Grand project

“A Study on Impact of NPA on Profitability and Liquidity In Public Sector Banks”

Table of Contents
Chapter - 1. INTRODUCTION TO TOPIC............................................................................... 2

What is an NPA? .............................................................................................................................................. 2
Chapter – 2. LITERATURE REVIEW ...................................................................................... 5
Chapter – 3. INTRODUCTION OF BANKING INDUSTRY .................................................. 8

History of Banking In India .......................................................................................................................... 8
Reserve Bank Of India (RBI).................................................................................................................... 11
Structure Of Indian Banking Industry.................................................................................................... 12
Aggregate Performance of the Banking Industry: ............................................................................. 18
Challenges facing by banking industry ................................................................................................. 20
Classification of Assets ............................................................................................................................... 23
Categories of NPAs ...................................................................................................................................... 23
Reporting Format For NPA – Gross And Net Npa ........................................................................... 25
Types Of Npa:................................................................................................................................................. 26
Impact Of Npa: ............................................................................................................................................... 26
Procedures for NPA Identification in India. ........................................................................................ 27
Chapter – 4. RESEARCH METHODOLOGY ..............................................................32

Scope of the study:- ...................................................................................................................................... 32
Research objective:- ..................................................................................................................................... 32
Methodology:- ................................................................................................................................................ 32
Tools and techniques: .................................................................................................................................. 32
Limitation ......................................................................................................................................................... 33
Tools and techniques: .................................................................................................................................. 33
Chapter – 5. DATA BASE AND METHODOLOGY .....................................................35

Hypothesis of the Study. ............................................................................................................................. 35
Chapter – 6. FINDING ............................................................................................................... 50
Chapter – 7. CONCLUSION ..................................................................................................... 51
Chapter – 8. REFERENCES ..................................................................................................... 52

S.K.Patel Institute of Management and Computer Studies (MBA)

Page 1


Chapter - 1. INTRODUCTION TO TOPIC
What is an NPA?
An asset becomes non-performing when it ceases to generate income to the Bank. Thus, a
non-performing asset (NPA) is defined as a credit facility in respect of which the interest and
or instalments of principal has remained „overdue‟ for a „specified period‟ of time. The
concept of „specified period‟ is reduced in a phased manner. The shortening of the period is
from 4 quarters in 1993 when the concept of IRAC norms was first introduced in India to the
present level of 90 days.
Thus from 31.3.2004 an advance or loan (other than direct agricultural advance) shall be
classified as an NPA where Interest and / or instalment of principal remain overdue for a period of more than 90 days
in respect of a term loan.
The account remains out of order in respect of an overdraft / cash credit for more than 90
days.
The bills remain overdue for a period of more than 90 days in the case of bills purchased
and discounted.
Any amount to be received remains overdue for a period more than 90 days in respect of
any other accounts.

In case of direct agricultural advances, w.e.f. 30.9.2004, a loan granted for short duration
crops will be treated as NPA, if the instalment of principal or interest thereon remains
overdue for 2 crop seasons. In the case of long duration crops, the loan will be treated as NPA
if the instalment of principal or interest thereon remains overdue for 1 crop season.
Explanation of some terms used in NPA management
Security Interest:
Security Interest means right, title and interest of any kind whatsoever upon property, created
in favour of any secured creditor and includes mortgage, charge, hypothecation and
assignment


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Wilful defaulters:
"A Wilful Default” would be deemed to have occurred if any of the following events is noted
The unit has defaulted in meeting its payment / repayment obligations to the lender even
when it has the capacity to honour the said obligations.
The unit has defaulted in meeting its payment / repayment obligations to the lender and
has not utilised the finance from the lender for the specific purposes for which finance
was availed of but has diverted the funds for other purposes.
The unit has defaulted in meeting its payment/repayment obligations to the lender and has
siphoned off the funds so that the funds have not been utilised for the specific purpose for
which finance was availed of, nor are the funds available with the unit in the form of
other assets.”
Factors contributing to NPAs:
According to a recent study conducted by the RBI, the underlying reasons for NPAs in India
can be classified into two heads, namely:
1. Internal Factors
2. External Factors
Internal Factors:
Diversion of funds for expansion/diversification/modernisation or for taking up new
projects
Diversion of funds for assisting or promoting associate concerns
Time or cost overrun during the project implementation stage
Business failures due to product failure, failure in marketing, etc
Inefficiency in management
Slackness in credit management & monitoring
Inappropriate technology or problems related to modern technology


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External Factors:
Recession in the economy as a whole
Input or power shortage
Price escalation of inputs
Exchange rate fluctuation
Accidents & natural calamities
Changes in government policies relating to excise & import duties, pollution control
orders, etc
Government loan waiver scheme
Other Factors:
Apart from the above factors, there are certain other factors which are responsible for
standard assets becoming NPAs. They are :
Liberalisation of the economy & the consequent pressures from liberalisation like
severe competition, reduction of tariffs, removal of restrictions
Poor monitoring of credits & the failure to recognise early warning signals shown by
standard assets
Promoters‟ over-optimism in setting up large projects
Sudden crashing of capital markets & the failure to raise adequate funds
Granting of loans to certain sectors on the basis of the Government‟s directives rather
than commercial imperatives
Mismatch of funding i.e. using loans granted for short term for long term transactions
High leveraging & high cost of borrowing


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Chapter – 2. LITERATURE REVIEW
There is several research paper studies for carried out or find out the impact of NPA on
profitability and liquidity in public sector banks.
Dr. A. Shyamala (June -2012), “NPAs in Indian banking sector: impact on profitability”
using secondary data for years 10 year, The data has been analyzed using ratio. Like Gross
NPA to Gross Advances, Net NPA to Net Advances, Gross NPA to Total Assets, Net NPA to
Total Assets and covered Area under study of SBI Group, Nationalized Banks Group and
Private Banks Group and
Shahbaz Haneef, Tabassum Riaz, Muhammad Ramzan, “Impact of Risk Management on
Non-Performing Loans and Profitability of Banking Sector of Pakistan” (April 2012,)using
secondary data for the years 10 year and covered Area under study 5 banks.
Mahipal Singh Yadav, ( June, 2011) “Impact of Non Performing Assets on Profitability and
Productvity of Public Sector Banks in India” has conclude that non-performing assets in
public sector banks affects fifty percent profitability.
Siraj K.K Prof. (Dr). P. Sudarsanan Pillai, (March|2012) “A Study on the Performance of
Non-Performing Assets (NPAs) of Indian Banking During Post Millennium Period” has
conclude that NPA remained as an area of concern as it indicates the real efficiency of credit
risk management)
Anshu bansal (January 15, 2012 “A study on recent trends in risk management of
nonperforming assets (npas) by public sector banks in india” Types of data: Primary and
secondary data years :2007-2011 Area under study: all Public Sector Banks in India. Scope:
30% banks as sample, based at Dehradun and nearby surrounding towns and cities The
research work has been divided into three major steps, (1)namely: Theoretical study of NPAs;
(2)Historical study of NPAs and (3)analyzing the recent trends of NPAs. (4)Mathematical
and statistical tools such as percentage, trend analysis conclude that NPA shows the actual
burden of banks.


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Dr.Hosmani ,Mr.Jagadish Hudagi (December 2011,) “Unearthing the epidemic of non-per
forming assets -a study with reference to public sector banks in india” Types of data:
secondary data years : 2005-2010. Area under study: Nonperforming assets in Commercial
banks operating in India wise public sector banks has been taken in to account Scope: Indian
banking sector for 5 year The study conducted on the topic unearthing the epidemic of non
performing assets with reference to public sector banks in India, found that there is a slight
improvement in the asset quality reflected by decline in the diverse NPA percentage.
Neha Kalra, Shaveta Gupta ,Rajesh Bagga “Non-Performing Assets: A Brunt on Financial
Performance of Banks” Types of data: secondary data years : (1998-2009 Area under study:
public sector: private sector: foreign banks: Scope: Indian & foreign banking sector for 10
year The money locked up in NPAs is not available for productive use and adverse effect on
banks' profitability is there)
Dr. Dhirajjain Ms. Nasreen Sheikh (September 2012, ) “A comparative study of loan
performance, npa and Net profit in selected indian private banks” Types of data: secondary
years : 2001-2011. Area under study: Axis, ICICI Bank, IDBI Bank, HDFC Bank, Induslnd
Bank, Kotak Mahindra Bank,Yes Bank,South Indian Bank, ING Vysya Bank, CITI Union
Bank Scope: Indian banking sector for 10 year The overall performance shows that it is the
moderately correlated.
P.Malyadri2.S.Sirisha“Asset Quality and Non Performing Assets of Indian Commercial
Banks” Types of data: secondary data years : 1996-2010, Area under study: NPA‟s of Indian
Scheduled Commercial Banks. Scope: Indian banking sector for 14 year The asset quality of
banks in India has been improving over the past few years as reflected in the declining NPA
to advances ratio.
Ms. Rajni Saluja, Dr. Roshan Lal (NOVEMBER-2007) “Comparative Analysis On
Non‐Performing Assets NPAS Of Public Sector, Private Sector And Foreign Banks In India”
Types of data: secondary data years: 2004‐2009 Area under study: public sector, private
sector and foreign banks are selected Scope: Indian banking sector for 5 year It can be
concluded that NPAs are not confined to PSBs alone but are present in private banks and
foreign banks as well. There is more of NPAs in non‐priority sector than priority sector.


