1Foreign Institutional Investment in India: Determinants and the impact of crises

1.0 Introduction

Foreign investment plays an important role in the long term economic development of a

country by augmenting availability of capital, bridging the gap betwee n savings and

investment, raising productivity and providing foreign exchange. I nternational capital

flows particularly in the form of portfolio investment to developing and emerging

markets is a phenomenon, which began at a reasonable scale in the early 1 990s. The

phenomenon emerged mainly on account of two reasons. First, a high potential for

growth coupled with the lack of availability of capital have made these economies

embrace foreign capital. Second, there is an increased preference on the part of

developed economies to divert their available capital to destinations where it earns

good returns. Both these factors together with the widespread liberalisation and

globalisation of financial markets have open ed a new era of capital mobility whereby

private capital flows have assumed an important source of finance for emerging

markets.

Following the liberalisation of Indian economy in 1991, there was a gradual shift in

capital flows from debt to non-debt creating flows by encouraging foreign investment

into the country. In this process of encouraging both direct and portfolio investment,

the Foreign Institutional Investors (FIIs) 1 were allowed to invest in domestic financial

markets from September, 1992. The FIIs have now become important players in the

1 The term foreign institutional investor has been defined by Securities and Exchange Board ofIndia (SEBI) under its (Foreign Institutional Investors) Regulations, 1995 as: "an institutionestablished or incorporated outside India which proposes to make investment in India insecurities. It also includes investment by a sub-account. The entities who are eligible to getregistered in India as FIIs include pension funds, mutual funds, banks, university funds,foundations, etc..

2domestic financial markets and the Foreign Institutional investment (FII) has become

an important source of portfolio investment in the country. The cumulative FII

investment (purchases) in India since November 1992 till end of November 2012 stood

at around Rs. 63,80,223 crores while the net investment for the same period stood at

around Rs. 701680.12 crores (www.sebi.gov.in). The total number of registered FIIs

was 1752 till the end of November 2012 and the total number of Registered Sub -

accounts was 6306 on the same date (www.sebi.gov.in). FII investments as a

percentage of market capitalization increased from 1.52 per cent in 1993 -94, to 12.96

per cent in 2000-01 and further to 14.51 per cent in 2010 -11 (SEBI, 2011). The total

turnover of the FIIs in the equ ity market constituted 15.30 percent of the total turnover

on the BSE and the NSE in 2010 2011, an improvement from 11.56 percent recorded

in 20092010 (ISMR, 2011).

1.1 Rationale and objective of the study

While there are many benefits associated with FII inflows, such as increased

investment rate and augmented foreign exchange, there are concerns regarding the

sustainability of these flows. Since the basic motive of these flows is profit, they are

always prone to sudden withdrawals. Portfolio flows in the form of FII, thus, tend to be

volatile and the sudden flight of this form of investment can have adverse consequences

on the recipient countrys economy as happened in the case of East Asian crisis and

Mexico crisis.

In view of the increasing significance of FIIs in the Indian markets, the present study

aims to analyse the nature and determinants of FII inflows to India, with special

reference to the impact of crises on these flows. The present paper contributes to the

existing literature in many ways. First, while identifying determinants of FIIs, w e have

tried to analyse the impact of crisis as well, particularly the global financial crisis of

32008. To identify for the existence of structural breaks in the FII inflows, we have

included the dummy variables for the East Asian Financial Crisis of 1997 and the

Global Financial Crisis of 2008. Second, we have used a more refined methodology of

Principal Component Analysis (PCA) to augment the analytical richness of the model

employed. Third, we have taken a longer time period as compared to many studies that

have a shorter reference period.

1.2 Layout of the Paper

The present study is divided into five sections. The first section as already discussed

introduces the topic and lays out the objectives. Section 2 presents an overview of the

existing literature in this area of research. Section 3 describes the choice of the dataset, and

explains the methodology used in the study. Section 4 presents and discusses the empirical

results. Section 5 gives conclusions and recommendations.

2.0 Review of Literature

International Studies

The literature on the relationship between portfolio investment and domestic stock

market returns has generally shown a positive contemporaneous relationship [(Bohn

and Tesar (1996), Clark and Berko (1996), Griffin et. al (2002) and Richards (2005)].

This positive relationship however need not necessarily imply that portfolio investment

flows into a country as a result of returns in domestic stock market ; rather the portfolio

inflows may be caused by exogenous changes in investor preferences (Brennan and

Cao, 1997). Further, this positive association between domestic stock market returns

and portfolio investment may alter during a currency crisis [Choe et al. (1998), Kim

and Wei (2000)].

