ANALYSIS TO STUDY THE IMPACT OF FII ON SENSEX : 2000-2006
Submitted in partial fulfillment of the requirement for MBA Degree of Bangalore University
R V RAVI KUMAR
04XQCM6067 Under the guidance of Prof. SANTHANAM
M.P.Birla Institute of Management Associate Bharatiya Vidya Bhavan Bangalore-560001 2004-2006
I hereby declare that the report titled “AN ANALYSIS TO STUDY THE IMPACT OF FII ON SENSEX: 2000-2006” prepared under the guidance of Prof. SANTHANAM in partial fulfillment of MBA degree of Bangalore University, and is my original work. This project does not form a part of any report submitted for degree or diploma of Bangalore University or any other university.
Place: Bangalore Date: R V RAVI KUMAR
This is to certify that Mr. R V RAVI KUMAR, bearing registration No: 04XQCM6067 has done a project and has prepared a report “AN ANALYSIS TO STUDY THE IMPACT OF FII ON SENSEX: 2000-2006” under the guidance of Prof. SANTHANAM, M P Birla Institute of Management, Bangalore. This has not formed a basis for the award of any degree/diploma for any other university.
Place: Bangalore Date:
Dr.NAGESH.S.MALLAVALLI PRINCIPAL MPBIM, Bangalore
I hereby declare that the research work embodied in this dissertation entitled “AN ANALYSIS TO STUDY THE IMPACT OF FII ON SENSEX: 20002006” has been undertaken and completed by Mr. R V RAVI KUMAR under My guidance and supervision.
I also certify that he has fulfilled all the requirements under the covenant governing the submission of dissertation to the Bangalore University for the award of MBA Degree.
Place: Bangalore Date:
Prof. SANTHANAM Internal guide MPBIM, Bangalore
The successful accomplishment of any task is incomplete without acknowledging the contributing personalities who both assisted, inspired and lead us to visualize the things that turn them into successful stories for our successors. First, I thank the Almighty God for his grace bestowed on us throughout this project. I thank Dr. N. MALAVALLI SIR for sufficient guidance and all the facilities for the completion of this project. My special thanks to my project guide Prof SANTHANAM, who guided us with the timely advice and expertise and helped us complete the project early.
Last, but not the least, I would like to thank my parents and all my friends for their wholehearted support and encouragement.
LIST OF TABLES AND GRAPHS
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This paper investigates the nature of the causal relationship between stock returns, net foreign institutional investment (FII) in India. By applying the techniques of unit–root tests, co integration and the long–run Granger non–causality test, we test the causal relationships using monthly data for the period January 1993 to March 2006.
Since the beginning of liberalization FII flows to India have steadily grown in importance. In this paper we analyze these flows and their relationship with other economic variables and arrive at the following major conclusions: While the flows are highly correlated with equity returns in India, they are more likely to be the effect than the cause of these returns;
CHAPTER 2 INTRODUCTION
May 2004 and May 2006, has some common thing to say to the history of Indian Stock Market, On may 2006, Global concerns over rising interest rates in the US and sustained FII outflows continued to take its toll on the market, dragging down the sensex to a threeand-a-half month low. Despite strong GDP growth and early onset of the monsoon, the mood remained extremely bearish as FIIs remained major sellers amid hints of a further rise in interest rates in the wake of inflationary trend in the US.
The foreign investors had made net investments of over $4bn during January-April ‘06. The FII-led recovery in the sensex may help ease down concerns about heavy FII selling that caused a crash in the market in the past few weeks. FIIs pulled out funds to the tune of $1.6bn in May, after pumping in over $4bn in January-April ’06. The ongoing transition in the international financial architecture is exhibiting several features of which capital inflows are perhaps the most important. Emerging economies have experienced massive capital inflows, which in some cases have proved to be traumatic later. Whether such traumatic situations occurred due to incompatible macro economic and exchange rate policies or imprudent banking policy or lack of liquidity in the market is a matter of debate at the theoretical plane and an issue of future policy concern under country specific conditions. Whatever be the reason, the issue boils down to the fact whether such flows are properly paced and properly sequenced such that the inflow of capital is not excessive relative to the maturity of the system in which it must be absorbed; only then the capital flows can be sustained and systemic stability ensured.
This may be attained through structural and operational realignment of the domestic and financial sector variables of the economies exposed to global financial network. It is in this context that the inter linkage among the stock market, the most sensitive sector of the economy, exchange rate, the barometer of external interaction and the FII flows, indicator of capital surge needs to be addressed
To what extent the stock market can internalize or in other words, can capture the information on these is a case in point. For otherwise there may arise the possibility of capital flow reversals. The present study focuses on this issue in the Indian context. In fact, from among the whole gamut of institutional reforms undertaken in India since the 1990’s, gradual abolishment of capital inflow barriers and foreign exchange restrictions, adoption of more flexible exchange rate arrangements deserve a special attention at this juncture to reexamine whether India is approaching towards achieving the twin goals of stability and efficiency of the financial system .
