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THE NATURE OF THE CAUSAL RELATIONSHIP BETWEEN FII AND
MACROECONOMIC AGGREGATES: AN EMPIRICAL ANALYSIS
Dr Gurcharan Singh,
Professor, School of Management Studies,
Punjabi University, Patiala.
Abstract
Foreign Institutional Investors are crowd - puller to magnetize the foreign investment in
India. The main objective of the study is to examine the causal relationship between FII and
macroeconomic aggregates namely BSE-SENSEX, Inflation rate (IR), Crude Oil Prices (CR)
and Exchange rate (EXR). The study is based on the secondary data which has been taken
from April 2000 to March 2013. The monthly average data has been considered for the study
including 156 observations. Econometric regression analysis indicated that Indian Stock
Market (SENSEX) and Exchange rate (EXR) had a significant and positive impact on FII.
Furthermore, As far as Granger causality relationship is concerned, a unidirectional
causality or one way causality is found from FII towards SENSEX and Exchange rate. It
leads to acceptance of null hypothesis at 5% level of significance. The study further
concluded that there is no relationship subsists between other indicators like inflation rate
and crude oil in both econometric analyses. However, FIIs inflows by and large are
determined by the performance of stock market (SENSEX) and other macroeconomic
aggregates in India
Keywords: FII, Macroeconomic Aggregates, Descriptive Statistics, Augmented Dickey
Fuller Test, Econometric Regression Analysis and Granger Causality Test.
INTRODUCTION
Stock market have become a key driver of modern market based economy and is one
of the major sources of raising resources for Indian corporate, thereby enabling financial
development and macroeconomic growth. In fact, Indian stock market is one of the emerging
markets in the world. Thus, an organised and well regulated stock market provides liquidity
to shares, ensures safety and a fair dealing in selling and buying of securities and helps in
monitoring of firms in the process of collection and use of funds by them (Agarwal, 2000).
With rapid changes in the economy because of liberal economic policies and fast pace
changes due to globalisation, Indian market has become a focus point for foreign investors.
Organisations tend to target for large volume of trade in this era of globalisation. Trade flows
are indeed one of the most visible aspects of globalization. FII refers to the investment made
by resident of one country in the financial capital and asset of another country (Shrivastav,
2013). Now a day, a significant portion of Indian corporate sector’s securities is held by
Foreign Institutional Investors, such as pension funds, mutual funds and insurance
companies. These investors are often viewed as sophisticated investors as these institutional
investors are better informed and better equipped to process information than individual
investors (Han and Wang, 2004). Consequently, policymakers have become increasingly
concerned about the factors determining international investment, the performance of foreign
capital investments, and the impact of foreign investment on local turnover and on the
volatility of stock prices (Tesar and Werner, 1995). FII inflows on domestic stock market are
important from government as well as investor point of view. The opening up of the market
for FII, increased speculation in the market makes the market more volatile and more
vulnerable to foreign shocks (Li, 2002). The immediate impact of market opening to FIIs is
the surge in trading volume and capital inflows to domestic stock markets, result of which the
boom in stock prices.
Foreign Institutional Investors (FIIs) were permitted to invest in all the listed
securities traded in Indian capital market for the first time in September, 1992. As per the
RBI, Report on Currency & Finance (2003-04), since 1991 there has been continuous move
towards the integration of the Indian economy with world economy. Since then the
regulations with regard to FIIs investment has become more liberal. International capital
inflows have both positive as well as negative impact on the health of the recipient economy.
On the positive side, these capital inflows raise the level of economic development by
augmenting the domestic investment and widen financial intermediation. But these capital
inflows also pose several threats to the domestic economic and financial system of the
recipient economy like inflation, appreciation in exchange rate, overheating of the economy
and possibility of sudden withdrawal.
In the present paper, an effort has been made to estimate the determinants of foreign
portfolio investment in India. Available empirical evidence suggests that FIIs inflows by and
large are determined by the performance of stock markets and macroeconomic aggregates of
the host country. Thus, FIIs investment is pulled toward an economy with sound
macroeconomic aggregates as follows:
Foreign Institutional Investors (FII) includes an investor or investment fund that is
from or registered in a country outside of the one in which it is currently investing. The term
is used most commonly in India to refer to outside companies investing in the financial
markets of India. FII is allowed to enter into our country only through stock exchanges either
in the form of equity or debt. Thus, it makes an impact on the rise or fall of SENSEX, since
FII is allowed to be purchased or sold daily. It has been observed that SENSEX increases
when there are positive inflows of FIIs & decreases when there are negative FII inflows.
