Multiple Linear Regression is found to be fit as R2 is 0.76 for Net Income. The
independent variables contribute 76 percent variation in the Net Interest Income and
statistically significant at 1 % level. It is found that Interest Expended to Total Income is
having positive association. The resulted equation also shows that Net Interest Income is
predicted by 3.911 decrease of Interest Expended to Total Income. Further Spread to
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An Examination of the Relationship Between Spread and Burden in Determining the Financial
Efficiency: A Study of New Generation Private Banks in India
Working Fund, Spread to Total Income, Interest Earned to Working Fund, Interest Earned to
Total Income and Interest Expended to Working Fund are excluded
5.6. Industrial Credit and Investment Corporation of India – Burden
The resulted equation is Net Interest Income = -1.087+1.520* Burden to Total Income. The
Multiple Linear Regression is found to be fit as R2 is 0.581 for Net Income. The independent
variables contribute 58 percent variation in the Net Interest Income and statistically
significant at 1 % level. It is found that Burden to Total Income is having positive association.
The resulted equation also shows that Interest Income is predicted by 1.520 increase of
Burden to Total Income. Further Burden to Working Fund, Non-Interest Income to Working
Fund, Non-Interest Income to Total Income, Non-Interest Expenditure to Working Fund and
Non-Interest Expenditure to Total Income are excluded.
5.7. Indusind Bank – Spread
The resulted equation is Net Interest Income = 14.434 – 6.213* Interest Expended to Total
Income. The Multiple Linear Regression is found to be fit as R2 is 0. 85 for Net Income. The
independent variables contribute 85 percent variation in the Net Interest Income and
statistically significant at 1 % level. It is found that Interest Expended to Total Income is
having positive association. The resulted equation also shows that Net Interest Income is
predicted by 6.213 decrease of Interest Expended to Total Income. Further Spread to
Working Fund, Spread to Total Income, Interest Earned to Working Fund, Interest Earned to
Total Income and Interest Expended to Working Fund are excluded.
5.8. Indusind Bank – Burden
The resulted equation is Net Interest Income = -6.175+4.057* Non-Interest Income to
Working Fund. The Multiple Linear Regression is found to be fit as R2 is 0.818 for Net
Income. The independent variables contribute 82 percent variation in the Net Interest Income
and statistically significant at 1 % level. It is found that Non-Interest Income to Working Fund
is having positive association. The resulted equation also shows that Interest Income is
predicted by 4.057 increase of Non-Interest Income to Working Fund. Further Burden to
Working Fund, Burden to Total Income, Non-Interest Income to Total Income, Non-Interest
Expenditure to Working Fund and Non-Interest Expenditure to Total Income are excluded.
5.9. Kodak Mahindra Bank – Spread
The resulted equation is Net Interest Income = 6.688 – 2.196* Spread to Working Fund. The
Multiple Linear Regression is found to be fit as R2 is 0. 89 for Net Income. The independent
variables contribute 90 percent variation in the Net Interest Income and statistically
significant at 1 % level. It is found that Spread to Working Fund is having positive
association. The resulted equation also shows that Net Interest Income is predicted by 2.196
decrease of Spread to Working Fund. Further Spread to Total Income, Interest Earned to
Working Fund, Interest Earned to Total Income, Interest Expended to Working Fund and
Interest Expended to Total Income are excluded.
5.10. Kodak Mahindra Bank – Burden
The resulted equation is Net Interest Income = 5.509 – 1.658 * Burden to Working Fund. The
Multiple Linear Regression is found to be fit as R2 is 0.81 for Net Income. The independent
variables contribute 81 percent variation in the Burden to Working Fund and statistically
significant at 1 % level. It is found that Burden to Working Fund is having positive
association. The resulted equation also shows that Interest Income is predicted by 1.658
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Dr. S. Sathyakala, Prof. Umaya Salma Shajahan and Dr. P. Kamalakannan
decrease of Burden to Working Fund. Further Burden to Total Income, Non-Interest Income
to Total Income, Non-Interest Income to Total Income, Non-Interest Expenditure to Working
Fund and Non-Interest Expenditure to Total Income are excluded.
