An Analysis of Determinants of Profitability in Public and Private Sector Banks in India

An Analysis of Determinants of Profitability in Public and Private Sector Banks in India Mrs. Somanadevi Thiagarajan Ph.D. Scholar, Management Sciences, Anna University of Technology, Coimbatore, India Lecturer (on leave) Faculty of Management, University of Belize, Belize Dr. S. Ayyappan Associate Professor in MBA Sakthi Institute of Information and Management Studies Pollachi -642001, India Dr. A. Ramachandran Director, SNR Institute of Management Sciences, SNR Sons College, Coimbatore, India Mr. M. Sakthivadivel Anna University of Technology Coimbatore, India Abstract An analysis was carried out to empirically evaluate the determinants of profitability in the public and private sector commercial banks in India. A combination of statistical tools such as the correlation analysis, multiple regression analysis and factor analysis were used to estimate the contribution of select bank specific variables towards profitability which was measured by using Return on Assets (RoA). The study revealed that the cost of borrowing and NPA has a significant negative correlation with profitability for public sector banks. Return on investments, return on advances and operating profit had a significant positive correlation with profitability for both public and private sector banks. The multiple regression analysis highlighted that the return on investments and return on advances has a significant influence on the profitability of private sector banks. The factor analysis has also shown that the NPA has a strong negative influence on the profitability for both public and private sector banks. Key Words: Profitablity, Public Sector Banks, Private Sector Banks, Determinants. Volume:01, Number:06, Oct-2011 Page 140

1. Introduction In the first half of the 19 th century, the British East India Company established three banks namely the Bank of Bengal in 1809, the Bank of Bombay in 1840 and the Bank of Madras in 1843. These three banks, also known as Presidency Banks, were independent units and functioned well. It was, however, considered that it would be in the interest of these banks and the country that they should be amalgamated. In 1920 was passed the Imperial Bank of India Act was passed amalgamating these three banks. The Imperial Bank of India was established in 1921. The Bank was authorized to hold Government balances and manage public debt. It was not however, given powers to issue currency notes. The issuing of the currency continued to be a close preserve of the Government of India. In the wake of the Swadeshi Movement, a number of banks with Indian management were established in the country. The Punjab National Bank was founded in 1895; The Bank of India Ltd in 1906; The Canara Bank Ltd in 1906; The Indian Bank Ltd in 1907; The Bank of Baroda Ltd in 1908; and the Central Bank of India in 1911. There have been a number of set-backs to the banking industry in the form of bank failures during the last 100 years. The series of bank crises particularly during the periods 1913-1917 and 1931-1938 wiped out many weak banks. The Reserve Bank of India was constituted in 1935 under the Reserve Bank of India Act, 1934, to regulate the issue of bank notes and the keeps of reserves with a view to securing monetary stability in India and generally to operate the currency and credit system of the country to its advantage. The bank is performing a number of functions as a central banking authority including the issue of bank notes. Under the Banking Regulation Act, 1949, (previously known as the Banking Companies Act, 1949), the bank is vested with large powers of supervision, control, direction and inspection of scheduled and non-scheduled banks. The banking sector in India has undergone remarkable changes. In 1969, 14 major banks were nationalized and in 1980, 6 major private sector banks were taken over by the government. Nationalization of commercial banks in 1968 and 1980 was a mixed blessing to the Indian banking sector. After nationalization, there was a shift of emphasis from industry to agriculture. The country witnessed rapid expansion in bank branches, even in rural areas. Banking development in India after nationalization was wonderful and received global compliments. The commercial banking system gained substantial strength to improve nation building programs. However, the nationalization process created its own problems such as excessive bureaucratization, and disruptive tactics of trade unions by bank employees. Reforms in the commercial banking sector have two distinct phases. The first phase of reforms introduced subsequent to the release of the Report of the Committee on Financial System (also known as the Narasimhan Committe), in 1992 which