BSE-SENSEX has been selected for this study as the representative of Indian stock market
and used to obtain a measure of market price movement of Indian securities since this index
is comprehensive.
Inflation is defined as a rise in the average level of price for all goods and services. It
can also be defined as a permanent increase in the aggregate price level which implies a
diminishing of the purchasing power and increase in the cost of living. Inflation considered
one of the macroeconomic phenomenon that still polarised attention of both developing and
development countries (Mousa et al., 2012). Inflation rate is computed as percentage rate of
change in Wholesale Price Index over a given period. Inflation Rate= WPI (t)-WPI (t-1)/WPI
(t-1).
Crude oil is an indispensable input for production and therefore, the price of oil is
included as a proxy for real macroeconomic activity. India is largely an importer of crude oil
and consequently, oil price takes part an imperative role in Indian economy. Therefore, for oil
importing countries like India, an increase in oil price will lead to an increase in production
costs and hence to decreased future cash flow, leading to a negative impact on the stock
market.
Exchange rate: The next macroeconomic variable used in this study has been the
exchange rate/dollar price, which represents the bilateral nominal rate of exchange of the
Indian Rupee (Rs.) against one unit of a foreign currency. US Dollar ($) has been taken to be
the foreign currency against which the Indian Rupee exchange rate is considered. This is
because the US Dollar has remained to be the most dominating foreign currency used for
trading and investment throughout the period of this study. Generally, a depreciating currency
causes a decline in stock prices because of expectations of inflation. On an average, exportoriented companies are adversely affectedly a stronger domestic currency while importoriented firms benefit from it.
REVIEW OF LITERATURE
Classens (1993) analysed the return and diversification benefits for an investor in an
industrial country of investing in emerging markets and barriers which prevent a free flow of
funds. Study found that equity portfolio flows can be affected by efficiency of domestic stock
market as well as market segmentation created by barriers. Investors’ perception and attitudes
may thus matter as much as formal barriers.
Batra (2003) made an attempt to build up an understanding of investment decisions,
trading strategies and behaviour of the FIIs in Indian equity market. The author scrutinized
the daily & monthly data to investigate the trading behaviour of FIIs & their impact on the
stability of stock market. It is found that the positive feedback investors and trend chasers of
FIIs on the basis of daily data analysis but no evidence of positive feedback trading while
analysed data on monthly basis.
Singh (2004) analysed the policy towards FIIs investment and highlighted the nature and
some determinants of FIIs flows. The study examined the extent of effect of significant
macroeconomic variables; inflation and exchange rate on the flows of Foreign Institutional
Investment in India. It has been found that the flow of investment by FIIs towards India was seasonal
and the more investment was done in the few months of calendar year.
Pal (2006) examined the impact of foreign portfolio investment on India’s economy
and industry. As FPI essentially interacts with the real economy via the stock market, the
effect of foreign portfolio on the country’s economic development examined. The result of
this study suggests that the entry of foreign portfolio investors will boost a country’s stock
market and consequently the economy, does not seem to be working in India.
Rai and Bhanumurthy (2006) analyzed the determinants of foreign institutional
investment in India using monthly data. The study revealed the positive association of FIIs
investment with return on BSE-SENSEX, inflation in US (home country) and negative
association with inflation in India (host country), return on S&P 500 index, ex-ante risk on
BSE and ex-ante risk on S&P 500 index. However, the ex-post risk neither in US nor in India
affected FII inflow to India.
Kaur and Dhillon (2010) aimed at exploring the determinants of FIIs investment in
India. The study found that returns on Indian stock market have positive impact whereas US
stock market returns have no significant influence on FIIs investment to India. Stock market
risk has negative influence on FIIs inflows to India. Market capitalization and stock market
turnover of India have significant positive influence only in short-run.
Dasgupta (2012) has attempted to explore the long-run and short-run relationships
between FII and Indian stock market with other macroeconomic variables of Indian economy.
Monthly data has been used for all the variables, i.e., FII, WPI, BSE SENSEX, EX and call
money rate. The Granger causality test has found no short-run unilateral or bilateral causal
relationships between FII with the macroeconomic variables like SENSEX.
