- 147 -
8 Determinants of
FIIs in Indian Stock Market
This chapter is designed to find out the factors that determine the flow of FIIs
to India. The nature and direction of causality between returns on Indian stock market
and FIIs investment flows have also been examined. This chapter is divided into eight
sections. Section I presents a brief background of the problem. Section II recaptures
the research methodology used here in. While descriptive statistics are given in
Section III, while Section IV outlines time series properties of data. Section V
presents the bi-variate analysis. Section VI gives the result of the multivariate analysis
while section VII describes the causality relationships and in the last section the
conclusion is given.
8.1 Background
The ongoing transition in the international financial markets is exhibiting
several features of which capital inflows are perhaps the most important. Emerging
economies have experienced massive capital inflows, which in some cases have
proved to be troublesome later. The question that arises is why do the capital flows
from foreign countries have brought in negative effects on the host countries?
Whether such situations occurred due to incompatible macro economic and exchange
rate policies or imprudent banking policy or lack of liquidity in the market. The
above-mentioned question is a subject of debate at the theoretical plane and issue of
future policy concern under country specific situations. Whatever be the reason, the
issue boils down to the fact whether such flows are properly paced and properly
sequenced such that the inflow of capital is not excessive relative to the maturity of
the system in which it must be absorbed; only then the capital flows can be sustained
and systemic stability ensured. As suggested by the Bhattacharya and Mukherjee
(2005), this may be attained through structural and operational realignment of the
domestic and financial sector variables of the economics exposed to global financial
- 148 -
network. It is in the context that the interlinkage among the FII flows, stock market
return and risk, exchange rate, interest rates prevailing in investing and host country
etc. needs to be addressed. On what factors foreign institutional investments depend is
a prime issue to be sorted out in this research beside others.
The present chapter focuses on this issue in the Indian context. In fact, from
among the whole gamut of institutional reforms undertaken in India since the 1990‟s,
gradual abolishment of capital flow barriers and foreign exchange restrictions,
adoption of more flexible exchange rate arrangements deserve a special attention at
these juncture to examine the determinants of FII flows. Some researchers made
attempts to identify the determinants of FII flows to India. Most of the existing studies
found that the equity return has a significant and positive impact on the FII
(Aggarwal, 1997; Chakrabarti, 2001; and Trivedi & Nair, 2003). But given the huge
volume of investments, foreign investors could play a role of market makers and book
their profits, i.e. they can buy financial assets when the prices are increasing (Gordon
and Gupta, 2003). Hence, there is a possibility of bi-directional relationship between
FII and the equity returns. Ahmad et al (2005) make a firm level analysis of FII‟s role
in the Indian equity market. At the aggregate level, FII investments and NSE Nifty
seem to have a strong bi-directional causality.
Asha C. Parsuna (2000) finds that mainly the return in the host country stock
market attract the FIIs investments, other factors are also creating impact on the arrival
of FIIs but they are statistically insignificant. Kumar (2001) has also shown the similar
result in his study. He inferred that FII flows do not respond to short-term changes or
technical position of the market and they are more driven by fundamentals. The study
finds that there is causality from FIIs to Sensex. This is in contradiction to Rai and
Bhanumurthy (2003) results using similar data but for a larger period. A study by
Panda (2005) also shows FII investments do not affect BSE Sensex. No clear causality
is found between FII and NSE Nifty. FIIs are found to follow positive feedback
strategy and to have return clustering tendency.
Following the Asian crisis and the brust of info-tech bubble internationally in
1998-99, the net FII investments have declined by US $ 61 million. But there was
not much effect on the equity returns. Chakrabarti (2001) has marked a regime shift
in the determinants of FII after Asian crisis. The study found that in the pre-Asian
crisis period any changes in FII found to have a positive impact on the equity returns.
- 149 -
But in the post-Asian crisis period a reverse relation was found, i.e. a change in FII
was mainly due to change in equity returns.
There are also a number of studies on exchange rate affecting stock prices
directly. Theory explained that a change in the exchange would affect a firm‟s foreign
operation and overall profits. This would, in turn, affect its stock prices. The nature of
the changes in stock prices would depend on multinational characteristics of the firm.
Conversely, a general downward movement of the stock market will motivate
investors to seek better returns elsewhere. This decreases the demand for money by
pushing interest rates down, causing further outflow of funds and hence depreciating
the currency. While the theoretical explanation was clear, empirical evidence was
mixed. Aggarwal (1981) found a significant positive correlation between the US dollar
and US stock prices while Soenen and Hennigan (1988) reported a significant negative
relationship. Soenen and Agarwal (1989) found mixed results among industrial
countries. Ma and Kao (1990) attributed the differences in results to the nature of the
countries i.e. whether the countries were export or import dominant. Morley and
Pentecost (2000), in their study on G-7 countries, argue that the reason for the lack of
strong relationship between exchange rate and stock prices may be due to the
exchange controls that were in effect in the 1980s.
Among the more recent studies Narayan and Smith (2005) in their study on
exchange rates and stock prices in South Asia found that exchange rate Granger cause
stock prices in India both in the long run and short run. Venkateshwarlu and Tiwari
(2005) analyzed bi-variate causality between stock prices and exchange rates.
Bhattacharya and Mukherjee (2006) investigate the nature of the causal relationship of
FIIs with stock return and exchange rate in India by applying co-integration and long
term Granger Causality test and find a bi-directional causality between stock return
and FIIs investments. But no causal relationship between exchange rates and net
investments by FIIs investments is found.
After going through the existing studies on the subject under reference of this
chapter, we are in a position to note some gaps in them. Firstly, the period under
reference of these research studies to relatively shorter. Secondly, the number of the
independent variables considered for examining their linkage with FIIs has remained
limited. Thirdly, the multiple regression model was applied without verifying the
properties of the times series data such as stationarity, autocorrelation etc. Lastly,
- 150 -
majority of the authors have proceeded with only one dependent variable i.e. either
purchase or sale or net investment by the FIIs. The present study is an improvement
over the earlier studies in several ways. First, it is a comprehensive study, as it
considers as many as 24 independent variables. Second, the period under reference is
comparatively longer than the earlier studies. Third, well-established model of
examining causality (i.e. Granger Cause) has been applied. Fourth, times series
properties of the data were examined prior to the application of regression models.
Lastly, as day-to-day variation in FII flows and market return is likely to contain
relatively large random components, one would not expect high correlation between
FII flows and market return. We therefore tried to explore whether the FII flow
variables would show any stronger dependence on market return if daily variation
were filtered through moving average smoothing.
Scope of the study is restricted to India because it is an appropriate case for
conducting such a study as portfolio investment has become the dominant path of
foreign investment in Indian economy. India liberalized its financial market and
allowed FIIs to participate in their domestic markets in 1992. The opening up of the
market resulted in a number of positive effects. First, the stock exchanges had to
improve the quality of their trading and settlement procedures in line with the best
practices of the world. Second, the transparency and information flows improved on
account of the entry of FIIs in India. However, people are also guessing negative
effects in the form of potential destabilization because of the bulk buying and selling
activity of FIIs. The period of the second-generation economic reform i.e. March
1999 to December 2006 is taken as reference period.
