Analysis of the Efficiency of NBFCs

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A Project Report
ON
“Analysis of the Efficiency of NBFCs and Determinants
of Profitability of Deposit taking NBFCs”
Submitted to
Reserve Bank of India, Kanpur
Submitted by
Vasudha Ruhela
Summer Trainee
DNBS, RBI Kanpur
Pursuing
MSc .Statistics (2yr)
IIT Kanpur

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Declaration
The project entitled “Analysis of the Efficiency of NBFCs and Determinants of Profitability
of Deposit taking NBFCs” is submitted to the Reserve Bank of India as the part of two
months summer internship.
The project is made under the guidance of Shri. Pradip Kumar Kar, General Manager,
DNBS, RBI Kanpur.
The research work has not been submitted elsewhere for award of any degree. The
material borrowed from other sources and incorporated in the thesis has been duly
acknowledged.
I understand that I myself could be held responsible and accountable for plagiarism, if any,
detected later on.
Signature
Vasudha Ruhela
Date
21st
July, 2015

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Acknowledgement
The pre-requisites for a task to be successfully completed are constant inspiration, guidance
and working environment. Fortunately all the requirements were satisfied by RBI, Kanpur for
the successful completion of my project work.
I express my sincere gratitude to Shri. Shekhar Bhatnagar, Regional Director, Reserve Bank
of India, Kanpur for giving me the opportunity to do the project work in Department of Non-
Banking Supervision, RBI Kanpur.
I also express my gratitude to Shri. Pradip Kumar Kar, General Manager, Department of
Non-Banking Supervision, RBI Kanpur for his constant support and guidance.
I would like to express due regards to my project incharge Shri. Gaurav Seth, Manager,
Department of Non-Banking Supervision, RBI Kanpur who took unusual pains and provided
me with his valuable advice during the preparation of work. Last but not least I would like to
thank all my teachers and friends for giving useful suggestions and ideas for improving my
project.

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Contents
Page No.
 Objectives 5
 Introduction 5
 Financial Institutions 6
 Shadow Banking 7
 Evolution of regulation of NBFCs in India 8
 Non-Banking Financial Companies (NBFCs) 8
 Types of NBFCs 9
 Growth of NBFCs 10
 NBFC segment witnessing consolidation
 Performance of Non Bank Financial Institutions 12
 Cross-Country Analysis 12
 Non-Deposit taking Systemically Important NBFCs 13
Asset quality 14
Capital Adequacy 15
Profitability 15
 Deposit taking NBFCs (NBFCs-D) 16
Asset quality 16
Profitability 16
Cost Income Ratio(CIR) 17
Net Interest Margin(NIM) 17
 Determinants of Profitability of NBFCs-D 18
 Criterion of Selection of Variables 19
 Description of data 20
 Data Analysis and Presentation Technique 21
 Scaling of Data 21
 Akaike Information Criterion(AIC) 21
 Description of Models 21
 Different Models Considering Return on Assets as
Profitability Indicator
22
Ordinary Least Squares Regression 22
Fixed Effects Model 23
Random Effects Model 24
 Different Models Considering Net Interest Margin
as Profitability Indicator
27
 Different Models Considering Cost Income Ratio as
Profitability Indicator
28
 Comparison of Kanpur Regional Office with Other
Regional Offices
29
 Conclusion and Recommendations 33
 Limitations 36

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Objectives
The study is conducted to fulfill the following objectives:
 NBFCs in India: An overview
 Current scenario of NBFCs in India
 Performance Analysis of NBFCs.
 To stain out the influential factors behind the NBFC–Deposit taking industry‟s
profitability.
 Comparison of the profitability indicators of NBFC-D of Kanpur-RO with that of the
other regional offices.
Introduction
This project examines the efficiency of firms in the Non Banking Financial Institution
(NBFIs) industry of India. Study of the current performance of NBFCs is shown. Analysis of
the determinants of profitability of Deposit taking NBFCs (NBFCs-D) is being done.
Comparison of the determinants of Kanpur-Regional Office with that of Other Regional
Offices is done. Financial Performance of a financial institution basically depends on its some
key financial determinants. Especially Non Performing Assets (NPA) and Net Owned Fund
(NOF) is main influencing factor. Besides it Operating Expenses, Capital Risk Adequacy
Ratio (CRAR), Net Interest Margin (NII), Provisions and Contingencies (P&C) and Non
Interest Income (NONII) significantly affect the profitability of any NBFI company. In
addition term Deposits, and Total Loans and Advances also affect the profitability. The used
accounting measures/ratios for an analysis of efficiency/profitability are cost-to-income ratio
(CI), net interest margin (NIM), and return on assets (ROA).
Different Statistical techniques such as multiple imputations, panel data regressions have
been used to determine the relationships between variables. And before doing regression
analysis, missing data is estimated using multiple imputation technique. The research is an
attempt to find out the statistically significant key determinants variables and their level of
influence over different profitability indicators cost-to-income ratio (CI), net interest margin
(NIM), and return on assets (ROA).

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Financial Institutions
Financial sector plays an indispensable role in the overall development of a country. The
most important constituent of this sector is the financial institutions, which act as a conduit
for the transfer of resources from net savers to net borrowers, that is, from those who spend
less than their earnings to those who spend more than their earnings. Besides, they provide
assistance to new enterprises, small and medium firms as well as to the industries established
in backward areas. Thus, they have helped in reducing regional disparities by inducing
widespread industrial development. The Government of India, in order to provide adequate
supply of credit to various sectors of the economy, has evolved a well developed structure of
financial institutions in the country. The Financial Institutions in India mainly comprises of
the Central Bank which is known as the Reserve Bank of India, the commercial banks, the
credit rating agencies, the securities and exchange board of India, insurance companies and
the specialized financial institutions in India.
The Reserve Bank of India is India's central banking institution, which is entrusted with the
responsibility of regulating and supervising the Non-Banking Financial Companies by virtue
of powers vested in Chapter III B of the Reserve Bank of India Act, 1934. The regulatory and
supervisory objective is to:
 Ensure healthy growth of the financial companies;
 Ensure that these companies function as a part of the financial system within the
policy framework, in such a manner that their existence and functioning do not lead to
systemic aberrations; and that
 The quality of surveillance and supervision exercised by the Bank over the NBFCs is
sustained by keeping pace with the developments that take place in this sector of the
financial system.1
Both banks and financial institutions are engaged in mobilizing fund from surplus unit to
deficit unit of the economy. Financial institutions are engaged in financial intermediation,
exchanging financial assets on own behalf and on customers behalf, assisting in creation of
financial assets providing investment advice and managing portfolio of participants.
The banks‟ lending to NBFCs
 forms a significant proportion of the NBFC liabilities;
 fluctuates in line with bank allocation to priority lending sectors;
 decreases as the banks expand in the rural areas relative to urban areas; but,
 is virtually non-existent for the largest state-owned bank, namely State Bank of India
(SBI) and its affiliates which have significant rural branch network.
Starting with the financial crisis of fall 2008, bank lending to NBFCs experienced a
permanent contraction shock related to the shift of term deposits toward SBI away from other
banks. These bank-NBFC linkages are present primarily for, and affect the credit growth of,
those NBFCs that do loans or asset financing but not the investment companies. Overall, the
findings suggest that in contrast to the prevailing views of shadow banking in the Western
1
https://www.rbi.org.in/Scripts/FAQView.aspx?Id=71