PAST RESEARCHES ON FII IMPACT
PAST RESEARCHES ON FII IMPACT
There is a general perception that FIIs play a major role in the movements of stock indices. This is because the swings in institutional demand for a particular stock or in a particular group of stocks have a larger effect on its prices than swings in individual demand. Foreign investors largely held responsible for the collapse of East Asian stock markets.
Radelet and Sachs (1998) attributed the East Asian economic crisis to financial panic due to sudden reversal of portfolio investment. Academicians often argue that foreign investors destabilize stock prices due to various reasons. More often, according to Dornbusch and Park (1995) foreign investors pursue positive feedback strategies, which makes stocks to overreact to change in fundamentals.
Very few studies have been carried out in India empirically to see the impact of FIIs investments on Indian stock market. Early studies by Samal (1997) and Pal (1998) found
co movement between FIIs flows and Bombay Stock Exchange (BSE) index to be high Chalapati Rao and et al. (1999) analyzed the investment exposure of five US-based India specific funds, which suggested a close resemblance between FIIs investment profile and trading pattern at the BSE. However, they did not study the causality between FIIs flows and BSE. Batra (2003), using both daily and monthly data attempted to understand the trading behavior of FIIs and returns in Indian equity market. He found the strong evidence of FIIs chasing trends and adopting positive feedback and herding trading strategies.
However, Batra did not find FIIs having any destabilizing impact on the equity market. Chakrabarti (2002), made an empirical investigation to see the interrelationship between FIIs flows and equity returns in India using monthly data. He came with the evidence that the FIIs flows are highly correlated with equity returns in India. He also found that FIIs flows are more likely to be the effect than the cause of these returns, which contradicted the view that the FIIs determine market returns in general.
CHAPTER 4 PRESENT STATE OF ART
THE PRESENT STATE OF ART
India embarked on a programme of economic reforms in the early 1990s to tie over its balance of payment crisis and also as a step towards globalization. An important milestone in the history of Indian economic reforms happened on September 14, 1992, when the FIIs (Foreign Institutional Investors) were allowed to invest in all the securities traded on the primary and secondary markets, including shares, debentures and warrants issued by companies which were listed or were to be listed on the stock exchanges in India and in the schemes floated by domestic mutual funds. Initially, the holding of a single FII and of all FIIs, NRIs (Non-Resident Indians) and OCBs (Overseas Corporate Bodies) in any company were subject to a limit of 5% and 24% of the company’s total issued capital respectively. In order to broad base the FII investment and to ensure that such an investment would not become a camouflage for individual investment in the nature of FDI, a condition was laid down that the funds invested by FIIs had to have at least 50 participants with no one holding more than 5%. Ever since this day, the regulations on FII investment have gone through enormous changes and have become more liberal over time.
There have been attempts to explain FII in India. All the existing studies found that the equity return has a significant and positive impact on the FII (Agarwal, 1997; Chakrabarti, 2001; and Trivedi & Nair, 2003). But given the huge volume of investments, foreign investors could play a role of market makers and book their profits, i.e., they can buy financial assets when the prices are declining thereby jacking-up the asset prices and sell when the asset prices are increasing (Gordon & Gupta, 2003). Hence, there is a possibility of bi-directional relationship between FII and the equity returns. Ahmad et al (2005) make a firm level analysis of FII’s role in the Indian equity market. At the aggregate level FII investments and NSE Nifty seem to have a strong bi-directional causality. At the firm level FIIs are influencing equity returns especially in the government owned companies. On the issue of market stability Mazumdar (2004) finds that FII flows have enhanced liquidity in the Indian stock market but not much evidence is there to support the hypothesis that FII flows have generated volatility in the returns.
Ahmed et al (2005) also confirms that there has been very little destabilizing effect of FII flows on individual equity returns of the firms during their period of study. Kumar (2001) inferred that FII flows do not respond to short-term changes or technical position of the market and they are more driven by fundamentals. The study finds that there is causality from FII to Sensex. This is in contradiction to Rai and Bhanumurthy (2003) results using similar data but for a larger period. A study by Panda (2005) also shows FII investments do not affect BSE Sensex. No clear causality is found between FII and NSE Nifty. FIIs are found to follow positive feedback strategy and to have return clustering tendency.
Following the Asian crisis and the bust of info-tech bubble internationally in 1998-99 the net FII has declined by US$ 61 million. But there was not much effect on the equity returns. Chakrabarti (2001) has marked a regime shift in the determinants of FII after Asian crisis. The study found that in the pre-Asian crisis period any change in FII found to have a positive impact on the equity returns. But in the post-Asian crisis period a reverse relation was found, i.e. a change in FII was mainly due to change in equity returns.