8.2 Assortment of Variable and Research Methodology
Dependent Variables: The present study consider six dependent variables namely:
FIIs sales (FIIS), purchases (FIIP), net investment (FIIN) and 7 days moving averages
of each of them denoted as FIIS_MA, FIIP_MA and FIIN_MA respectively. The logic
of taking these variables is as follows: Rationally, global investors would continuously
adjust investment portfolio round the clock using available market information and
thereby tracking the returns on all possible markets. The trading behaviour of these
investors can be classified into two categories: (I) Momentum or Positive Feedback
- 151 -
Trading and (II) Herding strategy. In the case of the Momentum Trading or Feedback
Trading, the investors have a tendency to buy and sell stocks based on their observed
return records i.e. to buy recent winners and sell recent losers. In case of Herding
strategy all investors behave in a similar manner and take decision by observing the
behaviour of other investors. To capture these behavioural patterns the investor‟s
action may be aggregated and summarized into two basic measures: (I) Sale and (II)
Purchase. Hence, we have chosen to examine the nature of FIIs flows to India in terms
of three variables: FIIs sales, FII purchases and FIIs net investment. Further, as the
time series data have been taken on the daily basis so to remove the effect of day to
day variation 7 days moving average of the above mentioned variables have also been
taken as dependent variables. Thus, in total 6 dependent variables are taken.
Explanatory Variables: The independent variables considered for this study includes:
(i) Stock return in the host country;
(ii) Stock return on the major investing country i.e. US;
(iii) Stock return in emerging countries represented by Morgan Stanley Capital
International World Index (MSCI);
(iv) One day Lag return of host country and investing country markets;
(v) Moving average of 7 days return of all index (host and Investing
countries);
(vi) Risk at host country and home country markets;
(vii) Exchange rate of US $ v/s Indian Rupee;
(viii) Federal Bank 3 months treasury bills interest rates;
(ix) Growth rate of Indian economy represented by the index of industrial
production (IIP);
(x) One day lag investment of foreign institutional investors;
(xi) Differential return between host country and investing country markets:
and
(xii) Link relation of host country and investing country market represented by
their Beta.
- 152 -
Methodology: Before making the data analysis, the time series properties of various
data series were identified. More specifically, the stationarity problem of both
dependent and independent variables were examined. For verifying time series
properties of data, we used the Augmented Dickey-Fuller Test (ADF) for checking the
unit root of the major variables. The following form of ADF regression equation was
used:
Yt = 1 + 2t+ Yt-1 + i m
i=1 Yt-i+ t----------------------------------------(8.1)
Where t is a pure white noise error term and Yt-1 additional lagged term, included
with an idea of ensuring that the errors are uncorrelated. 1, 2, , are the coefficients
where is the first difference operator, which is equal to (p-1), estimated to test the
null hypothesis that = 0. If is equal to zero it mean there is a Unit Root which
implies non-stationarity in the series under consideration. If a series is stationary at
level, it is also integrated of zero order and a series stationary at Ist difference is
integrated of 1st order and so on.
As the use of differenced variables instead of original ones may sometimes
result in the serious loss of long run information, it is essential to keep the long run
information on the variables and to avoid the problem of spurious regression. These
two problems have to be avoided simultaneously. For this purpose possible co-
integration between the variables has to be checked. To find out the co-integration
between variables, Augmented Engle-Granger Test (AEG) test was conducted. For this
very first we had to estimate co-integration regression using variables having the same
order integration. The co-integration equation by the OLS method is as follows:
Yt = a0 + a1X1 + a2X2 + anXn + Zt----------------------------------------(8.2)
The residuals (Zt) from the co-integration regression are subject to the test
stationary by applying Augmented Dickey Fuller unit root test based on the following
equation:
(ADF) Zt = 1 + 2t+ Zt-1 + i m
i=1 Zt-i+ t-----------------------------------
(8.3)
If the Z proved stationary, it means that calculated co-integration regression is
not spurious
- 153 -
After verifying the time series properties, we specify the proper regression
equation because if the regression equations are under or overstated then the results
may not be correct. To specify the proper regression equations data mining technique
was used. In this technique, first we used the bi-varaiate form of the OLS and find out
the respective regression coefficients, R-square values and Durbin-Watson values of
all the 24 independent variables with each of the 6 dependent variables. Only those
variables were used for the final multiple regression analysis which were significantly
associated with the particular dependent variable. The results of the bi-variate form of
OLS applied to examine the significance of various independent variables as
determinants of FII flows in India are given in Table 8.5.
After bi-variate analysis, we conducted multivariate analysis so as to study the
relationship between the various dependent variables with explanatory variables over
the period 1999-2007. For the purpose of multiple regression analysis we made
separate specification of the model for each of the dependent variables (i.e. FIIS, FIIP,
FIIN, FIIS_MA, FIIP_MA and FIIN_MA) with the appropriate independent variables
[decided as per data mining technique].
This analysis has carried out in two stages. In the first stage we took only
those variables as regressors, which are selected by data mining technique (as
specified in the equations 8.6 to 8.11). In the second stage to improve R2 and to make
the data free from the autocorrelation we introduced one more independent variable in
each model. That is FIIN_MA, FIIS_MA and FIIP_MA in case of FIIN, FIIS and
FIIP respectively while in case of other three variables it was their own one day lag
values. The results of (both the stages) multivariate form of OLS applied to examine
the significance of various independent variables as determinants of the FII
investments in India are given in Table 8.6 and 8.7.
In the last section of this chapter, the results of Granger causality test are
given. This test was used to determine causal relationship amongst the dependent and
independent variables under reference. Granger Causality Test is a bi-variate analysis
and involves estimates X(Y X) and Y(X Y) by using following pair of regressions:
Yt = 0 + n
i=1 i Xt-i + n
i=1 i Yt-i + 1t---------------------------------(8.4)
Xt = 0 + n
i=1 i Yt-i + n
i=1 i Xt-i + 2t---------------------------------(8.5)
- 154 -
In the above equations Yt, Xt are the variables to be tested and i, i, i i are
coefficients explaining the relation of dependent variable with the lag terms of
independent variable and lag terms of dependent variable in itself. 1t, 2t are mutually
uncorrelated white noise errors. t is the time period and i is the number of lags. The
null hypothesis is i,= I = 0. If the i is statistically significant but I is not then it
means X causes Y, in the reverse case Y causes X. But if both are significant then
causality runs both ways. We have taken the 2 lags as it is prescribed by the various
researchers that 2 lags are sufficient to explain causality.
8.3 Descriptive Statistics
To begin with let us glance through the summary measures exhibited in Table
8.1 and 8.2 concerning various dependent and independent variables, respectively. It
is evident from the former table that the average daily net FII inflows in India are Rs.
98.51 crore with a standard deviation of Rs. 316.22 crore, while average daily
purchases and sales are Rs. 656.53 and Rs. 552. 29 crore respectively with standard
deviation of Rs. 703.64 in the former and Rs. 627.53 in case of the later. It shows
greater fluctuation in purchases in comparison with sales. All the three dependent
variables are positively skewed and leptokurtic in nature. Thus, the FII investments
are not normally distribution, rather they are positively skewed.
