relationship between fii flows and nifty index
TRANSCRIPT
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-M P BIRLA INSTITUTE OF MANAGEMENT- 1 -
RESEARCH PROJECT
OnDETRMINATION OF RELATIONSHIPBETWEEN FII FLOWS
AND NIFTY INDEX
Submitted in partial fulfillment of the requirement for MBA
Degree of Bangalore University
BY
CHIPPLAKATTI SUNIL SUKUMARRegistration Number
04XQCM6019
Under the guidance of
DR T V N RAO
M.P.Birla Institute of Management
Associate Bharatiya Vidya Bhavan
Bangalore-560001
2004-2006
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DECLARATION
I hereby declare that the research project titled DETRMINATION OF
RELATIONSHIPBETWEEN FII FLOWS AND NIFTY INDEX is prepared under
the guidance of Dr T V N Rao in partial fulfillment of MBA degree of Bangalore
University, and is my original work.
This project does not form a part of any report submitted for degree or diploma
under Bangalore University or any other university.
Place: Bangalore Chippalakatti Sunil
Skumar
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PRINCIPALS CERTIFICATE
This is to certify that Mr.Chippalakatti Sunil Sukumar bearing
Registration No: 04XQCM6019 has done a research project on DETRMINATION
OF RELATIONSHIPBETWEEN FII FLOWS AND NIFTY INDEX under the
guidance ofDr T V N Rao M.P. Birla Institute of Management, Bangalore. This has
not formed a basis for the award of any degree/diploma for any other university.
Place: Bangalore Dr.N.S.MALLAVALLI
Date: PRINCIPAL
MPBIM, Bangalore
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GUIDES CERTIFICATE
I hereby declare that the research work embodied in this dissertation
entitledDETRMINATION OF RELATIONSHIPBETWEEN FII FLOWS AND
NIFTY INDEXhas been undertaken and completed by Mr. Chippalakatti Sunil
Skumar under my guidance and supervision.
I also certify that he has fulfilled all the requirements under the
covenant governing the submission of dissertation to the Bangalore University for the
award of MBA Degree.
Place: Bangalore Dr T V N Rao
Date: Research Guide
MPBIM, Bangalore
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ACKNOWLEDGEMENT
The successful accomplishment of any task is incomplete without
acknowledging the contributing personalities who both assisted and inspired and lead
us to visualize the things that turn them into successful stories for our successors.
First of all I thank the Almighty God for his grace bestowed on us throughout this
project.
My special thanks to my project Guide Dr T V N Rao, who guided me with the
timely advice and expertise and has helped remarkably to complete the project.
Last, but not the least, I would like to thank my Parents and all my Friends
for their wholehearted direct and indirect support and encouragement.
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CHAPTER CONTENTS PAGE
NO.
ABSTRACT
1.
2.
3.
4.
5.
6.
INTRODUCTION:
THOEORETICAL CONSIDERATIONS
LITERATURE REVIEW:
RESEARCH METHODOLOGY:
INTRODUCTION PROBLEM STATEMENT OBJECTIVES METHODOLOGY SCOPE OF THE STUDY SAMPLE OF SIZE DATA DATABASE
DATA ANALYSIS:
TEST FOR UNIT ROOT (ADF &PP) RESULTS FROM ADF & PP RESULTS FROM GRANGERS CAUSALITY
TEST TABLES.
FINDINGS AND CONCLUSION:
LIMITATION OF THE STUDY
1-15
16-26
27-30
31-40
41
42
BIBLIOGRAPHY: 43
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INTRODUCTION
Foreign Institutional Investment in India: An Overview
SEBIs definition of FIIs presently: Includes foreign pension funds, mutual funds,
charitable/endowment/university funds etc. As well as asset management companies
and other money managers operating on their Behalf
India embarked on a programme of economic reforms in the early 1990s to tie over
its balance of payment crisis and also as a step towards globalization. An important
milestone in the history of Indian economic reforms happened on September 14, 992,
when the FIIs (Foreign Institutional Investors) were allowed to invest in all thesecurities traded on the primary and secondary markets, including shares, debentures
and warrants issued by companies which were listed or were to be listed on the stock
exchanges in India and in the schemes floated by domestic mutual funds. Initially, the
holding of a single FII and of all FIIs, NRIs (Non-Resident Indians) and OCBs
Overseas Corporate Bodies) in any company were subject to a limit of 5% and 24%
of the companys total issued capital respectively. In order to broad base the FII
investment and to ensure that such an investment would not become a camouflage for
individual investment in the nature of FDI, a condition was laid down that the funds
invested by FIIs had to have at least 50 participants with no one holding more than
5%. Ever since this day, the regulations on FII investment have gone through
enormous changes and have become more liberal over time.
Net FII inflows into India increased steadily through the decade of the 1990s to reach
an annual peak of US$10.25 billion in 2004-05 Cumulatively, FII investments as on
October 31, 2005 have been US$ 39.27 billion.1 every year since FIIs were allowedto participate in the Indian market, FII net inflows into India have been positive,
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Except for 1998-99. This reflects the strong economic fundamentals of the country, as
well as the confidence of the foreign investors in the growth with stability of the
Indian market. The year 2003 marked a watershed in FII investment in India. FIIs
started the year 2003 in a big way by investing Rs. 985 crore in January itself.
