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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.

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  1. International Journal of Mechanical Engineering and Technology (IJMET)
    Volume 10, Issue 03, March 2019, pp. 1713–1724, Article ID: IJMET_10_03_173
    Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3
    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
    S. Sathyakala
    Assistant Professor, Sona College of Technology, Salem

    Umaya Salma Shajahan
    Assistant Professor, Sona College of Technology, Salem

    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: S. Sathyakala, Umaya Salma Shajahan and 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(3), 2019, pp.
    1713–1724.
    http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=3

    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 safe

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  2. An Examination of the Relationship Between Spread and Burden in Determining the Financial
    Efficiency: A Study of New Generation Private Banks in India

    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 and comprises of commercial

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  3. S. Sathyakala, Umaya Salma Shajahan and P. Kamalakannan

    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

    2.2. Variables commonly used in financial efficiency

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  4. An Examination of the Relationship Between Spread and Burden in Determining the Financial
    Efficiency: A Study of New Generation Private Banks in India

    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 Determinants Result
    Goyal and Kaur (2008) CAMEL Capital adequacy, It was concluded that the performance
    asset quality, of few banks were good during the
    employee efficiency, study period.
    earning quality,
    liquidity.
    Prasad and Ravinder Turkey, HSD Net profit It was concluded that HDFC and ICICI
    (2011) test out performed in terms of profit when
    compared with other two banks.
    Sehrish Gul et.al (2011) POLS Return on asset, The empirical results had found that
    return on equity, there was a strong evidence between
    return on capital internal and external factors on
    employed, net profitability.
    interest margin
    Ganga Naidu (2012) Compound Total expenditure, It was concluded that the ratios like
    annual growth total assets and interest earned, total expenditure, net
    rate, liabilities, interest profit to total funds have recorded low
    Coefficient of earned to total fund, which leads to decrease in profitability
    variation interest expended to ratios.
    total assets, spread as
    percentage of total

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  5. S. Sathyakala, Umaya Salma Shajahan and P. Kamalakannan

    fund, interest earned,
    non interest
    expenditure, net
    profit to total funds
    percentage spread
    Gurusamy (2012) 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
    Vijay Kumar (2012) 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.
    Vincent Okoth Ongore Regression Return on assets, It was determined that gross domestic
    Gemechu Berhanu Kusa return on equity, net product had a negative correlation with
    (2013) 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.
    Iveta Repkova (2015) 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.
    Abdul Kaium Magud Trend Deposits, loans and It was concluded that a bank with higher
    (2016) analysis advances, deposits, loans and advances,
    investment, income, investments, branches, employees did
    return on assets and not always mean that had better
    return on equity profitability performance.
    Kokobe Seyoum Alemu Regression Bank specific, It was determined that all the selected
    Birhanv Diriba Negasa Industry Specific and variables affect performance of the
    (2016) Macro economic banks significantly and there is a
    Variables negative relationship between inflation
    and bank financial performance.
    Serhat Yuksel Regression Return on assets, There is a negative relationship between
    Sinemis Zengin (2017) 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
    selected and for the period 2007-08 to 2016-17. The financial data were obtained from RBI
    website and from its various publications.

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  6. An Examination of the Relationship Between Spread and Burden in Determining the Financial
    Efficiency: A Study of New Generation Private Banks in India

    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 Description
    1 Spread to working fund This ratio enlightened the relationship between net interest
    margin and the working fund of the banks during the
    stated period.
    2 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.
    3 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.
    4 Interest earned to total income This ratio explains the relationship between interest earned
    to total income which consists of interest income, non-
    interest income and other income.
    5 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
    6 Interest expended to working This ratio explains the percentage of working fund
    fund constitutes interest cost
    7 Burden to working fund This ratio shows the relationship between burden and
    working funds of the bank.
    8 Burden to total income This reflects the relationship between burden and the
    bank’s total income during the stated period.
    9 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.
    10 Non-interest income to total This ratio reveals that the percentage share of non-interest
    income income to the total income of the banks.
    11 Non interest expenditure to This ratio shows the relationship between the interests
    working fund spent to the working fund of the banks.
    12 Non interest expended to total This ratio shows the percentage of interest expended by
    income the banks from its total income.

    4.1.a. Regression equation

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  7. S. Sathyakala, Umaya Salma Shajahan and P. Kamalakannan

    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 Non-interest
    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 Constant R R2 F– Ratio Significance
    Axis -6.326 0.961 0.924 97.263 0.00*
    DCB -7.781 0.923 0.851 45.782 0.00*
    ICICI 9.837 0.873 0.762 25.595 0.001*
    InduInd 14.434 0.923 0.852 45.982 0.00*
    Kotak Mahindhra 6.688 0.899 0.808 33.742 0.00*
    Yes -3.519 0.956 0.913 36.848 0.00*
    HDFC -55.430 0.959 0.920 40.112 0.00*

    Table 1 Regression estimates of Burden on Net Interest Income
    Name of the bank Constant R R2 F– Ratio Significance
    Axis -1.170 0.932 0.868 23.059 0.001*
    DCB 5.012 0.883 0.780 28.414 0.001*
    ICICI -1.087 0.762 0.581 11.089 0.010*
    InduInd -6.175 0.904 0.818 35.880 0.00*
    Kotak Mahindhra 5.509 0.899 0.809 33.843 0.00*
    Yes -2.996 0.962 0.926 43.872 0.00*
    HDFC 15.375 0.966 0.932 48.161 0.00*

    5. FINDINGS
    5.1. Axis bank – Spread

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  8. An Examination of the Relationship Between Spread and Burden in Determining the Financial
    Efficiency: A Study of New Generation Private Banks in India

    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 Non-Interest
    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 Working Fund, Spread to
    Total Income, Interest Earned to Working Fund, Interest Earned to Total Income and Interest
    Expended to Working Fund are excluded

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  9. S. Sathyakala, Umaya Salma Shajahan and P. Kamalakannan

    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 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.

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  10. An Examination of the Relationship Between Spread and Burden in Determining the Financial
    Efficiency: A Study of New Generation Private Banks in India

    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 Non-Interest
    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 Non-Interest Expenditure to
    Total Income are excluded.

    6. CONCLUSIONS AND FURTHER RESEARCH

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  11. S. Sathyakala, Umaya Salma Shajahan and P. Kamalakannan

    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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