an analysis of determinants of profitability in public and

Volume:01, Number:06, Oct-2011 40 www.theinternationaljournal.org

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 141 www.theinternationaljournal.org

1. Introduction

In the first half of the 19th

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).


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


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.

http://www.rbi.org.in/


X1 - Cash to deposit ratio; X2 - Credit to deposit ratio; X3 - (Credit+ Investment) to deposit

ratio

X4 - Ratio of term deposits to total deposits; X5 - Ratio of priority sector advances to total

advances; X6 - Ratio of term loan to total advances; X7 - Ratio of interest income to total

assets;

X8 - Ratio of net interest margin to total assets; X9 -Ratio of non -interest income to total

assets;

X10 - Ratio of wage bills to total expenses; X11 - Ratio of burden to total assets; X12 - Ratio of

operating profit to total assets; X13 - Return on equity; X14 - Cost of deposits; X15 - Cost of

borrowings; X16 - Return on advances; X17 - Return on investments; X18 - Business per

employee;

X19 - Profit per employee; X20- Capital adequacy ratio; X21 - Ratio of net NPA to net

advances;

X22 - Return on Net worth; X23 - 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 X18 (Business per employee) and X19 (Profit per employee)

are higher for the private sector for most years under study. The variable X10 (wages to total

expenses) and X22 (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 co-

efficient of all the selected variables with the dependent variable, as well as correlation

coefficients among different independent variables. The calculated correlation co-efficient


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 X12 (Ratio of

operating profit to total assets), X16 (Return on advances), X17 (Return on investments), X20

(Capital adequacy ratio) and X22 (Return on Net worth) have significant positive correlation

with bank profitability. Other two variables namely X15 (Cost of borrowings) and X21 (Ratio

of net NPA to net advances) have significant but negative correlation with bank profitability.

Out of these, the relationship of X16 and X20 is very high (0.754 and 0.752). In private sector

banks variables namely X1 (Cash to deposit ratio), X9 (Ratio of non -interest income to total

assets), X12 (Ratio of operating profit to total assets), X16 (Return on advances), X17 (Return

on investments), and X19 (Profit per employee) have significant positive correlation with bank

profitability. Out of these, the relationship of X12 and X17 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

X1 Cash to deposit ratio -.365 .150 .550 .050*

X2 Credit to deposit ratio .265 .229 .034 .463

X3 (Credit+ Investment) to deposit ratio .520 .062 -.103 .388

X4 Ratio of term deposits to total deposits -.131 .359 -.195 .295

X5 Ratio of priority sector advances to total

advances .374 .143 .300 .200

X6 Ratio of term loan to total advances .453 .094 .378 .141

X7 Ratio of interest income to total assets -.524 .060 .472 .084

X8 Ratio of net interest margin to total assets -.087 .406 .442 .101

X9 Ratio of non -interest income to total assets .417 .115 .790 .003**

X10 Ratio of wage bills to total expenses -.204 .286 -.266 .229

X11 Ratio of burden to total assets -.501 .070 -.131 .359

X12 Ratio of operating profit to total assets .854 .001** .847 .001**

X13 Return on equity .429 .108 .534 .056

X14 Cost of deposits -.513 .065 -.222 .269

X15 Cost of borrowings -.774 .004** -.322 .182

X16 Return on advances .754 .006** .826 .002**

X17 Return on investments .682 .015** .831 .001**

X18 Business per employee .302 .198 .391 .132

X19 Profit per employee .511 .066 .765 .005**

X20 Capital adequacy ratio .752 .006** .427 .109

X21 Ratio of net NPA to net advances -.576 .041* -.400 .126

X22 Return on Net worth .991 .000** .503 .069

X23 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)


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.

S.No. Ratio of

Multiple

Regression

Co-efficient

t‟ value p-value

X1 Cash to deposit ratio .035 .666 .526

X2 Credit to deposit ratio -.055 -1.161 .284

X3 (Credit+ Investment) to deposit ratio -.064 -1.170 .280

X4 Ratio of term deposits to total deposits -.055 -1.238 .256


advances .020 .391 .708

X6 Ratio of term loan to total advances -.059 -1.123 .298

X7 Ratio of interest income to total assets .060 1.076 .318

X8 Ratio of net interest margin to total assets .047 1.002 .350

X9 Ratio of non -interest income to total assets .073 1.635 .146

X10 Ratio of wage bills to total expenses .037 .760 .472

X11 Ratio of burden to total assets .047 .835 .431

X12 Ratio of operating profit to total assets .110 1.424 .198

X13 Return on equity -.075 -1.546 .166

X14 Cost of deposits .022 .390 .708

X15 Cost of borrowings .060 .762 .471

X16 Return on advances .105 1.844 .108

X17 Return on investments -.053 -.780 .461

X18 Business per employee -.069 -1.501 .177

X19 Profit per employee -.072 -1.365 .214

X20 Capital adequacy ratio -.087 -1.182 .276

X21 Ratio of net NPA to net advances .053 .906 .395

X22 Return on Net worth .066 1.449 .191

X23 Provision and Contingencies to total assets .991 21.526 .000**

R2

= 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 X1 -.055 X2 – 0.064 X3 -055 X4 + .02X5 -.059 X6 + 0.06 X7 + 0.047

X8 + 0.073 X9 + 0.037X10 + 0.047 X11 + .110 X12 – 0.075 X13 +0.022 X14 +0.06 X15 +

0.105 X16 – 0.053 X17 -0.069 X18 -.072 X19 -.087 X20 + 0.053X21+ 0.066X22+ 0.991X23


An insight into the public sector banks (Table 2) reveals that the multiple regression co-

efficient 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” (X23) 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 R2 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.

