: In this research paper, we have made an attempt to identify the key determinants of profitability of all the banks in India. The analysis is based on pool regression and fixed effect regression model(in the cases of time and individual banks). We used panel data from the year 2008 to 2012. The study has brought out that the explanatory power of some variables is significantly high. Such variables include Net Spread, Non Interest Income and Cash And Reserve. However, some variables namely Profit per employee, Cash-Deposit Ratio, Operating Expense, Rate Of Equity, Offices, Non Performing Assets and Business per employee are found with low explanatory power. Hence the variables Net spread, non interest income (NII) and cash and reserve (CNR) have a significant relationship with Net Profit, where spread has the maximum influence and Cash - Deposit Ratio has the least effect on net profit. On introducing Time and Bank Dummies under the fixed effect model, we have found that only Cash And Reserve has an effect on bank profitability.

1. 1 | P a g e
Project Report On
PROFITABILITY OF BANKS IN INDIA
Submitted in partial fulfillment of the requirements for the
degree of
BACHELOR OF BUSINESS ECONOMICS
By
Akanksha Garg(Roll No. 2531)
Archit Aggarwal (Roll No. 2524)
Pulkit Vig (Roll No. 2557)
Shivani Baghel (Roll No.2534)
Siddhant Kapur (Roll No. 2533)
Tanuj Mendiratta (Roll No.2569)
Supervisor : Mr. Abhishek Kumar
Assistant Professor
(University Of Delhi)

2. 2 | P a g e
DECLARATION
I hereby declare that the project work entitled “Profitability of Banks” submitted to
the University of Delhi, is a record of an original work done by us under the
guidance of Mr. Abhishek Kumar, Ram Lal Anand College(E) , and this project
work has not been partially or fully copied from any other project (diploma and
degree course). Also due credit has been provided to all sources from which the
data has been taken.
Akanksha Garg(Roll No. 2531)
Archit Aggarwal (Roll No. 2524) Supervisor :
Pulkit Vig (Roll No. 2557) Abhishek Kumar
Shivani Baghel (Roll No.2534) (Assistant Professor)
Siddhant Kapur (Roll No. 2533) (University Of Delhi)
Tanuj Mendiratta (Roll No.2569

3. 3 | P a g e
ACKNOWLEDGEMENT
This is to acknowledge the efforts of one and all to make this project success. Thanks to
everyone for their patience, hard work and sincerity, we are able to complete this project
effectively.
We are equally grateful to our mentor Mr. Abhishek Kumar for his consistent guidance,
encouragement and support during the development of this work.
We also want to thank Ms. Aastha, Coordinator, BA(H) Business Economics for extending her
support. We are also grateful to our Institution and our faculty members without whom this
project would have been a distant reality.
Also, We would like to express our eternal gratitude to our parents for their everlasting love and
support.
Thanking you
AKANKSHA GARG(Roll No. 2531)
ARCHIT AGGARWAL(Roll No. 2524)
PULKIT VIG(Roll No. 2557)
SHIVANI BAGHEL(Roll No.2534)
SIDDHANT KAPUR(Roll No. 2533)
TANUJ MENDIRATTA (Roll No.2569)

4. 4 | P a g e
INDEX
TOPIC PAGE NO.
Introduction 5
Literature Review 15
Database And Methodology 18
Regression Analysis 23
Conclusion 38
Bibliography 39

5. 1.INTRODUCTION
A Bank is a financial institution and a financial intermediary that accepts deposits and channels
those deposits into lending activities, either directly by loaning or indirectly through capital
markets. A bank links together customers that have capital deficits and customers with capital
surpluses. Due to their influential status within the financial system and upon
national economies, banks are highly regulated in most countries. They play a very crucial role
in shaping a country's economical and social background and hence act as an active agent in
determining their growth perspectives.
In this paper , we aim to analyze what determines the profitability of banks in India.. A dataset of
five years, from fiscal year 2008 to 2013 has been taken into consideration to analyze the various
aspects of bank profitability. 77 banks operational in India have been analyzed and interpreted on
various parameters like Net Spread, Cash Deposit Ratio, Cash and Reserves etc. The study has
been conducted with the help of various statistical and econometric tools of regression.
Banking in India originated in the last decades of the 18th century. The first banks were The
General Bank of India, which started in 1786, and Bank of Hindustan, which started in 1770;
both are now defunct. The oldest bank in existence in India is the State Bank of India, which
originated in the Bank of Calcutta in June 1806, which almost immediately became the Bank of
Bengal. This was one of the three presidency banks, the other two being the Bank of
Bombay and the Bank of Madras, all three of which were established under charters from the
British East India Company. For many years the Presidency banks acted as quasi-central banks,
as did their successors. The three banks merged in 1921 to form the Imperial Bank of India,
which, upon India's independence, became the State Bank of India in 1955.
Banking occupies one of the most important positions in the modern economic world. It is
necessary for trade and industry. Hence it is one of the great agencies of commerce. Although
banking in one form or another has been in existence from very early times, modern banking is
of recent origin. It is one of the results of the Industrial Revolution and the child of economic
5 | P a g e

