Impact of Foreign Institutional Investment on Indian Stock Market: A Cause and Effect Relationship

Abstract

The growing Indian market had attracted the foreign institutional investors (FIIs) to Indian equity market during post financial reform period. The present article made an attempt to explain the short-term and long-term causal relationship between FIIs and Indian stock market. The study uses the monthly time series data on advances to declines ratio (ADR) of Bombay Stock Exchange (BSE) and purchases to sales ratio of FIIs. The sample period spans from April 2001 to December 2012. To attain the intent of the study, the article employs the empirical techniques such as co-integration, Granger causality test and variance decomposition analysis as a part of research methodology.

The result of co-integrating relationship discloses the rejection of null hypothesis of no co-integrating vectors which implies the existence of a long-term relationship between the dependent and the independent variables. Further, the Granger causality reveals the presence of unidirectional causality running from FIIs to BSE-ADR during short as well as long span of period. The variance decomposition analysis clearly shows that the price changes in BSE are influenced to a very large extent by innovations in FIIs and confirms the dominant role of FIIs in information dissemination. Thus, the results suggest that BSE is significantly affected by FIIs and the latter is not influenced by variations in the BSE, that is, there exists unidirectional causality running from FIIs to BSE-ADR.

Keywords

Foreign institutional investment advance–decline ratio co-integration Granger causality variance decomposition analysis

The increasing influence of foreign institutional investors (FIIs) forms a decisive component of the stock market behaviour during the last two decades. The government of India opened the doors for foreign investment in September 1992 and prepared procedure and guidelines to promote the flow of foreign capital into India. FIIs can invest their funds in the country only under the norms prescribed by Security and Exchange Board of India (SEBI). The reform process led to far-reaching changes in foreign investment scenario where various limitations imposed on investments by the foreign investors in India were eased. The increasing role of FIIs has brought both quantitative and qualitative developments in the stock market.

Banaji (2000) conferred that the entry of FIIs triggered the regulations on reporting and disclosure standards and also initiated capital market reforms such as market transparency, computerisation and dematerialisation. Mohan (2006) laid emphasis on benefits of FIIs on Indian economy suggesting that it enhanced capital flows at a time when the balance of payment was not comfortable. Upadhyay (2006) also found allied results in her study proposing that FII flows complement and strengthen domestic savings and domestic investment without escalating the foreign debt of India. Further, there are apprehensions with regard to the position of FIIs in the stock market. According to some researchers, the FIIs help in dropping off the volatility in the stock market of India (S.S.S. Kumar, 2000). Additionally, Kim and Singal (1993), Banerjee and Sarkar (2006) did not find any destabilising impact of FIIs on stock prices. On the other hand, the majority of researchers believe that FIIs increase the volatility of the share prices. According to Chittedi (2008), the liquidity and volatility in BSE Sensex were decidedly influenced by the FII flows. Rai and Bhunumurthy (2004) and Porwal et al. (2005) also established higher volatility in the market due to the arrivals of FIIs.

This is not the case with India alone; the economists and researchers have observed that the stock market enjoys certain positive developments along with certain tribulations on the entry of FIIs world over. Dornbusch and Park (1995) and Richards (2002) hold a sense of worry with the entry of FIIs in their studies, as this flows into the opposite direction at the indication of crisis, thereby knocking off the stability of the stock market. According to Hamilton (2005), the unreliability for FIIs originates from their very nature since the purchases by FIIs boost the stock market and their sales plunge the stock market. Thus, the repercussion of foreign institutional investment is apparent, as every time the stock market goes up or down, it is endorsed to FII’s money. The reason may be that the investment decisions of FIIs are based on a team of well-informed experts who are skilled and qualified to explore the essential information in a well thought-out manner. Moreover, they have massive funds on hand for investment and, therefore, move unequivocally from one market to another.

