Project report on

AN ANALYSIS TO STUDY THE IMPACT OF FOREIGN INSTITUTIONAL

INVESTORS (FII) FLOWS ON INDIAN STOCK MARKET WITH SPECIAL

REFERENCE TO NIFTY 500: 2002-2015

Submitted by

Bhaskar Prasad

Roll no 14MBA0016

in partial fulfilment of the requirements for the award of the degree of Master of

Business Administration

Centre For Management Studies

Jamia Millia Islamia, New Delhi-110025

March 2016

AN ANALYSIS TO STUDY THE IMPACT OF FOREIGN

INSTITUTIONAL INVESTORS (FII) FLOWS ON INDIAN STOCK

MARKET WITH SPECIAL REFERENCE TO NIFTY 500: 2002-2015

Report submitted to

Jamia Millia Islamia

in partial fulfilment of the requirements for the award of the degree of Master of

Business Administration

By

Bhaskar Prasad

Roll No. 14-MBA-16

Under the Supervision of

Dr. Taufique Siddiqui

Assistant Professor

2 | Page

Centre for Management Studies

Jamia Millia Islamia, New Delhi-25

March 2016

DECLARATION

I, Bhaskar Prasad, hereby declare that the thesis entitled “AN ANALYSIS TO STUDY

THE IMPACT OF FOREIGN INSTITUTIONAL INVESTORS (FII) FLOWS ON

INDIAN STOCK MARKET WITH SPECIAL REFERENCE TO NIFTY 500: 20022015” in fulfilment of the requirements for the award of the degree of Master of Business

Administration which is submitted by me to the Centre for Management Studies, Jamia

Millia Islamia University, New Delhi has been done by me and that, to the best of my

knowledge and belief, it contains no material previously published or written by another

person nor material which has been accepted for the award of any other degree or diploma ,

Associateship, Fellowship or other similar title or recognition. This is the original work and

is the result of my own efforts.

Dated:

Place: New Delhi

3 | Page

Bhaskar Prasad

CERTIFICATE

On the basis of the declaration submitted by Bhaskar Prasad, a student of MBA (FullTime), I hereby certify that that the project report titled “AN ANALYSIS TO STUDY THE

IMPACT OF FOREIGN INSTITUTIONAL INVESTORS (FII) FLOWS ON INDIAN

STOCK MARKET WITH SPECIAL REFERENCE TO NIFTY 500: 2002-2015” which

is submitted to the Centre for Management Studies, Jamia Millia Islamia University, New

Delhi in partial fulfilment of the requirements for the award of the degree of Master of

Business Administration, is an original contribution with existing knowledge and faithful

record of research carried out by him/her under my guidance and supervision. Certified

further, that to the best of my knowledge the work reported herein does not form part of any

other project report or dissertation on the basis of which a degree or award was conferred on

an earlier occasion on this or any other candidate.

Dated: , March 2016

Place: New Delhi

Dr. Taufique Siddiqui

Assistant Professor

Centre for Management Studies

Jamia Millia Islamia, New Delhi

ACKNOWLEDGEMENTS

I would like to take this opportunity to express my deepest sense of gratitude to all those who

helped me directly or indirectly in the completion of the project work.

I express my gratitude towards Dr. Taufique Siddiqui, Assistant Professor Centre for

Management Studies Jamia Millia Islamia, New Delhi for guiding me with his valuable

advice and giving me proper direction throughout my study. The present work is an effort to

throw some light on type of relationship between FIIs and Indian stock market. This work

would not have possibly come to the present shape without the able guidance, supervision

and help of a number of people. I convey my heartfelt affection to all those people who

supported during the course, for contributing tremendously in completing my Research

Report.

Dated:

Place: New Delhi

Bhaskar Prasad

EXECUTIVE SUMMARY

The Foreign Institutional Investors (FIIs) have emerged as noteworthy players in the Indian

stock market and their growing contribution adds as an important feature of the development

of stock markets in India. The term Foreign Institutional Investor is defined by SEBI as

under: "Means an institution established or incorporated outside India which proposes to

make investment in India in securities. Provided that a domestic asset management company

or domestic portfolio manager who manages funds raised or collected or brought from

outside India for investment in India on behalf of a sub-account, shall be deemed to be a

Foreign Institutional Investor." These flows on one hand are criticised for being ‘hot money

flows” i.e. speculative capital flows that can move very quickly in and out of markets,

potentially leading to market instability and wreck the economy in times of need, as

happened to many countries during the Asian financial crisis of 1997-99. On other hand they

are said to bring numerous advantages in host countries like enhanced flows of equity

capital, improved capital markets, improved corporate governance, reduced cost of equity

capital, imparting stability to hosts Balance of Payments etc. Thus FIIs have both positive

and negative impacts associated with them.

In Indian context, foreign institutional investors (FIIs) have been allowed to invest in the

domestic financial market since 1992. This decision to open up the Indian financial market to

FII portfolio flows at that point in time was influenced by several factors such as the

complete disarray in India’s external finances in 1991 and a disorder in the country’s capital

market. Aimed primarily at ensuring non-debt creating capital inflows at a time of an

extreme balance of payment crisis and at developing and disciplining the nascent capital

market, foreign investment funds were welcomed to the country. From 1993 onwards FII

inflows have grown leaps and bounds coinciding with India’s unfolding growth story. And

with India becoming the highest growing major economy in the world surpassing China and

world at large are facing the problem of low growth if not outright deflation India is all set to

become cynosure of foreign investors portfolio in coming future.

But recent event like selloff in Chinese stock market coupled with mass exodus of FIIs from

china. Further back home headlines like ‘FII outflows in FY16 so far highest in last 7 yearsWed, Nov 18 2015, mint”, ‘Sensex leaps 463 points on FII buying- 2 march 2016, Business

line” and many more becoming a regular fixtures on financial dailies have intrigue researcher

to examine the role and impact FII inflows on Host economy and market especially in recent

times post 2008 economic crisis in world.

Hence this study attempts to analyse Fii inflows in Indian equity market along with NIFTY

500 taken as a substitute to Indian overall stock market,by taking monthly time sries data of

FII net inflows and NIFTY 500 value for a period of 13 years w.e.f January 2002 to

December 2015 and find answer to the following question: What is impact does FII inflows have on Indian stock market?

Does there is spill over of volatility of Fii inflows on the volatility of Indian Stock

market represent by NIFTY 500?

And finally how does Fii inflow influence future volatility in NIFTY 500?

After analysis and observation it was found that Fii share a mild correlation with NIFTY

500.there was reason to believe that there is spill over of volatility from Fii flows to NIFTY

500 volatility and it was found that FII flows have a calming influence on the future volatility

of NIFTY 500.Hence one can say that in the period considered there was no reason to

believe that FII flows are making Indian stock market unstable.

CHAPTER 1

INTRODUCTION

INTRODUCTION

Indian capital market

Conceptual Framework of Capital Market

Capital market is the financial market for the buying and selling of the long term debt or

equity backed securities. The market channels the wealth of savers to those who can put it to

long term productive use. Modern capital markets are hosted on computer based electronic

trading system which can be accessed by entities within the financial sector. The capital

market can be divided into:

1) Primary Market: It deals with issue of new securities .Companies, government, and

public sector institutions can obtain funds through sale of new stock or bonds issue.

2) Secondary Market: It is also called liquid market. In this market the securities are sold

by or transferred from one investor to another. Thus, this market gives liquidity to the long

term securities.

Overview of Indian Capital Market

The Indian capital market is more than a century old. Its history goes back to 1875, when 22

brokers formed the Bombay Stock Exchange (BSE). Over the period, the Indian securities

market has evolved continuously to become one of the most dynamic, modern, and efficient

securities markets in Asia. Today, Indian market confirms to best international practices and

standards both in terms of structure and in terms of operating efficiency .Indian securities

markets are mainly governed by a) The Company’s Act,2013 b) the Securities Contracts

(Regulation) Act 1956 (SCRA Act), and c) the Securities and Exchange Board of India

(SEBI) Act, 1992. A brief background of these above regulations are given below:

a) The Companies Act 2013 deals with issue, allotment and transfer of securities and various

aspects relating to company management. It provides norms for disclosures in the public

issues, regulations for underwriting, and the issues pertaining to use of premium and discount

on various issues.

b) SCRA provides regulations for direct and indirect control of stock exchanges with an aim

to prevent undesirable transactions in securities. It provides regulatory jurisdiction to Central

Government over stock exchanges, contracts in securities and listing of securities on stock

exchanges.

c) The SEBI Act empowers SEBI to protect the interest of investors in the securities market,

to promote the development of securities market and to regulate the security market.

The Indian securities market consists of primary (new issues) as well as secondary (stock)

market in both equity and debt. The primary market provides the channel for sale of new

securities, while the secondary market deals in trading of securities previously issued. The

issuers of securities issue (create and sell) new securities in the primary market to raise funds

for investment. They do so either through public issues or private placement. There are two

major types of issuers who issue securities. The corporate entities issue mainly debt and

equity instruments (shares, debentures, etc.), while the governments (central and state

governments) issue debt securities (dated securities, treasury bills). The secondary market

enables participants who hold securities to adjust their holdings in response to changes in

their assessment of risk and return. A variant of secondary market is the forward market,

where securities are traded for future delivery and payment in the form of futures and

options. The futures and options can be on individual stocks or basket of stocks like index.

Two exchanges, namely National Stock Exchange (NSE) and the Stock Exchange, Mumbai

(BSE) provide trading of derivatives in single stock futures, index futures, single stock

options and index options. Derivatives trading commenced in India in June 2000.

In the beginning of the twentieth century, the industrial revolution was on the way in India

with the Swadeshi Movement; and with the inauguration of the Tata Iron and Steel Company

Limited in 1907, an important stage in industrial advancement under Indian enterprise was

reached.

There are two major indicators of Indian capital market- SENSEX & NIFTY:

The Sensex is an "index". What is an index? An index is basically an indicator. It gives you a

general idea about whether most of the stocks have gone up or most of the stocks have gone

down. The Sensex is an indicator of all the major companies of the BSE. The Nifty is an

indicator of all the major companies of the NSE. If the Sensex goes up, it means that the

prices of the stocks of most of the major companies on the BSE have gone up. If the Sensex

goes down, this tells you that the stock price of most of the major stocks on the BSE have

gone down. Just like the Sensex represents the top stocks of the BSE, the Nifty represents the

top stocks of the NSE. The BSE and NSE are situated at Mumbai. These are the major stock

exchanges in the country. There are other stock exchanges like the Calcutta Stock Exchange

etc. but they are not as popular as the BSE and the NSE. Most of the stock trading in the

country is done though the BSE & the NSE. Besides Sensex and the Nifty there are many

other indexes. There is an index that gives you an idea about whether the mid-cap stocks go

up and down. This is called the “BSE Mid-cap Index”. There are many other types of index.

Unless stock markets provide professionalized service, small investors and foreign investors

will not be interested in capital market operations. And capital market being one of the major

source of long-term finance for industrial projects, India cannot afford to damage the capital

market path. In this regard NSE gains vital importance in the Indian capital market but if we

see the Sensex & nifty graph there is a great variation.

Recent Trends In Indian Capital Market

A new era in capital market in India was ushered in July, 1991, with starting of a process of

financial and economic deregulation. Beginning with the devaluation of rupee by about 20%

in July 1991, industrial policy was totally reshaped to dispense with licensing of all

industries except the 18 scheduled industrial groups. Further, removal of MRTP limit on

assets of companies, dilution of FERA and foreign trade liberalization etc., were some of the

other reforms.

Genesis of new order

The beginning of liberalized policies dates back to 1985 when the Seventh Five Year Plan

was started. The banking companies Amendment Act of 1983 gave new avenues of activities

to banks in the form of participation in non-funded activities and financial services such as

leasing, hire purchase, merchant banking, etc. The public sector banks have started setting up

subsidiaries for merchant banking, lease financing, mutual fund etc., since that time. After

1992 even private sector is allowed to enter into these financial services, including banking,

mutual funds, etc.

The SEBI was set up in April 1988 to oversee and control the capital market and it has been

given legal powers since April 1992 by an act. A number of new institutions like CRISIL for

credit rating and SHCIL for clearance and share depository services have been set up.

NSE

The National Stock Exchange (NSE) is India's leading stock exchange covering various cities

and towns across the country. NSE was set up by leading institutions to provide a modern,

fully automated screen-based trading system with national reach. The Exchange has brought

about unparalleled transparency, speed & efficiency, safety and market integrity. It has set up

facilities that serve as a model for the securities industry in terms of systems, practices and

procedures.

NSE has played a catalytic role in reforming the Indian securities market in terms of

microstructure, market practices and trading volumes. The market today uses state-of-art

information technology to provide an efficient and transparent trading, clearing and

settlement mechanism, and has witnessed several innovations in products & services viz.

demutualisation of stock exchange governance, screen based trading, compression of

settlement cycles, dematerialisation and electronic transfer of securities, securities lending

and borrowing, professionalisation of trading members, fine-tuned risk management systems,

emergence of clearing corporations to assume counterparty risks, market of debt and

derivative instruments and intensive use of information technology.

NIFTY 500

The Nifty 500 is India’s first broad based benchmark of the Indian capital market. The Nifty

500 Index represents about 95.77% of the free float market capitalization of the stocks listed

on NSE as on March 31, 2015.

The total traded value for the last six months ending March 2015, of all Index constituents is

approximately 91.97% of the traded value of all stocks on NSE. The Nifty 500 companies are

disaggregated into 73 industry indices viz. Nifty Industry Indices. Industry weightages in the

index reflect the industry weightages in the market. For e.g. if the banking sector has a 5%

weightage in the universe of stocks traded on NSE, banking stocks in the index would also

have an approx. representation of 5% in the index.

Method of Computation

Nifty 500 is computed using free float market capitalization* weighted method w.e.f.

October 11, 2010, wherein the level of the index reflects the total market value of all the

stocks in the index relative to a particular base period. The method also takes into account

constituent changes in the index and importantly corporate actions such as stock splits,

rights, etc without affecting the index value.

Base Date and Value

Nifty 500 Index is calculated with base date of 01-01-1995 and base value of 1000.

Criteria for Selection of Constituent Stocks.

The constituents and the criteria for the selection judge the effectiveness of the index.

Selection of the index set is based on the following criteria :

Market Capitalisation

A company's rank on free float market capitalisation is an important consideration for its

inclusion in the Index.

Industry Representation

Nifty 500 Equity Index reflects the market as closely as possible. In order to ensure that this

is accomplished, industry weightages in the index mirror the industry weightages in the

universe. Consequently, companies to be included in the index are selected from the

industries which are underrepresented in the index.

Nifty 500 Equity Index currently contains 71 industries, including one category of diversified

companies and one category of miscellaneous. The number of industries in the Index and the

number of companies within each industry have been kept flexible, in order to ensure that the

Index retains its objective of being a dynamic market indicator.

Trading Interest

Nifty 500 Equity Index includes those companies which have a minimum listing record of 6

months on the Exchange. In addition these companies must have demonstrated high turnover

and trading frequency.

Others

A company which comes out with a IPO will be eligible for inclusion in the index, if it fulfils

the normal eligibility criteria for the index for a 3 month period instead of a 6 month period.

Foreign portfolio investments or foreign institutional investors

Definition

An investor or investment fund that is from or registered in a country outside of the one in

which it is currently investing is known as Foreign Institutional Investment and investors are

known as Foreign Institutional Investors. Institutional investors include hedge funds,

insurance companies, pension funds and mutual funds. The term is used most commonly in

India to refer to outside companies investing in the financial markets of India.

History

Most of the under developed countries suffer from low level of income and capital

accumulation. Though, despite this shortage of investment, these countries have developed a

strong urge for industrialization and economic development. As we know the need for

Foreign capital arises due to shortage from domestic side and other reasons. Indian economy

has experienced the problem of capital in many instances. While planning to start the steel

companies under government control, due to shortage of resources it has taken the aid of

foreign countries. Likewise we have received aid from Russia, Britain and Germany for

establishing Bhilai, Rourkela and Durgapur steel plants. The present paper is a modest

attempt to study the impact of Foreign Institutional Investment on Indian Stock Market with

special reference to NSE NIFTY500 Index. The character of global capital flows to

developing countries underwent significant changes on many counts during the 'nineties. By

the time the East Asian financial crisis surfaced, the overall size of the flows more than

tripled. It stood at US$ 100.8 bn. in 1990 and rose to US$ 308.1 bn. by 1996. The increase

was entirely due to the sharp rise in the flows under private account that rose from US$ 43.9

bn. to 275.9 bn. during the same period. In relative terms the percentage of private account

capital flows increased from 43.55 to 89.55 per cent. Simultaneously, the Official

Development Assistance (ODA) declined both in relative and absolute terms. All the main

components of the private account capital transfers, namely, (a) commercial loans, (b)

foreign direct investments (FDI), and (c) foreign portfolio investments (equity and bonds)

(FPI) recorded significant increases. Portfolio flows increased at a faster rate than direct

investments on private account. As a result, starting with a low level of 11.16 percent, the

share of capital flows in the form of portfolio investments quadrupled to reach 37.22 per cent

in 1996 reflecting the enhanced emphasis on private capital flows with portfolio investments

forming the second important constituent of the flows during the 'nineties. In this process

multilateral bodies led by the International Finance Corporation (IFC) played a major role.

Following the East Asian financial crisis, initially there was a slowdown followed, by a

decline in private capital flows. While bonds and portfolio equity flows reacted quickly and

declined in 1997 it, loans from commercial banks dropped a year later in 1998. Decline in

FDI was also delayed. But the fall in FDI was quite small compared to the other three major

forms of private capital flows. Thus, starting with the resolve by the developed countries to

provide 1 per cent of their GNP as developmental aid, the industrialised world preferred to

encourage private capital transfers through direct investments instead of official assistance.

The declining importance of official development finance is attributed to budgetary

constraints in donor countries and the optimism of private investors in the viability of the

developing countries.