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economies, lending to NBFCs in India is viewed by banks as a substitute for direct lending in
the non-urban areas.
Chart1: Share of different sectors in total assets of the
Indian financial system
Source: www.rbi.org.in
Financial Stability Report (Including Trend and Progress of
Banking in India 2013-14) December 2014
Shadow Banking
The term “shadow bank” was coined by economist Paul McCulley in a 2007 speech at the
annual financial symposium hosted by the Kansas City Federal Reserve Bank inJackson
Hole, Wyoming. In McCulley‟s talk, shadow banking had a distinctly U.S. focus though there
were shadow banking institutions in the UK, Europe and even in China. He referred mainly
to non – bank financial institutions that engaged in what economists call Maturity
transformation. Thus, shadow banking activities include:-
 Credit intermediation – Any kind of lending activity including at least one
intermediary between the saver and the borrower
 Liquidity transformation – Usage of short-term debts like deposits or cash-like
liabilities to finance long-term investments like loans.
 Maturity transformation – Using short-term liabilities to fund investment in long-term
assets
Why are they called shadow banks?
Because there was so little transparency, it often was unclear who owed (or would owe later)
what to whom. As someone put it, the shadow banking entities were characterized by a lack
of disclosure and information about the value of their assets (or sometimes even what the
assets were).
Economist Paul Krugman said that the shadow banking system was the core cause of the
crisis. “As the shadow banking system expanded to rival or even surpass conventional
banking in importance, politicians and government officials should have realized that they
were recreating the kind of financial vulnerability that made the Great Depression possible –
and they should have responded by extending regulations and the financial safety net to cover
these new institutions. Influential figures should have proclaimed a simple rule: anything that

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does what a bank does, anything that has to be rescued in crises the way banks are, should be
regulated like a bank.”
Do we have shadow banks in India?
The answer is yes. It is yes, because we have Financial institutions which accept deposits and
extend credit like banks, but we do not call them shadow banks; we call them the Non-
Banking Finance Companies (NBFCs). Are they in fact shadow banks? No, because these
institutions have been under the regulatory Structure of the Reserve Bank of India, right from
1963 i.e. 50 full years before the developed west is doing so.
Evolution of regulation of NBFCs in India
In the wake of failure of several banks in the late 1950s and early 1960s in India, large
number of ordinary depositors lost their money. This led to the formation of the Deposit
Insurance Corporation by the Reserve Bank, to provide guarantee to the depositors. While
this provided the necessary safety net for the bank depositors, the Reserve Bank did note that
there were deposit taking activities undertaken by non-banking companies. Though they were
not systemically as important as the banks, the Reserve Bank initiated regulating them, as
they had the potential to cause pain to their depositors. Later in 1996, in the wake of the
failure of a big NBFC, the Reserve Bank tightened the regulatory structure over the NBFCs,
with rigorous registration requirements, enhanced reporting and supervision. Reserve Bank
also decided that no more NBFC will be permitted to raise deposits from the public. Later
when the NBFCs sourced their funding heavily from the banking system, it raised systemic
risk issues. Sensing that it can cause financial instability, the Reserve Bank brought asset side
prudential regulations onto the NBFCs.2
Non-Banking Financial Companies (NBFCs)
Non-banking financial companies (NBFCs) are fast emerging as an important segment of
Indian financial system. It is an heterogeneous group of institutions (other than commercial
and co-operative banks) performing financial intermediation in a variety of ways, like
accepting deposits, making loans and advances, leasing, hire purchase, etc. Thus, they have
broadened and diversified the range of products and services offered by a financial sector.
Gradually, they are being recognized as complementary to the banking sector due to their
customer-oriented services; simplified procedures; attractive rates of return on deposits;
flexibility and timeliness in meeting the credit needs of specified sectors; etc. NBFCs have
been made mandatory to get registered with the Reserve Bank under Section 45 IA of the
RBI Act, 1934 since January 1997.
The working and operations of NBFCs are regulated by the within the framework of the
(Chapter III B) and the directions issued by it under the Act. As per the RBI Act, a 'non-
banking financial company' is defined as:-
2
“Role of NBFCs in Financial Sector: Regulatory Challenges”, The Frank Moraes oration
lecture delivered by Shri R. Gandhi, Deputy Governor, Reserve Bank of India, available at
https://www.rbi.org.in/scripts/FS_Speeches.aspx?Id=901&fn=14, retrieved on June 30,2015.

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i. a financial institution which is a company;
ii. a non banking institution which is a company and which has as its principal business
the receiving of deposits, under any scheme or arrangement or in any other manner, or
lending in any manner;
iii. Such other non-banking institution or class of such institutions, as the bank may, with
the previous approval of the Central Government and by notification in the Official
Gazette, specify.
Under the Section 45-IA of the RBI Act, 1934, no Non-banking Financial company can
commence or carry on business of a non-banking financial institution without a) obtaining a
certificate of registration from the Bank and without having a Net Owned Funds of Rs. 25
lakhs(Rs two crore since April 1999).
The term 'NOF' means, owned funds (paid-up capital and free reserves, minus accumulated
losses, deferred revenue expenditure and other intangible assets) less, (i) investments in
shares of subsidiaries/companies in the same group/ all other NBFCs; and (ii) the book value
of debentures/bonds/ outstanding loans and advances, including hire-purchase and lease
finance made to, and deposits with, subsidiaries/ companies in the same group, in excess of
10% of the owned funds.3
Chart 2: Types of NBFCs registered with the RBI
W P S (DEPR): 21 / 2011 RBI working paper series
Inter-connectedness of Banks and NBFCs in India: Issues and Policy Implications
3
http://www.archive.india.gov.in/business/business_financing/non_banking.php, retrieved on June 30
th
,2015