Therefore, it has been a matter of concern for the economy of a country that whether the increasing number of FIIs over the years has destabilising impact on the stock market of a country. Therefore, an attempt has been made to study the causal relationship between FII flows and stock market of India. The study would be of high significance from policy perspective regarding inflow and outflow of foreign institutional investment. The remainder of the article is organised as follows. In the second section, we summarise several stands of literature that are relevant for our analysis. We discuss a few key studies that empirically analyse the relationship between foreign institutional investment and stock market performance. The third section gives an overview of the data and the methodology. The fourth section presents results with discussions and the last section concludes the study by highlighting the implication of the results.

Review of Literature

Considerable amount of research has been conducted on the impact of institutional investment on the stock prices of different economies with widespread econometric methods. S. Kumar (2001) studied the co-integration between FII and BSE Sensex and found long-term relationship between the two. Chakrabarti (2001) came with the evidence that the FII flows were highly correlated with equity returns in India and also found that the FII flows are the effect rather than the cause of these returns and hence it contradicted the view that FIIs determine the market return in general. Analogous results were obtained by Mukherjee, Bose and Coondoo (2002) on daily data from 1999 to 2002. The study concluded that although equity returns were the most important factor in influencing the FII flows into the country, FII flows do not have any significant impact on the returns. Trivedi and Nair (2003) have found in their investigation that the equity return shows a significant and positive impact on the FII. But given the huge volume of investments, there is a possibility of bidirectional relationship between FII and the equity returns. Gordon and Gupta (2003) analysed monthly data over the period 1993–2000 and observed causation running from FII inflows to returns in BSE. Rai and Bhanumurthy (2004) examined the determinants of the FIIs in India by taking the data from January 1994 to November 2002 and found a positive relation between FIIs and stock market return (BSE). Moreover, Griffin et al. (2004) found that foreign flows are significant predictors of returns for Korea, Taiwan, Thailand and India.

Badhani (2005) applied Granger Causality test on the monthly data from April 1993 to March 2004 and observed (i) bidirectional long-term causality between FII investment flows and stock prices, but no short-term causality could be traced between the variables; (ii) no long-term relationship between exchange rate and stock prices, but short-term causality runs from change in exchange rate to stock returns; and (iii) exchange rate long-term Granger causes FII investment flow, not vice versa. Bhattacharya and Mukherjee (2005) investigated 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 found bidirectional causality between stock return and FIIs. But, there exists no causal relationship between exchange rates and net investments by FIIs. A study conducted by Bansal and Pasricha (2009) analysed the change of market return and volatility after the entry of FIIs to Indian capital market and found that although there is no significant change in the average returns of the Indian stock market, volatility is significantly reduced after India unlocked its stock market to foreign investors. Stigler, Shah and Patnaik (2010) estimated a vector auto regression (VAR) involving five variables such as net FII, Nifty index, S&P 500 index, ADR premium index and INR/USD exchange rate. Causality tests indicate that a shock to net FII flows does not cause the Nifty index returns, but the reverse causality does hold. In fact, shocks to net FII flows do not feed through to any of the other four variables, whereas positive shocks to the exchange rate, ADR premium and S&P 500 all affect net FII flows.

A wide literature is available on causal relationship between FII and stock market returns, but most of the existing studies performed on Indian context have been mixed and thus are inconclusive on the issue of causality between FIIs and stock market of India. Therefore, there is a need to further investigate whether foreign institutional investment is the cause or effect of stock market fluctuations in India.

Objective of the Study

The present study is an attempt to analyse the co-integration and subsequently to unravel the nature of causal relationship, that is, is it unilateral or bilateral between foreign institutional investment and the stock market of India.

Data and Methodology

To achieve the objective, the study uses the monthly time series data on advances to declines ratio (ADR) of BSE. The ADR indicates the breadth of the stock market and captures the direction of entire market in unambiguous manner. Therefore, we use ADR for empirical analysis instead of returns. The foreign institutional investment is captured by taking the ratio of monthly purchases to sales. The sample period of the study spans from April 2001 to December 2012. The data on monthly FII flows and advances/declines have been collected from the Handbook of Statistics on Indian Securities Market. Further, the article uses the empirical tools such as co-integration, Granger causality test and variance decomposition analysis as a part of research methodology to accomplish the stipulated set of objectives of the study.