5.11. Yes Bank – Spread
The resulted equation is Net Interest Income = -3.519 +5.784* Spread to Working Fund 3.052 * Interest Expended to Working Fund. The Multiple Linear Regression is found to be
fit as R2 is 0.91 for Net Income. The independent variables contribute 91 percent variation in
the Net Interest Income and statistically significant at 1 % level. It is found that Spread to
Working Fund and Interest Expended to Working Fund are having positive association. The
resulted equation also shows that Net Interest Income is predicted by 5.784 increase of Spread
to Working Fund and 3.052 decrease of Interest Expended to Working Fund. Further Spread
to Total Income, Interest Earned to Working Fund, Interest Earned to Total Income and
Interest Expended to Total Income are excluded.
5.12 Yes Bank – Burden
The resulted equation is Net Interest Income = -2.996 +4.957 * Burden to Working Fund –
2.459* Non-Interest Expenditure to Working Fund. The Multiple Linear Regression is found
to be fit as R2 is 0.92 for Net Income. The independent variables contribute 92 percent
variation in the Burden to Working Fund and Non-Interest Expenditure to Working Fund and
both the variables are statistically significant at 1 % level. It is found that Burden to Working
Fund and Non-Interest Expenditure to Working Fund are having positive association. The
resulted equation also shows that Interest Income is predicted by 4.957increase of Burden to
Working Fund and 2.459 decrease of Non-Interest Expenditure to Working Fund. Further
Burden to Total Income, Non-Interest Income to Working Fund, Non-Interest Income to Total
Income and Non-Interest Expenditure to Total Income are excluded.
5.13. Housing Development and Finance Corporation of India – Spread
The resulted equation is Net Interest Income = -55.430 -4.155* Spread to Working Fund +
32.875 * Interest Earned to Total Income. The Multiple Linear Regression is found to be fit as
R2 is 0.92 for Net Income. The independent variables contribute 92 percent variation in the
Net Interest Income and statistically significant at 1 % level. It is found that Spread to
Working Fund and Interest Earned to Total Income are having positive association. The
resulted equation also shows that Net Interest Income is predicted by 4.155 decrease of
Spread to Working Fund and 32.875 increase of Interest earned to Total Income. Further
Spread to Total Income, Interest Expended to Total Income, Interest expended to Working
Fund and Interest expended to Total Income are excluded
5.14. Housing Development and Finance Corporation of India – Burden
The resulted equation is Net Interest Income = 15.375 – 2.256 * Burden to Working Fund –
4.420* Non-Interest Income to Total Income. The Multiple Linear Regression is found to be
fit as R2 is 0.93 for Net Income. The independent variables contribute 93 percent variation in
the Burden to Working Fund and Non-Interest Income to Total Income and both the variables
are statistically significant at 1 % level. It is found that Burden to Working Fund and NonInterest Income to Total Income are having positive association. The resulted equation also
shows that Interest Income is predicted by 2.256 decrease of Burden to Working Fund and
4.420 decrease of Non-Interest Income to Total Income. Further Burden to Total Income,
Non-Interest Income to Working Fund, Non-Interest Expenditure to Working Fund and NonInterest Expenditure to Total Income are excluded.
http://www.iaeme.com/IJMET/index.asp
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editor@iaeme.com
An Examination of the Relationship Between Spread and Burden in Determining the Financial
Efficiency: A Study of New Generation Private Banks in India
6. CONCLUSIONS AND FURTHER RESEARCH
The aim of this paper was to determine the financial efficiency of new generation banks over
the period 2007-08 to 2016-17. The multiple regression analysis were employed to estimate
the relationship between Net interest margin and Spread and Burden. is statistically fit for all
the banks. The variables like Spread to working fund, Spread to total income, Burden to total
income, burden to working fund, Non-interest income to working fund, Interest expended to
total income are positively correlated with net interest margin. As far as this model is
concerned, it is statistically fit for all the banks. Finally, it would be interesting to further
study an Indian banking industry as a whole since this work is restricted to new private banks
alone.
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