Meanwhile, corporate India continued to report good operational results. This, along
with good macroeconomic fundamentals, growing industrial and service sectors led
FIIs to perceive great RBI data generally shows that investment by FIIs has been
smaller, when compared with SEBI data. This discrepancy in the statistical system
needs to be corrected. One possible explanation may involve differences in the
treatment of reinvestment of profit earned. Potential for investment in the Indian
economy. In April 2003, prices of commodities like steel and aluminum went up,
propelling FII investment in May 2003 to Rs. 3,060 crore. Around the same time,
Morgan Stanley Capital International (MSCI) in its MSCI Emerging Markets Index
gave a weight of 4.3 per cent to India among the emerging markets of the world.2
Calendar year 2004 ended with net FII inflows of US$9.2 billion, an all-time highsince the liberalization.
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FII Investments in India
FOREIGN INVESTMENT INFLOWS
Year A. Direct investment B. Portfolio investment Total (A+B)
Rs. crore US $ million Rs. crore US $ million Rs. crore US $ million
1 2 3 4 5 6 7
1990-91 174 97 11 6 185 103
1991-92 316 129 10 4 326 133
1992-93 965 315 748 244 1713 559
1993-94 1838 586 11188 3567 13026 4153
1994-95 4126 1314 12007 3824 16133 5138
1995-96* 7172 2144 9192 2748 16364 4892
1996-97* 10015 2821 11758 3312 21773 6133
1997-98* 13220 3557 6696 1828 19916 5385
1998-99* 10358 2462 -257 -61 10101 2401
1999-00* 9338 2155 13112 3026 22450 5181
2000-01* 18406 4029 12609 2760 31015 6789
2001-02* 29235 6130 9639 2021 38874 8151
2002-03* 24367 5035 4738 979 29105 6014
2003-04* 21473 4673 52279 11377 73752 16050
2004-05* P 24870 5535 40029 8909 64899 14444
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FII Investment (% of GDP):
FII flows to India
Foreign investment inflows to india
-10000
0
10000
2000030000
40000
50000
60000
1990-91
1991
-92
1992
-93
1993
-94
1994
-95
1995
-96
1996
-97
1997-98
1998-99
1999-00
2000-01
2001
-02
2002-03
2003-04
2004-05
incrs
direct investment portfolio investment
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Growth of FII
Growth of FII
0
10000
20000
30000
40000
50000
60000
70000
80000
90-9191-9292
-9393
-9494
-9595
-9696
-9797-9898-9999-0000-012-Jan
3-Fe
b4-Mar
5-Ap
r
fiiinvestment
0
50000
100000
150000
200000
250000
300000
350000
400000
cuma
lativefiiinvestment
f ii investment cum fii
The sources of FII in India:
The closed-end country fund, The India Fund launched in June 1986 provided a
channel for portfolio investment in India before the stock market liberalization in
1992. Global Depository Receipts, American Depository Receipts, Foreign Currency
Convertible Bonds and Foreign Currency Bonds issued by Indian companies and
traded in foreign exchanges provide other routes for portfolio investment in India by
foreign investors. It is also possible for foreigners to trade in Indian securities withoutregistering as an FII but such cases require approval from the RBI or the Foreign
Investment Promotion Board. That these national affiliations do not necessarily mean
that the actual investor funds come from these particular countries. Given the
significant financial flows among the industrial countries, national affiliations are
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very rough indicators of the home of the FII investments. In particular institutions
operating from Luxembourg, Cayman Islands or Channel Islands, or even those based
at Singapore or Hong Kong are likely to be investing funds largely on behalf of
residents in other countries. Nevertheless, the regional breakdown of the FIIs does
provide an idea of the relative importance of different regions of the world in the FII
flows
The trickle of (FII) flows to India that began in January 1993 has gradually expanded
to an average monthly inflow of close to Rs. 4100 crores during the last six Months of
2005. Over 740 FIIs were registered with SEBI by end of 2005. The total Amount of
(FII) investment in India had accumulated to a formidable sum of over Rs. 3,50,000
crores during these timeThe sources of these FII flows are varied. The FIIs registered
with SEBI come from as many as 28 countries (including money management
companies operating in India on behalf of foreign investors). US-based institutions
accounted for slightly over 42%, those from the UK constitute about 20% with other
Western European countries hosting another 17% of the FIIs
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Holdings of the FII in Indian Company:
Most of there subsidiary FII having majority share and also There are so many Indian
companies where FII having more than 10% of share, following are the table shows the
shareholding pattern of FII in Nifty and Non nifty companies and the top 25 companies where
FII having more holdings.
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TOP 25 companies were FII holdings
Evolution of Policies and Regulation:
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Domestic institutional and individual investors, used as they are to the ongoing
practices of Indian corporates, often accept such practices, even when these do not
measure up to the international benchmarks of best practices. FIIs, with their vast
experience with modern corporate governance practices, are less tolerant of
malpractice by corporate managers and owners (dominant shareholder). FII
participation in domestic capital markets often lead to vigorous advocacy of sound
corporate governance practices, improved efficiency and better shareholder value.
Improvements to market efficiency:
A significant presence of FIIs in India can improve market efficiency through two
channels. First, when adverse macroeconomic news, such as a bad monsoon, 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 the 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 firms like Infosys, which are primarily export-oriented,
applying valuation principles that prevailed outside India for software servicescompanies.
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Costs:
There are concerns that foreign investors are chronically ill informed about India, and
this lack of sound information may generate herding (a large number of FIIs buying
or selling together) and positive feedback trading (buying after positive returns,
selling after negative returns). These kinds of behavior can exacerbate volatility, and
push prices away from fair values. FIIs behavior in India, however, so fardoes not
exhibit these patterns.