Hypothesis of the Study

H₀: There is no significant relation between FII and BSE-ADR.

H_a: There is a significant relation between FII and BSE-ADR.

Stationary and Order of Integration

The variables in a regression model must be stationary or co-integrated so as to avoid spurious regression. Therefore, unit root tests are conducted to verify the stationary properties of the time series data. A series is said to be integrated of order d, denoted I(d), if it has to be differenced d times before it becomes stationary. If a series, by itself, is stationary in level without having to be first differenced, then it is said to be I(0). We use augmented Dickey–Fuller (ADF) (Dickey & Fuller, 1979) test to know whether the variables are stationary. Consider the equation:

Xt_{1}_{2} t X_{t 1}_{i 1}^{k}_{i} X_{t 1}_{t}

(1)

where ΔX is the first difference of X series, α₁ is a constant term, t is a time trend, u is the white noise residual term of zero mean and constant variance and k is the lagged values of ΔXt which are included to allow for serial correlation in the residuals.

Co-integration Test

If all variables are found to be I(d), then the next step is to test for the existence of a co-integration between them. This is accomplished by using Johansen–Juselius (1990) co-integration techniques. The Johansen–Juselius uses the maximum likelihood approach. This method allows the empirical determination of the number of co-integrating relations and produces maximum likelihood estimators of the parameters of these relations. Two test statistics namely trace test statistic and the maximum eigen value test statistic are used to identify the number of co-integrating vectors. For trace test statistics null hypothesis is the number of co-integrating vectors is less than or equal to r, in which r is 0, 1, 2, 3, … so on. The alternative hypothesis against this is that r = n. Meanwhile the null hypothesis for maximum eigen value test is the existence of r co-integrating vector and the alternative hypothesis is r + 1 co-integrating vectors.

A finding of co-integration implies the existence of a long-term relationship between the dependent and the independent variables. If there is at least one co-integrating relationship among the variables, then the causal relationship among these variables can be determined by estimating the vector error correction model (VECM).

Granger Causality Test—Error Correction Model (ECM)

If the series is found co-integrated, there will exist an ECM including error correction term (ECT) obtained from the relevant co-integration regression. Engle and Granger (1987) have shown if the variables are integrated of degree I(1) and are co-integrated, then either unidirectional or bidirectional Granger causality must exist in at least the I(0) variables. If the variables are found co-integrated, the error correcting models are defined as in equations given below:

\begin{matrix} X_{t}_{1}_{i 1}^{m}_{1 i} X_{t 1} \\ _{i 1}^{m}_{1 i} Y_{t 1}_{1} {ECT}_{t 1} e_{t} \end{matrix}

(2)

\begin{matrix} Y_{t}_{2}_{i 1}^{m}_{2 i} Y_{t 1} \\ _{i 1}^{m}_{2 i} X_{t 1}_{2} {ECT}_{t 1} e_{t} \end{matrix}

(3)

The error correction terms (ECT_t–1) are the stationary residuals from the co-integration equations. The inclusion of ECTs in the above equations introduces an additional channel to detect causality. Given such a specification short-term and long-term causality can be tested. For instance in Equation 2, the null hypothesis that Y does not Granger cause X is rejected if not only c_1i are jointly significant from zero but also d₁ is significant. The ECMs allow for the fact that Y Granger causes X as long as the coefficient of the ECT is significant even if c_1i are not jointly significant. ECT is used for correcting disequilibrium and testing for long-run and short-run causality among co-integrated variables. The significance of c_1i terms implies Y Granger causes X in the short run and significance of ECT coefficient implies Y Granger causes X in the long run.