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NBFCs are categorized in:
a) In terms of the type of liabilities into Deposit and Non-Deposit accepting NBFCs,
b) Non deposit taking NBFCs by their size into systemically important and other non-deposit
holding companies (NBFC-NDSI and NBFC-ND) and
c) By the kind of activity they conduct.
Growth of NBFCs
In line with the global trend, NBFCs in India too emerged primarily to fill in the gaps in the
supply of financial services which were not generally provided by the banking sector, and
also to complement the banking sector in meeting the financing requirements of the evolving
economy. Over the years NBFCs have grown sizably both in terms of their numbers as well
as the volume of business transactions (RBI, 2009). The number of such financial companies
grew more than seven-fold from 7,063 in 1981 to 51,929 in 1996.8 .Thus, the growth of
NBFCs has been rapid, especially in the 1990s owing to the high degree of their orientation
towards customers and simplification of loan sanction requirements (RBI, 2000).
Further, the activities of NBFCs in India have undergone qualitative changes over the years
through functional specialization. NBFCs are perceived to have inherent ability and
flexibility to take quicker decisions, assume greater risks, and customize their services and
charges according to the needs of the clients. These features, as compared to the banks, have
tremendously contributed to the proliferation of NBFCs in the eighties and nineties. Their
flexible structures allowed them to unbundle services provided by banks and market the
components on a competitive basis. Banks on the other hand, had all along been known for
their rigid structure, especially the public sector banks. This compelled them carry out such
services by establishing „banking subsidiaries‟ in the form of NBFCs. The willingness of
NBFCs to engage in varied forms of financial intermediation, hitherto unavailable to the
banking system, has provided the valuable flexibility in financing new areas of business.
Though the NBFCs are different species and smaller in size as a segment when compared
with the banking system, their relevance to the overall economic development and to certain
specified areas cannot be undermined. The number of NBFCs-D declined considerably with
conversion into non-deposit taking companies, besides closure and mergers of weaker
companies. Incidentally, the regulatory regime also seems to be in favor of reducing the
number of deposit taking NBFCs and consequent migration of depositors towards the
banking system which is better regulated and supervised in line with the global standards.4
4
“Inter-connectedness of Banks and NBFCs in India: Issues
and Policy Implications”, W P S (DEPR) : 21 / 2011 RBI working paper series, 2011, retrieved
on June 30, 2015,pg 9

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NBFC segment witnessing consolidation
The total number of NBFCs registered with the Reserve Bank declined marginally to 12,225
as at end-June 2013 (Chart 3) and finally declined to 12,029 as of March 2014. The number
of NBFCs-D during 2012-13 declined mainly due to the cancellation of Certificates of
Registration (COR) and migration to non-deposit-taking category
Chart 3: Number of NBFCs registered with RBI
(Report on Trend and Progress of Banking in India 2012-13)
As of March 2014, there were 12,029 NBFCs registered with the Reserve Bank, of which 241
were deposit-accepting (NBFCs-D) and 11,788 were non deposit accepting (NBFCs-ND).
NBFCs-ND with assets of 1 billion and above had been classified as Systemically Important
Non-Deposit accepting NBFCs (NBFCs-ND-SI) since April 1, 2007 and prudential
regulations such as capital adequacy requirements and exposure norms along with reporting
requirements were made applicable to them. From the standpoint of financial stability, this
segment of NBFCs assumes importance given that it holds linkages with the rest of the
financial system.5
The share of NBFCs‟ assets in GDP (at current market prices) increased steadily from just 8.4
per cent as on March 31, 2006 to 14 per cent as on March 31, 2014; while the share of bank
assets increased from 75.4 per cent to 95 per cent during the same period (Table 1).In
developed countries this ratio is greater than 50%.In fact, if the assets of all the NBFCs below
Rs.100crore are reckoned, the share of NBFCs‟ assets to GDP would go further.
5
“Chapter II Financial Institutions: Developments and Stability”, Financial Stability Report
(Including Trend and Progress of Banking in India 2013-14) December 2014, available at
https://www.rbi.org.in/Scripts/PublicationsView.aspx?id=16165 , retrieved on June
30th
,2015.

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Table 1: Assets of NBFC and Banking (SCBs) Sectors as a % to GDP
Year
Ratio 2006 2007 2008 2009 2010 2011 2012 2013 2014
NBFC
Assets to
GDP (%)
8.4 9.1 10.1 10.3 10.8 10.9 11.9 12.5 14
Bank
Assets to
GDP (%)
75.4 80.6 86.8 93.0 93.0 92.2 92.7 95.5 95
Sources: i) Reports on Trend and Progress of Banking in India, 2006-2013
ii) Hand Book of Statistics on Indian Economy, 2012-2013
iii) 2nd National Summit: Non-Banking Finance Companies- “The way forward”,
The Associated Chambers of Commerce and Industry of India,23rd January, 2015 –
New Delhi
Note: Assets of NBFC sector include assets of all deposit taking NBFCs and Non -Deposit
Taking NBFCs having assets size Rs. 100 crore and above (NBFCs-ND)
Performance of Non Bank Financial Institutions
 Cross Country Analysis
As per the latest available report6
on Cross-Country analysis of NBFCs:
“Globally, the size of non-bank financial intermediation was equivalent to 117 percent of
GDP as at the end of 2012 for 20 jurisdictions and the euro area4. In absolute terms, total
assets of non-bank financial intermediaries remained at around $ 70 trillion as at end 2012.
US has the largest system of non-bank financial intermediation with assets of $ 26 trillion,
followed by the euro area ($ 22 trillion), the UK ($ 9 trillion) and Japan ($ 4 trillion).
On an average, the size of non-bank financial intermediation in terms of assets was
equivalent to 52 per cent of the banking system. However, there were significant cross-
country differences, ranging from 10 per cent to 174 per cent.
Non-bank financial intermediation is relatively small in the case of emerging market
economies compared to the level of GDP. In India, Turkey, Indonesia, Argentina, Saudi
Arabia the amount of non-bank financial activity remained less than 20 per cent of the GDP
as at end 2012. As such, the size of the non-banking financial sector in India is relatively low,
by global standards”.
6
“Non-Banking Finance Companies: Game Changers”,
Speech delivered by Shri P Vijaya Bhaskar, Executive Director, Reserve Bank of India ,available at
https://www.rbi.org.in/scripts/BS_SpeechesView.aspx?Id=870 ,retrieved on June 30
th
,2015,pg 3