Arya & Purohit (2012) found that FII has gained a significant role in Indian stock
markets. The beginning of 21st century has revealed the real dynamics of Indian stock
market and its various benchmarking indices. The study was mainly focused to check the
volatility of stock market & returns due to the existence of FIIs in India.
Shrivastav (2013) examined whether market movement in terms of stock prices could
be explained by foreign investors, and also examined the relationship between FII and Indian
stock market. The study found that FII is thus an important macroeconomic indicator which
can help us analyze a particular stock and the whole stock market in a better manner.
OBJECTIVE OF THE STUDY
The main objective is to investigate the causal relationship between Foreign
Institutional Investment (FII) and Indian stock market (BSE SENSEX) & other
macroeconomic aggregates namely Inflation rate (IR), Crude Oil Prices (CR) and Exchange
rate (EXR).
RESEARCH METHODOLOGY
In the study, a time span of 14 years has been chosen for this study from April, 2000
to March, 2013 uses monthly data to portray a larger view of the relationship including 156
observations. Descriptive statistics has been used which provide a useful quantitative
summary of macroeconomic indicators and BSE-SENSEX. For the purpose, measures of
central tendency (mean) and measures of variability (standard deviation, range, minimum and
maximum) to explain the dataset. Here, descriptive statistics provide a historical account of
variables behaviour and convey some future aspects of the distribution of dataset. The current
study unravels the linkage between FII & macroeconomic aggregates in the Indian context
using techniques like regression, Granger causality test, ADF test & Unit root test using
Eview7 Software. RBI website has been referred for FII, BSE-SENSEX, Inflation rate (WPI)
and Exchange rate. Lastly Index Mundi has been referred for database of crude oil.
1.1 Descriptive Statistics Analysis
Table1 presents a summary of descriptive statistics of all the variables in which sample
mean, standard deviation, sum, maximum, minimum and range have been reported. These
variables are FII, Bombay stock exchange’s main index i.e. SENSEX, inflation rate, crude oil
price and Exchange rate. In the group of 156 observations, FII mean is 39.35 and its standard
deviation is 73.74 which imply that there is a greater degree of variability in FII due to higher
standard deviation as compared to FII mean. It means that it is normally distributed among its
means. The mean of share price (SENSEX) is 10792.66, while its maximum price is 20249.7
for data series and the standard deviation is 6050.90 which considered being very high. It
reflects significant variability in stock prices (SENSEX). Inflation rate mean is 6.13 and
standard deviation is 2.58 implying that there is moderate variability in wholesale price index
(WPI). Maximum value of WPI is 11.10 and minimum is -0.400. Crude oil mean is 267.58
and its standard deviation is 217.62 respectively. There is highly moderate variability in
crude oil. The maximum price of crude oil is 803.75 for the data series and minimum is
43.60. Exchange rate mean is 46.43 and standard deviation is 3.40. It is seen from the Table 1
that there is no significant variability in Exchange rate driven from its mean. The maximum
and minimum values of Exchange rate are 56.03 and 39.37 respectively.
Table 1: Descriptive Statistics
Indicators
Mean
S.D
Sum
Max
Min
Range
FII
39.35
73.74
6139.08
295.070
-134.61
429.68
SENSEX
10792.66
6050.90
1683654
20249.75
2918.28
17331.47
Inflation rate
6.13
2.58
956.4
11.10
-0.400
11.5
Crude oil
267.58
217.62
41742.85
803.75
43.60
760.15
Exchange rate
46.43
3.40
49.84
56.03
39.37
16.66
Source: Compiled with Eviews 7 Software
1.2 Test for Stationary/ Non-Stationary
It is a recognized fact that many financial time series are random walk or nonstationary time series and contain unit root. Augmented Dickey-Fuller (ADF) Test is the
popular test for unit root testing of time series. If yt is the time series to be tested for unit-root,
then the test statistic for ADF unit root testing will be given by τ statistics, which is OLS
estimate of coefficient of yt-1 in equation (1), divided by its standard error:
n
yt  yt 1    t   i  yt  i  ut …
i 1
The results of Unit Root Test i.e. Augmented Dickey Fuller (ADF) Test applied on
FII and macroeconomic indicators by Akaike Information Criteria (AIC). The result indicates
that FII along with macroeconomic indicators are non-stationary at level but becomes
stationary at their first difference. The result of the Augmented Dickey Fuller (ADF) Test is