TABLE 8.1: SUMMARY STATISTICS OF DEPENDENT VARIABLES
(1999-2007)
(Amt. Rs. Crore)
Dependent Variables
Variable FIIN FIIP FIIS
Mean 98.51 656.53 552.29
Median 51.20 361.90 285.40
Max. 3319.60 5457.60 5427.40
Min. -2824.70 9.90 0.00
Std.Dev. 316.22 703.64 627.54
Skewness 1.31 2.01 2.32
Kurtosis 25.74 7.94 10.14
Jarque-Bera 42187.09 3244.25 5789.49
- 155 -
(P-Value) (0.00) (0.00) (0.00)
Table 8.2 provides that, both mean and standard deviation of the daily return in
case of Indian stock markets (BSE and NSE) are found higher than those of the US
market index (S&P) as well as the emerging market index (MSCI). It means that
Indian stock market has performed better than the US and other emerging markets over
the recent years in terms of return. However the risk involved is higher in the former
than the later. While the return series are negatively skewed in case of BSE and NSE
(Indian Markets), these series are positively skewed in case of S&P and MSCI. Table
8.2 further shows that, on an average, the exchange rate of Indian currency against US
dollar is Rs.45.176. During the study period it was maximum 48.65 and minimum
39.33. As regard the kurtosis, table shows that except the FBIR and IIP series all
posses leptokurtic distribution. As per the result of Jarque-Bera statistics, each variable
under the study turned non-normal.
TABLE 8.2: SUMMARY STATISTICS OF INDEPENDENT VARIABLES
(1999-2007)
Independent Variables
Summary
Measures
BSE Sensex
Return
(%)
NSE
Return (%)
MSCI
Return
(%)
S & P
Return
(%)
FBIR US_EX IIP
Mean 0.08 0.08 0.03 0.02 3.18 45.71 187.05
Median 0.16 0.16 0.03 0.05 3.33 45.75 180.00
Max. 8.25 8.30 6.73 5.73 6.24 48.65 253.60
Min. -11.14 -12.24 -5.31 -4.92 0.80 39.33 144.40
Std.Dev. 1.58 1.56 91 1.0 1.74 1.91 28.58
Skewness -0.3868 -0.4295 0.2697 0.16 0.12 1.85 0.56
Kurtosis 6.7398 7.8178 7.6028 5.08 1.53 28.30 2.23
Jarque-Bera 1177.06 1932.88 1733.34 359.55 177.97 52777.01 151.65
(P-Value) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
8.4 Time Series Properties of Data
Table 8.3 which provides the result of ADF test indicates clearly that that all
the dependent and independent variables are stationary at level except Federal Bank
- 156 -
Interest rate and Industrial Production Index which are stationary at Ist difference
and to make them integrated on the level we used differencing technique. The
differencing technique might result in loss of long run information which is
otherwise essential to return. To deal with the problem Augmented Engle Granger
Test was used. This test first estimate the co-integration regression using variables
having the same order integration. The co-integration was estimated using OLS
method. The stationarity of the residual of the co-integration regression was verified
by the use of ADF unit root test. The result of AEG and ADF are given in table 8.4.
It is obvious from this table that error term of the OLS regression explaining the net
FII investments (FIIN) by selected major independent variable (such as NSE and
MSCI return, risk, FBIR and IIP etc.) is significantly stationary. This implies that
the various series are co-integrated.
TABLE 8.3: RESULTS OF THE AUGMENTED DICKEY FULLER UNIT
ROOT TEST
Variable Constant, Without Trend Constant, With Trend
BSE -25.7218*, Level --25.78205*, Level
FIIN -15.8364*, Level -16.34354*, Level
FBIR -25.80197*, Ist Difference -26.01480*, Ist Difference
MSCI -25.85145*, Level -25.87004*, Level
NSE -25.6853*, Level -25.7309*, Level
S & P -27.67409*, Level -27.68075*, Level
US_EX -4.49117*, Level -4.481535*, Level
IIP -25.3460*, Ist Difference -25.35231*, Ist Difference
FIIS -5.658116* Level -9.012926* Level
FIIP -6.351345* Level -10.33795* Level
*Hypothesis rejected at 1% significance level
TABLE 8.4: RESULTS OF AUGMENTED ENGLE-GRANGER
TEST OF CO-INTEGRATION
ADF Test Statistic -21.92628 1% Critical Value* -3.4368
5% Critical Value -2.8636
- 157 -
8.5 Bi-Variate Analysis
As mentioned in the research methodology, in order to identify the
independent variables, which deserve to be included in the multiple regression
technique, the data mining technique was used. For this, bi-variate form of OLS was
used. More specifically, only those variables are considered for the multiple
regression models, which show significant relationship with the dependent variables.
Table 8.5 consists of the result of bi-variate form of OLS.
TABLE 8.5: REGRESSION COEFFICIENT AND OTHER STATISTICS
RESULTING FROM BI-VARIATE ANALYSIS
Dependent Variables
Independent Variables
(1)
FIIN
(2)
FIIN_MA
(3)
FIIS
(4)
FIIS_MA
(5)
FIIP
(6)
FIIP_MA
(7)
BSE Return Coefficient
(P-Value)
1097.86*
(.016)
1674.421*
(.00)
196.403
(.830)
113.004
(.891)
1254.41
(.220)
1513.759
(0.099)
R2
Adjusted R2
S.D
DW
F Value
.003
.002
315.8300
1.278
5.784
.018
.018
192.314
.124
36.314
.000
.000
627.6954
309
627.6954
.000
.001
.019
.372
.019
.001
.000
703.5544
.372
1.503
.001
.001
636.5429
.15
2.723
BSE_MA Coefficient 11092.336* 9342.632* 613.900 753.28 10043.27* 9194.46*
10% Critical Value -2.5679
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation
Method: Least Squares
Variable Coefficient Std. Error t-Statistic Prob.
LAG(-1) -0.921115 0.042010 -21.92628 0.0000
D(LAG(-1)) -0.190892 0.034278 -5.568990 0.0000
D(LAG(-2)) -0.141279 0.022979 -6.148240 0.0000
C -0.138156 6.233266 -0.022164 0.9823
R-squared 0.563334 Mean dependent var -0.347746
Adjusted R-squared 0.562638 S.D. dependent var 409.3216
S.E. of regression 270.6980 Akaike info criterion 14.04200
Sum squared resid 1.38E+08 Schwarz criterion 14.05376
Log likelihood -13237.61 F-statistic 809.3113
Durbin-Watson stat 2.020812 Prob(F-statistic) 0.000000
- 158 -
(P-Value) (.00) (0.0) (.791) (.721) (00) (0.0)
R2
Adjusted R2
S.D
DW
F Value
.047
.047
307.9105
1.332
95.316
.088
.088
185.8476
.124
186.216
.000
.000
623.0570
.314
.070
.000
.000
573.5035
.009
.128
.008
.007
700.2507
.370
14.904
.008
.007
634.4510
.015
15.483
Lagged BSE Return Coeff.
(P-Value)
3840.874*
(.00)
1730.858*
(.00)
32.303
(.972)
64.522
(.938) 2650.424*
(.012)
1657.326
(.071)
R2
Adjusted R2
S.D
DW
F Value
.037
.036
310.4841
1.311
74.040
.020
.019
192.6812
.131
38.841
.000
-.001
627.8015
.309
.001
.000
-.001
573.5216
.009
.006
.003
.003
702.7936
.374
6.263
.002
.001
636.4538
.016
3.264
BSE Return Volatility Coeff.
