Foreign Institutional Investment Flows and Indian Stock ... · need to encourage FII flows in a narrow and shallow stock market like that of India. There are conflicting theories

Foreign Institutional Investment Flows and Indian Stock Market

Returns � A Cause and Effect Relationship Study

By

DR. TANUPA CHAKRABORTY

Senior Lecturer

Department Of Commerce

University Of Calcutta

ADDRESS: FLAT NO. � 12 PHONE: (R) 2412-4973

23, CENTRAL ROAD (M) 9830175653

JADAVPUR E-MAIL: [email protected]

KOLKATA � 700 032 [email protected]

INDIA

Acknowledgement This research paper is originally published in �Indian Accounting Review�, Vol. 11, No. 1, June 2007, pp. 35-48.

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2

Foreign Institutional Investment Flows and Indian Stock Market

Returns � A Cause and Effect Relationship Study

Abstract

Foreign Institutional Investment (FII) flows, i.e., capital flows across national borders,

to emerging market economies (EMEs) have risen sharply over the past one and half decade

due to globalization and India is no exception in this regard. However, there is a lot of

apprehension regarding the volatile nature of such flows thereby raising questions about the

need to encourage FII flows in a narrow and shallow stock market like that of India.

There are conflicting theories on the issue of whether FII flows affect or are affected

by domestic stock market returns. So, the present empirical study has been undertaken to

throw some light on the direction of causality between FII flows and Indian stock market

returns using data on both the variables from over the period April 1997-March 2005.

I. INTRODUCTION

International portfolio flows, as are commonly known as Foreign Institutional

Investment (FII) flows, refer to capital flows made by individual and institutional investors

across national borders with a view to creating an internationally diversified portfolio. Unlike

Foreign Direct Investment (FDI) flows which refer to that category of international

investment aimed at obtaining a lasting interest by a resident entity in one economy in an

enterprise resident in another economy by way of exercising significant control over its

management, FII flows are not directed at acquiring management control over foreign

companies. FII flows were almost non-existent until 1980s. Global capital flows were

primarily characterized by syndicated bank loans in 1970s followed by FDI flows in 1980s.

But a strong trend towards globalization leading to widespread liberalization and

implementation of financial market reforms in many countries of the world had actually set

the pace for FII flows during 1990s. According to Bekaert and Harvey (2000), FII investment

as a proportion of a developing country's GDP increases substantially with liberalization as

such integration of domestic financial markets with the global markets permits free flow of




3

capital from 'capital-rich' to 'capital-scarce' countries in pursuit of higher rate of return and

increased productivity and efficiency of capital at global level.

Diversifying internationally i.e., holding a well-diversified portfolio of securities from

around the world in proportion to market capitalizations, irrespective of the investor's country

of residence, has long been advocated as the means to reduce overall portfolio risk and

maximize risk-adjusted returns by the classical capital asset pricing model (CAPM). But a

persistent 'home bias' (i.e., the tendency to hold a greater proportion of stocks from the home

country vis-a-vis the foreign country) was noticed in the portfolios of investors in capital-rich

industrialized countries in early 1990s. With more and more emerging market economies

(EMEs) 1 deregulating their financial markets by eliminating foreign exchange controls,

reducing taxes imposed on foreign investors, relaxing the restrictions on the purchase / sale of

securities by foreign investors in domestic markets etc., such 'home bias' has decreased over

the years. Today, EMEs, by virtue of their lower correlations in stock market returns with the

developed markets, offer greater scope to investors in developed countries to reduce their

overall portfolio risk and effectively enhance the portfolio performance and hence have

become the most preferred destinations for FII flows.

Several research studies on FII flows to EMEs over the world have highlighted that

financial market infrastructure such as the market size, market liquidity, trading costs, extent

of information dissemination etc., legal mechanisms relating to property rights etc.,

harmonization of corporate governance, accounting, listing and other rules with those

followed in developed markets, and strengthening of securities markets' enforcement are

important determinants of foreign portfolio investments into emerging markets. Of late, the

Securities and Exchange Board of India (SEBI) and Reserve Bank of India (RBI) have

initiated a string of measures like allowing overseas pension funds, mutual funds, investment

trusts, asset management companies, banks, institutional portfolio managers, university

funds, endowments, foundations or charitable trusts etc. but banning non-resident Indians