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 account
flows, this suggests that there is little evidence of vulnerability so far.40
Possibility of taking over companies:While FIIs are normally seen as pure portfolio investors, without interest in control,
portfolio investors can occasionally behave like FDI investors, and seek control of
companies that they have a substantial shareholding in. Such outcomes, however,
may not be inconsistent with India's quest for greater FDI. Furthermore, SEBI's
takeover code is in place, and has functioned fairly well, ensuring that all investors
benefit equally in the event of a takeover.
Complexities of monetary management
A policymaker trying to design the ideal financial system has three objectives. The
policy maker wants continuing national sovereignty in the pursuit of interest rate,
inflation and exchange rate objectives; financial markets that are regulated,
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supervised and cushioned; and the benefits of global capital markets. Unfortunately,
these three goals are incompatible. They form the impossible trinity. India's
openness to portfolio flows and FDI has effectively made the countrys capital
account convertible for foreign institutions and investors. The problems of monetary
management in general, and maintaining a tight exchange rate regime, reasonable
interest rates and moderate inflation at the same time in particular, have come to the
fore in recent times. The problem showed up in terms of very large foreign exchange
reserve inflows requiring considerable sterilization operations by the RBI to maintain
stable macroeconomic conditions. The Government had to introduce a Market
Stabilization Scheme (MSS) from April 1, 2004.
With the foreign exchange invested in highly liquid and safe foreign assets with low
rates of return, and payment of a higher rate of interest on the treasury bills issued
under MSS, sterilization involves a cost. With a rapid rise in foreign exchange
reserves, and the need for having an MSS-based sterilization involving costs,
questions have been raised about the desirability of encouraging more foreign
exchange inflows in general and FII inflows in particular. While there is indeed the
issue of timing the policy of encouragement appropriately, to avoid the pitfalls ofthrowing the baby with the bath water, there can not be a turnaround from the
avowed policy of gradual liberalization,
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2. Literature review
FII Flows to India Nature and Causes (RAJESH CHAKRABARTI 2001).
Introduction:
International capital flows and capital controls have emerged as important policy
issues in the Indian context as well. The danger of Mexico-style abrupt and sudden
outflows inherent with FII flows and their destabilizing effects on equity and foreign
exchange markets have been stressed. Some authors have argued that FII flows have,
in fact, had no significant benefits for the economy at large. While these concerns are
all well placed, comparatively less attention has been paid so far to analyzing the FII
flows data and understanding their key features. A proper understanding of the nature
and determinants of these flows, however, is essential for a meaningful debate about
their effects as well as for predicting the chances of their sudden reversals. In an
attempt to address this lacuna, this paper undertakes an empirical analysis of FII
investment flows to India. The broad objective of the present paper is to gain a better
understanding of the nature and determinants of FII flows. Towards this end we first
take a look at the FII investment flows data to bring out the key features of these
flows. Next we study the relationship between FII flows and the stock market returns
in India with a close look at the issue of causality. Finally we study the impact of
other factors identified in the portfolio flows literature on the FII flows to India. In all
of these investigations we make a distinction between the pre-Asian crisis period and
the post-Asian crisis period to check if there was a regime shift in the relationships
owing to the Asian crisis.
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Methodology:
To Study the relationship between FII flows and possible economic factors affecting
it, particularly stock returns in the Indian market. They conduct the Pair-wise Granger
causality tests between net FII inflows and monthly return on the BSE Index. Before
conducting the Granger causality tests author made ADF and PP test for check the
collected data are stationary or not,
Granger causality tests was conducted for 3 period
Panel A: Entire Sample: May1993 Dec 1999
Panel B: Pre-Asian Crisis period: May 1993 June 1997
Panel C: Asian Crisis and after: July 1997 Dec 1999
Collection of data:FII net flows from May 1993 to Dec 1999
Bse index monthly return from May 1993 to Dec 1999
Hypothesis:
H0: Returns do not cause FII flows
H0: Flows do not cause returns
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Main Findings and Conclusion:
This empirical investigation of FII flows to India has elicited the following stylized
facts about such flows:
1. FII flows are correlated with returns in the Indian markets.
2. This high correlation is not necessarily evidence of FII flows Causing price
pressureif anything, the causality is likely to be the other way around.
3. A collection of domestic and international variables likely to affect both flows
and returns fails to diminish the importance of returns in explaining FII flows.
4. Since the US and world returns are not significant in explaining The FII flows,
there is no evidence1of any informational Disadvantage of FIIs in comparison
with the domestic investors in India.
5. There appears to be significant differences in the nature of FII flows before
and after the Asian crisis. In the post Asian crisis period it seems that the
returns on the BSE National Index have become the sole driving force behind
FII
An Analysis of Stock Market Efficiency in the Light of Capital Inflows
and Exchange Rate Movements: The Indian Context (BHATTACHARYA &
MUKHERJEE)
Introduction:
There are number of studies on exchange rate affecting stock prices directly. Theory
explained that a change in the exchange rates would affect a firms foreign operation
and overall profits. This would, in turn, affect its stock prices. The nature of the
change in stock prices would depend on the multinational characteristics of the firm.
