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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

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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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Nội dung Text: An examination of the relationship between spread and burden in determining the financial efficiency: A study of new generation private banks in India

  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 http://www.iaeme.com/IJMET/index.asp 1713 editor@iaeme.com
  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 http://www.iaeme.com/IJMET/index.asp 1714 editor@iaeme.com
  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 http://www.iaeme.com/IJMET/index.asp 1715 editor@iaeme.com
  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 http://www.iaeme.com/IJMET/index.asp 1716 editor@iaeme.com
  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. http://www.iaeme.com/IJMET/index.asp 1717 editor@iaeme.com
  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 http://www.iaeme.com/IJMET/index.asp 1718 editor@iaeme.com
  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 http://www.iaeme.com/IJMET/index.asp 1719 editor@iaeme.com
  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 http://www.iaeme.com/IJMET/index.asp 1720 editor@iaeme.com
  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. http://www.iaeme.com/IJMET/index.asp 1721 editor@iaeme.com
  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 http://www.iaeme.com/IJMET/index.asp 1722 editor@iaeme.com
  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. REFERENCES [1] Abdul Kaium Masud (2016), Financial soundness measurement and trend analysis of commercial banks in Bangladesh: An observation of selected banks, European Journal of Business and Social sciences, Vol 4 No 10, pp 159-184 [2] Aggarwal, Priyanka (2016) An Empirical evidence of determining profitability indicators in the Indian public sector banks, International Journal of Economic Perspectives, Vol 10, No 2 pp -93-101 [3] Brahmananda (2001), Spread ratio in public sector banks, The Hindu, Business Line, December 9 pp 12 [4] Ganganaidu (2012) A study of financial performance of reputed public bank in India during 2006-2010, Asia Pacific Journal of Marketing and Management Review, Vol 1 No 3, pp 82-101 [5] Gurusamy (2012), Analysis of profitability performance of SBI and its associates, Zenith International Journal of Business economics and Management Research, Vol 2 No 1, pp 105 -125 [6] Hanumantha Rao (2011) Spread analysis of Indian banks for the period 2006 -2011, Paradigm, Vol XV, No 1and 2, pp 26-33 [7] Iveta Repkova (2015), Banking efficiency determinants in the Czech banking sector, Procedia Economics and Finance, 23. pp 191-196 [8] Kanhaiya Singh, Vinay Dutta 2013), Commercial Bank Management, McGraw Hill education Pvt Ltd, 1st Edition [9] Kokobe Seyoum, Alemu, Birhanu diriba negasa (2016), Determinants of financial performance of commercial banks in Ethiopia, Account and Financial Management, Vol 1, pp – 11-24 [10] Klimberg et al (2009), Proceedings for the North East region, Decision Science Institute, pp 514-519 [11] Krishna, Kavitha (2017), An analysis of the financial performance of Indian commercial banks, The IUP Journal of Bank Management, Vol XVI, No 1, pp 7-26 [12] Paneer Selvam, Radjaramane (2010), An analysis of financial performance of nationalized Banks in India: A post liberalization analysis, International Journal of Current Research, Vol 4, No 01, pp 262-267 [13] Prasad, Ravinder (2011), Performance evaluation of banks: A comparative study on SBI, PNB, ICICI and HDFC, Advances in Management, Vol 4, No 2, pp – 42-53 [14] Rahman, Bukair (2013), The influence of the shariah supervision board on corporate social responsibility disclosure by Islamic banks of gulf cooperation council countries, Asian Journal of Business and Accounting, Vol 6 No 2, pp 65-104 [15] Richa Verma, Bodla.B.S (2011), Evaluating performance of banks through CAMEL model: A case study of SBI and ICICI Decision, Vol, 38 No 1 pp 49-63 http://www.iaeme.com/IJMET/index.asp 1723 editor@iaeme.com
  12. An Examination of the Relationship Between Spread and Burden in Determining the Financial Efficiency: A Study of New Generation Private Banks in India [16] Rani S.Ladha (2017), Merger of Public sector banks in India under the rule of reason, Journal of emerging Market and Finance, Vol 16, No 3, pp 259-273 [17] Reserve Bank of India (2010-2017), Report on Trend and Progress of Banking in India, Mumbai [18] Sehrish Gul, Faiza Irshad, Khalid Zaman (2011), Factors affecting bank profitability in Pakistan, The Romanian Economic Journal Year, Vol XIV, No 39, pp 177-182 [19] Serhat Yuksel, Sinemis Zengin (2017), Influencing factors of Net interest margin of Turkish banking sector, International Journal of Economics and Financial Issues, Vol 7 No 1, pp 178-191 [20] Seema Sant, Chaudhari (2012), A study of the profitability of urban cooperatives banks, Zenith International Journal of Multidisciplinary Research, Vol 2, No 5, pp 124-129 [21] Shefali Verma, Rita Goyal, Priya Jindal 2013, Profitability of Commercial banks after the reforms: A study of selected banks, International Journal of Research in Finance and Marketing, Vol 3, No 2 [22] Sreekala, Shanthi, Senthilkumar (2016), Evaluating the financial health of selected commercial banks in Indian banking sector, International Journal of Advanced Engineering Technology, Vol VII, No 1, pp 38-45 [23] Subramnayam, Venkateswarlu (2012), Financial performance of Scheduled commercial banks in India – A study, Paripex – Indian Journal of Research, Vol 1, No 12, pp 17-20 [24] Vaidyanathan, Credit risk management for Indian Banks, 2013, Sage publications, 1st Edition [25] Vijayakumar (2012), Evaluating performance of banks through CAMEL model – A case study of State bank of India and its associates, Online International Interdisciplinary Research Journal, Vol II, No VI [26] Vincent Okoth Ongore, Gemechu Berhanu Kusa (2013), Determinants of financial performance of commercial banks in Kenya, International Journal of Economics and Financial Issues, Vol 3, No 1 pp – 237-252 [27] Thangasamy (2014), Financial health of state bank of India: A diagnostic study, International Journal of Business and Commerce, Vol 3 No 10, pp 51-68 http://www.iaeme.com/IJMET/index.asp 1724 editor@iaeme.com
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