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 Non-Deposit taking Systemically Important NBFCs (NBFCs-ND-SI)
In India, among the non-deposit taking NBFCs, the large NBFCs with Rs. 100 crore and
above assets size17 have been classified as systemically important financial institutions
(NBFC-ND-SI). As these NBFCs are not raising resources by way of public deposits, they
are regulated with fewer rigors compared with NBFCs-D. Even this type of reclassification of
NBFC-ND-SI came into existence since mid-2006 although, the Reserve Bank has initiated
measures effective 2000 to reduce the scope of „regulatory arbitrage‟ between banks, NBFCs-
D and NBFCs-ND (RBI, 2008) recognizing their importance, essentially from the systemic
stability point of view.
With the recent happening of global financial crisis and aftermath, the regulators‟ attention
world over has received increased attention towards the systemically important financial
institutions (SIFIs). Even in the case of India, the extant prudential regulation of NBFCs-ND-
SI are endeavored to bring convergence with that of the deposit taking NBFCs. Accordingly,
it is advisable to introduce the return relating to balance sheets on a monthly basis and a more
detailed returns encompassing the whole operations of the companies on completion of their
annual accounts, as against the quarterly, half yearly annual returns to be filed by the deposit
taking NBFCs.
During 2013-14, the overall balance sheet of NBFCs-ND-SI expanded by 9.5 per cent (Table
2).Loans and advances (a major component on the assets side) increased by 11.2 per cent.
Total borrowings, which constituted more than two-third of their liabilities, increased by 9.8
percent.
Table 2: Consolidated balance sheet of NBFCs-ND-SI
(As of March)
Source: Financial Stability Report
(Including Trend and Progress of Banking in India 2013-14)
(in billion Rs.)
Item 2013 2014P Percentage
Variation
1.Share Capital 674 695 7.40
2. Reserves & Surplus 2,276 2,457 8.00
3.Total Borrowings 8,104 8,902 9.80
4.Current Liabilities &
Provisions
574 647 12.80
Total Liabilities/Assets 11,601 12,701 9.50
1.Loans & Advances 7,600 8,455 11.20
2.Hire Purchase Assets 805 896 11.30
3.Investments 1,945 2,075 6.60
4. Other Assets 1,250 1,276 2.10
Memo Items
1. Capital Market
Exposure(CME)
885 1,029 16.40
2.CME to Total
Assets(percent)
7.6 8.1
3.Leverage Ratio 3.0 3.0

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The financial performance of NBFCs-ND-SI improved during 2013-14 as their net profit to
total income increased from 18.3 per cent to 20.2 per cent. As a result, return on assets rose to
2.3 per cent as of March 2014 from 2.0 per cent a year ago. (Table 3)
Table 3: Financial performance of NBFCs-ND-SI sector
(As of March)
(In billion Rs.)
Items 2013 2014P
1.Total Income 1,272 1,436
2.Total Expenditure 1,039 1,147
3.Net Profit 233 290
4.Total Assets 11,601 12,701
Financial Ratios(percent)
(i) Net Profit to Total Income 18.3 20.2
(ii) Net Profit to Total Assets 2 2.3
Source: RBI supervisory returns
Trend and Progress of Banking in India 2013-14
Asset quality
The asset quality of the NBFCs-ND-SI sector has been deteriorating since the quarter ended
March 2013 (Chart 4). The Reserve Bank issued separate guidelines for both banks and
NBFCs with an objective of mitigating the stress due to their NPAs. NBFCs were advised to
identify incipient stress in their accounts by creating a sub-asset category viz. „Special
Mention Accounts‟ (SMA), which was further divided into three sub-categories (viz., SMA-
0, SMA-1 and SMA-2), based on the extent of principal or interest payment overdue as also
the weakness of their accounts. They were also directed to report relevant credit information
to the Central Repository of Information on Large Credits (CRILC).
Chart4: Asset Quality of NBFCS-ND-SI
Trend and Progress of Banking in India 2013-14
0
0.5
1
1.5
2
2.5
Percent
Gross NPA to Total Advances Net NPA to Total Advances

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Capital adequacy
As per the guidelines, NBFCs-ND-SI are required to maintain a minimum capital consisting
of Tier-I26 and Tier-II capital, of not less than 15 per cent of their aggregate risk-weighted
assets. As of March 2014, by and large, the capital adequacy position of the NBFCs-ND-SI
remained comfortable and was well above prudential norms.
Nevertheless, CRAR of the NBFCs-ND-SI slipped from the peak of 29.0 per cent as of
September 2013 to 27.2 per cent as of March 2014. It subsequently recovered to 27.8 per cent
by the quarter ended September 2014 (Chart 5).
Chart5: CRAR of NBFCS-ND-SI
Financial Stability Report (Including Trend and Progress of Banking in India 2013-14
Profitability
ROA of NBFCs-ND-SI increased to 2.5 per cent in September 2014 after remaining at
around 2.3 percent in previous three quarters (Chart 6).7
Chart 6: Trends in Return on Assets of NBFCS-ND-SI
Financial Stability Report (Including Trend and Progress of Banking in India 2013-14
7
“Chapter II Financial Institutions: Developments and Stability”, Financial Stability Report (Including Trend and
Progress of Banking in India 2013-14) December 2014, available at
https://www.rbi.org.in/Scripts/PublicationsView.aspx?id=16165 , retrieved on June 30th,2015.
0
0.5
1
1.5
2
2.5
Percent
24
25
26
27
28
29
30
Percent

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 Deposit taking NBFCs (NBFCs-D)
Asset Quality
The asset quality of the NBFCs-D sector is shown from quarter ended June 2012 till the
quarter ended march 2015 (Chart 7).There is a decrease in Net NPA to Total Advances and
Gross NPA to Total Advances from 2.64 and 3.67 as on quarter ended December 2012 to
0.48 and 0.30 as on quarter ended March 2015.In between this time period there is no defined
trend followed by them.
Chart 7: Trend in Assets Quality of NBFCs-D
Source: COSMOS Section,
Department of Non Banking Supervision,
Profitability
ROA of NBFCs-D increased to 2.18 per cent in quarter ended March 2015 from 0.9 percent in
quarter ended June 2012(Chart 8) .There is a polynomial trend in between this period. In
previous four quarters, there is increasing linear trend as it increased from 1.11 percent in
quarter ended June 2014 to 1.89 percent in quarter ended March 2015 and finally reached 2.18
in quarter ended March 2015.
Chart 8: Trends in Return on Assets of NBFC-D
0
0.5
1
1.5
2
2.5
Percent
Return On Assets(ROA)
0
2
4
6
Dec-12 Mar-13 Jun-13 Sep-13 Dec-13 Mar-14 Jun-14 Sep-14 Dec-14 Mar-15
Percent
Net NPA to Total Advances Gross NPA to Total Advances