Results also vary across literature regarding the general question of whether external or

domestic factors are more important in influencing portfolio flows. While some studies

4find domestic factors to be more important determinants of FII movement [Chuhan et

al. (1998); Garibaldi et al (2002)], others find external factors to be more important

[Calvo et al. (1993); In-Mee Baek (2006)]. Still others find that both domestic and

external factors have an important role to play in determining FII flows [Taylor and

Sarno (1997); Portes and Rey (2005)]. Regional factors have also been considered as an

important determinant in a number of studies [ Buckberg (1996); Richards (2005);

Griffin et al. (2002); Froot et al. (2001)]

Indian Studies

The extant literature in the Indian context adopts a common approach in separating the

determinants of foreign portfolio flows into domestic influences such as domestic stock

market returns, the volatility and liquidity of the domestic stock markets, exchange

rates, and the external influences such as global stock market returns and interest rates

[Agarwal (1997); Pal (1998); Mukherjee, Bose and Coondoo (2002) ; Batra (2003);

Gordon and Gupta (2003); Rai and Bhanumurthy (2004) ; Roy (2007); Saraogi (2008);

Kaur and Dhillon (2010); Bhanumurthy and Singh (2011); Shankar (2011)] .

3.0 Data and Methodology

3.1 Sample Period and Data Sources

Sample Period

The study uses monthly time series data from April 1994 to December 2011. We have

chosen to use monthly data because of the lack of availabil ity of daily data on various

macroeconomic variables considered in the study, namely, Index of Industrial

Production (IIP), Wholesale Price Index (WP I), Call Money Rate (CMR), etc. Though,

the FII inflows to the country was allowed from September 1992, but the starting point

for the data has been selected from April 1994 as these two years can be regarded as

learning period for investors.

5Data Sources

The data on FII inflows has been taken from SEBIs website. The data on S&P 500 has

been taken from the website of yahoo finance (in.finance.yahoo.com). The data on

MSCI Emerging Market (EM) Index has been taken from the website of Morgan

Stanley Capital International. The data on London InterBank Offer Rate (LIBOR) is

taken from fedprimerate.com. The data o n Sensex has been taken from the website of

Bombay Stock Exchange (BSE) (www.bseindia.com). The data on exchange rate, call

money rate, index of industrial production and wholesale price index has been taken

from the website of Reserve Bank of India (www.rbi.org.in). The data on volatility and

market capitalisation has been taken from the SEBIs Handbook of Statistics on Indian

Securities Market, 2011.

3.2 Data Description

Dependent Variables

In the literature, researchers have used several alternative form s of the

dependent/explained variable. We have used three different specifications of the

dependent variable. First , we have taken gross FII equity inflows/purchases expressed

in absolute values in rupees crore. Second, we have also tried to identify the

determinants of FII net (inflows-outflows) equity inflows expressed in rupees crore. FII

net inflows are considered to serve as an overall measure of FIIs activities in the Indian

stock market. Third, we have used gross FII equity inflows as a percentage of market

capitalisation on the BSE. This form of dependent variable has been taken to capture

the relative importance of FIIs in the Indian stock market.

6Independent Variables

Regarding the choice of the independent variables, we have drawn upon the existing

literature to identify the primary determinants of global investors decision to invest in

the Indian markets. We classify the factors determining the FII inflows into domestic

and external factors. Domestic factors are also referred to as pull factors as they attract

the portfolio investment into the country, whereas external factors are referred to as

push factors as they try to push the portfolio investment out of the country.

We further classify the domestic determinants of FII inflows into those which relate to

financial market factors such as stock market returns and volatility, and those which

relate to the macroeconomic fundamentals of the country. If foreign investors focus on

the macroeconomic factors while deciding to invest in any country, they are described

as following a top-down approach. On the contrary, if they focus more on the financial

market variables, then they are said to follow a bottom -up approach. A general belief

regarding the FIIs in India is that they invest on a bottom-up basis. They invest in some

selected stocks and are less concerned with macroeconomic fundamentals. In this study

we try to assess whether the investment decisions of FIIs in case of India are affected

by Indias macroeconomic performance or not.

Before actually conducting a regression analysis we can form a priori expectations on

the behaviour of the selected independent variables with respect to FII investments.

Based on the above discussion, the determinants explaining the FII inflows to India

along with their expected relationship with each of the dependent variable can be stated

as follows.

7Global Variables

1. Monthly return2 on S&P 500 in order to represent global stock market returns. The

reason for taking U.S. market is that stock market in U.S. act s as a benchmark for

other countries indices. FII inflows are expected to have a negative relationship

with returns on S&P 500. An increase in the U.S stock markets returns will make

the FIIs to invest more in their home countries thereby reducing the flow o f funds to

emerging markets like India.