6. necessity. Its presence is very helpful to the economic activity and industrial progress of a
country.
Since the initiation of economic reforms in 1991-92, the banking sector in India has seen
numerous developments and policy changes. The more important reforms initiated in the
banking sector includes adoption of prudential norms in terms of capital adequacy, assets
classification and provisioning, deregulation of interest rates, lowering of Statutory Liquidity
Ratio (SLR) and Cash Reserve Ratio (CRR), opening of the sector to private participation,
permission to foreign banks to expand their operations through subsidiaries, the introduction of
Real Time Gross Settlement (RTGS) and liberalization of FDI norms. The main thrust of the
banking sector reforms has been the creation of efficient and stable financial institutions and
development of the banking industry. The reforms have been undertaken gradually with mutual
consent and wider debate amongst the participants and in a sequential pattern that is reinforcing
to the overall economy.
Banking sectors reforms have changed the face of INDIAN BANKING INDUSTRY. The
reforms have led to the increase in resource productivity, increasing level of deposits, credits and
profitability and decrease in non-performing assets. However, the profitability, which is an
important criteria to measure the performance of banks in addition to productivity, financial and
operational efficiency, has come under pressure because of changing environment of banking.
An efficient management of banking operations aimed at ensuring growth in profits and
efficiency requires up-to-date knowledge of all those factors on which the banks profit depends.
History
Merchants in Calcutta established the Union Bank in 1839, but it failed in 1840 as a consequence
of the economic crisis of 1848-49. The Allahabad Bank, established in 1865 and still functioning
today, is the oldest Joint Stock bank in India.(Joint Stock Bank: A company that issues stock
and requires shareholders to be held liable for the company's debt) It was not the first though.
That honor belongs to the Bank of Upper India, which was established in 1863, and which
survived until 1913, when it failed, with some of its assets and liabilities being transferred to
the Alliance Bank of Simla.
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7. Foreign banks too started to app, particularly in Calcutta, in the 1860s. The Comptoir d'Escompte
de Paris opened a branch in Calcutta in 1860, and another in Bombay in 1862; branches
in Madras and Pondicherry, then a French colony, followed. HSBC established itself
in Bengal in 1869. Calcutta was the most active trading port in India, mainly due to the trade of
the British Empire, and so became a banking center.
The first entirely Indian joint stock bank was the Oudh Commercial Bank, established in 1881
in Faizabad. It failed in 1958. The next was the Punjab National Bank, established in Lahore in
1895, which has survived to the present and is now one of the largest banks in India.
Around the turn of the 20th Century, the Indian economy was passing through a relative period
of stability. Around five decades had elapsed since the Indian Mutiny, and the social, industrial
and other infrastructure had improved. Indians had established small banks, most of which
served particular ethnic and religious communities.
The presidency banks dominated banking in India but there were also some exchange banks and
a number of Indian joint stock banks. All these banks operated in different segments of the
economy. The exchange banks, mostly owned by Europeans, concentrated on financing foreign
trade. Indian joint stock banks were generally under capitalized and lacked the experience and
maturity to compete with the presidency and exchange banks. This segmentation let Lord Curzon
to observe, "In respect of banking it seems we are behind the times. We are like some old
fashioned sailing ship, divided by solid wooden bulkheads into separate and cumbersome
compartments."
The period between 1906 and 1911, saw the establishment of banks inspired by
the Swadeshi movement. The Swadeshi movement inspired local businessmen and political
figures to found banks of and for the Indian community. A number of banks established then
have survived to the present such as Bank of India, Corporation Bank, Indian Bank, Bank of
Baroda, Canara Bank and Central Bank of India.
The fervor of Swadeshi movement lead to establishing of many private banks in Dakshina
Kannada and Udupi district which were unified earlier and known by the name South Canara (
South Kanara ) district. Four nationalized banks started in this district and also a leading private
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8. sector bank. Hence undivided Dakshina Kannada district is known as "Cradle of Indian
Banking".
During the First World War (1914–1918) through the end of the Second World War (1939–
1945), and two years thereafter until the independence of India were challenging for Indian
banking. The years of the First World War were turbulent, and it took its toll with banks simply
collapsing despite the Indian economy gaining indirect boost due to war-related economic
activities. At least 94 banks in India failed between 1913 and 1918 as indicated in the following
table:
YEARS NUMBERS OF
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BANK THAT
FAILED
AUTHORISED
CAPITAL (Rs. Lacs)
PAID-UP CAPITAL
(Rs. Lacs)
1913 12 274 35
1914 42 710 109
1915 11 56 5
1916 13 231 4
1917 9 76 25
1918 7 209 1

9. Post-Independence
The partition of India in 1947 adversely impacted the economies of Punjab and West Bengal,
paralyzing banking activities for months. India's independence marked the end of a regime of
the Laissez-faire for the Indian banking. The Government of India initiated measures to play an
active role in the economic life of the nation, and the Industrial Policy Resolution adopted by the
government in 1948 envisaged a mixed economy. This resulted into greater involvement of the
state in different segments of the economy including banking and finance. The major steps to
regulate banking included
 'The Reserve Bank of India, India's central banking authority, was established in April
1935, but was nationalized on January 1, 1949 under the terms of the Reserve Bank of
India (Transfer to Public Ownership) Act, 1948 (RBI, 2005b).[1]
 In 1949, the Banking Regulation Act was enacted which empowered the Reserve Bank of
India (RBI) "to regulate, control, and inspect the banks in India".
 The Banking Regulation Act also provided that no new bank or branch of an existing
bank could be opened without a license from the RBI, and no two banks could have
common directors.
Nationalization
Despite the provisions, control and regulations of Reserve Bank of India, banks in India except
the State Bank of India or SBI, continued to be owned and operated by private persons. By the
1960s, the Indian banking industry had become an important tool to facilitate the development of
the Indian economy. At the same time, it had emerged as a large employer, and a debate had
ensued about the nationalization of the banking industry. Indira Gandhi, then Prime Minister of
India, expressed the intention of the Government of India in the annual conference of the All
9 | P a g e