Variance Decomposition (VDC) Analysis

Once the VECM model is estimated, then we employ VDC. The analyses allow us to investigate the behaviour of an error shock to each variable on its own future dynamics as well as on the future dynamics of the other variables in the VECM system. VDC is used to detect the causal relations among the variables. It explains the degree at which a variable is explained by the shocks in all the variables in the system (Mishra, 2004).

Results and Discussion

FIIs have been the most dynamic source of capital to Indian market and the surge of FII flows to India is by and large a phenomenon of the 1990s. There has been a growing presence of FIIs in Indian stock market as evidenced by increase in their net investments over the years (Table 1). The net flows to India have been positive other than in the year 1998–1999. This may be attributed to the nuclear tests and East Asian crisis which slowed down the flows but as stated by Gordon and Gupta (2003), their effects were short-lived with regard to India. There has been a phenomenal increase in the investment by FIIs in the Indian market in the subsequent years with the trend reversal in 2008–2009, which was due to the global financial crisis. Again 2009–2010 has witnessed coming back of FIIs with a bang and positive net flow continues in subsequent years. Several factors are responsible for this increasing confidence of FIIs in the Indian market which comprises strong macro-economic fundamentals of the economy, transparent regulatory system and encouraging corporate results. Prasanna (2008) inferred that among financial performance variables, higher sensex indices, share returns, earning per share and higher price earning ratios attract more foreign investment in India. He also established that the foreign investment is more in companies with higher volume of publicly held shares, that is, the promoters’ holdings and the foreign investments are inversely related.

Table 2 presents the result of ADF unit root test conducted to verify the stationary properties of a time series data. The result shows that the absolute value of calculated ADF test statistic is greater than its critical value at 5 per cent level of significance in both the series understudy. It indicates that the integration of both series is of order I(0) and there is no unit root. Thus, the null hypothesis of non-stationary data cannot be accepted and both the series fulfil the prerequisite of VAR model and are fit for further research.

Table 1.

Trends in FII Investment in India

Year	Gross Purchase (₹ Crore)	Gross Sales (₹ Crore)	Net Investment (₹ Crore)
1992–1993	18	4.0	13
1993–1994	5,593	467	5,127
1994–1995	7,631	2,835	4,796
1995–1996	9,694	2,752	6,942
1996–1997	15,554	6,980	8,575
1997–1998	18,695	12,737	5,958
1998–1999	16,115	17,699	(1,584)
1999–2000	56,857	46,735	10,122
2000–2001	74,051	64,118	9,933
2001–2002	50,071	41,308	8,763
2002–2003	47,062	44,372	2,689
2003–2004	144,855	99,091	45,764
2004–2005	216,951	171,071	45,880
2005–2006	346,976	305,509	41,467
2006–2007	520,506	489,665	30,841
2007–2008	948,018	881,839	66,179
2008–2009	614,576	660,386	(45,811)
2009–2010	846,438	703,780	142,658
2010–2011	992,596	846,158	146,438
2011–2012	921,285	827,562	93,723
April 2012–December 2012	619,554	519,553	100,001

Source: Handbook of Statistics on Indian Securities Market, 2012.

Note: Figures in parenthesis are net outflows.

Table 2.

Augmented Dickey–Fuller Unit Root Test

Variable	ADF Test Statistics at Level
Variable	Constant	With Constant & Trend
FII	–7.9602* (0)	–8.0322* (0)
BSE-ADR	–11.5816* (0)	–12.0429* (0)

Source: Authors.

Notes: i. The optimum lag length is indicated within parenthesis determined by Schwarz criteria. The critical value of ADF statistics are –4.0265 and –3.4429 at the 1 per cent and 5 per cent level of significance, respectively.

ii. * Denotes statistically significant at 1 per cent level.

After having established that both series are stationary, we proceed to examine whether the series understudy is co-integrated or not. Co-integration analysis is used to determine the extent to which two series have moved together towards long-run equilibrium relationship. We use Johansen and Juselius (1990) co-integration approach for this purpose. The co-integrating tests examine that there are r co-integrating relationships against the r + 1 and hence, not accepting the null hypothesis implies that the co-integrating relationships exceed r number of relationships by one. Here, Akaike information criterion (AIC) is used to select the optimal lag length and all related calculations have been done embedding that lag length.