2. Monthly returns on MSCI Emerging Market (EM) Index which is one of the most

popular emerging market index and is widely used by foreign portfolio investors for

making investment decisions in emerging markets. FIIs in vestment in India is

expected to have a negative relationship with returns on MSCI EM index. A

negative relationship would suggest that India compete s with other emerging

markets in order to attract FII.

3. Changes in 3-month London InterBank Offered Rate (LIBOR) to represent short

term global interest rates. Declining foreign interest rates would lead to an increase

in FII inflows to India because of cheaper availability of funds. Another reason for

this inverse relationship can be that an increase in LIBOR would mean high returns

on the LIBOR linked investments and therefore lesser investment to stock markets

of countries like India because of funds flowing towards high yield instruments.

Thus we expect a negative relationship.

Domestic Variables

A. Financial Variables

2 Returns have been calculated as the excess of the logarithm of the month -end index valueover the logarithm of the index value on the previous month -end.

81. Monthly returns on BSE Sensex to represent domestic stock market returns. An

increase in BSE Sensex would lead to an increase in FIIs investment in India and

vice-versa thereby suggesting positive feedback trading. FIIs will invest in Indian

markets in search of higher returns. Thus a positive relationship is expected.

2. Lagged monthly returns on BSE Sensex with a lag of one month. FIIs may also take

into account the past domestic stock market returns while taking their current

investment decisions. Thus, the relationship is expected to be positive.

3. Volatility3 in monthly returns on BSE Sensex used as a proxy for risk associated

with investing in Indian equities of the index. In theory volatility is expected to

have a negative sign with FII inflows. The markets with high volatility would see

less investment by FIIs.

Apart from these financial market variables, we also consider various macroecon omic

variables. These variables are taken to reflect the variations in the fund amentals of the

Indian economy.

B. Macroeconomic Variables

4. Monthly returns in exchange rate denoted by variations in the rupee -dollar

exchange rates. A positive return means depreciation of Indian rupee against US

dollar. FII inflows are expected to go up (down) when there are expectations of

domestic currency appreciation (depreciation). Going by our definition of the

monthly exchange rate return , it is expected to have a negative relationship with FII

inflows

3 Volatility is calculated as the standard deviation of the natural log of daily returns in Sensex.

95. Changes in Call Money Rate (CMR) as a proxy for short term domestic interest

rates and real economic activity. We expect a positive relationship of CMR with FII

inflows

6. Index of Industrial Production 4 (IIP) as a proxy for short run real income changes.

An increasing IIP suggests an increase in the level of industrial production thereb y

an increase in growth rate of the economy. Hence FII investments in India should

be a positive function of IIP.

7. Wholesale Price Index (WPI)5 as an indicator of inflation rate in the Indian

economy. An increasing WPI suggest an increase in general price l evels. A high

rate of inflation is a signal for macroeconomic instability and erodes the returns of

FIIs in rupee terms. Hence, we expect that FII investments in India should be a

negative function of inflation or the WPI index.

8. Lagged value of the dependent variable with a lag of one time period that is, one

month. The relationship of FII investment with its own lagged value is expected to

be positive. Such a positive relation would suggest that FIIs indulge in herd

behaviour.

Impact of the Crises

In addition to the above variables, we have introduced two dummy variables, one for

East Asian financial crisis of 1997 and the other for the Global financial crisis of the

year 2008. Specifically, we want to capture the impact of these two crises on FII

inflows to India.

Thus, the general form of the function is:

4 Sector-wise index number (general index) of industrial production with b ase year 1993-94.5 Monthly average of wholesale price index (all commodities) with base year 1993 -94.

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FII = f (S&P_RET, MSCI_RET, LIB, BSE_RET, BSE_RETL, ER_RET, VOL, CMR,

IIP, WPI, D2, D3, LDV)

where

S&P_RET : Returns on US S&P 500 index

MSCI_RET : Returns on MSCI EM index

LIB : Changes in LIBOR

BSE_RET : Returns on BSE Sensex

BSE_RETL : One month lagged returns on BSE Sensex

ER_RET : Return on US dollar -rupee exchange rate

VOL : Volatility in returns on BSE Sensex

CMR : Changes in call money rate

IIP : Index of Industrial Production

WPI : Wholesale Price Index

D2 : Dummy variable for East Asian financial crisis (value of 1 for all months

after the onset of crisis, (i.e., July 19976 till September 2008 and 0 otherwise) and

D3 : Dummy variable for Global financial crisis (value of 1for all months after

October 20087 and 0 otherwise)

LDV : One month Lagged dependent variable

3.3 Methodology

Multiple regression

6 This is the date generally accepted in the literature: see Corsetti et al (1999).7 The crisis marked its beginning around the time of August 2007, however it aggravatedaround October 2008 when the markets came down heavily. Seehttp://www.nber.org/papers/w14631

11

The study uses multiple regression analysis to identify the determinants of FII

investment. As a first step, we check for the stationarity of the underlying time series .