Portfolio investments spread risk for foreign investors, and provide an opportunity to share

the fruits of growth of developing countries which are expected to grow faster. Investing in

emerging markets is expected to provide a better return on investments for pension funds and

private investors of the developed countries. For developing countries, foreign portfolio

equity investment has different characteristics and implications compared to FDI. Besides

supplementing domestic savings, FDI is expected to facilitate transfer of technology,

introduce new management and marketing skills, and helps expand host country markets and

foreign trade. Portfolio investments supplement foreign exchange availability and domestic

savings but are most often not project specific.

FPI, are welcomed by developing countries since these are non-debt creating. FPI, if

involved in primary issues, provides critical risk capital for new projects. Since FPI takes the

form of investment in the secondary stock market, it does not directly contribute to creation

of new production capabilities. To enable FPI flows which prefer easy liquidity, multilateral

bodies, led by the International Finance Corporation (IFC), have been encouraging

establishment and strengthening of stock markets in developing countries as a medium that

will enable flow of savings from developed countries to developing countries. FPI, it is

expected, could help achieve a higher degree of liquidity at stock markets, increase priceearning (PE) ratios and consequently reduce cost of capital for investment. FPI is also

expected to lead to improvement in the functioning of the stock market as foreign portfolio

investors are believed to invest on the basis of well-researched strategies and a realistic stock

valuation. The portfolio investors are known to have highly competent analysts and access to

a host of information, data and experience of operating in widely differing economic and

political environments. Host countries seeking foreign portfolio investments are obliged to

improve their trading and delivery systems which would also benefit the local investors. To

retain confidence of portfolio investors host countries are expected to follow consistent and

business friendly liberal policies. Having access to large funds, foreign portfolio investors

can influence developing country capital markets in a significant manner especially in the

absence of large domestic investors. Portfolio investments have some macroeconomic

implications. While contributing to build-up of foreign exchange reserves, portfolio

investments would influence the exchange rate and could lead to artificial appreciation of

local currency. This could hurt competitiveness. Portfolio investments are amenable to

sudden withdrawals and therefore these have the potential for destabilising an economy. The

volatility of FPI is considerably influenced by global opportunities and flows from one

country to another. Though it is sometime argued that FDI and FPI are both equally volatile

the Mexican and East Asian crises brought into focus the higher risk involved in portfolio

investments.

Foreign Institutional Investors in India

India opened her doors to foreign institutional investors in September, 1992. This event

represents a landmark event since it resulted in effectively globalizing its financial services

industry. Beginning 1996-97, the group was expanded to include registered university funds,

endowment, foundations, charitable trusts and charitable. Since then, FII flows which form a

part of foreign portfolio investments have been steadily growing in importance in India.

The Government of India gave preferential treatment to FIIs till 1999-2000 by subjecting

their long term capital gains to lower tax rate of 10 percent while the domestic investors had

to pay higher long-term capital gains tax. The Indo-Mauritius Double Taxation Avoidance

Convention 2000 (DTAC), exempts Mauritius-based entities from paying capital gains tax in

India - including tax on income arising from the sale of shares. This gives an incentive for

foreign investors to invest in Indian markets taking the Mauritius route. Consequently, we

now see investments coming from Mauritius while there were none before 2000.

Difference between FII and FDI

FII, Foreign Institutional Investors, foreign investors invest in the markets of a foreign

country for example, insurance companies, investment companies, charitable organizations

etc. The concept of FII is very common in India. Such companies are just required to register

on the stock exchange or in the markets to make investments and there is no board

controlling it. FII is when a foreign company buys equity in a company through the stock

markets. Therefore, in this case, FII would not give the foreign company any control in the

company. On the other hand in FDI, Foreign Direct Investment, in this foreign country

investor has to get at least 10% of voting stocks. It increases the management interest in the

enterprise of the other country. In very simple words, FDI is an investment which a parent

enterprise made in a foreign country. For example, Multinational companies present the best

examples of FDI like Telenor; the seventh largest company in the world has a number of

companies in various countries. The daughter companies of Telenor Group act as the FDI for

the host countries. In most of the countries there is an FDI board which is responsible to

handle FDI coming in the country like in India, one of the attractive FDI country, there is

Foreign Investment Promotion Board (FIPB). FDI is when a foreign company brings capital

into a country or an economy to set up a production or some other facility. FDI gives the

foreign company some control in the operations of the company.

Inflow of foreign capital, whether in the form of FDI or FII, is good for any recipient country

provided they are allowed in a measured way. If FDIs are invited more in the infrastructure

sectors, it could prove to be a boom for the economy. But if these capital inflows come more

in the consumer sectors, chances are that in the long run it could be proved to be disastrous

for the economy/country. For example if the recipient country of FDIs invites more and more

capital into the automobile sector to have vast varieties of cars, scooter, motorcycles etc. in

the country, but if the growth of the automobile sector is not matched by the proper roads as

well as the capacity of the country to generate or import petrol or diesel, at a macro level, the

growth of automobile sector might prove to be a liability for the country simply because the

country will be more dependent on the international markets for buying petrol/diesel. Poor

roads, inefficient and outdated railway's technology, outdated railways tracks etc. etc. will

add the consumption of petrol/diesel. This way whatever would come in as a foreign

exchange through an inflow of foreign capital will be drained out in buying petrol/diesel

from international markets for sheer consumption purposes? Similar cases could be in other

sectors.

Investment Limits For FII

Foreign Institutional Investors (FIIs) are allowed to invest in the primary and secondary

capital markets in India through the portfolio investment scheme (PIS).Under this scheme,

FIIs can acquire shares/debentures of Indian companies through the stock exchanges in India.

The ceiling for overall investment for FIIs is 24 percent of the paid up capital of the Indian

company. The limit is 20 per cent of the paid up capital in the case of public sector banks,

including the State Bank of India. The ceiling of 24 per cent for FII investment can be raised

upto sectoral cap/statutory ceiling, subject to the approval of the board and the general body

of the company passing a special resolution to that effect. And the ceiling of 10 per cent for

NRIs/PIOs can be raised to 24 per cent subject to the approval of the general body of the

company passing a resolution to that effect. The ceiling for FIIs is independent of the ceiling

of 10/24 per cent for NRIs/PIOs.

The Reserve Bank of India monitors the ceilings on FII/NRI/PIO investments in Indian

companies on a daily basis. For effective monitoring of foreign investment ceiling limits,the

Reserve Bank has fixed cut-off points that are two percentage points lower than the actual

ceilings. The cut-off point, for instance, is fixed at 8 per cent for companies in which NRIs/

PIOs can invest up to 10 per cent of the company’s paid up capital. The cut-off limit for

companies with 24 per cent ceiling is 22 percent and for companies with 30 per cent ceiling,

is 28 per cent and so on. Similarly, the cut-off limit for public sector banks (including State

Bank of India) is 18 per cent.Once the aggregate net purchases of equity shares of the

company by FIIs/NRIs/PIOs reach the cut-off point, which is 2% below the overall limit, the

Reserve Bank cautions all designated bank branches so as not to purchase any more equity

shares of the respective company on behalf of FIIs/NRIs/PIOs without prior approval of the

Reserve Bank. The link offices are then required to intimate the Reserve Bank about the total

number and value of equity shares/convertible debentures of the company they propose to

buy on behalf of FIIs/NRIs/PIOs. On receipt of such proposals, the Reserve Bank gives

clearances on a first-come first served basis till such investments in companies reach 10 / 24 /

30 / 40/ 49 per cent limit or the sectoral caps/statutory ceilings as applicable. On reaching the

aggregate ceiling limit, the Reserve Bank advises all designated bank branches to stop

purchases on behalf of their FIIs/NRIs/PIOs clients. The Reserve Bank also informs the

general public about the caution’ and the ‘stop purchase’ in these companies through a press

release.

Basis for calculating FII investment limit:

Investment limit by all registered FIIs or sub accounts in primary or secondary markets

Under Portfolio Investment Scheme is subject to a ceiling of 24% of issued share capital of a

Company. The limit can be extended upto 49% per sectoral cap if the general body of the

Company approves it.

Advantages of FII Investments

The advantages of having FII Investment can be broadly classified under the following

categories:

(A). Enhanced flows of equity capital:

FIIs are well known for a greater appetite for equity than debt in their asset structure. For

examples, pension funds in the United Kingdom and United states are known to have grater

allocation to equity than debt. Thus, opening up the economy to FIIs is in line with the

accepted preference for non-debt creating foreign inflows over foreign debt. Furthermore,

because of this preference for equities over bonds, FIIs can help in compressing the yielddifferential between equity and bonds and improve corporate capital structures.

(B). Improving capital markets:

FIIs as professional bodies of asset managers and financial analysts enhance competition and

efficiency of financial markets. Equity market development aids economic development. By

increasing the availability of riskier long term capital for projects, and increasing firms’

incentives to supply more information about themselves, the FIIs can help in the process of

economic development. The increasing role of institutional investors has brought both

quantitative and qualitative developments in the stock markets viz., expansion of securities

business, increased depth and breadth of the market, and above all their dominant investment

philosophy of emphasizing the fundamentals has rendered efficient pricing of the stocks.

(C). Improved corporate governance:

Good corporate governance is essential to overcome the principal-agent problem between

share-holders and management. Information asymmetries and incomplete contracts between

share-holders and management are at the root of the agency costs. Dividend payment, for

example, is discretionary. Bad corporate governance makes equity finance a costly option.

With boards often captured by managers or passive, ensuring the rights of shareholders is a

problem that needs to be addressed efficiently in any economy. Incentives for shareholders to

monitor firms and enforce their legal rights are limited and individuals with small shareholdings often do not address the issue since others can free-ride on their endeavor.

(D). Managing uncertainty and controlling risks:

Institutional investors promote financial innovation and development of hedging instruments.

Institutions, for example, because of their interest in hedging risks, are known to have

contributed to the development of zero-coupon bonds and index futures. FIIs, as professional

bodies of asset managers and financial analysts, not only enhance competition in financial

markets, but also improve the alignment of asset prices to fundamentals. Institutions in

general and FIIs in particular are known to have good information and low transaction costs.

By aligning asset prices closer to fundamentals, they stabilize markets. Fundamentals are

known to be sluggish in their movements. Thus, if prices are aligned to fundamentals, they

should be as stable as the fundamentals themselves. Furthermore, a variety of FIIs with a

variety of risk-return preferences also help in dampening volatility.

(E). Reduced cost of equity capital:

FII inflows augment the sources of funds in the Indian capital markets. In a common sense

way, the impact of FIIs upon the cost of equity capital may be visualized by asking what

stock prices would be if there were no FIIs operating in India. FII investment reduces the

required rate of return for equity, enhances stock prices, and foster investments by Indian

firms in the country. From the perspective of international investors, the rapidly growing

emerging markets offer potentially higher rates of return and help in diversifying portfolio

risk.

(F) Imparting stability to India’s Balance of Payments:

For promoting growth in a developing country such as India, there is need to augment

domestic investments, over and beyond domestic saving, through capital flows. The excess

of domestic investment over domestic savings result in a current account deficit and this

deficit is financed by capital flows in the balance of payments. Prior to 1991, debt flows and

official development assistance dominated these capital flows. This mechanism of funding

and current account deficit is widely believed to have played a role in the emergence of

balance of payments difficulties in 1981 and 1991. Portfolio flows in the equity markets, and

FDI as opposed to debt creating flows, are important as safer and more sustainable

mechanisms for funding the current account deficit.

(G). Knowledge Flows:

The activities of international institutional investors help strengthen Indian finance.FIIs

advocate modern ideas in market design, promote innovation development of sophisticated

products such as financial derivatives, enhance competition in financial intermediation, and

lead to spillovers of human capital by exposing Indian participants to modern financial

techniques, and international best practices and systems.

(H).Improvements to market efficiency:

A significant presence of FIIs in India can improve market efficiency through two channels.

First, when adverse macro-economic news, such as bad monsoons, unsettles many domestic

investors, it may be easier for a globally diversified portfolio manager to be more

dispassionate about India’s prospects and engage in stabilizing trades. Second, at a level of

individual stocks and industries, FIIs may act as a channel through which knowledge and

ideas about valuation of a firm or an industry can more rapidly propagate into India. For

example, foreign investors were rapidly able to assess the potential of the firms like Infosys,

which are primarily expert oriented, applying valuation principles, and the prevailed outside

India for software services companies.

Disadvantages of FII Investment

There are also some disadvantages of FII Investment which are broadly classified under the

following categories:

(A). Volatility and capital outflows:

There is also increasing possibility of abrupt and sudden outflows of capital if the inflows are

of a short-term nature as in the case of portfolio inflows of FIls. The recent experience of

reversal of private capital flows observed in Global crisis of 2008, Asian crisis in 1997 and in

Mexico during the latter part of 1994 due to sudden change in FIIs' investment sentiment

provides a vivid illustration of such risks. Usually, FIls take into account some specific risks

in emerging markets such as (i) political instability and economic mismanagement, (ii)

liquidity risk and (iii) currency movement. Currency movement can have a dramatic impact

on equity returns of FIIs, a depreciation having an adverse effect.Thus, short-term flows

including portfolio flows of FIIs to developing countries in particular are inherently unstable

and increases volatility of the emerging equity markets. They are speculative and respond

adversely to any instability either in the real economy or in financial variables. Investment in

emerging markets by FIIs can at times be driven more by a perceived lack of opportunities in

industrial countries than by sound fundamentals in developing countries including India.

Emerging stock markets of India and other developing countries have a low, even negative

correlation with the stock markets in industrial nations. So, when the latter goes down, FIIs

invest more in the former as a means to reduce overall portfolio risk. On the other hand, if

there is boom in industrial countries, there may be reverse flow of funds of FIIs from India

and other developing countries. Of course, there is pull for international private portfolio

investment of FIIs due to the impact of wide-ranging macro-economic and structural reforms

including liberalization or elimination of capital restrictions, improved flow of financial

information, strengthening investors' protection and the removal of barriers on FIIs'

participation in equity markets in India and other emerging markets.

(B). Price rigging:

Bear hammering by FIIs has been alleged in case of almost all companies in India tapping

the GDR market. The cases of SBI and VSNL are most illuminating to show how the FIIs

manipulate domestic market of a company before its GDR issues. The manipulation of FIIs,

working in collusion operates in the following way. First, they sell en masse and then when

the price has been pulled down enough, pick up the some shares cheaply in the GDR market.

Though FIIs have the freedom of entry and exit, they alone have the access to both the

domestic as well as the GDR market but the GDR market is not open to domestic investors.

Hence FIIs gain a lot at the cost of domestic investors due to their manipulation which is

possible owing to integration of Indian equity market with global market consequent upon

liberalization.

(C).BOP vulnerability:

There are concerns that in an extreme event, there can be a massive flight of foreign capital

out of India, triggering difficulties in the balance of payments front. India’s experience with

FIIs so far, however, suggests that across episodes like the Pokhran blasts, or the 2001 stock

market scandal, no capital flight has taken place. A billion or more of US dollars of portfolio

capital has never left India within the period of one month. When juxtaposed with India’s

enormous current account and capital flows, this suggests that there is little evidence of

vulnerability so far.

(D). Money laundering:

The movement of hot money of FIIs due to integration of emerging markets of India and

other countries with global market have helped the hawala traders and criminal elements an

easy means to launder international money from illegal activities which in consequence have

also an impact on equity market. Sometimes FIIs act as an agent For money laundering.It is

also argued that the FII indulge in price rigging by collusive operation. Another ill effect of

opening up of the capital market to FIIs has been the possibility of FIIs trying to gain control

of indigenous companies.

CHAPTER 2

LITERATURE REVIEW

LITERATURE REVIEW

A literature review is a text of a scholarly paper, which includes the current

knowledge including substantive findings, as well as theoretical and

methodological contributions to a particular topic.

There is plethora of research work, essays and journals etc. are available on the

topic of relationship between FII and stock markets conducted both in India and

abroad. I have gone through diverse content available on the topic and found the

following research thesis pertinent to my study:

I.

FII Flows to India: Nature and Causes by Rajesh Chakrabarti*

Dupree College of Management, Georgia Institute of Technology: In this

paper the author analyzes FII flows and their relationship with other economic

variables and arrive at the following major conclusions: While the flows are

highly correlated with equity returns in India, they are more likely to be the

effect than the cause of these returns; The FIIs do not seem to be at an

informational disadvantage in India compared to the local investors; The Asian

Crisis marked a regime shift in the determinants of FII flows to India with the

domestic equity returns becoming the sole driver of these flows since the crisis.

II.

Does the stock market rise or fall due to fiis in india? By Dr. Ambuj

Gupta Assistant Professor Asia Pacific Institute of Management New Delhi:

In the above paper the author examines the relationship between Indian stock

market and FIIs investment in India and finds that both, Indian stock market and

FIIs influence each other; however, their timing of influence is different.

III. Analytical study of impact of fii on indian stock market with special

reference to bse sensex by Vikram K. Joshi* & Miss Richa Saxena: In this

paper the author attempts to analyse the impact of variation in FII on Sensex

and to study the degree of relationship between them in various FII movement

scenarios.

IV. Impact of Flow of FDI & FII on Indian Stock Market by Dr. Syed

Tabassum Sultana, Prof. S Pardhasaradhi: In this paper the authors makes an

attempt to study the relationship and impact of FDI & FII on Indian stock

market using statistical measures correlation coefficient and multi regression.

Sensex and Nifty were considered as the representative of stock market as they

are the most popular Indian stock market indices. Based on 11 years data

starting from 2001 to 2011, it was found that the flow of FDI & FII was moving

in tandem with Sensex and Nifty. The study concludes that Flow of FDIs and

FIIs in India determines the trend of Indian stock market.

V.