(P-Value)
-7197.466*
(.00)
-7280.704*
(.00)
114.349
(.956)
-968.965
(.607) -11708.2*
(.00)
-12662.7*
(.00)
R2
Adjusted R2
S.D
DW
F Value
.025
.024
313.3609
1.293
48.918
.068
.067
188.3673
.100
138.869
.000
-.001
628.9148
.310
.003
.000
-.001
574.2313
.009
.265
.013
.013
700.3709
.376
25.504
.019
.019
631.557
013
37.368
NSE Return Coefficient
(P-Value)
972.094*
(.036)
1674.323*
(.00)
404.267
(.663)
146.562
(.861)
1275.874
(.219)
1517.470
(.103)
R2
Adjusted R2
S.D
DW
F Value
.002
.002
313.3609
1.293
43.918
.018
.017
192.8602
.124
35.191
.000
.000
627.6717
.309
.191
.000
-.001
573.5180
.009
.030
.001
.000
703.5229
.372
1.511
.001
.001
636.5544
.015
2.653
NSE_MA Coefficient
(P-Value)
11.41.690*
(.00)
9332.683*
(0.0)
728.060
(.756)
739.129
(.730) 9935.040*
(.00)
9163.094*
(0.0)
R2
Adjusted R2
S.D
DW
F Value
.046
.045
308.8388
1.338
91.829
.086
.085
186.1056
.123
180.357
.000
.000
623.0528
.314
.096
.000
.000
573.5047
.009
.120
.007
.007
700.3580
.370
14.205
.008
.007
634.5333
.015
14.978
Lagged NSE Return Coeff.
(P-Value)
3781.190*
(.00)
1786.314*
(.00)
-14.256
(.988)
99.831
(.905) 2443.094*
(.018)
1679.365
(.072)
R2
Adjusted R2
S.D
DW
F Value
.035
.034
310.7556
1.308
69.469
.020
.020
192.6176
.130
40.141
.000
-.001
627.8017
.309
.000
.000
-.001
573.5205
.009
.014
.003
.002
702.9189
.374
702.9189
.011
.010
634.3131
.013
20.405
NSE Return Volatility Coeff.
(P-Value)
-6881.910*
(.00)
-6968.443*
(.00)
2612.480
(.213)
1844.175
(.331) 8683.420*
(.00)
9464.073*
(.00)
R2
Adjusted R2
S.D
DW
F Value
.022
.022
313.7556
1.102
1.416
.061
.061
189.0254
.099
124.571
.001
.000
628.6588
.009
1.550
.000
.000
574.1296
.009
.946
.007
.007
702.5264
.373
13.712
.011
.010
634.3131
.013
20.405
MSCI Return Coefficient
(P-Value)
-938.814
(.234)
577.221
(.234)
1522.864
(.335)
1509.085
(.291)
565.404
(.749)
1979.229
(.213)
R2
Adjusted R2
S.D
DW
F Value
.001
.000
316.1867
1.258
1.416
.001
.000
194.5401
.091
1.415
.000
.000
627.5502
.310
.932
.001
.000
573.3571
.010
1.113
.000
.000
703.8119
.370
.102
.001
.000
636.7358
.014
1.553
Lagged MSCI Return Coeff.
(P-Value)
345.173
(.662)
663.077
(.172)
1401.896
(.372)
1322.918
(.355)
2158.801
(.220)
1949.707
(.219)
R2
Adjusted R2
.000
.000
.000
.000
.000
.000
.000
.000
.001
.000
.001
.000
- 159 -
S.D
DW
F Value
316.3679
1.259
.191
194.5172
.093
1.870
627.6710
.310
.797
573.3953
.010
.857
703.6670
.371
1.540
636.7431
.014
1.509
MSCI_MA Coefficient
(P-Value)
4119.270*
(.049)
2985.306*
(.021)
11735.28*
(.005)
10594.66*
(.005)
15343.75*
(.001)
13009.13*
(.002)
R2
Adjusted R2
S.D
DW
F Value
.002
.001
315.1231
1.276
3.891
.003
.002
194.3408
.92
5.377
.004
.004
621.7594
.316
8.039
.004
.004
572.3654
.04
7.807
.006
.005
700.7993
.368
10.812
.005
.004
635.4209
.014
9.551
MSCI Return Volatility Coeff.
(P-Value)
-9422.803*
(.00)
-9784.286*
(.00)
-61124.2*
(.00)
-60122.8*
(.00)
-72662.4*
(.00)
-72363.6*
(.00)
R2
Adjusted R2
S.D
DW
F Value
.013
.013
315.1933
1.276
26.127
.038
.038
191.299
.367
310.716
.144
.144
581.7272
.37
32.379
.162
.162
645.2719
.450
368.198
.196
.196
571.7218
.024
468.065
.135
.135
514.369
.028
478.035
S & P 500 Return Coefficient
(P-Value)
-324.179
(.616)
745.502
(.061)
743.040
(.565)
667.639
(.569)
668.729
(.644)
1205.141
(.355)
R2
Adjusted R2
S.D
DW
F Value
.000
.000
316.2821
1.258
.251
.002
.001
194.4343
.093
3.516
.000
.000
627.6485
.310
.332
.000
.000
573.4744
.010
.324-
.000
.000
703.7913
.317
.214
.000
.000
636.8507
.013
.856
S & P_MA 500 Return Coeff.
(P-Value)
6361.99*
(.001)
4935.861*
(.00)
6692.181
(.073)
5825.854
(.088) 11424.82*
(.007)
9778.225*
(.010)
R2
Adjusted R2
S.D
DW
F Value
.001
.000
316.2972
1.262
1.055
.002
.002
194.3687
.096
4.821
.000
.000
627.7681
.309
.205
.000
.000
573.4886
.010
.229
.001
.000
703.7635
.371
.979
.000
.000
636.8364
.014
.940
Lagged S & P Return Coeff.
(P-Value)
663.920
(.305) 872.312*
(.028)
585.454
(.651)
580.571
(.633)
1433.312
(.323)
1262.233
(.332)
R2
Adjusted R2
S.D
DW
F Value
.006
.005
314.5018
1.272
11.519
.009
.009
193.6939
.095
18.318
.002
.001
622.5437
.315
3.217
.002
.001
573.0897
.010
2.915
.004
.003
701.6051
.368
7.381
.003
.002
635. 8936
.014
6.670
S & P Return Volatility Coeff.