10. India Congress Meeting in a paper entitled "Stray thoughts on Bank Nationalisation." [2] The
meeting received the paper with enthusiasm.
Thereafter, her move was swift and sudden. The Government of India issued an ordinance
('Banking Companies (Acquisition and Transfer of Undertakings) Ordinance, 1969'))
and nationalised the 14 largest commercial banks with effect from the midnight of July 19, 1969.
These banks contained 85 percent of bank deposits in the country.[2]Jayaprakash Narayan, a
national leader of India, described the step as a "masterstroke of political sagacity." Within two
weeks of the issue of the ordinance, the Parliament passed the Banking Companies (Acquisition
and Transfer of Undertaking) Bill, and it received the presidential approval on 9 August 1969.
A second dose of nationalization of 6 more commercial banks followed in 1980. The stated
reason for the nationalization was to give the government more control of credit delivery. With
the second dose of nationalization, the Government of India controlled around 91% of the
banking business of India. Later on, in the year 1993, the government merged New Bank of
India with Punjab National Bank. It was the only merger between nationalized banks and
resulted in the reduction of the number of nationalised banks from 20 to 19. After this, until the
1990s, the nationalised banks grew at a pace of around 4%, closer to the average growth rate of
the Indian economy.
Liberalization
In the early 1990s, the then Narasimha Rao government embarked on a policy of liberalization,
licensing a small number of private banks. These came to be known as New Generation tech-savvy
banks, and included Global Trust Bank (the first of such new generation banks to be set
up), which later amalgamated with Oriental Bank of Commerce, UTI Bank (since renamed Axis
Bank), ICICI Bank and HDFC Bank. This move, along with the rapid growth in the economy of
India, revitalized the banking sector in India, which has seen rapid growth with strong
contribution from all the three sectors of banks, namely, government banks, private banks and
foreign banks.
The next stage for the Indian banking has been set up with the proposed relaxation in the norms
for Foreign Direct Investment, where all Foreign Investors in banks may be given voting rights
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11. which could exceed the present cap of 10%,at present it has gone up to 74% with some
restrictions. The new policy shook the Banking sector in India completely. Bankers, till this time,
were used to the 4-6-4 method (Borrow at 4%;Lend at 6%;Go home at 4) of functioning. The
new wave ushered in a modern outlook and tech-savvy methods of working for traditional banks.
All this led to the retail boom in India. People not just demanded more from their banks but also
received more.
Current Scenario
By 2013 , banking in India was generally fairly mature in terms of supply, product range and
reach-even though reach in rural India still remains a challenge for the private sector and foreign
banks. In terms of quality of assets and capital adequacy, Indian banks are considered to have
clean, strong and transparent balance sheets relative to other banks in comparable economies in
its region. The Reserve Bank of India is an autonomous body, with minimal pressure from the
government. The stated policy of the Bank on the Indian Rupee is to manage volatility but
without any fixed exchange rate-and this has mostly been true.
With the growth in the Indian economy expected to be strong for quite some time-especially in
its services sector-the demand for banking services, especially retail banking, mortgages and
investment services are expected to be strong. One may also expect M&As, takeovers, and asset
sales.
In March 2006, the Reserve Bank of India allowed Warburg Pincus to increase its stake in Kotak
Mahindra Bank (a private sector bank) to 10%. This is the first time an investor has been allowed
to hold more than 5% in a private sector bank since the RBI announced norms in 2005 that any
stake exceeding 5% in the private sector banks would need to be vetted by them.
In recent years critics have charged that the non-government owned banks are too aggressive in
their loan recovery efforts in connexion with housing, vehicle and personal loans. There are press
reports that the banks' loan recovery efforts have driven defaulting borrowers to suicide.
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12. Adoption Of Banking Technology
The IT revolution had a great impact in the Indian banking system. The use of computers had led
to introduction of online banking in India. The use of the modern innovation and computerisation
of the banking sector of India has increased many fold after the economic liberalisation of 1991
as the country's banking sector has been exposed to the world's market. The Indian banks were
finding it difficult to compete with the international banks in terms of the customer service
without the use of the information technology and computers.
Number of branches of scheduled banks of India as of March 2005
The RBI in 1984 formed Committee on Mechanisation in the Banking Industry (1984) whose
chairman was Dr C Rangarajan, Deputy Governor, Reserve Bank of India. The major
recommendations of this committee was introducing MICR Technology in all the banks in the
metropolis in India.This provided use of standardized cheque forms and encoders.
In 1988, the RBI set up Committee on Computerisation in Banks (1988) headed by Dr. C.R.
Rangarajan which emphasized that settlement operation must be computerized in the clearing
houses of RBI in Bhubaneshwar, Guwahati, Jaipur, Patna and Thiruvananthapuram.It further
stated that there should be National Clearing of inter-city cheques at
Kolkata,Mumbai,Delhi,Chennai and MICR should be made Operational.It also focused on
computerisation of branches and increasing connectivity among branches through computers. It
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13. also suggested modalities for implementing on-line banking. The committee submitted its reports
in 1989 and computerisation began form 1993 with the settlement between IBA and bank
employees' association.
In 1994, Committee on Technology Issues relating to Payments System, Cheque Clearing and
Securities Settlement in the Banking Industry (1994) was set up with chairman Shri WS Saraf,
Executive Director, Reserve Bank of India. It emphasized on Electronic Funds Transfer (EFT)
system, with the BANKNET communications network as its carrier. It also said that MICR
clearing should be set up in all branches of all banks with more than 100 branches.
Committee for proposing Legislation On Electronic Funds Transfer and other Electronic
Payments (1995) emphasized on EFT system. Electronic banking refers to DOING BANKING
by using technologies like computers, internet and networking, MICR,EFT so as to increase
efficiency, quick service, productivity and transparency in the transaction.
Number of ATMs of different Scheduled Commercial Banks Of India as on end March 2005
Apart from the above mentioned innovations the banks have been selling the third party products
like Mutual Funds, insurances to its clients. Total numbers of ATMs installed in India by various
banks as on end March 2005 is 17,642. The New Private Sector Banks in India is having the
largest numbers of ATMs which is fol off site ATM is highest for the SBI and its subsidiaries
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14. and then it is followed by New Private Banks, Nationalised banks and Foreign banks. While on
site is highest for the Nationalised banks of India.
BANK GROUP NUMBER OF
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BRANCHES
ON SITE
ATM
OFF SITE
ATM
TOTAL
ATM
NATIONALISED BANKS 33627 3205 1567 4772
STATE BANK OF INDIA 13661 1548 3672 5220
OLD PRIVATE SECTOR
BANKS
4511 800 441 1241
NEW PRIVATE SECTOR
BANKS
1685 1883 3729
5612
FOREIGN BANKS 242 218 579 797
Since the initiation of economic reforms, the banking sector in India has seen numerous
developments and policy changes. The more important reforms initiated in the banking sector
includes adoption of prudential norms in terms of capital adequacy, assets classification and
provisioning, deregulation of interest rates, lowering of SLR and CRR, opening of the sector to
private participation, permission to foreign banks to expand their operations through subsidiaries,
the introduction of Real Time Gross Settlement (RTGS) and liberalization of FDI norms. The
main thrust of the banking sector reforms has been the creation of efficient and stable financial
institutions and development of the banking industry. The reforms have been undertaken
gradually with mutual consent and wider debate amongst the participants and in a sequential
pattern that is reinforcing to the overall economy.
In the project of ours we are concentrating on the parameters such as Spread, Non Interest
Income, Credit/Deposit Ratio, NPA as a percentage to Net Advances, BPE(Business per
Employee), PPE(Profit per Employee), Cash and Reserve, Operating Expense and Return on
Assets.