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Conversely, a general downward movement of the stock market will motivate
investors to seek better returns elsewhere. This decreases the demand for money,
pushing interest rates down, causing further outflow of funds and hence depreciating
the currency. While the theoretical explanation was clear, empirical evidence was
mixed. If FIIs use positive feedback trading strategies, causality may run from stock
prices to foreign investment. The portfolio balancing efforts of foreign investors
would also put pressure on demand for (or supply) of currency, which may affect its
exchange rate. On the other hand, the payoff of foreign investors depends on
exchange rate movements as well as on stock price movements, and they may
rebalance their portfolio in response to an (an anticipated) change in exchange rate.
The relationship of FII investment with stock prices on the one hand, and with
exchange rate on the other hand may produce indirect relation between exchange rate
and stock prices
Methodology and Data Sources:
TO test for the causal relationship between two variables, the standard Granger
(1969) test has been employed. This test states that, if past values of a variable Y
significantly contribute to forecast the value of another variable Xt+1 then Y is said to
Granger cause X and vice versa.
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 seven macroeconomic
variables, namely, money supply, index of industrial production, real GDP, rate of
inflation, real effective exchange rate, foreign exchange reserves and value of trade
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balance. In order to avoid a spurious regression situation the variables in a regression
model must be stationary or cointegrated. Therefore, in the first step, we perform unit
root tests on these eight time series variables to investigate whether they are
stationary or not. The Augmented Dickey-Fuller (ADF) unit root test is used for this
purpose.
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 accepted and the series are non-stationary.
Data collection:
They used monthly data series for three variables for the period Jan 1993 to Mar2005.
The monthly return on stock prices is calculated by taking a percentage change in the
BSE Sensitive
Net investments by FIIs (in equities) in the Indian capital market and The indices of
Real Effective Exchange Rate of the Indian Rupee (36-country bilateral weight with
base 1985=100).
The major findings are:
1. A bi-directional causality between stock price and the net foreign institutional
Investment, thus implying that the market informational efficiency hypothesis
Can be rejected for BSE Sensitive Index with respect to the FII,
2. Uni-directional causality runs from change in exchange rate to stock returns
(At 10% level of significance), not vice versa, implying that the exchange rate
Movements lead the BSE sensitive index, and
3. No causal relationship exists between exchange rate and net investment by
FIIs.
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IN THE INDIAN EQUITY MARKET: A FIRM LEVEL ANALYSIS
(Khan Masood ahmad, Shahid Ashraf, Shahid Ahmed)
Introduction:
The FII investment in India, as a percentage of market capitalization, has been
improving. It ranged between 8 to 10% during 2002-04. Further, the percentage of
FIIs shareholding improves if we consider only the floating stock of the companies.
The FIIs have also been very enthusiastic in subscribing to some of the large IPOs of
government enterprises like Maruti, ONGC and NTPC, as well as private companies
like TCS. If we focus on the FIIs shareholding in some top market capitalized
companies ranging from government owned to private companies across software,
petroleum, finance and old economy, the FIIs shareholding as percentage of total
equity seems to range from a low of 1.88% to a high of 62.04%. Invariably, in all the
government owned companies, the FIIs are the second largest shareholders after the
government. The lowest FIIs shareholding are in Indian Oil and Steel Authority. In
both of these companies the government shareholding is above 80%. Further dilution
of equity will most probably improve the FIIs shareholding in these companies. Two
major financial institutions, ICICI Bank and HDFC, with no Indian promoters, have
majority shareholding of the FIIs. If we exclude the shares of the promoters and
consider only the possible floating stock then the percentage of FIIs holding would go
up substantially, and the chances of price influencing trades on these stocks would
also go up. The retail investors with their individual small trades would hardly affectthe market and the influence of the mutual funds and UTI would also possibly below.
The role of FIIs becomes important in influencing equity returns at the firm level,
especially in the government owned companies. It seems that FIIs are value investing
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in anticipation of further reforms that is driving up the equity returns. There is
volatility clustering in individual series but no transmission from one to another,
except for one key company. Therefore, their is very little destabilizing effect of FII
flows on individual equity returns of the firms during the period of study.
Methodology:
The present study attempts to analyze daily equity returns at the firm level with that
of FIIs net investments. As data on FIIs individual trades on specific firms are not
available publicly, the analysis is confined to the daily aggregate FIIs investments.
Even though the FIIs flows are taken in the aggregate it does give an overall picture
of the investment environment. The study has analyzed 36 firms listed on the NSE,
forming part of the NSE Nifty and they give a reasonable representation of the market
capitalization.
We conduct a Granger causality test between the FII and market returns (R) to see the
direction of causality at the firm level. For any time series analysis, all data series
must be stationary. Stationarity condition has been tested using Augmented Dickey
Fuller (ADF) and Phillips Perron tests (Dickey and Fuller, 1979, 1981; Enders, 1995;Gujarat, 2003; Phillips and Perron, 1988).
GARCH Model:
Volatility in individual series and spillover effect on each other has wider economic
implications. The volatility of individual series has been tested by applying GARCH
MODEL (Generalized ARCH).
Spillover effect of volatility:
In order to test the spillover effect of volatility of one series to another series, they
apply Granger causality test on residuals generated from GARCH mean equation.
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Collection of data:
The daily data was collected from August 2002 to August 2004.
Returns of 36 listed companies
Net FII flows
The analysis has been carried out to capture the lead lag dynamics between net equity
purchases by FIIs and equity returns at the firm.
Hypotheses:
Ho: Firm level returns do not Granger-cause FII flows.
Ho: FII flows do not Granger/cause Firm level returns.