(P-Value)
-11032.1*
(.00)
-11182.8*
(.00)
-62329.5*
(.00)
-61620.9*
(.00)
-76515.4*
(.00)
-76164.5*
(.00)
R2
Adjusted R2
S.D
DW
F Value
.026
.026
313.1299
1.293
51.820
.071
.071
187.9698
.101
147.577
.214
.213
557.6749
.403
516.167
.250
.250
497.2324
.24
640.371
.256
.256
608.0437
.508
654.325
.310
.310
529.6443
.033
862.246
Exchange Rate Coefficient
(P-Value)
-14.699*
(.00)
-14.853*
(.00)
-81.839*
(.00)
-80.137*
(.00)
-100.593*
(.00)
-98.357*
(.00)
R2
Adjusted R2
S.D
DW
F Value
.008
.007
315.0505
1.270
15.380
.021
.021
192.5305
.097
41.923
.062
.062
607.9289
.344
126.472
.071
.071
522.6991
.026
148.092
.075
.074
677.0991
.415
154.031
.087
.087
608.6305
.034
183.967
FBIR Coefficient
(3MTB) (P-Value)
-10.812*
(.009)
-10.402*
(.00)
67.579*
(.00)
65.643*
(.00)
57.980*
(.00)
57.155*
(.00)
R2
Adjusted R2
S.D
DW
.004
.003
315.7412
1.263
.009
.008
193.7645
.092
.035
.035
316.5424
.321
.040
.039
561.9872
.010
.021
.020
696.5312
.377
.024
.024
629.1493
.013
- 160 -
F Value 6.874 16.899 69.884 80.002 40.305 48.392
IIP Coefficient
(P-Value)
2.144*
(.00)
2.247*
(.00)
16.928*
(.00)
16.819*
(.00)
19.550*
(.00)
19.621*
(.00)
R2
Adjusted R2
S.D
DW
F Value
.037
.037
310.3917
1.310
75.166
.108
.108
183.7941
.102
234.110
.595
.595
399.3286
.772
2418.823
.698
.698
315.2068
.045
4458.098
.632
.632
427.2453
1.018
3279.459
.770
.770
305.4745
.071
6459.703
Lagged Investment Coefficient
(P-Value)
.445*
(.00)
.398*
(.00)
.160*
(.00)
.144*
(.00)
.515*
(.00)
.534*
(.00)
R2
Adjusted R2
S.D
DW
F Value
.198
.197
283.6665
.225
474.911
.414
.414
149.0851
1.040
1361.752
.007
.006
626.3820
.313
12.570
.006
.006
572.3013
.081
12.137
.054
.054
685.3819
.529
108.953
.070
.069
614.9565
.106
144.236
Deferential Return 1 Coeff.
(BSE- S & P500) (P-Value)
895.189*
(.020)
928.172*
(.00)
-124.578
(.872)
-155.725
(.823)
655.134
(.449)
651.232
(.400)
R2
Adjusted R2
S.D
DW
F Value
.003
.002
315.8606
1.274
5.409
.008
.007
193.8328
.108
15.529
.000
-.001
627.6987
.309
.026
.000
.000
573.5157
.009
.050
.000
.000
703.5250
.370
.574
.000
.000
636.8752
.013
.708
Deferential Return 2 Coeff.
(BSE- MSCI) (P-Value)
1066.985*
(.007)
11117.062*
(.00)
-233.511
(.768)
-292.879
(.683)
806.782
(.564)
644.309
(.419)
R2
Adjusted R2
S.D
DW
F Value
.001
.000
318.0102
1.262
1.168
.011
.010
193.5464
.113
21.208
.000
.000
327.6888
.309
.087
.000
.000
573.4978
.010
.167
.000
.000
703.6792
.370
.823
.000
.000
636.8841
.013
.654
Beta_MSCI Coefficient
(P-Value)
1.210*
(.00)
1.359*
(.00)
1.495*
(.00)
1.461*
(.00)
1.670*
(.00)
1.679*
(.00)
R2
Adjusted R2
S.D
DW
F Value
.007
.007
317.3311
1.268
13.452
.023
.022
193.4266
.094
44.819
.275
.275
536.3765
.432
716.036
.308
.308
478.5412
.034
846.715
.273
.273
601.8127
.525
710.096
.330
.330
522.4681
.041
938.255
Beta_S&P 500 Coefficient
(P-Value)
2.996
(.280)
1.764
(.300)
9.414
(.087) 1.325*
(.008)
2.529*
(.00)
1.576*
(.005)
R2
Adjusted R2
S.D
DW
F Value
.001
.000
318.0102
1.262
1.168
.001
.000
195.1193
.093
1.076
.002
.001
628.8434
.319
2.941
.004
.003
574.355
.018
7.012
.009
.008
703.6537
.372
17.004
.004
.004
637.7951
.021
8.041
Note: 1) Figures in brackets are P values that show the significance of value of t- test.
2) * Significant at a level of 5 percent
3) DW is the Durbin-Watson Value
This table presents the values of regression coefficients, coefficients of
determination (R2) and adjusted R
2 values, Standard deviation and Durbin-Watson
value. The coefficient of determination (R2) indicates the percentage of the total
variance in the dependent variable, explained by the independent variables. It is
noteworthy from the table that the values of the coefficients of determination (R2)
resulting from the various regression equations are very low irrespective of the
- 161 -
nature of the variable. The highest explanatory power amongst the various
independent variables, works out in case of lagged investment by FIIs (41.4%),
followed with wide difference, in down side, by Industrial Production Index (10.8%),
7 days moving average of BSE Sensex return (8.8%), moving average of NSE Nifty
return (8.6%), and volatility of BSE return (6.8%). Though the above-mentioned
values of R2
are obtained when moving average of net investments by FIIs (i.e.
FIIN_MA) is taken as the dependent variables for fitting the OLS equations, the
aforesaid phenomenon holds good in case of all of the dependent variables with a
few exceptions. Thus, the results make indication of the dependency of FII flows
primarily on past investments by FIIs, industrial growth in the host country, stock
market return and volatility.
Table 8.5 further shows that the regression coefficients concerning BSE
Return, BSE_MA, Lagged BSE Return, NSE Return, NSE_MA, Lagged NSE Return
and Differential Return 1 and 2 are positive and statistically significant at 5 percent
level of significance, insofar as the dependent variable FIIN and its seven days
moving average (FIIN_MA) are concerned. The next revelation of the table is that
both BSE_MA as well as NSE_MA have obtained positive and statistically
significant regression coefficient in case of the regression equations having FIIN,
FIIN_MA, FIIP and FIIP_MA as the dependent variables. Similarly, lagged BSE
Return and Lagged NSE Return have established positive relation with three
dependent variables namely FIIN, FIIN_MA and FIIP. Thus net inflows as well as
gross purchases of securities are dependent on both current and past information
regarding stock returns in Indian capital market.
Regarding the relationship between the return in the foreign stock markets and
FIIs flows to India, it may be seen from the table under reference that the return has a
negative relationship with FIIN, but positive relationship with rest of the dependent
variables. However, the regression coefficients pertaining to the two variables MSCI
return and S&P return are found insignificant irrespective of the dependent variable. It
is also noteworthy that seven days moving average series of the MSCI and S&P 500
is found having positive and significant values of regression coefficient in case of four
dependent variables FIIN, FIIN_MA, FIIP and FIIP_MA.
The volatility in return of both domestic as well as foreign stock markets
affects negatively the FII flows to Indian stock market. To be precise, volatility in
- 162 -
return of BSE, NSE, MSCI and S&P 500 have obtained negative values of regression
coefficient against majority of the dependent variables. Moreover, these coefficients
are significant at 1 percent level. The effect of Beta, measures of co- movement of
domestic and foreign market in both cases (S&P and MSCI) have obtained positive
values. While the coefficient of BETA_MSCI turned significant at 1 percent against
each of the model specifications, BETA_S&P is found significant in case of only three
dependent variables namely, FIIS_MA, FIIP and FIIP_MA.
Regarding the effect of the various macro-economic factors taken as
determinants of FIIs, table shows that Exchange Rate of Indian Rupee with US Dollar
has a significant negative relationship with each and every dependent variable
representing foreign portfolio investment in India. In contrast, Federal Bank Interest
Rate has significant positive relationship with four of the dependent variables namely,
FIIS, FIIS_MA, FIIP and FIIP_MA. The Federal Interest Rate has a significant
negative relationship with FIIN and FIIN_MA. As expected, FII flows to India are
found having positive relationship with growth of Indian economy represented by
industrial production index.