focused mainly on enabling and strengthening measures of the banking sector. The second phase of reforms, introduced subsequent to the recommendations of the Committee on Banking Sector Reforms in 1998 placed greater emphasis on structural measures and an improvement in standards of disclosure and levels of transparency. The main objectives of the reform measures were to align the Indian standards with the best international practices. These reforms have resulted in considerable improvements, as reflected in various parameters relating to capital adequacy, asset quality, profitability and operational efficiency of the Indian banking sector. Given the historical journey of Indian banking sector, the current study is undertaken to analyse the bank specific variables influencing the profitability of public and private sector commercial banks in India during the post reform period (2000 2010). Volume:01, Number:06, Oct-2011 Page 141

2. Review of Literature Report of the committee on productivity, efficiency, and profitability in banking (1977) set up by the Reserve Bank of India stressed the need for adopting planning and budgeting in banks and stated "The performance budget helps the management to proceed along the projected goals and the performance evaluation at monthly or quarterly intervals indicates the deviations and corrective actions that should be initiated. The committee analysed the various issues related to the planning, budgeting and marketing in commercial banks, bank management information system, criteria for evaluation of bank performance, annual accounts of banks, trends in earnings and expenses of banks, and profitability as well as pricing of banking services. Seshadri (1981) conducted a research study in which she selected 14 public sector banks and 13 private sector banks as scope of the study. Some peculiar features of this study are the assessment of temporal behaviour of selected variables for growth analysis and the use of suitable techniques to evaluate the economies of scale in banking industry. The study brings out that the profitability ratios have been higher for the selected group of private sector banks than for the nationalised banks and this is so in spite of the fact that the private banks had a higher proportion of establishment cost. The study also concluded that the private sector banks have taken banking service to a large number of centers and competed well with the public sector banks in spite of the inherent advantages the public sector banks. Verghese (1983) conducted a detailed study on profits and profitability of commercial banks during the decade 1970-79. They reported the reasons for the decline the profits and profitability of Indian Commercial Banks in the seventies and also highlighted the main determinants of profits and profitability of the Indian banks during this period. Raut and Das (1996) have attempted to examine, measure and analyse the profitability trends of the Indian banking sector over the period 1980-92. They have highlighted various factors responsible for the variations in banks profitability in either direction. They have also incorporated empirical analysis of profitability as well as of its determinants of the sample bank groups. Chen (2002) assessed the management performance of banks in Taiwan by incorporating operating efficiency, marketing efficiency and financial performance in the study. He reported that the banks with public ownership exhibited superior profitability performance, whereas privately owned banks tend to perform better with regard to operational capabilities. Furthermore, the relatively large banks exhibited superior performance on profitability, whereas the smaller ones tend to perform better with regard to operational capabilities. Bodla and Verma (2006) studied the determinants of profitability of public sector banks in India by using a multivariate analysis for the period from 1992 to 2004 and reported that non-interest income, operating expenses, provisions and contingencies and spread have significant influence on the profitability of the public sector banks. Chen and Lin (2007) while analyzing the efficiency of Australian banks for a period from 1996 to 2004, reported that return on assets (RoA) is an important financial factor affecting positively the performance of Australian banks. They have also noted that Australian banks showed better operational efficiency than their American counterparts for the period 2001-2004. Sufian (2009) examined the determinant of bank profitability in Malaysian commercial banks and reported that Malaysian with higher credit risk and higher loan concentration exhibit lower profitability level. They also revealed that banks with higher level of capitalisation and higher proportion of income from non interest sources and higher operating cost tend to exhibit Volume:01, Number:06, Oct-2011 Page 142