shown in Table2:
Table 2: Unit Root Test Result
Series
ADF Unit Root Test Statistics
At Level
FII
At First
Difference
At Level
None
With Intercept
With Trend and Intercept
-6.438
-7.6263
-8.4054
(0.000)
(0.000)
(0.000)
-13.955
-13.9173
-9.8333
(0.000)
0.386
(0.000)
-1.034
(0.000)
-3.328
(0.794)
(0.740)
(0.066)
-5.087
-5.192
-5.176
(0.000)
(0.000)
(0.000)
-0.518
-1.941
-2.999
(0.490)
(0.312)
(0.136)
-5.158
-5.136
-5.090
(0.000)
(0.000)
(0.000)
2.475
1.219
-1.260
(0.996)
(0.998)
(0.893)
-3.881
-4.532
-4.919
(0.000)
(0.000)
(0.000)
0.6056
-1.5542
-1.7819
(0.8462)
(0.5035)
(0.7089)
-8.680
-8.696
-8.7091
(0.000)
(0.000)
(0.000)
SENSEX
At First
Difference
At Level
Inflation rate
At First
Difference
At level
Crude oil
At First
Difference
At Level
Exchange rate
At First
Difference
Source: Compiled with Eviews7 Software
The result in Table 2 indicates that all the time series variables SENSEX and the other
selected macroeconomic aggregates like Inflation Rate (IR), Crude oil (CR) and Exchange
rate (EXR) are a random walk or non-stationary at their level but becomes stationary at their
first difference. Subsequently, Only FII investment is stationary at their level where mean and
variance are constant. Hence now granger causality test & regression analysis can be applied
using first differencing of the variables.
1.3 Econometric Regression Analysis
Econometric Regression analysis is a technique to check the effect of
macroeconomics variables on FII and found some interesting results for the relationship.
Foreign Institutional Investors (FII) has a dependent relationship among other
macroeconomic aggregates such as BSE-SENSEX, Inflation Rate (IR) and Crude oil (CR).
The null hypothesis has been tested on the basis of the p-value while the overall significance
of model has been tested on the basis of F-sign. If the p-value and F- sign is less than the
critical p-value and F-sign at 5% of significance level, then the null hypothesis is rejected and
there will be a significant relation between the variables. The following statement of
hypotheses is as follows:
H0: There is no significant difference between FII and each macroeconomic aggregate
(SENSEX, IR, CR and EXR).
Ha: There is a significant difference between FII and each macroeconomic aggregate
(SENSEX, IR, CR and EXR).
The following variables which are stationary at the first differencing, take the log of
original values and make a new variable which can be expressed as symbolically:
∆SENSEX= d log (SENSEX)
∆IR = d log (IR)
∆CR = d log (CR)
∆EXR = d log (EXR)
The result of regression model is represented in Table 3 which can be expressed
mathematically as:
Y= α+β1∆SENSEX+β2∆IR+β3∆CR+β4∆EXR
Where Y= ∆FII,
α= Intercept,
β1………… β3 = Slopes
Independent variables or macroeconomic indicators= ∆SENSEX, ∆IR, ∆CR and ∆EXR
Table 3: Econometric Regression Model by Equation between FII and other Macroeconomic
Aggregates
Α
(Intercept)
β
Macroeconomic
R2
Aggregates
(R-square)
∆SENSEX
0.2180
34.8872
519.277
6.339
∆IR
0.0023
39.4631
15.8883
∆CR
0.0017
39.0584
∆EXR
0.2183
42.253
T-stat
F-Stat
Remark
0.000
40.195
Reject H0
0.602
0.5474
0.363
Accept H0
23.7077
0.517
0.711
0.605
Accept H0
-1969.77
-6.537
0.000
42.742
Reject H0
(Slope)
pvalue
Source: Compiled with eview7 Software
*Significant at 5% level
The table above shows simple regression test for four macroeconomic indicators and
FII. It was found through p-value and F-sign that there is a significant relationship between
FII and SENSEX. That, means SENSEX does affect FII. BSE-SENSEX is considered as the
representative of Indian stock market and used to obtain a measure of market price movement
of Indian securities. Thus, Indian Stock market (SENSEX) had a significant impact on Foreign
Institutional Investors (FII). It leads to the rejection of null hypothesis (H0) as p-value is less
than 0.05 at level of significance. Further, EXR also had a significant influence on FII (Fvalue= 42.74, p<0.05 respectively). Thus, H0 is rejected from which it can be inferred that
there is a significant difference between macroeconomic aggregate (EXR) and FII. Rest of
the variables i.e. IR and CR had no effect on FII respectively. R2 shows the model fitness of a
regression equation and also explains the variation in dependent variable which is made by an
independent variable. Table 3 presented that inflation rate vis-a-vis crude oil explain very low
variation in FII while SENSEX and EXR both explain approximately 22 per cent of variation
in FII respectively. In the table, there are Intercept values and Slope values which help in
forming regression equations in the form Yi = β0 + β1 Xi.