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Chandan Chatterjee Jeet Mukherjee 3.Dr. Ratan Das (November 2012,) Management Of
Non Performing Assets - A Current Scenario” Types of data: secondary data years :20052011 Area under study: study of NPA‟ s of public sector banks, private sector banks and
foreign sector banks Scope: Indian banking sector for 6 year The NPAs have a negative
influence on the achievement of capital adequacy level, funds mobilization and deployment
policy, banking system credibility, productivity and overall economy.


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Chapter – 3. INTRODUCTION OF BANKING INDUSTRY
Definition of Bank
“An organization, usually a corporation, chartered by a state or federal government, which
does most or all of the following: receives demand deposits and time deposits, honors
instruments drawn on them, and pays interest on them; discounts notes, makes loans, and
invests in securities; collects checks, drafts, and notes; certifies depositor's checks; and issues
drafts and cashier's checks.”

Definition of banking
In general terms, “The business activity of accepting and safeguarding money owned by other
individuals and entities, and then lending out this money in order to earn a profit” So we can
say that Banking is a company, which transacts the business of banking. The Banking
Regulations Acts defines the business as banking by stating the essential function of a banker.
The term banking is defined as “Accepting for the purpose of leading or investment, deposits
of money from the public, repayable on demand or otherwise and withdrawal by cheque,
draft, order or otherwise.”

History of Banking In India
Without a sound and effective banking system in India it cannot have a healthy economy. The
banking system of India should not only be hassle free but it should be able to meet new
challenges posed by the technology and any other external and internal factors.
For the past three decades India's banking system has several outstanding achievements to its
credit. The most striking is its extensive reach. It is no longer confined to only metropolitans
or cosmopolitans in India. In fact, Indian banking system has reached even to the remote
corners of the country. This is one of the main reasons of India's growth process.
The government's regular policy for Indian bank since 1969 has paid rich dividends with the
nationalization of 14 major private banks of India. Not long ago, an account holder had to
wait for hours at the bank counters for getting a draft or for withdrawing his own money.


Page 8

Today, he has a choice. Gone are days when the most efficient bank transferred money from
one branch to other in two days. Now it is simple as instant messaging or dials a pizza.
Money has become the order of the day. The first bank in India, though conservative, was
established in 1786. From 1786 till today, the journey of Indian Banking System can be
segregated into three distinct phases. They are as mentioned below:
PHASE I
The General Bank of India was set up in the year 1786. Next were Bank of Hindustan and
Bengal Bank. The East India Company established Bank of Bengal (1809), Bank of Bombay
(1840) and Bank of Madras (1843) as independent units and called it Presidency Banks.
These three banks were amalgamated in 1920 and Imperial Bank of India was established
which started as private shareholders banks, mostly Europeans shareholders.
In 1865 Allahabad Bank was established and first time exclusively by Indians, Punjab
National Bank Ltd. was set up in 1894 with headquarters at Lahore.
Between 1906 and 1913, Bank of India, Central Bank of India, Bank of Baroda, Canara
Bank, Indian Bank, and Bank of Mysore were set up. Reserve Bank of India came in 1935.
During the first phase the growth was very slow and banks also experienced periodic failures
between 1913 and 1948. There were approximately 1100 banks, mostly small.
To streamline the functioning and activities of commercial banks, the Government of India
came up with The Banking Companies Act, 1949 which was later changed to Banking
Regulation Act 1949 as per amending Act of 1965 (Act No. 23 of 1965).
PHASE II
Government took major steps in this Indian Banking Sector Reform after independence. In
1955, it nationalized Imperial Bank of India with extensive banking facilities on a large scale
especially in rural and semi-urban areas. It formed State Bank of India to act as the principal
agent of RBI and to handle banking transactions of the Union and State Governments all over
the country.
Seven banks forming subsidiary of State Bank of India was nationalized in 1960 on 19th July,
1969, major process of nationalization was carried out. It was the effort of the then City


Page 9

Minister of India, Mrs. Indira Gandhi. 14 major commercial banks in the country were
nationalized.
Second phase of nationalization Indian Banking Sector Reform was carried out in 1980 with
seven more banks. This step brought 80% of the banking segment in India under Government
ownership. The following are the steps taken by the Government of India to Regulate
Banking Institutions in the Country:
©. 1949: Enactment of Banking Regulation Act.
©. 1955: Nationalization of State Bank of India.
©. 1959: Nationalization of SBI subsidiaries.
©. 1961: Insurance cover extended to deposits.
©. 1969: Nationalization of 14 major banks.
©. 1971: Creation of credit guarantee corporation.
©. 1975: Creation of regional rural banks.
©. 1980: Nationalization of seven banks with deposits over 200 crore.
Banking in the sunshine of Government ownership gave the public implicit faith and
immense confidence about the sustainability of these institutions.
PHASE III
This phase has introduced many more products and facilities in the banking sector in its
reforms measure. In 1991, under the chairmanship of M Narasimham, a committee was set up
by his name which worked for the liberalization of banking practices.
The country is flooded with foreign banks and their ATM stations. Efforts are being put to
give a satisfactory service to customers. Phone banking and net banking is introduced.
The entire system became more convenient and swift. Time is given more importance than
money. The financial system of India has shown a great deal of resilience. It is sheltered from


Page 10

any crisis triggered by any external macroeconomics shock as other East Asian Countries
suffered.

Reserve Bank Of India (RBI)
The central bank of the country is the Reserve Bank of India (RBI). It was established in
April 1935 with a share capital of Rs. 5 crores on the basis of the recommendations of the
Hilton Young Commission.
The share capital was divided into shares of Rs. 100 each fully paid which was entirely
owned by private shareholders in the beginning. The Government held shares of nominal
value of Rs. 2, 20,000Reserve Bank of India was nationalized in the year 1949.
The general superintendence and direction of the Bank is entrusted to Central Board of
Directors of 20 members, the Governor and four Deputy Governors, one Government official
from the Ministry of Finance, ten nominated Directors by the Government to give
representation to important elements in the economic life of the country, and four nominated
Directors by the Central Government to represent the four local Boards with the headquarters
at Mumbai, Kolkata, Chennai and New Delhi. Local Boards consist of five members each
Central Government appointed for a term of four years to represent territorial and economic
interests and the interests of co-operative and indigenous banks.
The Reserve Bank of India Act, 1934 was commenced on April 1, 1935. The Act, 1934 (II of
1934) provides the statutory basis of the functioning of the Bank.
The Bank was constituted for the need of following:
To regulate the issue of banknotes to maintain reserves with a view to securing monetary
stability and
To operate the credit and currency system of the country to it


Page 11


Structure Of Indian Banking Industry
Banking Industry in India functions under the sunshade of Reserve Bank of India - the
regulatory, central bank. Banking Industry mainly consists of:
• Commercial Banks
• Co-operative Banks

The commercial banking structure in India consists of: Scheduled Commercial Banks
Unscheduled Bank. Scheduled commercial Banks constitute those banks which have been
included in the Second Schedule of Reserve Bank of India (RBI) Act, 1934. RBI in turn
includes only those banks in this schedule which satisfy the criteria laid down vide section 42
(60) of the Act. Some co-operative banks are scheduled commercial banks although not all
co-operative banks are. Being a part of the second schedule confers some benefits to the bank
in terms of access to accommodation by RBI during the times of liquidity constraints. At the
same time, however, this status also subjects the bank certain conditions and obligation
towards the reserve regulations of RBI.

For the purpose of assessment of performance of banks, the Reserve Bank of India categorize
them as public sector banks, old private sector banks, new private sector banks and foreign
banks.


Page 12


Sr.No.

1

2

3

4

5

Nationalized
Banks

Allahabad Bank
Ltd.

Andhra Bank Ltd.

Old Private
Sector
Banks

Bank

8

Ltd.

Bank Ltd.

Express Bank

Bank Ltd.

Bank of India Ltd.

Federal Bank Ltd

Bank of

ING Vysya Bank

Maharashtra Ltd.

Ltd.

Kashmir

Central Bank of

Karnataka Bank

India Ltd.

Ltd.

Corporation Bank

Karur Vysya Bank

Ltd.

Ltd.

10

IDBI Bank Ltd.

11

Indian Bank Ltd.

Bank
American

Ltd.

Dena Bank Ltd.

Commercial

Development Credit

Dhanalakshmi

9

Axis Bank Ltd.

City Union Bank

Bank Ltd.
7

Foreign Banks

Abu Dhabi

Ltd.

Bank of Baroda

Canara Bank Ltd.

Banks

Catholic Syrian

Jammu and
6

New Private Sector

Lakshmi Vilas
Bank Ltd.
Nainital Bank
Ltd.
Ratnakar Bank
Ltd.

Bank
HDFC Bank Ltd.

Internasional
Indonesia

ICICI Bank Ltd.

IndusInd Bank Ltd.

Kotak Mahindram
Bank Ltd.

Yes Bank Ltd.

-

Bank of
America NA
Bank of Ceylon
Bank of Nova
Scotia
(Scotia Bank)
Bank of Tokyo
Mitsubishi UFJ
Barclays Bank
PLC

-

BNP Paribas

-

Calyon Bank
Chinatrust

-


Commercial
Bank

Page 13


12

13

14

15

16

17

18

19

20

Indian Overseas
Bank Ltd.