The results of Johansen and Juselius co-integration relationship between FII and BSE-ADR are presented in Table 3. The null hypothesis of r co-integrating vectors is given in column 1 of the table. The maximum eigen value and trace statistics are used to interpret whether the null hypothesis of r = 0 is rejected at 5 per cent level of significance. The result of bivariate co-integrating vectors discloses the rejection of null hypothesis of no co-integrating vectors under both the trace statistics and maximal eigen value forms of test. The rejection of null hypothesis implies that the variables understudy are tied together for long-term relationship and indicate that at least one of the variables tests reacts to deviations from the long-run relationship.

Table 3.

Johansen Co-integration Test between FII and BSE

R	Eigen Value	Trace Statistics	Critical Value (5%)	Max. Eigen Value	Critical Value	Result
r = 0	0.3370	88.648*	15.49	56.723*	14.26	Co-integrated
r = 1	0.2065	31.925*	3.841	31.925*	3.84

Source: Authors.

Notes: ‘r’ denotes number of co-integrating vectors and * denotes statistically significant at 5 per cent level.

In other words, presence of co-integration rules out non-causality among the variables and confirms the possibility of at least unidirectional causality from one variable to the other. Therefore, to achieve the objective as to which variable causes the other, the Granger causality test with ECT is applied to detect short-run and long-run causality. Here, the causal relationship can be gauged by examining the statistical significance and relative magnitude of error correction coefficient. The ECM takes into account the lag term in the technical equation that invites the short-term adjustment towards the long run. In other words, the error correction coefficient acts as an evidence of direction of causal relation and reveals the speed at which discrepancy from equilibrium is corrected or minimised. The explanation of estimating ECT is to know which sample variable plays decisive role in information flow and leads lag relationship.

The result of Granger causality test with ECM is reported in Table 4. The structure lag is chosen on the basis of VAR model by using Akaike’s minimum final prediction error (FPE) criterion. To make consistency, the same lag length has been chosen for the co-integration test. The ECT represents the long-run impact of one variable on the other while the change of the lagged independent variable describes the short-run causal impact. The results of Granger causality between FII and BSE-ADR disclose that BSE does not Granger cause FII in the long run as ECT is not significant, whereas FII Granger cause BSE-ADR in the long run as indicated by statistically significant ECT (Table 4).

This indicates that FIIs are the cause of ups and downs in the BSE stock market, although the fluctuations in BSE do not affect the FIIs. There exists only one co-integrating vector in the form of FII that causes ups and downs in BSE-ADR in the long-run relationship. The comparable results are also relevant for short-term interaction between the understudy variables. Thus, there exists unidirectional Granger causality between the variables in the short run as well as the long run.

Thus, it can be inferred from the estimating ECT that FIIs lead the BSE-ADR, thereby playing a crucial role in information flow and leading lag relationship. In other words, since there is a statistical evidence that foreign institutional activity can influence the forecasts about market direction in Indian stock market therefore, it can be concluded that FIIs are not feedback traders and they do not respond to changes or technical position of the market, rather the investments by FIIs are more driven by fundamentals.

Table 4.

Granger Causality Test with ECT between FII and BSE

Particulars	DFII (–1)	DBSE (–1)	ECT
DFII	–0.3406(3.9243)*	–0.0046(0.5134)	0.0169(0.4002)
DBSE	–1.3792(–1.5908)**	0.0867(0.9686)	3.3616(7.9649)*

Source: Authors.

Notes: i. Figures in parenthesis indicate ‘t’ values.

ii. and ** denote statistically significant at 5 per cent level and 10 per cent level, respectively.