Based on the above mentioned bi-variate analysis we established the following
multiple regression models:
FIIN = (Constant, BSE, BSE_MA, L_BSE, R_BSE, NSE, NSE_MA, L_NSE,
R_NSE, MSCI_MA, R_MSCI, S&P_MA, R_S&P, US_EX, FBIR, IIP, L_FIIN,
D_RET1, D_RET2, BETA_MSCI, Error term-------(8.6)
FIIN_MA = (Constant, BSE, BSE_MA, L_BSE, R_BSE, NSE, NSE_MA, L_NSE,
R_NSE, MSCI_MA, R_MSCI, S&P_MA, L_S&P, R_S&P, US_EX, FBIR, IIP,
L_FIIN, D_RET1,D_RET2, BETA_MSCI, Error term-------(8.7)
FIIS = (Constant, MSCI_MA, R_MSCI, R_S&P, US_EX, FBIR, IIP, L_FIIN,
BETA_MSCI, Error term-------(8.8)
FIIS_MA = (Constant, MSCI_MA, R_MSCI, R_S&P, US_EX, FBIR, IIP, L_FIIN,
BETA_MSCI, BETA_S&P Error term-------(8.9)
FIIP = (Constant, BSE_MA, L_BSE, R_BSE, NSE_MA, L_NSE, R_NSE,
MSCI_MA, R_MSCI, S&P_MA, R_S&P, US_EX, FBIR, IIP, L_FIIN, BETA_MSCI,
BETA_S&P Error term-------(8.10)
- 163 -
FIIP_MA = (Constant, BSE_MA, R_BSE, NSE_MA, R_NSE, MSCI_MA, R_MSCI,
S&P_MA, R_S&P, US_EX, FBIR, IIP, L_FIIN, BETA_MSCI, BETA_S&P Error term----
(8.11)
The description of various equations is available in Chapter III, (Research
Methodology).
8.6 Results of Multiple Regression Model
It is clearly shown by Table 8.6 that the coefficients of determination (R2), and
adjusted value of R2
in case of stage I of the model vary from 0.24 in case of FIIN to
as high as 0.52 when the moving average of the purchase is taken as dependent
variable. It means the finally selected explanatory variables explain 24 to 52 percent
of the variation in various dependent variables. Hence, if taken jointly, these variables
are the important determinants of foreign portfolio investment on the Indian bourses.
What is interesting to note is the R2 and adjusted R
2 obtained at stage 2. Both of the
model (Table 8.7) indicates the extent of variation in dependent variables have got
substantially higher values in comparison to those at stage I of the model. At the stage
II, these indicators have obtained largest value (i.e. 0.988) in the case of the sixth
specification of the model and lowest value is 0.44 in case of first model. The values
of Durbin-Watson statistics pointed out absence of autocorrelation in various models
as they are close to 2.
Regarding the relationship between the returns offered by the Indian stock
market and FII flows, we might observe from the table that these variables are
positively associated. The regression coefficient of both NSE and BSE return are
having positive relationship with net investment by FIIs in India. The relationship is
judged significant at 10 percent level of significance. Similar to the study carried out
by Bose, Suchismita (2002) we found that Lagged BSE return as well as moving
average of NSE return are having positive relationship with net foreign portfolio
investments. And the same is found significant at 5 percent level. It implies that the
net foreign investment is significantly affected by the current and past information
regarding the Indian market return. Higher the return higher would be the net FII
inflows to India. Lagged BSE return is also positively correlated with gross purchases
by foreign institutional investors. In contrast 7 days moving average of BSE‟s return
- 164 -
(BSE_MA) was seen negatively associated with gross purchase and its moving
average by FIIs at Indian bourses.
TABLE 8.6: REGRESSION COEFFICIENTS AND OTHER STATISTICS
RESULTS FROM MULTIVARIATE ANALYSIS
(First Stage)
Dependent
Variables
Independent
Variables(1)
FIIN
(2)
FIIN_MA
(3)
FIIS
(4)
FIIS_MA
(5)
FIIP
(6)
FIIP_MA
(7)
Constant 435.54*
(.014)
437.86*
(.000)
1504.10*
(.000)
2916.40*
(.000)
1687.09*
(.000)
1659.76*
(.000)
BSE Return 2542.41**
(.071)
1555.29*
(.000)
BSE_MA -4218.1**
(.073)
-3412.7**
(.057)
Lagged BSE
Return
2943.61*
(.000)
1636.881**
(.062)
BSE Return
Volatility
-3817.21*
(.000)
-51430.83*
(.000)
-52215.1*
(.000)
NSE Return 2451.33**
(086)
NSE_MA 2754.59*
(033)
3246.14*
(.000)
Lagged NSE
Return
670.909*
(.003)
NSE Return
Volatility
-3237.27*
(.001)
50472.83*
(.000)
50551.34*
(.000)
MSCI Return
Lagged MSCI
Return
MSCI_MA 9481.82*
(.007)
8280.548*
(.005)
MSCI Return
Volatility
3370.57*
(.040)
14575.2*
(.018)
24541.41*
(.000)
23427.81*
(.000)
24262.24*
(.000)
- 165 -
S & P 500
Return
S & P_MA 500
Return
3023.461*
(.009)
14863.183*
(.000)
14003.21*
(.000)
Lagged S & P
500 Return
568.96**
(.052)
S & P 500
Return
Volatility
-4088.16*
(.009)
-5878.8*
(.000)
48564.5*
(.000)
-42127.3*
(.000)
-58974.68*
(.000)
-59783.7*
(.000)
Exchange Rate
(Rs V/s US $)
-6.396**
(.099)
-6.498*
(.002)
17.075*
(.016)
-47.107*
(.000)
-18.955*
(.017)
-18.178*
(.006)
Federal Bank
Interest Rate
(3MTB)
-88.366*
(.000)
Industrial
Production
Process
8.165*
(.007)
-10.740**
(.064)
Lagged
Investment by
FIIs
0218*
(.000)
353*
(.000)
-.09438*
(.003)
.310*
(.000)
.307*
(.000)
Deferential
Return 1
(BSE- S &
P500)
-680.98*
(.020)
Deferential
Return 2
(BSE- MSCI)
Beta_MSCI .000345*
(.034)
.0113*
(.000)
.01652*
(.000)
.01312*
(.000)
.01320*
(.000)
Beta_S&P 500 .000076**
(.059)
.0000183*
(.000)
.0000852*
(.028)
R-Squared .246 .495 .355 .442 .435 .522
Adjusted R-
Squared
.239 .490 .353 .440 .430 .519
S.E.of
Regression
277.0898 139.8983 503.3723 431.0069 533.2597 443.5306
- 166 -
Durbin-Watson
Stat.
2.228 1.195 1.496 1.153 1.770 1.193
*Significant at 5%
** Significant as 10 %
Figure in parenthesis are p-value of the regression coefficients.
Regarding the relationship between the return in the foreign stock markets and
FII flows to India, it may be seen from the table under reference that the return in
emerging markets based on MSCI and S&P 500 indicators were excluded by
backward elimination alternative of stepwise model. The above also holds true about
lagged MSCI returns. The reason of their exclusion may be the multiple co- linearity
brought in by these variables. However, 7 days moving average of MSCI‟s return and
S&P 500 returns was included in the final regressors. The former (i.e. MSCI_MA)
amongs the above-motioned variables is positively associated with gross sales and
moving average of sales. The coefficients are found significant at 1 percent level of
significance. Moving average of S&P 500 return (S&P_MA) is having positive and
significant relationship with FIIN, FIIP and FIIP_MA. Hence, grouped information
about foreign return affects FIIs. Coondoo, Dipankor (2002) find more or less similar
results about the investing country market return and its moving average relation with
FIIN, FIIP and FIIS and their moving averages.