The Impact of FII Regulations in India: A Time-Series Intervention

Analysis of Equity Flows: by Suchismita Bose ,ICRA Ltd,Dipankor

Coondoo Indian Statistical Institute, New Delhi - Economic Research Unit:

This paper examines the impact of reforms of the foreign institutional investors'

(FIIs) investment policy, on FII portfolio flows to the Indian stock markets, an

aspect, studies on determinants of FII flows to India so far have not taken into

consideration and finds results that strongly suggest that liberalisation policies

have had the desired expansionary effect and have either increased the mean

level of FII inflows and/or the sensitivity of these flows to a change in BSE

return and/or the inertia of these flows. On the other hand, interestingly, the

restrictive measures aimed at achieving greater control over FII flows also do

not show any significant negative impact on the net inflows; we find that these

policies mostly render FII investments more sensitive to the domestic market

returns and raise the inertia of the FII flows.

VI. Volatility Of FII In India By Dipankor Coondoo And Paramita

Mukherjee: In this paper, the authors have examined volatility of the day-today movements of foreign institutional investment (FII) in India, along with

some other related variables like the stock market returns and the call money

rate. For the purpose of this study, a new technique of analysis has been used

that defines and examines three different aspects of volatility, viz. strength,

duration and persistence of volatility. The results suggest that the over-time

movements of the daily values of FII and stock market returns contain a fair

amount of volatility. Also, the strength and duration of volatility of stock market

returns are more or less similar to those of the FII flows. Another interesting

finding is that the strength of volatility of FII flows are positively correlated

both with that of stock market returns and call money rate. The overall finding

is that the FII and stock market returns in India exhibit quite high volatility in

terms of both extent and duration. More importantly, there is also evidence that

their volatility is interrelate.

VII. Causal relationships between Foreign Institutional Investments and

stock returns in India by M. Suresh Babu,K.P. Prabhees: This paper

examines the dynamic interaction between FII flows and stock market returns in

Indian stock market. Using daily data from January 2003 to February 2007,

VAR framework and Granger causality test, we find the existence of

bidirectional causality between FII flows and stock returns. Further analysis

through impulse response function indicates that FII flows are more stock return

driven. We also find support for information revelation hypothesis and

momentum trading hypothesis.

VIII. Foreign Institutional Investors: Investment Preferences in India by P.

Krishna Prasanna: this paper examines the contribution of foreign institutional

investment particularly among companies included in sensitivity index (Sensex)

of Bombay Stock Exchange. Also examined is the relationship between foreign

institutional investment and firm specific characteristics in terms of ownership

structure, financial performance and stock performance. It is observed that

foreign investors invested more in companies with a higher volume of shares

owned by the general public. The promoters’ holdings and the foreign

investments are inversely related. Foreign investors choose the companies

where family shareholding of promoters is not substantial. Among the financial

performance variables the share returns and earnings per share are significant

factors influencing their investment decision.

IX. International Capital Flows and Growth in India: The Recent

Experience by Narayan Sethi: In the study the author attempts to examine the

impact of international capital flows on economic growth. The study also

examines trends and composition of capital inflows, changing pattern capital

flows in view of economic reform, ascertain the impact of domestic financial

policy variables on international capital flows and suggest policy implication

thereof. By using monthly time series data, the author found that Foreign Direct

Investment (FDI) is positively affecting the economic growth direct

contribution, while Foreign Institutional Investment (FII) is negatively affecting

the growth although, in a small way and make a preliminary attempt to test

whether the international capital flows has positive impact on financial markets

and economic growth. The empirical analysis using the time series data between

April 1995 to April 2007 shows that FDI plays unambiguous role in

contributing to economic growth.

X.

Impact Of Foreign Institutional Investment on Stock Market by

Karan Walia, Dr. Rimpi Walia and Monika Jain: In this paper examines the

contribution of foreign institutional investment in sensitivity index (Sensex).

Also attempts to understand the behavioral pattern of FII during the period of

2001 to 2010 and examine the volatility of BSE Sensex due to FII. The data for

the study uses the information obtained from the secondary resources like

website of BSE Sensex. We attempted to explain the impact of foreign

institutional investment on stock market and Indian economy. Also attempts to

present the correlation between FII and BSE Sensex by the Karl Pearson’

Coefficient of correlation test.

CHAPTER 3

RESEARCH METHODOLOGY

OBJECTIVES OF STUDY:

Below listed points constitute the objectives of the study:

I. To study the correlation between CNX NIFTY 500 and FIIs between the

period 2002 to 2015.

II. To build a regression model to depict the relationship between dependent

variable and independent variable.

III. To find out how FII net flows impacts the future volatility in NIFTY 500

index using GARCH (1,1).

IV. To find out whether volatility in net flows spillover to influence the

volatility in NIFTY 500 index.

SCOPE AND NEED OF STUDY:

Scope of study is broader and it covers FIIs in the form of equity. But, study is

only going to cover only one stock index, i.e. NIFTY 500. The time period

under study is limited from January 2002 to December 2015, as it will give

exact impact in both bullish and bearish trend.

This study also provides a clear picture about how FIIs impact the Indian

capital market and also NSE.

RESEARCH METHODOLOGY:

According to John.W.Best “Research is a systematic and objective analysis and

recording of controlled observations that may lead to the development of

generalizations, principles, theories and concepts, resulting in prediction for

seeing and possibly ultimate control of events.”

The research methodology includes the following:

•

Research problem

•

Research design

•

Sampling design

•

Sampling technique

•

Data collection method

RESEARCH PROBLEM:

There is an old dictum by John Dewey, which says “a problem well put is half

solved”. The project deals with the “Impact of Foreign Institutional Investors on

Indian Capital Market”. This project studies the relation between Foreign

Institutional Investments and one of the stock indices i.e. Nifty 500. To identify

the degree of relation between CNX Nifty 500 and Foreign Institutional

Investments, net investments (i.e. total inflows minus total outflows) in the

equity segments by the FIIs are considered. This project also studies the trends

in the investment of FIIs along with the economic figures provided.

There may be multiple factors affecting the stock index, like:

•

Government Policies

•

Budgets

•

Economic and political condition of the country

•

FDI

•

Rupee./Dollar exchange rate

•

Inflation

•

Bullion market

But for the above research problem we will be considering FII flows as

independent variable and ignoring the other variables for the simplicity of the

model.

RESEARCH DESIGN:

A research design is the framework or plan for a study used as a guide in

collecting and analyzing data.

There are three basic types of research design:

•

Exploratory design

•

Descriptive design

•

Casual design

In this study, exploratory research has been conducted. Exploratory research is

conducted to provide a better understanding of a situation. This study aims to

find the new insights in terms of finding the relation between FII’S and Nifty

500 index.

To describe the relation between FII’S and CNX Nifty 500, a hypothesis is set

up in relation with the study being conducted.

NULL HYPOTHESIS (Ho): There is no relationship between CNX Nifty 500

volatility and FIIs.

ALTERNATE HYPOTHESIS (H1): There is relationship between S&P CNX

Nifty volatility and FIIs.

In this study, foreign institutional investment is considered as an independent

variable and CNX Nifty 500 is considered as a dependent variable.

SAMPLING DESIGN:

UNIVERSE:

In this study the universe is finite and will take into account all the events and

announcements that happened in the last few years.

SAMPLING UNIT:

As this study mainly concentrates on foreign institutional investment and

National stock exchange, the sampling unit is confined to them.

SAMPLING TECHNIQUE:

Convenient Sampling: Study conducted on the basis of availability of the data

and requirement of the project. Study requires the events that have impact on

the Indian stock market.

DATA COLLECTION METHOD:

Secondary Data: Data is collected from various literatures, journals, magazines,

books, web links are used. As there are no possibilities of collecting data

personally, there is no questionnaire prepared.

Monthly data of NIFTY 500 and net flows of FII’s net flows for period of 13

years w.e.f. January 2002 to December 2015 is considered for the project.

RESEARCH ANALYSIS TOOLS:

a) Unit Root Test:

Empirical studies (for example, Engle and Granger, 1987) have shown that

many time series variables are non-stationary or not integrated of order zero.

The time series variables considered in this paper are the stock prices and fii

flows. In order to avoid a spurious regression situation the variables in a

regression model must be stationary. Therefore, in the first step, we perform unit

root tests to investigate whether they are stationary or not.

The Augmented Dickey-Fuller (ADF) unit root test is used for this purpose. The

ADF regression equations are:

where åô is white noise. The additional lagged terms are included to ensure that

the errors are uncorrelated.

HYPOTHESIS ADF UNIT ROOTS TEST

The tests are based on the

null hypothesis (H0): Yt is not I (0). If the calculated ADF statistics are less than

their critical values from Fullers table then the null hypothesis (H0) is rejected

and the series are stationary or not integrated of order zero.

b) TREND ANALYSIS AND CORRELATION ANALYSIS:

Trend analysis:

The term “trend analysis” refers to the concept of the collecting information and

attempting to spot a pattern, or a trend in the information. In some fields of

study, the term “trend analysis” has more formally-defined meanings.

In project management trend analysis is a mathematical technique that uses

historical results to predict future outcome. This is achieved by tracking

variances in cost and schedule performance. In this context, it is a project

management quality control tool. The analysis of a variable's past value changes

to determine if a trend exists and if so. What the trend indicates? A technical

analyst may graph a stock's price throughout a period of time to determine

whether a trend has been established. This is a type of technical analysis that is

used /or attempt to predict the future movement of a stock or institutional of

fund.

Correlation analysis:

This analysis tool and its formulas measure the relationship between two data

sets that are scaled to be independent of the unit of measurement. It is a measure

that determines the degree to which two variable's movements are associated. It

is used to understand:

•

Whether the relationship is positive or negative,

•

The strength of relationship.

Correlation is a powerful tool that provides these vital pieces of information.

Positive correlation occurs when large values of one set are associated with

large values of the other and Negative correlation occurs when small values of

one set are associated with large values of the other and when values in both

sets are unrelated then correlation is said to be zero.

Correlation analysis will be used in the study to

1- Find out whether or not fii net flows are correlated with NIFTY 500 or

not

2- Find out whether volatility of net flows of FII impact volatility on NIFTY

500 index.

c) REGRESSION ANALYSIS

In statistical modeling, regression analysis is a statistical process for estimating

the relationships among variables. It includes many techniques for modeling

and analyzing several variables, when the focus is on the relationship between a

dependent variable and one or more independent variables (or 'predictors').

More specifically, regression analysis helps one understand how the typical

value of the dependent variable (or 'criterion variable') changes when any one of

the independent variables is varied, while the other independent variables are

held fixed. Most commonly, regression analysis estimates the conditional

expectation of the dependent variable given the independent variables – that is,

the average value of the dependent variable when the independent variables are

fixed. Less commonly, the focus is on a quantile, or other location parameter of

the conditional distribution of the dependent variable given the independent

variables. In all cases, the estimation target is a function of the independent

variables called the regression function. In regression analysis, it is also of

interest to characterize the variation of the dependent variable around the

regression function which can be described by a probability distribution.

Here regression will be used in order to arrive at a regression model

specifying the relationship between NIFTY 500(Dependent variable) and

FII’s net flows over the period considered.

d) GRANGER CAUSALITY TEST:

Granger causality is a statistical concept of causality that is based on prediction.

According to Granger causality, if a signal X1 "Granger-causes" (or "G-causes")

a signal X2, then past values of X1 should contain information that helps predict

X2 above and beyond the information contained in past values of X2 alone. Its

mathematical formulation is based on linear regression modeling of stochastic

processes (Granger 1969). More complex extensions to nonlinear cases exist,

however these extensions are often more difficult to apply in practice.

G-causality is normally tested in the context of linear regression models. For

illustration, consider a bivariate linear autoregressive model of two variables X1

and X2 :

X1(t)=∑j=1pA11,jX1(t−j)+∑j=1pA12,jX2(t−j)+E1(t)X2(t)=∑j=1pA21,jX1(t−j)

+∑j=1pA22,jX2(t−j)+E2(t)(1)

where p is the maximum number of lagged observations included in the model

(the model order), the matrix A contains the coefficients of the model (i.e., the

contributions of each lagged observation to the predicted values of X1(t) and

X2(t) , and E1 and E2 are residuals (prediction errors) for each time series. If

the variance of E1 (or E2) is reduced by the inclusion of the X2 (or X1) terms in

the first (or second) equation, then it is said that X2 (or X1) Granger-(G)-causes

X1 (or X2). In other words, X2 G-causes X1 if the coefficients in A12 are

jointly significantly different from zero. This can be tested by performing an Ftest of the null hypothesis that A12 = 0, given assumptions of covariance

stationarity on X1 and X2 . The magnitude of a G-causality interaction can be

estimated by the logarithm of the corresponding F-statistic (Geweke 1982).

Note that model selection criteria, such as the Bayesian Information Criterion

(BIC, (Schwartz 1978)) or the Akaike Information Criterion (AIC, (Akaike

1974)), can be used to determine the appropriate model order p

Granger causality (or "G-causality") was developed in 1960s and has been

widely used in economics since the 1960s. However it is only within the last

few years that applications in neuroscience have become popular.

This test will be used to find out whether the volatility in net flows of FII

have any causal relationship between the volatility of NIFTY 500 index.

e) AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY

(ARCH)

In econometrics, autoregressive conditional heteroskedasticity (ARCH) models

are used to characterize and model time series. They are used at any point in a

series, the error terms are thought to have a characteristic size or variance. In

particular ARCH models assume the variance of the current error term or

innovation to be a function of the actual sizes of the previous time periods' error

terms: often the variance is related to the squares of the previous innovations.

Such models are often called ARCH models (Engle, 1982), although a variety of

other acronyms are applied to particular structures that have a similar basis.

ARCH models are commonly employed in modeling financial time series that

exhibit time-varying volatility clustering, i.e. periods of swings interspersed

with periods of relative calm. ARCH-type models are sometimes considered to

be in the family of stochastic volatility models, although this is strictly incorrect

since at time t the volatility is completely pre-determined (deterministic) given

previous values.

GARCH(1,1):

If an autoregressive moving average model (ARMA model) is assumed for the

error variance, the model is a generalized autoregressive conditional

heteroskedasticity (GARCH, Bollerslev (1986)) model.

In that case, the GARCH (p, q) model (where p is the order of the GARCH

terms

and q is the order of the ARCH terms ), following the notation of

original paper is given by

Generally, when testing for heteroskedasticity in econometric models, the best

test is the White test. However, when dealing with time series data, this means

to test for ARCH and GARCH errors.

Here,Garch (1,1) analysis will be conducted to find out the nature of impact

thatFII’S net flows have on future volatility of the NIFTY 500 .in this case

the fii inflows will be exogeneous regressors.

CHAPTER 4

ANALYSIS

ANALYSIS

TREND OF FII(NET FLOWS) AND NIFTY 500 OVER THE LAST 13

YEARS

40000

30000

20000

NET FLOWS

10000

NSE 500

0

-10000

-20000

GRAPH SHOWING FII NET FLOWS AND NIFTY 500 MOVEMENTS OVER THE PERIOD JAN 2002-DEC 2015

SOURCE: TABLE 1 APPENDIX

Interpretation

From the above chart we can see how FII net flows and NIFTY 500 index have

moved since January 2002 to December 2015.over the period considered

NIFTY 500 has moved with a upward bias showing smaller corrections along

its path in contrast FII net flows have shown both upward and downward

swings at regular intervals.one striking point is that beyond mid 2007‘s i.e. after

happening of infamous US subprime crisis FII net flows have shown a greater

volatility as compared to before. Also quantum of inflows has increased

manifolds.

DESCRIPTIVE STATISTICS

Mean

Median

Maximum

Minimum

Std. Dev.

Skewness

Kurtosis

Jarque-Bera

NET_FLOWS

4438.135

3219.950

28562.90

-16877.00

8368.443

0.473046

3.409292

NIFTY_500

3513.234

3818.550

7239.450

691.0000

1789.384

0.122846

2.250842

7.438283

4.351216

Probability

0.024255

0.113539

Sum

Sum Sq. Dev.

745606.6

1.17E+10

590223.2

5.35E+08

Observations

168

168

Interpretation

By looking at the descriptive statistics table we can see mean value of FII

net investments is 4438.135 cr while that of NIFTY 500 is 3513.234.

The standard deviation shows there is a larger spread or deviation in FII

net flows as compared to NSE500 in the relevant period.

Median of FII net flows comes out to be 3219.950 cr vis-à-vis NIFTY

500 is 3818.550.

UNIT ROOT TEST OF NET FII INVESTMENTS

At level

Null Hypothesis: NET_FLOWS has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=13)

Augmented Dickey-Fuller test statistic

Test critical values:

1% level

5% level

10% level

t-Statistic

Prob.*

-8.857154

-3.469691

-2.878723

-2.576010

0.0000

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(NET_FLOWS)

Method: Least Squares

Date: 03/26/16 Time: 18:24

Sample (adjusted): 2002M02 2015M12

Included observations: 167 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

NET_FLOWS(-1)

C

-0.646055

2875.942

0.072942

691.2088

-8.857154

4.160743

0.0000

0.0001

R-squared

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

F-statistic

Prob(F-statistic)

0.322240

0.318133

7870.325

1.02E+10

-1734.089

78.44917

0.000000

Mean dependent var

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

Durbin-Watson stat

-19.40299

9531.094

20.79149

20.82883

20.80665

2.066548

Interpretation

The ADF t-test has value t = -8.857154 and has p-value of 0.0000 which is

smaller than the 5% critical value of -1.9448.

Therefore the ADF t-test rejects the null hypothesis of a trend (at the 5%

significance level).Since, the null hypothesis of a trend is rejected by the ADF

F-test. Thus the trend indicates the trend is deterministic.

net flows

30,000

20,000

10,000

0

-10,000

-20,000

02

03

04

05

06

07

08

09

10

11

12

13

14

15

Similarly, conducting ADF UNIT ROOT TEST for the NIFTY 500,

At level

Null Hypothesis: NIFTY_500 has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=13)

Augmented Dickey-Fuller test statistic

Test critical values:

1% level

5% level

10% level

t-Statistic

Prob.*

-0.667057

-3.469691

-2.878723

-2.576010

0.8508

*MacKinnon (1996) one-sided p-values.

Unit root test of NIFTY 500 Reveals That It has t- statistics = -0.667057 and

corresponding p-value of 0.8508 which is more than the 5% critical value of

-1.9448.hence we can accept the null hypothesis (at the 5% significance

level).Since, the null hypothesis of a trend is accepted by the ADF F-test. Thus

it indicates the trend is non-stationery at level.