Further, to supplement the Granger causality test results, variance decomposition analysis is performed. The VDC analysis indicates the relative impact that one variable has upon another variable within VECM model. It measures the percentage of forecast error of a variable that is explained by another variable within short-run dynamics and interactions. The analysis helps in assessing the economic significance of these impacts as the percentage of the forecast error for a variable sum to one.

Table 5 shows the result of VDC of FII and BSE-ADR for a variance period of 10 months. In case of bi-variate modelling of FII and BSE-ADR, the results reflecting the information share show that the FII explains 99 per cent of its own forecast error variance, whereas BSE explains trivial, that is, less than 0.5 per cent of FII variance. On the other hand, BSE explains 90 per cent to 56 per cent of its own forecast and remaining 10 per cent to 44 per cent is explained by FIIs over the time period of 10 months. Thus, variations in variables are predominantly attributed to their own variations which decline gradually over the increase in the time horizon. However, the VDC of BSE clearly shows that the price changes in BSE are influenced to a very large extent by innovations in FIIs and the variability increases with time.

Therefore, the result evidently shows that more information flows from FIIs to BSE, and thus the price changes in BSE can be considered due to FIIs. This indicates that FII defines BSE more and the information share depicted by VDC confirms the dominant role of FIIs in information dissemination, which concludes that FII is the cause of BSE in the short-run causal relationship and BSE is influenced by the developments of FIIs.

Table 5.

Variance Decomposition of FII and BSE

Period	Variance Decomposition of FII			Variance Decomposition of BSE
Period	S.E.	FII	BSE	S.E.	FII	BSE
1	0.2287	100.00	0.0000	2.2849	9.5279	90.4721
2	0.2733	99.4637	0.5363	2.3847	14.5310	85.4690
3	0.3252	99.5756	0.4244	2.4761	20.2796	79.7204
4	0.3653	99.6122	0.3878	2.5530	24.8542	75.1458
5	0.4029	99.6437	0.3563	2.6276	29.0480	70.9520
6	0.4368	99.6608	0.3392	2.6995	32.7684	67.2316
7	0.4684	99.6744	0.3255	2.7698	36.1296	63.8704
8	0.4980	99.6848	0.3152	2.8384	39.1657	60.8344
9	0.5259	99.6931	0.3069	2.9053	41.9262	58.0738
10	0.5524	99.6998	0.3002	2.9708	44.4458	55.5542

Source: Authors.

Conclusions

The article examines the causal relationship between FIIs and the sentiments of the Indian stock market by using analytical tools such as co-integration, Granger’s causality test and variance decomposition analysis. The analysis done in the article is concluded with the results that there exists co-integration between FII and BSE-ADR, suggesting that the variables share common long-term information with each other. Further, the results of the Granger causality test with ECT indicated that there exists unidirectional causal relationship proceeding from FII to BSE-ADR for long as well as short period. FIIs with positive and statistically significant ECT indicate that they are the market makers and invest in the market to fetch the rising returns. Therefore, the FIIs with their huge investment have capacity to cause fluctuations in BSE. The result of variance decomposition analysis also confirms the dominant role of FIIs in information dissemination signifying the significant competence of FIIs to influence the Indian stock market. Thus, the considerable impact of FIIs in passing on direction to BSE in the short run as well as the long run reveals that these investors are proficient in explaining the market movements, that is, the rise and fall of the Indian stock market is due to inclusion and withdrawal of money from FIIs. In other words, it can be interpreted that Indian stock market is in the control of FIIs as BSE-ADR is more an effect than a cause of FII flows in India. According to Biswas (2005), FII influences the share price movements in Indian stock market but their role in the development of Indian stock market is still questionable. Therefore, the increasing presence of this class of investors with their causal impact on Indian stock market emphasises upon the call for needed watchful reforms in terms of trading and transaction systems by the regulatory authorities so that they cannot induce volatility in the share market.

The research on causal association depicting the rising role of FIIs will always be of significance for policy formulation by a country. The present study has been conducted on monthly data. There is a further scope to examine this relationship on daily data and also with other parameters of stock market such as market capitalisation or stock index prices.

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