SBI Commercial
and International

South Indian Bank

Commerce Ltd.

Ltd.

Bank Ltd.
Punjab National
Bank Ltd.
Syndicate Bank
Ltd.
UCO Bank Ltd.
Union Bank of
IndiaLtd.
United Bank of
India Ltd.
Vijaya Bank Ltd.

Citibank N.A.

-

DBS Bank

Bank Ltd.

Oriental Bank of

Punjab and Sind

-

Tamilnad
Mercantile

-

Bank Ltd.

Deutsche Bank
AG

-

-

HSBC

-

-

-

-

-

-

-

-

-

-

-

-

Shinhan Bank

-

-

Société Générale

-

-

Sonali Bank

-

-

-

-

JPMorgan Chase
Bank
Krung Thai
Bank
Mashreq Bank
psc
Mizuho
Corporate Bank
Royal Bank of
Scotland

State Bank of
21

Bikaner
and Jaipur Ltd.

22

23

24

25

State Bank of
Hyderabad Ltd.
State Bank of India
Ltd.
State Bank of
Mysore Ltd.
State Bank of
Patiyala Ltd.


Standard
Chartered Bank
State Bank of
Mauritius

Page 14


26
27

State Bank of

-

-

UBS

-

Travankore

-

VTB

Industry scenario of Indian Banking Industry:

The growth in the Indian Banking Industry has been more qualitative than quantitative and it
is expected to remain the same in the coming years. Based on the projections made in the
"India Vision 2020" prepared by the Planning Commission and the Draft 10th Plan, the report
forecasts that the pace of expansion in the balance-sheets of banks is likely to decelerate. The
total assets of all scheduled commercial banks by end-March 2010 is estimated at Rs
40,90,000 crores. That will comprise about 65 per cent of GDP at current market prices as
compared to 67 per cent in 2002-03. Bank assets are expected to grow at an annual composite
rate of 13.4 per cent during the rest of the decade as against the growth rate of 16.7 per cent
that existed between 1994-95 and 2002-03. It is expected that there will be large additions to
the capital base and reserves on the liability side.

The Indian Banking industry, which is governed by the Banking Regulation Act of India,
1949 can be broadly classified into two major categories, nonscheduled banks and scheduled
banks. Scheduled banks comprise commercial banks and the co-operative banks. In terms of
ownership, commercial banks can be further grouped into nationalized banks, the State Bank
of India and its group banks, regional rural banks and private sector banks (the old/ new
domestic and foreign). These banks have over 67,000 branches spread across the country.

The Public Sector Banks(PSBs), which are the base of the Banking sector in India account for
more than 78 per cent of the total banking industry assets. Unfortunately they are burdened
with excessive Non Performing assets (NPAs), massive manpower and lack of modern
technology. On the other hand the Private Sector Banks are making tremendous progress.
They are leaders in Internet banking, mobile banking, phone banking, ATMs. As far as
foreign banks are concerned they are likely to succeed in the Indian Banking Industry. In the
Indian Banking Industry some of the Private Sector Banks operating are IDBI Bank, ING
Vyasa Bank, SBI Commercial and International Bank Ltd, Bank of Rajasthan Ltd. and banks
from the Public Sector include Punjab National bank, Vijaya Bank, UCO Bank, Oriental


Page 15

Bank, Allahabad Bank among others. ANZ Grindlays Bank, ABN-AMRO Bank, American
Express Bank Ltd, Citibank are some of the foreign banks operating in the Indian Banking
Industry.
As far as the present scenario is concerned the Banking Industry in India is going through a
transitional phase. The first phase of financial reforms resulted in the nationalization of 14
major banks in 1969 and resulted in a shift from Class banking to Mass banking. This in turn
resulted in a significant growth in the geographical coverage of banks. Every bank had to
earmark a minimum percentage of their loan portfolio to sectors identified as “priority
sectors”. The manufacturing sector also grew during the 1970s in protected environs and the
banking sector was a critical source. The next wave of reforms saw the nationalization of 6
more commercial banks in 1980. Since then the number of scheduled commercial banks
increased four-fold and the number of bank branches increased eight-fold.
After the second phase of financial sector reforms and liberalization of the sector in the early
nineties, the Public Sector Banks (PSB) s found it extremely difficult to compete with the
new private sector banks and the foreign banks. The new private sector banks first made their
appearance after the guidelines permitting them were issued in January 1993. Eight new
private sector banks are presently in operation. These banks due to their late start have access
to state-of-the-art technology, which in turn helps them to save on manpower costs and
provide better services.
During the year 2000, the State Bank Of India (SBI) and its 7 associates accounted for a 25
percent share in deposits and 28.1 percent share in credit. The 20 nationalized banks
accounted for 53.2 percent of the deposits and 47.5 percent of credit during the same period.
The share of foreign banks (numbering 42), regional rural banks and other scheduled
commercial banks accounted for 5.7 percent, 3.9 percent and 12.2 percent respectively in
deposits and 8.41 percent, 3.14 percent and 12.85 percent respectively in credit during the
year 2000.


Page 16

Current Scenario:
The industry is currently in a transition phase. On the one hand, the PSBs, which are the
mainstay of the Indian Banking system are in the process of shedding their flab in terms of
excessive manpower, excessive non Performing Assets (Npas) and excessive governmental
equity, while on the other hand the private sector banks are consolidating themselves through
mergers and acquisitions.
PSBs, which currently account for more than 78 percent of total banking industry assets are
saddled with NPAs (a mind-boggling Rs 830 billion in 2000), falling revenues from
traditional sources, lack of modern technology and a massive workforce while the new
private sector banks are forging ahead and rewriting the traditional banking business model
by way of their sheer innovation and service. The PSBs are of course currently working out
challenging strategies even as 20 percent of their massive employee strength has dwindled in
the wake of the successful Voluntary Retirement Schemes (VRS) schemes.
The private players however cannot match the PSB‟ s great reach, great size and access to
low cost deposits. Therefore one of the means for them to combat the PSBs has been through
the merger and acquisition (M& A) route. Over the last two years, the industry has witnessed
several such instances. For instance, HDFC BANK‟ s merger with Times Bank ICICI
BANK‟ s acquisition of ITC Classic, Anagram Finance and Bank of Madura. Centurion
Bank, Indusind Bank, Bank of Punjab, Vysya Bank are said to be on the lookout. The UTI
bank- Global Trust Bank merger however opened a pandora‟ s box and brought about the
realization that all was not well in the functioning of many of the private sector banks.
Private sector Banks have pioneered internet banking, phone banking, anywhere banking,
mobile banking, debit cards, Automatic Teller Machines (ATMs) and combined various other
services and integrated them into the mainstream banking arena, while the PSBs are still
grappling with disgruntled employees in the aftermath of successful VRS schemes. Also,
following India‟ s commitment to the W To agreement in respect of the services sector,
foreign banks, including both new and the existing ones, have been permitted to open up to
12 branches a year with effect from 1998-99 as against the earlier stipulation of 8 branches.


Page 17

Talks of government diluting their equity from 51 percent to 33 percent in November 2000
have also opened up a new opportunity for the takeover of even the PSBs. The FDI rules
being more rationalized in Q1FY02 may also pave the way for foreign banks taking the M&
A route to acquire willing Indian partners.
Meanwhile the economic and corporate sector slowdown has led to an increasing number of
banks focusing on the retail segment. Many of them are also entering the new vistas of
Insurance. Banks with their phenomenal reach and a regular interface with the retail investor
are the best placed to enter into the insurance sector. Banks in India have been allowed to
provide fee-based insurance services without risk participation, invest in an insurance
company for providing infrastructure and services support and set up of a separate
jointventure insurance company with risk participation.

Aggregate Performance of the Banking Industry:
Aggregate deposits of scheduled commercial banks increased at a compounded annual
average growth rate (Cagr) of 17.8 percent during 1969-99, while bank credit expanded at a
Cagr of 16.3 percent per annum. Banks‟ investments in government and other approved
securities recorded a Cagr of 18.8 percent per annum during the same period.
In FY01 the economic slowdown resulted in a Gross Domestic Product (GDP) growth of only
6.0 percent as against the previous year‟ s 6.4 percent. The WPI Index (a measure of
inflation) increased by 7.1 percent as against 3.3 percent in FY00. Similarly, money supply
(M3) grew by around 16.2 percent as against 14.6 percent a year ago.
The growth in aggregate deposits of the scheduled commercial banks at 15.4 percent in FY01
percent was lower than that of 19.3 percent in the previous year, while the growth in credit by
SCBs slowed down to 15.6 percent in FY01 against 23 percent a year ago.
The industrial slowdown also affected the earnings of listed banks. The net profits of 20 listed
banks dropped by 34.43 percent in the quarter ended March 2001. Net profits grew by 40.75
percent in the first quarter of 2000-2001, but dropped to 4.56 percent in the fourth quarter of
2000-2001. On the Capital Adequacy Ratio (CAR) front while most banks managed to fulfill
the norms, it was a feat achieved with its own share of difficulties. The CAR, which at
present is 9.0 percent, is likely to be hiked to 12.0 percent by the year 2004 based on the
Basle Committee recommendations. Any bank that wishes to grow its assets needs to also