If FIIs use positive feedback trading strategies, causality may run from stock prices to foreign investment. The portfolio balancing efforts of foreign investors would also put pressure on demand for (or supply) of currency, which may affect its exchange rate. On the other hand, the payoff of foreign investors depends on exchange rate movements as well as on stock price movements, and they may rebalance their portfolio in response to an (an anticipated) change in exchange rate. The relationship of FII investment with stock prices on the one hand, and with exchange rate on the other hand may produce indirect relation between exchange rate and stock prices
In the contemporary Indian scenario, study on interlinkage of stock prices, net FII investment and exchange rate is scarce. Using monthly data from April 1993 to March 2004, Badhani (2005) observed (i) bi-directional long-term causality between FII investment Flow 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, not vice versa, and (iii) exchange rate long term granger causes FII investment flow, not vice versa. The issue of the above interlinkage among stock price, FII and exchange rate gains importance from its own course of happenings.
The current pattern of concomitant advancement of FII flows and the Sensex does pose concern for the market analyst and the researchers. These may have multifaceted ramifications in terms of economic and financial stability of the economy in which exchange rate does have a role to play as discussed above.
Apart from exhibiting some concern, the market analysts do not currently observe any destabilizing motion in the current scenario nor do they apprehend it for the future; however, we feel that the time is perhaps ripe for analyzing the interlinkage in the stock price –FII- exchange rate nexus and examine to what extent the stock price can capture the publicly available information on the other two. This is true for the other two variables as well.
The concern lies in the fact that if such information is not captured, then there is scope for profitable trading as discussed above which at faster pace of occurrence may trigger off higher volatility and generate systemic risk. Some studies do reveal volatility clustering at the firm level equity returns but till now these are not transferred to other firms. It may be feared that at a higher pitch such transfer may occur endangering systemic risk in the entire equity market. Attempts are also being currently made by the authorities to check intra-day volatility through ‘Block Deals’. Although destabilizing motion is not presently apprehended, some concern are expressed on the sustainability of FII flows since new traders are setting in the market posing edgy character and selling at the slightest concern.
There also prevails optimistic outlook which puts forward that FIIs are not ‘villains’. In most of the market crashes the FIIs were net buyers (e.g.stock market crash of 2001,
market collapse of 1998). Even in the 17 May 2004 Black Monday episode FIIs were not the culprits. Though there was a net outgo there was also a come back in the next month June as a net inflow. Further, improved risk management system was also seen to withstand volatility of ‘8’ sigma against the normal built in capacity of ‘3-6’ sigma variations internationally.
It is further argued that FIIs tend to support stock market purely to ensure stability and safety of their own investments. All these reflect much concern on issue of FII, Stock prices and exchange rate interlinkage and this itself justifies our study done in terms of Efficient Market Hypothesis (EMH).
CHAPTER 5 FOREIGN INSTITUTIONAL INVESTORS
Foreign Institutional Investment in India: An Overview
India opened its stock markets to foreign investors in September 1992 and has, since 1993, received considerable amount of portfolio investment from foreigners in the form of Foreign Institutional Investor’s (FII) investment in equities. This has become one of the main channels of international portfolio investment in India for foreigners13. In order to trade in Indian equity markets, foreign corporations need to register with the SEBI as Foreign Institutional Investors (FII)14. SEBI’s definition of FIIs presently includes foreign pension funds, mutual funds, charitable/endowment/university funds etc. as well as asset management companies and other money managers operating on their behalf.
AVERAGE MONTHLY FII FLOWS
The trickle of FII flows to India that began in January 1993 has gradually expanded to an average monthly inflow of close to Rs. 1900 crores during the first six months of 2001. By June 2001, over 500 FIIs were registered with SEBI. The total amount of FII investment in India had accumulated to a formidable sum of over Rs. 50,000 crores. In terms of market capitalization too, the share of FIIs has steadily climbed to about 9% of the total market capitalization of BSE (which, in turn, accounts for over 90% of the total market capitalization in India).
The sources of these FII flows are varied. The FIIs registered with SEBI come from as many as 28 countries (including money management companies operating in India on behalf of foreign investors). US-based institutions accounted for slightly over 41%, those from the UK constitute about 20% with other Western European countries hosting another 17% of the FIIs (see Fig. 2). It is, however, instructive to bear in mind that these national affiliations do not necessarily mean that the actual investor funds come from these particular countries. Given the significant financial flows among the industrial
countries, national affiliations are very rough indicators of the ‘home’ of the FII investments. In particular institutions operating from Luxembourg, Cayman Islands or Channel Islands, or even those based at Singapore or Hong Kong are likely to be investing funds largely on behalf of residents in other countries. Nevertheless, the regional breakdown of the FIIs does provide an idea of the relative importance of different regions of the world in the FII flows.