Ho: there is no volatility in the individual series.
Ho: FII volatility does not influence volatility of Firm level returns.
Ho: Volatility of Firm level returns does not influence FII volatility.
Findings:
Existence of bi-directional causality between stock returns and FII flows and
vice-versa in 13 firms,
Uni-directional causality running from stock returns to FII flows in 21 firms.
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FOREIGN INSTITUTIONAL INVESTMENT IN THE INDIAN
EQUITY MARKET
(Paramita mukherjee, Suchismita bose and Dipankor coondoo)
This paper explores the relationship of foreign institutional investment (FII) flows to
the Indian equity market with its possible covariates based on a daily data-set for the
period January 1999 to May 2002. The set of possible covariates considered
comprises two types of variables. The first type includes variables reflecting daily
market return and its volatility in domestic and international equity markets as well as
measures of co-movement of returns in these markets (viz., relevant betas). The
second type of variables, on the their hand, are essentially macroeconomic ones like
exchange rate, short-term interest rate and index of industrial production (IIP)viz.,
variables that are likely to affect foreign investors expectation about return in Indian
equity market.
Methodology:
TO test for the causal relationship between two variables, the standard Granger(1969) test has been employed. This test states that, if past values of a variable Y
significantly contribute to forecast the value of another variable Xt+1 then Y is said to
Granger cause X and vice versa.
In this paper they Used
Grangers causality test find out Direction of causation between FII flows and Return
in the Indian stock market
Multiple regression models had been used to find the relation between the FII flows
and other variables
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Collection of Data:
Daily data set for the period January 1999 to May 2002 collected for the following
variables:
Daily return in the Indian market calculated on the basis of day to day
variations in the value of BSE Sensex
Volatility of daily return in the Indian market calculated as the standard
deviation of previous 7/ 15/ 30 days daily returns based on the BSE Sensex
Daily return in the international equity market based on the day to day
variations in the value of the MSCI World Index
Daily return in the US equity market based on the day to day variations in theS&P500
Volatility of daily return in the international equity market calculated as the
standard deviation of previous 7/ 15/ 30 days daily returns based on MSCI
World index
Volatility of daily return in the US equity market calculated as the standard
deviation of previous 7/ 15/ 30 days daily returns based on the S&P500 index
Extent of co-movement of daily returns in Indian and International equity
markets as measured by the beta of returns from BSE Sensex and MSCI
World Index
Extent of co-movement of daily returns in Indian and the US equity markets
as measured by the beta of returns from BSE Sensex and S&P500 Index
Daily return from day to day variations in the RupeeUSD exchange rate
The second set, on the other hand, includes two macroeconomic variablesviz., the
index of industrial taken as a proxy for short run real income changes and the call
money rate henceforth denoted by CMR) taken as a proxy for short term interest rate.
These two variables, taken to reflect the short run variations in the fundamentals of
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the Indian economy, have been used together with the equity market-related variables
to see whether or not global investors take into account their expectations about the
state of the Indian economy. The sample period of the daily data set is January
1999May 2002, which wholly relates to the post- Asian crisis period. For a fuller
description of these variables and the sources of data on them,
Findings and conclusion:
Flows to and from the Indian market tend to be caused by return in the
domestic equity market and not the other way round;
Returns in the Indian equity market is indeed an important (and perhaps the
single most important) factor that influences FII flows into the country;
While FII sale and FII net inflow are significantly affected by the performance
of the Indian equity market, FII purchase is not responsive to this market
performance;
FII investors do not seem to use Indian equity market for the purpose of
diversification of their investment;
Return from exchange rate variation and fundamentals of the Indian economymay have
Influence on FII decisions, but such influence does not seem to be strong, and;
Daily FII flows are highly auto-correlated and this auto-correlation could not
be accounted for by the all or some of the covariates considered.
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3.1) INTRODUCTION:
Over the past ten years India has gradually emerged as an important destination of
global investors investment in emerging equity markets. Today India has a share of
about 20 per cent in the total global investment in all emerging equity markets
together and the outstanding FII investment in India stood around Rs.86, 287crore,as
on end march, 2002.FII investments as a percentage of market capitalization
increased from7.06 per cent in 1999-00,to 13.5per cent in 2000-01 and further to 14.1
per cent in 2001-02.Given this growing importance of FII for the Indian economy and
in year 2005 only FII are invested more than 47000 crs, It is apparent that the nature
and causation of such fund flows deserve careful examination.
3.2) PROBLEM STATEMENT:
Stock markets become more receptive to foreign investment as the economy liberalizes.
The process of liberalization leads to stock price appreciation followed by in flows from
foreign investors. A concern with the entry of FIIs is that they are positive feedback
traders, buying when the market increases and selling when the market falls. This could be
destabilizing as the sales would lead the stock market to fall and buys would make the
stock market go up. These traders could possibly push the stock prices away from the
fundamentals. Gray area of the study is analyze the relationship between the FII net flow
and return from the stock market and what extent they are correlated with each other.
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3.3) OBJECTIVE OF THE STUDY:
The present study has been undertaken to meet the following specific
objectives,
To Check the impact of Net FII Flows And also Purchase made by the
FII (i.e. only inflow) to stock market
To examine the empirical relationship between FII net flows and Stock
market return (S&P CNX nifty)
To examine the empirical co-movements between the FII net flows and
Stock market return (S&P CNX nifty)
3.4) METHODOLOY:
The present study deals with the analyses of relation between two variables
namely FII net flows and Stock market return; to study the relationship between these
two variables we are conducting the Grangers causality test.