1.4 Granger Causality Test for the Relationship between Turnover and Macroeconomic
Aggregates
A statistical approach proposed by Clive W Granger (1969) to infer cause and effect
relationship between two or more time series is known as Granger Causality. Granger
Causality is based on the simple logic that effect cannot precede cause. It is important to note
that the statement “x Granger causes y” does not imply that y is the effect or the result of x. In
other words, Granger Causality is a technique for determining whether one time series is
useful in forecasting another. It analyses the causal relationship between FII and
macroeconomic aggregates like SENSEX, Inflation rate, Crude oil and Exchange rate. The
study compares FII with other macroeconomic aggregates from the period of April 2000 to
March 2013. For any time series data analysis, all data series get stationary. To study the
stationary of the data series, the ADF have been conducted above. As the test confirmed that
the data is stationary in nature. The direction of the causal relationship between the variables
has been conducted by applying Granger Causality Test. The various hypotheses have been
developed for the purpose as shown in the Table 4. If calculated p-value less than table value
of 0.05, the null hypothesis is rejected which implies x Granger cause y. The results of the
Granger Causality Test are presented in Table 4.
Table 4: PAIRWISE GRANGER CAUSALITY TESTS FOR FII
Null Hypothesis
F-Statistic
p-value
Result
Relationship
SENSEX does not Granger Cause FII
0.379
0.684
Accept H0
Unidirectional
FII does not granger cause SENSEX
5.039
0.001
Reject H0
Relation
Inflation rate does not Granger Cause FII
2.805
0.063
Accept H0
FII does not granger cause Inflation rate
0.531
0.588
Accept H0
Crude oil does not Granger Cause FII
0.840
0.433
Accept H0
FII does not granger cause Crude oil
2.402
0.094
Accept H0
No relation
Exchange rate does not Granger Cause FII
0.852
0.428
Accept H0
Unidirectional
FII does not granger cause Exchange rate
28.712
0.000
Reject H0
Relation
No relation
Source: Compiled with Eviews 7 Software
It is observed from the Table 4 that the variables like inflation rate and crude oil do
not granger cause FII as indicated by statistic values as the p-values are calculated more than
that of 0.05 which leads to acceptance of null hypothesis at 5 per cent level. It indicates that
there is no relation exists between inflation rate & FII and Crude oil & FII. Further, the
results reveal that FII causes SENSEX and Exchange rate as presented in Table that the Fstatistic is 5.039, 28.712 respectively and p-value is 0.001 & 0.000 respectively less than
critical value i.e. 0.05 which implies that the null hypothesis is rejected i.e. it proves that FII
granger causes SENSEX and Exchange rate. As far as causality relationship is concerned, a
unidirectional causality or one way causality is found from FII towards SENSEX and
Exchange rate. Thus FII affects Indian stock market (SENSEX) and Exchange rate as well.
CONCLUSION
For exploring the determinants of FIIs investment, FIIs net investment has been
modelled by including, both financial and macro-economic variables together. BSE-SENSEX
has positive and significant impact on FII. Among other macroeconomic determinants,
Exchange rate has significant and positive impact on FIIs investment inflows to India both in
long-run and short-run. The paper employed Granger causality test and regression analysis to
examine such relationships. The results are interesting and useful in understanding the Indian
stock market pricing mechanism as well as its return generating process. The inferences
drawn on the bases of the granger causality test has shown that FII granger causes BSESENSEX and Exchange rate, however FIIs inflows by and large are determined by the
performance of stock market (SENSEX) and macroeconomic aggregates in India as shown by
regression analysis. Thus, FIIs investment is pulled towards an economy with sound
macroeconomic factors.
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WEBSITES

http://dbie.rbi.org.in/

http://www.indexmundi.com/india/

ww.ssrn.com

www.bseindia.com