higher profitability level. They also suggested that there was an inverse relationship between economic growth and profitability in Malaysian banks and a positive relationship between inflation and profitability. 3. Methodology 3.1 Data The data for the study have been collected mainly from the secondary sources comprising various audited reports and publications of the Reserve Bank of India. Detailed information were collected mainly from the various volumes of the Statistical Tables Relating to Banks in India covering the period from 2000-2010 which were published by the Statistical Department of Reserve Bank of India, Mumbai from the website www.rbi.org.in. The concepts and definitions and data for certain macroeconomic and bank specific variables were gathered from the Report on Trend and Progress of Banks in India various issues covering the period from 2000-2010 which were published by the Statistical Department of RBI, Mumbai, RBI Bulletins (Monthly), Bombay Stock Exchange Official Directory, etc. In view of the problem and the scope of the study, we included all public and private sector Indian scheduled commercial banks functioning in India for the financial period from 2000-01 to 2009-2010 that were listed in Bombay Stock Exchange and had data for the entire period of study. The banks were grouped into two categories: i.e., Public Sector Banks Group (22 Banks) and Private Banks Group (15 Banks). The detailed list of banks selected under each group is as follows: 3.2. Public Sector Banks 1) Allahabad Bank; 2) Andhra Bank; 3) Bank of Baroda; 4) Bank of India; 5) Bank of Maharashtra; 6) Canara Bank; 7) Central Bank of India; 8) Corporation Bank; 9) Dena Bank; 10) Indian Bank; 11) Indian Overseas Bank; 12) Oriental Bank of Commerce; 13) Punjab National Bank; 14) State Bank of Bikaner and Jaipur 15) State Bank of India; 16) State Bank of Mysore; 17) State Bank of Travancore; 18) Syndicate Bank; 19) UCO Bank; 20) Union Bank of India; 21) United Bank of India; 22) Vijaya Bank. 3.3. Private Sector Banks 1) Axis Bank; 2) Bank of Rajasthan; 3) City Union Bank; 4) Development Credit Bank; 5) Dhanalakshmi Bank; 6) Federal Bank; 7) HDFC Bank; 8) ICICI Bank; 9) IndusInd Bank; 10) ING Vysya Bank; 11) Jammu and Kashmir Bank; 12) Karnataka Bank; 13) Karur Vysya Bank; 14) Lakshmi Vilas Bank; 15) South Indian Bank. 3.4. The Variables The performance of a bank can be measured by a number of indicators. Among these, profitability is the most important and reliable indicator as it gives a broad indication of the capability of a bank to increase its earning. An analysis was carried out to identify the extent of influence of the factors on the profitability of the scheduled commercial banks. For the purpose of applying the multivariate techniques, the ratio of Return on Assets is taken as dependent variable (Y) and the following 23 variables are considered as independent variables. Volume:01, Number:06, Oct-2011 Page 143

X 1 - Cash to deposit ratio; X 2 - Credit to deposit ratio; X 3 - (Credit+ Investment) to deposit ratio X 4 - Ratio of term deposits to total deposits; X 5 - Ratio of priority sector advances to total advances; X 6 - Ratio of term loan to total advances; X 7 - Ratio of interest income to total assets; X 8 - Ratio of net interest margin to total assets; X 9 -Ratio of non -interest income to total assets; X 10 - Ratio of wage bills to total expenses; X 11 - Ratio of burden to total assets; X 12 - Ratio of operating profit to total assets; X 13 - Return on equity; X 14 - Cost of deposits; X 15 - Cost of borrowings; X 16 - Return on advances; X 17 - Return on investments; X 18 - Business per employee; X 19 - Profit per employee; X 20 - Capital adequacy ratio; X 21 - Ratio of net NPA to net advances; X 22 - Return on Net worth; X 23 - Provision and Contingencies to total assets. The Tables 1a and 1b show that most variables show similar trend for the public and private sector banks. However variables X 18 (Business per employee) and X 19 (Profit per employee) are higher for the private sector for most years under study. The variable X 10 (wages to total expenses) and X 22 (return on Net worth) has been higher for public sector banks over their private sector counterpart. 3.5. The Data Analysis To identify the prominent factors responsible for the profitability of scheduled commercial banks and to measure the extent of influence of the independent variables on the dependent variable the following tools were used: a) Correlation Analysis b) Multiple Regression Analysis and c) Factor Analysis The data of selected variables have been pooled together for each of the groups of banks. The basic idea underlying the pooling of the data is to make the data more representative. Therefore, it is desirable to mitigate the effect of such fluctuations by having more information, spread over equally. Moreover, pooling has been done to have more number of observations, which is required to avoid any problem associated with lesser degrees of freedom. Normally, pooling of the data is considered helpful from a statistical point of view in the sense that it leads to larger samples, and therefore, to more reliable results. 