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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.

Page 9 of 36

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

Source: www.rbi.org.in

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

Source: www.rbi.org.in

(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

Source: RBI supervisory returns

Trend and Progress of Banking in India 2013-14

0

0.5

1

1.5

2

2.5

Pe

rcen

t

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

Source: RBI supervisory returns

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

Source: RBI supervisory returns

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

Pe

rce

nt

24

25

26

27

28

29

30

Pe

rcen

t

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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,

Reserve Bank of India, Kanpur

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

Source: COSMOS Section,

Department of Non Banking Supervision,

Reserve Bank of India, Kanpur

0

0.5

1

1.5

2

2.5

Pe

rce

nt

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

Pe

rce

nt

Net NPA to Total Advances Gross NPA to Total Advances

Page 17 of 36

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

Source: COSMOS Section,

Department of Non Banking Supervision,

Reserve Bank of India, Kanpur

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

Source: COSMOS Section, Department of Non Banking Supervision,

Reserve Bank of India, Kanpur

00.20.40.60.8

11.21.41.61.8

Per

cen

t

Net Interest Margin(NIM)

0

20

40

60

80

100

120Ju

n-1

2

Sep

-12

Dec

-12

Mar

-13

Jun

-13

Sep

-13

Dec

-13

Mar

-14

Jun

-14

Sep

-14

Dec

-14

Mar

-15

Per

cen

t

Cost Income Ratio

Page 18 of 36

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

Source: COSMOS Section,

Department of Non Banking Supervision,

Reserve Bank of India, Kanpur

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 30th

,2015.

-20.0000

0.0000

20.0000

40.0000

60.0000

80.0000

100.0000

2011-2012 2012-2013 2013-2014 2014-2015

Pe

rce

nta

ge

Financial Year

ROA

NIM

CIR

Page 19 of 36

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),

Page 20 of 36

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.

Page 21 of 36

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.

Page 22 of 36

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

Page 23 of 36

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:

Page 24 of 36

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:

Page 25 of 36

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.

Page 26 of 36

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

Page 27 of 36

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.

Page 28 of 36

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

Page 29 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

Page 30 of 36

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

Page 31 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,

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, 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.

Page 32 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

Page 33 of 36

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 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 *(+)

Page 34 of 36

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 *(+) *(-) *(+)

Page 35 of 36

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).

Page 36 of 36

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.