2559
Dynamic Interaction Between Foreign
Institutional Investors And Market Return In An
Emerging Economy: Evidence From India
Faisal Usmani ., Javaid Akhter
Abstract: Foreign institutional investors plays key role in emerging markets including India. Emerging economies need foreign investors to fund their various demands and build up their foreign reserves. While foreign institutional investors invest in emerging market to diversify risk and take benefit of higher growth rate. Vector autoregression (VAR) techniques has been applied to study the dynamic interaction between the variables of interest. The result of the study shows that foreign institutional investors in India are positive feedback traders. It is also found that they are strongly impacted by their own lagged values that imply they follow each other. This has strong implication for Indian market returns as foreign investors trade in huge volumes they might affect market with their herd behavior. The study suggest more liberalized regime for foreign institutional investors with some indispensible capital control techniques. Better economic environment and proper policy implementation encourage foreign investors.
Keywords: Foreign institutional investors, Market returns, Vector autoregression, Positive feedback trading.
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1
INTRODUCTION
An amplified development of financial integration and economic liberalization world over has significantly fostered massive upsurge in foreign institutional investment towards developing economies. Foreign institutional investors invest in emerging market for diversification benefits and international risk sharing and thereby playing role in restructuring the local markets as mentioned by Vinh Vo (2017). As a result, the topic of foreign institutional investment and its impact on market returns have been attracting the attention of researchers for policymakers. In India, foreign institutional trading activity has been increased substantially. India has been as one of the most favored destination for foreign investment. Several liberalizing policies and substantial reformatory measure along with strong fundamental factors has encouraged foreign investors to invest in India. The study of Vardhan and Sinha, (2016) stated that wing to balance of payment crises, India liberalized its economy for foreign investors on the recommendation of Rangarajan committee. Initially, the responses of foreign investors were slow but from 2000 onwards it has increased substantially. Later on, FII flows that form a part of foreign portfolio investments have gained massive importance in India. Subsequently, global financial crises in 2008 triggered a big debate on the role of FIIs in emerging market like India. This issue motivates to investigate the dynamic interaction of foreign flows and Indian capital market. In this study, strong evidence has been found that foreign institutional investors gross, purchase and sale are significantly affected by their own lags. This proves that foreign investors follow other investors in trading. They usually trade in large volumes and this might have strong impact on Indian markets. However we have found evidence of positive feedback trading by foreign investors as they follow lead in the Indian markets. This is different from other in several ways. Firstly, this study has used daily data, to analyze dynamic impact of foreign institutional investors on Indian capital market form 2/3/ 2008 to 30/3/2018. Secondly, this study has covered crises period also and used dummy to analyze impact of crises also. Thirdly, this study has used FII gross also as overall foreign investment. The rest of the paper is
organized as follows. In the following section, the review of existing literature will be presented. Followed by data collection and methodology. Next is empirical analysis discussion. In last section, conclusion is presented along with implications and suggestions.
2
LITERATURE
REVIEW
studied daily FII flows from January 1999 to May 2002. They found that FII movements are caused by the return in the domestic market and not the other way round. There are number of studies that have investigated various aspect of foreign institutional investors in Indian capital market. This background highlights the importance of foreign capital flows and demand for further study to understand the trading behavior of foreign capital flows in India. Considering this background, it is necessary to study the dynamics of the foreign institutional investment flows and Indian market returns. Srinivasan and Bhat, (2009) also emphasized that India is an important emerging Asian economy, where the market liberalization steps are being taken regularly. It makes India lucrative destination for foreign institutional investors. The study of Samarakoon (2009), using Bivariate VAR, found in the Sri Lankan market that purchases as well as sales of domestic and foreign investors are positively linked with past returns signifying that investors exhibits positive feedback trading in foreign inflow and contrarian trading behavior in foreign outflow. However, the behavior of foreign investors reverses in the times of crisis. The investors demonstrate negative feedback trading in buying trades and momentum trading in selling. This study attempts to investigate dynamic impact of foreign institutional investors on market return. This paper endeavors to analyze the relationship between foreign institutional investors and market returns in dynamic setting using bivariate vector autoregression (BVAR) model.