4. Results and Discussion 4.1. Correlation Analysis Correlation analysis attempts to study the relationship that exists between two variables. The correlation co-efficient of the selected independent variables with the bank profitability has been worked out in order to identify the most important variable, which have relationship with the dependent variable. Also, the correlation co-efficient among the different variables has been worked out so as to arrive at a correlation matrix, which incorporates correlation coefficient of all the selected variables with the dependent variable, as well as correlation coefficients among different independent variables. The calculated correlation co-efficient Volume:01, Number:06, Oct-2011 Page 144

values were compared with a critical value of simple correlation co-efficient available in the statistical tables for its significance. The correlation coefficient matrices of the selected variables with the dependent variable, i.e., return on total assets of public and private sector banks for the periods from 2000-01 to 2009-10 are given in Table 1. For public sector banks, five variables namely X 12 (Ratio of operating profit to total assets), X 16 (Return on advances), X 17 (Return on investments), X 20 (Capital adequacy ratio) and X 22 (Return on Net worth) have significant positive correlation with bank profitability. Other two variables namely X 15 (Cost of borrowings) and X 21 (Ratio of net NPA to net advances) have significant but negative correlation with bank profitability. Out of these, the relationship of X 16 and X 20 is very high (0.754 and 0.752). In private sector banks variables namely X 1 (Cash to deposit ratio), X 9 (Ratio of non -interest income to total assets), X 12 (Ratio of operating profit to total assets), X 16 (Return on advances), X 17 (Return on investments), and X 19 (Profit per employee) have significant positive correlation with bank profitability. Out of these, the relationship of X 12 and X 17 is very high (0.847 and 0.831). Table 1: Correlation Analysis between Return on Assets (RoA) and Selected Variables. S.No. Ratio of PUBLIC SECTOR BANKS PRIVATE SECTOR BANKS r p- value r p-value X 1 Cash to deposit ratio -.365.150.550.050* X 2 Credit to deposit ratio.265.229.034.463 X 3 (Credit+ Investment) to deposit ratio.520.062 -.103.388 X 4 Ratio of term deposits to total deposits -.131.359 -.195.295 X 5 Ratio of priority sector advances to total advances.374.143.300.200 X 6 Ratio of term loan to total advances.453.094.378.141 X 7 Ratio of interest income to total assets -.524.060.472.084 X 8 Ratio of net interest margin to total assets -.087.406.442.101 X 9 Ratio of non -interest income to total assets.417.115.790.003** X 10 Ratio of wage bills to total expenses -.204.286 -.266.229 X 11 Ratio of burden to total assets -.501.070 -.131.359 X 12 Ratio of operating profit to total assets.854.001**.847.001** X 13 Return on equity.429.108.534.056 X 14 Cost of deposits -.513.065 -.222.269 X 15 Cost of borrowings -.774.004** -.322.182 X 16 Return on advances.754.006**.826.002** X 17 Return on investments.682.015**.831.001** X 18 Business per employee.302.198.391.132 X 19 Profit per employee.511.066.765.005** X 20 Capital adequacy ratio.752.006**.427.109 X 21 Ratio of net NPA to net advances -.576.041* -.400.126 X 22 Return on Net worth.991.000**.503.069 X 23 Provision and Contingencies to total assets.369.147.274.222 **Correlation is significant at the 0.01 level (p<0.01) *Correlation is significant at the 0.05 level (p<0.05) Volume:01, Number:06, Oct-2011 Page 145

4.2. Multiple Regression Analysis Multiple regression co-efficient analysis measures separately the relationship between two variables in such a way that the effects of other related variables are eliminated. In other words, it measures the relation between a dependent variable and a particular independent variable by holding all other variables constant. Each multiple regression co-efficient measures the effect of its independent variable on the dependent variable. The results for the multiple regression analysis for the public and private sector banks for the periods from 2000-01 to 2009-10 are given in Table 2 and 3 respectively. Table 2. Multiple Regression Analysis of the Selected Variable with the ratio of Return on Assets for Public Sector