The volatility of stock returns at host country market is having found its
negative affect on 7 days moving average of net investments, gross purchases and
moving average of gross purchases. The above relationship is found significant at one
percent level. Interestingly the same holds true in case of S&P 500 return volatility.
However, MSCI return volatility is found positively associated with each of the
dependent variables except net investment by FIIs. It means more the volatility in the
emerging markets more will be the flows to foreign funds to India.
The analysis is further provided that the purchases, sales, moving average of
purchases and sales do have positive relation with Beta of MSCI as well as Beta of
S&P 500 return series. While the coefficient of BETA_MSCI turned significant at 5
percent against all the model speciation except the FIIN, BETA_S&P is found
significant only in case of only two dependent variables namely FIIP and FIIP_MA.
- 167 -
The positive association of beta values with purchases by the foreign investors implies
that FIIs want to reap the benefits of portfolio diversification by switching over the
markets when risk profile of the markets get changed. It is found that the differential
return of BSE and S&P are found positively related with FIIN_MA. While past
information regarding the FIIs Investment is found having statistically significant
positive relationship with all the dependent variable except FIIS.
Regarding the macro-economic factors taken as determinants of FIIs, table
shows that exchange rate of Indian rupee with US dollar has a significant negative
relationship with each and every dependent variable except the FIIS. This is quite
contrast to the study given by Mukherjee, Paramita (2002), who found no significant
relation of exchange rate with any of the six dependent variables. The FIIS showed
positive association. In contrast the Federal Bank interest rate (FBIR) has significant
negative relation with FIIS_MA. As expected, FII flows to India are found
significantly related with growth of Indian economy represented by Index of
Industrial Production (IIP).
As stated previously, the multiple regression analysis was also conducted is
stage two where in the moving average of the basic variables was an additional
independent variable and for seven days moving average their one day lag was taken
as additional variable. The results of second stage model are given in Table 8.7.
It is obvious from the table that now the value R2 has risen considerably in
case of each of the six models under appreciation. While R2is found the lowest
(0.447) in case of FIIN, it was the highest in case of 0.988. Hence, the explanatory
power of the models have increased significantly. Similarly to stage one results,
lagged BSE Return, NSE‟s Return, FIIN_MA turned significance in case of model
one (i.e. FIIN as dependent variable). The regression coefficient of BSE‟s return,
NSE_MA, lagged FIIN_MA turned as significant. Hence, these are the determinants
of FIIN_MA. While FIIS is dependent only on FIIS_MA. MSCI‟s volatility and its
beta are found determinants of FIIS_MA. FIIP is caused by lagged BSE return,
Beta_S&P 500and FIIP_MA.
- 168 -
TABLE 8.7: REGRESSION COEFFICIENTS AND OTHER STATISTICS
RESULTS FROM MULTIVARIATE ANALYSIS
(Second Stage)
Dependent
Variables
Independent
Variables(1)
FIIN
(2)
FIIN_MA
(3)
FIIS
(4)
FIIS_MA
(5)
FIIP
(6)
FIIP_MA
(7)
Constant -51.229
(.740)
-19.387
(.624)
16.55
(.913)
52.15
(.214)
8.433
(.966)
26.774
(.586)
BSE Return 361.268*
(.015)
BSE_MA 916.475*
(.001)
Lagged BSE
Return
2289.45*
(.000)
1011.434*
(.031)
NSE Return 2699.77*
(.025)
NSE_MA 1786.489*
(.000)
MSCI Return
Volatility
1265.818**
(.062)
S & P 500
Return
Volatility
-1451.304*
(.016)
Deferential
Return 1
(BSE- S &
P500)
-311.649*
(.009)
Beta_MSCI .000249*
(.024)
.000193*
(.048)
Beta_S&P 500 .000000971*
(.000)
- 169 -
FIIN_MA 1.034*
(.000)
FIIS_MA 1.000*
(.000)
FIIP_MA 1.007*
(.000)
1 Lag
FIIN_MA
.927*
(.000)
1 Lag
FIIS_MA
.991*
(.000)
1 Lag
FIIP_MA
.985*
(.000)
R-Squared .447 .915 .852 .991 .832 .988
Adjusted R-
Squared
.444 .914 .852 .991 .832 .988
S.E.of
Regression
236.1591 57.2738 239.7555 55.6346 288.8921 71.0380
Durbin-Watson 2.263 1.659 2.167 1.710 2.162 2.122
*Significant at 5%
** Significant as 10 %
Figure in parenthesis are p-value of the regression coefficients
The first stage of daily FII flows indicated that these were stationary in nature
(i.e., contained no significant time trend but were auto-correlated). Except the first
equation this auto-correlation got reflected in all the regression equation estimated to
find out statistically significant covariates of various of the measures of FII flows.
This means none of the covariates-be it related to equity market performance or to the
performance of Indian economy could explain singly or jointly observed auto-
correlation of the FII flows. Use of lagged values of the concerned FII flow variable
as a regressor, however, removed the auto-correlation altogether. But inclusion of the
lagged value of FII flow variable in most of the cases caused the erstwhile significant
determinants to turn non-significant. As a result, the only statistically satisfactory
regression results turned out to be the ones having market return and lagged value of
the concerned FII flow. Such regression results would have economic explanation in
terms some kind of dynamic adjustment mechanism being involved in the
determination of current daily values a given FII flow. In other words, these results
- 170 -
may be taken to mean that for individual FII flow there is a desired level determined
solely by BSE return or some variant of it and the actual value constantly tries to
reach this desired level.
8.7 Causal Relationship
After identifying the determinants of FIIs purchases, sales and net
investments and the moving average of their values, we made an attempt to
investigate whether there is any casual relationship between various dependent and
independent variables under reference of the study. For this objective Granger
Casualty Test was used. Table 8.8 shows the result of this test. The table indicates
that the hypothesis „FIIN does not Granger causes BSE returns‟ is accepted. In
contrast, its corresponding hypothesis „BSE return does not Granger causes FIIN‟
gets rejected at 5 percent level of significance. It refers that net foreign portfolio
investment are influenced by the stock market return of the host country. But the
vice-versa is not found true. The above result confirms to that found by Chakrabarti
(2001). Lagged return (one day lag) is also found causing net foreign investment
significantly. Thus the host country return is the strongest force that attracts foreign
institutional investors to India. The above result is also supported by the study
carried out by the Mukherjee, Paramita et al. (2002).
The table further reveals that seven days moving average of the NSE return
showed bi-directional causality with FIIN which implies that net investment is
attracted by current and past information about the return. Risk in the host country is
also caused by the foreign institutional investments. Further, risk in the investing
country (US Market) affects the quantum of foreign investment in host country.
Index of industrial production has a bi-directional causality with net foreign
institutional investments. It implies that the industrial growth of a country attracts the
foreign investments and later on these investments lead to further development in the
host country economy. Similar to the study of Bhattacharya and Mukherjee (2005),
this work found no causal relation between exchange rate and foreign portfolio
investments.
