Do Country ETFs Influence Foreign Stock Market Index? Evidence from India ETFs

Abstract

We examine the influence of country exchange traded funds (ETFs) on the country’s stock market indices, irrespective of their underlying benchmark. A pooled ordinary least square (OLS) analysis of a sample of 28 India ETFs listed in the US, UK, Canada, France, Japan, Israel and Singapore reveals that India ETFs have a significant impact on the country’s stock indices. We also document reverse causal dynamics between country ETFs and the country’s stock indices. The results are robust even after controlling for global effects, stock market volatility, foreign institutional investor (FII) flows, foreign exchange rate and asset size of India ETFs. The findings of the study have implications for global investors and policymakers in both emerging and developed markets. Policymakers would find it compelling to monitor country ETFs’ fund flows into the underlying country, as withdrawal of country ETFs could have a cascading effect on the economy.

JEL Classification: G11, G15, G23

Keywords

Mutual funds country ETFs foreign portfolio investment

1. Introduction

Emerging market exchange traded funds (ETFs) and country ETFs, which focus on a particular country, are increasingly used to get a definite exposure to international markets. The two largest country ETFs in the world—iShares MSCI Japan ETF (EWJ) and WisdomTree Japan Hedged Equity Fund—alone have roughly assets worth US$14 billion and US$11 billion, respectively (iShares by BlackRock, 2014). Country ETFs are spread across 38 countries and hold assets worth US$83 billion (iShares by BlackRock, 2014) and have an enormous impact on the markets that they track, especially the developing and emerging markets. There is abundant literature which examines if ETFs track their underlying benchmark (Ackert & Tian, 2000; Alexander & Barbosa, 2005; Blitz, Huij, & Swinkels, 2012), but no prior study has analysed the impact of country ETFs on the underlying country’s stock market index, irrespective of their underlying benchmark index. Hence, this study differs from prior studies, by examining the influence of country ETFs, specifically India ETFs, on the country’s stock market indices, namely, CNX NIFTY and S&P BSE SENSEX.

India’s spectacular growth in the last few decades has not only attracted lot of interest among foreign investors, but also become one of their most favoured investment destinations. India, a US$2 trillion economy, is one of the largest economies in the world and is poised to be a US$3 trillion economy by 2019 (IMF World Economic Outlook Report 2014). The liberalisation of the Indian economy in 1991 and the subsequent entry of foreign institutional investors (FII) paved way for the enormous foreign flows into the country. The FII inflows, what started as a trickle in 1993–1994 with a net inflow of ₹130 million, has grown manifold over the years and the net inflow stood at a staggering ₹1,683,670 million, at the end of FY 2012–2013 (Securities and Exchange Board of India Ltd). Foreign inflows into India predominantly come through India focussed offshore funds and India exchange traded products (ETPs). India focussed offshore funds and ETFs, at their peak in 2010, had total assets worth US$55.7 billion. At the end of September 2014, India ETFs held US$8.4 billion assets, while India focussed offshore equity funds held US$29.8 billion (MorningStar India Offshore Fund Spy, 2014). India ETFs have been increasing their asset base steadily and now form about 22 per cent (which was around 9% in 2010; MorningStar India Offshore Fund Spy, 2014) of the total India focussed funds. One of the prime reasons for this upswing has been the lower expense ratio of India ETFs, which stands at 0.8 per cent. Moreover, ETFs can be easily exited, unlike offshore funds which charge an exit load. The top two India ETFs are the Wisdom Tree India Earnings (EPI) and iShares MSCI India ETF (INDA) that have the largest assets under management (AUM), of US$1.95 billion and US$1.43 billion, respectively, as of September 2014 (Factsheet of WisdomTree Indian Earnings and iShares). Prior research shows evidence of pricing efficiency, price discovery and volatility spill over between country ETFs and other market ETFs and also between country ETFs and underlying securities, but the impact of country ETFs on the country’s stock market is yet to be explored. Even though, India ETFs have large AUM, the impact of these funds on the underlying stock market behaviour is not known.

In this study, we examine the impact of India based country ETPs on stock market returns in India, after controlling for global effects (S&P 500 index), stock market volatility (India Volatility Index VIX), FII flows, foreign exchange rate and asset size of the India ETFs. The analysis of India ETPs listed in the US, UK, France, Japan, Israel and Singapore show that India ETPs have a significant influence on the Indian stock indices, both CNX NIFTY and S&P BSE SENSEX. The study also found a reverse causal relationship exists between the India ETPs and the stock indices. The findings of our study have important implications for policymakers as they would find it compelling to monitor India ETFs’ fund flows into the Indian market. It is pertinent to note that the sudden withdrawal of India offshore funds and India ETFs could have a cascading effect on the economy. From the academic point of view, this is a pioneering study which dwells on the influence of country ETFs on their country indices and would add value to the literature as the first research article on India ETFs.

The rest of the article is structured as follows: Section 2 deals with literature survey, Section 3 explains the data and sample, Section 4 discusses the methodology used, Section 5 explains the empirical results and Section 6 summarises the findings and brings out the implications of the study.

2. Literature Survey

ETFs, one of the best innovations in the financial markets, are increasingly used by investors globally. ETFs have numerous advantages over traditional mutual funds, such as, less affected by capital gains tax, have low expense ratios can be short sold and traded like stocks throughout the day. S&P Depository Receipts (SPDRs), launched in 1993, was the first ETF to be launched in the world. Since then, the last decade has seen ETFs making rapid advancements in the world of financial markets.

Country ETFs track indices from different parts of the world. For instance, KODEX 200 tracks South Korea’s KOSPI index, while Tracker Fund of Hong Kong (TraHK) tracks Hong Kong’s Hang Seng Index. Similarly, TOPIX and China 50 ETF track Japan’s NIKKEI index and Shanghai Stock Exchange (SSE 50) index, respectively. iShares MSCI United Kingdom Index ETF (EWU), iShares MSCI Spain Index (EWP) and iShares MSCI Italy Index (EWI) track the European stock market indices. Country ETFs trade at a premium or discount to their underlying index, to factor for the time difference that exists between countries. Several studies have looked into the pricing efficiency of country ETFs. Ackert and Tian (2000) examined the performance of country ETFs and US ETFs and found that US ETFs were priced closer to their NAVs than the country ETFs. Engle and Sarkar (2006) examined the premium and discounts for 21 US ETFs and 16 country ETFs and found that the premiums or discounts were lower for US ETFs compared with country ETFs. They also reported that the price deviations for US ETFs were very small and did not persist. Ackert and Tian (2008) investigated the pricing deviation of 28 country ETFs and US ETFs and found that country ETFs which track emerging markets had higher pricing deviation compared with that of ETFs tracking the developed markets. Harper, Madura, and Schnusenberg (2006) studied the performance of country ETFs of 14 countries with that of closed ended funds and concluded that country ETFs exhibited higher mean returns and performed better than closed ended funds. They attributed this better performance to lower expense ratios of ETFs. The time difference between the countries ensures the non-synchronised trading hours among the different markets. The Australian and Asian markets open earlier and also close earlier than the European markets. On the other hand, at least, a part of the trading hours of European markets synchronise with the trading hours of US market. Jares and Lavin (2004) investigated iShares ETFs for Japan and Hong Kong and reported that there were deviations between the country ETFs and their underlying securities and that these lagged differences were positively related to the country ETFs returns. Delcoure and Zhong (2007) reported significant premium for 20 country ETFs, even after controlling for transaction cost. They also reported that exchange rate volatility, financial and political crises, trading activity and institutional holders were the factors that cause the premiums.