Page 18

shore up its capital at the same time so that its capital as a percentage of the risk-weighted
assets is maintained at the stipulated rate. While the IPO route was a much-fancied one in the
early „90s, the current scenario doesn‟ t look too attractive for bank majors.
Consequently, banks have been forced to explore other avenues to shore up their capital base.
While some are wooing foreign partners to add to the capital others are employing the M& A
route. Many are also going in for right issues at prices considerably lower than the market
prices to woo the investors.
Interest Rate Scene:
The two years, post the East Asian crises in 1997-98 saw a climb in the global interest rates.
It was only in the latter half of FY01 that the US Fed cut interest rates. India has however
remained more or less insulated. The past 2 years in our country was characterized by a
mounting intention of the Reserve Bank Of India (RBI) to steadily reduce interest rates
resulting in a narrowing differential between global and domestic rates.
The RBI has been affecting bank rate and CRR cuts at regular intervals to improve liquidity
and reduce rates. The only exception was in July 2000 when the RBI increased the Cash
Reserve Ratio (CRR) to stem the fall in the rupee against the dollar. The steady fall in the
interest rates resulted in squeezed margins for the banks in general.
Governmental Policy:
After the first phase and second phase of financial reforms, in the 1980s commercial banks
began to function in a highly regulated environment, with administered interest rate structure,
quantitative restrictions on credit flows, high reserve requirements and reservation of a
significant proportion of lendable resources for the priority and the government sectors. The
restrictive regulatory norms led to the credit rationing for the private sector and the interest
rate controls led to the unproductive use of credit and low levels of investment and growth.
The resultant „financial repression‟ led to decline in productivity and efficiency and erosion
of profitability of the banking sector in general.
This was when the need to develop a sound commercial banking system was felt. This was
worked out mainly with the help of the recommendations of the Committee on the Financial
System (Chairman: Shri M. Narasimham), 1991. The resultant financial sector reforms called
for interest rate flexibility for banks, reduction in reserve requirements, and a number of
structural measures. Interest rates have thus been steadily deregulated in the past few years


Page 19

with banks being free to fix their Prime Lending Rates(PLRs) and deposit rates for most
banking products. Credit market reforms included introduction of new instruments of credit,
changes in the credit delivery system and integration of functional roles of diverse players,
such as, banks, financial institutions and non-banking financial companies (Nbfcs). Domestic
Private Sector Banks were allowed to be set up, PSBs were allowed to access the markets to
shore up their Cars.

Challenges facing by banking industry
The banking industry in India is undergoing a major transformation due to changes in
economic condition and continuous deregulation. These multiple changes happening one
after other has a ripple effect on a bank trying to graduate from completely regulated sellers
market to completed deregulated customers market.
Deregulation
This continuous deregulation has made the banking market extremely competitive with
greater autonomy, operational flexibility, and decontrolled interest rate and liberalized norms
for foreign exchange. The deregulation of the industry coupled with decontrol in interest rates
has led to entry of a number of players in the banking industry. At the same time reduced
corporate credit off thanks to sluggish economy has resulted in large number of competitors
battling for the same pie
New Rules:
As a result, the market place has been redefined with new rules of the game. Banks are
transforming to universal banking, adding new channels with lucrative pricing and freebees
to offer. Natural fall out of this new players, new channels squeezed spreads, demanding
customers better service, marketing skills heightened competition, new rules of the
game pressure on efficiency missed opportunities. Need for new orientation diffused
customer loyalty. Bank has led to a series of innovative product offerings catering to various
customer segments, specifically retail credit


Page 20

Efficiency:
This in turn has made it necessary to look for efficiencies in the business. Bank need to
access low cost funds and simultaneously improve the efficiency. The banks are facing
pricing pressure, squeeze on spread and have to give thrust on retail assets.
Diffused customer loyalty:
This will definitely impact customer preferences, as they are bound to react to the value
added offerings. Customers have become demanding and the loyalties are diffused. These are
multiple choices; the wallet share is reduced per bank with demand on flexibility and
customization. Given the relatively low switching costs; customer retention calls for
customized service and hassle free, flawless service delivery.
Misaligned mindset:
These changes are creating challenges, as employees are made to adapt to changing
conditions. There is resistance to change from employees and the seller market mindset is yet
to be changed coupled with fear of uncertainty and control orientation. Acceptance of
technology in but the utilization is not maximized.
Competency gap:
Placing the right skill at the right place will determine success. The competency gap needs to
be addressed simultaneously otherwise there will be missed opportunities. The focus of
people will be doing work but not providing solutions, on escalating problems rather than
solving them and on disposing customers instead of using the opportunity to cross sell.
Strategic Options with Banks to Cope With the Challenges:
Leading players in the industry have embarked on a series of strategic and tactical initiatives
to sustain leadership. The major initiatives include:
Investing in state of the start of the art technology as the back bone of to ensure reliable
service delivery.
Leveraging the branch network and sales structure to mobilize low cost current and
savings deposits.


Page 21


Making aggressive forays in the retail advances segments of home and personal loans.
Implementing organization wide initiatives involving people, process and technology to
reduce the fixed costs and the cost per transaction.
Focusing on fee based income to compensate foe squeezed spread.
Innovating products to capture customer „mind share‟ to begin with and later the wallet
share.
Improving the asset quality as Basel II norms.


Page 22


Classification of Assets
Standard Assets:
The standard assets consist of assets which are totally regular, safe and conducted as per
norms of sanction. However, during the operations of such accounts, some of them, at times,
show signs of deviations, sickness, out of order position wherein they became irregular.
When such irregularities are noticed, they are classified as „Watch Category” assets with
Code No. 12 but continues to be a part of “Standard Asset”. These accounts need higher level
of monitoring and have to be regularised before these irregularities continue for more than 90
days. Provision requirement for a standard asset (including Watch Category asset) is given
below:

Categories of NPAs
Banks are required to classify NPAs further into following categories, based on the period for
which asset has remained non-performing and realisability of dues.
 Sub-standard Asset
 Doubtful Asset
 Loss Asset
Substandard Assets:
With effect from 31st March 2005, substandard asset is one which has remained NPA for a
period less than or equal to 12 months. Its Asset Code is 20. The provision requirement in
substandard asset was earlier flat 10% of the outstanding dues, irrespective of the category of
the advance (secured or clean).
Now RBI has removed the CAP on the unsecured exposures and individual Bank Boards
were given the freedom to formulate their own policy guidelines for prudential norms on
unsecured exposures. Simultaneous with this liberalisation, RBI has made norms of provision
requirement on unsecured exposure of Banks more stringent. Unsecured exposure is defined
as an exposure where the realisable value of security as stipulated and ascertained by the
valuation is not more than 10% `ab initio‟. That means all clean / unsecured advances when
they become NPA as substandard asset, will now (w.e.f. 31.3.2005) require a provision at
20% of the outstanding


Page 23

Balances. As against this, the normal secured advances, when moving to NPA as substandard
asset will require 10% of the outstanding balance as provision (no change from existing
system). Thus from 31.3.2005 onwards the substandard asset will have 2 segments with
different provision requirement as below:(a) Substandard – secured assets – Code 21 – provision at 20% of outstanding dues
(b) Substandard – unsecured assets – Code 22 – provision at 20% of outstanding dues
Doubtful Assets:
It consists of 3 stages - Doubtful I, Doubtful II and Doubtful III. The provision requirement in
each stage of Doubtful asset will be as under:
Doubtful I (Code 31) - Assets remaining for a period of 12 months in Doubtful category –
provision requirement shall be 20% of RVS + 100% of shortfall in security (i.e. NPAs over
12 months upto 24 months)
Bank
Group
Year

Standard Assets
Amt

%

523724
90.6
2002-03
610435
92.2
2003-04
830029
94.6
2004-05
1092607
96.2
2005-06
1425519
97.3
2006-07
1778476
97.8
2007-08
2237556
97.9
2008-09
2673534
97.8
2009-10
3272914 97.77
2010-11
3825500
97
2011-12
AVERAGE 1827029.4 95.92
18270294
TOTAL

Sub-standard
assets

Doubtful assets

Loss assets

Total NPAs

Amt

%

Amt

%

Amt

%

Amt

%

14909
16909
11068
11453
14275
17290
26603
28791
34973
62300
23857.1
238571

2.6
2.6
1.3
1
1
1
0.9
1
1
1.6
1.4

32340
28756
30799
25028
19873
19291
21019
25383
33180
49000
28466.9
284669

5.6
4.3
3.5
2.2
1.4
1.1
0.9
0.9
0.99
1.2
2.21

6840
5876
5929
5636
4826
4018
4296
5750
6463
6000
5563.4
55634

1.2
0.9
0.7
0.5
0.3
2
0.1
0.2
0.19
0.2
0.63

52807
50149
45619
41378
38602
39739
44043
57301
71326
83772
52473.6
524736

9.4
7.8
5.4
3.7
2.7
2.2
2.2
2.1
2.1
2
4


Page 24


Reporting Format For NPA – Gross And Net Npa
Name of the Bank:
Position as on………
PARTICULARS
1) Gross Advanced *
2) Gross NPA *
3) Gross NPA as %age of Gross Advanced
4) Total deduction( a+b+c+d )
( a ) Balance in interest suspense a/c **
( b ) DICGC/ECGC claims received and held pending
adjustment
( c ) part payment received and kept in suspense a/c
( d ) Total provision held ***
5) Net advanced ( 1-4 )
6) Net NPA ( 2-4 )
7) Net NPA as a %age of Net Advance

*excluding Technical write-off of Rs.________crore.
**Banks which do not maintain an interest suspense a/c to park the accrued interest on
NPAs may furnish the amount of interest receivable on NPAs.
***Excluding amount of Technical write-off (Rs.______crore) and provision on standard
assets. (Rs._____crore).