15. 2.LITERATURE REVIEW
A lot of research work has so far taken place concerning the views about the role of financial and
banking development in economic growth [McKinnon (1973); Shaw (1973); Rajan and Zingales
(1998); Levine (2004); Singh (2005)].Similarly some studies have been undertaken for
measuring the productivity and operational efficiency of banks in India. More recent among
them includes- Cheema and Agarwal (2002), Ketkar, Noulas and Agarwal (2003), Singh (2003).
Insofar as our information is concerned, however, very scanty work has been done with the
objective of identifying the determinants of profitability of banks in India. The recent studies of
Chandan and Rajput (2002) and Saggar (2005) have examined the factors determining
profitability of banks in India. Therefore, the onus of conducting more research studies lies on
the researchers so as to identify the determinants of profitability of banks. It is in this context that
the present study titled-‘.Indian Banking Industry’: A Multivariate Analysis. has been performed.
Some of these studies have been mentioned :
McKinnon (1973)
McKinnon’s (1973) complementary hypothesis predicts that money and investment are
complementary due to a self-financed investment, and that a real deposit rate is the key
determinant of capital formation for financially constrained developing economies.
Shaw (1973):
The paper attempts to demonstrate the problematic nature of `market liberalisation' by
concentrating in an area where renewed interest has resurfaced, this being financial markets.
More precisely, the focus of this contribution will be on the setting of financial prices by central
banks, especially in developing countries, a fairly common practice in the 1950s and 1960. The
paper ascribed the poor performance of investment and growth in developing countries to
interest rate ceilings, high reserve requirements and quantitative restrictions in the credit
allocation mechanism. These restrictions were sources of `financial repression', the main
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16. symptoms of which were low savings, credit rationing and low investment. They propounded
instead the thesis which has come to be known as `financial liberalisation', which can be
succinctly summarised as amounting to ‘freeing’ financial markets from any intervention and
letting the market determine the allocation of credit.
Rajan and Zingales (1998):
The paper examines whether financial development facilitates economic growth by scrutinizing
one rationale for such a relationship: that financial development reduces the costs of external
finance to firms. Specifically, the authors ask whether industrial sectors that are relatively more
in need of external finance develop disproportionately faster in countries with more-developed
financial markets. They find this to be true in a large sample of countries over the 1980s. The
authors show this result is unlikely to be driven by omitted variables, outliers, or reverse
causality. Copyright 1998 by American Economic Association.
Ketkar, Noulas and Agarwal (2003):
The paper seeks to determine the impact of various market and regulatory initiatives on
efficiency improvements and profitability of Indian banks since the implementation of financial
sector reforms following the recommendations of the Narasimham Committee in1992 and 1997.
The reform process has shifted the focus of public sector dominated banking system from social
banking to a more efficient and profit oriented industry. While the reform process has resulted in
the private sector replacing the government as the source of resources for public sector banks
(PSBs), the infusion of private equity capital has led to shareholders challenges to bureaucratic
decision making. PSBs also face increasing competition not only from private and foreign
banks but also from growing non- banking financial intermediaries like mutual funds and other
capital market entities. The competitive pressures to improve efficiency in the banking sector
has resulted in a switch from traditional paper based banking to electronic banking, use
information technology and shift of emphasis from brick and mortar banking to use of ATMs.
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17. Levine (2004):
This paper reviews, appraises, and critiques theoretical and empirical research on the connections
between the operation of the financial system and economic growth. While subject to ample
qualifications and countervailing views, the preponderance of evidence suggests that both
financial intermediaries and markets matter for growth and that reverse causality alone is not
driving this relationship. Furthermore, theory and evidence imply that better developed financial
systems ease external financing constraints facing firms, which illuminates one mechanism
through which financial development influences economic growth. The paper highlights many
areas needing additional research.
H. Semih Yildirim & George C. Philippatos (2007)
Efficiency of Banks: Recent Evidence from the Transition Economies of Europe, 1993–2000.
This study examines the cost and profit efficiency of banking sectors in twelve transition
economies of Central and Eastern Europe (CEE) over the period 1993–2000, using the stochastic
frontier approach (SFA) and the distribution- free approach (DFA). The managerial inefficiencies
in CEE banking markets were found to be significant, with average cost efficiency level for 12
countries of 72% and 77% by the DFA and the SFA, respectively. The alternative
profit efficiency levels are found to be significantly lower relative to cost efficiency. According
to the SFA, approximately one-third of banks' profit are lost to inefficiency, and almost one-half
according to the DFA. The results of the second-stage regression analyses suggest that higher
efficiency levels are associated with large and well-capitalized banks . The degree of competition
has a positive influence on cost efficiency and a negative one on profit efficiency, while market
concentration is negatively linked to efficiency. Finally, foreign banks are found to be more cost
efficient but less profit efficient relative to domestically owned private banks and state-owned
banks.
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18. 3. DATABASE AND METHODOLOGY
The Indian financial system comprises an impressive network of Nationalized Banks,
Commercial Banks CBs), Co-operative banks (CPB), Development Finance Institutions (DFIs)
and Non-banking Finance Companies (NBFCs). The commercial banks comprise public sector,
private sector and foreign sector banks. Though the number of foreign and private banks
operating in India has increased from 21 and 23 in 1991 to 33 and 30, respectively in 2004, the
public sector banks dominate the banking industry in terms of branch expansion, market share in
deposits and lending etc. Accordingly, the scope of the present study is limited to Nationalized ,
Foreign , Public and Private Sector Banks operating in India. We have considered the data for 77
banks currently operating in India : Nationalized, Public sector, private sector and foreign banks.
The dataset covers a period of fiver years, 2008-2013.
The variables considered for the present study include Spread (S), Non-Interest Income (NII),
Credit Deposit Ratio, Cash and Reserve, Business per employee, Office, Operating Expenses,
Profit Per Employee and Returns on Equity.
The data relating to these variables have been collected from the Reserve Bank of India. Bulletin
and Internet ( www.rbi.org.in)
The variables used are explained as:
Cash And Reserves: Bank reserves are banks' holdings of deposits in accounts with
their central bank (for instance the European Central Bank or the Federal Reserve, in the latter
case including federal funds), plus currency that is physically held in the bank's vault (vault
cash). The central banks of some nations set minimum reserve requirements. Even when no
requirements are set, banks commonly wish to hold some reserves, called desired reserves,
against unexpected events such as unusually large net withdrawals by customers or even bank
runs.
 Reserves on deposit – deposit accounts at the central bank, owned by banks.
 Vault cash – reserves held as cash in bank vaults rather than being on deposit at the central
bank.
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19.  Borrowed reserves – bank reserves that were obtained by borrowing from the central bank.
 Non-borrowed reserves – bank reserves that were not obtained by borrowing from the
central bank.
 Required reserves – the amount of reserves that banks are required to hold, determined by
the central bank as a function of a bank's deposit liabilities.
 Excess reserves - bank reserves in excess of the reserve requirement. A portion of excess
reserves (or even all of them) may be desired reserves.
 Free reserves - the amount by which excess reserves exceed borrowed reserves.
Total reserves – all bank reserves: vault cash plus reserves on deposit at the central bank, also
borrowed plus non-borrowed, also required plus excess.
Non Interest Income: Bank and creditor income derived primarily from fees. Examples
of non-interest income include deposit and transaction fees, insufficient funds (NSF) fees, annual
fees, monthly account service charges, inactivity fees, check and deposit slip fees,
etc. Institutions charge fees that provide non-interest income as a way of generating revenue and
ensuring liquidity in the event of increased default rates.
Non-interest income makes up a significant portion of most banks' and credit card companies’
revenue. In 2008 alone, credit card issuers took in over $19 billion in penalty-fee income alone –
this includes late fees and over-the-limit fees, among others. The passage of the Credit Card
Accountability, Responsibility and Disclosure (CARD) Act of 2009 included sweeping
restrictions on credit card companies’ ability to generate non-interest income
Profits per Employee / Net Income per Employee: Profits per Employee
(Net Income per Employee) = net income / number of employees. This ratio indicate the average
profit generated per person employed.
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20. The profits per employee (or net income per employee) ratio is included in the financial
statement ratio analysis spreadsheets highlighted in the left column, which provide formulas,
definitions, calculation, charts and explanations of each ratio. The profits per employee ratio is
listed in our net income ratios.
Net Spread: Net spread refers to the difference in borrowing and lending rates of financial
institutions (such as banks) in nominal terms. It is considered analogous to the gross margin of
non-financial companies. Net spread is
expressed as interest yield on earning assets (any asset, such as a loan, that generates interest
income) minus interest rates paid on borrowed funds. Net spread is similar to net interest margin;
net interest spread expresses the nominal average difference between borrowing and lending
rates, without compensating for the fact that the amount of earning assets and borrowed funds
may be different. Spread = Total Interest
Income - Total Interest Expended
Return On Equity: It is the ratio relating net profit (net income) to shareholder's equity.
Here, shareholder's equity refers to share capital reserve and surplus of bank.
Formula = Profit after tax/ Total Equity + Total Equity at the end of previous year) / 2)*100
Cash Deposit Ratio: Cash deposit Ratio is the ratio between the sum of liquid cash in
hand and the amount of balance kept with Reserve Bank Of India and the deposits held.
Cash-deposit ratio = (Cash in hand + Balances with RBI) / Deposits
Operating Expense: Operating expenses are the expenses incurred in conducting the
bank’s ongoing operations. An important component of a bank’s operating expenses is the
interest payments that it must make on its liabilities, particularly on its deposits.Just as interest
income varies with the level of interest rates, so do interest expenses. Non - interest expense
consists of salaries for employees, expense on premises and equipment, rent on bank buildings,
servicing costs. Operating expenses also accounts for loss in loans.
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21. Business Per Employee: This ratio is most useful when compared against other
companies in the same industry. Ideally, a company wants the highest revenue per employee
possible, as it denotes higher productivity. It is calculated as: Revenue/ No. Of employee .
Non Performing Assets: A Non-performing asset (NPA) is defined as a credit facility in
respect of which the interest and/or installment of principal has remained ‘past due’ for a
specified period of time. NPA is a classification used by financial institutions that refer to loans
that are in jeopardy of default. Once the borrower has failed to make interest or principal
payments for 90 days the loan is considered to be a non-performing asset. Non-performing assets
are problematic for financial institutions since they depend on interest payments for income.
Troublesome pressure from the economy can lead to a sharp increase in non-performing
loans and often results in massive write-downs.
Typically we have three types of data sets which we use in economics:
1) Time series – This is the most common form of data that we use and they are quite easily
accessible. You can see time series data in the Taiwan Statistical Databook, Central Banks
websites and publications, the Economic Report of the President, the Bureau of Labor Statistics,
the Census Bureau, the Asian Development Bank and at websites like economagic.com and the
Directorate of Budget Accounting and Statistics (DGBAS). Time series regression must face the
formidable problems of autocorrelation and structural change.
2) Cross Section – This is data usually observed over geographic or demographic groups. A
regression, which uses these cross section data sets, is called a cross sectional regression. Cross
sectional regressions usually suffer from the problem of heteroskedasticity. Moreover, they are
really only true for a moment in time and therefore there is always the lingering question of
whether they can adequately represent the unchanging structure we are researching.
21 | P a g e