Another strand of literature looks at the price discovery process of country ETFs. The different trading hours of country ETFs and their underlying indices give rise to another natural question—how does the information flow between country ETFs and their benchmarks happen? Olienyk, Schwebach, and Zumwalt (1999) examined the cointegration between World Equity Benchmark Shares (WEBS) and closed ended country funds and found substantial cointegration existing among WEBS and as well as between WEBS and closed ended country funds. Durand and Scott (2003) found that US investors investing in Australia over- reacted to the lagged returns in the US market. Country ETFs give access to growing economies around the world. International investors seek to maximise returns by investing in different countries. Yavas, Rezayat, and Bilici (2004) studied the co-movement of country ETFs that track American, Japanese and European markets and reported interdependence among these markets and there was scope for further portfolio diversification among these markets. Tse and Martinez’s (2007) analysis of the price discovery process of 24 international iShares ETFs revealed that country ETFs trading in US market was driven by the information released in the local markets’ trading hours and not during the trading hours of US session. Mazumder, Chu, Miller, and Prather (2008) investigated the day of the week pattern for 17 iShares country ETFs and SPDRs and found that iShares exhibited day of the week pattern; information that could be utilised by informed traders to maximise their profits. Levy and Lieberman’s (2013) examination of the price formation of country ETFs revealed a structural difference between synchronised and non-synchronised trading hours. They found that NAVs played a prominent role in impacting the ETF prices during synchronised trading hours and that S&P 500 index had a dominant effect on non-synchronised trading hours.

Thus, the review of literature on country ETFs shows that country ETFs are priced higher than US ETFs (Ackert & Tian, 2000; Engle & Sarkar, 2006), premiums are higher (Delcoure & Zhong, 2007), pricing deviations are higher (Ackert & Tian, 2008) and they perform better than closed ended funds (Harper et al., 2006). The literature on price discovery reveals that cointegration exists between country ETFs and closed ended funds (Olienyk et al., 1999) and there is evidence of interdependence among country ETFs (Yavas et al., 2004); According to Tse and Martinez (2007), information is released during local market trading hours; country ETFs exhibit day of the week pattern and structural difference exists between synchronised and non-synchronised trading hours (Levy & Liebermann, 2013) and follow their indices closely (Ackert & Tian, 2000; Engle & Sarkar, 2006). Based on the research gap, we postulate the following questions: What is the influence of country ETFs on the underlying country stock market index? Is there any reverse causal relationship between country ETFs returns and its underlying country stock index returns? We, therefore, attempt to investigate the influence of country ETFs on its underlying country index and the reverse casual effect of the index on country ETFs performance. Most of the prior studies on ETFs focus on scheme wise analysis and do not consider the analysis of all schemes together. There is a need to analyse various ETF schemes together to get a more holistic and conclusive inference. In this study, we use a pooled ordinary least square (OLS) analysis to evaluate all the ETF schemes together controlling for the difference because of scheme wise effects.

3. Data and Sample

Country ETFs are a special classification of ETFs market and are specifically designed to track the indices of foreign countries. Interestingly, country ETFs are traded in two different countries—the ETFs are traded in the local market, whereas the underlying indices track the portfolio of shares of a foreign country. For instance, iShares China Large Cap ETF (FXI US) is traded in the US and it tracks FTSE China 25 index. Emerging markets and BRICS countries have attracted lot of fund flows because of their higher growth potential. There are several emerging market ETFs that invest in a portfolio of emerging markets, which includes India. This study has considered only those country ETFs that specifically invest in India alone, and has excluded emerging market ETFs where India is also included in its portfolio. In other words, this study has considered country ETPs that exclusively invest in India—including Exchange Traded Certificates (ETCs) and Exchange Traded Notes (ETNs). Henceforth, we would be referring to them as country ETPs.

The inception period for each India ETP is different and some India ETPs like KSM India, an ETC from Israel, was launched as early as 2005. However, to have a common time period, data from 2 January 2010 until 31 August 2014 have been considered. Moreover, the ETPs should have been in existence for at least a minimum period of 1 year to be included in our sample. Based on the above criteria, the final sample consists of daily closing prices of 28 India ETPs from America, Canada, France, Hong Kong, Israel, Japan, Singapore and UK. WisdomTree India Earnings (EPI US), iShares MSCI India ETF (INDA US) and Lyxor ETF MSCI India are some of the largest India ETPs in the world. It is interesting to note that though CNX NIFTY and S&P BSE SENSEX are the most prominent indices in India, most India ETPs have different underlying benchmark indices. For instance, WisdomTree India Earnings (EPI US) has WisdomTree India Earning TR which is a proprietary index and iShares MSCI India ETF has MSCI Emerging Markets India as their underlying benchmark. Similarly, iShares India 50 ETF and iShares S&P BSE SENSEX India have CNX NIFTY and S&P BSE SENSEX as their underlying benchmark indices. Even though each India ETP has different underlying benchmarks, we have examined how the India ETPs collectively impact the Indian stock indices, namely, CNX NIFTY and S&P BSE SENSEX.

While examining the impact of ETPs on stock returns, we control for other factors that impact returns. We control for volatility by considering Volatility index, since several authors have found an inverse relationship between VIX and stock returns (Fleming, Ostdiek, & Whaley, 1995). According to Whaley (2009), VIX has been dubbed as the ‘investor fear gauge’ and is also used to hedge the portfolio. In short, VIX tends to act as the portfolio insurance. Lubnau and Todorova (2014) explored the predictive power of VIX and find that low volatility levels are followed by significantly positive returns in the shorter term. The literature is replete with studies that document VIX’s strong inverse relationship with stock indices (Lubnau & Todorova, 2012; Sarwar, 2012), mutual funds (Petajisto, 2013) and ETF premiums (Petajisto, 2011). While the aforementioned studies document the relationship between CBOE VIX and stock indices, our study documents the relationship between India VIX and India ETPs. We use India VIX (IVIX) which measures the volatility expectations in the near term.

A large body of literature deals with the co-movements between stock markets (Cotter, 2004; Gilmore & MacManus, 2004; Longin & Solnik, 1995; I. Meric & Meric, 1989). S&P 500 impacts stock markets across the globe. There are numerous studies that have examined the relationship between US and other countries stock markets. Barclay, Litzenberger, and Warner (1990) found a positive correlation between the US and Japanese markets. King and Wadhwani (1990) examined the relationship and also showed that contagion effect exists among the US, UK and the Japanese stock markets. Hence, we hypothesise that S&P would also have an impact on Indian stock indices and since the US stock market opens after the Indian market has closed, we expect the S&P 500 index and India ETPs to impact the Indian market only on the next day. Therefore, we control for global market changes by including the lag of S&P 500 index.

Foreign exchange rate is a vital macroeconomic variable, which has a bearing on the country’s growth and the exchange rate dynamics play an important role in the portfolio choice of the investors. If the domestic currency is not strong enough, foreign investors would be wary of bringing in the requisite funds into a country.

India ETFs is one of the channels through which FII invest. Since FIIs play a prominent role in the fund flows to India, we also control for this factor by taking into consideration the net FII investment.

Funds with higher asset size attract a lot of investors and a few India ETPs such as WisdomTree India Earnings (EPI US) and iShares MSCI India ETF (INDA US) hold around US$2 billion worth of assets. Hence, we also examine whether the asset size of India ETPs has any impact on the index returns. Finally, after controlling for the above variables, we investigate whether the India ETPs impact the returns of Indian stock indices. Further, we hypothesise that two lags of India ETPs might have a bearing on the indices and consider two lags since it usually takes a few sessions for the markets to comprehend the flow of information.

The data include daily data on India ETPs price and ETPs asset size, which are sourced from Bloomberg database. The daily returns of India ETPs are calculated using the continuously compounded rate of return as follows R_p = ln (P_t/P_{t – 1}), where, P_t and P_t_{– 1} represents the closing prices of India ETPs at time ‘t’ and ‘t – 1’, respectively. The daily closing prices of India VIX and CNX NIFTY data have been obtained from NSE India, and S&P BSE SENSEX data are obtained from BSE India. The FII net investment flows data are obtained from Securities and Exchange Board of India (SEBI) and foreign exchange data are obtained from International Monetary Fund (IMF) database. The daily closing prices of S&P 500 data are obtained from Federal Reserve Bank of St. Louis.