We use the Augmented Dickey-Fuller (ADF) test and Phillips -Perron (PP) test to

investigate whether the time serie s of dependent and independent variables are

stationary or not. Having tested for stationarity, we then conduct a multiple regression

analysis to identify the determinants of FII inflows to India. As mentioned earlier, we

make use of dummy variables in ord er to test for the presence of structural break in our

data. In our case we make use of two dummy variables to identify structural breaks ; one

for the East Asian Financial Crisis of 1997 and the second for the Global Financial

Crisis of 2008. The dummy var iables take the value 1 during the crisis period and 0

otherwise.

After introducing the dummy variables, e ach specification of the dependent variable

was regressed on the independent variables in following regression framework or

model:

FII = + 1 S&P_RET + 2MSCI_RET + 3LIB + 4BSE_RET + 5BSE_RETL +

6ER_RET + 7VOL + 8CMR + 9IIP + 10WPI + 11LDV + 12D2 + 13D3 + u

.(1)

where

FII: different specification of the dependent variable

: constant term

(1 to 13) : the respective beta coefficients for each independent variable

u : error term

All other variables carry their usual meaning as stated in section 3.2.

The original regression results were found to be suffering from t he problem of

multicollinearity due to a large number of expl anatory variables. When the data suffers

12

from the problem of multicollinearity then though the estimation of regression

coefficients is possible but their standard errors tend to be large. In such a situation ,

while overall measure of goodness of fit (R 2) can be very high, the estimated

coefficients of most of the explanatory variables are largely insignificant.

Principal Component Analysis

As seen above, retaining all variables leads to multicollinearity. If we try to avoid

multicollinearity by dropping a ll correlated variables, there is a great loss of

information. Alternatively, we could use Principal Component Analysis to determine

the principal variables. By using this procedure, we eliminate some variables without

affecting the explanatory power of the equation because the retained variables contain

the information of those which are eliminated.

The basic principle behind the application of factor analysis is that the initial set of

variables should be highly correlated. This could be checked with the help of a test

known as Barttlet test of sphericity, which tests the null hypothesis that the variables

are uncorrelated. Another condition which needs to be fulfilled before a factor analysis

could be carried out is the value of Kaiser -Meyer-Olkin (KMO) measure of sampling

adequacy. This test statistic takes a value between 0 & 1. For the application of factor

analysis, however, value of KMO statistics greater than 0.5 is considered desirable

(Malhotra and Dash, 2011).

The following consideration should be kept in mind while applying PCA:

1. For determining the retained component we need a criterion.

2. The PCA methodology tells us the total variance explained by each retained principal

component as well as the cumulative percentage of the explained var iation. This is a

measure of the explanatory power of the component for the information content of the

procedure.

13

3. There were various methods of rotation but the most popular method is the Varimax

with the Kaiser normalization. The purpose of the rotatio n is to make the interpretation

of the PCA more meaningful. Method of rotation however retains the same information

and explanatory power.

After doing these procedures there was a choice between retained principal components

in a regression framework or se lecting the principal variables that are associated with

each of those components. This involves the Jolliffe procedure. In the first case

regression is known as principal component regression and in the second case it is

known as principal variable regres sion. We have chosen the latter because it is difficult

to interpret the principal component regression. We have used the Joliffes procedure

for selecting principal variables. We take up each rotated component and select the

variable that has the highest component score. Then we move to the next component

and so on. This way we get the principal variables which represent the maximum

information and eliminate the variables that are correlated to them and hence create

multicollinearity.

4.0 Empirical Results and Analyses

The summary statistics of the dependent and independent variables are presente d in

Table 1 of the appendix.

4.1 Unit Root Tests Results

To detect the presence of unit root in the underlying series of all variables , two unit root

tests are employed, namely Augmented Dickey Fuller Test (ADF) and Phillip Perron

(PP) test. The lag length, for the ADF tests is chosen so as to minimize Schwarz

Information Criterion (SIC) where the upper bound on the lag length of 14 was

selected. The bandwidths fo r the PhillipsPerron test follow the NeweyWest

14

suggestion using Bartlett kernel. The null hypothesis under these tests is that the

underlying time series is non-stationary or has a unit root. Rejection of null hypothesis

would signify that the underlying variable is stationary. Table 4.1 summarises the

results of unit root tests.