S.No. Ratio of

Multiple

Regression

Co-efficient

t‟ value p-value

Y Return on assets -4.891 .003

X1 Cash to deposit ratio -.034 -1.111 .317

X2 Credit to deposit ratio .002 .049 .963

X3 (Credit+ Investment) to deposit ratio -.001 -.027 .980

X4 Ratio of term deposits to total deposits .000 -.004 .997


advances

-.061 -.916 .402

X6 Ratio of term loan to total advances .183 4.085 .006**

X7 Ratio of interest income to total assets .059 .918 .401

X8 Ratio of net interest margin to total assets .033 .864 .427

X9 Ratio of non -interest income to total assets .039 .942 .389

X10 Ratio of wage bills to total expenses .018 .689 .521

X11 Ratio of burden to total assets .019 .400 .706

X12 Ratio of operating profit to total assets .022 .338 .749

X13 Return on equity -.003 -.074 .944

X14 Cost of deposits -.053 -1.711 .148

X15 Cost of borrowings .044 .586 .583

X16 Return on advances .692 20.951 .000**

X17 Return on investments .430 8.996 .000**

X18 Business per employee -.040 -1.130 .310

X19 Profit per employee .010 .340 .748

X20 Capital adequacy ratio .084 .983 .371

X21 Ratio of net NPA to net advances -.051 -1.166 .296

X22 Return on Net worth .025 1.049 .342

X23 Provision and Contingencies to total assets -.035 -.688 .522

R2=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 X1 +0.002 X2 – 0.001 X3 + 0.0001 X4 -0.061 X5 + .183 X6 +

0.059 X7 + 0.033 X8 + 0.039 X9 +0.018 X10 +0.019 X11 +0.022 X12 – 0.003X13 –

0.0.053 X14 + 0.044 X15 + 692 X16 + 0.430 X17 -0.04 X18 + 0.01 X19 +0.084 X20 -

0.051X21.+ 0.025X22 -0.035X23


An insight into the private sector banks (Table 3) reveals that the multiple regression co-

efficient of the three variables namely ratio of term loan to total advances (X6), Return on

advances (X16) and return on investments (X16) 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 R2 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:

Zj = aj1F1 + lj2F2 + …… ajmFm + djUj

where „Z‟ stands for a variable, „F‟ for common factors, „U‟ for a unique factor and „ajm‟ and

„dj‟ for regression weights. For example, the first row may be written in the regression

formula as:

Z = 0.537F1 + 0.023F2 + 0.814F3

It is clear from Table 5 that the important determinant of Factor 1 is X21 (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 X6 (Ratio of term loan to total advances), X7 (Ratio of

interest income to total assets), X3 ((Credit+ Investment) to deposit ratio), X17 (Return on

investments), X2 (Credit to deposit ratio), X13 (Return on equity), X19 (Profit per employee),

X15 (Cost of borrowings), X18 (Business per employee), X20 (Capital adequacy ratio) and X14

(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 C2

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


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 X21 accounted

for by Factor I are:

a2

21 = (-0.992)2 = 0.9841

that is 98.41 per cent of total variations in X12 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 X6,X7, X3,X17,X2,X13,X19,X15,X18,X20 and X14 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 X21, X6,X7, X3,X17,X2,X13,X19,X15,X18,X20 and X14 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 X4

(Ratio of term deposits to total deposits), X5 (Ratio of priority sector advances to total

advances), X8 (Ratio of net interest margin to total assets), X10 (Ratio of wage bills to total

expenses) and X11 (Ratio of burden to total assets) respectively. Similarly, X16 (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 X16. The c2

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


total variation in X6 (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 X21(Ratio of net NPA to net advances),

X14(Cost of deposits), X5(Ratio of priority sector advances to total advances), X4 (Ratio of

term deposits to total deposits), X17 (Return on investments) and X2 (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, X21(Ratio of net NPA to net advances), X14(Cost of

deposits), X5(Ratio of priority sector advances to total advances), X4 (Ratio of term deposits

to total deposits), X17 (Return on investments) and X2 (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 X20 (Capital adequacy ratio), X1 (Cash to deposit ratio), X13 (Return on equity),

X19 (Profit per employee), X18 (Business per employee) and X12 (Ratio of operating profit to

total assets) respectively. Similarly, X16 (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 X16. Next, X23 has relatively high factor loading with Factor III and all the four

together could explain nearly 75.34 per cent of the variations in X23. Finally, X3 (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 X3. The c2

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 C2

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


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

values (in %) 38.794 65.285 77.485 88.115 93.973

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

The study reveals that there three variables namely X12 (Ratio of operating profit to total

assets), X16 (Return on advances), and X17 (Return on investments) had positive correlation

with the profitability for both public and private sector banks. The two variables namely X15

(Cost of borrowings) and X21 (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 (X6), Return on advances (X16) and

return on investments (X16) 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 (X6) 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 (X6) 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 X19 (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 X10 (wages to total expenses) and X22 (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


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.

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