[Fig. 2 about here]
CHAPTER 6 FINANCIAL MARKET OVERVIEW
FINANCIAL MARKET OVERVIEW
In the new financial year (2006-07), there has been overall buoyancy across the markets, with the equity stock indices ruling at unprecedented levels, gilt-edged markets sloshing in surfeit of liquidity and improved volumes in commodities market. This bullishness has been backed by the healthy state of the macro-economy at a sustained growth rate of 8 per cent and RBI’s positive outlook on growth and price stability and sound corporate earnings–particularly the impressive performance by the top three IT companies. The market has been pleasantly surprised by the RBI governor’s decision to keep short-term rates unchanged. The international rating agency Standard and Poor’s decision to upgrade the sovereign rating by a notch has further propelled market sentiments. In the equity market, the bourses has displayed immense volatility, nevertheless have retained their bullishness as BSE sensex has crossed yet another milestone of 12,000 mark amidst a highly volatile market. A notable feature during the month has been the countervailing role played by mutual funds supporting the bourses to sustain the FIIs selling spree, which in turn has been induced by the relatively expensive domestic equity market in comparison to other emerging markets on account of a higher P/E ratio of more than 21. Meanwhile, following NSE’s hike in margins in both cash and derivative segments, the derivative market has registered a marginal increase in its turnover as compared to the previous month. In the debt market, the prevailing surplus liquidity has boosted market sentiments, reinforced by the surprisingly neutral interest rate stance in the Annual Credit Policy announced on April 19. The secondary market for gilt-edged securities has remained buoyant. However, the corporate bond market has turned subdued and the total resources mobilised through it have nose-dived on account of the on–going slack season and uncertainty prevailing over the hardening of interest rates. In the foreign exchange market the rupee has depreciated against the dollar following the spiralling international crude oil prices as well as RBI intervention in the market. Meanwhile, in the commodity futures market, the Forward Market Commission (FMC) has, on April 25, classified real time trading in commodity by opening the terminals of foreign commodity exchanges without prior approval of the Centre or FMC as illegal and punishable.
Trends in the Equity Market i) Primary Issues
According to Ernst & Young’s third annual global IPO report –‘Accelerating Growth’, 29 countries, including India, Brazil, Eygpt, Greece, Isreal, Kazakhstan, Malaysia, Poland, Saudi Arabia, South Korea and the UAE have, each floated more than $1 billion worth of IPO in 2005, a trend which is expected to continue in 2006. As per the report, India has evoked lively investor interest. While the amounts raise have fallen from $2.9 billion in 2004 to $2.3 billion in 2005, reflecting fewer privatisation, the number of transactions (issues) have surged from 21 to 53,
placing India in the fourth position in Asia, following China, Japan, and South Korea. While the financial sector dominated the overall funds mobilised, IPO activity in India has been fairly spread across sectors with companies from energy and power, airlines, engineering, industrial, healthcare, consumer goods, technology and media segments accessing the capital market.
ii) Secondary Market
In April, the stock markets have remained volatile, yet bullish with both the bourses touching yet newer all time highs reflecting strong macroeconomic fundamentals, excellent corporate results and sound business outlook, further reaffirmed by, the global rating agency, Standard and Poor’s decision to revise its outlook on India’s sovereign credit rating and confirm its ‘BB+/B’ ratings for the sovereign after hinting a scope of upgradation to investment grade if the country’s public finances improve. Moreover, the RBI’s decision to keep the reverse repo and repo rate unchanged in its Annual Credit Policy has further fuelled the market sentiments. Notably, what has stood out is the extreme volatility that has gripped the market in the latter half of the month. Amidst the above stated positive developments, coupled with the overwhelming response to the Reliance Petroleum initial public offer and the surprise bonus share announcement by Infosys, the BSE sensex has surged to cross yet another milestone of 12,000 points on April 20 as it closed at 12,039.55 points, within just 16 trading sessions. However, it could not sustain the 12,000 level as the surge in international crude oil prices, worries over reports regarding below-normal monsoon as well as NSE’s hike in margins in both cash and derivative segments led to a massive fall of around 383.52 points in just two days as the sensex closed at 11646.78 points on April 25. But, irrespective of FIIs being net sellers to the tune of Rs 508.8 crore, sensex has seen a rise by 292 points on the very next day supported by encouraging corporate results and a dip in the international crude oil prices. Then again following a dip of 104 points on April 27, it has risen by around 208 points to close the month at 12042.56 points, making a gain of 6.76 per cent over its previous month’s close. Meanwhile, BSE Mid-Cap and BSE Small-Cap have registered gains of 9.28 per cent and 12.22 per cent, respectively, both above that recorded by sensex
Monthly Percentage Change in the Stock Indices of BSE