Table 4.1: Unit Root Test Results for Dependent and Independent Variables

Variables ADF test statistics PP test statistics InferencesIntercept and

no trendIntercept and

trendIntercept and no

trendIntercept and

trendFIIP -1.858987 -3.545008** -1.994177 -4.299723* Stationary at levelFIIN -5.018195* -5.367088* -10.73613* -11.04012* Stationary at levelFIIPCAP -2.039284 -2.516148 -2.916575** -5.611506* Stationary at levelS&P_RET -13.05018* -13.17062* -13.13599* -13.22413* Stationary at levelMSCI_RET -12. 18680* -12. 18155 * -12. 30543* -12. 26394* Stationary at levelLIB -10.26279* -10.26893* -10.68751* -10.69920* Stationary at levelBSE_RET -13. 81063* -13. 80263* -13. 89546* -13. 87156 * Stationary at levelER_RET -11.66984* -11.65376* -11.65360* -11.60982* Stationary at levelVOL -4.791111* -4.777311* -9.217622* -9.219286* Stationary at levelCMR -13. 60035* -13. 56707* -26. 11990* -26. 04323 * Stationary at level

IIP 1.326333 - 0.545931 - Non-stationary atlevel

WPI 2.270884 -0.207717 2.758014 0.398913 Non-stationary atlevel*denotes significance at 1% level of significance** denotes significance at 5% level of significance-Means that the E.Views was showing insufficient number of observations

Table 4.1 suggests that except for the Index of Industrial production (IIP) and

Wholesale Price Index (WPI), all other series came out to be stationary at level

implying that these variables can be used at level in regression analysis. FIIPCAP was

found to be non-stationary at level using ADF tests, however it came out to stationary

at level using PP test. Since PP test is considered to be a powerful test than ADF test,

so we take the FIIPCAP series to be stationary at level. As far as the IIP and WPI are

concerned we took their first difference and again checked for their stationarity. Both

these series came out to be stationary at first difference. The first differenced series of

15

these variables are now onwards denoted by DIIP and DWPI, respectively. The resul ts

are reported in the table 4.2.

Table 4.2: Unit Root Test Results for IIP and WPI after differencing

Variables ADF test statistics PP test statistics InferencesIntercept and

no trendInterceptand trend

Intercept andno trend

Interceptand trend

DIIP -25.56906* -25.56661* -37.03304* -40.77019* Stationary atfirst difference

DWPI -8.781424* -9.189777* -8.950275* -9.317136* Stationary atfirst difference

*denotes significance at 1% level of signifi cance

4.2 Preliminary Regression Results

Each specification of the dependent variable was regressed on the independent

variables. The results are summarised in Table 4.3. The table shows that inspite of high

R2, only few coefficients turns out to be signi ficant in explaining FII inflows to India . A

high R2 with few significant t-ratios is a symptom of multicollinearity. The correlation

matrix (given in table 2 of appendix) also reveals that there are indeed high correlations

between quite a few of our inde pendent variables. We address the problem of

multicollinearity in our data using factor analysis technique through Principal

Component analysis (PCA).

Table 4.3: Preliminary Regression Results

Dependent VariablesFIIP FIIN FIIPCAP

Independent Variables Coefficients p-values Coefficients p-values Coefficients p-valuesConstant -695.1622 0.7187 1921.153 0.0550*** 0.000157 0.7306

S&P_RET -228.0027 0.2708 -21.60837 0.8391 -0.000119 0.0162**MSCI_RET 125.1649 0.4167 94.51165 0.2347 5.36E-05 0.1448

LIB -448.8021 0.8622 -81.96247 0.9507 0.000368 0.5449BSE_RET 158.6252 0.1489 158.0085 0.0056* -1.14E-05 0.6591

BSE_RETL 211.1818 0.0103** 28.68618 0.5421 5.89E-05 0.0024*ER_RET -822.0009 0.0344** -914.4197 0.0000* -0.000193 0.0355**

VOL 760.9154 0.4076 -997.6255 0.0373** 0.000387 0.0790***CMR -102.3592 0.5636 -14.22695 0.876 -6.44E-05 0.1245

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DIIP 788.4217 0.1165 247.6811 0.3171 0.000126 0.267DWPI 83.00815 0.0627*** 17.28118 0.4509 2.25E-05 0.0323**

Lagged DependentVariable

(FIIPL/FIINL/FIIPCAPL)

0.898972 0.0000* 0.186724 0.0042* 0.781689 0.0000*

D2 1080.742 0.5315 689.8973 0.4239 0.001162 0.0132**D3 3239.726 0.2001 3673.959 0.0012* 0.001065 0.0627***

ADJ. R2 0.897926 0.416348 0.803692Prob. (F-statistic) 0.000000 0.000000 0.000000

*denotes significance at 1% level of significance,**denotes significance at 5% level of significance,***denotes significance at 10% level of significance

4.3 Principal Component Analysis (PCA)

As a first step we identify whether the technique of factor analysis is applicable to our

set of data. For this we use two measures namely, Barttlet test of sphericity and Kaiser -

Meyer-Olkin (KMO) measure of sampling adequacy. Since we have taken three

specifications of dependent variables, therefore the lagged dependent varia ble which

has been used as an independent variable in our analysis will be different in each case.