(NRIs) and overseas corporate bodies (OCBs) from trading as foreign portfolio investors,

raising the caps for FII from 24% to 49% of a non-bank company's issued capital subject to

sectoral caps / statutory ceiling as applicable, enhancing the individual investment limit from

1. A term coined in 1981 by Antoine W. van Agtmael of the International Finance Corporation of the World Bank, an emerging, or developing, market economy (EME) is an economy with low-to-middle per capita income that is in the transitional phase of moving from a closed to an open market economy by embarking on an economic reform program and building accountability within the system at the same time. EMEs constitute approximately 80% of the global population representing about 20% of the world's economies and include countries like Argentina, Brazil, Chile, China, Colombia, India, Hungary, Indonesia, Malaysia, Mexico, Korea, Pakistan, Poland etc.




4

5% to 10% of issued capital, permitting foreign investors to trade in Government securities

and derivatives, easing the norms for FII registration, reducing procedural delays, lowering

fees, mandating stricter disclosure norms, improved regulatory standards etc. with a view to

improving the scope, coverage and quality of FII flows into India. As a result, India, also

supported by her strong economic fundamentals, has become one of the attractive

destinations for FII flows in the emerging market space today. The expansionary effect of

various reform measures on FII flows over the years can be gauged from the fact that net (i.e.,

gross purchases minus gross sales) FII flows into India have risen sharply from Rs. 5126

crore in 1993-1994 2 to Rs. 46,215 crore in 2004-2005, with the number of foreign

institutional investors being registered with SEBI increasing from 3 in 1993-1994 to 685 in

2004-2005 (Source : SEBI website). This increasing dominance of foreign investors in Indian

market has necessitated research on the implications of FII flows for the Indian stock market

time and again.

Although FII flows help supplement the domestic savings and augment domestic

investments without increasing the foreign debt of the recipient countries, correct current

account deficits in the external balance of payments' position, reduce the required rate of

return for equity, and enhance stock prices of the host countries, yet there are worries about

the vulnerability of recipient countries' capital markets to such flows. FII flows, often referred

to as 'hot money' (i.e., short-term and overly speculative), are extremely volatile in character

compared to other forms of capital flows. Foreign portfolio investors are regarded as 'fair-

weather friends' who come in when there is money to be made and leave at the first sign of

impending trouble in the host country thereby destabilizing the domestic economy of the

recipient country. Often, they have been blamed for exacerbating small economic problems in

the host nation by making large and concerted withdrawals at the slightest hint of economic

weakness. It is also alleged that as they make frequent marginal adjustments to their

portfolios on the basis of a change in their perceptions of a country's solvency rather than

variations in underlying asset value, they tend to spread crisis even to countries with strong

fundamentals thereby causing 'contagion' in international financial markets (FitzGerald,

1999). Further, it is feared that too much of FII inflows may build up sizeable surpluses on a

country's balance of payments, create excess liquidity and hence exert upward pressure on the

exchange rate of the domestic currency or on domestic prices. The fear of foreigners

capturing a large part of the securities' market is also associated with FII flows. Accordingly,

2. FII flows into India began in January 1993 following the promulgation of Guidelines for Foreign Institutional Investment by the Government of India in September, 1992.




5

it is viewed that as securities markets in developing countries like India are narrow and

shallow and as the foreign investors have command over considerable funds and occupy a

dominant position in the capital market, FII flows have the potential for major capital flight

out of India driving the prices down sharply and hence inducing considerable instability in

the Indian stock market. The dangers of 'abrupt and sudden outflows' inherent with FII flows

have been highlighted in several research studies. Froot, O'Connell, and Seasholes (2001), in

their pioneering work on the Tequila crisis in Mexico (which began in December 1994 with

the devaluation of Mexican currency 'peso'), East Asian Crisis in July 1997 (triggered by

devaluation of Thai currency 'baht') and the Russian devaluation resulting into Long Term

Capital Management crisis in September, 1998, have found evidences of a strong negative

effect on global capital flows, especially to emerging markets. These crisis episodes have

made the Indian policy makers all the more wary about FII flows as questions have begun to

be raised about the wisdom in promoting such flows.

However, the issue of whether FII flows affect stock market returns or the other way

round is a matter of some controversy. It has been perceived in some quarters that FII flows

are the major drivers of stock markets in India and hence a sudden reversal of such flows may

harm the stability of its markets. Contrary to this belief, it is viewed by others that FII flows

react to the existing crisis in the stock market, possibly exacerbating it rather than causing it.