Banks. Multiple S.No. Ratio of Regression t value p-value Co-efficient X 1 Cash to deposit ratio.035.666.526 X 2 Credit to deposit ratio -.055-1.161.284 X 3 (Credit+ Investment) to deposit ratio -.064-1.170.280 X 4 Ratio of term deposits to total deposits -.055-1.238.256 X 5 Ratio of priority sector advances to total advances.020.391.708 X 6 Ratio of term loan to total advances -.059-1.123.298 X 7 Ratio of interest income to total assets.060 1.076.318 X 8 Ratio of net interest margin to total assets.047 1.002.350 X 9 Ratio of non -interest income to total assets.073 1.635.146 X 10 Ratio of wage bills to total expenses.037.760.472 X 11 Ratio of burden to total assets.047.835.431 X 12 Ratio of operating profit to total assets.110 1.424.198 X 13 Return on equity -.075-1.546.166 X 14 Cost of deposits.022.390.708 X 15 Cost of borrowings.060.762.471 X 16 Return on advances.105 1.844.108 X 17 Return on investments -.053 -.780.461 X 18 Business per employee -.069-1.501.177 X 19 Profit per employee -.072-1.365.214 X 20 Capital adequacy ratio -.087-1.182.276 X 21 Ratio of net NPA to net advances.053.906.395 X 22 Return on Net worth.066 1.449.191 X 23 Provision and Contingencies to total assets.991 21.526.000** R 2 = 0..983; R = 0..991; F-value 463.368; **significant at 1% level. The following equation has been fitted to estimate the ratio of return on total assets for public sector banks: Y = -40.4896 +0.035 X 1 -.055 X 2 0.064 X 3-055 X 4 +.02X 5 -.059 X 6 + 0.06 X 7 + 0.047 X 8 + 0.073 X 9 + 0.037X 10 + 0.047 X 11 +.110 X 12 0.075 X 13 +0.022 X 14 +0.06 X 15 + 0.105 X 16 0.053 X 17-0.069 X 18 -.072 X 19 -.087 X 20 + 0.053X 21 + 0.066X 22 + 0.991X 23 Volume:01, Number:06, Oct-2011 Page 146

An insight into the public sector banks (Table 2) reveals that the multiple regression coefficient of the one variable with the ratio of return on total assets are significant. The calculated t values are significant for the variable Provision and Contingencies to Total Assets (X 23 ) when the other variables are kept constant:. It is indicating that the one factor, individually contribute significantly to variations in the ratio of return on total assets when the influence of other variables are kept constant. The R 2 value in terms of these variables is 0. 983. Table 3. Multiple Regression Analysis of the Selected Variable with the ratio of Return on Assets for Private Sector Banks. Multiple S.No. Ratio of Regression t value p-value Co-efficient Y Return on assets -4.891.003 X 1 Cash to deposit ratio -.034-1.111.317 X 2 Credit to deposit ratio.002.049.963 X 3 (Credit+ Investment) to deposit ratio -.001 -.027.980 X 4 Ratio of term deposits to total deposits.000 -.004.997 X 5 Ratio of priority sector advances to total -.061 -.916.402 advances X 6 Ratio of term loan to total advances.183 4.085.006** X 7 Ratio of interest income to total assets.059.918.401 X 8 Ratio of net interest margin to total assets.033.864.427 X 9 Ratio of non -interest income to total assets.039.942.389 X 10 Ratio of wage bills to total expenses.018.689.521 X 11 Ratio of burden to total assets.019.400.706 X 12 Ratio of operating profit to total assets.022.338.749 X 13 Return on equity -.003 -.074.944 X 14 Cost of deposits -.053-1.711.148 X 15 Cost of borrowings.044.586.583 X 16 Return on advances.692 20.951.000** X 17 Return on investments.430 8.996.000** X 18 Business per employee -.040-1.130.310 X 19 Profit per employee.010.340.748 X 20 Capital adequacy ratio.084.983.371 X 21 Ratio of net NPA to net advances -.051-1.166.296 X 22 Return on Net worth.025 1.049.342 X 23 Provision and Contingencies to total assets -.035 -.688.522 R 2 =0. 997; R = 0..999; F-value 758.09; **significant at 1% level. The following equation has been fitted to estimate the ratio of return on total assets for private sector banks: Y = -36.6362-0.034 X 1 +0.002 X 2 0.001 X 3 + 0.0001 X 4-0.061 X 5 +.183 X 6 + 0.059 X 7 + 0.033 X 8 + 0.039 X 9 +0.018 X 10 +0.019 X 11 +0.022 X 12 0.003X 13 0.0.053 X 14 + 0.044 X 15 + 692 X 16 + 0.430 X 17-0.04 X 18 + 0.01 X 19 +0.084 X 20-0.051X 21. + 0.025X 22-0.035X 23 Volume:01, Number:06, Oct-2011 Page 147