As a result, we have applied PCA thrice with a different lagged dependent variable

each time. The other independent variables remain the same. The results o f KMO and

Barttlet test are reported in the table 4.4.

Table 4.4: Results of KMO and Bartletts testDependent Variable Kaiser-Meyer-Olkin (KMO)

measure of sampling adequacyBarttlet test of Sphericity

FIIP 0.621 481.844 (0.000*)FIIN 0.647 504.652 (0.000*)FIIPCAP 0.624 474.488 (0.000*)Figures in parentheses represent p -values. *denotes 1% level of significance

The results indicate that PCA can be applied to the set of independent variables as the

value of KMO statistics is greater than 0.5 and the B artlett test is significant in all three

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cases thereby leading to the rejection of null hypothesis that correlation matrix is

insignificant.

In all the three cases, four factors/components were extracted following Kaisers rule,

the Eigen values of all these factors was greater than 1. The total variation explained in

each of the three cases was 60.650%, 61.148% and 59.878% respectively .

In order to facilitate a better interpretation of factor loadings, we rotate the initial

component matrix using varimax r otation, thereby resulting in a Rotated Component

Matrix. The Rotated component matrix was then used to select the principal variables.

The cut-off point was taken to be 0.60 and negative values were ignored. The variable

with the highest loading factor o n each of the component was taken to be as a principal

variable and was used as an independent variable in the final regression.

Rotated Component Matrix for FIIP

The Rotated Component Matrix for FIIP is presented in the table 4.5. On the basis of

setting 0.60 as cut-off, the four principal variables that can explain FII inflows to India

are MSCI_RET, LIB, FIIPL and DIIP. The selection of last two variables has been

done on the basis of theoretical considerations.

Table 4.5: Rotated Component Matrix a for FIIPComponent

Variables 1 2 3 4S_P_RET 0.765MSCI_RET 0.907LIB 0.785BSE_RET 0.800BSE_RETLER_RET -0.665VOL -0.607CMR 0.706DIIP 0.673DWPI 0.797FIIPL 0.790Extraction Method: Principal Component Analys is.Rotation Method: Varimax with Kaiser Normalization.

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Extraction Method: Principal Component Analys is.Rotation Method: Varimax with Kaiser Normalization.a. Rotation converged in 6 iterations.

Rotated Component Matrix for FIIN

The Rotated Component Matrix for FIIN is p resented in the table 4.6. The four

variables that are selected are MSCI_RET, FIINL, DIIP and LIB.

Table 4.6: Rotated Component Matrix a in case of FIIN

Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.a. Rotation converged in 6 iterations.

Rotated Component Matrix for FIIPCAP

The Rotated Component Matrix for FII PCAP is presented in the table 4.7. The four

selected principal variables are MSCI_RET, FIIPCAPL, DWPI and BSE_RETL. The

variables, FIICAPL and BSE_RETL have been selected on the basis of theoretical

considerations.

Table 4.7: Rotated Component Matrix a in case of FIIPCAP

ComponentVariables 1 2 3 4S_P_RET 0.766MSCI_RET 0.908LIB 0.857BSE_RET 0.815BSE_RETL 0.791ER_RET -0.640VOLCMRDIIP 0.765DWPI -0.614FIINL 0.815

ComponentVariables 1 2 3 4S_P_RET 0.751MSCI_RET 0.909LIBBSE_RET 0.806BSE_RETL 0.634

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Extraction Method: Principal Component Analysis.Rotation Method: Varimax with Kaiser Normalization.a. Rotation converged in 6 iterations.

Thus, the variables selected as independent variables under Factor Analysis accounts

for the maximum variance in the data. The selected variables in all the three cases are

summarized in table 4.8.

Table 4.8: Principal Variables Selected after Factor AnalysisDependent variables

FIIP FIIN FIIPCAP

Independentvariables

MSCI_RET MSCI_RET MSCI_RETLIB FIINL FIIPCAPLFIIPL DIIP DWPIDIIP LIB BSE_RETL

4.4 Principal Variable Regression Results

After selecting the principal variables to be used in further regression, we again ran the

regressions taking the principal variables extracted in factor analysis and the two

dummy variables, namely D2 and D3. The regression results are presented in the table

4.9.