Page 25


TYPES OF NPA:
1. Gross NPA
2. Net NPA
Gross NPA:
Gross NPAs are the sum total of all loan assets that are classified as NPAs as per RBI
guidelines as on Balance Sheet date. Gross NPA reflects the quality of the loans made by
banks. It consists of all the nonstandard assets like as sub-standard, doubtful, and loss assets.
It can be calculated with the help of following ratio: Gross NPAs Ratio = Gross NPAs
Gross Advances
Net NPA:
Net NPAs are those type of NPAs in which the bank has deducted the provision regarding
NPAs. Net NPA shows the actual burden of banks. Since in India, bank balance sheets
contain a huge amount of NPAs and the process of recovery and write off of loans is very
time consuming, the provisions the banks have to make against the NPAs according to the
central bank guidelines, are quite significant. That is why the difference between gross and
net NPA is quite high. It can be calculated by following: Net NPAs = Gross NPAs –
Provisions
Gross Advances – Provisions

IMPACT OF NPA:

Profitability:
NPA means booking of money in terms of bad asset, which occurred due to wrong choice of
client. Because of the money getting blocked the prodigality of bank decreases not only by
the amount of NPA but NPA lead to opportunity cost also as that much of profit invested in
some return earning project/asset. So NPA doesn‟t affect current profit but also future stream
of profit, which may lead to loss of some long-term beneficial opportunity. Another impact of
reduction in profitability is low ROI (return on investment), which adversely affect current
earning of bank.


Page 26


Liquidity:
Money is getting blocked, decreased profit lead to lack of enough cash at hand which lead to
borrowing money for shortest period of time which lead to additional cost to the company.
Difficulty in operating the functions of bank is another cause of NPA due to lack of money
Routine payments and dues.
Involvement of management: Time and efforts of management is another indirect cost which
bank has to bear due to NPA. Time and efforts of management in handling and managing
NPA would have diverted to some fruitful activities, which would have given good returns.
Now day‟s banks have special employees to deal and handle NPAs, which is additional cost
to the bank.
Credit loss: Bank is facing problem of NPA then it adversely affect the value of bank in terms
of market credit. It will lose its goodwill and brand image and credit which have negative
impact to the people who are putting their money in the banks

Procedures for NPA Identification in India.

Internal Checks and Control
Since high level of NPAs dampens the performance of the banks identification of potential
problem accounts and their close monitoring assumes importance. Though most banks have
Early Warning Systems (EWS) for identification of potential NPAs, the actual processes
followed, however, differ from bank to bank. These early warning signals used by banks are
generally independent of risk rating systems and asset classification norms prescribed by
RBI. The major components/processes of a EWS followed by banks in India as brought out
by a study conducted by Reserve Bank of India at the instance of the Board of Financial
Supervision are as follows:
Designating Relationship Manager/ Credit Officer for monitoring account/s
Preparation of `know your client' profile
Credit rating system
Identification of watch-list/special mention category accounts
Monitoring of early warning signals


Page 27


Relationship Manager/Credit Officer
The Relationship Manager/Credit Officer is an official who is expected to have complete
knowledge of borrower, his business, his future plans, etc. The Relationship Manager has to
keep in constant touch with the borrower and report all developments impacting borrowable
account. As a part of this contact he is also expected to conduct scrutiny and activity
inspections. In the credit monitoring process, the responsibility of monitoring a corporate
account is vested with Relationship Manager/Credit Officer
Know your client' profile (KYC)
Most banks in India have a system of preparing `know your client' (KYC) profile/credit
report. As a part of `KYC' system, visits are made on clients and their places of
business/units. The frequency of such visits depends on the nature and needs of relationship.
Credit Rating System
The credit rating system is essentially one point indicator of an individual credit exposure
and is used to identify measure and monitor the credit risk of individual proposal. At the
whole bank level, credit rating system enables tracking the health of banks entire credit
portfolio. Most banks in India have put in place the system of internal credit rating. While
most of the banks have developed their own models, a few banks have adopted credit rating
models designed by rating agencies. Credit rating models take into account various types of
risks viz. financial, industry and management, etc. associated with a borrowable unit. The
exercise is generally done at the time of sanction of new borrowable account and at the time
of review renewal of existing credit facilities.
Watch-list/Special Mention Category
The grading of the bank's risk assets is an important internal control tool. It serves the need
of the Management to identify and monitor potential risks of a loan asset. The purpose of
identification of potential NPAs is to ensure that appropriate preventive / corrective steps
could be initiated by the bank to protect against the loan asset becoming non-performing.
Most of the banks have a system to put certain borrowable accounts under watch list or
special mention category if performing advances operating under adverse business or
economic conditions are exhibiting certain distress signals.


Page 28


Early Warning Signals It is important in any early warning system, to be sensitive to signals
of credit deterioration. A host of early warning signals are used by different banks for
identification of potential NPAs. Most banks in India have laid down a series of operational,
financial, transactional indicators that could serve to identify emerging problems in credit
exposures at an early stage. Further, it is revealed that the indicators which may trigger early
warning system depend not only on default in payment of installment and interest but also
other factors such as deterioration in operating and financial performance of the borrower,
weakening industry characteristics, regulatory changes, general economic conditions, etc.
Early warning signals can be classified into five broad categories viz.

a) Financial
b) Operational
c) Banking
d) Management and
e) External factors
Financial:
Related warning signals generally emanate from the borrowers' balance sheet, income
expenditure statement, statement of cash flows, statement of receivables etc. Following
common warning signals are captured by some of the banks having relatively developed
EWS.
Financial warning signals
Persistent irregularity in the account
Default in repayment obligation
Devolvement of LC/invocation of guarantees
Deterioration in liquidity/working capital position
Substantial increase in long term debts in relation to equity
Declining sales
Operating losses/net losses
Rising sales and falling profits
Disproportionate increase in overheads relative to sales


Page 29

Rising level of bad debt losses Operational warning signals
Low activity level in plant
Disorderly diversification/frequent changes in plan
Nonpayment of wages/power bills
Loss of critical customer/s
Frequent labor problems
Evidence of aged inventory/large level of inventory
Management related warning signals
Lack of co-operation from key personnel
Change in management, ownership, or key personnel
Desire to take undue risks
Family disputes
Poor financial controls
Fudging of financial statements
Diversion of funds
Banking related signals
Declining bank balances/declining operations in the account
Opening of account with other bank
Return of outward bills/dishonored cheques
Sales transactions not routed through the account
Frequent requests for loan
Frequent delays in submitting stock statements, financial data, etc.

Signals relating to external factors
Economic recession
Emergence of new competition
Emergence of new technology
Changes in government / regulatory policies
Natural calamities


Page 30


Willful Defaulters
RBI has issued revised guidelines in respect of detection of willful default and diversion and
siphoning of funds. As per these guidelines a willful default occurs when a borrower defaults
in meeting its obligations to the lender when it has capacity to honor the obligations or when
funds have been utilized for purposes other than those for which finance was granted. The list
of willful defaulters is required to be submitted to SEBI and RBI to prevent their access to
capital markets. Sharing of information of this nature helps banks in their due diligence
exercise and helps in avoiding financing unscrupulous elements. RBI has advised lenders to
initiate legal measures including criminal actions, wherever required, and undertake a
proactive approach in change in management, where appropriate.


Page 31


Chapter – 4. RESEARCH METHODOLOGY
Scope of the study:This research report is based on historical data of public sector banks and the source
for the data is Trends and progress report of banking industry from RBI website. For the
analysis the main NET NPA and NET PROFIT are being taken etc. and area of research in
banking industry very wide but my report is limited to these public sector banks only and
time period of data is ten year it‟s to get probable output and on the basis of this forecasting
can be done.

Research objective:To analysis the impact of non-performing assets on profitability of Public sector
banks.
To evaluate the impact of non-performing assets on profitability with other variables.
To examine the impact of non-performing assets on efficiency and Liquidity.
To know the ratio of NPA and Advances of public sector banks

Methodology:Types of data: secondary data
Sampling unit: - All public sector banks
Period of the study: 10 year (1/4/2002 to 31/3/2012)
Data collection: journals, articles, internet, books

Tools and techniques:
Descriptive test
Correlation analysis
Regression analysis
Jarque-Bera,
Kurtosis,
Skewness,
Pairwise Granger Causality Tests,
Johansen Cointegration Test, and
Ratio of NPA and Advances.


Page 32


Limitation
This project Report study only past 10 year data so the chances of relationship between
variable me be wrong.
We have taken assumption for find out liquidity result, the Net advances will return back as
their perfect time period, so no outstanding amt remaining at end of the year.
Report study only on public sector banks.

Tools and techniques:
Correlation Analysis:
There can be both short-run and long-run relationships between financial time series.
Correlation coefficients are used for examining short-run co-movements and multicollinearity among the variables. If correlation coefficient is greater than 0.8, it indicates that
multi collinearity exists. The population correlation coefficient, p, (-1 ≤ p ≤ 1) measures the
degree of linear association between two variables.