An analysis of the direction of causality to understand the possible devastating effect of

volatility of FII flows on the Indian economy is important from the viewpoint of Indian

policy makers especially when such flows have recorded a sharp rise over the last decade.

But, as very few studies have been done so far in this regard, the present empirical study has

been undertaken to throw some light on the cause and effect relationship between FII flows

and Indian stock market returns.

Accordingly, the remainder of the paper is organized as follows. The next section

deals with literature survey while the third section outlines the objective of the case study.

Section four elaborates the data source, hypothesis and research methodology. Section five

presents the findings of the case study. Finally, conclusions are drawn in section six.

II. LITERATURE SURVEY

The nature of relationship between FII flows and Indian stock market returns can be

explained in terms of 'cumulative informational disadvantage' of foreign portfolio investors

vis-à-vis local investors. The theory says that local investors possess greater knowledge about




6

Indian financial markets than foreign investors and this information asymmetry leads to

'positive feedback trading' by the foreign portfolio investors. Feedback trading or 'return-

chasing behaviour' refers to investors' reaction to recent changes in stock prices. A positive

feedback trading strategy leads to buy or (sell) decisions following a rise (or fall) in stock

prices and hence brings in more portfolio inflows into the market after a gain in market

values. The reverse behaviour of selling (or buying) while the stock prices are rising (or

falling) is termed as 'negative feedback trading'. Accordingly, it may be reasoned that local

investors, by virtue of their informational advantage, may trade in stocks in response to some

new information and cause a price change, and this price change may, in turn, lead to FII

flows due to positive feedback trading by foreign investors. Thus, the hypothesis of

informational disadvantage resulting into positive feedback trading suggests that Indian stock

market returns should lead flows.

The contrarian theory of flows affecting contemporaneous and future stock market

returns coexists. Froot, O'Connell and Seasholes (2001) have demonstrated that international

capital flows 'predict' i.e., lead price changes. They have found evidence that a one-basis

point shock to international portfolio flows results in a 40 basis point increase in equity

prices. The 'herding behaviour' of foreign investors is cited as the possible explanation for the

reverse direction of causality from FII flows to stock market returns. Herding refers to the

tendency of a majority of investors to follow each other and to either only buy or only sell at

the same time. With the incentive structure for fund managers being linked to their

performance relative to that of other funds, there is a great deal of incentive for a foreign

portfolio investor to suffer the consequences of being wrong when everyone is wrong, rather

than taking the risk of being wrong when some others are right. Thus, herding by foreign

investors may be quite rational in market place. But such herding can lead to stock prices

spiraling up (or down) in times of price-rise (or fall) and hence push the prices far away from

their fair values and overshoot the market equilibrium.

In 1990s, several research studies have explored the cause and effect relationship

between FII flows and domestic stock market returns but the results have been mixed in

nature. Tesar and Werner (1994,1995), Bohn and Tesar (1996), and Brennan and Cao (1997)

have examined the estimates of aggregate international portfolio flows on a quarterly basis

and found evidence of positive, contemporaneous correlation between FII inflows and stock

market returns. Jo (2002) has shown empirically tested instances where FII flows induce

greater volatility in markets compared to domestic investors while Bae et.al. (2002) have

proved that stocks traded by foreign investors experience higher volatility than those in which




7

such investors do not have much interest. On the contrary, Gordon and Gupta (2003) have

shown that lagged domestic stock market returns are an important determinant of FII flows.

Bekaert and Harvey (1998), and Errunza (2001) have found evidences that FII flows do not

have significant impact in increasing volatility of stock returns. In Indian context,

Chakrabarti (2001) has observed that foreign institutional investors do not appear to be at an

informational disadvantage compared to domestic investors in the Indian markets. Using a

monthly data-set for the period May 1993 to December 1999, he has found that FII net

inflows are not only correlated with the returns in Indian equity market but are more likely

the effect than the cause of the Indian equity market returns. Contrary to the general

perception of foreign investors' activities having a strong demonstration effect and driving the

domestic stock market in India, evidence from causality tests conducted by Mukherjee, Bose

and Coondoo (2002) suggests that FII flows to and from the Indian market tend to be caused

by returns in the domestic equity market and not the other way round. In a subsequent study,

Bose and Coondoo (2004) have found mild evidence of bi-directional causality between

returns on the BSE stock index and FII net inflows and reasoned that it may have been due to

heightened FII inflows caused by an upsurge in global equity markets.