4. Methodology

Prior research has focussed on evaluating each ETF separately in terms of performance and price discovery and generalises results based on the findings of various schemes. In this study, we examine the collective influence of India ETPs on the Indian stock market indices. The Hausman test (1978) (Prob. > ϰ² = 0.8373) shows that random effect model is more appropriate than fixed effect panel regression and Breusch Pagan Lagan Multiplier (BPLM) test (Prob. > ϰ² = 1) shows that pooled OLS method is more appropriate than random effect panel model. Hence, in this study, we perform pooled OLS regression analysis to examine the impact of India ETPs on the Indian indices, by considering all India ETPs together and we also control for scheme wise effects. The pooled regression model used in this study is as follows: _i,t₀₁_i,t₂_i,t–1₃_i,t₄_i,t₅_i,t₆_i,t₇_i,t₈_{i, t–1}₉_i,t–2u_it

where Nifty_i,t refers to the NIFTY returns, S&P_i,t–1 refers to a lag of S&P 500 returns, IVIX_i,t refers to India Volatility Index, FX_i,t refers to USD exchange rate of respective countries, FII_i,t refers to the net foreign institutional investment, Tot_assets_IETP_i,t refers to the asset size of India ETPs, IndiaETP_i,t–1 refers to a lag of India ETPs returns and IndiaETP_{i,t – 2} refers to the second lag of India ETPs returns. Also, μ_it represents the error term in the above pooled OLS equation.

Similarly, we also examine the impact of India ETPs on S&P BSE SENSEX and the pooled regression model is as below: _i,t₀₁_i,t₂_i,t–1₃_i,t₄_i,t₅_i,t₆_i,t₇_i,t₈_i,t–1₉_i,t–2u_it

where Sensex_i,t refers to S&P BSE SENSEX returns and the other terms remain the same as in the previous equation.

Apart from the above analysis, we further explore the impact of India ETPs by dividing them into sub-samples. In the first sub-sample, we consider India ETPs that either have CNX Nifty or S&P BSE Sensex alone as their underlying index. Later, we analyse by including India ETPs which have other underlying benchmarks except CNX Nifty or S&P BSE Sensex. Finally, we explore India ETPs based on their asset size and in specific include India ETPs that have a minimum of US$100 million as their AUM.

Further, we also hypothesise that the Indian stock indices would influence the movements in India ETPs. Broadly, the model to examine the relationship between India ETPs and CNX NIFTY and S&P BSE SENSEX is expressed as follows: _i,t₀₁_i,t₂_i,t₃_i,t₄_i,t₅_i,t₆_i,t₇_i,t–1u_it _i,t₀₁_i,t₂_i,t₃_i,t₄_i,t₅_i,t₆_i,t₇_i,t–1u_it

where, IndiaETP_i,t refers to India ETPs returns, Nifty_i,t refers to CNX NIFTY returns, Sensex_i,t refers to S&P BSE SENSEX returns and the other variables remain the same.

5. Empirical Results

5.1 Characteristics of India ETPs

In recent years, India, being a favoured destination for emerging market ETFs and country ETFs, has witnessed an explosion of India ETPs across the globe. Apart from the traditional country ETFs, which invest in India, India ETCs such as TACHLIT India and KSM India are also making their presence felt. ETFs are preferred over mutual funds because of their low expense ratios. India ETPs are no exception to this and have lower expense ratios compared with India offshore funds. WisdomTree Indian Earnings (EPI US) is one of the largest India ETFs in the world and has assets worth US$2,049 million. WisdomTree India Earnings, listed in the US, has its own proprietary index called WisdomTree India Earning Index, and it comprises 189 stocks from different sectors of the economy. iShares MSCI India ETF (INDA US), another popular India ETF listed in the US, belongs to BlackRock and has assets worth US$1,457 million with MSCI Emerging Markets India as its underlying benchmark index. Table 1 shows the summary statistics of India ETPs considered in the sample.

Table 1.
Characteristics of India ETPs

Ticker India Exchange Traded Products Underlying Index Listed Country Inception Date Expense/Mgt. Fees (%) Asset Size (in Million USD)

EPI US WisdomTree India Earnings WisdomTree India Earning TR USA 22/02/2008 0.84 2,049.01

INDA US iShares–MSCI India ETF MSCI Emerging Markets India USA 03/02/2012 0.67 1,457.00

INDY US iShares– India 50 ETF CNX NIFTY USA 20/11/2009 0.94 22.15

SMIN US iSharesMSCI India Small Cap MSCI India Small Cap Net USA 09/02/2012 0.74 0.35

INP US iPath MSCI India Index MSCI India Total Return Index USA 19/12/2006 0.89 394.5

PIN US Powershares India Portfolio Indus India Index TR USA 05/03/2008 0.82 25.2

ICN US WisdomTree Indian Rupee Strategy Money market rates USA 14/05/2008 0.45 1.2

EIA FP EASY ETF EIA FP EQUITY Dow Jones India 15 TR USA 24/09/2009 0.55 26.76

SCIF US Market Vectors India S/C MV India Small Cap TR USA 25/08/2010 0.85 7.42

INXX US EGShares India Infra ETF INDXX Infra Structure TR USA 11/08/2010 0.86 4.35

INCO US EGShares India Consumer ETF INDXX Ind Cons TR USA 10/08/2011 0.89 0.25

INDL US Direxion Daily India Bull 3X Shares Indus India Index USA 11/03/2010 0.95 72

INR US Market Vectors Indian Rupee/USD S&P Total Return Index USA 14/03/2008 0.55 1.78

ZID CN BMO India Equity Index ETF BNYM India CAD HT Canada 21/01/2010 0.75 3.46

XID CN iShares India Index ETF CNX NIFTY Canada 21/01/2010 0.99 1.28

INR FP Lyxor ETF MSCI India MSCI Emerging Markets India Net France 07/11/2006 0.85 4.55

CI2 FP Amundi ETF MSCI India MSCI Emerging Markets IndiaNet France 03/03/2009 0.8 105.95

LYINR SW LYXOR ETF MSCI India MSCI Emerging Markets India Net France 16/01/2007 0.85 8.7

2836 HK iShares S&P BSE SENSEX India S&P BSE SENSEX Hong Kong 02/11/2006 1.15 10.61

KSMINDIA IT KSM India CNX NIFTY Israel 06/09/2005 1.00 19.86

PMMSCI8 IT KSM MSCI India MSCI India Israel 05/06/2007 1.00 n/a

TCSP21 IT TACHLIT India CNX NIFTY Israel 04/03/2010 1.00 n/a

HRNFT30 IT Harel Sal India Nifty CNX NIFTY Israel 01/12/2009 1.00 n/a

MNFTY67 IT PSAGOT SAL CNX Nifty CNX NIFTY Israel 30/12/2010 0.80 19.21

1678 JP NEXT Funds CNX NIFTY linked CNX NIFTY Japan 26/11/2009 0.95 46.3

1549 JP NIKKO ETF CNX Nifty India ST Generic 1st IH Future Japan 29/10/2010 0.45 7.39

XCS5 LN DBX MSCI India MSCI Emerging Markets India Net London 12/11/2010 0.75 106.18

INDIA SP iShares MSCI India Index ETF MSCI India Index Singapore 15/06/2006 1.09 714.47