Change Base Index Year Closing Closing (per cent) High 1978-79 11279.96 12042.56 12102 SENSEX 1983-84 5904.17 6251.39 6265.23 BSE 100 1989-90 1412.62 1500.48 1502.34 BSE 200 1998-99 4516.73 4829.73 4835.21 BSE 500 2002-03 5348.62 5843.29 5848.73 BSE Mid-Cap 2002-03 6591.66 7397.48 7462.51 BSE Small-Cap Apr 02,2001 2713.12 2745.43 2903.03 BSE TECk 1998-99 6114.88 6147.31 6329.82 BSE PSU BSE AUTO Feb 01,1999 5322.73 5548.91 5586.3 BANKEX Jan 01,2003 5265.24 5245.79 5558.02 BSE CG Feb 01,1999 8170.56 8811.05 8932.69 BSE CD Feb 01,1999 3212.33 3458.62 3515.51 BSE FMC Feb 01,1999 2211.45 2312.19 2358.27 BSE HC Feb 01,1999 3858.10 3894.84 4119.57 BSE IT Feb 01,1999 4030.29 4034.92 4343.63 Feb 01,1999 8869.91 10762.23 10870.22 BSE METAL BSE OIL&GAS Feb 01,1999 4918.98 5687.90 5745.45 Source: BSE (www.bseindia.com) Low 11008.43 5745.18 1376.42 4413.61 5277.7 6613.15 2588.8 5837.85 5088.67 4853.39 8092.27 3129.9 2129.34 3656.16 3828.7 8929.83 4953.69 6.76 5.88 6.22 6.93 9.25 12.22 1.19 0.53 4.25 -0.37 7.84 7.67 4.56 0.95 0.11 21.33 15.63 March April April over month
Foreign Institutional Investments (FIIs)
During the month, FIIs investments in the equit y market have dipped to Rs 522 crore with purchases worth Rs 44,645 crore and sales of Rs 44,123 crore as against Rs 6,689 crore in March, irrespective of the rise in the number of FIIs registered with Sebi to 906 from 882 in the previous month. During the first four months of the calendar year 2006, FIIs have invested US $ 4.03 billion as against US $ 3.5 billion in the corresponding period last year. Meanwhile, the share of FIIs in the total cash turnover of both the bourses has stood at 33.68 per cent during the month. Sebi has, on April 05, allocated new limits for FIIs to invest in the debt market in line with the announcement made in this regard in the budget 2006-07, wherein the government had increased the limit on FII investment in government securities from $1.75 billion to $2 billion and in corporate debt from $0.5 billion to $1.5 billion. Under the revised limits, the $2 billion investment in government securities by FIIs will be divided into a proportion of $1.75 billion for
100 per cent debt FIIs and $250 million for 70:30 FII/sub-accounts. Similarly, the corporate debt limit of $1.50 billion will be shared as $ 1.35 billion for 100 per cent debt FIIs and $150 million for 70:30 FIIs. Further, it has also been clarified that FII investments will be restricted to listed debt securities of companies.
In the financial year 2005-06 there has been a huge surge in the mobilisations by the private sector mutual funds through new fund offers (NFOs). The total amount mobilised via NFOs has risen by almost 173 per cent to Rs 70,583 crore from Rs 25,811 crore in the previous financial year. Likewise, the assets under management (AUM) of the mutual funds have increased by almost 27 per cent to Rs 23,25,841 crore from 18,33,602 crore in the last financial year. In April, the total amount mobilised via NFOs has registered a decline by almost 87 per cent to Rs 2,918 crore from Rs 22,868 crore in the previous month, while the AUM have increased to Rs 2,57,499 crore from Rs 2,31,862 crore in March. Meanwhile, the share of mutual funds in the total cash turnover of both the bourses has stood at 8.49 per cent.
Foreign Exchange Market
The rupee–dollar exchange rate in April has sharply depreciated against the dollar by around 36 paise from Rs 44.61 to Rs 44.97 as on April 28. During the month, a variety of factors such as escalation in international crude oil prices and the resultant heavy dollar demand by oil companies have adversely affected the rupee–dollar exchange rate. In April, the RBI continued to intervene in the market to modulate the rupee movement, which has further aggravated the currency’s weakness against the dollar. The month began with the rupee trading at Rs 44.61 per dollar amid robust FIIs inflows into the surging domestic equity market. But, subsequently the rupee movement has been adversely affected by the increased volatility in the domestic stock market due to a selling spree of the FIIs coupled with the RBI’s intervention in the market as it breached the psychologically important Rs 45 mark and has traded above the Rs 45 level for most part of the month. However, in the latter part of the month with the Chinese Central Bank raising its basic lending rates by 27 basis points to 5.85 per cent and the Fed’s Chief signalling that it may pause its interest rate–tightening policy has resulted in the rupee recovering smartly to end the month at Rs 44.97 per dollar. Meanwhile, in the forward premia market, all three tenures,
viz., one-month, three-month and twelve –month, have converged in the initial part of the month; though the one-month annualised premia have ruled above the other two tenures in the later part of the month .
CHAPTER 8 METHODOLOGY
A plethora of researches are being conducted to understand the current working of the economic and the financial system. Interesting results are emerging particularly for the developing countries where the markets are experiencing new relationships which are not perceived earlier. One of the most important changes that Indian capital market witnessed with the reforms is the entry of foreign institutional investors. Since then the country has been receiving increasing amounts of portfolio investment. The analysis of the interrelationship between stock prices, net foreign institutional investment (FII) and exchange rate in the present study runs in terms of efficiency of Indian stock markets.
The Efficient Market Hypothesis (semi-strong form), states that in a semi strong efficient market, everyone has perfect knowledge of all publicly available information in the market. The idea is, whether in a mutually interactive framework, stock market can effectively digest and incorporate all available information about economic variables.