3
DATA
COLLECTION
AND
METHODOLOGY
The daily data for this study has been collected from national securities depository limited (NSDL). The data set for this study includes FIIs purchase and FIIs sales and FIIs net (difference between purchase and sales). The Nifty 50 data is collected from NSE website. The study of Dhingra et.al (2016) have used Nifty 50 for computing Indian stock market return, as it is termed as the benchmark index representing the whole of the Indian capital market. The data ranges from 02/03/2008 to 30/3/2018. This period is crucial as far as relationship between foreign capital flow and market returns because of the financial crises of 2007-08. This study has control the impact of financial crises by using exogenous dummy variable.
UNIT ROOT TEST
It is necessary to use stationary data for analysis in time series. As it is the requirement of most the models and techniques used in time series. For, non-stationary data will provide unreliable and spurious results. In order to check stationarity of the data series, this study has used two popular tests that are frequently used by many authors like Vardhan and Sinha (2016), Thenmozhi and Kumar (2009) in testing stationary of the time series data.
AUGMENTED DICKEY FULLER (ADF) TEST
△ 𝑌 = 𝛽 + 𝛽𝑡 + 𝛿𝑌 + ∑ 𝛼 △ 𝑌 + ℰ ……… (1)
Where ℰ a pure white noise error is term and where
△ 𝑌 = (𝑌 − 𝑌 ), △ 𝑌 = (𝑌 − 𝑌 ) etc. The
number of lagged difference term to include is often determined empirically, the idea being to include enough terms so that the error term in the ADF test equation is serially uncorrelated.
PHILLIPS AND PERRON TEST
Phillips and Perron (1988) propose an alternative (nonparametric) method of controlling for serial correlation when testing for a unit root. The PP method estimates the non-augmented DF test equation;
𝑌 = 𝛼 + 𝛿𝑌 + 𝜗 ……….(2) The Phillips -Perron test tends to be more robust to a wide range of serial correlation and time dependent heteroscedasticity (Lee and Rui, 2002).
4
METHODOLOGY
In order to properly explore the impact of foreign institutional investors on future stock returns and impact of previous market return on future a foreign investors trading, we estimated vector autoregression (VAR) models. Patnaik (2013) stated in her study that many studies have used VAR model to study the trading behavior of foreign investors in the Indian capital market. Vinh Vo, (2017) observed that the VAR framework has several advantages and has been considered as standard method in this line of research. According to Ulku, (2012) using VAR approach is relevant in investigating the relationship between foreign institutional investment and market returns in developing economies.
TRADING EQUATION:
𝐹𝐼𝐼 𝑓𝑙𝑜𝑤 =
𝛼 + ∑ 𝛼 𝐹𝐼𝐼 𝑓𝑙𝑜𝑤 + ∑ 𝛼 𝑅𝑒𝑡𝑢𝑟𝑛 +
𝛿𝐷𝑢𝑚𝑚𝑦 𝜀 ………. (3)
RETURN EQUATION:
𝑅𝑒𝑡𝑢𝑟𝑛 = 𝛽 + ∑ 𝛽 𝐹𝐼𝐼 𝑓𝑙𝑜𝑤 + ∑ 𝛽 𝑅𝑒𝑡𝑢𝑟𝑛 + 𝛿𝐷𝑢𝑚𝑚𝑦 𝜀 ……… (4)
Where 𝐹𝐼𝐼 𝑓𝑙𝑜𝑤 is natural log foreign institutional investment in Indian capital market in day t. while, 𝑅𝑒𝑡𝑢𝑟𝑛
is the market return in day t and 𝜀 and 𝜀 are the error
terms. Market return is calculated as log=(Pt /Pt-1). Pt market close while Pt-1 is previous market close. In the above equations FII flow and Returns are endogenous variables. However, a dummy crisis is the exogenous variable used as a proxy for global financial crisis. Global financial crisis of 2008 has adversely affected many developed and developing countries and India was no exception. This study has run the same equation for LFIIG, LFIIP and LFIIS with returns.