Ticker	India Exchange Traded Products	Underlying Index	Listed Country	Inception Date	Expense/Mgt. Fees (%)	Asset Size (in Million USD)
EPI US	WisdomTree India Earnings	WisdomTree India Earning TR	USA	22/02/2008	0.84	2,049.01
INDA US	iShares–MSCI India ETF	MSCI Emerging Markets India	USA	03/02/2012	0.67	1,457.00
INDY US	iShares– India 50 ETF	CNX NIFTY	USA	20/11/2009	0.94	22.15
SMIN US	iSharesMSCI India Small Cap	MSCI India Small Cap Net	USA	09/02/2012	0.74	0.35
INP US	iPath MSCI India Index	MSCI India Total Return Index	USA	19/12/2006	0.89	394.5
PIN US	Powershares India Portfolio	Indus India Index TR	USA	05/03/2008	0.82	25.2
ICN US	WisdomTree Indian Rupee Strategy	Money market rates	USA	14/05/2008	0.45	1.2
EIA FP	EASY ETF EIA FP EQUITY	Dow Jones India 15 TR	USA	24/09/2009	0.55	26.76
SCIF US	Market Vectors India S/C	MV India Small Cap TR	USA	25/08/2010	0.85	7.42
INXX US	EGShares India Infra ETF	INDXX Infra Structure TR	USA	11/08/2010	0.86	4.35
INCO US	EGShares India Consumer ETF	INDXX Ind Cons TR	USA	10/08/2011	0.89	0.25
INDL US	Direxion Daily India Bull 3X Shares	Indus India Index	USA	11/03/2010	0.95	72
INR US	Market Vectors Indian Rupee/USD	S&P Total Return Index	USA	14/03/2008	0.55	1.78
ZID CN	BMO India Equity Index ETF	BNYM India CAD HT	Canada	21/01/2010	0.75	3.46
XID CN	iShares India Index ETF	CNX NIFTY	Canada	21/01/2010	0.99	1.28
INR FP	Lyxor ETF MSCI India	MSCI Emerging Markets India Net	France	07/11/2006	0.85	4.55
CI2 FP	Amundi ETF MSCI India	MSCI Emerging Markets IndiaNet	France	03/03/2009	0.8	105.95
LYINR SW	LYXOR ETF MSCI India	MSCI Emerging Markets India Net	France	16/01/2007	0.85	8.7
2836 HK	iShares S&P BSE SENSEX India	S&P BSE SENSEX	Hong Kong	02/11/2006	1.15	10.61
KSMINDIA IT	KSM India	CNX NIFTY	Israel	06/09/2005	1.00	19.86
PMMSCI8 IT	KSM MSCI India	MSCI India	Israel	05/06/2007	1.00	n/a
TCSP21 IT	TACHLIT India	CNX NIFTY	Israel	04/03/2010	1.00	n/a
HRNFT30 IT	Harel Sal India Nifty	CNX NIFTY	Israel	01/12/2009	1.00	n/a
MNFTY67 IT	PSAGOT SAL CNX Nifty	CNX NIFTY	Israel	30/12/2010	0.80	19.21
1678 JP	NEXT Funds CNX NIFTY linked	CNX NIFTY	Japan	26/11/2009	0.95	46.3
1549 JP	NIKKO ETF CNX Nifty India ST	Generic 1st IH Future	Japan	29/10/2010	0.45	7.39
XCS5 LN	DBX MSCI India	MSCI Emerging Markets India Net	London	12/11/2010	0.75	106.18
INDIA SP	iShares MSCI India Index ETF	MSCI India Index	Singapore	15/06/2006	1.09	714.47

Source: Statistical analysis performed by the authors.

Note: Table 1 shows the summary statistics of India exchange traded products. The ticker denotes the Bloomberg symbol of India ETPs. The table shows India ETPs along with its underlying benchmark, the country where the ETP is traded, the inception date and the expense ratio/management fees. The asset size of India ETPs is obtained from Bloomberg as on August 2014.

A few India ETPs have CNX NIFTY and S&P BSE SENSEX as their underlying benchmark indices. Apart from the above benchmarks, there are also other benchmarks, namely, MSCI Emerging Markets India, Indus India Index and INDXX Infrastructure Index (refer to Table 1). It is observed that the expense ratio is in the range of 0.45 to 1 per cent, and it is also not surprising that most India ETPs trade in the US as it is the largest market for ETFs in the world.

5.2 Descriptive Statistics of India ETPs

The descriptive statistics of India ETPs given in Table 2 show that iShares MSCI India Small ETF has the highest annualised return of 22 per cent followed by EG Shares India Consumer ETF with an annualised return of 20 per cent.

Table 2.
Descriptive Statistics of India Exchange Traded Products

Ticker India Exchange Traded Products Mean(%) Std. Dev. (%) Max (%) Min (%) No. of Obs.

EPI US WisdomTree India Earnings 0.67 26.16 2.93 –14.6 1,173

INDA US iShares MSCI India ETF 7.46 25.36 6.26 –6.87 602

INDY US iShares India 50 Etf 3.86 27.22 12.62 –6.94 1,173

SMIN US iShares MSCI India Small 22.13 30.03 6.54 –5.37 440

INP US iPath MSCI India Index 2.43 26.84 6.90 –7.07 1,173

PIN US Powershares India Portfolio 0.16 27.62 6.61 –6.91 1,173

EIA FP EASY ETF EIA FP EQUITY 3.33 23.5 5.56 –7.42 1,193

SCIF US Market Vectors India S/C –12.98 31.84 7.04 –8.28 1,010

INXX US EGShares India Infra ETF –13.73 31.3 6.93 –11.7 957

INCO US EGShares India Consumer 20.59 27.8 8.36 –7.49 591

INDL US Direxion Daily India Bull –10.31 69.43 17.58 –18.86 1,126

INR US Market Vectors Indian Rupee/USD –1.16 18.41 5.83 –8.18 1,064

ZID CN BMO India Equity Index ETF 4.06 23.66 5.35 –5.87 1,081

XID CN iShares India Index ETF 5.70 23.33 5.48 –6.81 1,137

INR FP Lyxor ETF MSCI India 3.86 22.49 5.12 –7.2 1,189

CI2 FP Amundi ETF MSCI India 4.16 22.92 5.23 –7.61 1,192

LYINR SW Lyxor ETF MSCI India 2.23 25.73 6.53 –7.84 1,172

2836 HK iShares S&P BSE SENSEX India 4.03 23.54 5.59 –5.53 1,150

KSMINDIA IT KSM India 2.82 23.32 10.45 –7.32 1,140

PMMSCI8 IT KSM MSCI India –0.39 21.6 6.71 –8.27 1,140

TCSP21 IT TACHLIT India 1.74 23.42 10.84 –9.86 1,077

HRNFT30 IT Harel Sal India Nifty 2.50 21.97 6.98 –10.07 1,140

MNFTY67 IT PSAGOT SAL CNX Nifty –0.24 22.95 14.61 –8.63 896

1678 JP NEXT Funds CNX NIFTY Linked 3.83 24.9 6.11 –8.00 1,146

1549 JP NIKKO ETF CNX Nifty India 7.56 22.2 4.99 –7.12 942

XCS5 LN DBX MSCI India –0.60 25.26 5.82 –8.10 935

INDIA SP iShares MSCI India Index ETF 2.03 30.72 23.87 –23.12 1,170

Ticker	India Exchange Traded Products	Mean(%)	Std. Dev. (%)	Max (%)	Min (%)	No. of Obs.
EPI US	WisdomTree India Earnings	0.67	26.16	2.93	–14.6	1,173
INDA US	iShares MSCI India ETF	7.46	25.36	6.26	–6.87	602
INDY US	iShares India 50 Etf	3.86	27.22	12.62	–6.94	1,173
SMIN US	iShares MSCI India Small	22.13	30.03	6.54	–5.37	440
INP US	iPath MSCI India Index	2.43	26.84	6.90	–7.07	1,173
PIN US	Powershares India Portfolio	0.16	27.62	6.61	–6.91	1,173
EIA FP	EASY ETF EIA FP EQUITY	3.33	23.5	5.56	–7.42	1,193
SCIF US	Market Vectors India S/C	–12.98	31.84	7.04	–8.28	1,010
INXX US	EGShares India Infra ETF	–13.73	31.3	6.93	–11.7	957
INCO US	EGShares India Consumer	20.59	27.8	8.36	–7.49	591
INDL US	Direxion Daily India Bull	–10.31	69.43	17.58	–18.86	1,126
INR US	Market Vectors Indian Rupee/USD	–1.16	18.41	5.83	–8.18	1,064
ZID CN	BMO India Equity Index ETF	4.06	23.66	5.35	–5.87	1,081
XID CN	iShares India Index ETF	5.70	23.33	5.48	–6.81	1,137
INR FP	Lyxor ETF MSCI India	3.86	22.49	5.12	–7.2	1,189
CI2 FP	Amundi ETF MSCI India	4.16	22.92	5.23	–7.61	1,192
LYINR SW	Lyxor ETF MSCI India	2.23	25.73	6.53	–7.84	1,172
2836 HK	iShares S&P BSE SENSEX India	4.03	23.54	5.59	–5.53	1,150
KSMINDIA IT	KSM India	2.82	23.32	10.45	–7.32	1,140
PMMSCI8 IT	KSM MSCI India	–0.39	21.6	6.71	–8.27	1,140
TCSP21 IT	TACHLIT India	1.74	23.42	10.84	–9.86	1,077
HRNFT30 IT	Harel Sal India Nifty	2.50	21.97	6.98	–10.07	1,140
MNFTY67 IT	PSAGOT SAL CNX Nifty	–0.24	22.95	14.61	–8.63	896
1678 JP	NEXT Funds CNX NIFTY Linked	3.83	24.9	6.11	–8.00	1,146
1549 JP	NIKKO ETF CNX Nifty India	7.56	22.2	4.99	–7.12	942
XCS5 LN	DBX MSCI India	–0.60	25.26	5.82	–8.10	935
INDIA SP	iShares MSCI India Index ETF	2.03	30.72	23.87	–23.12	1,170

Source: Statistical analysis performed by the authors.