For conduct Grangers causality test the time series must be stationary, to calculate
stationarity condition has been tested using augmented dickey fuller test.
Augmented dickey fuller test is based on the following regression:
The following are the methodology to check for Stationarity,
Stationarity:
Ho: = 0
H1: < 1.
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If the null hypothesis of only a unit root cannot be rejected, then the stock prices
follow a random walk.
The existence of unit roots is firstly tested using the Augmented Dickey-Fuller test
(ADF) (Dickey and Fuller, 1981) through the following relationship:
St = + T + St-1 + St-1 + t
Where tS = t S t1 S,t S is the index of the spot market, and kis chosen so that
the deviations t u to be white noise. The same relationship is used to determine the
order of the futures price index (t F). The null and the alternative hypothesis for theexistence of unit root in t S and t Fis
Once the Stationarity has been tested and if the series are Stationarity then we can test
the Grangers causality test, to test the causation between two series,
The test is based on the following regression:
Where Yt and Xt are the variables to be tested, and ut & vt are mutually uncorrelated
white noise errors, and t denotes the time period and kand lare the number of
lags.The null hypothesis is = = 0 for all ls versus the alternative hypothesis that
0 and 0 for at least some ls. If the coefficient s arestatistically significant
but s are not, then X causes Y. In the reverse case, Y causes X. But if both and
are Significant, then causality runs both ways.
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In our study for Gangers causality test hypotheses would be:
TEST1 H0: FII net flows does not cause S&P CNX Nifty return,
H1: FII net flows cause S&P CNX Nifty return.
TEST 2 H0: S&P CNX Nifty return does not cause FII net flows.H1: S&P CNX Nifty return cause FII net flows.
3.5) SCOPE OF THE STUDY:
In this study we are analyzing the causation between FII and Nifty return, but from
this single study we cant come to the conclusion that FII flows affecting the Indian
market return or Indian market return affect the FII flows, Because there are so many
factors (Internal and external) are affecting both
For eg: Exchange rate, Inflation, Growth, Savings, Interest rate etc,
Therefore our study is limited to analyze the relationship between the FII and Nifty
return with the help of Grangers causality test.
3.6) SAMPLE SIZE OF THE STUDY:
Daily FII net flow (Purchase sale) from 1 April 2001 to 31 March 2006.
Daily Nifty return from the 1 April 2001 to 31 March 2006.
3.7) DATABASE:
Data pertaining to the FII flows are taken from www.moneycontrol.com.
Data regarding Nifty return will be taken from www.nseindia.com.
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4 DATA ANLYSIS:
ADF Test of FII Net flows:
ADF Test Statistic -24.80915 1% Critical Value* -2.5674
5% Critical Value -1.9396
10% Critical Value -1.6157
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D (NETFLOWS)
Method: Least SquaresDate: 06/05/06 Time: 19:59
Sample (adjusted): 1/02/2001 10/24/2005
Included observations: 1255 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
NETFLOWS (-1) -0.659129 0.026568 -24.80915 0.0000
R-squared 0.329230 Mean dependent var 0.274502
Adjusted R-squared 0.329230 S.D. dependent var 406.9663
S.E. of regression 333.3076 Akaike info criterion 14.45681
Sum squared resid 1.39E+08 Schwarz criterion 14.46090
Log likelihood -9070.645 Durbin-Watson stat 2.133951
Interpretation:
ADF Test statistic of net FII flows (Purchase-sale) would be 24.80915,
Whereas Critical Value at 1% level is -2.5674
5% level is -1.9396
10% level is 1.6517
Since the ADF Test statistic result would be the greater than the Critical value,
therefore null hypothesis is rejected it means that data i.e. net FII flows is Stationary.
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ADF Test of Nifty Daily Return:
ADF Test Statistic -31.27959 1% Critical Value* -2.5674
5% Critical Value -1.939610% Critical Value -1.6157
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test EquationDependent Variable: D (NIFTY)Method: Least SquaresDate: 06/05/06 Time: 20:00Sample (adjusted): 1/02/2001 10/25/2005Included observations: 1256 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
NIFTY (-1) -0.876014 0.028006 -31.27959 0.0000
R-squared 0.438080 Mean dependent var 5.28E-07Adjusted R-squared 0.438080 S.D. dependent var 0.002426
S.E. of regression 0.001819 Akaike info criterion -9.780489
Sum squared resid 0.004152 Schwarz criterion -9.776400
Log likelihood 6143.147 Durbin-Watson stat 1.969839
Interpretation:
ADF Test statistic of Nifty return would be 31.27959
Whereas Critical Value at 1% level is -2.5674
5% level is -1.9396
10% level is -1.6157
Since the ADF Test statistic result would be the greater than the Critical value,
therefore null hypothesis is rejected it means that data i.e. Nifty return is Stationary.
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Phillips-Perron Test:
PP Test of FII net flows:
PP Test Statistic -24.81731 1% Critical Value* -2.56745% Critical Value -1.9396
10% Critical Value -1.6157
*MacKinnon critical values for rejection of hypothesis of a unit root.