At 1st difference

Null Hypothesis: D(NIFTY_500) has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=13)

Augmented Dickey-Fuller test statistic

Test critical values:

1% level

5% level

10% level

t-Statistic

Prob.*

-12.61490

-3.469933

-2.878829

-2.576067

0.0000

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(NIFTY_500,2)

Method: Least Squares

Date: 03/26/16 Time: 18:45

Sample (adjusted): 2002M03 2015M12

Included observations: 166 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(NIFTY_500(-1))

C

-0.984932

35.34441

0.078077

19.37759

-12.61490

1.823983

0.0000

0.0700

R-squared

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

F-statistic

Prob(F-statistic)

0.492473

0.489379

247.0263

10007606

-1149.114

159.1357

0.000000

Mean dependent var

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

Durbin-Watson stat

-0.087048

345.6955

13.86884

13.90634

13.88406

2.000000

Interpretation

The ADF t-test of NSE500 at 1st difference has t-statistics =-12.61490 and has

p-value of 0.0000 which is smaller than the 5% critical value of -1.9448.

Therefore the ADF t-test rejects the null hypothesis of a trend (at the 5%

significance level).Since, the null hypothesis of a trend is rejected by the ADF

F-test. Thus the test indicates the trend is deterministic at 1st difference.

nifty 500

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

02

03

04

05

06

07

08

09

10

11

12

13

14

15

REGRESSION ANALYSIS BETWEEN FII NET FLOWS AND NIFTY

500

The following regression model is developed to depict the relationship between

FII’s (independent variable) and NIFTY 500 (dependent variable)

Dependent Variable: DNIFTY

Method: Least Squares

Date: 03/23/16 Time: 20:12

Sample (adjusted): 2002M02 2015M12

Included observations: 167 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

NET_FLOWS

-0.005057

4.14E-06

0.005753

6.07E-07

-0.878948

6.824046

0.3807

0.0000

R-squared

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

F-statistic

Prob(F-statistic)

0.220107

0.215381

0.065592

0.709884

219.0013

46.56760

0.000000

Mean dependent var

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

Durbin-Watson stat

0.013425

0.074050

-2.598818

-2.561477

-2.583662

1.818296

Intrepetation

Analyzing regression model with respect to 7 assumptions for a good regression

yield the following results:

Assumption 1: R square value of the model is 0.22 or 22% ideally it should 50%

or more.

Assumption 2: Independent variable should be individually significant here in

this case independent variable is net flows which is having t-statistics =

6.824046 and p value=0.00 which is less than 5 percent confidence level.

Hence we can say that net flows are individually significant to influence the

dependent variable.

Assumption 3 Joint Significance: Independent variables should be jointly

significant to explain dependent variable. Here F Statistic is 46.56760

probabilty of F Statistics is 0.000% which is less 5% hence we can conclude

that independent variables can jointly influence the dependent variable.

Assumption 4: It is common assumption that FII have a positive relationship

with stocks market. Further in this model also FII have coefficient which is

according to the popular economic theory.

Residual analysis

Assumption 5: Test for serial or auto correlation: Serial correlation is a

statistical term used to the describe the situation when the residual is correlated

with lagged values of itself. In other words, If residuals are correlated, we call

this situation serial correlation which is not desirable. An approach of detecting

serial correlation is Breusch-Godfrey serial correlation LM test: BG test

The LM: BG test reveals the following results:Null hypothesis (H0): There Is No Serial Or Auto Correlation.

Breusch-Godfrey Serial Correlation LM Test:

F-statistic

Obs*R-squared

0.628995

1.278990

Prob. F(2,163)

Prob. Chi-Square(2)

0.5344

0.5276

Test Equation:

Dependent Variable: RESID

Method: Least Squares

Date: 03/23/16 Time: 20:14

Sample: 2002M02 2015M12

Included observations: 167

Presample missing value lagged residuals set to zero.

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

NET_FLOWS

RESID(-1)

RESID(-2)

0.000185

-3.86E-08

0.087108

0.005024

0.005771

6.11E-07

0.078517

0.078544

0.032119

-0.063193

1.109419

0.063963

0.9744

0.9497

0.2689

0.9491

R-squared

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

F-statistic

Prob(F-statistic)

0.007659

-0.010605

0.065740

0.704447

219.6433

0.419330

0.739370

Mean dependent var

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

Durbin-Watson stat

-4.45E-18

0.065394

-2.582554

-2.507872

-2.552242

1.995470

Interpretation

Here observed r square value = 1.278990 and corresponding probability chisquare value is 0.5276 is greater than 5% confidence value.

Hence we can accept the null hypothesis i.e. there is no serial or auto

correlation.

Assumption 6: The variance of the residual (u) is constant (Homoscedasticity)

Heteroscedasticity is a situation when the variance of the residuals from a model

is not constant. When the variance of the residuals is constant, we call it

homoscedasticity. Homoscedasticity is desirable. If residuals do not have

constant variance, we call it hetersocedasticty, which is not desirable.

This can be check using Breusch-Pegan-Godfrey Test

Conducting the bpg test we get the following results

Null hypothesis Ho: Homoscedasticity (the variance of residual (u) is constant)

Alternative hypothesis H1 : Heteroscedasticity (the variance of residual (u) is

not constant )

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic

Obs*R-squared

Scaled explained SS

0.696892

0.702372

1.224280

Prob. F(1,165)

Prob. Chi-Square(1)

Prob. Chi-Square(1)

0.4050

0.4020

0.2685

Test Equation:

Dependent Variable: RESID^2

Method: Least Squares

Date: 03/23/16 Time: 20:34

Sample: 2002M02 2015M12

Included observations: 167

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

NET_FLOWS

0.004529

-6.23E-08

0.000707

7.46E-08

6.402653

-0.834800

0.0000

0.4050

R-squared

0.004206

Mean dependent var

0.004251

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

F-statistic

Prob(F-statistic)

-0.001829

0.008064

0.010731

569.0312

0.696892

0.405037

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

Durbin-Watson stat

0.008057

-6.790792

-6.753451

-6.775636

1.901167

Interpretation

Here observed r square value = 0.702372 and corresponding probability chisquare value is 0.4020 is greater than 5% confidence value.

Hence we can accept the null hypothesis i.e. Homoscedasticity (the variance of

residual (u) is constant).

Assumption no. 7: Residuals (u ) should be normally distributed.

Null hypothesis Ho : Normal distribution (the residual (u) follows a normal

distribution)

Alternative hypothesis H1: Not normal distribution (the residual (u) follows not

normal distribution)

Conducting histogram normality test we find the following results

40

Series: Residuals

Sample 2002M02 2015M12

Observations 167

35

30

25

20

15

10

5

Mean

Median

Maximum

Minimum

Std. Dev.

Skewness

Kurtosis

-4.45e-18

0.006021

0.217635

-0.249265

0.065394

-0.331499

4.571152

Jarque-Bera

Probability

20.23543

0.000040

0

-0.2

-0.1

0.0

0.1

0.2

Interpretation

Here Jarque-Bera Statistics is 20.23543 and p value is 0.000040 is less than

0.05(5%) hence, we can reject null and accept the alternative, that is residuals

(u) are not normally distributed.

Conclusion

Out of the seven assumption 5 assumption are holding their ground. Moreover

in the model we find that Dublin Watson stat (1.818296) is having a greater

value than R square value (0.22) which shows that there is no spurious

relationship between the variables.

Thus we can say the regression model is good model as it fulfil majority of the

assumptions.

TEST TO FIND OUT WHETHER THERE IS SPILLOVER OF

VOLATILITY IN FII’S NET FLOW TO VOLATILITY IN NIFTY 500.

To find out the above we can conduct a Gramger causality test among the

residuals series of FII net flows and NIFTY 500

Pairwise Granger Causality Tests

Date: 03/23/16 Time: 20:31

Sample: 2002M01 2015M12

Lags: 2

Null Hypothesis:

Obs

F-Statistic

Prob.

RESID01 does not Granger Cause DNIFTYR

DNIFTYR does not Granger Cause RESID01

165

0.50249

1.32045

0.6060

0.2699

From the table below, we interpret the following findings and conclusions:

HYPOTHESIS: We took the null hypothesis, RESID01(residual series of FII net

flows) does not Granger Cause DNIFTYR (residual series of NSE500)

In case 1: RESID01 does not Granger Cause DNIFTYR

Here, f-statistic value is 0.50249 and p value is 0.6060 which is greater than the

5 % ( 0.05) significance level. Thus we accept the null hypothesis and can say

resid01(residual series of FII net flows) does not causes the dniftyr (residual

series of NSE500).

On the other hand analysing the impact of DNIFTYR on RESID01 we find that

F-statistic value is 1.32045 and p value is 0.2699 which is greater than the 5 %

significance level.

Thus we can accept the null hypothesis and can say that DNIFTYR does not

Granger Cause RESID01

Now following is the finding for grangers’ causality test.:

There is no bi-directional causality between volatility(residual series)of FII net

flows and the volatility (residual series) of NSE500 , thus implying that there is

no volatility spill over between the above two variable.

TO SEE IMPACT OF FII NET FLOWS ON FUTURE VOLATILITY OF

NIFTY 500 USING GARCH(1,1) MODEL.

Dependent Variable: DNIFTY

Method: ML - ARCH (Marquardt) - Normal distribution

Date: 03/23/16 Time: 20:41

Sample (adjusted): 2002M02 2015M12

Included observations: 167 after adjustments

Convergence achieved after 12 iterations

Presample variance: backcast (parameter = 0.7)

GARCH = C(1) + C(2)*RESID(-1)^2 + C(3)*GARCH(-1)

Variable

Coefficient

Std. Error

z-Statistic

Prob.

0.880149

2.480190

10.70062

0.3788

0.0131

0.0000

Variance Equation

C

RESID(-1)^2

GARCH(-1)

R-squared

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

Durbin-Watson stat

0.000178

0.165274

0.812602

-0.033067

-0.026881

0.075038

0.940331

208.0421

1.750621

0.000202

0.066638

0.075940

Mean dependent var

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

0.013425

0.074050

-2.455594

-2.399582

-2.432860

Here if we add the alpha(0.16) and beta (0.81)values we will get value of 0.96

with is close to 1.this implies moving forward volatility will remain the same.

Now if we use FII net flow as variance regressors then the result changes to

following

Dependent Variable: DNIFTY

Method: ML - ARCH (Marquardt) - Normal distribution

Date: 03/23/16 Time: 20:39

Sample (adjusted): 2002M02 2015M12

Included observations: 167 after adjustments

Convergence achieved after 4 iterations

Presample variance: backcast (parameter = 0.7)

GARCH = C(2) + C(3)*RESID(-1)^2 + C(4)*GARCH(-1) + C(5)*NET_FLOWS

Variable

Coefficient

Std. Error

z-Statistic

Prob.

C

0.013425

0.012514

1.072834

0.2833

4.239152

0.673887

3.186200

-41.51676

0.0000

0.5004

0.0014

0.0000

Variance Equation

C

RESID(-1)^2

GARCH(-1)

NET_FLOWS

0.005204

0.149999

0.599996

-2.37E-07

R-squared

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

Durbin-Watson stat

-0.000000

-0.000000

0.074050

0.910233

182.8497

1.808508

0.001228

0.222588

0.188311

5.70E-09

Mean dependent var

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

0.013425

0.074050

-2.129937

-2.036584

-2.092047

Interpretation

Here if we add the value of alpha(0.14) and beta(0.59) we get 0.73 which is less

than 1 or unitary.this implies that goimg forward the volatility is set to decrease.

Conclusion comparing results of garch (11) model one with net flows as

variance regressor and otherwise we find that future volatility tend to decrease if

we keep fii inflows as variance regressor.

This implies that FII flows doenot increase the voaltity of NIFTY 500 instead

one can see a reduction in future volatility.

CHAPTER 5

CONCLUSION

FINDING AND CONCLUSION

In this paper I have attempted to estimate the impact of FII net flows on NIFTY 500

over a time period of 13years (2002-2015)using the techniques of Regression

Analysis, Granger Causality Test and Garch (1 1) .

On the basis of above discussion and data analysis, the following can be concluded.

From the current study it is evident that there is moderate positive correlation between

FII & NIFTY 500. Table 1 presents the summary of the model developed. In the model

NIFTY 500 is taken as a dependent variable, FII net flows was found to be significant

predictor. Hence it can be concluded that there is impact of flow of FII on Indian stock

market is significant.

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

NET_FLOWS

-0.005057

4.14E-06

0.005753

6.07E-07

-0.878948

6.824046

0.3807

0.0000

Table1

Secondly the granger Causality test was used to determine whether the volatility in FII

net flows have made the NIFTY 500 .the result of the test reveals that there is no such

evidence that signify any spillover of volatility from one FII to NIFTY 500

Thirdly Garch (11) was used examine the influence of FII on the future stability of

NIFTY 500 and it was found that FII investment has had calming influence on future

stability of the index.

Hence, after conducting the above analysis one can say FII have a mild

correlation with Indian stock market and contrary to popular belief FII

investment has not shown properties of temporary and transient flows instead

they have had a calming influencing Indian stock market.

CHAPTER 6

REFERENCES

REFERENCES

Software resources

EViews 8

Websites

http://www.sebi.gov.in/sebiweb/investment/statistics.jsp?s=fii

http://www.thehindubusinessline.com/markets/stock-markets/markets-live-update/article8303443.ece

http://www.livemint.com/Money/XEBulFzWstlFvtCdzcoVxM/FII-outflows-in-September-quarterhighest-in-five-years.html

https://in.finance.yahoo.com/q?s=%5ECRSLDX&ql=0

RESEARCH PAPERS

Chakrabarti Rajesh , “FII Flows to India: Nature and Causes”

Dr Gupta Ambhuj ,Does the stock market rise or fall due to fiis in india?

Joshi Vikram K. & Saxena Richa Analytical study of impact of fii on indian stock market with special reference

to Bse Sensex.

Dr. Sultana Tabassum Syed, Prof. Pardhasaradhi S. Impact of Flow of FDI & FII on Indian Stock Market.

Bose Suchismita & Coondoo Dipankor The Impact of FII Regulations in India: A Time-Series Intervention

Analysis of Equity Flows.

Coondoo Dipankor And Mukherjee Paramita :Volatility Of FII In India.

Babu Suresh M. and Prabhees K.P. : Causal relationships between Foreign Institutional Investments and stock

returns in India.

Prasanna Krishna P. : Foreign Institutional Investors: Investment Preferences in India.

Sethi Narayan International Capital Flows and Growth in India: The Recent Experience.

Walia Karan , Dr. Walia Rimpi and Jain Monika: Impact Of Foreign Institutional Investment on Stock.

CHAPTER 7

APPENDIX

APPENDIX

1) Raw Data Of Monthly Time Series Used In Above Study(Source Sebi.Com,

Yahoo.Finance .Com)

NET

FLO

WS(I

NR

CAR

ORES

)

N

IF

T

Y

50

0

423.3

71

4.

5

1966.3

76

7.

6

391.4

77

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5

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3

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73

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77

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85

348.5

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6.

65

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g02

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73

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p02

468.6

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2

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70

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2581.9

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913.5

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AN ANALYSIS TO STUDY THE IMPACT OF FOREIGN INSTITUTIONAL

INVESTORS (FII) FLOWS ON INDIAN STOCK MARKET WITH SPECIAL

REFERENCE TO NIFTY 500: 2002-2015

Submitted by

Bhaskar Prasad

Roll no 14MBA0016

in partial fulfilment of the requirements for the award of the degree of Master of

Business Administration

Centre For Management Studies

Jamia Millia Islamia, New Delhi-110025

March 2016

AN ANALYSIS TO STUDY THE IMPACT OF FOREIGN

INSTITUTIONAL INVESTORS (FII) FLOWS ON INDIAN STOCK

MARKET WITH SPECIAL REFERENCE TO NIFTY 500: 2002-2015

Report submitted to

Jamia Millia Islamia

in partial fulfilment of the requirements for the award of the degree of Master of

Business Administration

By

Bhaskar Prasad

Roll No. 14-MBA-16

Under the Supervision of

Dr. Taufique Siddiqui

Assistant Professor

2 | Page

Centre for Management Studies

Jamia Millia Islamia, New Delhi-25

March 2016

DECLARATION

I, Bhaskar Prasad, hereby declare that the thesis entitled “AN ANALYSIS TO STUDY

THE IMPACT OF FOREIGN INSTITUTIONAL INVESTORS (FII) FLOWS ON

INDIAN STOCK MARKET WITH SPECIAL REFERENCE TO NIFTY 500: 20022015” in fulfilment of the requirements for the award of the degree of Master of Business

Administration which is submitted by me to the Centre for Management Studies, Jamia

Millia Islamia University, New Delhi has been done by me and that, to the best of my

knowledge and belief, it contains no material previously published or written by another

person nor material which has been accepted for the award of any other degree or diploma ,

Associateship, Fellowship or other similar title or recognition. This is the original work and

is the result of my own efforts.

Dated:

Place: New Delhi

3 | Page

Bhaskar Prasad

CERTIFICATE

On the basis of the declaration submitted by Bhaskar Prasad, a student of MBA (FullTime), I hereby certify that that the project report titled “AN ANALYSIS TO STUDY THE

IMPACT OF FOREIGN INSTITUTIONAL INVESTORS (FII) FLOWS ON INDIAN

STOCK MARKET WITH SPECIAL REFERENCE TO NIFTY 500: 2002-2015” which

is submitted to the Centre for Management Studies, Jamia Millia Islamia University, New

Delhi in partial fulfilment of the requirements for the award of the degree of Master of

Business Administration, is an original contribution with existing knowledge and faithful

record of research carried out by him/her under my guidance and supervision. Certified

further, that to the best of my knowledge the work reported herein does not form part of any

other project report or dissertation on the basis of which a degree or award was conferred on

an earlier occasion on this or any other candidate.