This is important because, if it does so, then the rational behavior of market participants ensures that past and current information is fully reflected in stock prices. Otherwise, (a) the market participants are able to develop profitable trading rules and thereby can consistently earn more than average market returns, and (b) the stock market is not likely to play an effective role in channeling financial resources to the most productive sector of the economy. The use of Granger Causality Test in examining market informational efficiency has been there for quite some time. This is based on the hypothesis that informational efficiency exists if a uni-directional lagged causal relationship from an economic variable to stock prices or bi-directional lagged causal relationship from an economic variable to stock price or from the latter to the former, could not be detected. It implicates that the economic variable neither influences nor is influenced by stock price fluctuations, and the two series are independent of each other and the market is informationally efficient.
This paper makes use of the Granger Causality test. The study investigates the empirical relationship between stock prices, net FII investment and using monthly data from January 2000 to May 2006.
Unit Root Test and Co integration:
Empirical studies (for example, Engle and Granger, 1987) have shown that many time series variables are non-stationary or not integrated of order zero. The time series variables considered in this paper are the stock prices and fii flows. In order to avoid a spurious regression situation the variables in a regression model must be stationary or cointegrated. Therefore, in the first step, we perform unit root tests to investigate whether they are stationary or not. The Augmented Dickey-Fuller (ADF) unit root test is used for this purpose. The ADF regression equations are:
where åô is white noise. The additional lagged terms are included to ensure that the errors are uncorrelated.
ADF UNIT ROOTS TEST
The tests are based on the null hypothesis (H0): Yt is not I (0). If the calculated ADF statistics are less than their critical values from Fuller’s table
then the null hypothesis (H0) is rejected and the series are stationary or not integrated of order zero.
In the second step we estimate cointegration regression using variables having the same order of integration. The cointegration equation estimated by the OLS method is given as:
In the third step residuals (Zt) from the cointegration regression are subject to the stationarity test based on the following equations:
Toda and Yamamoto Version of Granger Causality:
Toda and Yamamoto (1995) proposed a simple procedure requiring the estimation of an ‘augmented’ VAR, even when there is cointegration, which guarantees the asymptotic distribution of the statistic. Therefore, the Toda-Yamamoto causality procedure has been labelled as the long-run causality tests. All one needs to do is to determine the maximal order of integration dmax, which we expect to occur in the model and construct a VAR in their levels with a total of (k + dmax) lags. Toda and Yamamoto point out that, for d=1, the lag selection procedure is always valid, at least asymptotically, since k 1 . =d. If d=2, then the procedure is valid unless k=1. Moreover, according to Toda and Yamamoto, the statistic is valid regardless whether a series is I (0), I (1) or I (2), non-cointegrated or cointegrated of an arbitrary order.
In order to clarify the principle, let us consider the simple example of a bivariate model, with one lag (k=1). That is,
The statistic will be asymptotically distributed as a Chi Square, with degrees of freedom equal to the number of "zero restrictions", irrespective of whether x1t and x2t are I (0), I (1) or I (2), non-cointegrated or cointegrated of an arbitrary order. In this study, we used monthly data series for three variables for the period January 1993 to March 2005 forming around 147 observations. The monthly return on stock prices (RBSE) is calculated by taking a percentage change in the BSE Sensitive Index (base: 197879=100).
The other two variables included in our study are net investments by FIIs (in equities) in the Indian capital market and the indices (36-country bilateral weight with base 1985=100). Incidentally, it may be noted that FIIs were allowed to invest in the Indian capital market securities from September 1992. However, investments made by them were first made in January 1993. The data has been compiled from Handbook of Statistics on Indian Economy published by Reserve Bank of India and various issues of RBI Bulletin.
CHAPTER 9 DATA DESCRIPTION
We obtain the aggregate monthly FII flow data from the time India opened her doors to FIIs, that is, from January 2000 to June 2006 from India Infoline and Equity Master. We also have data on each of the three components of the flows, that is, the purchases, sales and the net flows (purchases less sales) with the markets returns. The Reserve Bank of India and the Securities Exchange Board of India also provide the data on FII flows. We look at the relationship between flows and market returns using both, the returns on the Bombay stock exchange (BSE) and National stock exchange (NSE). The data on market index is obtained from the respective stock exchanges. The return for market i for month t would be given by;
Established in 1875, BSE is not only the oldest stock exchange in India, but is also the oldest in Asia. It accounts for over one-third of the total trading volume in the country. The National Stock Exchange (NSE), located in Bombay, was set up in 1993 to encourage stock exchange reform through system modernization and competition. It opened for trading in mid-1994. Since then the NSE has made major strides and is now the dominant stock exchange in the country. Most other studies on Indian market use the BSE Sensex index to compute market returns. With NSE being an equally prominent stock exchange in India, we also use the S&P CNX Nifty index to compute returns.
Between the two exchanges, NSE being demutualized provides a better market quality. With lower execution cost, lower price volatility and higher liquidity compared to BSE, NSE has emerged to be superior by providing improved market quality and high standards of investor protection .