171
TABLE 8.8: RESULTS OF GRANGER CAUSALITY TEST (with FIIN)
H0 / Lags 2 3 4 5 6 7
BSE does not Granger Cause FIIN
FIIN does not Granger Cause BSE
47.1021*
2.2314
31.5781*
2.46645
23.4463 *
1.45525
18.8483*
1.38726
15.8335*
1.074
13.5569*
1.26521
L_BSE does not Granger Cause FIIN
FIIN does not Granger Cause L_BSE
10.9746*
2.71584
8.58891*
2.86813*
6.44354*
1.83620
5.18584*
1.87128
4.32176*
1.67609
3.77838*
1.33846
NSE does not Granger Cause FIIN
FIIN does not Granger Cause NSE
45.8000*
2.83612
30.5540*
1.86114
22.9999*
0.92290
18.4615*
0.95504
15.4936*
0.80272
13.2155*
0.89740
NSE_MA does not Granger Cause FIIN
FIIN does not Granger Cause NSE_MA
22.6492*
5.78518*
16.0593*
6.16215*
12.2312*
5.79970*
12.4918*
5.58126*
10.6547*
4.69646*
9.59090*
4.97615*
R_NSE does not Granger Cause FIIN
FIIN does not Granger Cause R_NSE
6.17590*
7.63659*
2.36998
5.28720*
1.43693
4.13413*
1.16284
3.58659*
1.02523
3.21135*
0.86375
2.72261*
R_S&P does not Granger Cause FIIN
FIIN does not Granger Cause R_S&P
7.84588*
1.33067
3.41252*
2.54626
2.19745
2.85686*
1.86850
2.29138*
1.78131
1.93221*
1.38975
1.67961
S&P_MA does not Granger Cause FIIN
FIIN does not Granger Cause S&P_MA
1.67575*
2.55236*
1.69683*
2.69358*
1.76736*
2.15457*
1.39057*
1.70162*
1.73627*
1.44859*
1.62171*
1.42282*
US_EX does not Granger Cause FIIN
FIIN does not Granger Cause US_EX
1.92866
1.54418
0.88674
1.40103
0.63133
1.54481
0.94387
1.67121
0.83979
1.83839
0.71730
2.01256
IIP does not Granger Cause FIIN
FIIN does not Granger Cause IIP
12.5275*
3.76028*
6.01352*
3.27252*
4.21989*
2.88887*
3.17777*
2.60935*
2.81033*
2.27366*
2.13818*
2.15229*
L_FIIN does not Granger Cause FIIN
FIIN does not Granger Cause L_FIIN
21.9383*
6675.75*
2.53136*
5147.56*
3.48017*
4433.48*
1.58851*
3596.49*
3.49175*
3156.90*
4.58444*
3067.16*
F-Values are given in table * H0 Rejected at 5% Level of Significance
172
After describing the results of Granger Casualty test with reference to the net
investment by the FIIs, let us now present the results of this test in case of gross sales.
Table 8.9 reveals that sale of securities by FIIs has turned as a cause of seven days
moving average of return of BSE as well as NSE. FIIS shows a bi-directional
relationship with interest rate of three months Treasury bills issued by Federal Bank
(FBIR) and Beta of MSCI with BSE, which implies the co-movement in both markets.
Sale has also been found as a cause of index of industrial production.
TABLE 8.9: RESULTS OF GRANGER CAUSALITY TEST (with FIIS)
H0 Value of F-Static at lag 2
BSE_MA does not Granger Cause FIIS
FIIS does not Granger Cause BSE_MA
0.16210
4.18233*
NSE_MA does not Granger Cause FIIS
FIIS does not Granger Cause NSE_MA
0.97739
3.28154*
R_NSE does not Granger Cause FIIS
FIIS does not Granger Cause R_NSE
1.70421
1.24028
US_EX does not Granger Cause FIIS
FIIS does not Granger Cause US_EX
2.84845
1.52264
FBIR does not Granger Cause FIIS
FIIS does not Granger Cause FBIR
4.03831*
5.32049*
IIP does not Granger Cause FIIS
FIIS does not Granger Cause IIP
67.1341*
1.26211
L_FIIN does not Granger Cause FIIS
FIIS does not Granger Cause L_FIIN
0.75498
0.69146
BETA_MSCI does not Granger Cause FIIS
FIIS does not Granger Cause BETA_MSCI
13.5005*
4.96754*
* Rejected at a significance level of 5 percent
Besides net investments and gross sales the bi-variate Granger Causality Test was
also applied to the gross purchase done by FIIs in the Indian stock market. The results of
the same are shown in table 8.10. It is clearly shown by the table that purchases done by
the foreign institutional investors in Indian stock market are having cause and effect
173
relationship with Federal Bank Interest Rate (FBIR), Index of Industrial Production (IIP),
one day lagged investment made by the FIIs in Indian market and Beta of MSCI with
Indian Stock Market (BSE). But gross purchases by FIIs were found a cause of Beta of
S&P and exchange rate of Indian rupess v/s US dollar.
TABLE 8.10: RESULTS OF GRANGER CAUSALITY TEST (with FIIS)
H0 Value of F-Static at
lag 2
L_BSE does not Granger Cause FIIP
FIIP does not Granger Cause L_BSE
2.76832
0.96979
BSE_MA does not Granger Cause FIIP
FIIP does not Granger Cause BSE_MA
1.76902
1.02979
NSE_MA does not Granger Cause FIIP
FIIP does not Granger Cause NSE_MA
1.77779
0.48288
R_NSE does not Granger Cause FIIP
FIIP does not Granger Cause R_NSE
2.19904
1.43212
FBIR does not Granger Cause FIIP
FIIP does not Granger Cause FBIR
3.97360*
9.80895*
IIP does not Granger Cause FIIP
FIIP does not Granger Cause IIP
110.924*
4.49611*
L_FIIN does not Granger Cause FIIP
FIIP does not Granger Cause L_FIIN
52.5724*
359.707*
BETA_MSCI does not Granger Cause FIIP
FIIP does not Granger Cause BETA_MSCI
20.2460*
4.57112*
BETA_SP does not Granger Cause FIIP
FIIP does not Granger Cause BETA_SP
4.71766*
1.84593
US_EX does not Granger Cause FIIP
FIIP does not Granger Cause US_EX
3.31516*
2.36270
* Rejected at a significance level of 5 percent
174
8.8 Conclusion
The results suggest that though FII flows to India are significantly affected by
return in the domestic equity market, the later is not influenced by variation in these
flows. It is also found that apart from the return in domestic market there are other
covariates of such flows. While the dependence of the net FII flows on daily return in the
domestic equity market- at a day‟s lag, to be more specific – is suggestive of foreign
investors return chasing behavior. Their decision seems to get affected also by the recent
history of the market return and its volatility in international and domestic market as well.
We also found that the set of factors affecting FII Sales and purchases were not the same.
It appeared that some factors would affect purchase or sale decision of foreign investors,
but not the corresponding net FII flows. For example, while FII purchase and sale to the
Indian stock market appeared to be sensitive to the volatility of the emerging market
returns (both purchase and sale responding positively to the risk in emerging markets),
but corresponding net inflows shows no association with that. One more interesting fact
we have found out that the foreign institutional investors reaped the benefits of portfolio
diversification by investing in Indian market as the betas of the BSE Sensex with respect
to the MSCI world and S&P 500 indices turned positively associated with FIIs purchase.
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175
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