Co-integration Test:
Johansen's cointegration test (Johansen and Juselius, 1990) has been applied to check whether
the long run equilibrium relationship exists between the variables. The Johansen approach to
cointegration test is based on two test statistics, viz., trace statistic, and maximum eigenvalue
statistic. The trace statistic can be specified as:
Trace (r, k) = - T∑ ln (1-λi)

(1)

Where λi is the i th largest eigenvalue of matrix Π and T is the number of observations. In the
trace test, the null hypothesis is that the number of distinct cointegrating vector(s) is less than
or equal to the number of cointegration relations (r). From the above, it is clear that λ

trace

equals Zero when all λ= 0. The maximum eigenvalue test examines the null hypothesis of
exactly r cointegrating relations against the alternative of r + 1 cointegrating relations with
the test statistic:

λ max (r, r+1) = -T ln (1- λr+1)


(2)

Page 33


Granger Causality test:
At the end, the Granger Causality test (Engle and Granger, 1987) has been used to find out
the direction of causality between the variables. To test for Granger Causality, the following
bivariate regression model can be used:
m
n
yt = α0 + ∑ αiYt-1 + ∑ βjXt-1 + εt
i=1

(3)

j=1

m
n
xt = ω0 + ∑ γiYt-1 + ∑ θjXt-1 + εt
i=1

(4)

j=1

The null hypothesis is H0: ∑ βj = 0 in the first regression equation of y i.e. lagged X terms do
not belong in the regression means X does not cause y.
If all the coefficients of x in the first regression equation of y, i.e. βj for j = 1...... are
significant, then the null hypothesis that x does not cause y is rejected.


Page 34


Chapter – 5. DATA BASE AND METHODOLOGY
Hypothesis of the Study.
Ho= There is no significant association between gross NPAs to gross advances of the
public sector banks.
Ho= There is no significant association between priority sector, non priority sector, public
sector & from NPAs point of view.
Ho= There is no significant reduction in the portion of gross NPAs to gross advances.
Ho= There is no significant relation between Net NPA and NET PROFIT.


Page 35


(1) To analysis the impact of Non-performing assets on profitability

of Public sector banks.
Table-1: Net Npa to Net Profit of Public Sector Banks. (Amount in Crores)

Year

Net NPA

Net Profit

2002-03

24877

12295

2003-04

19335

16546

2004-05

16904

15784

2005-06

14566

16539

2006-07

14145

20152

2007-08

17726

26592

2008-09

21033

34394

2009-10

29644

57109

2010-11

36071

70331

2011-12

39423

81700

(Source: Report On Trend And Progress of Banking In India from 2003 to 2012)
Table No-1.1 : Descriptive Statistics
Mean
Median
Std. Dev.
Skewness
Kurtosis
Jarque-Bera
Probability

NET NPA
23472.30
20184.00
8826.273
0.758552
2.151510
1.258974
0.532865

NET PROFIT
35144.20
23372.00
25332.07
0.857480
2.150133
1.526400
0.466172

Analysis:
Jarque-Bera: From the Data it is clear that Probability is more than 0.05 that‟s shows that
data follow the normality in the past 10 year in NPA & PROFIT. The probability is
respectively 0.532 & 0.4466 that‟s show normally distributed.
Kurtosis: From the result it is clear that data follow the Platykurtic. The standard is less than
3 then data follow Platykurtic, if more than 3 then data follow the Leptokurtic. Here both
variable NPA & PROFIT result is 2.1589 &2.1501 respectively.


Page 36

Skewness: The standard of the test is in negative or positive; here result shows positive in
both the variable its show the normally distributed follow by data.
Table No-1.2: Correlation Analysis:
NET NPA
1
0.912

NET NPA
NET PROFIT

NET PROFIT
1

Analysis:
Correlation result shows the positive correlation between NET NPA and NET PROFIT. Its
show the one of the objective to know the impact of npa on profitability its clear in result that
Correlation is 0.912 is more than 0.800, it indicate high correlation between them.
Table No-1.3: Regression analysis.

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C
NET NPA

-26288.31
2.617234

10378.30
0.416446

-2.533007
6.284688

0.0351
0.0002

R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)

0.831569
0.810516
11027.00
9.73E+08
-106.1547
39.49731
0.000237

Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat

35144.20
25332.07
21.63094
21.69146
21.56455
0.468111

Analysis:
The regression test on NET NPA and NET PROFIT show the R squared is .831569 means
the both variable in the test show the relation between each other positive and data will affect
with each other. If the variable is more than 2 then the chances of getting R-square is 1 means
High relationship between them, therefore we can applied the test of Adjusted R-squared.
Here the variable more than two does not affect with each other. And Adjusted R-square is
0.810516 it shows high relationship between NPA and PROFIT if the NPA increase its affect
the PROFIT margin of public sector Banks.


Page 37

Table No-1.4: Granger Causality Tests.
Null Hypothesis
NETPROFIT does not Granger Cause NETNPA

F-Statistic
0.07989

Probability
0.9251

Decision
Accepted

NETNPA does not Granger Cause NETPROFIT

.86378

0.5055

Accepted

Analysis:
Granger Causality test say if the probability is less than 0.05 reject the null hypothesis, if
more than 0.05 accepted the hypothesis. Here in Net Profit to Net NPA the probability is
0.9251 means Accepted and NET NPA to NET PROFIT is 0.5055 the null hypothesis
Accepted and will affected the each other, here if Net Profit Decreases its means the affected
by NPA.
Table No-1.5: Johansen Cointegration Test.
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)

Eigenvalue

Trace
Statistic

0.05
Critical Value

Prob.**

None *
At most 1

0.950581
0.001385

24.07038
0.011090

15.49471
3.841466

0.0020
0.9159

Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)

Eigenvalue

None *
At most 1

0.950581
0.001385

Max-Eigen
0.05
Statistic Critical Value
24.05929
0.011090

14.26460
3.841466

Prob.**
0.0011
0.9159

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level


Page 38


(2) To evaluate the impact of non-performing assets on profitability with other
variables.
Table No-2: NPA of Priority, Non-priority, and Remaining Public to Net Profit
(Amount in Crores)
Year
2002-03
2003-04
2004-05
2005-06
2006-07
2007-08
2008-09
2009-10
2010-11
2011-12

Priority Sector
24939
23841
21926
22374
22954
25287
24318
30848
41245
48524

Non-priority Sector
26781
25698
23249
18664
15158
14153
19251
25929
29803
34502

Public Sector
1087
610
444
341
490
299
474
524
278
746

Net Profit
12295
16546
15784
16539
20152
26592
34394
57109
70331
81700

(Source: Report on Trend And Progress of Banking In India from 2003 to 2012)
Table No-2.1: Descriptive test.

Mean
Median
Std. Dev.
Skewness
Kurtosis
Jarque-Bera
Probability

PRIORITY
28625.60
24628.50
9082.669
1.390414
3.408916
3.291758
0.192843

NON PRIORITY
23318.80
24473.50
6501.273
0.111797
2.064054
0.385829
0.824553

PUBLIC
529.3000
482.0000
242.2244
1.224214
3.841249
2.792707
0.247498

NET PROFIT
35144.20
23372.00
25332.07
0.857480
2.150133
1.526400
0.466172

Analysis:
Jarque-Bera: From the Data it is clear that Probability is more than 0.05 that‟s show the data
follow the normality in the past 10 year in NPA & PROFIT. The probability is respectively in
priority sector, Non priority sector & Public sector to Net Profit like 0.1928 & 0.82450 &
0.2474 & 0.4661 that‟s shows normally distributed.
3 in both the variable like Non priority & Net Profit and more than 3 than data follow the
Leptokurtic. Both variable priority sector and public sector result is 3.4089 &3.8141
respectively.


Page 39

Skewness: The standard of the test is in negative or positive; here result shows positive in the
entire variable its show the normally distributed follow by data.
Table No-2.2: Correlation Analysis.
PRIORITY
NON PRIORITY
PUBLIC
NET PROFIT

PRIORITY
1
0.77145
0.08411
0.94424

NONPRIORITY

PUBLIC

NETPROFIT

1
0.45452
0.6574

1
-0.09704

1

Analysis:
Correlation results show the positive correlation between PRIORITY and NET PROFIT and
Average correlation between NON PRIORITY and NET PROFIT and no correlation between
PUBLIC SECTOR and NET PROFIT. The one of the objective to know the impact of npa on
profitability with other variable it‟s clear in result that Correlation is respectively 0.94424,
0.6574 and -0.09704. If correlation more than 0.800 then it indicate high relation and between
0 .8 to 0.5 then it indicate average relation.
Table No-2.3: Regression Test.
Variable

Coefficient

Std. Error

t-Statistic

Prob.