BSE Sensex is a basket of 30 constituent stocks representing a sample of large, liquid and representative companies. The base year of SENSEX is 1978-79 and the base value is 100. The index is widely reported in both domestic and international markets through print as well as electronic media. The Index was initially calculated based on the ‘Fullmarket capitalization’ methodology but was shifted to the ‘Free-float methodology’ with effect from September 1, 2003.
Under the 'Full-market capitalization' methodology, the total market capitalization of a company, irrespective of who is holding the shares, is taken into consideration for computation of an index. Recognizing the limitation with this methodology, BSE now uses the Free-float market capitalization of a company for index calculation, like MSCI, FTSE, S&P and Dow Jones. Using ‘Free-float methodology’, generally excludes promoters' holding, government holding, strategic holding and other locked-in shares, which are not tradable in the normal course. Thus, the market capitalization of each company in a Free-float index is reduced to the extent of its Free-float available in the market.
S&P CNX Nifty is a well diversified 50 stock index accounting for 23 sectors of the economy. S&P CNX Nifty is computed using market capitalization weighted method, wherein the level of the index reflects the total market value of all the stocks in the index relative to a particular base period. The method also takes into account constituent changes in the index and importantly corporate actions such as stock splits, rights, etc without affecting the index value. The base period selected for S&P CNX Nifty index is the close of prices on November 3, 1995, which marks the completion of one year of operations of NSE's Capital Market Segment. The base value of the index has been set at
1000 and a base capital of Rs.2.06 trillion. The stocks in these indices, both BSE Sensex and S&P CNX Nifty, are the ones in which the FIIs are most likely to invest in. Figure 4 shows the movement of BSE Sensex with FII flows starting January 1993. Figure 5 shows the same relation between S&P CNX Nifty and flows.
The purchases and sales seem to be more correlated with the indices from late 90’s.We scale the flows for the month by market capitalization at the beginning of the month, that is, market capitalization as at the end of previous month. Regressions looking at relationship between BSE returns and flows have flows normalized by BSE market capitalization. For analyzing the relationship between NSE returns and flows, we scale the flows by NSE market capitalization. With the monthly BSE market capitalization data available from April 1993; we have the scaled flows starting May 1993 to June 2006 giving us scaled flow data for 122 months. The monthly market capitalization data for NSE is available from November 1994, giving the scaled flows from December 1994 to June 2006.
Therefore, with the scaled flow data for 103 months the sample size becomes smaller for the analysis using NSE returns. The data on market capitalization has been taken from the respective stock exchange. The seasonal pattern in the average monthly FII flows
(scaled) to India. Consistent with Rao et al. (1999) the graph shows that the month of January witnesses the highest inflow and the flow (especially purchases) decreases as the year progresses. The fund managers might be investing most of the funds allocated to a market, at the beginning of the year and the investment reduces subsequently with the reduction in availability of funds. Rao et al. (1999) make an interesting observation that BSE Sensex and FII investments decline in the fourth quarter and this may be because the local market players might be looking toward FIIs for leads.
CHAPTER 10 EMPIRICAL RESULTS
UNIT ROOT TEST OF NET FII INVESTMENTS
ADF Test Statistic -4.584336 1% Critical Value* 5% Critical Value 10% Critical Value -2.5945 -1.9448 -1.6181
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(FII) Method: Least Squares Date: 06/11/06 Time: 22:13 Sample(adjusted): 2000:02 2006:02 Included observations: 73 after adjusting endpoints Variable FII(-1) R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood Coefficient -0.454260 0.225923 0.225923 0.036740 0.097187 138.1052 Std. Error 0.099090 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Durbin-Watson stat t-Statistic -4.584336 Prob. 0.0000 0.000201 0.041759 -3.756306 -3.724930 2.227419
The ADF t-test has value t = -4.584336 which is smaller than the 5% critical value of 1.9448
and also at 1 % critical value i.e -2.5945
Therefore the ADF t-test rejects the null hypothesis of a trend (at the 5% significance level, and also at the 1% significance level).
Since, the null hypothesis of a trend is rejected by the ADF F-test. Thus the trend indicates the trend is deterministic.
-0.05 00 01 02 03 04 FII 05 06
Similarly, the ADF UNIT ROOT TEST for the sensex returns of the year 2000-06, states that
ADF Test Statistic
1% Critical Value* 5% Critical Value 10% Critical Value
-3.52001 -2.9007 -2.58736
*MacKinnon critical values for rejection of hypothesis of a unit root.
Augmented Dickey-Fuller Test Equation Dependent Variable: D(SENSEX) Method: Least Squares Date: 06/09/06 Time: 12:59 Sample(adjusted): 2000:02 2006:03 Included observations: 74 after adjusting endpoints Variable SENSEX(-1) C R-squared Adjusted R-squared Coefficient -0.933779 0.009799 0.4636679 0.456218918 Std. Error 0.118357 0.008302 t-Statistic -7.8896 1.1804 Prob. 2.46445e-5 0.2418 0.000609 0.0958866410 032 S.E. of regression Sum squared resid 0.07070827 0.35997539 Akaike info criterion Schwarz criterion -2.4339 -2.37159
Mean dependent var S.D. dependent var
Log likelihood Durbin-Watson stat
We see, the Augmented Dickey-Fuller Test Equation, for which the t-statistics value of 7.889 is less than the critical value of -1.94467 at 5 % significance level . hence the null hypothesis is rejected for the above values. Hence we conclude the returns of the sensex are stationary, and the trend is deterministic.