Dated: , March 2016

Place: New Delhi

Dr. Taufique Siddiqui

Assistant Professor

Centre for Management Studies

Jamia Millia Islamia, New Delhi

ACKNOWLEDGEMENTS

I would like to take this opportunity to express my deepest sense of gratitude to all those who

helped me directly or indirectly in the completion of the project work.

I express my gratitude towards Dr. Taufique Siddiqui, Assistant Professor Centre for

Management Studies Jamia Millia Islamia, New Delhi for guiding me with his valuable

advice and giving me proper direction throughout my study. The present work is an effort to

throw some light on type of relationship between FIIs and Indian stock market. This work

would not have possibly come to the present shape without the able guidance, supervision

and help of a number of people. I convey my heartfelt affection to all those people who

supported during the course, for contributing tremendously in completing my Research

Report.

Dated:

Place: New Delhi

Bhaskar Prasad

EXECUTIVE SUMMARY

The Foreign Institutional Investors (FIIs) have emerged as noteworthy players in the Indian

stock market and their growing contribution adds as an important feature of the development

of stock markets in India. The term Foreign Institutional Investor is defined by SEBI as

under: "Means an institution established or incorporated outside India which proposes to

make investment in India in securities. Provided that a domestic asset management company

or domestic portfolio manager who manages funds raised or collected or brought from

outside India for investment in India on behalf of a sub-account, shall be deemed to be a

Foreign Institutional Investor." These flows on one hand are criticised for being ‘hot money

flows” i.e. speculative capital flows that can move very quickly in and out of markets,

potentially leading to market instability and wreck the economy in times of need, as

happened to many countries during the Asian financial crisis of 1997-99. On other hand they

are said to bring numerous advantages in host countries like enhanced flows of equity

capital, improved capital markets, improved corporate governance, reduced cost of equity

capital, imparting stability to hosts Balance of Payments etc. Thus FIIs have both positive

and negative impacts associated with them.

In Indian context, foreign institutional investors (FIIs) have been allowed to invest in the

domestic financial market since 1992. This decision to open up the Indian financial market to

FII portfolio flows at that point in time was influenced by several factors such as the

complete disarray in India’s external finances in 1991 and a disorder in the country’s capital

market. Aimed primarily at ensuring non-debt creating capital inflows at a time of an

extreme balance of payment crisis and at developing and disciplining the nascent capital

market, foreign investment funds were welcomed to the country. From 1993 onwards FII

inflows have grown leaps and bounds coinciding with India’s unfolding growth story. And

with India becoming the highest growing major economy in the world surpassing China and

world at large are facing the problem of low growth if not outright deflation India is all set to

become cynosure of foreign investors portfolio in coming future.

But recent event like selloff in Chinese stock market coupled with mass exodus of FIIs from

china. Further back home headlines like ‘FII outflows in FY16 so far highest in last 7 yearsWed, Nov 18 2015, mint”, ‘Sensex leaps 463 points on FII buying- 2 march 2016, Business

line” and many more becoming a regular fixtures on financial dailies have intrigue researcher

to examine the role and impact FII inflows on Host economy and market especially in recent

times post 2008 economic crisis in world.

Hence this study attempts to analyse Fii inflows in Indian equity market along with NIFTY

500 taken as a substitute to Indian overall stock market,by taking monthly time sries data of

FII net inflows and NIFTY 500 value for a period of 13 years w.e.f January 2002 to

December 2015 and find answer to the following question: What is impact does FII inflows have on Indian stock market?

Does there is spill over of volatility of Fii inflows on the volatility of Indian Stock

market represent by NIFTY 500?

And finally how does Fii inflow influence future volatility in NIFTY 500?

After analysis and observation it was found that Fii share a mild correlation with NIFTY

500.there was reason to believe that there is spill over of volatility from Fii flows to NIFTY

500 volatility and it was found that FII flows have a calming influence on the future volatility

of NIFTY 500.Hence one can say that in the period considered there was no reason to

believe that FII flows are making Indian stock market unstable.

CHAPTER 1

INTRODUCTION

INTRODUCTION

Indian capital market

Conceptual Framework of Capital Market

Capital market is the financial market for the buying and selling of the long term debt or

equity backed securities. The market channels the wealth of savers to those who can put it to

long term productive use. Modern capital markets are hosted on computer based electronic

trading system which can be accessed by entities within the financial sector. The capital

market can be divided into:

1) Primary Market: It deals with issue of new securities .Companies, government, and

public sector institutions can obtain funds through sale of new stock or bonds issue.

2) Secondary Market: It is also called liquid market. In this market the securities are sold

by or transferred from one investor to another. Thus, this market gives liquidity to the long

term securities.

Overview of Indian Capital Market

The Indian capital market is more than a century old. Its history goes back to 1875, when 22

brokers formed the Bombay Stock Exchange (BSE). Over the period, the Indian securities

market has evolved continuously to become one of the most dynamic, modern, and efficient

securities markets in Asia. Today, Indian market confirms to best international practices and

standards both in terms of structure and in terms of operating efficiency .Indian securities

markets are mainly governed by a) The Company’s Act,2013 b) the Securities Contracts

(Regulation) Act 1956 (SCRA Act), and c) the Securities and Exchange Board of India

(SEBI) Act, 1992. A brief background of these above regulations are given below:

a) The Companies Act 2013 deals with issue, allotment and transfer of securities and various

aspects relating to company management. It provides norms for disclosures in the public

issues, regulations for underwriting, and the issues pertaining to use of premium and discount

on various issues.

b) SCRA provides regulations for direct and indirect control of stock exchanges with an aim

to prevent undesirable transactions in securities. It provides regulatory jurisdiction to Central

Government over stock exchanges, contracts in securities and listing of securities on stock

exchanges.

c) The SEBI Act empowers SEBI to protect the interest of investors in the securities market,

to promote the development of securities market and to regulate the security market.

The Indian securities market consists of primary (new issues) as well as secondary (stock)

market in both equity and debt. The primary market provides the channel for sale of new

securities, while the secondary market deals in trading of securities previously issued. The

issuers of securities issue (create and sell) new securities in the primary market to raise funds

for investment. They do so either through public issues or private placement. There are two

major types of issuers who issue securities. The corporate entities issue mainly debt and

equity instruments (shares, debentures, etc.), while the governments (central and state

governments) issue debt securities (dated securities, treasury bills). The secondary market

enables participants who hold securities to adjust their holdings in response to changes in

their assessment of risk and return. A variant of secondary market is the forward market,

where securities are traded for future delivery and payment in the form of futures and

options. The futures and options can be on individual stocks or basket of stocks like index.

Two exchanges, namely National Stock Exchange (NSE) and the Stock Exchange, Mumbai

(BSE) provide trading of derivatives in single stock futures, index futures, single stock

options and index options. Derivatives trading commenced in India in June 2000.

In the beginning of the twentieth century, the industrial revolution was on the way in India

with the Swadeshi Movement; and with the inauguration of the Tata Iron and Steel Company

Limited in 1907, an important stage in industrial advancement under Indian enterprise was

reached.

There are two major indicators of Indian capital market- SENSEX & NIFTY:

The Sensex is an "index". What is an index? An index is basically an indicator. It gives you a

general idea about whether most of the stocks have gone up or most of the stocks have gone

down. The Sensex is an indicator of all the major companies of the BSE. The Nifty is an

indicator of all the major companies of the NSE. If the Sensex goes up, it means that the

prices of the stocks of most of the major companies on the BSE have gone up. If the Sensex

goes down, this tells you that the stock price of most of the major stocks on the BSE have

gone down. Just like the Sensex represents the top stocks of the BSE, the Nifty represents the

top stocks of the NSE. The BSE and NSE are situated at Mumbai. These are the major stock

exchanges in the country. There are other stock exchanges like the Calcutta Stock Exchange

etc. but they are not as popular as the BSE and the NSE. Most of the stock trading in the

country is done though the BSE & the NSE. Besides Sensex and the Nifty there are many

other indexes. There is an index that gives you an idea about whether the mid-cap stocks go

up and down. This is called the “BSE Mid-cap Index”. There are many other types of index.

Unless stock markets provide professionalized service, small investors and foreign investors

will not be interested in capital market operations. And capital market being one of the major

source of long-term finance for industrial projects, India cannot afford to damage the capital

market path. In this regard NSE gains vital importance in the Indian capital market but if we

see the Sensex & nifty graph there is a great variation.

Recent Trends In Indian Capital Market

A new era in capital market in India was ushered in July, 1991, with starting of a process of

financial and economic deregulation. Beginning with the devaluation of rupee by about 20%

in July 1991, industrial policy was totally reshaped to dispense with licensing of all

industries except the 18 scheduled industrial groups. Further, removal of MRTP limit on

assets of companies, dilution of FERA and foreign trade liberalization etc., were some of the

other reforms.

Genesis of new order

The beginning of liberalized policies dates back to 1985 when the Seventh Five Year Plan

was started. The banking companies Amendment Act of 1983 gave new avenues of activities

to banks in the form of participation in non-funded activities and financial services such as

leasing, hire purchase, merchant banking, etc. The public sector banks have started setting up

subsidiaries for merchant banking, lease financing, mutual fund etc., since that time. After

1992 even private sector is allowed to enter into these financial services, including banking,

mutual funds, etc.

The SEBI was set up in April 1988 to oversee and control the capital market and it has been

given legal powers since April 1992 by an act. A number of new institutions like CRISIL for

credit rating and SHCIL for clearance and share depository services have been set up.

NSE

The National Stock Exchange (NSE) is India's leading stock exchange covering various cities

and towns across the country. NSE was set up by leading institutions to provide a modern,

fully automated screen-based trading system with national reach. The Exchange has brought

about unparalleled transparency, speed & efficiency, safety and market integrity. It has set up

facilities that serve as a model for the securities industry in terms of systems, practices and

procedures.

NSE has played a catalytic role in reforming the Indian securities market in terms of

microstructure, market practices and trading volumes. The market today uses state-of-art

information technology to provide an efficient and transparent trading, clearing and

settlement mechanism, and has witnessed several innovations in products & services viz.

demutualisation of stock exchange governance, screen based trading, compression of

settlement cycles, dematerialisation and electronic transfer of securities, securities lending

and borrowing, professionalisation of trading members, fine-tuned risk management systems,

emergence of clearing corporations to assume counterparty risks, market of debt and

derivative instruments and intensive use of information technology.

NIFTY 500

The Nifty 500 is India’s first broad based benchmark of the Indian capital market. The Nifty

500 Index represents about 95.77% of the free float market capitalization of the stocks listed

on NSE as on March 31, 2015.

The total traded value for the last six months ending March 2015, of all Index constituents is

approximately 91.97% of the traded value of all stocks on NSE. The Nifty 500 companies are

disaggregated into 73 industry indices viz. Nifty Industry Indices. Industry weightages in the

index reflect the industry weightages in the market. For e.g. if the banking sector has a 5%

weightage in the universe of stocks traded on NSE, banking stocks in the index would also

have an approx. representation of 5% in the index.

Method of Computation

Nifty 500 is computed using free float market capitalization* weighted method w.e.f.

October 11, 2010, wherein the level of the index reflects the total market value of all the

stocks in the index relative to a particular base period. The method also takes into account

constituent changes in the index and importantly corporate actions such as stock splits,

rights, etc without affecting the index value.

Base Date and Value

Nifty 500 Index is calculated with base date of 01-01-1995 and base value of 1000.

Criteria for Selection of Constituent Stocks.

The constituents and the criteria for the selection judge the effectiveness of the index.

Selection of the index set is based on the following criteria :

Market Capitalisation

A company's rank on free float market capitalisation is an important consideration for its

inclusion in the Index.

Industry Representation

Nifty 500 Equity Index reflects the market as closely as possible. In order to ensure that this

is accomplished, industry weightages in the index mirror the industry weightages in the

universe. Consequently, companies to be included in the index are selected from the

industries which are underrepresented in the index.

Nifty 500 Equity Index currently contains 71 industries, including one category of diversified

companies and one category of miscellaneous. The number of industries in the Index and the

number of companies within each industry have been kept flexible, in order to ensure that the

Index retains its objective of being a dynamic market indicator.

Trading Interest

Nifty 500 Equity Index includes those companies which have a minimum listing record of 6

months on the Exchange. In addition these companies must have demonstrated high turnover

and trading frequency.

Others

A company which comes out with a IPO will be eligible for inclusion in the index, if it fulfils

the normal eligibility criteria for the index for a 3 month period instead of a 6 month period.

Foreign portfolio investments or foreign institutional investors

Definition

An investor or investment fund that is from or registered in a country outside of the one in

which it is currently investing is known as Foreign Institutional Investment and investors are

known as Foreign Institutional Investors. Institutional investors include hedge funds,

insurance companies, pension funds and mutual funds. The term is used most commonly in

India to refer to outside companies investing in the financial markets of India.

History

Most of the under developed countries suffer from low level of income and capital

accumulation. Though, despite this shortage of investment, these countries have developed a

strong urge for industrialization and economic development. As we know the need for

Foreign capital arises due to shortage from domestic side and other reasons. Indian economy

has experienced the problem of capital in many instances. While planning to start the steel

companies under government control, due to shortage of resources it has taken the aid of

foreign countries. Likewise we have received aid from Russia, Britain and Germany for

establishing Bhilai, Rourkela and Durgapur steel plants. The present paper is a modest

attempt to study the impact of Foreign Institutional Investment on Indian Stock Market with

special reference to NSE NIFTY500 Index. The character of global capital flows to

developing countries underwent significant changes on many counts during the 'nineties. By

the time the East Asian financial crisis surfaced, the overall size of the flows more than

tripled. It stood at US$ 100.8 bn. in 1990 and rose to US$ 308.1 bn. by 1996. The increase

was entirely due to the sharp rise in the flows under private account that rose from US$ 43.9

bn. to 275.9 bn. during the same period. In relative terms the percentage of private account

capital flows increased from 43.55 to 89.55 per cent. Simultaneously, the Official

Development Assistance (ODA) declined both in relative and absolute terms. All the main

components of the private account capital transfers, namely, (a) commercial loans, (b)

foreign direct investments (FDI), and (c) foreign portfolio investments (equity and bonds)

(FPI) recorded significant increases. Portfolio flows increased at a faster rate than direct

investments on private account. As a result, starting with a low level of 11.16 percent, the

share of capital flows in the form of portfolio investments quadrupled to reach 37.22 per cent

in 1996 reflecting the enhanced emphasis on private capital flows with portfolio investments

forming the second important constituent of the flows during the 'nineties. In this process

multilateral bodies led by the International Finance Corporation (IFC) played a major role.

Following the East Asian financial crisis, initially there was a slowdown followed, by a

decline in private capital flows. While bonds and portfolio equity flows reacted quickly and

declined in 1997 it, loans from commercial banks dropped a year later in 1998. Decline in

FDI was also delayed. But the fall in FDI was quite small compared to the other three major

forms of private capital flows. Thus, starting with the resolve by the developed countries to

provide 1 per cent of their GNP as developmental aid, the industrialised world preferred to

encourage private capital transfers through direct investments instead of official assistance.

The declining importance of official development finance is attributed to budgetary

constraints in donor countries and the optimism of private investors in the viability of the

developing countries.

Portfolio investments spread risk for foreign investors, and provide an opportunity to share

the fruits of growth of developing countries which are expected to grow faster. Investing in

emerging markets is expected to provide a better return on investments for pension funds and

private investors of the developed countries. For developing countries, foreign portfolio

equity investment has different characteristics and implications compared to FDI. Besides

supplementing domestic savings, FDI is expected to facilitate transfer of technology,

introduce new management and marketing skills, and helps expand host country markets and

foreign trade. Portfolio investments supplement foreign exchange availability and domestic

savings but are most often not project specific.

FPI, are welcomed by developing countries since these are non-debt creating. FPI, if

involved in primary issues, provides critical risk capital for new projects. Since FPI takes the

form of investment in the secondary stock market, it does not directly contribute to creation

of new production capabilities. To enable FPI flows which prefer easy liquidity, multilateral

bodies, led by the International Finance Corporation (IFC), have been encouraging

establishment and strengthening of stock markets in developing countries as a medium that

will enable flow of savings from developed countries to developing countries. FPI, it is

expected, could help achieve a higher degree of liquidity at stock markets, increase priceearning (PE) ratios and consequently reduce cost of capital for investment. FPI is also

expected to lead to improvement in the functioning of the stock market as foreign portfolio

investors are believed to invest on the basis of well-researched strategies and a realistic stock

valuation. The portfolio investors are known to have highly competent analysts and access to

a host of information, data and experience of operating in widely differing economic and

political environments. Host countries seeking foreign portfolio investments are obliged to

improve their trading and delivery systems which would also benefit the local investors. To

retain confidence of portfolio investors host countries are expected to follow consistent and

business friendly liberal policies. Having access to large funds, foreign portfolio investors

can influence developing country capital markets in a significant manner especially in the

absence of large domestic investors. Portfolio investments have some macroeconomic

implications. While contributing to build-up of foreign exchange reserves, portfolio

investments would influence the exchange rate and could lead to artificial appreciation of

local currency. This could hurt competitiveness. Portfolio investments are amenable to

sudden withdrawals and therefore these have the potential for destabilising an economy. The

volatility of FPI is considerably influenced by global opportunities and flows from one

country to another. Though it is sometime argued that FDI and FPI are both equally volatile

the Mexican and East Asian crises brought into focus the higher risk involved in portfolio

investments.

Foreign Institutional Investors in India

India opened her doors to foreign institutional investors in September, 1992. This event

represents a landmark event since it resulted in effectively globalizing its financial services

industry. Beginning 1996-97, the group was expanded to include registered university funds,

endowment, foundations, charitable trusts and charitable. Since then, FII flows which form a

part of foreign portfolio investments have been steadily growing in importance in India.