22. 3) Panel Data – This type combines the first two types. Here we have a cross section, but we
observe the cross section over time. If the same people or states or counties, sampled in the cross
section, are then re-sampled at a different time we call this a longitudinal data set, which is a very
valuable type of panel data set. Longitudinal data sets are very common in medical and
biostatistical studies. Panel data sets are becoming more and more popular due to the widespread
use of the computer making it easy to organize and produce such data.
Comprehending from the description above , we understand that the data considered for 77 banks
across five years from 2008 to 2013 is a panel data. However, to work competently for panel
data , we need data set for a longer period of time. Having only a dataset for five years restrains
us from studying and analyzing Panel Data regression in detail. The advanced complexities of
panel data are again a barrier to it.
Therefore, we have used both regression models in our study , namely
The Multiple Regression Model : Pooled Regression Model
and , The Fixed Effect Model for analyzing the panel data
using Bank Dummies
and Time Dummies
22 | P a g e

23. 4.REGRESSION ANALYSIS
 The Multiple Regression Model
Multiple linear regression is a generalization of linear regression by considering more than one
independent variable, and a specific case of general linear models formed by restricting the
number of dependent variables to one. The general linear model incorporates a number of
different statistical models: ANOVA, ANCOVA, MANOVA, MANCOVA, ordinary linear
regression, t-test and F-test.
In multivariate tests the columns of Y are tested together, whereas in univariate tests the columns
of Y are tested independently, i.e., as multiple univariate tests with the same design matrix.
The multiple regression model is given by;
23 | P a g e
풚 = 휶 + 휷ퟏ 풙ퟏ + 휷ퟐ풙ퟐ + 휷ퟑ풙ퟑ … + 휷풏풙풏
Where,
α – Intercept
β – Slope Coefficient (rate of change of y with respect to x ,keeping all other things constant)
x – Independent variable
y – Dependent variable
Here β is known as the partial regression coefficient.
The multiple regression model is applied considering profitability as the dependent variable and
all other variables as independent variables.