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Cost Income Ratio (CIR)
CIR is maintained around 55 percent between the Quarter ended September 2012 and quarter
ended June 2012(Chart 9).The highest Cost Income Ratio(CIR) in the time period from
quarter ended June 2012 till quarter ended March 2015 was recorded as 109.18 percent in
quarter ended September 2014.It declined to 51.5 percent in quarter ended march 2015.
Chart 9: Trends in Cost Income Ratio of NBFCS-D
Net Interest Margin (NIM)
The Net Interest Margin of NBFCs-D has considerably increased from 0.12 percent as on
quarter ended June 2012 to 1.66 percent as on quarter ended March 2015(Chart 10).There is a
polynomial trend followed by NIM in between this period.
Chart 10: Trends in Net Interest Margin of NBFCs-D
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
Percent
Net Interest Margin(NIM)
0
20
40
60
80
100
120
Jun-12
Sep-12
Dec-12
Mar-13
Jun-13
Sep-13
Dec-13
Mar-14
Jun-14
Sep-14
Dec-14
Mar-15
Percent
Cost Income Ratio

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Determinants of profitability of NBFCs-D in India
Sustained improvements in efficiency of the banking sector are desirable as they contribute
towards
(a) Higher economic growth – an efficient banking sector can render its basic function of
mobilization and allocation of resources more effectively aiding economic growth (Mohan,
2005);
(b) Mitigation of risks – the more efficient the banking system, the more is the likelihood that
it can withstand and absorb shocks. This link essentially stems from the ability of the banking
sector to channel improvements in efficiency towards creating more capital buffers. In fact,
studies find a negative and significant relationship between cost efficiency and the risk of a
bank failure (Podpiera and Podpiera, 2005);
(c) improved financial inclusion – the more efficient the banking system, the more it can aid
financial inclusion, particularly because it can make the delivery of banking services cost-
effective and can thereby ensure that improved access to banking services results in improved
usage (Chakrabarty, 2013).Some of the commonly used accounting measures/ratios for an
analysis of efficiency/profitability are cost-to-income ratio (CI), net interest margin (NIM),
and return on assets (ROA).8
Thus, the standard accounting measures/ratios suggest the trend (Chart 11) in the efficiency
of the Indian NBFCs-Deposit taking sector over recent years.
Chart 11: Trends in Accounting Measures reflecting Efficiency
8
“Operations and Performance of Commercial Banks”, Trend and Progress of Banking in India, RBI, available
at https://www.rbi.org.in/scripts/PublicationsView.aspx?id=15440 retrieved on June 30
th
,2015.
-20.0000
0.0000
20.0000
40.0000
60.0000
80.0000
100.0000
2011-2012 2012-2013 2013-2014 2014-2015
Percentage
Financial Year
ROA
NIM
CIR

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Criterion of Selection of Variables
Independent
Variables
Definition Source
NII Net Interest Income= Interest Earned -
Interest Paid
Research
Papers:“Determinants of
Profitability of Banks In
India” (B.S.Badola and
Richa Verma) and
“Determinants of
Profitability of Non Bank
Financial Institutions‟ in a
developing country
:Evidence from
Bangladesh” (Md.Sogir
Hossain Khandoker et.al)
NONII Non-Interest Income= Total Income - Interest
Income
Loans Total Loans and Advances
Deposits Total Deposits
NPA Non-performing Assets as percentage to Net
Advances
PC Provision and Contingencies (P&C)
OE Operating Expenses : Includes establishment
expenditure, salary expenditure and –
expenditure on technology up gradation
NOF Net Owned Fund
GDP Gross Domestic Product Researcher‟s Own Interest
CRAR Capital Risk Adequacy Ratio
Dependent Variables
CIR Cost-to-Income ratio : (operating
costs(including provisions and contingencies )
/Total Income) * 100
Trend and Progress of
Banking in India(2012-
2013)
NIM Net interest margin : (net interest income /
Total assets )*100
ROA Return on Assets: (net profit/Total
assets)*100
The variables considered for this study include Net Interest Income(NII),GDP, Non-Interest
Income (NII), Total Loans and Advances, NPA as percentage to Net Advances (NPA),
Provision and Contingencies (P&C),Operating Expenses (OE),Net Owned Fund(NOF),

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Capital Adequacy Risks Assets Ratio(CRAR),Gross domestic product(GDP),Cost Income
Ratio(CIR) and Net Interest Margin(NIM).
 GDP: In terms of year-over-year growth rate, the NBFC sector beat the banking sector
in most years between 2006 and 2013. On an average, it grew 22% every year. Even
when the country‟s GDP growth slowed to 6.3% in 2011-12 from 10.5% in 2010-11,
the NBFC sector clocked a growth of 25.7%. This shows that it is contributing more
to the economy every year.9
Description of data
I have taken panel data of 254 NBFCs –Deposit taking for my study. In econometrics, the
term panel data refers to multi-dimensional data frequently involving measurements over
time. Panel data contain observations of multiple phenomena obtained over multiple time
periods for the same firms or individuals. This data is generated by pooling time-series
observations from 2011 to 2014 across 254 Deposit taking NBFCs.
Country : India Panel Data of NBFCs –D
Collected by COSMOS
Years Available 2011,2012,2013,2014 (of 4 quarters each)
Data Description
This data is of different deposit taking
NBFCs present in all the regions
(AHM,BAN,CAL,CHA,CHE,DEL,HYD,J
AI,JAM,LUC,MUM,PAT and THI) 10
in
India.
Variables
Net Interest Income(NII), Non-Interest
Income (NII), NPA as percentage to Net
Advances (NPA), Provision and
Contingencies (P&C), Operating Expenses
(OE), NET owned Fund (NOF) ,cost-to-
income ratio(CIR) ,Net interest margin
(NIM), Return on assets (ROA),Total
Loans and Advances, Investments
,Deposits, and Borrowings of the different
companies
9
“Non-Banking Finance Companies: Game Changers”,
Speech delivered by Shri P Vijaya Bhaskar, Executive Director, Reserve Bank of India ,available at
https://www.rbi.org.in/scripts/BS_SpeechesView.aspx?Id=870 ,retrieved on June 30
th
,2015,pg 3
10
AHM,BAN,CAL,CHA,CHE,DEL,HYD,JAI,JAM,LUC, MUM,PAT and THI stands for Ahmedabad, Bangalore,
Kolkata, Chandigarh, Chennai, Delhi, Hyderabad, Jaipur, Jammu, Lucknow, Mumbai, Patna and
Thiruvananthpuram.