The Government of India gave preferential treatment to FIIs till 1999-2000 by subjecting

their long term capital gains to lower tax rate of 10 percent while the domestic investors had

to pay higher long-term capital gains tax. The Indo-Mauritius Double Taxation Avoidance

Convention 2000 (DTAC), exempts Mauritius-based entities from paying capital gains tax in

India - including tax on income arising from the sale of shares. This gives an incentive for

foreign investors to invest in Indian markets taking the Mauritius route. Consequently, we

now see investments coming from Mauritius while there were none before 2000.

Difference between FII and FDI

FII, Foreign Institutional Investors, foreign investors invest in the markets of a foreign

country for example, insurance companies, investment companies, charitable organizations

etc. The concept of FII is very common in India. Such companies are just required to register

on the stock exchange or in the markets to make investments and there is no board

controlling it. FII is when a foreign company buys equity in a company through the stock

markets. Therefore, in this case, FII would not give the foreign company any control in the

company. On the other hand in FDI, Foreign Direct Investment, in this foreign country

investor has to get at least 10% of voting stocks. It increases the management interest in the

enterprise of the other country. In very simple words, FDI is an investment which a parent

enterprise made in a foreign country. For example, Multinational companies present the best

examples of FDI like Telenor; the seventh largest company in the world has a number of

companies in various countries. The daughter companies of Telenor Group act as the FDI for

the host countries. In most of the countries there is an FDI board which is responsible to

handle FDI coming in the country like in India, one of the attractive FDI country, there is

Foreign Investment Promotion Board (FIPB). FDI is when a foreign company brings capital

into a country or an economy to set up a production or some other facility. FDI gives the

foreign company some control in the operations of the company.

Inflow of foreign capital, whether in the form of FDI or FII, is good for any recipient country

provided they are allowed in a measured way. If FDIs are invited more in the infrastructure

sectors, it could prove to be a boom for the economy. But if these capital inflows come more

in the consumer sectors, chances are that in the long run it could be proved to be disastrous

for the economy/country. For example if the recipient country of FDIs invites more and more

capital into the automobile sector to have vast varieties of cars, scooter, motorcycles etc. in

the country, but if the growth of the automobile sector is not matched by the proper roads as

well as the capacity of the country to generate or import petrol or diesel, at a macro level, the

growth of automobile sector might prove to be a liability for the country simply because the

country will be more dependent on the international markets for buying petrol/diesel. Poor

roads, inefficient and outdated railway's technology, outdated railways tracks etc. etc. will

add the consumption of petrol/diesel. This way whatever would come in as a foreign

exchange through an inflow of foreign capital will be drained out in buying petrol/diesel

from international markets for sheer consumption purposes? Similar cases could be in other

sectors.

Investment Limits For FII

Foreign Institutional Investors (FIIs) are allowed to invest in the primary and secondary

capital markets in India through the portfolio investment scheme (PIS).Under this scheme,

FIIs can acquire shares/debentures of Indian companies through the stock exchanges in India.

The ceiling for overall investment for FIIs is 24 percent of the paid up capital of the Indian

company. The limit is 20 per cent of the paid up capital in the case of public sector banks,

including the State Bank of India. The ceiling of 24 per cent for FII investment can be raised

upto sectoral cap/statutory ceiling, subject to the approval of the board and the general body

of the company passing a special resolution to that effect. And the ceiling of 10 per cent for

NRIs/PIOs can be raised to 24 per cent subject to the approval of the general body of the

company passing a resolution to that effect. The ceiling for FIIs is independent of the ceiling

of 10/24 per cent for NRIs/PIOs.

The Reserve Bank of India monitors the ceilings on FII/NRI/PIO investments in Indian

companies on a daily basis. For effective monitoring of foreign investment ceiling limits,the

Reserve Bank has fixed cut-off points that are two percentage points lower than the actual

ceilings. The cut-off point, for instance, is fixed at 8 per cent for companies in which NRIs/

PIOs can invest up to 10 per cent of the company’s paid up capital. The cut-off limit for

companies with 24 per cent ceiling is 22 percent and for companies with 30 per cent ceiling,

is 28 per cent and so on. Similarly, the cut-off limit for public sector banks (including State

Bank of India) is 18 per cent.Once the aggregate net purchases of equity shares of the

company by FIIs/NRIs/PIOs reach the cut-off point, which is 2% below the overall limit, the

Reserve Bank cautions all designated bank branches so as not to purchase any more equity

shares of the respective company on behalf of FIIs/NRIs/PIOs without prior approval of the

Reserve Bank. The link offices are then required to intimate the Reserve Bank about the total

number and value of equity shares/convertible debentures of the company they propose to

buy on behalf of FIIs/NRIs/PIOs. On receipt of such proposals, the Reserve Bank gives

clearances on a first-come first served basis till such investments in companies reach 10 / 24 /

30 / 40/ 49 per cent limit or the sectoral caps/statutory ceilings as applicable. On reaching the

aggregate ceiling limit, the Reserve Bank advises all designated bank branches to stop

purchases on behalf of their FIIs/NRIs/PIOs clients. The Reserve Bank also informs the

general public about the caution’ and the ‘stop purchase’ in these companies through a press

release.

Basis for calculating FII investment limit:

Investment limit by all registered FIIs or sub accounts in primary or secondary markets

Under Portfolio Investment Scheme is subject to a ceiling of 24% of issued share capital of a

Company. The limit can be extended upto 49% per sectoral cap if the general body of the

Company approves it.

Advantages of FII Investments

The advantages of having FII Investment can be broadly classified under the following

categories:

(A). Enhanced flows of equity capital:

FIIs are well known for a greater appetite for equity than debt in their asset structure. For

examples, pension funds in the United Kingdom and United states are known to have grater

allocation to equity than debt. Thus, opening up the economy to FIIs is in line with the

accepted preference for non-debt creating foreign inflows over foreign debt. Furthermore,

because of this preference for equities over bonds, FIIs can help in compressing the yielddifferential between equity and bonds and improve corporate capital structures.

(B). Improving capital markets:

FIIs as professional bodies of asset managers and financial analysts enhance competition and

efficiency of financial markets. Equity market development aids economic development. By

increasing the availability of riskier long term capital for projects, and increasing firms’

incentives to supply more information about themselves, the FIIs can help in the process of

economic development. The increasing role of institutional investors has brought both

quantitative and qualitative developments in the stock markets viz., expansion of securities

business, increased depth and breadth of the market, and above all their dominant investment

philosophy of emphasizing the fundamentals has rendered efficient pricing of the stocks.

(C). Improved corporate governance:

Good corporate governance is essential to overcome the principal-agent problem between

share-holders and management. Information asymmetries and incomplete contracts between

share-holders and management are at the root of the agency costs. Dividend payment, for

example, is discretionary. Bad corporate governance makes equity finance a costly option.

With boards often captured by managers or passive, ensuring the rights of shareholders is a

problem that needs to be addressed efficiently in any economy. Incentives for shareholders to

monitor firms and enforce their legal rights are limited and individuals with small shareholdings often do not address the issue since others can free-ride on their endeavor.

(D). Managing uncertainty and controlling risks:

Institutional investors promote financial innovation and development of hedging instruments.

Institutions, for example, because of their interest in hedging risks, are known to have

contributed to the development of zero-coupon bonds and index futures. FIIs, as professional

bodies of asset managers and financial analysts, not only enhance competition in financial

markets, but also improve the alignment of asset prices to fundamentals. Institutions in

general and FIIs in particular are known to have good information and low transaction costs.

By aligning asset prices closer to fundamentals, they stabilize markets. Fundamentals are

known to be sluggish in their movements. Thus, if prices are aligned to fundamentals, they

should be as stable as the fundamentals themselves. Furthermore, a variety of FIIs with a

variety of risk-return preferences also help in dampening volatility.

(E). Reduced cost of equity capital:

FII inflows augment the sources of funds in the Indian capital markets. In a common sense

way, the impact of FIIs upon the cost of equity capital may be visualized by asking what

stock prices would be if there were no FIIs operating in India. FII investment reduces the

required rate of return for equity, enhances stock prices, and foster investments by Indian

firms in the country. From the perspective of international investors, the rapidly growing

emerging markets offer potentially higher rates of return and help in diversifying portfolio

risk.

(F) Imparting stability to India’s Balance of Payments:

For promoting growth in a developing country such as India, there is need to augment

domestic investments, over and beyond domestic saving, through capital flows. The excess

of domestic investment over domestic savings result in a current account deficit and this

deficit is financed by capital flows in the balance of payments. Prior to 1991, debt flows and

official development assistance dominated these capital flows. This mechanism of funding

and current account deficit is widely believed to have played a role in the emergence of

balance of payments difficulties in 1981 and 1991. Portfolio flows in the equity markets, and

FDI as opposed to debt creating flows, are important as safer and more sustainable

mechanisms for funding the current account deficit.

(G). Knowledge Flows:

The activities of international institutional investors help strengthen Indian finance.FIIs

advocate modern ideas in market design, promote innovation development of sophisticated

products such as financial derivatives, enhance competition in financial intermediation, and

lead to spillovers of human capital by exposing Indian participants to modern financial

techniques, and international best practices and systems.

(H).Improvements to market efficiency:

A significant presence of FIIs in India can improve market efficiency through two channels.

First, when adverse macro-economic news, such as bad monsoons, unsettles many domestic

investors, it may be easier for a globally diversified portfolio manager to be more

dispassionate about India’s prospects and engage in stabilizing trades. Second, at a level of

individual stocks and industries, FIIs may act as a channel through which knowledge and

ideas about valuation of a firm or an industry can more rapidly propagate into India. For

example, foreign investors were rapidly able to assess the potential of the firms like Infosys,

which are primarily expert oriented, applying valuation principles, and the prevailed outside

India for software services companies.

Disadvantages of FII Investment

There are also some disadvantages of FII Investment which are broadly classified under the

following categories:

(A). Volatility and capital outflows:

There is also increasing possibility of abrupt and sudden outflows of capital if the inflows are

of a short-term nature as in the case of portfolio inflows of FIls. The recent experience of

reversal of private capital flows observed in Global crisis of 2008, Asian crisis in 1997 and in

Mexico during the latter part of 1994 due to sudden change in FIIs' investment sentiment

provides a vivid illustration of such risks. Usually, FIls take into account some specific risks

in emerging markets such as (i) political instability and economic mismanagement, (ii)

liquidity risk and (iii) currency movement. Currency movement can have a dramatic impact

on equity returns of FIIs, a depreciation having an adverse effect.Thus, short-term flows

including portfolio flows of FIIs to developing countries in particular are inherently unstable

and increases volatility of the emerging equity markets. They are speculative and respond

adversely to any instability either in the real economy or in financial variables. Investment in

emerging markets by FIIs can at times be driven more by a perceived lack of opportunities in

industrial countries than by sound fundamentals in developing countries including India.

Emerging stock markets of India and other developing countries have a low, even negative

correlation with the stock markets in industrial nations. So, when the latter goes down, FIIs

invest more in the former as a means to reduce overall portfolio risk. On the other hand, if

there is boom in industrial countries, there may be reverse flow of funds of FIIs from India

and other developing countries. Of course, there is pull for international private portfolio

investment of FIIs due to the impact of wide-ranging macro-economic and structural reforms

including liberalization or elimination of capital restrictions, improved flow of financial

information, strengthening investors' protection and the removal of barriers on FIIs'

participation in equity markets in India and other emerging markets.

(B). Price rigging:

Bear hammering by FIIs has been alleged in case of almost all companies in India tapping

the GDR market. The cases of SBI and VSNL are most illuminating to show how the FIIs

manipulate domestic market of a company before its GDR issues. The manipulation of FIIs,

working in collusion operates in the following way. First, they sell en masse and then when

the price has been pulled down enough, pick up the some shares cheaply in the GDR market.

Though FIIs have the freedom of entry and exit, they alone have the access to both the

domestic as well as the GDR market but the GDR market is not open to domestic investors.

Hence FIIs gain a lot at the cost of domestic investors due to their manipulation which is

possible owing to integration of Indian equity market with global market consequent upon

liberalization.

(C).BOP vulnerability:

There are concerns that in an extreme event, there can be a massive flight of foreign capital

out of India, triggering difficulties in the balance of payments front. India’s experience with

FIIs so far, however, suggests that across episodes like the Pokhran blasts, or the 2001 stock

market scandal, no capital flight has taken place. A billion or more of US dollars of portfolio

capital has never left India within the period of one month. When juxtaposed with India’s

enormous current account and capital flows, this suggests that there is little evidence of

vulnerability so far.

(D). Money laundering:

The movement of hot money of FIIs due to integration of emerging markets of India and

other countries with global market have helped the hawala traders and criminal elements an

easy means to launder international money from illegal activities which in consequence have

also an impact on equity market. Sometimes FIIs act as an agent For money laundering.It is

also argued that the FII indulge in price rigging by collusive operation. Another ill effect of

opening up of the capital market to FIIs has been the possibility of FIIs trying to gain control

of indigenous companies.

CHAPTER 2

LITERATURE REVIEW

LITERATURE REVIEW

A literature review is a text of a scholarly paper, which includes the current

knowledge including substantive findings, as well as theoretical and

methodological contributions to a particular topic.

There is plethora of research work, essays and journals etc. are available on the

topic of relationship between FII and stock markets conducted both in India and

abroad. I have gone through diverse content available on the topic and found the

following research thesis pertinent to my study:

I.

FII Flows to India: Nature and Causes by Rajesh Chakrabarti*

Dupree College of Management, Georgia Institute of Technology: In this

paper the author analyzes FII flows and their relationship with other economic

variables and arrive at the following major conclusions: While the flows are

highly correlated with equity returns in India, they are more likely to be the

effect than the cause of these returns; The FIIs do not seem to be at an

informational disadvantage in India compared to the local investors; The Asian

Crisis marked a regime shift in the determinants of FII flows to India with the

domestic equity returns becoming the sole driver of these flows since the crisis.

II.

Does the stock market rise or fall due to fiis in india? By Dr. Ambuj

Gupta Assistant Professor Asia Pacific Institute of Management New Delhi:

In the above paper the author examines the relationship between Indian stock

market and FIIs investment in India and finds that both, Indian stock market and

FIIs influence each other; however, their timing of influence is different.

III. Analytical study of impact of fii on indian stock market with special

reference to bse sensex by Vikram K. Joshi* & Miss Richa Saxena: In this

paper the author attempts to analyse the impact of variation in FII on Sensex

and to study the degree of relationship between them in various FII movement

scenarios.

IV. Impact of Flow of FDI & FII on Indian Stock Market by Dr. Syed

Tabassum Sultana, Prof. S Pardhasaradhi: In this paper the authors makes an

attempt to study the relationship and impact of FDI & FII on Indian stock

market using statistical measures correlation coefficient and multi regression.

Sensex and Nifty were considered as the representative of stock market as they

are the most popular Indian stock market indices. Based on 11 years data

starting from 2001 to 2011, it was found that the flow of FDI & FII was moving

in tandem with Sensex and Nifty. The study concludes that Flow of FDIs and

FIIs in India determines the trend of Indian stock market.

V.

The Impact of FII Regulations in India: A Time-Series Intervention

Analysis of Equity Flows: by Suchismita Bose ,ICRA Ltd,Dipankor

Coondoo Indian Statistical Institute, New Delhi - Economic Research Unit:

This paper examines the impact of reforms of the foreign institutional investors'

(FIIs) investment policy, on FII portfolio flows to the Indian stock markets, an

aspect, studies on determinants of FII flows to India so far have not taken into

consideration and finds results that strongly suggest that liberalisation policies

have had the desired expansionary effect and have either increased the mean

level of FII inflows and/or the sensitivity of these flows to a change in BSE

return and/or the inertia of these flows. On the other hand, interestingly, the

restrictive measures aimed at achieving greater control over FII flows also do

not show any significant negative impact on the net inflows; we find that these

policies mostly render FII investments more sensitive to the domestic market

returns and raise the inertia of the FII flows.

VI. Volatility Of FII In India By Dipankor Coondoo And Paramita

Mukherjee: In this paper, the authors have examined volatility of the day-today movements of foreign institutional investment (FII) in India, along with

some other related variables like the stock market returns and the call money

rate. For the purpose of this study, a new technique of analysis has been used

that defines and examines three different aspects of volatility, viz. strength,

duration and persistence of volatility. The results suggest that the over-time

movements of the daily values of FII and stock market returns contain a fair

amount of volatility. Also, the strength and duration of volatility of stock market

returns are more or less similar to those of the FII flows. Another interesting

finding is that the strength of volatility of FII flows are positively correlated

both with that of stock market returns and call money rate. The overall finding

is that the FII and stock market returns in India exhibit quite high volatility in

terms of both extent and duration. More importantly, there is also evidence that

their volatility is interrelate.

VII. Causal relationships between Foreign Institutional Investments and

stock returns in India by M. Suresh Babu,K.P. Prabhees: This paper

examines the dynamic interaction between FII flows and stock market returns in

Indian stock market. Using daily data from January 2003 to February 2007,

VAR framework and Granger causality test, we find the existence of

bidirectional causality between FII flows and stock returns. Further analysis

through impulse response function indicates that FII flows are more stock return

driven. We also find support for information revelation hypothesis and

momentum trading hypothesis.

VIII. Foreign Institutional Investors: Investment Preferences in India by P.

Krishna Prasanna: this paper examines the contribution of foreign institutional

investment particularly among companies included in sensitivity index (Sensex)

of Bombay Stock Exchange. Also examined is the relationship between foreign

institutional investment and firm specific characteristics in terms of ownership

structure, financial performance and stock performance. It is observed that

foreign investors invested more in companies with a higher volume of shares

owned by the general public. The promoters’ holdings and the foreign

investments are inversely related. Foreign investors choose the companies

where family shareholding of promoters is not substantial. Among the financial

performance variables the share returns and earnings per share are significant

factors influencing their investment decision.