Note: Table 2 shows the annualised mean and the number of observations of India ETPs returns. The daily closing prices of India ETPs have been obtained from Bloomberg and the above descriptive statistics are for the time period 4 January 2010 to 29 August 2014.

EG Shares India Infra ETF is the least performing India ETF because after falling off from the cliff in 2008, infrastructure stocks have continued to underperform the market. It also shows that Direxion Daily India Bull has the highest standard deviation (69.43%), and this is because it is a leveraged ETF. Market Vectors India ETF has a standard deviation of 31.8 per cent. The results show that the standard deviation for most India ETPs is in the range of 22 to 30 per cent. It is to be kept in mind that after the severe crash of financial markets in 2008, stock markets were still in a volatile phase, which could have resulted in higher standard deviations.

5.3 Impact of India ETPs on Underlying Stock Index

Emerging markets and BRICS countries have continued to perform well in the last few decades. India along with China, from the Asian region, has continued to outperform other markets consistently. After liberalisation, Indian economy has continued to grow strongly over the years. India’s GDP has consistently been on the upward swing in the last decade. The GDP, from a mere 1.43 per cent in 1991–1992, touched upwards of 9 per cent during 2005–2008 (Planning Commission, Government of India). The robust performance of the Indian economy has resulted in a vibrant Indian stock market and has also resulted in a deluge of foreign inflows into the country. FIIs continue to play a very prominent role in the Indian equity markets, and their importance can be gauged from the fact that the net FII inflows into India were US$13.9 billion as on September 2014. The foreign flows have come through a plethora of participants, namely, insurance companies, sovereign wealth funds, hedge funds, offshore funds and India ETPs. India focussed offshore funds, and ETFs had a net inflow of US$1.8 billion for the third quarter of 2014. At the end of September 2014, the total value of FII investments stood at US$294 billion (MorningStar), and it is interesting to note that India dedicated offshore funds and ETFs have assets worth US$38 billion. Major part of the funds invariably finds its way into stocks that form S&P BSE SENSEX and CNX NIFTY—the two leading stock indices from India.

The spate of fund flows into India has resulted in the indices putting up a stellar performance. CNX NIFTY which was 5,232 in January 2010 went up to 8,282 at the end of December 2014. Similarly, S&P BSE SENSEX had gone up from 17,379 in January 2010 to 27,346 (BSE Ltd) at the end of December 2014. The India Offshore funds and India ETPs have been the biggest contributors to the fund flows into India. Although they follow different underlying benchmarks, India ETPs would also impact CNX NIFTY and S&P BSE SENSEX. Hence, we examine the factors that have a bearing on the indices, purely from India ETFs’ perspective.

The analysis shows that a lag of S&P 500 has tremendous influence over CNX Nifty. It can also be seen that a lag of S&P 500 strongly impacts CNX Nifty even in the sub-samples. It is interesting to note that even when India ETPs have different underlying benchmarks, a lag of S&P 500 still has a sway over CNX Nifty (refer to Model 3 in Table 3[a]). The importance of S&P 500 also can be gauged from the fact, that even when only a handful of the largest India ETPs are considered, they still have an impact on CNX Nifty. The results corroborate the fact that a lag of S&P 500 has a strong impact on CNX Nifty, more than the contemporaneous S&P 500. The results are in line with our expectations since S&P 500 leads the capital markets across the world and the analysis proves that India has no exception to the above tenet. The exposure to foreign exchange rate also weighs heavily on the minds of international investors. The portfolio choice of the investors depends heavily on the foreign exchange rate dynamics. Francis, Hasan, and Hunter (2006) also showed that a dynamic relationship exists between currency markets and equity markets. Our study also shows that foreign exchange is a major influencing factor that has an effect on CNX Nifty. The sub-sample of the largest India ETPs by asset size alone shows that foreign exchange does not significantly influence CNX Nifty, probably, such ETPs, do not always consider the exchange rate mechanism while investing. Presumably, larger funds typically would like to deploy their funds at the earliest rather than wait to take advantage of the exchange rate fluctuations. A negative correlation between the forex rates and the stock market returns reduces the volatility of the home currency and makes the portfolio investment attractive. The analysis of foreign exchange rate showed that it has a significant impact on CNX NIFTY, that is, as the currency strengthens, the CNX NIFTY also goes up.

Table 3(a).
Impact of India ETPs on CNX NIFTY

(A)
(B)
(C)
(D)
(E)
(F)
(G)
(H)
(I)
(J)
(K)
(L)

(Model 1) (Model 1) (Model 1) (Model 1) (Model 1) (Model 1) (Model 1) (Model 1) (Model 1) (Model 2) (Model 3) (Model 4)

Constant 0.0001 –0.00003 –0.0000 –0.0003 –0.0003 –0.0031 0.0016 0.0025 0.0027 0.0011 0.0032 –0.0002

(1.72) 1.2 (–0.55)* (–0.92) –0.86 (–1.29) (0.76) (1.2) (1.3) (0.24) (1.31) (–0.06)

S&P 0.2701 0.2771 0.2772 0.1754 0.1708 0.1779 –0.0021 –0.0069 –0.0086 –0.0005 –0.0140 –0.0984

(35.05)* (36.80)* (36.08)* (27.39)* (26.00)* (25.99)* (-0.28) (–0.93) (–1.15) (–0.03) (–1.63) (–4.62)

S&P lag 0.2904 0.2941 0.1436 0.1399 0.1486 0.1690 0.1174 0.1166 0.0697 0.1275 0.1201

(34.87)* (35.27)* (19.83⁾* (18.39)* (19.08)* (23.82)* (14.8)* (14.62)* (4.23)* (14.11)* (6.61)*

FX –0.0788 –0.0391 –0.0383 –0.0230 –0.0377 –0.0563 –0.0618 –0.1465 –0.0325 –0.0253

(–6.05⁾* (–3.78)* (–3.06)* (–2.06)* (–3.9)* (–5.86)* (–6.43)* (–6.90)* (–2.81)* (–1.26)

VIX –0.1004 –0.1022 –0.0996 –0.0718 –0.0726 –0.0729 –0.0748 –0.0718 –0.0581

(–56.83)* (–55.41)* (51.8⁾* (–37.66)* (–38.02)* (–38.11)* (–19.01)* (–33.0)* (–13.29)*

FII 3.11E–07 1.88E–07 2.27E–07 1.06E–06 1.14E–06 1.43E–06 1.07E–06 1.22E–06

(0.52) (0.31) (0.4) (1.73)* (1.89)* (1.16) (1.55) (1.05)

Tot_assets 0.0001 –8E–05 –0.0001 –0.0001 –4.3E–05 –0.0002 1.01E–05

(1.22) (–0.78) (–1.21) (–1.3) (–0.21) (–1.31) (0.07)

India ETPs 0.2722 0.2773 0.2769 0.3307 0.2690 0.3931

(39.95)* (41.42)* (41.31)* (29.5)* (35.74)* (14.25)*

India ETPs (lag1) 0.0654 0.0670 0.0754 0.0642 0.0799

(14.42)* (15.01)* (8.16)* (13.00)* (5.24)*

India ETPs (lag2) 0.0211 0.0188 0.0212 0.0271

(6.23)* (2.53) (5.71)* (4.21)***

No. of obs. (N) 25,062 23,753 23,612 22,508 21,627 19,744 19,744 19,744 19,743 4,468 15,274 4,801