III. OBJECTIVE OF THE STUDY

The discussion of Indian as well as international studies in the preceding

section suggests that the issue of direction of causality between FII flows and

domestic stock returns still remains unresolved. So, in order to provide an insight

into the sensitivity of FII flows to domestic market returns or otherwise, the empirical study

aims at determining whether FII flows to India are caused by or are the causes of national

stock market returns in the light of the above-mentioned research studies.

IV. DATA SOURCE, HYPOTHESES AND RESEARCH

METHODOLOGY

There have been quite a few episodes of volatility in the Indian stock market over the

past decade induced by several adverse exogenous developments like East Asian Crisis in

mid - 1997, imposition of economic sanctions subsequent to Pokhran Nuclear explosion in

May 1998, Kargil War in June 1999, Stock Market Scam of early 2001 and the Black




8

Monday of May 17, 2004 when the market was halted for the first time in the wake of a sharp

fall in the index. A sharp decline in FII flows coincided with the above events and this has

prompted the Indian policy makers to announce a number of changes in FII regulations like

enhancing the aggregate FII investment limit (in February 2001), permitting foreign investors

to trade in exchange traded derivatives (in December 2003) etc. in order to regenerate the

foreign investors� interests in the Indian capital market. So, to facilitate a better

understanding of the causal linkage between FII flows and contemporaneous stock market

returns, a period of eight consecutive financial years ranging from April 1997 to March 2005

is selected for the empirical study.

Monthly net FII flows (i.e., gross purchases ─ gross sales by foreign investors) into

the Indian equity market and monthly averages of BSE National Index constitute the two key

variables in the study. BSE National Index is a market capitalization- weighted index of

equity shares of 100 companies from the 'Specified' and 'Non-specified' list of the five stock

exchanges - Mumbai, Calcutta, Delhi, Ahmedabad and Madras - and its monthly values are

averages of daily closing indices. Since the market for equity shares is subject to much larger

fluctuations than the bond market, the emphasis is on equity market in the present study. Both

the secondary data for the relevant sample period are obtained from RBI website. However,

the statistical analysis of whether FII flows cause stock market returns or vice-versa is based

an two estimated variables - monthly net FII flows as a proportion of the preceding month's

BSE market capitalization and monthly returns on BSE National Index. As mentioned earlier,

BSE National Index is a representative market capitalization weighted index of five major

stock exchanges of the country and hence use of BSE National Index monthly returns as the

measure of Indian stock market returns in the case analysis appears justified. Since a

particular month�s market capitalization is an important determinant of domestic as well as

foreign portfolio investment decisions in the immediately succeeding month, the ratio of

monthly net FII flows to previous month�s market capitalization is used as FII flow measure

in the empirical study. Moreover, as BSE National Index values are derived with reference to

the base year 1983-1984, monthly net FII flows are calculated in relation to previous month's

market capitalization in order to make the dataset of the two variables comparable over the

sample period. BSE market capitalization data for the months over the sample period are also

obtained from RBI website. Month-wise returns on the BSE National Index are calculated on

continuous compounding basis - i.e., as the excess of the logarithm of the average index value

in a particular month over the logarithm of the average index value in the previous month.

Since the sample period is not quite long, it is assumed for convenience of exposition that the




9

observed time series is stationary and does not contain a trend and/or perceptible cyclical

component.

A histogram and descriptive statistics like maximum, minimum, mean, median,

standard deviation, skewness and kurtosis are shown for each of the two time series in order

to depict the trend in the two estimated variables over the sample period. Then correlations

between the two estimated variables' series, each of the estimated variables� series with its

own lag one series and between each of the estimated variables� series with the other

estimated variable's lag one series are determined to have a preliminary understanding of the

nature of relationship between stock market returns and FII flows. But since positive

correlations, if found, do not shed much light an the direction of causality, pair-wise Granger

causality tests are conducted between monthly net FII flows as a proportion of preceding

month's BSE market capitalization and monthly returns on BSE National Index. Before

applying Granger causality tests, monthly net FII flows as a proportion of the previous

month's BSE market capitalization are regressed on monthly returns on the BSE National

Index and vice-versa to examine the explanatory power of predictor variables and to find

evidence of autocorrelated residuals from the Durbin-Watson (D-W) statistic. The

regressions are run using the SPSS statistical software and the first order autocorrelation

parameters (i.e., a measure of correlation of the series of residuals with its own lag one series)

are estimated for use in the Granger causality tests, if the regression residuals are found to be

autocorrelated.