Lag0 truncation for Bartlett kernel: (Newey-West suggests: 7)Residual variance with no correction 110930.8
Residual variance with correction 110930.8
Phillips-Perron Test EquationDependent Variable: D (NETFLOWS)
Method: Least SquaresDate: 06/06/06 Time: 19:34Sample (adjusted): 1/02/2001 10/25/2005
Included observations: 1256 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
NETFLOWS (-1) -0.659124 0.026559 -24.81731 0.0000
R-squared 0.329199 Mean dependent var 0.375398Adjusted R-squared 0.329199 S.D. dependent var 406.8198
S.E. of regression 333.1954 Akaike info criterion 14.45613Sum squared resid 1.39E+08 Schwarz criterion 14.46022Log likelihood -9077.450 Durbin-Watson stat 2.133734
Interpretation
PP Test statistic of net FII flows would be -24.81731
Whereas Critical Value at 1% level is -2.5674
5% level is -1.9396
10% level is -1.6157
Since the PP Test statistic result would be the greater than the Critical value, therefore
null hypothesis is rejected it means that data i.e. Nifty return is Stationary.
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PP Test of Daily Nifty return:
PP Test Statistic -11.27146 1% Critical Value* -2.5674
5% Critical Value -1.939610% Critical Value -1.6157
*MacKinnon critical values for rejection of hypothesis of a unit root.
Lag0 truncation for Bartlett kernel: (Newey-West suggests: 7)
Residual variance with no correction 2.06E-05
Residual variance with correction 2.06E-05
Phillips-Perron Test Equation
Dependent Variable: D (NIFTY)
Method: Least Squares
Date: 06/06/06 Time: 19:35
Sample (adjusted): 1/02/2001 10/27/2005
Included observations: 1258 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
NIFTY (-1) -0.787887 0.069901 -11.27146 0.0000
R-squared 0.091230 Mean dependent var 0.000119
Adjusted R-squared 0.091230 S.D. dependent var 0.004766
S.E. of regression 0.004543 Akaike info criterion -7.949561
Sum squared resid 0.025946 Schwarz criterion -7.945477Log likelihood 5001.274 Durbin-Watson stat 1.151407
Interpretation
PP Test statistic of Nifty return would be -11.27146
Whereas Critical Value at 1% level is -2.5674
5% level is -1.9396
10% level is -1.6157
Since the PP Test statistic result would be the greater than the Critical value, therefore
null hypothesis is rejected it means that data i.e. Nifty return is Stationary.
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ADF TEST FOR RESIDUALS (FII DEPEDENT AND INDEPENDENTNIFTY RESUIDALS)
ADF Test Statistic -3.310602 1% Critical Value* -2.5674
5% Critical Value -1.9396
10% Critical Value -1.6157
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(FII)
Method: Least Squares
Date: 06/11/06 Time: 13:40
Sample(adjusted): 1/02/2001 10/24/2005Included observations: 1255 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
FII(-1) -0.017736 0.005357 -3.310602 0.0010
R-squared 0.008661 Mean dependent var -0.760080
Adjusted R-squared 0.008661 S.D. dependent var 407.4407
S.E. of regression 405.6725 Akaike info criterion 14.84977
Sum squared resid 2.06E+08 Schwarz criterion 14.85386
Log likelihood -9317.228 Durbin-Watson stat 2.893844
Interpretation:
To calculate the ADF test for residuals, first we have to find out Residuals of
variables, For that we drew the regression analysis with each other by taking the FII
dependent first after that Nifty dependent, with help of results we got from the
regression i.e. Alpha and Beta, we are calculated the residuals,
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Calculation of residuals for Dependent variable i.e. FII :
+ * Nifty (Independent)
Calculation of residuals for Dependent variable i.e. Nifty:
+ * FII flows (Independent)
Since the ADF Unit root value is -3.310602 less than MacKinnon critical values@ 1% Critical Value* is -2.5674@ 5% Critical Value is -1.9396@ 10% Critical Value is -1.6157
Therefore Null hypothesis is rejected that means Residuals of FII are
stationary
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ADF TEST FOR RESIDUALS (NIFTY DEPEDENT ANDINDEPENDENT FII RESUIDALS)
ADF Test Statistic -10.98859 1% Critical Value* -2.56745% Critical Value -1.9396
10% Critical Value -1.6157
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Dependent Variable: D(NIFTY)
Method: Least Squares
Date: 06/11/06 Time: 13:42
Sample(adjusted): 1/02/2001 10/24/2005
Included observations: 1255 after adjusting endpoints
Variable Coefficient Std. Error t-Statistic Prob.
NIFTY(-1) -0.175622 0.015982 -10.98859 0.0000
R-squared 0.087834 Mean dependent var 0.004328
Adjusted R-squared 0.087834 S.D. dependent var 56.95182
S.E. of regression 54.39320 Akaike info criterion 10.83115
Sum squared resid 3710110. Schwarz criterion 10.83524
Log likelihood -6795.548 Durbin-Watson stat 2.675462
Interpretation:
Since the ADF Unit root value is -10.98859 less than MacKinnon critical values@ 1% Critical Value* is -2.5674@ 5% Critical Value is -1.9396@ 10% Critical Value is -1.6157
Therefore Null hypothesis is rejected that means Residuals of Nifty are stationary.