An insight into the private sector banks (Table 3) reveals that the multiple regression coefficient of the three variables namely ratio of term loan to total advances (X 6 ), Return on advances (X 16 ) and return on investments (X 16 ) with the Return on Assets are significant (Table 3) indicating that these three factors, individually contribute significantly to variations in the ratio of net profit to working funds when the influence of other variables are kept constant. The calculated R 2 value in terms of these variables is 0.997. 4.3. Factor Analysis The procedure of factor analysis attempts to estimate the value for the coefficients of regression when the variables are regressed upon the factors. These coefficients are referred to as factor loading. The matrix of factor loadings provides the basis for grouping the variables into common factors. Each variable is assigned to the factor, where it has the highest loading. The Varimax Rotation method is used in factor analysis. Table 4 presents the factor loading of the selected variables of public sector banks for the period from 2000-01 to 2009-10. The columns in the table stand for different factors and the numbers in each row represent regression weights. More specially, the basic factor postulate can be written as below: Z j = a j1 F 1 + l j2 F 2 + a jm F m + d j U j where Z stands for a variable, F for common factors, U for a unique factor and a jm and d j for regression weights. For example, the first row may be written in the regression formula as: Z = 0.537F 1 + 0.023F 2 + 0.814F 3 It is clear from Table 5 that the important determinant of Factor 1 is X 21 (Ratio of net NPA to net advances), and its influence on the other common factors is very less. Likewise, the other significant variables in Factor I are X 6 (Ratio of term loan to total advances), X 7 (Ratio of interest income to total assets), X 3 ((Credit+ Investment) to deposit ratio), X 17 (Return on investments), X 2 (Credit to deposit ratio), X 13 (Return on equity), X 19 (Profit per employee), X 15 (Cost of borrowings), X 18 (Business per employee), X 20 (Capital adequacy ratio) and X 14 (Cost of deposits). In regression equation, the hypothetical factors are said to control and account for a certain proportion of the variations in the variable set. Table 4. Factor Loading for Public Sector Banks of the Measurement Scale Items on Extracted Factors. Variables Factor I Factor II Factor III C 2 X21 -.992.056 -.037 0.989 X6.991.040 -.100 0.994 X7 -.967.181 -.028 0.969 X3.960.181 -.007 0.954 X17.915.271.165 0.938 X2.905.243 -.301 0.969 X13.885.449 -.049 0.987 X19.868.466.074 0.976 X15 -.838.060 -.448 0.907 X18.823.530 -.161 0.984 X20.715.198.547 0.850 x14 -.714.654 -.191 0.974 Volume:01, Number:06, Oct-2011 Page 148

X4.105.936 -.221 0.936 X5.398 -.888.200 0.987 X8 -.479 -.839.236 0.989 X10 -.485 -.835.139 0.952 X11 -.676 -.717 -.133 0.989 X16 -.122 -.138.961 0.957 X12.191 -.307.928 0.992 X9 -.457 -.095.847 0.935 y.537.023.814 0.951 X22.585.046.786 0.962 X23 -.341 -.607.675 0.940 X1.042.364 -.586 0.478 Eigan values 12.510 7.096 2.949 Variance (in %) 52.126 29.567 12.288 Cumulative Eigan values (in %) 52.126 81.694 93.981 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. The importance of a given variable can exactly be expressed in terms of the variations in the variable that can be accounted for by the factor. For instance, the variations of X 21 accounted for by Factor I are: a 2 21 = (-0.992) 2 = 0.9841 that is 98.41 per cent of total variations in X 12 are accounted by Factor I. Similarly, it is seen that nearly 98.21 per cent, 93.51 per cent, 92.16 per cent, 83.72 per cent, 81.9 per cent, 78.32 per cent, 75.34 per cent, 70.22 per cent, 67.73 per cent, 51.12 per cent and 50.98 per cent variations in X 6,X 7, X 3,X 17,X 2,X 13,X 19,X 15,X 18,X 20 and X 14 respectively are explained by Factor I and 28.84 per cent of the variations in the profitability (Y) are explained by Factor I. This shows that though Factor I is an important factor as far as explaining the variations in twelve variables namely X 21, X 6,X 7, X 3,X 17,X 2,X 13,X 19,X 15,X 18,X 20 and X 14 are concerned but in terms of profitability, its explanation is fairly low. But all the three derived factors taken together could explain; (0.537) 2 + (0.023) 2 + (814) 2 = 95.15 per cent of the variations in the profitability of banks. This shows that no individual factor can be solely responsible for the variations in the profitability of public sector banks but it is the combinations of different factors which are associated with the profitability. Factor II, it is seen that the 87.61 per cent, 78.85 per cent, 70.39 per cent, 69.72 per cent and 51.41 per cent of total variations in X 4 (Ratio of term deposits to total deposits), X 5 (Ratio of priority sector advances to total advances), X 8 (Ratio of net interest margin to total assets), X 10 (Ratio of wage bills to total expenses) and X 11 (Ratio of burden to total assets) respectively. Similarly, X 16 (Return on advances) has relatively high factor loading with Factor III and all the three together could explain nearly 95.74 per cent of the variations in X 16. The c 2 represent the communalities column. This is the amount of variance a variable shares with all other variables being considered with all the variables. Table 5 shows the factors loadings of the selected variables of private sector banks for the period from 2000-01 to 2009-10. It can be observed from the above table 90.06 per cent of Volume:01, Number:06, Oct-2011 Page 149