Granger causality test, developed in 1969 and popularized by Sims in 1972, involves

using F-tests to examine whether lagged information on a predictor (i.e., independent)

variable 'X' provides any statistically significant information about the response (i.e.,

dependent) variable 'Y' in the presence of lagged 'Y'. If not, then 'X does not Granger - cause

Y'. That is, autoregressive technique is used to estimate the following unrestricted (i.e.,

including all lagged terms) and restricted (i.e., not including lagged terms of exogenous

variable) equations in this test :-

Unrestricted equation :

Yt = Ct + 1

p

i i Yt-i +

1

p

i iX t-i + ut




10

Restricted equation :

Y/t = C/

t +

1

p

i i Yt-i + et

where, Yt , Y/t = dependent variables

Yt-i = lagged terms of dependent variable

X t-i = lagged terms of exogenous variable

Ct , C/t = constants i.e., intercepts measuring mean level of dependent variables.

iii = slope coefficients of independent variables where coefficient

corresponding to lagged terms of dependent variable indicate the

inertia of dependent variable and the coefficient associated with

lagged terms of exogenous variable measures the sensitivity of

dependent variable to variations in exogenous variable

p = autoregressive lag length

ut, et = residuals in regression estimation

Use of SPSS statistical software estimates the above two equations and yields the sum

of squared residuals of unrestricted equation (RSSUR ) and of restricted equation (RSSR)

where - RSSUR = 1

T

t ût

2 ; RSSR =

1

T

t êt

2

and T= total number of observations in the time series.

If the test statistic

(RSSR - RSSUR ) / p

RSSUR / (T-2p-1)

is greater than the specified critical F- value (i.e., Fp, T-2p-1), then the null hypothesis that 'X

does not Granger - cause Y' is rejected.

Since previous research studies have found that other than its own autoregressive

effect (i.e., dependence on its own lagged value), FII flows to India are also dependent on one

period lagged returns of the domestic stock market, use of Granger - causality tests to

determine causality in the present study appears justified. As the direction of causality is to be

tested, there are two null hypotheses in this case study ―

S =




11

Test -1

HO : Monthly BSE National Index Return does not Granger - cause monthly net FII flows as

a proportion of previous month's BSE market capitalization.

[In this autoregressive analysis, net FII flows / BSE market capitalization is the dependent

variable with its own lagged terms and lagged values of BSE National Index returns as the

two independent variables in unrestricted equation].

Test - 2

HO : Monthly net FII flows as a proportion of previous month's BSE market capitalization

does not Granger - cause monthly BSE National Index Return.

[Here, BSE National Index return is the dependent variable with its own lagged terms and

lagged values of net FII flows/ BSE market capitalization as the two independent variables in

unrestricted equation].

In the case analysis, one period lagged values are used and hence autoregressive lag length

(p) is taken as 1.

V. FINDINGS

The histogram and summary descriptive statistics of monthly net FII flows, monthly

net FII flows as a proportion of previous month's BSE market capitalization and monthly

returns on BSE National Index over a 8 - year window (April 1997- March 2005) are

presented in figures and tables below.




12

Figure (1) : Monthly Net FII Inflows (Rs. Crore) from April 1997 to March 2005

-4000

-2000

0

2000

4000

6000

8000

10000

July

Nov

embe

r

Mar

ch

July

Nov

embe

r

Mar

ch

July

Nov

embe

r

Mar

ch

July

Nov

embe

r

Mar

ch

July

Nov

embe

r

Mar

ch

July

Nov

embe

r

Mar

ch

July

Nov

embe

r

Mar

ch

July

Nov

embe

r

Mar

ch

Year/Months

Net

FII

Infl

ow

s

Figure (2) : Monthly net FII flows as a proportion of previous month's BSE market capitalisation from April