24. 24 | P a g e
Model 1: Pooled Regression Model
Pooled Regression is usually carried out on Time-Series Cross-Sectional data- data that has
observations over time for several different units or ‘cross-sections’. Pooled regression works
similar to regular regression, except an extra intercept or ‘dummy’ is added for each store. It is
important to remember that Pooled Regression Coefficients do not measure demand effect
separately for each store, but yield an ‘overall’ measure of demand.
This approach can be used when the groups to be pooled are relatively similar or homogenous.
Level differences can be removed by 'mean-centering' (similar to Within-Effects Model) the data
across the groups (subtracting the mean or average of each group from observations for the
group). The model can be directly run using Ordinary Least Squares on the concatenated groups.
groups are not all that homogenous and a more advanced approach like Random Effects Model
may be more appropriate.
F Statistic: An F-test is any statistical test in which the test statistic has an F-distribution under
the null hypothesis. It is most often used when comparing statistical models that have been fitted
to a dataset, in order to identify the model that best fits the population from which the data were
sampled. Exact F-tests mainly arise when the models have been fitted to the data using least
squares. It analyzes the fitness of the regression model.
For example, the null hypothesis of F-statistic that the model is not a fit model can be rejected if
the probability-value of F-statistic is less than 5% level of significance, with 95% confidence
interval.
The multiple regression model is applied considering profitability as the dependent variable and
all other variables as independent variables.
푵풆풕 푷풓풐풇풊풕 = 휶 + 휷ퟏ 푺풑풓풆풂풅 + 휷ퟐ푷푷푬 + 휷ퟑ푵푰푰 + 휷ퟒ푪푵푹 + 휷ퟓ푪푫풓풂풕풊풐
+ 휷ퟔ푶푬 + 휷ퟕ푹푶푬 + 휷ퟖ푶풇풇풊풄풆풔 + 휷ퟗ푩푷푬 + 휷ퟏퟎ푵푷푨 + 풆풊
푯ퟎ : All the independent variables taken in this model DO NOT AFFECT the net profit.
푯ퟏ : All the independent variables taken in this model AFFECT the net profit.
The null hypothesis of the regression model is that All the independent variables taken in this
model DO NOT AFFECT the net profit. Which means that there is no impact of independent
variables on the dependent variable.
The analysis of fitness of the regression model is done with the help of F-Statistics.
With 95% confidence interval. If the probability value of F-Statistics is less than 5% (0.05) level

25. of significance then we reject the null hypothesis. In other words, The independent variables do
AFFECT the NET PROFIT
In the table given below, R represents, the multiple correlation coefficient between dependent
and independent variables considered in the regression model.
The square of R, popularly known as 푅2 , is the coefficient of determination. It explains the
percentage of variation in dependent variables which can be explained by the independent
variables.
25 | P a g e
TABLE 1: Model Summary
Model R R Square Adjusted
R Square
Standard Error of
the Estimate
F- test
(significance)
0.853 0.728 0.720 10028.81938
99.661
(0.000)
The result indicates that the F-Statistics of the regression model is 99.661 and the probability
value of F-statistics is 0.000 which is less than 5% level of significance. Hence, The Null
hypothesis of the F-statistics that the independent variables taken in this model DO NOT
AFFECT the net profit is Rejected. Hence the model is a good fit.
푅 2of the model is 72.8%. This implies that the model explains 72.8% of the profitability with
the help of the considered factors in the model.
The adjusted 푅 2 of the model is found to be 72% it is the value of 푅 2 adjusted with degree of
freedom.
This table tells us the significance of each independent variable on measuring the dependent
variable i.e. Net Profit. The negative standardized coefficients imply that those independent
variables with negative beta values have negative relation with the net profit. The constant is the
value of the net profit when all the independent variables are zero.
Standardized Coefficients: In statistics, standardized coefficients or beta coefficients are the
estimates resulting from an analysis carried out on independent variables that have been
standardized . Therefore, standardized coefficients refer to how many standard deviations a
dependent variable will change, per standard deviation increase in the predictor variable.
Standardization of the coefficient is usually done to answer the question of which of the
independent variables have a greater effect on the dependent variable in a multiple
regression analysis, when the variables are measured in different units of measurement.
It uses z-score of Y and X-variables. Standardizing all variables in a multiple regression yields
standardized regression coefficients that show the change in the dependent variable measured

26. in standard deviations. The coefficients ignore the independent variable's scale of units, which
makes comparisons easy.
Unstandardized Coefficients : Unstandardized relationships are expressed in terms of the variables'
original, raw units. The Beta depends upon the unit of the variable, that is, if unit changes the
variable also changes. It's estimated using original values of X and Y. Unstandardized
coefficients are usually used for forecasting.
T-statistic : In statistics, the t-statistic is a ratio of the departure of an estimated parameter from
its notional value and its standard error. It is used in hypothesis testing. Let be an estimator of
parameter 훽 in some statistical model. Then a t-statistic for this parameter is any quantity of the
form
where β0 is a non-random, known constant, and is the standard error of the
estimator . By default, statistical packages report t-statistic with β0 = 0 (these t-statistics are
used to test the significance of corresponding regressor). However, when t-statistic is needed
to test the hypothesis of the form H0: β = β0, then a non-zero β0 may be used.
If 훽 is an ordinary least squares estimator in the classical linear regression model (that is,
with normally distributed and homoskedastic ( error terms), and if the true value of
parameter β is equal toβ0, then the sampling distribution of the t-statistic is the Student’s t-distribution
26 | P a g e
with (n − k) degrees of freedom, where n is the number of observations, and k is
the number of regressors (including the intercept).
In the majority of models the estimator is consistent for 훽 and distributed asymptotically
normally. If the true value of parameter 훽 is equal to β0 and the quantity correctly
estimates the asymptotic variance of this estimator, then the t-statistic will have
asymptotically the standard normal distribution.