UNIT ROOT TEST
TABLE 1
AUGMENTED DICKEY FULLER (ADF) TEST
Variables T-statistics Probability value
FIIP -6.689256 0.0000
FIIS -8.172286 0.0000
FIIG -6.513129 0.0000
Nifty 50 returns -46.63135 0.0001 Note: *** denotes significant at 1% level
TABLE 2 PHILLIP-PERRON TEST
2561
FIIP -41.93789 0.0000
FIIS -41.04004 0.0000
FIIG -40.59147 0.0000
Nifty 50 returns -46.57536 0.0001 Note: *** denotes significant at 1% level
In the above table 1 and table 2, the stationarity of the variables of interest are presented. If a time series is not stationary, it will lead to spurious regression and unreliable results. In order to avoid these issues, this study has conducted the unit root test using popular augmented dickey fuller (ADF) test and Phillips–Perron (PP) test. Usually, econometrics software automatically choose the lag length for unit root test according to the nature and frequency of the data. While checking the stationarity of the variables, this study has used 2561 chwarz information criterion (SIC) to determine the appropriate lag length. The result of the above tests shows that the series is stationary at level and need not to be differenced. However, the series of Nifty 50 returns is calculated by using log difference. The study of Dhingra et.al (2016) has also used stationary time series at level to study dynamic interaction between foreign investment and Indian stock market. In all series, probability value is less than 5% that is required to reject the null hypothesis that the series contains unit root. T-statistics is greater than the critical values at 1% level. This satisfies the
second condition of stationarity. This study has used natural log of FII purchase, FII sales and FII gross (Vinh Vo, 2017) to neutralize size and outliers effect. This study has calculated log return of the Nifty 50 series. Briefly, the series of foreign flows are I (0) and Nifty 50 returns I (1). Foreign flow series are integrated at order zero, while Nifty 50 is integrated at order one.
LAG LENGTH SELECTION CRITERIA
Gangadharan and Yoonus (2012) mentioned that there are two basic conditions before applying VAR model. The time series must be stationary and optimum lag length should be selected. The lag length of VAR model is ascertained from different information criteria. The optimal lag length is smallest value of multivariate information criteria based on Akaike information criterion (AIC), Schwarz criterion (SC) and Hannan–Quinn (HQ) criterion. The tables of lag length criterion have not been shown to save space. This study has relied on Schwarz information criterion for lag selection. Most of the studies based on high frequency data used SIC as appropriate lag length criteria. In this study, the three VAR equations has been estimated with different foreign flow variables and market return. According to information criterion, four lags have been selected in LFIIG, LFIIS to returns while five lags have been selected for LFIIP and returns
TABLE 3
VAR ESTIMATES OF SYSTEM EQUATIONS
GROSS PURCHASE SALES
LGFII RETURNS LFIIP RETURNS LFIIS RETURNS
LGFII(-1) 0.3431 -0.0015 LFIIP(-1) 0.3034 -0.0012 LFIIS(-1) 0.3456 -0.0015
-0.0201 -0.0008 -0.0203 -0.0007 -0.0201 -0.0007
[ 17.0947] [-1.97663] [ 14.9716] [-1.65553] [ 17.1968] [-2.07130]
LGFII(-2) 0.1655 0.0011 LFIIP(-2) 0.1631 0.0011 LFIIS(-2) 0.1560 0.0007
-0.0211 -0.0008 -0.0210 -0.0008 -0.0211 -0.0008
[ 7.83132] [ 1.37739] [ 7.76164] [ 1.37710] [ 7.38418] [ 0.94490]
LGFII(-3) 0.1190 -0.0004 LFIIP(-3) 0.0932 -0.0006 LFIIS(-3) 0.1352 0.0000
-0.0212 -0.0008 -0.0212 -0.0008 -0.0211 -0.0008
[ 5.62572] [-0.54236] [ 4.39315] [-0.82796] [ 6.39729] [ 0.02110]
LGFII(-4) 0.1784 -0.0007 LFIIP(-4) 0.1361 -0.0005 LFIIS(-4) 0.1698 -0.0007
-0.0201 -0.0008 -0.0210 -0.0008 -0.0199 -0.0007
[ 8.88728] [-0.96000] [ 6.47472] [-0.68216] [ 8.52564] [-0.91404]
LFIIP(-5) 0.1150 -0.0003
-0.0203 -0.0007
[ 5.68151] [-0.36152]
RETURNS(-1) -0.6492 0.0519 RETURNS(-1) 2.4592 0.0517 RETURNS(-1) -4.1705 0.0517
-0.5334 -0.0204 -0.5586 -0.0204 -0.5530 -0.0204
[-1.21715] [ 2.54864] [ 4.40246] [ 2.53339] [-7.54160] [ 2.53989]
RETURNS(-2) 0.6851 -0.0317 RETURNS(-2) 2.0653 -0.0291 RETURNS(-2) -0.4115 -0.0364