Table 4.9: Principal Variable Regression Results

Dependent variablesFIIP FIIN FIIPCAP

Independent variables Coefficients p-values Coefficients p-values Coefficients p-values

Constant -29.92675 0.9835 310.2478 0.6949 0.000406 0.2535MSCI_RET 239.9998 0.0041* 323.0616 0.0000* 4.81E-06 0.8068LIB 315.302 0.8997 188.5094 0.891 N.A. N.A.Lagged DependentVariable(FIIPL/FIINL/FIIPCAPL)

0.919211 0.0000* 0.25441 0.0000* 0.810874 0.0000*

ER_RET -0.673VOL 0.634CMR 0.686DIIPDWPI 0.771FIIPCAPL 0.738

20

DIIP 74.92327 0.0921*** 20.54843 0.3972 N.A. N.A.DWPI N.A. N.A. N.A. N.A 2.35E-05 0.8293BSE_RETL N.A. N.A. N.A. N.A 5.84E-05 0.0023*D2 1941.72 0.2599 784.3273 0.3821 0.00128 0.0071*D3 3619.616 0.1591 3379.987 0.0035* 0.001251 0.0302**ADJ. R2 0.892024 0.302995 0.792185Prob. (F-statistic) 0.000000 0.000000 0.000000

*denotes significance at 1% level of significance**denotes significance at 5% level of significance*** denotes significance at 10% level of significanceN.A. means not applicable

Regression Analysis for FIIP as the Dependent Variable

The regression results for FIIP as a dependent variable indicate that 89.20 per cent

variation in gross FII inflows is explained by the selected principal variables. The

estimated coefficient of MSCI_RET is positive and significa nt. Thus, the regional

factor represented by MSCI Emerging Market Index has come out to be an important

determinant of FII inflows. While we had expected a negative sign for the coefficient,

we get a positive sign. This could be due to the reason that FIIs look at emerging

markets as a whole and therefore an increase in the EM index wou ld mean that India

also receive greater FII inflows. However this positive association also suggests the

contagion effect in the sense that adverse changes in any other emerg ing economy

would also lead to an outflow of FII from Indian economy.

The one month lagged dependent variable turns out to be a significant determinant of

gross FII inflows. Further, we get a positive sign for the estimated coefficient which

can possibly be explained as herd behavior by FIIs. The FIIs are positively influenced

by the amount of investments in the preceding month . DIIP is positively associated to

gross FII inflows to India. This independent variable was found to be significant at 10%

level of significance. The significance of this variable may suggest that FII flows

follow a top-down approach while investing and are affected by the growth rate of the

21

country. Our results are in contradiction with the general belief that FIIs are only

interested in stock market returns and not least concerned about the growth rate of the

economy. Both the dummy variables are insignificant. Thus we can say that there was

no significant impact of both the crises on FII inflows to India.

Thus the principal variables explaining FII inflows to India are MSCI EM returns,

lagged values of FII inflows and the growth rate of India.

Regression Analysis for FIIN as the Dependent Variable

The regression results for FIIN as a dependent variable indicate that 30.30 per cent

variation in net FII inflows (inflows -outflows) is explained by the independent

variables. The coefficient of MSCI_RET is positively and significantly associated with

FII net inflows. The results are consistent with the results for FIIP as FIIP is one of th e

components of FIIN. One month lagged dependent variable is positive and significant .

DIIP which is acting as a measure of growth is insignificant for FII net inflows. This

can be explained by the fact that while investing in India, FIIs do consider the g rowth

of the economy but for withdrawing money from the Indian markets, they are not really

influenced by growth. Thus for net inflows which also incorporates the effect of FIIs

sales, the growth rate of India is an insignificant factor. While the dummy variable for

East Asian Financial Crisis is insignificant , the dummy variable for the global financial

crisis of 2008 has a significant impact on net FII inflows. A possible reason for this

could be that India had stricter capital controls in 1997 as compare d to other South-East

Asian economies and it was relatively less integrated with other economies of the

world. Consequently, FII movement was less and hence there were not sudden large

withdrawals by the FIIs from the Indian markets as was seen in case of other emerging

markets like Malaysia and Thailand. The crisis of 2008, however, marks a structural

break in the net FII inflows to India. This result is supported by the fact that capital

22

controls had eased in India by this time as compared to 1997. Hence, Indian markets

were affected more by the crisis of 2008 than the crisis that happened in 1997. There

were sudden withdrawals by the FIIs in 2008 indicating increasing linkages of the

Indian economy with the other economies in the world. Thus the principal variables

explaining net FII inflows to India are MSCI EM returns, lagged values of FII net

inflows. Also, the crisis of 2008 had a significant impact on the net FII inflows to India.