IX. International Capital Flows and Growth in India: The Recent

Experience by Narayan Sethi: In the study the author attempts to examine the

impact of international capital flows on economic growth. The study also

examines trends and composition of capital inflows, changing pattern capital

flows in view of economic reform, ascertain the impact of domestic financial

policy variables on international capital flows and suggest policy implication

thereof. By using monthly time series data, the author found that Foreign Direct

Investment (FDI) is positively affecting the economic growth direct

contribution, while Foreign Institutional Investment (FII) is negatively affecting

the growth although, in a small way and make a preliminary attempt to test

whether the international capital flows has positive impact on financial markets

and economic growth. The empirical analysis using the time series data between

April 1995 to April 2007 shows that FDI plays unambiguous role in

contributing to economic growth.

X.

Impact Of Foreign Institutional Investment on Stock Market by

Karan Walia, Dr. Rimpi Walia and Monika Jain: In this paper examines the

contribution of foreign institutional investment in sensitivity index (Sensex).

Also attempts to understand the behavioral pattern of FII during the period of

2001 to 2010 and examine the volatility of BSE Sensex due to FII. The data for

the study uses the information obtained from the secondary resources like

website of BSE Sensex. We attempted to explain the impact of foreign

institutional investment on stock market and Indian economy. Also attempts to

present the correlation between FII and BSE Sensex by the Karl Pearson’

Coefficient of correlation test.

CHAPTER 3

RESEARCH METHODOLOGY

OBJECTIVES OF STUDY:

Below listed points constitute the objectives of the study:

I. To study the correlation between CNX NIFTY 500 and FIIs between the

period 2002 to 2015.

II. To build a regression model to depict the relationship between dependent

variable and independent variable.

III. To find out how FII net flows impacts the future volatility in NIFTY 500

index using GARCH (1,1).

IV. To find out whether volatility in net flows spillover to influence the

volatility in NIFTY 500 index.

SCOPE AND NEED OF STUDY:

Scope of study is broader and it covers FIIs in the form of equity. But, study is

only going to cover only one stock index, i.e. NIFTY 500. The time period

under study is limited from January 2002 to December 2015, as it will give

exact impact in both bullish and bearish trend.

This study also provides a clear picture about how FIIs impact the Indian

capital market and also NSE.

RESEARCH METHODOLOGY:

According to John.W.Best “Research is a systematic and objective analysis and

recording of controlled observations that may lead to the development of

generalizations, principles, theories and concepts, resulting in prediction for

seeing and possibly ultimate control of events.”

The research methodology includes the following:

•

Research problem

•

Research design

•

Sampling design

•

Sampling technique

•

Data collection method

RESEARCH PROBLEM:

There is an old dictum by John Dewey, which says “a problem well put is half

solved”. The project deals with the “Impact of Foreign Institutional Investors on

Indian Capital Market”. This project studies the relation between Foreign

Institutional Investments and one of the stock indices i.e. Nifty 500. To identify

the degree of relation between CNX Nifty 500 and Foreign Institutional

Investments, net investments (i.e. total inflows minus total outflows) in the

equity segments by the FIIs are considered. This project also studies the trends

in the investment of FIIs along with the economic figures provided.

There may be multiple factors affecting the stock index, like:

•

Government Policies

•

Budgets

•

Economic and political condition of the country

•

FDI

•

Rupee./Dollar exchange rate

•

Inflation

•

Bullion market

But for the above research problem we will be considering FII flows as

independent variable and ignoring the other variables for the simplicity of the

model.

RESEARCH DESIGN:

A research design is the framework or plan for a study used as a guide in

collecting and analyzing data.

There are three basic types of research design:

•

Exploratory design

•

Descriptive design

•

Casual design

In this study, exploratory research has been conducted. Exploratory research is

conducted to provide a better understanding of a situation. This study aims to

find the new insights in terms of finding the relation between FII’S and Nifty

500 index.

To describe the relation between FII’S and CNX Nifty 500, a hypothesis is set

up in relation with the study being conducted.

NULL HYPOTHESIS (Ho): There is no relationship between CNX Nifty 500

volatility and FIIs.

ALTERNATE HYPOTHESIS (H1): There is relationship between S&P CNX

Nifty volatility and FIIs.

In this study, foreign institutional investment is considered as an independent

variable and CNX Nifty 500 is considered as a dependent variable.

SAMPLING DESIGN:

UNIVERSE:

In this study the universe is finite and will take into account all the events and

announcements that happened in the last few years.

SAMPLING UNIT:

As this study mainly concentrates on foreign institutional investment and

National stock exchange, the sampling unit is confined to them.

SAMPLING TECHNIQUE:

Convenient Sampling: Study conducted on the basis of availability of the data

and requirement of the project. Study requires the events that have impact on

the Indian stock market.

DATA COLLECTION METHOD:

Secondary Data: Data is collected from various literatures, journals, magazines,

books, web links are used. As there are no possibilities of collecting data

personally, there is no questionnaire prepared.

Monthly data of NIFTY 500 and net flows of FII’s net flows for period of 13

years w.e.f. January 2002 to December 2015 is considered for the project.

RESEARCH ANALYSIS TOOLS:

a) Unit Root Test:

Empirical studies (for example, Engle and Granger, 1987) have shown that

many time series variables are non-stationary or not integrated of order zero.

The time series variables considered in this paper are the stock prices and fii

flows. In order to avoid a spurious regression situation the variables in a

regression model must be stationary. Therefore, in the first step, we perform unit

root tests to investigate whether they are stationary or not.

The Augmented Dickey-Fuller (ADF) unit root test is used for this purpose. The

ADF regression equations are:

where åô is white noise. The additional lagged terms are included to ensure that

the errors are uncorrelated.

HYPOTHESIS ADF UNIT ROOTS TEST

The tests are based on the

null hypothesis (H0): Yt is not I (0). If the calculated ADF statistics are less than

their critical values from Fullers table then the null hypothesis (H0) is rejected

and the series are stationary or not integrated of order zero.

b) TREND ANALYSIS AND CORRELATION ANALYSIS:

Trend analysis:

The term “trend analysis” refers to the concept of the collecting information and

attempting to spot a pattern, or a trend in the information. In some fields of

study, the term “trend analysis” has more formally-defined meanings.

In project management trend analysis is a mathematical technique that uses

historical results to predict future outcome. This is achieved by tracking

variances in cost and schedule performance. In this context, it is a project

management quality control tool. The analysis of a variable's past value changes

to determine if a trend exists and if so. What the trend indicates? A technical

analyst may graph a stock's price throughout a period of time to determine

whether a trend has been established. This is a type of technical analysis that is

used /or attempt to predict the future movement of a stock or institutional of

fund.

Correlation analysis:

This analysis tool and its formulas measure the relationship between two data

sets that are scaled to be independent of the unit of measurement. It is a measure

that determines the degree to which two variable's movements are associated. It

is used to understand:

•

Whether the relationship is positive or negative,

•

The strength of relationship.

Correlation is a powerful tool that provides these vital pieces of information.

Positive correlation occurs when large values of one set are associated with

large values of the other and Negative correlation occurs when small values of

one set are associated with large values of the other and when values in both

sets are unrelated then correlation is said to be zero.

Correlation analysis will be used in the study to

1- Find out whether or not fii net flows are correlated with NIFTY 500 or

not

2- Find out whether volatility of net flows of FII impact volatility on NIFTY

500 index.

c) REGRESSION ANALYSIS

In statistical modeling, regression analysis is a statistical process for estimating

the relationships among variables. It includes many techniques for modeling

and analyzing several variables, when the focus is on the relationship between a

dependent variable and one or more independent variables (or 'predictors').

More specifically, regression analysis helps one understand how the typical

value of the dependent variable (or 'criterion variable') changes when any one of

the independent variables is varied, while the other independent variables are

held fixed. Most commonly, regression analysis estimates the conditional

expectation of the dependent variable given the independent variables – that is,

the average value of the dependent variable when the independent variables are

fixed. Less commonly, the focus is on a quantile, or other location parameter of

the conditional distribution of the dependent variable given the independent

variables. In all cases, the estimation target is a function of the independent

variables called the regression function. In regression analysis, it is also of

interest to characterize the variation of the dependent variable around the

regression function which can be described by a probability distribution.

Here regression will be used in order to arrive at a regression model

specifying the relationship between NIFTY 500(Dependent variable) and

FII’s net flows over the period considered.

d) GRANGER CAUSALITY TEST:

Granger causality is a statistical concept of causality that is based on prediction.

According to Granger causality, if a signal X1 "Granger-causes" (or "G-causes")

a signal X2, then past values of X1 should contain information that helps predict

X2 above and beyond the information contained in past values of X2 alone. Its

mathematical formulation is based on linear regression modeling of stochastic

processes (Granger 1969). More complex extensions to nonlinear cases exist,

however these extensions are often more difficult to apply in practice.

G-causality is normally tested in the context of linear regression models. For

illustration, consider a bivariate linear autoregressive model of two variables X1

and X2 :

X1(t)=∑j=1pA11,jX1(t−j)+∑j=1pA12,jX2(t−j)+E1(t)X2(t)=∑j=1pA21,jX1(t−j)

+∑j=1pA22,jX2(t−j)+E2(t)(1)

where p is the maximum number of lagged observations included in the model

(the model order), the matrix A contains the coefficients of the model (i.e., the

contributions of each lagged observation to the predicted values of X1(t) and

X2(t) , and E1 and E2 are residuals (prediction errors) for each time series. If

the variance of E1 (or E2) is reduced by the inclusion of the X2 (or X1) terms in

the first (or second) equation, then it is said that X2 (or X1) Granger-(G)-causes

X1 (or X2). In other words, X2 G-causes X1 if the coefficients in A12 are

jointly significantly different from zero. This can be tested by performing an Ftest of the null hypothesis that A12 = 0, given assumptions of covariance

stationarity on X1 and X2 . The magnitude of a G-causality interaction can be

estimated by the logarithm of the corresponding F-statistic (Geweke 1982).

Note that model selection criteria, such as the Bayesian Information Criterion

(BIC, (Schwartz 1978)) or the Akaike Information Criterion (AIC, (Akaike

1974)), can be used to determine the appropriate model order p

Granger causality (or "G-causality") was developed in 1960s and has been

widely used in economics since the 1960s. However it is only within the last

few years that applications in neuroscience have become popular.

This test will be used to find out whether the volatility in net flows of FII

have any causal relationship between the volatility of NIFTY 500 index.

e) AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY

(ARCH)

In econometrics, autoregressive conditional heteroskedasticity (ARCH) models

are used to characterize and model time series. They are used at any point in a

series, the error terms are thought to have a characteristic size or variance. In

particular ARCH models assume the variance of the current error term or

innovation to be a function of the actual sizes of the previous time periods' error

terms: often the variance is related to the squares of the previous innovations.

Such models are often called ARCH models (Engle, 1982), although a variety of

other acronyms are applied to particular structures that have a similar basis.

ARCH models are commonly employed in modeling financial time series that

exhibit time-varying volatility clustering, i.e. periods of swings interspersed

with periods of relative calm. ARCH-type models are sometimes considered to

be in the family of stochastic volatility models, although this is strictly incorrect

since at time t the volatility is completely pre-determined (deterministic) given

previous values.

GARCH(1,1):

If an autoregressive moving average model (ARMA model) is assumed for the

error variance, the model is a generalized autoregressive conditional

heteroskedasticity (GARCH, Bollerslev (1986)) model.

In that case, the GARCH (p, q) model (where p is the order of the GARCH

terms

and q is the order of the ARCH terms ), following the notation of

original paper is given by

Generally, when testing for heteroskedasticity in econometric models, the best

test is the White test. However, when dealing with time series data, this means

to test for ARCH and GARCH errors.

Here,Garch (1,1) analysis will be conducted to find out the nature of impact

thatFII’S net flows have on future volatility of the NIFTY 500 .in this case

the fii inflows will be exogeneous regressors.

CHAPTER 4

ANALYSIS

ANALYSIS

TREND OF FII(NET FLOWS) AND NIFTY 500 OVER THE LAST 13

YEARS

40000

30000

20000

NET FLOWS

10000

NSE 500

0

-10000

-20000

GRAPH SHOWING FII NET FLOWS AND NIFTY 500 MOVEMENTS OVER THE PERIOD JAN 2002-DEC 2015

SOURCE: TABLE 1 APPENDIX

Interpretation

From the above chart we can see how FII net flows and NIFTY 500 index have

moved since January 2002 to December 2015.over the period considered

NIFTY 500 has moved with a upward bias showing smaller corrections along

its path in contrast FII net flows have shown both upward and downward

swings at regular intervals.one striking point is that beyond mid 2007‘s i.e. after

happening of infamous US subprime crisis FII net flows have shown a greater

volatility as compared to before. Also quantum of inflows has increased

manifolds.

DESCRIPTIVE STATISTICS

Mean

Median

Maximum

Minimum

Std. Dev.

Skewness

Kurtosis

Jarque-Bera

NET_FLOWS

4438.135

3219.950

28562.90

-16877.00

8368.443

0.473046

3.409292

NIFTY_500

3513.234

3818.550

7239.450

691.0000

1789.384

0.122846

2.250842

7.438283

4.351216

Probability

0.024255

0.113539

Sum

Sum Sq. Dev.

745606.6

1.17E+10

590223.2

5.35E+08

Observations

168

168

Interpretation

By looking at the descriptive statistics table we can see mean value of FII

net investments is 4438.135 cr while that of NIFTY 500 is 3513.234.

The standard deviation shows there is a larger spread or deviation in FII

net flows as compared to NSE500 in the relevant period.

Median of FII net flows comes out to be 3219.950 cr vis-à-vis NIFTY

500 is 3818.550.

UNIT ROOT TEST OF NET FII INVESTMENTS

At level

Null Hypothesis: NET_FLOWS has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=13)

Augmented Dickey-Fuller test statistic

Test critical values:

1% level

5% level

10% level

t-Statistic

Prob.*

-8.857154

-3.469691

-2.878723

-2.576010

0.0000

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(NET_FLOWS)

Method: Least Squares

Date: 03/26/16 Time: 18:24

Sample (adjusted): 2002M02 2015M12

Included observations: 167 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

NET_FLOWS(-1)

C

-0.646055

2875.942

0.072942

691.2088

-8.857154

4.160743

0.0000

0.0001

R-squared

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

F-statistic

Prob(F-statistic)

0.322240

0.318133

7870.325

1.02E+10

-1734.089

78.44917

0.000000

Mean dependent var

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

Durbin-Watson stat

-19.40299

9531.094

20.79149

20.82883

20.80665

2.066548

Interpretation

The ADF t-test has value t = -8.857154 and has p-value of 0.0000 which is

smaller than the 5% critical value of -1.9448.

Therefore the ADF t-test rejects the null hypothesis of a trend (at the 5%

significance level).Since, the null hypothesis of a trend is rejected by the ADF

F-test. Thus the trend indicates the trend is deterministic.

net flows

30,000

20,000

10,000

0

-10,000

-20,000

02

03

04

05

06

07

08

09

10

11

12

13

14

15

Similarly, conducting ADF UNIT ROOT TEST for the NIFTY 500,

At level

Null Hypothesis: NIFTY_500 has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=13)

Augmented Dickey-Fuller test statistic

Test critical values:

1% level

5% level

10% level

t-Statistic

Prob.*

-0.667057

-3.469691

-2.878723

-2.576010

0.8508

*MacKinnon (1996) one-sided p-values.

Unit root test of NIFTY 500 Reveals That It has t- statistics = -0.667057 and

corresponding p-value of 0.8508 which is more than the 5% critical value of

-1.9448.hence we can accept the null hypothesis (at the 5% significance

level).Since, the null hypothesis of a trend is accepted by the ADF F-test. Thus

it indicates the trend is non-stationery at level.

At 1st difference

Null Hypothesis: D(NIFTY_500) has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic - based on SIC, maxlag=13)

Augmented Dickey-Fuller test statistic

Test critical values:

1% level

5% level

10% level

t-Statistic

Prob.*

-12.61490

-3.469933

-2.878829

-2.576067

0.0000

*MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(NIFTY_500,2)

Method: Least Squares

Date: 03/26/16 Time: 18:45

Sample (adjusted): 2002M03 2015M12

Included observations: 166 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

D(NIFTY_500(-1))

C

-0.984932

35.34441

0.078077

19.37759

-12.61490

1.823983

0.0000

0.0700

R-squared

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

F-statistic

Prob(F-statistic)

0.492473

0.489379

247.0263

10007606

-1149.114

159.1357

0.000000

Mean dependent var

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

Durbin-Watson stat

-0.087048

345.6955

13.86884

13.90634

13.88406

2.000000

Interpretation

The ADF t-test of NSE500 at 1st difference has t-statistics =-12.61490 and has

p-value of 0.0000 which is smaller than the 5% critical value of -1.9448.

Therefore the ADF t-test rejects the null hypothesis of a trend (at the 5%

significance level).Since, the null hypothesis of a trend is rejected by the ADF

F-test. Thus the test indicates the trend is deterministic at 1st difference.

nifty 500

8,000

7,000

6,000

5,000

4,000

3,000

2,000

1,000

0

02

03

04

05

06

07

08

09

10

11

12

13

14

15

REGRESSION ANALYSIS BETWEEN FII NET FLOWS AND NIFTY

500

The following regression model is developed to depict the relationship between

FII’s (independent variable) and NIFTY 500 (dependent variable)

Dependent Variable: DNIFTY

Method: Least Squares

Date: 03/23/16 Time: 20:12

Sample (adjusted): 2002M02 2015M12

Included observations: 167 after adjustments

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

NET_FLOWS

-0.005057

4.14E-06

0.005753

6.07E-07

-0.878948

6.824046

0.3807

0.0000

R-squared

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

F-statistic

Prob(F-statistic)

0.220107

0.215381

0.065592

0.709884

219.0013

46.56760

0.000000

Mean dependent var

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

Durbin-Watson stat

0.013425

0.074050

-2.598818

-2.561477

-2.583662

1.818296

Intrepetation

Analyzing regression model with respect to 7 assumptions for a good regression

yield the following results:

Assumption 1: R square value of the model is 0.22 or 22% ideally it should 50%

or more.

Assumption 2: Independent variable should be individually significant here in

this case independent variable is net flows which is having t-statistics =

6.824046 and p value=0.00 which is less than 5 percent confidence level.