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Data Analysis & Presentation Technique
In order to analyze gathered data, I have used statistical software R and SPSS for imputation
technique and panel data regression. The data is presented through graphs and charts for
better visual understanding.
Scaling of data
Since some variables like NII, NONII, NPA, PC, OE, NOF, NIM, Total Loans and Advances,
and Deposits are varying factors according to the company‟s profile. .Thus, these are divided
by the corresponding total assets size of the companies so that the proper weightage can be
given to each company.
Data Analysis
An important problem that arises in making inferences about individual regression coefficient
is multicollinearity, the problem of correlation among the independent variables themselves.
Due to this the standard errors of the individual slope estimators become usually high,
making the slope coefficient seem statistically not significant. The variables causing
multicollinearity are dropped from the model by using Akaike Information Criterion in order
to identify the variables that have high explanatory powers and are, therefore, more important
in managing the operations of a NBFCs.
Akaike Information Criterion
The Akaike information criterion (AIC) is a measure of the relative quality of a statistical
model, for a given set of data. As such, AIC provides a means for model selection.AIC deals
with the trade-off between the goodness of the model and the complexity of the model.
In the general case, the AIC is
AIC= 2K-Ln (L)
Where,
K is the number of parameters in the statistical model, and
L is the maximized value of the likelihood function for the estimated model. Given a set of
candidate models for the data, the preferred model is the one with the minimum AIC value.
Hence AIC not only rewards goodness of fit, but also includes a penalty that is an increasing
function of the number of estimated parameters. This penalty discourages over fitting
(increasing the number of free parameters in the model improves the goodness of fit,
regardless of the number of free parameters in the data-generating process).
Description of Models
Since analysis is based on panel data. So we are considering three different models, Ordinary
least Squares regression model (OLS), fixed and random regression models to best determine
our results.
Assumptions
 Normality of errors is expected to follow according to central limit theorem. Since
total data points are 3331.
 Heteroscedasticity is assumed, as the data comprises of different companies in
different time periods.

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 Cross-sectional dependence is a problem in macro panels with long time series. This
is not much of a problem in micro panels (few years and large number of cases. Here
we have only four year data quarter wise.11
Different Models Considering Return on Assets as Profitability
Indicator
The independent variables considered for these models are Net Interest Income(NII), Non-
Interest Income (NONII),NPA as percentage to Net Advances (NPA), Provision and
Contingencies (P&C), Operating Expenses (OE), NET owned Fund (NOF) , Total Loans and
Advances, Deposits and Return on assets (ROA) as dependent variable. There is no
multicollinearity in the model. Hence the models fitted using Return on Assets (ROA) as
dependent variable and taking all the independent variables as regressors are described
below:
Ordinary Least Squares Regression (OLS regression)
Here we have unbalanced panel data of 254 companies and 16 time periods from Quarter
ended June 2011 to Quarter ended March 2015.Since the response is ROA, the usual
regression analysis using the assumption of normality of errors is expected to follow
according to central limit theorem. The MLR model fitted using ROA as response variable
and Repressors are Net Interest Income(NII), Non-Interest Income (NONII), NPA as
percentage to Net Advances (NPA), Provision and Contingencies (P&C), Operating Expenses
(OE), NET owned Fund (NOF) , Total Loans and Advances, Deposits and Return on assets
(ROA) as dependent variable gives the following estimates:
11
“Getting Started in Fixed/Random Effects Models using R (ver. 0.1-Draft)”, available at
http://dss.princeton.edu/training/, retrieved on June 30th
,2015

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If the p-value for F statistic number is < 0.05 then the model is ok. This is a test (F) to see
whether all the coefficients in the model are different than zero. Since it is less than 0.05, thus
model is appropriate. Pr (>|t|) = Two-tail p-values test the hypothesis that each coefficient is
different from 0. To reject this, the p-value has to be lower than 0.05 (95%, you could choose
also an alpha of 0.10), if this is the case then you can say that the variable has a significant
influence on your dependent variable (y).So all the variables are coming out to be significant
except GDP and PC at 5% level of significance. Since regular OLS regression does not
consider heterogeneity across groups or time. This model is providing us biased results. Now
we will consider some other models.
Fixed Regression Model
In panel data analysis, the term fixed effects estimator (also known as the within estimator) is
used to refer to an estimator for the coefficients in the regression model. If we assume fixed
effects, we impose time independent effects for each entity that are possibly correlated with
the regressors. The fixed effect allows the heterogeneity or individuality among 254
companies by allowing to have its own intercept value. The term fixed effect is due to the
fact that although the intercept may differ across the companies, but intercept does not vary
over time, that it is time invariant. The Fixed effect regression model fitted using ROA as
response variable and Repressors gives the following estimates:

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NII, NONII, OE and NOF are significant at 5 % level of significance, hence, are determinants
of ROA. Within a quarter, if there is change in NII, NONII of a company by one unit, there
will be a considerable increase in ROA. If there is a change in OE or NOF, there will be a
decrease in ROA.
Random Effects Model
In statistics, a random effect(s) model, also called a variance components model, is a kind of
hierarchical linear model. It assumes that the dataset being analyzed consists of a hierarchy of
different populations whose differences relate to that hierarchy. In econometrics, random
effects models are used in the analysis of hierarchical or panel data when one assumes no
fixed effects (it allows for individual effects). The random effects model is a special case of
the fixed effects model. The random effect regression model fitted using ROA as response
variable gives the following estimates:

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Here, Interpretation of the coefficients is quite tricky since they include both the within-entity
and between-entity effects. In this case, these represent the average effect of X over Y when
X changes across time and between companies by one unit. Calculate averages of Dependent
and independent variables over time and then regress one over the other. Here, NII , NONII
and OE are coming out to be significant at 5 % level of significance.ROA have a positive
change when the average values of NII or NONII over time are increased by one unit and
negative impact in the case of OE.
Unbiasedness
In general, random effects are efficient, and should be used (over fixed effects) if the
assumptions underlying them are believed to be satisfied. For random effects to work here it
is necessary that the company-specific effects be uncorrelated to the other covariates of the
model. This can be tested by running fixed effects, then random effects, and doing a
Hausman specification test. If the test rejects, then random effects is biased and fixed effects
is the correct estimation procedure.