27. 27 | P a g e
TABLE 2
Model Unstandardized Coefficients Standardized
Coefficients
t-test
(significance)
훽 Standard Error 훽
(Constant) 2929.483 1451.647 2.018
(0.044)
Spread 0.299 0.100 0.722 3.003
(0.003)
Profit Per Employee -57.017 141.399 -0.012 -0.403
(0.0687)
Non-Interest
Income 0.375 0.150 0.409
2.504
(0.013)
Cash and Reserve 0.051 0.019 0.315
2.736
(0.007)
Cash/Deposit
Ratio
32.971 450.391 0.002
0.073
(0.942)
Operating
Expenses
-0.338 0.190 -0.543
-1.779
(0.076)
Return on Equity 5.738 73.608 0.002
0.078
(0.938)
Offices -0.483 0.728 -0.049
-0.664
(0.507)
Business Per
Employee
-10.418 5.735 -0.054
-1.816
(0.070)
Non-Performing -315.655 398.936 -0.023 -.791

28. Asset (0.429)
In Table 2, the regression coefficients resulting from the application of multiple regression model
reveal that 3 independent variables have exerted influence on profitability. These variables
include Spread, Non Interest Income & Cash and Reserves.
T- test
The hypothesis is given by;
160000
140000
120000
100000
80000
60000
40000
20000
0
-20000
28 | P a g e
푯ퟎ : 휷풋 = ퟎ
푯ퟏ : 휷풋 > 0
Taking 95% level of significance if the P-value is less than 5% we will reject the null hypothesis.
In the others, the independent variables have an impact on net profitability.
It can be concluded that Spread has the maximum effect and significance in the net profit and the
CD ratio has the least effect.
We can remove those variables that do not have significant effect on Net Profit
Variables Removed: PPE, CD ratio, OE, ROE, Offices, NPA and BPE.
-40000
Model 1
1
14
27
40
53
66
79
92
105
118
131
144
157
170
183
196
209
222
235
248
261
274
287
300
313
326
339
352
365
378
Predicted Value Actual Value

29. Model 2: Pooled Regression (Only Significant Variables)
29 | P a g e
푵풆풕 푷풓풐풇풊풕 = 휶 + 휷ퟏ푺풑풓풆풂풅 + 휷ퟐ푵푰푰 + 휷ퟑ푪푵푹 + 풆풊
푯ퟎ : All the independent variables taken in this model DO NOT AFFECT the net profit.
푯ퟏ : All the independent variables taken in this model AFFECT the net profit.
Table 3: Model Summary
Model R R Square Adjusted
R Square
Standard Error of
the Estimate
F- test
(significance)
0.849 0.721 0.719 10040.98751
328.545
(0.000)
Since we have removed the variables that were not significant, the values still have not varied
vastly. Only these three factors together explain 72.1% of variations in the net profit. Thus this
model is better as compared to the previous model.
TABLE 4
Model Unstandardized Coefficients Standardized
Coefficients
t-test
(significance)
훽 Standard Error 훽
(Constant)
1178.055 593.423
1.985
(0.048)
Spread
0.111 0.034 0.267
3.230
(0.001)
Non- Interest
Income 0.189 0.100 0.206
1.898
(0.05)
Cash and Reserve
0.063 0.017 0.392
3.798
(0.00)

30. T- test
The hypothesis is given by;
160000
140000
120000
100000
80000
60000
40000
20000
0
-20000
30 | P a g e
푯ퟎ : 휷풋 = ퟎ
푯ퟏ : 휷풋 > 0
Taking 95% level of significance if the P-value is less than 5% we will reject the null hypothesis.
In the others, the independent variables have an impact on net profitability.
-40000
Model 2
1
13
25
37
49
61
73
85
97
109
121
133
145
157
169
181
193
205
217
229
241
253
265
277
289
301
313
325
337
349
361
373
385
Predicted Value Actual Value

31.  Regression with Panel Data Approach
Panel data (also known as longitudinal or cross- sectional time-series data) is a dataset in which
the behavior of entities are observed across time. Panel data allows you to control for variables
you cannot observe or measure like cultural factors or difference in business practices across
companies; or variables that change over time but not across entities (i.e. national policies,
federal regulations, international agreements, etc.). This is, it accounts for individual
heterogeneity.
With panel data you can include variables at different levels of analysis (i.e. students, schools,
districts, states) suitable for multilevel or hierarchical modeling.
Some drawbacks are data collection issues (i.e. sampling design, coverage), non-response in the
case of micro panels or cross-country dependency in the case of macro panels.
Fixed Effect Model and Random Effect Model are two techniques of analyzing Panel data used
widely. However , we will be using only the Fixed Effect Model in our study.
Model 3: Fixed Effect Model with Time Dummies
Fixed Effect Model : Use fixed-effects (FE) whenever you are only interested in analyzing the
impact of variables that vary over time. FE explore the relationship between predictor and
outcome variables within an entity (country, person, company, etc.). Each entity has its own
individual characteristics that may or may not influence the predictor variables (for example
being a male or female could influence the opinion toward certain issue or the political system of
a particular country could have some effect on trade or GDP or the business practices of a
company may influence its stock price). When using FE we assume that something within the
individual may impact or bias the predictor or outcome variables and we need to control for this.
This is the rationale behind the assumption of the correlation between entity’s error term and
predictor variables. FE remove the effect of those time-invariant characteristics from the
predictor variables so we can assess the predictors’ net effect. Another important assumption of
the FE model is that those time-invariant characteristics are unique to the individual and should
not be correlated with other individual characteristics. Each entity is different therefore the
31 | P a g e

32. entity’s error term and the constant (which captures individual characteristics) should not be
correlated with the others. If the error terms are correlated then FE is no suitable since inferences
may not be correct and you need to model that relationship (probably using random-effects).
Fixed effects
The equation for the fixed effects model becomes:
32 | P a g e
Yi= β1Xi+ α+ ui
Where
-αi(i=1….n) is the unknown intercept for each intercept
-Y is the dependent variable (DV) where i= entity and t= time.
-Xi represents one independent variable (IV),
- β1 is the coefficient for that IV,
-ui is the error term
Dummy Variables : Dummy variables are "proxy" variables or numeric stand-ins
for qualitative facts in a regression model. In regression analysis, the dependent variables may be
influenced not only by quantitative variables (income, output, prices, etc.), but also by qualitative
variables (gender, religion, geographic region, etc.). A dummy independent variable, or a dummy
explanatory variable, which for some observation has a value of 0 will cause that
variable's coefficient to have no role in influencing the dependent variable, while when the
dummy takes on a value 1 its coefficient acts to alter the intercept.
Dummy variables are used frequently in time series analysis with regime switching, seasonal
analysis and qualitative data applications.
Time Dummies: Time dummies have been used in this model to counteract any variation
generated over a period of five years, from 2008 to 2013 .