Hence we can say that net flows are individually significant to influence the

dependent variable.

Assumption 3 Joint Significance: Independent variables should be jointly

significant to explain dependent variable. Here F Statistic is 46.56760

probabilty of F Statistics is 0.000% which is less 5% hence we can conclude

that independent variables can jointly influence the dependent variable.

Assumption 4: It is common assumption that FII have a positive relationship

with stocks market. Further in this model also FII have coefficient which is

according to the popular economic theory.

Residual analysis

Assumption 5: Test for serial or auto correlation: Serial correlation is a

statistical term used to the describe the situation when the residual is correlated

with lagged values of itself. In other words, If residuals are correlated, we call

this situation serial correlation which is not desirable. An approach of detecting

serial correlation is Breusch-Godfrey serial correlation LM test: BG test

The LM: BG test reveals the following results:Null hypothesis (H0): There Is No Serial Or Auto Correlation.

Breusch-Godfrey Serial Correlation LM Test:

F-statistic

Obs*R-squared

0.628995

1.278990

Prob. F(2,163)

Prob. Chi-Square(2)

0.5344

0.5276

Test Equation:

Dependent Variable: RESID

Method: Least Squares

Date: 03/23/16 Time: 20:14

Sample: 2002M02 2015M12

Included observations: 167

Presample missing value lagged residuals set to zero.

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

NET_FLOWS

RESID(-1)

RESID(-2)

0.000185

-3.86E-08

0.087108

0.005024

0.005771

6.11E-07

0.078517

0.078544

0.032119

-0.063193

1.109419

0.063963

0.9744

0.9497

0.2689

0.9491

R-squared

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

F-statistic

Prob(F-statistic)

0.007659

-0.010605

0.065740

0.704447

219.6433

0.419330

0.739370

Mean dependent var

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

Durbin-Watson stat

-4.45E-18

0.065394

-2.582554

-2.507872

-2.552242

1.995470

Interpretation

Here observed r square value = 1.278990 and corresponding probability chisquare value is 0.5276 is greater than 5% confidence value.

Hence we can accept the null hypothesis i.e. there is no serial or auto

correlation.

Assumption 6: The variance of the residual (u) is constant (Homoscedasticity)

Heteroscedasticity is a situation when the variance of the residuals from a model

is not constant. When the variance of the residuals is constant, we call it

homoscedasticity. Homoscedasticity is desirable. If residuals do not have

constant variance, we call it hetersocedasticty, which is not desirable.

This can be check using Breusch-Pegan-Godfrey Test

Conducting the bpg test we get the following results

Null hypothesis Ho: Homoscedasticity (the variance of residual (u) is constant)

Alternative hypothesis H1 : Heteroscedasticity (the variance of residual (u) is

not constant )

Heteroskedasticity Test: Breusch-Pagan-Godfrey

F-statistic

Obs*R-squared

Scaled explained SS

0.696892

0.702372

1.224280

Prob. F(1,165)

Prob. Chi-Square(1)

Prob. Chi-Square(1)

0.4050

0.4020

0.2685

Test Equation:

Dependent Variable: RESID^2

Method: Least Squares

Date: 03/23/16 Time: 20:34

Sample: 2002M02 2015M12

Included observations: 167

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

NET_FLOWS

0.004529

-6.23E-08

0.000707

7.46E-08

6.402653

-0.834800

0.0000

0.4050

R-squared

0.004206

Mean dependent var

0.004251

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

F-statistic

Prob(F-statistic)

-0.001829

0.008064

0.010731

569.0312

0.696892

0.405037

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

Durbin-Watson stat

0.008057

-6.790792

-6.753451

-6.775636

1.901167

Interpretation

Here observed r square value = 0.702372 and corresponding probability chisquare value is 0.4020 is greater than 5% confidence value.

Hence we can accept the null hypothesis i.e. Homoscedasticity (the variance of

residual (u) is constant).

Assumption no. 7: Residuals (u ) should be normally distributed.

Null hypothesis Ho : Normal distribution (the residual (u) follows a normal

distribution)

Alternative hypothesis H1: Not normal distribution (the residual (u) follows not

normal distribution)

Conducting histogram normality test we find the following results

40

Series: Residuals

Sample 2002M02 2015M12

Observations 167

35

30

25

20

15

10

5

Mean

Median

Maximum

Minimum

Std. Dev.

Skewness

Kurtosis

-4.45e-18

0.006021

0.217635

-0.249265

0.065394

-0.331499

4.571152

Jarque-Bera

Probability

20.23543

0.000040

0

-0.2

-0.1

0.0

0.1

0.2

Interpretation

Here Jarque-Bera Statistics is 20.23543 and p value is 0.000040 is less than

0.05(5%) hence, we can reject null and accept the alternative, that is residuals

(u) are not normally distributed.

Conclusion

Out of the seven assumption 5 assumption are holding their ground. Moreover

in the model we find that Dublin Watson stat (1.818296) is having a greater

value than R square value (0.22) which shows that there is no spurious

relationship between the variables.

Thus we can say the regression model is good model as it fulfil majority of the

assumptions.

TEST TO FIND OUT WHETHER THERE IS SPILLOVER OF

VOLATILITY IN FII’S NET FLOW TO VOLATILITY IN NIFTY 500.

To find out the above we can conduct a Gramger causality test among the

residuals series of FII net flows and NIFTY 500

Pairwise Granger Causality Tests

Date: 03/23/16 Time: 20:31

Sample: 2002M01 2015M12

Lags: 2

Null Hypothesis:

Obs

F-Statistic

Prob.

RESID01 does not Granger Cause DNIFTYR

DNIFTYR does not Granger Cause RESID01

165

0.50249

1.32045

0.6060

0.2699

From the table below, we interpret the following findings and conclusions:

HYPOTHESIS: We took the null hypothesis, RESID01(residual series of FII net

flows) does not Granger Cause DNIFTYR (residual series of NSE500)

In case 1: RESID01 does not Granger Cause DNIFTYR

Here, f-statistic value is 0.50249 and p value is 0.6060 which is greater than the

5 % ( 0.05) significance level. Thus we accept the null hypothesis and can say

resid01(residual series of FII net flows) does not causes the dniftyr (residual

series of NSE500).

On the other hand analysing the impact of DNIFTYR on RESID01 we find that

F-statistic value is 1.32045 and p value is 0.2699 which is greater than the 5 %

significance level.

Thus we can accept the null hypothesis and can say that DNIFTYR does not

Granger Cause RESID01

Now following is the finding for grangers’ causality test.:

There is no bi-directional causality between volatility(residual series)of FII net

flows and the volatility (residual series) of NSE500 , thus implying that there is

no volatility spill over between the above two variable.

TO SEE IMPACT OF FII NET FLOWS ON FUTURE VOLATILITY OF

NIFTY 500 USING GARCH(1,1) MODEL.

Dependent Variable: DNIFTY

Method: ML - ARCH (Marquardt) - Normal distribution

Date: 03/23/16 Time: 20:41

Sample (adjusted): 2002M02 2015M12

Included observations: 167 after adjustments

Convergence achieved after 12 iterations

Presample variance: backcast (parameter = 0.7)

GARCH = C(1) + C(2)*RESID(-1)^2 + C(3)*GARCH(-1)

Variable

Coefficient

Std. Error

z-Statistic

Prob.

0.880149

2.480190

10.70062

0.3788

0.0131

0.0000

Variance Equation

C

RESID(-1)^2

GARCH(-1)

R-squared

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

Durbin-Watson stat

0.000178

0.165274

0.812602

-0.033067

-0.026881

0.075038

0.940331

208.0421

1.750621

0.000202

0.066638

0.075940

Mean dependent var

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

0.013425

0.074050

-2.455594

-2.399582

-2.432860

Here if we add the alpha(0.16) and beta (0.81)values we will get value of 0.96

with is close to 1.this implies moving forward volatility will remain the same.

Now if we use FII net flow as variance regressors then the result changes to

following

Dependent Variable: DNIFTY

Method: ML - ARCH (Marquardt) - Normal distribution

Date: 03/23/16 Time: 20:39

Sample (adjusted): 2002M02 2015M12

Included observations: 167 after adjustments

Convergence achieved after 4 iterations

Presample variance: backcast (parameter = 0.7)

GARCH = C(2) + C(3)*RESID(-1)^2 + C(4)*GARCH(-1) + C(5)*NET_FLOWS

Variable

Coefficient

Std. Error

z-Statistic

Prob.

C

0.013425

0.012514

1.072834

0.2833

4.239152

0.673887

3.186200

-41.51676

0.0000

0.5004

0.0014

0.0000

Variance Equation

C

RESID(-1)^2

GARCH(-1)

NET_FLOWS

0.005204

0.149999

0.599996

-2.37E-07

R-squared

Adjusted R-squared

S.E. of regression

Sum squared resid

Log likelihood

Durbin-Watson stat

-0.000000

-0.000000

0.074050

0.910233

182.8497

1.808508

0.001228

0.222588

0.188311

5.70E-09

Mean dependent var

S.D. dependent var

Akaike info criterion

Schwarz criterion

Hannan-Quinn criter.

0.013425

0.074050

-2.129937

-2.036584

-2.092047

Interpretation

Here if we add the value of alpha(0.14) and beta(0.59) we get 0.73 which is less

than 1 or unitary.this implies that goimg forward the volatility is set to decrease.

Conclusion comparing results of garch (11) model one with net flows as

variance regressor and otherwise we find that future volatility tend to decrease if

we keep fii inflows as variance regressor.

This implies that FII flows doenot increase the voaltity of NIFTY 500 instead

one can see a reduction in future volatility.

CHAPTER 5

CONCLUSION

FINDING AND CONCLUSION

In this paper I have attempted to estimate the impact of FII net flows on NIFTY 500

over a time period of 13years (2002-2015)using the techniques of Regression

Analysis, Granger Causality Test and Garch (1 1) .

On the basis of above discussion and data analysis, the following can be concluded.

From the current study it is evident that there is moderate positive correlation between

FII & NIFTY 500. Table 1 presents the summary of the model developed. In the model

NIFTY 500 is taken as a dependent variable, FII net flows was found to be significant

predictor. Hence it can be concluded that there is impact of flow of FII on Indian stock

market is significant.

Variable

Coefficient

Std. Error

t-Statistic

Prob.

C

NET_FLOWS

-0.005057

4.14E-06

0.005753

6.07E-07

-0.878948

6.824046

0.3807

0.0000

Table1

Secondly the granger Causality test was used to determine whether the volatility in FII

net flows have made the NIFTY 500 .the result of the test reveals that there is no such

evidence that signify any spillover of volatility from one FII to NIFTY 500

Thirdly Garch (11) was used examine the influence of FII on the future stability of

NIFTY 500 and it was found that FII investment has had calming influence on future

stability of the index.

Hence, after conducting the above analysis one can say FII have a mild

correlation with Indian stock market and contrary to popular belief FII

investment has not shown properties of temporary and transient flows instead

they have had a calming influencing Indian stock market.

CHAPTER 6

REFERENCES

REFERENCES

Software resources

EViews 8

Websites

http://www.sebi.gov.in/sebiweb/investment/statistics.jsp?s=fii

http://www.thehindubusinessline.com/markets/stock-markets/markets-live-update/article8303443.ece

http://www.livemint.com/Money/XEBulFzWstlFvtCdzcoVxM/FII-outflows-in-September-quarterhighest-in-five-years.html

https://in.finance.yahoo.com/q?s=%5ECRSLDX&ql=0

RESEARCH PAPERS

Chakrabarti Rajesh , “FII Flows to India: Nature and Causes”

Dr Gupta Ambhuj ,Does the stock market rise or fall due to fiis in india?

Joshi Vikram K. & Saxena Richa Analytical study of impact of fii on indian stock market with special reference

to Bse Sensex.

Dr. Sultana Tabassum Syed, Prof. Pardhasaradhi S. Impact of Flow of FDI & FII on Indian Stock Market.

Bose Suchismita & Coondoo Dipankor The Impact of FII Regulations in India: A Time-Series Intervention

Analysis of Equity Flows.

Coondoo Dipankor And Mukherjee Paramita :Volatility Of FII In India.

Babu Suresh M. and Prabhees K.P. : Causal relationships between Foreign Institutional Investments and stock

returns in India.

Prasanna Krishna P. : Foreign Institutional Investors: Investment Preferences in India.

Sethi Narayan International Capital Flows and Growth in India: The Recent Experience.

Walia Karan , Dr. Walia Rimpi and Jain Monika: Impact Of Foreign Institutional Investment on Stock.

CHAPTER 7

APPENDIX

APPENDIX

1) Raw Data Of Monthly Time Series Used In Above Study(Source Sebi.Com,

Yahoo.Finance .Com)

NET

FLO

WS(I

NR

CAR

ORES

)

N

IF

T

Y

50

0

423.3

71

4.

5

1966.3

76

7.

6

391.4

77

5.

5

11.7

77

1.

3

-56

73

9.

55

-381.1

77

2.

85

348.5

70

6.

65

Au

g02

204.3

73

7.

15

Se

p02

468.6

69

1

-776.4

69

1.

95

M

O

NT

H

Ja

n02

Fe

b02

Ma

r02

Ap

r02

Ma

y02

Ju

n02

Jul

-02

Oc

t02

No

v02

De

c02

Ja

n03

Fe

b03

Ma

r03

Ap

r03

Ma

y03

Ju

n03

Jul

-03

Au

g03

Se

p03

Oc

t03

No

v03

De

c-

601.8

74

1.

55

427.2

77

2.

85

888.1

74

9.

1

378.8

76

2

411.7

70

1.

35

430.2

69

7.

2

1220.3

80

7.

2

2581.9

89

4.

5

2346.5

93

8.

55

2091.3

11

00

.4

5

3851.1

11

38

.5

5

6797.3

12

18

.3

3300.6

12

85

.4

6161

15

31

03

.3

5

Ja

n04

3176.8

14

59

.8

2397.4

14

42

.8

5604.6

14

57

.5

7638.2

15

07

.5

5

3246.8

12

26

.5

5

Fe

b04

Ma

r04

Ap

r04

Ma

y04

Ju

n04

Jul

-04

Au

g04

Se

p04

Oc

t04

No

v04

De

c04

12

48

516.3

913.5

13

51

.4

5

2892.2

13

77

.2

2385.4

14

78

.7

5

3263.1

15

02

.0

5

6740.7

16

53

.2

6683.7

18

04

.9

457

17

68

.2

5

8376.1

18

27

.4

7502

17

72

.8

5

-654.2

16

88

.6

5

1140.2

18

34

.8

5

5328.5

19

06

.2

7934.1

20

27

.4

5051

21

26

.3

5

Se

p05

4646.7

22

74

Oc

t05

3693.7

20

67

.8

4038.8

23

06

.1

5

De

c05

9335

24

59

.2

Ja

3677.7

25

Ja

n05

Fe

b05

Ma

r05

Ap

r05

Ma

y05

Ju

n05

Jul

-05

Au

g05

No

v05

n06

85

.9

5

Fe

b06

7587.8

26

58

.9

5

6688.6

29

10

.3

5

522.1

30

64

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Ma

r06

Ap

r06

Ma

y06

Ju

n06

Jul

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Au

g06

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p06

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t06

No

v06

De

c06

7354.1

26

35

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5

479.6

25

62

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1144.9

25

62

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5

4642.8

28

07

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5

5424.6

29

88

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31

14

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5

9379.9

32

80

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5

3667.3

32

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5

Ja

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33

93

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7239.7

31

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31

45

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5

41

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20590.

8

48

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69

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5

De

5579.2

53

Fe

b07

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r07

Ap

r07

Ma

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c07

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6

43

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60

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38

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5011.5

39

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10095.

7

32

03

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5

Jul

-08

1836.8

34

56

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1211.5

34

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5

8278.4

30

58

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t08

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22

25

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2598.1

20

93

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1750

22

95

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5

Fe

b08

Ma

r08

Ap

r08

Au

g08

Se

p08

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4245.3

22

09

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5

2436.4

21

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37

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45

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5

De

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1

43

29

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Ja

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Ma

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p09

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t09

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41

56

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5

1216.9

41

27

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5

19928

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13

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5

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43

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4

44

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4

44

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5

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9

49

72

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5

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1

47

81

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2049.6

49

40

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5

Ja

n10

Fe

b10

Ma

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Ap

r10

Jul

-10

Au

g10

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p10

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t10

No

v10

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c10

Ja

n11

4813.2

44

24

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4585.5

42

47

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5

6897.8

46

26

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5

Ap

r11

7213.3

46

15

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y11

6614.4

44

92

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4572.2

45

22

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5

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-11

8030.1

44

24

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5

Au

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10833.

6

40

38

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5

-158.3

39

78

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5

1677.4

42

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4197.9

38

11

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5

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35

97

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5

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10357.

7

40

82

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5

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75

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5

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41

78

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13

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5

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41

70

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5

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10272.

7

41

26

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5

Au

g12

10803.

9

41

29

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19261.

3

45

04

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5

11364

44

48

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5

9577.1

46

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Ja

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25087.

7

47

43

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5

Ja

n13

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b13

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r13

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Ma

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n13

Jul

-13

Au

g13

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p13

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t13

No

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c13

22059

47

95

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24439

44

77

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9124

44

38

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5

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46

41

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22169

46

81

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5

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43

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41

75

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92

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47

70

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14

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Ja

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b14

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r14

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r14

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y14

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n14

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p14

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47

09

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5

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49

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20077

52

24

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5

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52

55

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5

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74

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61

94

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5

5430

63

60

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5

5103

64

15

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66

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5

13753

69

18

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5

1036

67

73

14

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5

Ja

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12919

71

66

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11476

72

39

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5

12078

69

78

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5

11721

67

49

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5

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69

59

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5

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97

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06

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66

69

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5

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66

46

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6650

67

50

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5

-7074

66

86

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Fe

b15

Ma

r15

Ap

r15

Ma

y15

Ju

n15

Jul

-15

Au

g15

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p15

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t15

No

v15

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c15

-2817

67

24

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5