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Hausman test
Null Hypothesis: Random-effects model is appropriate
Alternative hypothesis: Fixed-effects model is appropriate.
If I get the statistically significant value, I shall use the fixed-effect model otherwise
Random-effect model. Now I have applied Hausman Test to find out which model( Fixed
Effect or Random Effect) is suitable to accept.
Since the p-value is less than 0.05, so random effects model is appropriate over the fixed
effects model. This is justified too because our data depends both on different companies and
time periods. The overall summary12
of Different Models Considering Return on Assets
(ROA) as Profitability Indicator:
12
Note: The Asterisk (*) sign shows that the corresponding variable is significant at 5 % level of significance
ROA OLS
regression
Within or Fixed Effects Random Effects
GDP -0.03 -0.0647 -0.07
NONII 37.53* 8.38* 7.73*
NII 49.42* 8.61* 7.72*
Loans -0.85* -0.147 -0.183
Deposits 1.30* 0.126 -0.325
OE -3.58* -7.865* -7.245*
PC -1.54* -0.793 -1.003
NOF 0.828* -0.412* -0.212
CRAR -0.00003* -0.00001 -0.00002
NPA -0.00005* -0.00001 -0.000014
Adj R Sq. 0.74 0.49 0.52
F-Statistic 971.7 358.19 362.9

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 Hausman test shows that the random model is best fitted to the data. Hence, NONII, NII and
OE are coming out to be significant at 5 % level of significance.
 Results shows that the higher values of NONII and NII are associated with higher values of
ROA for all estimators while OE has negative impact on ROA.
 52% of the total variation is explained by the random model.
Different Models Considering Net Interest Margin as Profitability
Indicator
The independent variables considered for these models are NONII,GDP, NPA, PC, OE, NOF
,Loans, CRAR, Deposits and NIM as dependent variable. Then, Multicollinearity in the
model is removed using AIC technique. Proceeding in the same way as above, different
models are fitted using as dependent variable and GDP, NONII, OE, NPA, Loans, NOF,
Deposits and PC as regressors. The overall summary of appropriate fitted Models
Considering Net Interest Margin (NIM) as Profitability Indicator:
NIM OLS
regression
Within or Fixed Effects Random Effects
GDP 0.403* -0.0775 -0.0686
NONII -70.78* -70.41* -70.39*
OE 67.49* 66.89* 66.85*
NPA 0.000003* 0.00012* 0.00012*
Loans 3.412* -2.8617* -2.576*
NOF -0.823* -1.401* -1.552*
Deposits -2.93* 1.085 1.129
PC -2.371 -0.282 -2.873
Adj R Sq 0.99 0.994 0.99
F-statistic 2.11E+05 65871.5 67735.4
 Hausman test shows that the fixed effect model is better fitted but both the models random
and fixed are providing the same results. Hence, NONII, OE, NPA, Loans and NOF are
coming out to be significant at 5 % level of significance.

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 Result shows that the higher values of Loans, NOF and NONII leads to decrease in NIM
while higher values of NPA and OE are associated with increase in NIM for all estimators.
 99% of the total variation is explained by the fixed or within model. It is a good fit.
Different Models Considering Cost Income Ratio as Profitability
Indicator
The independent variables considered for these models are NONII,GDP, NPA, PC, OE, NOF
, Loans, CRAR, Deposits and NIM as dependent variable. Then, Multicollinearity in the
model is removed using AIC technique. Proceeding in the same way as above, different
models are fitted using as dependent variable and NOF, GDP, NPA, Loans, and Deposits as
regressors.
The overall summary of appropriate fitted model Considering Cost Income Ratio (CIR) as
Profitability Indicator:




 Except OLS regression model, other models are inappropriate in sense of F statistic. Results
show that the NOF, GDP, NPA, and Loans are coming out to be significant at 5 % level of
significance.
 Around 43% of the total variation is explained by the fixed or within model. It is a good fit.
 Higher values of loans results in decrease in CIR.
CIR OLS
regression
NOF 66.7*
GDP 7.38*
NPA 0.0028*
Loans -36.27*
Deposits -51.85
Adj R Sq 0.06089
F-statistic 43.13

of 36
Comparison of Kanpur Regional Office with Other Regional Offices
The overall summary of Different Models Considering Return on Assets (ROA) as
Profitability Indicator separately for Kanpur Regional office and other regional offices13
:
ROA_other OLS regression Fixed Effects Random Effects
NONII 32.88* 9.697* 9.293*
OE -31.41* -9.117* -8.7215*
Loans -0.631* -0.2596 -0.2499
PC -1.56 2.504* 1.8512
NII 42.9* 10.469* 9.926*
Deposits 1.95* 0.0813 -0.0509
NOF 1.378* 0.2054 0.2109
NPA -0.000039* -0.000025* -0.000024*
GDP -0.0802* -0.08250 -0.0854
R Sq 0.787 0.59865 0.6315
F Statistic 1145 531.372 533.849
13
Other regional offices includes AHM,BAN,CAL,CHA,CHE,DEL,HYD,JAI,JAM,LUC,MUM,PAT,THI
ROA_LUC OLS regression Fixed Effects
NONII 80.5396* 9.460*
NII 107.471* 12.2630
Loans -0.8818* 6.9120*
OE -80.0125* -9.6672*
PC -1.938 -2.5181
Adj R Sq. 0.7447 0.0401
F-Statistic 38.9 4.57