C
PRIORITY
NONPRIORITY
PUBLIC

-31528.83
2.689335
-0.026662
-18.30542

11185.74
0.562783
0.879563
15.07430

-2.818664
4.778634
-0.030313
-1.214346

0.0304
0.0031
0.9768
0.2702

R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)

0.922970
0.884455
8610.854
4.45E+08
-102.2430
23.96392
0.000970

Mean dependent var
S.D. dependent var
Schwarz criterion
Durbin-Watson stat

35144.20
25332.07
21.24861
21.36964
21.11583
1.433864

Analysis:
The regression test on NPA of Priority sector, Non-Priority sector and Public sector and NET
PROFIT show the R squared is 0.922970 means the both variable in the test show the relation
between each other is very high and data will affect with each other. If the variable is more
than 2 the chances of getting R-square is 1 means High relationship between them, therefore
we can applied the test of Adjusted R-squared. Here the variable more than two does not


Page 40

affect with each other. And Adjusted R-square is 0.884455 it show high relationship between
NPA of Priority sector, Non- priority sector, public sector and PROFIT. If the NPA of
different sector increase the decrease in the PROFIT margin of public sector Banks
Table No-2.4: Pairwise Granger Causality Tests.
Null Hypothesis

F-Statistic

Probability

Decision

NETPROFIT does not Granger Cause PRIORITY

11.9584

0.0372

Rejected

PRIORITY does not Granger Cause NETPROFIT

3.7898

0.1510

Accepted

NETPROFIT does not Granger Cause NONPRIORITY

1.87085

0.2968

Accepted

NONPRIORITY does not Granger Cause NETPROFIT

13.1264

0.0328

Rejected

NETPROFIT does not Granger Cause PUBLIC

1.57091

0.3414

Accepted

PUBLIC does not Granger Cause NETPROFIT

1.75564

0.3127

Accepted

Analysis:
Granger Causality test say if the probability is less than 0.05 reject the null hypothesis and if
more than 0.05 accepted the null hypothesis. Here in Net Profit to Priority Sector the
probability is 0.0372 means Rejected the null hypothesis and Net Profit to Non Priority sector
the probability is 0.2968 means Accepted the null hypothesis and Net Profit to Public Sector
the probability is 0.3414 means Accepted the null hypothesis and other like Priority to Net
profit is accepted, Non priority to Net profit Rejected and Public to Net profit Accepted. The
results show the accepted and rejection of null hypothesis, and will affected the each other,
here if Net Profit Decreases its means the affected by NPA.


Page 41


(3) To examine the impact of non-performing assets on efficiency and Liquidity.
Table No-3: Gross NPA to Gross Advance and Ratio of Gross NPA to Gross
Advance. (Amount in Crores)

Year

Gross NPA

Gross Advance

Gross NPA To Gross
Advances

2002-03

56,473

577813

6.85

2003-04

54,090

661975

7.79

2004-05

52,880

877825

5.53

2005-06

41,358

1134724

3.64

2006-07

38,968

1464493

2.66

2007-08

40,595

1819074

2.23

2008-09

44,957

2282081

2.19

2009-10

59,926

2736347

2.23

2010-11

74,614

3265245

2.5

2011-12

117,200

3645235

3.1

(Source: Report On Trend And Progress of Banking In India from 2003 to 2012)
Table No-3.1: Descriptive Statistics of Public sector bank.

Mean
Median
Std. Dev.
Skewness
Kurtosis
Jarque-Bera
Probability

GROSS ADVANCE
1846481.
1641784.
1097176.
0.393184
1.780627
0.877186
0.644943

GROSS NPA
58106.10
53485.00
23429.14
1.742166
5.165252
7.012038
0.030016

Analysis:
Jarque-Bera: From the Data it is clear that Probability is more than 0.05 that‟s show the data
follow the normality in the past 10 year in GROSS NPA & GROSS ADVANCES. Here the
probability is respectively 0.030016 & 0.644943 that‟s show normally distributed in GROSS
ADVANCE and GROSS NPA Do not follow the normal distribution.


Page 42

3 in gross advances and gross NPA it follow the Leptokurtic. Here the result respectively in
GROSS ADVANCES and GROSS NPA is 1.780627 and 5.165252.
GROSS ADVANCE
1
0.67952

GROSS ADVANCE
GROSS NPA

GROSS NPA
1

Analysis:
Correlation result shows the positive correlation between GROSS NPA and GROSS
ADVANCE its shows the one of the objective to know the liquidity impact of NPA in public
sector Banks, it‟s clear in result that Correlation is 0.67952 if more than 0.800 it indicate high
correlation between them, but here not more correlation between gross advance and gross npa
its result the npa does not affected greater to Liquidity.
Table No-3.3: Regression analysis.

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

31312.51

11740.57

2.667035

0.0285

GROSSADVANCE

0.014511

0.005539

2.619754

0.0307

R-squared
Adjusted R-squared

0.461755
0.394474

Mean dependent var
S.D. dependent var

58106.10
23429.14

S.E. of regression

18231.52


22.63655

Sum squared resid

2.66E+09

Schwarz criterion

22.69706

Log likelihood

-111.1827


22.57016

F-statistic

6.863112

Durbin-Watson stat

0.687269

Prob(F-statistic)

0.030662

Analysis:
The regression test on GROSS ADVANCES and GROSS NPA show the R squared is
0.461755 means the both variable in the test show the relation between each other are not
positive and data will affect with each other is lesser. If the variable is more than 2 the
chances of getting R-square is 1 means High relationship between them, therefore we can


Page 43

applied the test of Adjusted R-squared. Here the variable more than two does not affect with
each other. And Adjusted R-square is 0.394474 it show less relationship between GROSS
ADVANCES and GROSS NPA if the GROSS NPA increase it does not affect the GROSS
ADVANCES of public sector Banks.
Null Hypothesis

F-Statistic

Probability

Decision

GROSS NPA does not Granger Cause GROSS ADVANCE

4.22619

0.1341

Accepted

GROSS ADVANCE does not Granger Cause GROSS NPA

6.35724

.0834

Accepted

Analysis:
Granger Causality test say if the probability is less than 0.05 reject the null hypothesis if more
than 0.05 accepted the null hypothesis. Here in GROSS NPA and GROSS ADVANCES the
probability is 0.1341 means accepted the null hypothesis and in GROSS ADVANCES and
GROSS NPA probability 0.0834 accepted the null hypothesis.
Table No-3.5: Johansen Cointegration test.
Hypothesized
No. of CE(s)

Eigenvalue

Trace
Statistic

0.05
Critical Value

Prob.**

None *
At most 1 *

0.969779
0.688035

37.31267
9.318926

15.49471
3.841466

0.0000
0.0023


Hypothesized
No. of CE(s)

Eigenvalue

Max-Eigen
Statistic

0.05
Critical Value

Prob.**

None *
At most 1 *

0.969779
0.688035

27.99374
9.318926

14.26460
3.841466

0.0002
0.0023



Page 44

Ratio analysis of ratio between gross advances and gross NPA
On the basis of Ratio of last 10 year gross advances and gross NPA is high in 2002-03 to
2005-06, is approximately between in 2003-04 is 7.79 thereafter slowly decreases and low in
the year 2008-09 is 2.19. If we taken average of all the ratio is 3.871 is good than individual
year ratio.


Page 45


(4) To know the ratio of NPA and Advances of public sector banks.
Table No-4: Net NPA to Net Advance and Ratio of NPA and Advance.
Year

Net NPA

Net Advance

Net NPA To Net
Advances

2002-03

24877

5,49,351

3.03

2003-04

19335

6,31,383

2.99

2004-05

16904

8,48,912

2.06

2005-06

14566

11,06,128

1.32

2006-07

15144

14,40,123

1.05

2007-08

17726

17,97,504

0.99

2008-09

21033

22,60,156

1.09

2009-10

29644

26,32,236

1.09

2010-11

36071

32,03,125

1.1

2011-12

39423

35,21,563

1.4

(Source: Report on Trend and Progress of Banking in India from 2003 to 2012)
Table No-4.1: Descriptive Statistics of Public sector.
Mean
Median
Std. Dev.
Skewness
Kurtosis
Jarque-Bera
Probability

NET ADVANCE
1799048.
1618814.
1068864.
0.362051
1.757141
0.862093
0.649829

NET NPA
23472.30
20184.00
8826.273
0.758552
2.151510
1.258974
0.532865

Analysis:
Jarque-Bera: From the Data it is clear that Probability is more than 0.05 it show the data
follow the normality in the past 10 year in NET ADVANCES and NET NPA. The probability
is respectively 0.64989 & 0.532865 it show normally distributed.
3 then data follow Platykurtic, if more than 3 then data follow the Leptokurtic. Here both
variable NET ADVANCE & NET NPA result is 1.757141 &2.151510 respectively.


Page 46

NETADVANCE
1
0.7969

NETADVANCE
NETNPA

NET NPA
1

Analysis:
Correlation result shows the positive correlation between NET NPA and NET ADVANCE.
Its show the one of the objective to know the impact of npa on liquidity. It‟s clear in result the
Correlation is 0.7969 is equal to 0.800 it indicate high correlation between them.
Table No-4.3: Regression test.
Variable

Coefficient

Std. Error

t-Statistic

Prob.

C
NETADVANCE

11632.64
0.006581

3641.754
0.001763

3.194240
3.731944

0.0127
0.0058

R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)

0.635160
0.589555
5654.641
2.56E+08
-99.47599
13.92740
0.005772

Mean dependent var
S.D. dependent var
Schwarz criterion
Durbin-Watson stat

23472.30
8826.273
20.29520
20.35571
20.22881
0.459668

Analysis:
The regression test on NET NPA and NET ADVANCE shows the R squared is 0.635160
means the both variable in the test show the relation between each other positive and data will
affect with each other. If the variable is more than 2 then the chances of getting R-square is 1,
means High relationship between them, therefore we can applied the test of Adjusted Rsquared. Here the variable more than two does not affect with each other. And Adjusted Rsquare is 0.589555 it show average relationship between NET NPA and NET ADVANCE.