1997 to March 2005

-0.004

-0.002

0

0.002

0.004

0.006

0.008

June

O

ctobe

r

F

ebru

ary

June

O

ctobe

r

F

ebru

ary

June

O

ctobe

r

F

ebru

ary

June

O

ctobe

r

F

ebru

ary

June

O

ctobe

r

F

ebru

ary

June

O

ctobe

r

F

ebru

ary

June

O

ctobe

r

F

ebru

ary

June

O

ctobe

r

F

ebru

ary

Year/Months

FII

/ BS

E M

arke

t C

apit

alis

atio

n




13

Table (1) : Descriptive Statistics of monthly net FII flows as a proportion of previous month�s BSE market capitalization

from April 1997 to March 2005

Number of Observations Maximum

96 0.007437377

Minimum -0.002510294 Mean 0.001378058

Median 0.000949626 Standard Deviation 0.00189808

Skewness 0.854838891 Kurtosis 1.110623181

Figure (3) : Monthly Returns on BSE National Index from April 1997 to March 2005

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

May

A

ugus

t

Nov

embe

r

F

ebru

ary

May

A

ugus

t

Nov

embe

r

F

ebru

ary

May

A

ugus

t

Nov

embe

r

F

ebru

ary

May

A

ugus

t

Nov

embe

r

F

ebru

ary

May

A

ugus

t

Nov

embe

r

F

ebru

ary

May

A

ugus

t

Nov

embe

r

F

ebru

ary

May

A

ugus

t

Nov

embe

r

F

ebru

ary

May

A

ugus

t

Nov

embe

r

F

ebru

ary

Year/Months

BS

E N

atio

nal

Ind

ex R

etu

rn




14

Table (2) : Descriptive Statistics of monthly returns on BSE National Index from April 1997 to March 2005

It is clear from the above charts that monthly net FII flows as a proportion of previous

month's BSE market capitalization exhibit a near similar trend as that of monthly returns on

BSE National Index over the sample period. However, BSE National Index return series

show greater variability than net FII flows / BSE market capitalization series as indicated by

their respective standard deviations. Both the distributions are asymmetric with net FII flows/

BSE market capitalization being positively skewed while BSE index returns being negatively

skewed ones. Again, both the distributions are lepto - kurtic with net FII flows / BSE market

capitalization being more steep than BSE index return distribution.

The cross - correlations between net FII flows / BSE market capitalization and BSE

National Index returns and their lagged terms for the sample period are shown in Table (3)

below.

Number of Observations Maximum

96 0.080259724

Minimum -0.098931258 Mean 0.003580077

Median 0.004931805 Standard Deviation 0.033484812

Skewness Kurtosis

-0.542529569 0.423768839




15

Table (3) : Correlation Matrix

[Period of Study : April 1997 - March 2005]

Monthly returns on BSE National Index

Monthly returns on BSE National Index lagged one month

Monthly net FII flows as a proportion of previous month's BSE market capitalization lagged one month

Monthly net FII flows as a proportion of previous month's BSE market capitalization

0.294 0.152 0.455

Monthly net FII flows as a proportion of previous month's BSE market capitalization lagged one month

0.239 - 1

Monthly returns on BSE National Index lagged one

month

0.234 1 -

The above correlation coefficients suggest that there is a positive relationship between

the variables, though not quite significant in nature. The estimated variables also appear to be

positively correlated with its own lagged terms thereby indicating that past FII flows or BSE

index returns respectively affect contemporaneous flows or returns. However, a positive

correlation does not, in itself, imply causality. As both directions of causation are equally

plausible, autoregressions are run to test Granger causality between monthly net FII flows as

a proportion of preceding month�s BSE market capitalization and monthly BSE National

Index returns.

Regression of each of the estimated variables on the other is run using the SPSS

software in order to test for serial correlation among regression residuals from D-W statistic.

The regression results are summarized in table below.




16

Table (4) : Regression Results

Particulars Panel A [Regression of monthly net FII flows as a proportion of

previous month's BSE market capitalization on monthly BSE National

Index returns]

Panel B [Regression of monthly BSE

National Index returns on monthly net FII flows as a

proportion of previous month's BSE market capitalization]

Number of observations

Dependent variable

96

Monthly net FII flows /

previous month�s BSE

market capitalization

96

Monthly BSE National Index

returns

Independent variable Monthly BSE National Index

returns

Monthly net FII flows / previous

month�s BSE market capitalization

Unstandardized

coefficients :

Constant 0.001

(0.000)*

-0.004

(0.004)*

Independent variable 0.017

(0.006)*

5.181

(1.739)*

Adjusted R2 0.077 0.077

Durbin � Watson 1.190 1.646

*Figures in parentheses indicate standard errors of the coefficients

The above regression results indicate that both the regressors have the same

explanatory power as shown by their adjusted R2 values. Since the Durbin-Watson statistic is

close to 2 in Panel B, the regression in Panel B confirms to the absence of serial correlation

among regression residuals. But as Durbin-Watson in Panel A is smaller than 2, the lag 1

autocorrelation coefficient among regression residuals in Panel A is estimated at 0.389 using

SPSS software. This autocorrelation parameter is used to test Granger causality running from

returns to flows (i.e., Test � 1).