-0.2 00 01 02 03 04 SENSEX 05 06
The cointegration test, suggests the likelihood of the existence of the other model due to the initial model. Thus calculating the likelihood ratio will show the probability of the regression of te other equation with initial equation.
Date: 06/11/06 Time: 22:50 Sample: 2000:01 2006:12 Included observations: 73 Test assumption: Linear deterministic trend in the data Series: FII SENSEX Lags interval: No lags Eigenvalue 0.622208 0.310836 *(**) denotes rejection of the hypothesis at 5%(1%) significance level L.R. test indicates 2 cointegrating equation(s) at 5% significance level Unnormalized Cointegrating Coefficients: FII -2.519865 2.595813 Normalized Cointegrating Coefficients: 1 Cointegrating Equation(s) FII 1.000000 SENSEX -0.598028 (0.08712) 232.1339 C -0.022068 SENSEX 1.506949 0.854860 Likelihood Ratio 98.23513 27.17616 5 Percent Critical Value 15.41 3.76 1 Percent Critical Value 20.04 6.65 Hypothesized No. of CE(s) None ** At most 1 **
Here, the likelihood ratio is 84.525 which is greater than critical values at 5 % significance value. Hence by looking at the table we can say that rejection of the hypothesis at 5%(1%) significance level.
-0.2 00 01 02 03 FII 04 05 06
GRANGER’S CAUSALITY TEST
Granger causality is a technique for determining whether one time series is useful in forecasting another. Ordinarily, regressions reflect "mere" correlations. A time series X is said to Granger-cause Y if it can be shown, usually through a series of F-tests on lagged values of X (and with lagged values of Y also known), that those X values provide statistically significant information on future values of Y. The test works by first doing a regression of ÄY on lagged values of ÄY. Once the appropriate lag interval for Y is proved significant (t-stat or p-value), subsequent regressions for lagged levels of ÄX are performed and added to the regression provided that they 1) are significant in of themselves and 2) add explanatory power to the model. This can be repeated for multiple ÄX's (with each ÄX being tested independently of other ÄX's, but in conjunction with the proven lag level of ÄY). More than 1 lag level of a variable can be included in the final regression model, provided it is statistically significant and provides explanatory power.
FROM THE TABLE BELOW, WE INTERPRETE THE FOLLOWING , FINDINGS AND CONCLUSIONS
Pairwise Granger Causality Tests Date: 06/11/06 Time: 22:52 Sample: 2000:01 2006:12 Lags: 1 Null Hypothesis: SENSEX does not Granger Cause FII FII does not Granger Cause SENSEX Obs 73 F-Statistic 3.91007 9.85815 Probability 0.05194 0.00248
We took the null hypothesis , that sensex doesn’t granger cause netfii and vice versa,
In case 1: Sensex on granger cause to netfii
Here, f-statistic value is 3.7002 which is greater than the critical value of 1.53 and 1.40 at 5 % and 1 % significance level respectively.
Thus we reject the null hypothesis and can say sensex causes the netfii investments
On the other hand analysing the impact of net fii on sensex for monthly basis we see that
F-statistic value is 9.86 which is greater than the critical value of 1.53 and 1.40 at 5 % and 1 % significance level respectively.
Thus we again reject the null hypothesis and can say net fii has cause on sensex returns.
Now following is the finding for grangers causality test.:
a bi-directional causality between stock price and the net foreign institutional investment, thus implying that the market informational efficiency hypothesis can be rejected for BSE Sensitive Index with respect to the FII,
CHAPTER 11 CONCLUSION
The main objective of the present paper is to determine impact and relationship between the Indian stock market, net foreign institutional investment. To test this, we employ the methodology of Granger non-causality recently proposed by Toda and Yamamoto (1995) for the sample period January 2000 to
March 2006. In this study, the returns on BSE Sensitive Index are used as a proxy for the Indian stock market. The result suggests uni-directional causality between stock price and the net foreign institutional investment, thus implying that the market informational efficiency hypothesis can be rejected for BSE Sensitive Index with respect to the FII. It suggests the policy implication that the authorities can focus on domestic economic policies to stabilize the stock market.
Our results imply that stock prices can capture information on neither the FIIs. Investors can therefore apply profitable trading rules to earn supernormal profits. Under the circumstances, the Indian stock market seems to be bearing the underlying strain not currently visible at the surface. The implementation of profitable trading strategy may at any point of time generate over-enthused investment and this, if coupled with market overreaction, may result in a destabilized system. A point also to be noted here is the current concentration of FII funds in the IT and Banking sector, which in any event of flow reversals may worsen the situation.
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