33. 푵풆풕 푷풓풐풇풊풕 = 휶 + 휷ퟏ푺풑풓풆풂풅 + 휷ퟐ푵푰푰 + 휷ퟑ푪푵푹 + 휷ퟒ푻푫ퟏ + 휷ퟓ푻푫ퟐ +
휷ퟔ푻푫ퟑ + 휷ퟕ푻푫ퟒ + 풆풊
Here, TD represents the Time Dummies
푯ퟎ : All the independent variables taken in this model DO NOT AFFECT the net profit.
푯ퟏ : All the independent variables taken in this model AFFECT the net profit.
33 | P a g e
Table 5: Model Summary
Model R R Square Adjusted
R Square
Standard Error of
the Estimate
F- test
(significance)
0.850 0.722 0.717 10074.88994
140.065
(0.000)
TABLE 6
Model Unstandardized Coefficients Standardized
Coefficients
t-test
(significance)
훽 Standard Error 훽
(Constant)
545.022 1182.590
0.461
(0.645)
Spread
0.107 0.035 0.258
3.047
(0.002)
Non-Interest
Income 0.201 0.107 0.220
1.889
(0.060)
Cash And
Reserves 0.062 0.017 0.387
3.609
(0.000)

34. In this table ,We took 2008 as the base year and created 4 dummies for the next 4 years. The
lower significance level signifies more explanatory power to dependent variable and hence it can
be concluded that the most significant variable in all the years have been Cash and Reserve It
contributed the most in variations in the net profit.
160000
140000
120000
100000
80000
60000
40000
20000
0
-20000
-40000
34 | P a g e
Model 3
1
14
27
40
53
66
79
92
105
118
131
144
157
170
183
196
209
222
235
248
261
274
287
300
313
326
339
352
365
378
Predicted Value Actual Value

35. 35 | P a g e
Model 4: Fixed Effect Model with Bank Dummies
Bank Dummies: Banks have been used as dummies so as to generalize the individual impact of
all the variations in 77 banks.
푵풆풕 푷풓풐풇풊풕 = 휶 + 휷ퟏ푺풑풓풆풂풅 + 휷ퟐ푵푰푰 + 휷ퟑ푪푵푹 + 휷ퟒ푩푫ퟏ + 휷ퟓ푩푫ퟐ +
휷ퟔ푩푫ퟑ +… … + 휷ퟕퟗ푩푫ퟕퟔ + 풆풊
Here , BD represents Bank Dummies
푯ퟎ : All the independent variables taken in this model DO NOT AFFECT the net profit.
푯ퟏ : All the independent variables taken in this model AFFECT the net profit.
Table 5: Model Summary
Model R R Square Adjusted
R Square
Standard Error of the
Estimate
F- test
(significance)
0.938 0.880 0.849 7358.80005
28.347
(0.000)

36. 36 | P a g e
TABLE 6
Model Unstandardized Coefficients Standardized
Coefficients
t-test
(significance)
훽 Standard Error 훽
(Constant)
-10748.541 17895.521
-0.601
(0.549)
Spread
-0.019 0.059 -0.045
-0.318
(0.750)
Non-Interest Income
0.175 0.152 0.191
1.148
(0.252)
Cash And Reserves
0.132 0.030 0.821
4.348
(0.000)
In this table ,We took SBI as the base bank and created 76 dummies for the remaining 76 banks.
The lower significance level signifies more explanatory power to dependent variable and hence it
can be concluded that the most significant variable in all the Banks have been Cash and Reserve
It contributed the most in variations in the net profit.

37. 37 | P a g e
-100000
-50000
0
50000
100000
150000
1
15
29
43
57
71
85
99
113
127
141
155
169
183
197
211
225
239
253
267
281
295
309
323
337
351
365
379
Predicted Value Actual Value
Model 4

38. 5.CONCLUSION
In this paper, we have made an attempt to identify the key determinants of profitability of all the
banks in India. The analysis is based on pool regression and fixed effect regression model(in the
cases of time and individual banks). We used panel data from the year 2008 to 2012. The study
has brought out that the explanatory power of some variables is significantly high. Such
variables include Net Spread, Non Interest Income and Cash And Reserve. However, some
variables namely Profit per employee, Cash-Deposit Ratio, Operating Expense, Rate Of Equity,
Offices, Non Performing Assets and Business per employee are found with low explanatory
power. Hence the variables Net spread, non interest income (NII) and cash and reserve (CNR)
have a significant relationship with Net Profit, where spread has the maximum influence and
Cash - Deposit Ratio has the least effect on net profit. On introducing Time and Bank Dummies
under the fixed effect model, we have found that only Cash And Reserve has an effect on bank
profitability.
Although this research was carefully prepared, we are still aware of its limitations and
shortcomings.
First of all, even though the research tried to cover all operating nationalised, private and public
sector banks in India, some were left out due to unavailability of data.
Second, only a period of five years have been covered in this study. It would have been better if
a larger time period was covered. It'd have given a better picture of the banking system in India.
Third, the project is done on few variables affecting the profitability. There are bound to be
other variables which may considerably affect the profitability of banks.
Fourth, Panel Data cannot be studied and analyzed using the software package we've used for
applying regression, that is, The SPSS Package.
Fifth, the variable ‘age of the company’ being the influential factor to profit per employee has
been excluded as the earnings of private banks have been better instead of the public banks, the
old members of the said industry.
However, the banks are now facing a number of challenges such as frequent changes in
technology required for modern banking, stringent prudential norms, increasing competition,
38 | P a g e

39. worrying level of NPA.s, rising customer expectations, increasing pressure on profitability,
assets-liability management, liquidity and credit risk management, rising operating expenditure,
shrinking size of spread and so on. The reforms in banking sector have also brought the
profitability under pressure. RBI.s efforts to adopt international banking standards have further
forced the banks to shift the focus to profitability for survival. Hence, profitability has become
major area of concern for bank’s management. In fact, profit is an important criteria to measure
the performance of banks in addition to productivity, financial and operational efficiency.
39 | P a g e

40. 40 | P a g e
BIBLIOGRAPHY
The following sources have been very helpful in designing this paper and helping us to
understand and analyze the various aspects of banking, economics and statistical arenas.
 www.rbi.org
 www.wikipedia.com
 www.princeton.edu
 www.ebw.in
 B.S. Badola, Richa Verma (2006), “DETERMINANTS OF PROFITABILITY OF
BANKS IN INDIA-A MULTIVARIATE ANALYSIS”
 Damodar N. Gujarati, Sangeetha(2011), “Basic Econometrics’’
 http://www.investopedia.com/