The next table presents the results of lag 1 autoregression and Granger causality tests

of the two null hypotheses.




17

Table (5) : Results of Autoregression and Granger Causality Tests

Null Hypotheses (HO)

Particulars Test � 1

[ Monthly BSE National

Index return does not

Granger � cause monthly net

FII flows as a proportion of

previous month's BSE

market capitalization]

Test � 2

[ Monthly net FII flows as a

proportion of previous month's

BSE market capitalization does

not Granger-cause monthly

BSE National Index return]

Unrestricted equation Yt = 0.00185 - 0.24527 Yt-1

-0.01139 Xt-1

Yt = - 0.0018 - 0.16388 Yt-1

+ 4.51231 Xt-1

Restricted equation

Y/t = 0.00052 + 0.633998 Yt-1

Y/t = 0.00285 + 0.250248 Yt-1

Sum of squared

residuals of unrestricted

equation (RSSUR)

0.00025

0.09538

Sum of squared

residuals of restricted

equation (RSSR)

0.00027

0.10044

Test - statistic under

Granger causality (S)

7.41*

4.88 **

* Since the test statistic under Test - 1 is greater than the critical value of F- statistic at both

1% and 5% levels of significance, Granger causality test rejects the null hypothesis that

returns do not cause FII flows at both the levels of significance.

** However, as the Test-2 statistic is greater than critical value of F-statistic only at 5% level

of significance, non-causality is rejected only at that level and accepted at 1% level of

significance.




18

Thus, the lag 1 test results using monthly observations over the period April 1997 �

March 2005 suggest that there is support for causality running from returns to flows and a

mild evidence of reversal of causality as FII flows causing returns are significant only at 5%

level. These results indicate that FII flows are more an effect than a cause of stock market

returns in India. Such a finding is suggestive of foreign investors� return-chasing behaviour

and hence supports the theory of �cumulative informational disadvantage� of foreign

investors vis-à-vis local investors in the Indian stock market. On the other hand, the

weakness of evidence of causality from FII flows to stock returns contradicts the age-old

perception that FII flows determine market returns in general, although �herding� effects,

particularly with local investors imitating foreign investors� moves, may well be present in

cases of individual stocks. Such a relationship, therefore, suggests that, given the thinness of

the Indian stock market and its evident susceptibility to manipulations, FII flows can, in fact,

aggravate the occurrence of equity market bubbles though they may not actually start them.

VI. CONCLUSIONS

The empirical investigation of the direction of causation between FII flows to India

and Indian stock market returns over the time period April 1997- March 2005 has thus

revealed that FII flows are caused by rather than causing the national stock market returns.

The slight evidence of a reversion of causality running from flows to returns as well has

policy implications because of the potential of FII flows to aggravate the crisis already set in

the stock market. But, given the ability of FII flows to augment the sources of funds in the

Indian capital markets, strengthen the market liquidity and efficiency, advocate modern ideas

in market design and sound corporate governance practices, and expose the Indian investors

to modern financial techniques and international best practices and systems, it can be

effectively argued that the role of foreign investors in developing and strengthening the

functioning of Indian capital markets cannot be underplayed. However, the Indian policy

makers must adopt a cautious approach while further liberalizing the FII policy by instituting

built-in-cushion within the system against the possible destabilizing effects of sudden

reversal of FII flows.

It may be noted that as information flows in financial markets drive both stock market

returns and investment flows, the test of causality between returns and FII flows can be

highly model-specific. In such a situation, the model-free approach of Granger causality

holds immense potential in detecting the direction of causality between FII flows and Indian




19

stock market returns. In view of the limitations of using monthly, instead of daily,

observations and of a shorter sample period, a more detailed study using daily data for a

longer period or, even, disaggregated data showing the transactions of individual foreign

investors at the stock level to help address questions regarding the extent of their herding or

return - chasing behaviour can be identified as potential areas for future research.

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_______________. (1995). " Home Bias and High Turnover", Journal of International

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Foreign Institutional Investment Flows and Indian Stock ... · need to encourage FII flows in a narrow and shallow stock market like that of India. There are conflicting theories