Grangers causality test:
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Grangers causality test: At lag 2
Pair wise Granger Causality Tests
Date: 06/06/06 Time: 19:37Sample: 1/01/2001 12/29/2006
Lags: 2
Null Hypothesis: Obs F-Statistic Probability
NIFTY does not Granger Cause FII 1255 4.25025 0.01447
FII does not Granger Cause NIFTY 13.1296 2.3E-06
Grangers causality test: At lag 3
Pair wise Granger Causality Tests
Date: 06/06/06 Time: 19:37
Sample: 1/01/2001 12/29/2006
Lags: 3
Null Hypothesis: Obs F-Statistic Probability
NIFTY does not Granger Cause FII 1254 3.11353 0.02545
FII does not Granger Cause NIFTY 7.60004 4.9E-05
Grangers causality test: At lag 4
Pair wise Granger Causality Tests
Date: 06/06/06 Time: 19:38
Sample: 1/01/2001 12/29/2006
Lags: 4
Null Hypothesis: Obs F-Statistic Probability
NIFTY does not Granger Cause FII 1253 2.36694 0.05101
FII does not Granger Cause NIFTY 5.41595 0.00025
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Grangers causality test: At lag 5
Pair wise Granger Causality Tests
Date: 06/06/06 Time: 19:39Sample: 1/01/2001 12/29/2006
Lags: 5
Null Hypothesis: Obs F-Statistic Probability
NIFTY does not Granger Cause FII 1252 2.09044 0.06415
FII does not Granger Cause NIFTY 4.56355 0.00040
Grangers causality test: At lag 6
Grangers causality test: At lag 7
F distribution critical value would be 1.0000 @ 5% significance level
F distribution critical value would be 1.0000 @ 1% significance level
Pair wise Granger Causality Tests
Date: 06/06/06 Time: 19:39
Sample: 1/01/2001 12/29/2006
Lags: 6
Null Hypothesis: Obs F-Statistic Probability
NIFTY does not Granger Cause FII 1251 1.88372 0.08041
FII does not Granger Cause NIFTY 3.88686 0.00075
Pair wise Granger Causality Tests
Date: 06/06/06 Time: 19:40
Sample: 1/01/2001 12/29/2006
Lags: 7
Null Hypothesis: F-Statistic Probability
NIFTY does not Granger Cause FII 1.87508 0.07012FII does not Granger Cause NIFTY 3.52561 0.00094
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Interpretation:
Since in all the lags F-Statistic value would be the greater than F distribution table
therefore
Null hypothesis i.e. NIFTY does not Granger Cause FII
FII does not Granger Cause NIFTY rejected
It means that Nifty return does cause FII and Nifty return has impact on net FII flows.
And also net FII flows does cause Nifty return.
There is Bi-directional relationship existence between the FII Flows and Nifty return .
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Findings and conclusion:
The results reveal that FII have been Attracted to India as an Important investment
destination. FII investment in certain Indian companies shows majority shareholding,
the flow FII seems to be attracted by the Indian equity returns.
The relationship between the FII investment and the Indian market return still remains
highly debatable, the our test for casual relationship between the FII and Nifty return
Existence of bi-directional causality between Nifty return and FII flows
FII flows are correlated with contemporaneous returns in the Indian markets.
This high correlation is not necessarily evidence of FII flows causing price
Pressure if anything, the causality is likely to be the other way around.
A collection of domestic and international variables likely to affect both flows and
Returns fail to diminish the importance of contemporaneous returns in explaining
FII flows.
Since the US and world returns are not significant in explaining the FII flows,
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Limitation of the study:
Since the our research topic is Impact of FII on Nifty return, in this we are tried to
analyze the relationship between the FII and Nifty and Impact by each other But
Practically numbers of variables are affected to the market like company related
information, economic factors, external factors etc, so we cant say that or cant
conclude that FII flows only determine the Nifty return, this is major hurdle for our
project and other limitation for our project is time constraint and Historical
availability of data, Because we are taking Daily FII net flows and nifty return
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Bibliography:
Books:Modern econometric model by Pindic
Journals:
ICFAI journal of applied Finance
ICRA journal Money and Finance
Websites:
www.nseindia.comwww.rbi.org.in
www.moneycontrol.com
www.valuenotes.com
http://www.valuenotes.com/http://www.moneycontrol.com/http://www.rbi.org.in/http://www.nseindia.com/ -
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EXECUTIVE SUMMARY
Over the past ten years India has gradually emerged as an important destination of
global investors investment in emerging equity markets.Today India has a share of
about 20 per cent in the total global investment in all emerging equity markets
together and the outstanding FII investment in India stood around Rs.86, 287crore,as
on end march, 2002.FII investments as a percentage of market capitalization
increased from7.06 per cent in 1999-00,to 13.5per cent in 2000-01 and further to 14.1
per cent in 2001-02.Given this growing importance of FII for the Indian economy and
in year 2005 only FII are invested more than 47000 crs, It is apparent that the nature
and causation of such fund flows deserve careful examination.
A concern with the entry of FIIs is that they are positive feedback traders, buying when
the market increases and selling when the market falls. This could be destabilizing as the
sales would lead the stock market to fall and buys would make the stock market go up.
These traders could possibly push the stock prices away from the fundamentals. Gray area
of the study is to analyze the relationship between the FII net flow and return from the
stock market and what extent they are correlated with each other.
The present study deals with the analyses of relation between two variables namely
FII net flows and Stock market return; to study the relationship between these two
variables we are conducted the Grangers causality test. To conduct Grangers causality
test the time series must be stationary, to calculate stationarity condition has been
tested using augmented dickey fuller test and Philips peron test. For that Daily Net
FII flows and NIFTY returns are collected from 1-april-2001 to 31-march-2006
From the empirical research we can conclude that there is Bi-directional causal
relationship between Net FII flows and Nifty return.
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