Controlled for scheme effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Adj. R square 0.0641 0.1314 0.1321 0.3568 0.3567 0.3494 0.5085 0.5185 0.5199 0.5344 0.5125 0.6071

	(A)	(B)	(C)	(D)	(E)	(F)	(G)	(H)	(I)	(J)	(K)	(L)
Constant	0.0001	–0.00003	–0.0000	–0.0003	–0.0003	–0.0031	0.0016	0.0025	0.0027	0.0011	0.0032	–0.0002
	(1.72)	1.2	(–0.55)***	(–0.92)	–0.86	(–1.29)	(0.76)	(1.2)	(1.3)	(0.24)	(1.31)	(–0.06)
S&P	0.2701	0.2771	0.2772	0.1754	0.1708	0.1779	–0.0021	–0.0069	–0.0086	–0.0005	–0.0140	–0.0984
	(35.05)***	(36.80)***	(36.08)***	(27.39)***	(26.00)***	(25.99)***	(-0.28)	(–0.93)	(–1.15)	(–0.03)	(–1.63)	(–4.62)
S&P lag		0.2904	0.2941	0.1436	0.1399	0.1486	0.1690	0.1174	0.1166	0.0697	0.1275	0.1201
		(34.87)***	(35.27)***	(19.83⁾***	(18.39)***	(19.08)***	(23.82)***	(14.8)***	(14.62)***	(4.23)***	(14.11)***	(6.61)***
FX			–0.0788	–0.0391	–0.0383	–0.0230	–0.0377	–0.0563	–0.0618	–0.1465	–0.0325	–0.0253
			(–6.05⁾***	(–3.78)***	(–3.06)***	(–2.06)***	(–3.9)***	(–5.86)***	(–6.43)***	(–6.90)***	(–2.81)***	(–1.26)
VIX				–0.1004	–0.1022	–0.0996	–0.0718	–0.0726	–0.0729	–0.0748	–0.0718	–0.0581
				(–56.83)***	(–55.41)***	(51.8⁾***	(–37.66)***	(–38.02)***	(–38.11)***	(–19.01)***	(–33.0)***	(–13.29)***
FII					3.11E–07	1.88E–07	2.27E–07	1.06E–06	1.14E–06	1.43E–06	1.07E–06	1.22E–06
					(0.52)	(0.31)	(0.4)	(1.73)*	(1.89)*	(1.16)	(1.55)	(1.05)
Tot_assets						0.0001	–8E–05	–0.0001	–0.0001	–4.3E–05	–0.0002	1.01E–05
						(1.22)	(–0.78)	(–1.21)	(–1.3)	(–0.21)	(–1.31)	(0.07)
India ETPs							0.2722	0.2773	0.2769	0.3307	0.2690	0.3931
							(39.95)***	(41.42)***	(41.31)***	(29.5)***	(35.74)***	(14.25)***
India ETPs (lag1)								0.0654	0.0670	0.0754	0.0642	0.0799
								(14.42)***	(15.01)***	(8.16)***	(13.00)***	(5.24)***
India ETPs (lag2)									0.0211	0.0188	0.0212	0.0271
									(6.23)***	(2.53)**	(5.71)***	(4.21)***
No. of obs. (N)	25,062	23,753	23,612	22,508	21,627	19,744	19,744	19,744	19,743	4,468	15,274	4,801
Controlled for scheme effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Adj. R square	0.0641	0.1314	0.1321	0.3568	0.3567	0.3494	0.5085	0.5185	0.5199	0.5344	0.5125	0.6071

Source: Statistical analysis performed by the authors.

Note: Table 3(a) shows results of pooled OLS done for the following equation Nifty_i,t = α₀ + β₁S&P_i,t + β₂S&P_{i,t– 1} + β₃FX_i,t + β₄IVIX_i,t + β₅FII_i,t + β₆Tot_assets_IETP_i,t + β₇IndiaETP_i,t + β₈IndiaETP_i,t–1 + β₉IndiaETP_i,t–2 + u_it. The time period is 4 January 2010 to 29 August 2014. Model 1 refers to the full sample of India ETPs and has different underlying benchmark. Model 2 refers to the sample that has only CNX Nifty or S&P BSE Sensex as the benchmark. Model 3 refers to the sample of India ETPs having benchmark other than CNX Nifty or S&P BSE Sensex. Model 4 refers to the sample that has only the largest India ETPs in terms of asset size. FX refers to the foreign exchange rate. IVIX refers to India VIX and is measured as returns. FII refers to the change in net investment flows, India ETPs (lag1) refers to first lag of India ETPs returns and India ETPs (lag2) refers to second lag of India ETPs returns. Tot_assets refers to the asset size of India ETFs and is measured as the natural log of asset size. S&P refers to a lag of S&P 500 index returns. The above values have been corrected for robust standard errors. The values of t-statistics are given in the brackets. Values are significant only at 1% and 5% levels which are represented by *** and **.

In our study, India VIX has a substantial influence on CNX Nifty across all the models. FIIs play a prominent role in bringing the foreign exchange, which goes a long way in improving the exchange reserve of a country. FIIs play a dominant role in emerging markets, especially so in India. FII investments in stocks touched US$40 billion in 2014 (India Brand Equity Foundation). FII investments through participatory notes (P-Notes) have increased to the highest level in the last few years and touched ₹2.65 trillion in 2014 (Securities and Exchange Board of India [SEBI]). The FII outflows during times of underperformance of the economy have severe repercussions. In our study, the analysis shows that FII investments do not have a major influence on CNX Nifty.

India offshore funds such as Aberdeen Global Indian Equity funds and HSBC GIF Indian equity funds have assets worth US$5.1 billion and US$2.4 billion (Figures at the end of September 2014, MorningStar Offshore Fund Spy Report) and play a prominent role in the Indian stock market. India ETFs such as WisdomTree India Earnings and iShares MSCI India ETF have large assets under their management. Our analysis proves, across different sub-samples, that the asset size of India ETPs is not significant. It is quite probable that investors do take into consideration other factors apart from the fund size. Finally, after controlling for the above factors, we examine the impact of India ETP–by including two lags of India ETPs–on CNX NIFTY. We find that the first lag of India ETP significantly impacts CNX NIFTY. It can also be seen that, across different models, in Model 1 a lag of India ETPs has more impact on CNX Nifty compared with other models. More importantly, we also find that the second lag of India ETPs still has an impact on CNX Nifty. This shows that India ETPs have a sizable effect on CNX Nifty, and they reverberate in the market even after a few days. The findings that India ETPs have substantial influence on CNX Nifty are crucial because they form one of the biggest modes of foreign portfolio investment and should be closely monitored.

Apart from India ETPs impacting CNX Nifty, we also posit that India ETPs would also influence S&P BSE Sensex, the other major index. The analysis showed that a lag of S&P 500 index and India VIX have a substantial impact on S&P BSE Sensex too (refer to Table 3[b]), similar to the findings in the previous section. However, it can be seen that the significance on S&P BSE Sensex is slightly lower compared with CNX Nifty. A plausible explanation could be the enormous growth of CNX Nifty index in the last decade. Agarwal (1997) and Chakrabarti (2001) have also examined the FII activities in India and found that a significant relationship exists between FIIs and stock market returns. Our study also shows that FII flows have a higher traction on S&P BSE Sensex compared with CNX Nifty. It is important to mention that S&P BSE Sensex, though outgrown by CNX Nifty in recent times, still finds favour among several foreign portfolio investors. The analysis shows that investors do not consider fund size to be of paramount importance. Finally, we find both the lags of India ETPs have extensive influence on S&P BSE Sensex. However, it is alluring to observe that the significance of India ETPs on CNX Nifty and S&P BSE Sensex is almost similar.

Table 3(b).