-0.5339 -0.0204 -0.5604 -0.0205 -0.5598 -0.0206
RETURNS(-3) 0.2659 -0.0210 RETURNS(-3) 0.1023 -0.0218 RETURNS(-3) 0.6078 -0.0222
-0.5341 -0.0204 -0.5615 -0.0205 -0.5594 -0.0206
[ 0.49786] [-1.03073] [ 0.18212] [-1.06014] [ 1.08645] [-1.07724]
RETURNS(-4) -0.5778 -0.0252 RETURNS(-4) -0.4009 -0.0226 RETURNS(-4) -0.3045 -0.0244
-0.5316 -0.0203 -0.5614 -0.0205 -0.5576 -0.0206
[-1.08696] [-1.24117] [-0.71403] [-1.09993] [-0.54612] [-1.19002]
RETURNS(-5) -0.4320 -0.0243
-0.5576 -0.0204
[-0.77476] [-1.19117]
C 1.7084 0.0144 C 1.5398 0.0135 C 1.5600 0.0122
-0.1635 -0.0062 -0.1616 -0.0059 -0.1470 -0.0054
[ 10.4508] [ 2.30345] [ 9.52609] [ 2.29368] [ 10.6151] [ 2.25375]
DUMMY_CRISIS -0.0827 -0.0032 DUMMY_CRISE
S -0.0892 -0.0034
DUMMY_CRISE
S -0.0760 -0.0030
-0.0251 -0.0010 -0.0269 -0.0010 -0.0256 -0.0009
[-3.29280] [-3.31853] [-3.31862] [-3.48332] [-2.97468] [-3.20687]
R-squared 0.4802 0.0114 R-squared 0.4692 0.0119 R-squared 0.4872 0.0110
Adj. R-squared 0.4783 0.0077 Adj. R-squared 0.4668 0.0074 Adj. R-squared 0.4853 0.0073
F-statistic 246.6736 3.0732 F-statistic 192.8558 2.6319 F-statistic 253.6613 2.9811 Source: Author’s calculation using Eviews 10 student
version
VECTOR AUTOREGRESSION (VAR) ESTIMATES
Table 3 presents the VAR estimates of the system equations. Equation 3 and equation 4 is estimated for all the variables. As it is mentioned above, in VAR estimates, optimum lag selection is very important. This study has relied on SIC lag length selection criteria. The minimum value of information criteria was at length 4 in case of LFIIG and LFIIS while it is 5 in case of LFIIP The model is significant as the F-statistics is significant in all the series. It can ascertained from the above table, that the coefficient of past market returns in the first and second lag are positive and significantly explaining the FII purchases and market return significantly negative in explaining the FII sales at first lag. This confirms that the foreign investors are positive feedback traders in Indian capital market. However, this study found that first lag of FII gross and FII sales have significant impact on Nifty returns .The FIIs sale and FIIs purchase variables are significantly influenced by the lagged market returns. Each of the flow variables are significantly and positively impacted by their own lags. This indicates that foreign investors tend to follow the other foreign investors in Indian market. Furthermore, this study has also used exogenous variable that is dummy for global financial crisis as it was drastic event that affected Indian market. It is evident from the above results that global financial crises have significant negative impact on market return as well as on FII flows.
TABLE 4
VARRESIDUAL SERIAL CORRELATION LMTESTS
Null hypothesis: No serial correlation at lag h
Lag LRE* stat df Prob. Rao
F-stat df Prob.
1 23.24438 4 0.0001 5.823972 (4, 4800.0) 0.0001
2 34.88901 4 0.0000 8.752198 (4, 4800.0) 0.0000
3 39.90551 4 0.0000 10.01587 (4, 4800.0) 0.0000
4 38.07847 4 0.0000 9.555478 (4, 4800.0) 0.0000
5 5.568628 4 0.2338 1.392674 (4, 4800.0) 0.2338
6 3.593598 4 0.4638 0.898549 (4, 4800.0) 0.4638
7 5.462435 4 0.2431 1.366101 (4, 4800.0) 0.2431
8 3.774536 4 0.4374 0.943808 (4, 4800.0) 0.4374
9 0.938901 4 0.9189 0.234699 (4, 4800.0) 0.9189
10 4.305902 4 0.3662 1.076734 (4, 4800.0) 0.3662
In table 4 above, residual serial correlation is presented. It is important to ensure that there is lo autocorrelation beyond the optimum lag selection. This study has relied on SC information criteria to select optimum lag length among all the information criteria. It is shown in this table that there is no autocorrelation beyond lag four. Which proves that this study has selected proper lag length. This study has checked VAR residual autocorrelation for others models as well but shown only one to save space. There is no autocorrelation beyond four lags in others models also. In VAR framework, VAR residual autocorrelation is an important residual test that shows the stability and reliability of the VAR model. If there is still autocorrelation beyond the selected lag, it would indicate that lag length selection is not optimum and VAR model is not reliable.