total variation in X 6 (Ratio of term loan to total advances) is accounted by Factor I. Similarly, it is seen that nearly 90.06 per cent, 79.92 per cent, 77.09 per cent, 68.89 per cent, 64.16 per cent, 48.30 per cent and 43.034 per cent variations in X 21 (Ratio of net NPA to net advances), X 14 (Cost of deposits), X 5 (Ratio of priority sector advances to total advances), X 4 (Ratio of term deposits to total deposits), X 17 (Return on investments) and X 2 (Credit to deposit ratio) respectively and 14.06 per cent of the variations in the return on total assets (Y) are explained by Factor I. This shows that though Factor I is an important factor as far as explaining the variations in sixteen variables namely, X 21 (Ratio of net NPA to net advances), X 14 (Cost of deposits), X 5 (Ratio of priority sector advances to total advances), X 4 (Ratio of term deposits to total deposits), X 17 (Return on investments) and X 2 (Credit to deposit ratio) respectively. Factor I in term of return on total assets is quite low. But all the five derived factors taken together explain 98.95 per cent variations in the return on total assets of bank. It is observed that no individual factor can be solely responsible for the variations in the return of bank; it is the combinations of different factors which are associated. Factor II, it is seen that the 90.25 per cent, 61.47 per cent, 58.98 per cent, 57.30 per cent, 48.02 per cent and 35.76 of total variations in X 20 (Capital adequacy ratio), X 1 (Cash to deposit ratio), X 13 (Return on equity), X 19 (Profit per employee), X 18 (Business per employee) and X 12 (Ratio of operating profit to total assets) respectively. Similarly, X 16 (Return on advances) has relatively high factor loading with Factor III and all the four together could explain nearly 81.36 per cent of the variations in X 16. Next, X 23 has relatively high factor loading with Factor III and all the four together could explain nearly 75.34 per cent of the variations in X 23. Finally, X 3 (Credit+ Investment) to deposit ratio) has relatively high factor loading with Factor V and all the five factors together could explain nearly 96.63 per cent of the variations in X 3. The c 2 represent the communalities column. This is the amount of variance a variable shares with all other variables being considered with all the variables to the extent of more than 69 percent. The importance of a given variable can exactly be expressed in terms of the variations in the variable than can be accounted for by the factor. Table 5. Factor Loading for Private Sector Banks of the Measurement Scale Items on Extracted Factors. Variables Factor I Factor II Factor III Factor IV Factor V C 2 X6.949.228 -.086 -.026 -.181 0.993 X21 -.894 -.203 -.014 -.063.381 0.990 x14 -.878.274 -.167.343.023 0.992 X5.830.386 -.293 -.061.189 0.963 X4 -.801 -.337.275.333 -.134 0.960 X17.695.588.253.270.182 0.999 X2.656.221 -.361.123 -.610 0.997 X20.138.950 -.099 -.074 -.100 0.947 x1.096.784.094.205.123 0.690 X13.605.768 -.135.044.105 0.987 X19.097.757.370.417 -.104 0.904 X18.631.693 -.304.086.071 0.983 X12.343.598.456.459.023 0.894 X16 -.142.292.902.212.155 0.988 X22 -.187 -.242.877.176.195 0.932 X15.125.166 -.775.391.121 0.811 Volume:01, Number:06, Oct-2011 Page 150

Y.375.495.716.258.157 0.990 X9 -.009.231.713.604 -.005 0.927 X11.578.082 -.670.062.396 0.950 X23.022.210 -.115.868 -.039 0.813 X10.282.059 -.316 -.842.112 0.904 X3.097.056 -.060.086 -.983 0.990 X8.486.513 -.128 -.056.666 0.962 X7 -.243.375.161.613.623 0.989 Eigan values 9.311 6.358 2.928 2.551 1.406 Variance (in %) 38.794 26.491 12.199 10.630 5.858 Cumulative Eigan 38.794 65.285 77.485 88.115 93.973 values (in %) Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. The study reveals that there three variables namely X 12 (Ratio of operating profit to total assets), X 16 (Return on advances), and X 17 (Return on investments) had positive correlation with the profitability for both public and private sector banks. The two variables namely X 15 (Cost of borrowings) and X 21 (Ratio of net NPA to net advances) have significant but negative correlation with the profitability of public sector banks. Bodla and Verma (2006) reported that the Non Performing Loans had a negative influence on the profitability of the public sector banks in India. The regression study for private sector banks reveals that the three variables namely ratio of term loan to total advances (X 6 ), Return on advances (X 16 ) and return on investments (X 16 ) with the Return on Assets have significant positive influence on the profitability of this sector. The factor analysis highlights that the ratio of net NPA to net advances (X 21 ) had a major negative contribution to the