Page 47

Null Hypothesis

F-Statistic

Probability

Decision

NET NPA does not Granger Cause NET ADVANCE

44.7597

.0058

Rejected

NET ADVANCE does not Granger Cause NET NPA

1.76669

0.3112

Accepted

Analysis:
Granger Causality test say if the probability is less than 0.05 reject the null hypothesis if more
than 0.05 accepted the null hypothesis. Here in Net ADVANCE to Net NPA the probability is
0.3112 means Accepted and NET NPA to NET ADVANCE is 0.0058 the null hypothesis
rejected and NET NPA does not cause NET ADVANCE and in second test NET ADVANCE
cause the NET NPA.
Table No-4.5: Johansen Cointegration Test.
Hypothesized
No. of CE(s)

Eigenvalue

Trace
Statistic

0.05
Critical Value

Prob.**

None *
At most 1

0.978742
0.157973

32.18377
1.375550

15.49471
3.841466

0.0001
0.2409


Hypothesized
No. of CE(s)

Eigenvalue

Max-Eigen
Statistic

0.05
Critical Value

Prob.**

None *
At most 1

0.978742
0.157973

30.80822
1.375550

14.26460
3.841466

0.0001
0.2409

Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level


Page 48

Ratio analysi between NET ADVANCES and GROSS NPA
On the basis of Ratio of last 10 year net advances and net NPA is average in 2002-03 to
2011-12 compare to Gross Advances to Gross Npa and approximately between heights in
2002-03 is 3.03 thereafter slowly decreases and low in year 2007-08 is 0.99. If we taken
average of all the ratio is 1.1612 is good than individual year ratio.


Page 49


Chapter – 6. FINDING
a. Form the Analysis we came to know that there is a positive relationship between NPA
and Profitability in public sector banks.
b. After analysis of impact of non-performing assets on profitability with other variables.
Like priority sector, non priority sector, and other variable, show high correlation ship
between NPA with Priority sector and average correlation ship between NPA and NonPriority sector and other public sector.
c. After analysis of the impact of non-performing assets on efficiency and Liquidity show
Average correlation ship.
d. The ratio of NPA and Advances of public sector banks is high.


Page 50


Chapter – 7. CONCLUSION
The NPAs of public sector banks in absolute terms has shown increasing trend till 2003-04 to
2011-12 and declined later on in 2004-05 to 2007-08, where as its test applied in the NET
NAP and NET ADVANCE also prove that‟s the significant impact of NPA on profitability in
public sector banks. If the talk about profitability from applied all the test it‟s null hypothesis
rejected and correlation is more than 0.8 and regression R-square is also good and Johansen
Cointegration Test give the result that null hypothesis is also rejected at 0.05 level.
Result of other variable of priority sector, non-priority sector and remaining public sector its
result of impact on profit is also more and other result for liquidity show there is not much
more but only average impact on liquidity. Indian banking sector is facing a serious problem
of NPA. The extent of NPA is comparatively higher in public sectors banks than the private
sector.
The impact of NPA on profitability them from applied a test we concluded that Correlation
result show the positive correlation between NET NPA and NET PROFIT its show
Correlation is 0.912 is more than 0.800 the indicate high correlation between them. Also
other tools analysis Granger Causality test says if Net Profit Decreases its means the affected
by NPA. The Correlation result show the positive correlation between PRIORITY and NET
PROFIT and Average relation between NON PRIORITY and NET PROFIT and no
correlation between PUBLIC SECTOR And NET PROFIT show the one of the objective to
know the impact of npa on profitability with other variable its clear in result the Correlation is
respectively like 0.94424, 0.6574 and -0.09704 is more than 0.800 the indicate high
correlation and between0 .8 to 0.5between them indicate average.
Correlation result show the positive correlation between GROSS NPA and GROSS
ADVANCE its show the one of the objective to know the liquidity impact of NPA in public
sector Banks, it‟s clear in result the Correlation is 0.67952 if more than 0.800 the indicate
high correlation between them, but here the not more correlation between gross advance and
gross npa its result the npa does not affected greater to Liquidity. Correlation result show the
positive correlation between NET NPA and NET ADVANCE its show the one of the
objective to know the impact of npa on liquidity its clear in result the Correlation is 0.7969 is
more than 0.800 the indicate high correlation between them.


Page 51


Chapter – 8. REFERENCES
1.

Anshu Bansal* (2012) " A Study On Recent Trends In Risk Management Of Non
Performing Assets (Npas) By Public Sector Banks In India”- Journal of Information and
Operations Management ISSN: 0976–7754 & E-ISSN: 0976–7762 , Volume 3, Issue 1,
, pp-50– 56.

2.

Chandan Chatterjee*, Jeet Mukherjee; Dr.Ratan Das (November 2012) “ Management
Of Non Performing Assets - A Current Scenario”

International Journal of Social

Science & Interdisciplinary Research Vol.1 Issue 11, , ISSN 2277 3630.
3.

Dr. A. Shyamala Assistant Professor of Economics,“Npas In Indian Banking Sector:
Impact On Profitability” Vol.1,Issue.V/June; 12pp.1-4 Indian Streams Research Journal

4.

Damodar gujarati “basic Economatrix” Eviews software.

5.

Dr. Anindita Chakraborty*( January -- June 2012) “Employees‟ Perception towards
NPAs: A Comparative Study of Public Sector and Private Sector Banks” Volume-I,
No.-3, Business Spectrum ISSN-2249-4804.

6.

Dr. Dhiraj Jain*, Ms. Nasreen Sheikh,( September 2012) “A Comparative Study Of
Loan Performance

Npa And Net Profit In Selected Indian Private Banks” IRJC

International Journal of Marketing, Financial Services & Management Research Vol.1
Issue 9, , ISSN 2277 3622.
7.

Dr. Namita Rajput, Anu Priya Arora, Baljeet Kaur “Management Of Non-Performing
Assets A Study Of Indian Public Sector Banks” IJMIE Volume 2, Issue 4 ISSN: 22490558.

8.

Dr. Viplaw Kishore Pandey* Mrs. Harmeet Kaur, (May, 2012) “Npa In Banking Sector:
Some Correlational Evidence” Volume 2, Issue 5 ISSN: 2249‐ 7323

9.

Dr.Hosmani.A.P*, Mr.Jagadish Hudagi, (December 2011) “Unearthing The Epidemic Of
Non-Per Forming Assets -A Study With Reference To Public Sector Banks In India”
International Journal of Multidisciplinary Research Vol.1 Issue 8, ISSN 2231 5780.

10. K. Veerakumar*,(2012) “Non-Performing Assets in Priority Sector: A Threat to Indian
Scheduled Commercial Banks

International Research Journal of Finance and

Economics” ISSN 1450-2887 Issue 93 (© EuroJournals Publishing, Inc. 2012
11. Ms. Rajni Saluja & Dr. Roshan Lal, (2010), “Comparative Analysis On
Non‐ Performing Assets (Npas) Of Public Sector, Private Sector And Foreign Banks In
India” Volume No. 1 Issue No. 7 (November)


Page 52

12. Mahipal Singh Yadav,(June, 2011) “Impact Of Non Performing Assets On Profitability
And Productvity Of Public Sector Banks In India” AFBE Journal Volume 4, No. 1, Issn
2071-7873
13. Mr. Sandeep Aggarwal, Assistant Professor, Indira Gandhi P.G. Regional CenterMirpur.Ms. Parul Mittal, Non-Performing Assest: “Comparative Position of Public and
Private Sector Banks in India” International Journal of Business and Management
Tomorrow Vol. 2 No. 1
14. Namita Rajput; Monika Gupta; Mr. Ajay Kumar Chauhan,( September 2012)
“Profitability And Credit Culture Of Npas: An Empirical Analysis Of PSBs”
International Journal of Marketing, Financial Services & Management Research Vol.1
Issue 9, ISSN 2277 3622
15. Neha Kalra, Shaveta Gupta, Rajesh Bagga, “Non-Performing Assets: A Brunt on
Financial Performance of Banks” AFBE JOURNAL Volume 5, No. 2, December, 2012
ISSN 2071-7873
16. P.Malyadri, Sirisha,* , (June 2012) “Asset Quality and Non Performing Assets of Indian
Commercial Banks” Advances in Asian Social Science 224Vol. 1, No. 2 Copyright
©World Science Publisher, United States
17. “RBI issue of trends and progress of banking sector in india” for last 10 year data.
18. Shahbaz Haneef*, Tabassum Riaz Muhammad Ramzan Mansoor Ali Rana Hafiz
Muhammad Ishaq Yasir Karim , Pakistan, (April 2012) “ Impact of Risk Management
on Non-Performing Loans and Profitability of Banking Sector of Pakistan” International
Journal of Business and Social Science Vol. 3 No. 7;
19. Siraj. K.K, Prof. (Dr). P. Sudarsanan Pillai, “A Study on the Performance of NonPerforming Assets (NPAs) of Indian Banking During Post Millennium Period”
International Journal of Business and Management Tomorrow Vol. 2 No. 3


Page 53

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