2563
Figure 1: Impulse Response Function of foreign
institutional investors and market return.
Figure 1 shows the impulse response function of foreign institutional investors and market returns. Impulse response function is popularly used to analyze dynamic interaction between the two variables. The solid line between the two dotted line represents Impulse Response coefficients and the two dotted lined around it represent bootstrapped 90 percent confidence band (Dhingra et.al, 2016). Using impulse response function, an attempt has been made to study the dynamic response of foreign flow variables that are LFIIG, LFIIP and LFIIS to one unit standard deviation shock to market return. The impulse response function shows that a shock to market return raises the LFIIP significantly, however its impact is short-lived and disappear at the fifth lag. LFIIS is negative affected and its impact disappears at fourth lag and marginal effect on LFIIG up to few lags. This further confirms the VAR results that market return posses information of foreign flows but foreign flows do not contain information about market return.
EMPIRICAL ANALYSIS AND DISCUSSION
This study has found a significant and positive relation between market return lagged one and lagged two period and FIIP. It implies that FIIs have been positive feedback traders on daily basis that is consistent with the findings of Dhingra and Gandhi (2016). Moreover, FIIs tend to buy following the positive development in the Indian capital market that is in line with the findings of Batra (2003). Another major finding is that LFIIG, LFIIP and LFIIS follow their own daily trade and the trade of other foreign investors as they are significantly influenced by their own lags. Therefore, it can be inferred from the finding that the growth of market returns have a higher positive impact on the
growth of FIIs purchases and negative impact on FII sales. This provides evidence than foreign investment tends to buy more when market rises and sell more when markets are down. This finding is in line with the findings of Naik and Padhi (2014) and Vardhan and Sinha (2016). Around 48% variation in gross FII can be explained by lagged values of gross FIIs and market return. However, slightly more than 1% variation in market return is explained by gross FIIs flow. Similar results are found with FII purchase and sales also. It suggests that foreign flows capacity to explain market return is low, which again corroborates with findings of Naik and Padhi (2014) Strong positive autocorrelation is found for all the three flows up to four days lag that means increase or decrease in foreign flow tends to stimulate other foreign investors to follow the same direction which is similar with the findings of Oh and Parwada (2007) in the Korean stock market. This study has found bidirectional impact between FII purchase and market returns. FII gross significantly affects the market return in one lag period. This study has used dummy variable for global financial crises. The coefficient of dummy variable is negatively significant in case of foreign capital flows and capital market returns. This suggests that global financial crises have negatively affected the foreign capital flows and market returns as well. The finding is consistent with finding of Bajaj (2014) that foreign investors liquidate there investment at the of crises and start booking prices by selling at the available options.
5
CONCLUSION
ignore market fundamentals and often times exacerbate price fluctuations in the market. The impulse response function shows that an innovation or shock to market return raises the LFIIP significantly, however its impact is short-lived and disappear at the fifth lag. LFIIS is negative affected and its impact disappears at fourth lag and marginal effect on LFIIG up to few lags. The findings suggest that India still needs capital control policies to prevent destabilization and provide stability in markets. Keeping in mind the importance and necessity foreign investment, India need to provide more stable economic environment to encourage foreign investors. Government should consider increasing sectoral limits for foreign investors in Indian companies. Better corporate governance practices and smooth policy implementations has huge potential to attract foreign investors. In this study only foreign flows and nifty returns has been considered that is why the study should be considered with some caution. While this study focuses on foreign institutional investors, there is scope and need to study the behavior of foreign individual investors along with other type of investors. The Indian government has come up with several reforms in FII debts in last few years that have increased foreign inflows in debt. This could also be a fruitful topic to explore. As in practice there are various other variables that affect the market returns and foreign flows, adding them in the study may give more robust findings. The researchers may look forward to address these issues in future researches. [Note: This paper is extracted from ongoing research work of the first author of this study]
6
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