profitability of the public sector banks (-.992) where as the ratio of term loans to total advances (X 6 ) was a major positive factor (-.991) in determining the profitability of the public sector banks. For private sector banks also a similar effect was seen where the ratio of term loans to total advances (X 6 ) was accounted as Factor I (.949) followed by the ratio of net NPA to net advances (X 21 ) which had a major negative contribution (-894). Given the recent trend in the gradual increase in NPA in the commercial banking sector (Thiagarajan et al. 2011), we can expect the impact of the NPA on the profitability in the coming years. Studies also reveal that credit risk is negatively correlated with the profitability of the banks (Duca and McLaughlin 1990). Based on the Factor analysis it is reveled that the ratio of term loans to total loans has a strong positive influence in determining the profitability in both public and private sector banks. The reason could be that the short term loans give more return and less default. Other critical factors that can improve profitability in both public and private sector are the return on advances and return on investments. Although X 19 (Profit per employee) was higher for the private sector for most years under study and showed a significant positive correlation on Return on Assets, the multiple regression result did not show it as a determinant. The variables X 10 (wages to total expenses) and X 22 (return on Net worth) were higher for public sector banks over their private sector counterpart but neither of those had any significant influence on profitability of the public sector banks. 5. Conclusion The study reveals that the level of Non Performing Assets (Credit Risk) has a significant negative influence on the profitability of both public and private sector banks. The negative Volume:01, Number:06, Oct-2011 Page 151

influence of the NPA to total advances is a critical variable that not only affect the profitability of the banks but also can undermine the very existence of the banking sector. In addition to bank specific factors, credit risk is influenced by macroeconomic conditions such as GDP and inflation. NPA is positively influenced by GDP and negatively influenced by Inflation. Given the recent trend of lower GDP and higher Inflation, we can expect the NPA to rise and hence the profitability to decline. Other prudential measures should be taken to avert the accumulation of NPA in the banking sector and this is more so for the private sector banks as the NPA level is higher in the private sector banks than in the public sector banks. Acknowledgement The authors thank Dr. Thyagarajan, Senior Lecturer in Commerce of the SNS College for his technical assistance during the data analysis. References: Bolda B.S., & Verma, R. (2007). Determinants of Profitability of Banks in India: A Multivariate Analysis, Journal of Services Research, Vol.6, 75-89. Chandan, C. L., & Rajput, P.K. (2002). Profitability analysis of banks in India: A multiple regression approach, Indian Management Studies Journal. 6, 119-129. Chen, T.Y. (2002). Measuring Operation, Market and Financial Efficiency in the Management of Taiwan s Bank, Services Market Quarterly. 24, 15-25 Chen, Y., & Lin, C., (2007). Empirical study on the efficiency analysis of Australian banks, Banks and Bank Systems, 2, 38 49. Duca, J.V & McLaughlin, M. (1990). Developments affecting the profitability of commercial banks, Federal Reserve Bulletin (U.S.), July, 477-499 Report of the committee on productivity, efficiency, and profitability in banking (1977), Reserve Bank of India, II-2. Report of the Committee on the Financial System (Narasimhan Committee-1). (1991). Government of India, New Delhi. Report of the Committee on the Banking System Reforms (Narashimhan Committee-2). (1998). Government of India, New DewDelhi. 1998 Raut K.C., & Das, S.K. (1996). Commercial Banks in India Profitability, Growth and Development, Krishna Publishers Distributors, Darya Ganj, New Delhi. Seshadri, I.J.H. (1981). Banks Since Nationalisation, Economic Research Division, Birla Institute of Scientific Research, New Delhi. Shah, S.G. (1978). Bank Profitability The Real Issues, The Journal of the Indian Institute of Bankers, 4, 130-144. Sufian, F. (2009). Factors influencing bank profitability in a developing economy: Empirical Evidence from Malasia, Global Business Review, 10, 225-241. Thiagarajan, S., S. Ayyappan & Ramachandran, A., (2011). Credit Risk Determinants of Public and Private Sector Banks in India. European Journal of Economy, Finance and Administrative Sciences. (in press) Verghese, S.K. (1983). Profits and Profitability of Indian Commercial Banks in Seventies, Economic and Political Weekly, 18, 145-157. *** Volume:01, Number:06, Oct-2011 Page 152