Impact of India ETPs on S&P BSE Sensex

	(A)	(B)	(C)	(D)	(E)	(F)	(G)	(H)	(I)	(J)	(K)	(L)
	(Model 1)	(Model 1)	(Model 1)	(Model 1)	(Model 1)	(Model 1)	(Model 1)	(Model 1)	(Model 1)	(Model 2)	(Model 3)	(Model 4)
constant	0.0002	–0.0001	0.0000	–0.0003	–0.0003	–0.0021	–0.0036	–0.0025	–0.0020	0.0001	0.0031	–0.0030
	(0.42)	(–0.14)	(–0.07)	(–0.90)	(–0.82)	(–1.12)	(–2.19)	(–1.55)	(–1.24)	(0.03)	(1.30)	(–1.11)
S&P	0.2684	0.2748	0.2700	0.1782	0.1736	0.1802	0.0130	0.0111	0.0096	0.0049	–0.0082	–0.0924
	(35.47)***	(37.12) ***	(36.40) ***	(29.81) ***	(28.37) ***	(26.22) ***	(1.71)*	(1.50)	(1.29)	(0.34)	(–0.97)	(–4.14)
S&P lag		0.2845	0.2880	0.1394	0.1391	0.1427	0.1565	0.1121	0.1116	0.0720	0.1294	0.1179
		(34.53) ***	(34.92) ***	(20.41) ***	(19.62) ***	(17.11) ***	(20.40) ***	(13.58) ***	(13.41) ***	(4.36) ***	(14.28) ***	(5.96) ***
FX			–0.0765	–0.0408	–0.0387	–0.0364	–0.0496	–0.0650	–0.0709	–0.1431	–0.0278	–0.0015
			(–5.96)***	(–3.92)***	(–3.64)***	(–3.01)***	(–4.79)***	(–6.34)***	(–6.90)***	(–7.09)***	(–2.40)**	(–0.07)
VIX				–0.0960	–0.0979	–0.0975	–0.0707	–0.0711	–0.0713	–0.0728	–0.0698	–0.0528
				(–56.21)***	(–55.03)***	(–49.45)***	(–35.92)***	(–36.29)***	(–36.34⁾***	(–18.41⁾***	(–31.97)***	(–11.56)***
FII					8.58E–07	8.12E–07	8.31E–07	1.50E–06	1.58E–06	2.02E–06	1.55E–06	1.75E–06
					(1.66)*	(1.55)	(1.66)*	(2.82)***	(3.01)***	(1.86)	(2.49)**	(1.68⁾*
Tot_assets						9.26E–05	0.0002	0.0001	0.0001	3.70E–08	–0.0002	0.0002
						(1.04)	(2.23)**	(1.59)	(1.29)	(0.00)	(–1.30)	(1.16)
India ETPs							0.2671	0.2717	0.2711	0.3209	0.2609	0.3928
							(38.27)***	(39.57)***	(39.45)***	(29.04)***	(35.46)***	(13.42)***
India ETPs (lag1)								0.0598	0.0614	0.0685	0.0584	0.0777
								(13.20)***	(13.65)***	(7.40)***	(11.97)***	(4.74)***
India ETPs (lag2)									0.0211	0.0200	0.0226	0.0310
									(5.97)***	(2.69)***	(6.17)***	(4.56)***
Controlled for scheme effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
No. of obs. (N)	25,618	23,764	23,623	22,524	2,1642	18,594	18,594	18,594	18,593	4,467	15,274	4,305
Adj. R²	0.0658	0.1324	0.133	0.3539	0.3559	0.3381	0.4978	0.5066	0.5080	0.5273	0.5125	0.6023

Source: Statistical analysis performed by the authors.

Note: The table shows results of pooled OLS done for the following equation Sensex_i,t = α₀ + β₁S&P_i,t + β₂S&P_i,t–1 + β₃FX_i,t + β₄IVIX_i,t + β₅FII_i,t + β₆Tot_assets_IETP_i,t + β₇IndiaETP_i,t + β₈IndiaETP_i,t–1 + β₉IndiaETP_i,t–2 + u_it. The time period is 4 January 2010 to 29 August 2014. Model 1 refers to the full sample of India ETPs and has different underlying benchmark. Model 2 refers to the sample that has only CNX Nifty or S&P BSE Sensex as the benchmark. Model 3 refers to the sample of India ETPs having benchmark other than CNX Nifty or S&P BSE Sensex. Model 4 refers to the sample that has only the largest India ETPs in terms of asset size. FX refers to the foreign exchange rate. IVIX refers to India VIX and is measured as returns. FII refers to the change in net investment flows, India ETPs (lag1) refers to first lag of India ETPs returns and India ETPs (lag2) refers to second lag of India ETPs returns. Tot_assets refers to the asset size of India ETFs and is measured as the natural log of asset size. S&P refers to a lag of S&P 500 index returns. The above values have been corrected for robust standard errors. The values of t-statistics are given in the brackets. ***, ** and * represent the values are significant at 1%, 5% and 10% significance levels, respectively.

The findings that India ETPs affect both CNX Nifty and S&P BSE Sensex are noteworthy because it shows that, apart from foreign flows that come from various channels, the flows that come through India ETPs should also be taken seriously. It is well known that foreign flows into countries, especially emerging markets, have a considerable impact on the fortunes of the stock indices. India ETPs represent only a sizable portion of the foreign flows but have a profound impact on both the indices. The finding that India ETPs have a discerning influence on both CNX NIFTY and S&P BSE SENSEX is noteworthy, as the fund flows from India ETPs is still smaller when compared with the gamut of foreign flows that come to India. The other interesting aspect is that the growth and popularity of India ETPs are still in a nascent stage. Unlike developed economies of the West where the economic growth potential is smaller, the economic growth potential of India remains high. The India growth story would be attracting several investors in the future too and foreign investors who wish to have exposure to India would increasingly use India ETPs. Hence, the finding that India ETPs have a significant impact on the indices is something to take cognizance of.

5.4 Determinants of India ETPs

Country’s stock market index and country ETPs have a strong relationship and, in many instances, country ETPs have the country’s stock index as its underlying index. As already mentioned India ETPs have different underlying benchmarks and are not restricted to S&P BSE SENSEX or CNX NIFTY alone. For example, WisomTreeIndia Earnings is a proprietary index and has neither S&P BSE SENSEX nor CNX NIFTY as the benchmark. However, as S&P BSE SENSEX and CNX NIFTY are the major indices we would like to investigate their influence on India ETPs. In this section, we examine the influence of Indian stock indices on India ETPs. The analysis shows after controlling for other variables CNX NIFTY has a strong influence on India ETPs (refer to Table 4[a]). It can also be observed that even a lag of CNX Nifty has an impact on India ETPs.

Table 4(a).
Impact of CNX Nifty on India ETPs

(A) (B) (C) (D) (E) (F) (G)

Constant –0.0001 –0.0001 –0.0004 –0.0007 –0.0161 –0.0141 –0.0137

(–0.27) (–0.27) (–0.55) (–0.92) –0.86 (–1.29) (0.76)

S&P 0.6963 0.6960 0.6248 0.6192 0.6751 0.5343 0.5359

(51.18)* (50.77)* (47.36)* (45.58)* (45.38)* (39.28)* (39.35)*

FX 0.0095 0.0506 0.0530 0.0436 0.0448 0.0359

(0.52) (2.73)* (2.76)* (2.14) (2.48) (1.98)

VIX –0.0985 –0.1001 –0.0984 –0.0053 –0.0067

(–40.52)* (–39.9)* (–37.16)*** (–1.67)* (–2.07)

FII 2.30E–07 –4.95E–08 8.51E–08 5.77E–07

(0.16) (–0.03) (0.06) (0.43)