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In Kanpur RO,
 Fixed Effects model is considered since random effects model is inappropriate fit in sense of
F-Statistic. Results based on this shows that NONII, Loans and OE are coming out to be
significant at 5% level of significance. The higher values of NONII and loans lead to increase
in ROA.
 Around 4% of the total variation is explained by this model.
In Other RO,
 Hausman test shows that fixed effects model is better than random effects. However the
estimates are nearly the same in both the models.
 Results of the fixed effects model shows that NONII, NII, PC, OE and NPA are coming out
to be significant at 5 % level of significance.
 Each additional quarter with increase of NII, NONII and PC above the average for the
company leads to increase in ROA.
The overall summary of Different Models Considering Net Interest Margin(NIM) as
Profitability Indicator separately for Kanpur Regional office and other regional offices :
NIM_LUC OLS regression Random Effects
NONII -34.29* -3.898*
OE 33.28* 4.7711*
Loans 1.65* 2.8609*
NOF 0.651* 0.1896
Deposits -3.732* 1.013
CRAR -0.00003 1.6E-06
Adj R Sq 0.5307 0.023
F-statistic 103.7 2.168

of 36
NIM_other OLS regression Within or Fixed Effects Random Effects
NONII -70.82* -70.489* -70.46*
Loans 3.54* -2.48* -2.402*
Deposits -3.752* -1.216 -0.6918*
OE 67.56* 67.022* 66.967*
NOF -1.687* -2.96* -3.418*
GDP 0.488* 0.0129 0.0473
Adj R Sq 0.99 0.915 0.99
F-statistic 2.65E+05 80150.7 82330.5
In Kanpur RO,
 Since the fixed model was coming out to be inappropriate in sense of F statistic. Results of
the random model shows that NONII, Loans and OE are coming out to be significant at 5 %
level of significance.
 Each additional quarter with increase of NONII above the average for the company leads to
decrease in NIM.
In Other RO,
 Results of the fixed effects model shows that NONII, Loans, OE and NOF are coming out to
be significant at 5 % level of significance.
 Each additional quarter with increase of Loans, NONII and NOF above the average for the
company leads to decrease in NIM.

of 36
The overall summary of Different Models Considering Cost Income Ration (CIR) as
Profitability Indicator separately for Kanpur Regional office and other regional offices:
In Kanpur RO,
 The random model was coming out to be inappropriate in sense of F statistic. Results of the
fixed model shows that only CRAR is coming out to be significant at 5 % level of
significance.
 Each additional quarter with increase of CRAR above the average for the company leads to
increase in CIR.
CIR_LUC OLS regression Fixed Effects
NONII -345.1* -47.02
OE 333.0* 57.85
Deposits -0.648* 0.783
NOF 14.11* 4.17
GDP 10.11* -1.46
CRAR 0.00376* 0.0043*
NII -435.4* -96.45
Adj R Sq 0.515 0.1157
F-statistic 83.75 10.3081
CIR_other OLS
regression
Within or Fixed
Effects
Random
Effects
NPA 0.003966* 0.00395* 0.00327*
Loans -32.8186* -43.302 -8.1623
NOF 143.792* 44.14 31.8713
R Sq 0.05809 0.0037 0.0038
F Statistic 58.28 3.45116 3.5902

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In Other RO,
 Results of the fixed effects model shows that NPA is coming out to be significant at 5 % level
of significance.
 Each additional quarter with increase of NPA above the average for the company leads to
increase in CIR.
Conclusions
In this study, I have made an attempt to identify the key determinants of profitability of all
the Deposit taking NBFCs (NBFCs-D) in India. The analysis is based on ordinary least
squares regression, random effects regression model and fixed effect regression. I used the
panel data from the year 2011 to 2014 quarter wise. The study has brought out that the
explanatory power of some variables is significantly high. The final results are summarized
below in the tables.14
Comparison of the profitability indicators of NBFC-D of Kanpur-RO with that of the
other regional offices:
14
The Asterisk (*) sign shows that the corresponding variable is significant at 5 % level of significance. The plus
(+) and minus (-) sign in the brackets tells us the positive and negative relationship between the variable and
the profitability indicator resp.
Profitability
Indicators
Region NONII NII Loans OE NOF CRAR NPA
ROA LUC *(+) *(+) *(-)
OTHER *(+) *(+) *(-) *(-)
NIM LUC *(-) *(+) *(+)
OTHER *(-) *(-) *(+) *(-)
CIR LUC *(+)
OTHER *(+)

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1. Comparison of Kanpur-RO with Other RO’s :
ROA:
In Kanpur RO, Decrease in OE and Increase in NONII and Loans leads to increase in ROA
while in other RO‟s, instead of Loans, increase in NII and decrease in NPA leads to ROA‟s
increase.
NIM:
In Kanpur RO, Increase in NONII and decrease in Loans and OE leads to decrease in NIM
while in other RO‟s, increase in NONII, Loans and NOF and decrease in OE leads to NIM‟s
decrease.
CIR:
In Kanpur RO, decrease in CRAR leads to decrease in CIR while in other RO‟s, decrease in
NPA leads to CIR‟s decrease.
Influential factors behind the NBFC–Deposit taking industry’s profitability:
2. Fee based income i.e. Non Interest Income, Net Interest Income and Operating expenditure are
main determinants of ROA. Therefore In general, For increasing Returns on Asset (ROA) ,
NBFCs-D may increase their lines of credit and increase their Fee based income business
apart from focusing on their main line of business which will improve their Margins too if
they can simultaneously control their Operating expenses (OE). Probably they can look for
automation in Infrastructure development to reduce OE.
3. For decreasing NIM, they have to increase NONII, NOF and Loans while need to decrease OE
and NPA. NBFCs -D should look for improving capital base i.e.Net Owned Fund (NOF) so
that they can implement their IT infrastructure smoothly since it involves huge cost. This will
also help in stress testing of their loans which are being deployed thereby keeping a check to
improve their asset quality.
4. Increase in deployment of Loans and improvement in the Asset Quality leads to obtain a
optimum level of CIR.
Profitability
Indicators
GDP NONII NII Loans OE NOF NPA
ROA *(+) *(+) *(-)
NIM *(-) *(-) *(+) *(-) *(+)
CIR *(+) *(-) *(+)

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Recommendations:
 NBFCs should increase their NOF: NBFCs may face decline in their profitability indicators
in the short run but in the longer run it will benefit them. Since introduction of new small and
payment banks will increase competition in this sector catering the same customer base and
will pose a tough challenge to these NBFCs.
 Improvement in CRAR will lead to increase in the loss absorbing capacity of the company;
increase in capital will help them in improving the market perception of their financial
soundness. This may lead to ease in raising their resources and funds to shore up their NOF.
With higher NOF and income, companies will be in better position to implement IT
infrastructure which initially requires high cost but after reaching break even it can reduce
their operation costs and thus leading to improvement in Cost to income ratios too.
 Since this data is of much use subject to its reliability and availability and this study leaves
room for further study in different areas of NBFI functions such as products of productivity
analysis, Data Envelopment Analysis (DEA).

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Limitations
Although this study was carefully prepared, I am still aware of its limitations and
shortcomings.
 Time Period: In this study, it would have been better if a larger time period was
covered. It'd have given a better picture of the NBFCs sector in India.
 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.

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