Regression Analysis for FIIPCAP as the Dependent Variable

The third set of regression analysis was conducted with FII inflows as a percentage of

market capitalization at BSE (FIIPCAP) as a dependent variable. This was primarily

done to gauge the relative importance of the FIIs in the Indian markets. The results

indicate that 79.22 per cent variation in dependent variable is expl ained by independent

variables. Returns on MSCI Emerging Market index is insignificant here but the returns

on BSE SENSEX with one month lag are positive and significant. The lagged

dependent variable is also positive and significant. Both the dummy variables are

significant. Thus the relative importance of the FII inflows in the Indian market was

significantly impacted by both the crisis.

5.0 Summary and Conclusions

This study aimed at identifying the determinants of FII inflows to India , with special

reference to financial crisis . We have incorporated two dummy variables in our study to

account for the East Asian financial crisis of 1997 and the global financial crisis of

2008 to examine if any structural break occurred in FII inflows due to these crises.

The most important factors affecting FII inflows to India were found to be the returns

on MSCI Emerging Market Index, past values of FII inflows and the growth rate of the

Indian economy. The East Asia n financial crisis of 1997 and the global financial crisis

of the year 2008 had no significant impact on the gross FII inflows to India though the

23

global financial crisis of the year 2008 had a significant impact on net FII inflows

which confirms our view that India was more affected by the crisis of 2008 than the

crisis of 1997. This was probably because of the fact that Indian economy was less

integrated with the other economies of the world in 1997 than in 2008. On the other

hand both the crises had a significant impact on FII inflows as a proportion of market

capitalisation.

Thus our results suggest that the FII inflows to India are in response to a combination

of both global as well as domestic factors. However, the tendency of return chasing and

herd behavior on part of FIIs makes the FII inflows coming to the Indian equity

markets volatile. These results have important implications for Indian policymakers.

The policymakers in India should try to develop some in -built cushions to protect the

economy from the ill effects resulting from volatile nature of FIIs. One such way could

be to encourage both the retail as well as domestic institutional investor to actively

participate in the Indian equity markets so as to broaden the investor base of the Indian

equity markets. Another way could be to discourage the speculative part of the FII

investment. Only then India would be able to fully reap the benefits arising out of

increasing foreign investment.

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Appendix

Table 1: Descriptive Statistics of Dependent and Independent VariablesVariables Mean Median Std. Dev. Skewness Kurtosis

S_P_RET 0.480271 1.103473 4.611877 -0.82497 4.358435MSCI_RET 0.293171 0.725651 7.348009 -1.12747 6.241315LIB -0.01929 0 0.24487 -2.43362 18.48055BSE_RET 0.661246 0.941215 7.641777 -0.28913 3.392817ER_RET 0.250824 0.049086 1.793185 0.685827 6.621019VOL 1.498578 1.3 0.714974 1.752654 7.312886CMR 0.015146 0.03 3.498632 -0.01733 21.7891IIP 205.3997 182.2 72.5657 0.656605 2.352937WPI 181.3606 171.6 49.06582 0.577103 2.375278FIIN 2085.965 633.5 5739.767 1.484582 8.177801FIIP 22956.05 7831.7 26695.84 1.152142 3.631151FIIPCAP 0.007918 0.008234 0.004545 0.190227 2.20638

28

Table 2: Correlations between Dependent and Independent Varia bles

Correlation

S_P_RET

MSCI_RET LIB

BSE_RET

ER_RET

VOL

CMR IIP

WPI

FIIN

FIIP FIIP

CAPS_P_RET 1MSCI_RET

0.747* 1

LIB 0.012 -0.066 1

BSE_RET

0.435* 0.655*

-

0.019 1

ER_RET

-

0.300*

-

0.458*0.117***

-

0.445* 1

VOL

-

0.243*

-

0.285*

-

0.194*

-

0.302*

0.145** 1

CMR-

0.018

-

0.116***

0.051

-

0.176**

-

0.021

-

0.010 1

IIP-

0.072 0.0425

-

0.065 0.023

-

0.0350.030

0.015 1

WPI-

0.100 0.0123

-

0.057 0.006

0.0123

0.038

0.007

0.982 1

FIIN0.302* 0.442*

-

0.019

0.455*

-

0.473*

-

0.285*

-

0.035

0.278*

0.244* 1

FIIP-

0.064 0.046

-

0.098 0.040

-

0.0720.109

0.0119

0.857*

0.833*

0.371* 1

FIIPCAP

-

0.152** 0.060

-

0.030 0.056

-

0.125***

0.197*

-

0.025

0.646*

0.655*

0.294*

0.726* 1

*denotes significance at 1% level of significance , **denotes significance at 5% level ofsignificance, ***denotes significance at 10% level of significance