Tot_assets 0.0008 0.0007 0.0006

(3.43)* (3.40)* (3.32)*

Nifty 0.8653 0.8586

(61.40)* (59.83)*

Nifty (lag1) 0.0481

(4.57)***

No. of obs. (N) 26,846 26,681 23,422 22,400 20,438 20,438 20,427

Controlled for scheme effects Yes Yes Yes Yes Yes Yes Yes

Adj. R² 0.1601 0.159 0.2494 0.2472 0.2544 0.4251 0.4258

	(A)	(B)	(C)	(D)	(E)	(F)	(G)
Constant	–0.0001	–0.0001	–0.0004	–0.0007	–0.0161	–0.0141	–0.0137
	(–0.27)	(–0.27)	(–0.55)	(–0.92)	–0.86	(–1.29)	(0.76)
S&P	0.6963	0.6960	0.6248	0.6192	0.6751	0.5343	0.5359
	(51.18)***	(50.77)***	(47.36)***	(45.58)***	(45.38)***	(39.28)***	(39.35)***
FX		0.0095	0.0506	0.0530	0.0436	0.0448	0.0359
		(0.52)	(2.73)***	(2.76)***	(2.14)**	(2.48)**	(1.98)**
VIX			–0.0985	–0.1001	–0.0984	–0.0053	–0.0067
			(–40.52)***	(–39.9)***	(–37.16)***	(–1.67)*	(–2.07)**
FII				2.30E–07	–4.95E–08	8.51E–08	5.77E–07
				(0.16)	(–0.03)	(0.06)	(0.43)
Tot_assets					0.0008	0.0007	0.0006
					(3.43)***	(3.40)***	(3.32)***
Nifty						0.8653	0.8586
						(61.40)***	(59.83)***
Nifty (lag1)							0.0481
							(4.57)***
No. of obs. (N)	26,846	26,681	23,422	22,400	20,438	20,438	20,427
Controlled for scheme effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Adj. R²	0.1601	0.159	0.2494	0.2472	0.2544	0.4251	0.4258

Source: Statistical analysis performed by the authors.

Note: Table 4(a) shows results of pooled OLS done for the following equation IndiaETP_i,t = α₀ + β₁S&P_i,t + β₂FX_i,t + β₃IVIX_i,t + β₄FII_i,t + β₅Tot_asset_IETPs_i,t + β₆Nifty_i,t + β₇Nifty_i,t–1 + u_it The time period is 4 January 2010 to 29 August 2014. S&P refers to S&P 500 index returns. FX refers to the foreign exchange rate. IVIX refers to India VIX and is measured as returns. FII refers to the change in net investment flows. Tot_assets refers to the asset size of India ETFs and is measured as the natural log of asset size and Nifty (lag1) refers to first lag of CNX Nifty returns. The above values have been corrected for robust standard errors. The values of t-statistics are given in the brackets. ***, ** and * represent the values are significant at 1%, 5% and 10% significance levels, respectively.

We predict that the dissemination of India ETP information flow is much higher initially and abates considerably on the following day. Similar analysis was performed to examine the relationship between S&P BSE SENSEX and India ETPs. The results (refer to Table 4[b]) are identical to that of the above model and prove that S&P BSE SENSEX also has an impact on India ETPs.

Table 4(b).

Impact of S&P BSE Sensex on India ETPs

	(A)	(B)	(C)	(D)	(E)	(F)	(G)
Constant	–0.0001	–0.0001	–0.0004	–0.0006	0.0073	0.0070	0.0081
	(–0.27)	(–0.27)	(–0.76)	(–1.13)	(2.52)**	(2.72)***	(2.72)***
S&P	0.6963	0.6960	0.6079	0.5999	0.6050	0.4575	0.4935
	(51.18)***	(50.77)***	(43.66)***	(41.73)***	(35.41)***	(29.00)***	(33.87)***
FX		0.0095	0.0348	0.0367	0.0361	0.0666	0.0610
		(0.52)	(1.82)***	(1.85)**	(1.61)**	(3.40)***	(3.23)***
VIX			–0.0906	–0.0928	–0.0955	–0.0040	–0.0081
			(–39.20)***	(–38.76)***	(–35.75)**	(–1.23)	(–2.44)**
FII				–2.71E–08	–2.69E–07	–1.02E–06	–1.99E–07
				(–0.02)	(–0.19)	(–0.79)	(–0.15)
Tot_assets					–0.0004	–0.0004	–0.0004
					(–2.80)***	(–3.00)***	(–3.28)***
Sensex						0.8835	0.8704
						(61.17)***	(58.03)***
Sensex (lag1)							0.0556
							(4.95)***
No. of obs. (N)	26,846	26,681	24,321	23,238	19,977	19,977	19,219
Controlled for schemes effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Adj. R²	0.1601	0.1590	0.2320	0.2299	0.2136	0.3945	0.4059

Source: Statistical analysis performed by the authors.

Note: The table shows results of pooled OLS done for the following equation IndiaETP_i,t = α₀ + β₁S&P_i,t + β₂FX_i,t + β₃IVIX_i,t + β₄FII_i,t + β₅Tot_asset_IETPs_i,t + β₆Sensex_i,t + β₇Sensex_i,t–1 + u_it The time period is 4 January 2010 to 29 August 2014. S&P refers to S&P 500 index returns. FX refers to the foreign exchange rate. IVIX refers to India VIX and is measured as returns. FII refers to the change in net investment flows. Tot_assets refers to the asset size of India ETFs and is measured as the natural log of asset size and Sensex (lag1) refers to first lag of S&P BSE Sensex returns. The above values have been corrected for robust standard errors. The values of t-statistics are given in the brackets. Values are significant only at 1% and 5% levels which are represented by *** and **.

Overall, it can be found that both the indices have a dominant influence on India ETPs. International investors willing to have exposure to Indian markets would consider the performance of stock indices before investing. Hence, it can be seen that though India ETPs have different underlying benchmarks, the Indian stock market indices still have influence over India ETPs. In short, it can be concluded that investors investing in India ETPs follow both CNX NIFTY and S&P BSE SENSEX before committing their investible surplus.

6. Conclusion

Country ETFs have made rapid strides in the last decade, as they provide investors with an opportunity to invest in emerging markets and developed markets across the world. India ETPs have proliferated in the last few years and provide investors with a chance to have exposure to India’s booming economy. Country ETPs have been consistently innovating and have come up with newer underlying benchmarks to attract the investors. It is well known that the underlying constituents of ETFs would have a bearing on ETFs and vice versa. In this study, we proceed to investigate the impact of country ETPs—which have different underlying benchmarks—on the two major stock indices of India. An analysis of 28 India ETPs from US, UK, France, Hong Kong, Israel, Singapore and Japan shows that India ETPs have a significant impact on both CNX NIFTY and S&P BSE SENSEX. It is interesting to observe that even across different sub-samples, we find India ETPs to have a discerning impact on both the indices in equal measure. Emerging market ETFs and country ETFs are increasingly finding favour with foreign portfolio investors because of their low expense ratios, ability to trade like stocks and also have tax advantages. India ETPs are gaining popularity because of India’s growth story and the findings have a considerable bearing on the way India ETPs should be looked at in the future, as India ETPs are one of the main channels of foreign portfolio investment. The study also documents that a reverse relationship exists between the indices and India ETPs. We find that both CNX NIFTY and S&P BSE SENSEX have a sizable impact on India ETPs.

The results of this study are of significance to policymakers as they clearly prove that India ETPs have a perceptible influence on Indian stock indices. FIIs’ flows have provided the much needed foreign exchange cushion in the last few decades. However, this ‘hot money’ also has the capability of reversing and turning into outflows in double quick time. Foreign flows are infamously volatile, and investors pull out money at the slightest hint of trouble in the global economy. The FIIs withdrawal to the tune of ₹477,060 million during the 2008–2009 global financial crisis is a case in point that the sudden withdrawal of funds from the Indian stock market exacerbates the fall in stock markets. It is imperative that local market regulators such as Securities Exchange Board of India and Reserve Bank of India regularly monitor the fund flows from India ETPs, and more importantly, from India offshore funds, which have a larger asset base.

Country ETFs are increasingly finding favour with investors who look at international diversification in both developed and emerging markets. The findings have specific implications for emerging markets and other BRICS nations, as country ETFs attract larger investor flows to these countries and also impact their financial system. Overall, the study contributes to country ETFs’ literature and shows that India ETPs have a significant bearing on India’s stock market performance, irrespective of their underlying benchmark and countries need to keep track of country ETFs to monitor the impact of foreign investment flows. The outcome of this study has significant implications for FII, as they need to keep track of the nexus between the country ETFs and the underlying country index when investing in other countries.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

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