Description
Market participants include individual retail investors, institutional investors such as mutual funds, banks, insurance companies and hedge funds, and also publicly traded corporations trading in their own shares.
Foreign Institutional Investment in the Indian Equity Market
An Analysis of Daily Flows during January 1999-May 2002
PARAMITA MUKHERJEE , SUCHISMITA BOSE ( AND DIPANKOR COONDOO
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Abstract
This paper explores the relationship of foreign institutional investment (FII) flows to the Indian equity market with its possible covariates based on a daily data-set for the period January 1999 to May 2002. The set of possible covariates considered comprises two types of variables. The first type includes variables reflecting daily market return and its volatility in domestic and international equity markets as well as measures of co-movement of returns in these markets (viz., relevant betas). The second type of variables, on the other hand, are essentially macroeconomic ones like exchange rate, shortterm interest rate and index of industrial production (IIP)—viz., variables that are likely to affect foreign investors’ expectation about return in Indian equity market. It may be mentioned that our analysis has been primarily motivated by the research done in this area by Chakrabarti (2001), results of which appeared in a recent issue of Money & Finance. Briefly, using a monthly dataset Chakrabarti examined the nature and causes of FII net inflow into the Indian equity market during the period May 1993 to December 1999. He obtained some interesting results: viz., (1) the FII net inflow is correlated with the return in Indian equity market and the former is more likely to be the effect than the cause of the Indian equity market return; (2) so far as investment in Indian equity market is concerned, foreign investors do not seem to be at an informational disadvantage compared to domestic investors; and (3) the Asian crisis marked a regime shift in the sense that in the post-Asian crisis period the return in the Indian equity market turned out to be the sole driver of the FII inflow, whereas for the pre-Asian crisis period other covariates reflecting return in other competing markets, urge for diversification etc., were also found to be correlated with FII net inflow.
This paper explores the relationship of foreign institutional investment (FII) flows to the Indian equity market with its possible covariates based on a daily data set.
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Monetary Research Project, ICRA Ltd., Kolkata Economic Research Unit, Indian Statistical Institute, Kolkata
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Whereas the preMexican crisis period 1990-1994 saw most of the emerging markets performing much better compared to the matured markets in terms of both return and associated risk, the pattern reversed during 1995-2001.
Our endeavour has been to see if these results would carry through when the phenomenon of FII flows was examined using a set of daily data on the relevant variables. The data-set incorporates day to day variations and hence is better suited for examination of various interrelationships, including Granger causality for equity market operations that are typically short run issues. Also, we tried to relate daily FII flows (distinguishing between three kinds of flows—viz., FII flows into the country or FII purchases, FII flows out of the country or FII sales and the net FII inflows into the country or FII net). We later modify the model specification to include a short past history of the variables over different time frames, like a week or fortnight. We have also made an attempt at relating FII flows to macroeconomic fundamentals for the Indian economy. Our results show that: (1) FII flows to and from the Indian market tend to be caused by return in the domestic equity market and not the other way round; (2) returns in the Indian equity market is indeed an important (and perhaps the single most important) factor that influences FII flows into the country; (3) while FII sale and FII net inflow are significantly affected by the performance of the Indian equity market, FII purchase is not responsive to this market performance; (4) FII investors do not seem to use Indian equity market for the purpose of diversification of their investment; (5) return from exchange rate variation and fundamentals of the Indian economy may have influence on FII decisions, but such influence does not seem to be strong, and; finally, (6) daily FII flows are highly auto-correlated and this auto-correlation could not be accounted for by the all or some of the covariates considered in our study.
I. Introduction
The performance of the emerging equity markets1 vis-à-vis their matured counterparts in the developed world have shown repeated reversals in recent times. Thus, whereas the pre-Mexican crisis period 1990-1994 saw most of the emerging markets performing much better compared to the matured markets in terms of both return and associated risk, the pattern reversed during 1995-2001; a period which in most part was affected by the Asian crisis and the associated contagion. The recent past (i.e., the first quarter of 2002) has seen another reversal of performance in which, the emerging markets (those of Asia and Latin America, in particular) have shown a remarkable recovery, in terms of both the level of return and risk while the matured markets have experienced drop in return and rise in risk (IMF, June and September 2002). The reasons behind these reversals may vary from one reversal to another. However, one thing seems to be pretty clear—viz., such
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1 This consists of capital markets in China, India, Indonesia, Korea, Malaysia, Pakistan, The Philippines, Sri Lanka, Taiwan and China in Asia, Czech Republic, Greece, Hungary, Poland Russia, Slovakia, Turkey, Egypt, Israel and Jordan in Europe and the Middle East, Morocco, South Africa and Zimbabwe in Africa and Brazil, Chile, Colombia, Mexico, Peru and Venezuela in Latin America.
reversals of market performances make foreign equity investment extremely volatile—a phenomenon which is capable of destabilising the domestic economy of the recipient country. It may therefore be essential to evolve appropriate built-in mechanisms in the economies of the countries receiving foreign equity investment such that destabilisation and damages can be minimised in case foreign investors suddenly withdraw from the equity market. It is in this context that a careful examination of the nature of foreign institutional investment (FII) flow into an economy is important, as it may help identify the strength of various factors (including macroeconomic ones like level of production, interest rate, etc.) that are likely to affect such flows and also the possible impact of such flows on the performance of the equity market concerned. Over the past ten years or so India has gradually emerged as an important destination of global investors’ investment in emerging equity markets. Today India has a share of about 20 per cent2 in the total global investment in all emerging equity markets together and the outstanding FII investment3 in India stood at around Rs. 86,287 crore, as on end-March, 2002. FII investments as a percentage of market capitalisation increased from 7.06 per cent in 1999-00, to 13.5 per cent in 2000-01 and further to 14.1 per cent in 2001-02. Given this growing importance of FII for the Indian economy, it is apparent that the nature and causation of such fund flows deserve careful examination. In a recent issue of Money & Finance a comprehensive analysis by Chakrabarti (2001) of the nature and causes of FII equity flows4 into the Indian market appeared. In his analysis Chakrabarti mostly used monthly data on FII net inflow as a proportion of the previous month’s market capitalisation, relevant stock market variables and other financial market indicators (like the deposit rate, exchange rate, etc.) and obtained the following main results: (a) though FII flows are highly correlated with equity returns in India, they are more likely to be the effect than the cause of such returns; (b) so far as investment in the Indian equity markets is concerned, global investors do not seem to be at an informational disadvantage compared to local investors; and (c) the Asian crisis marked a regime shift in the determinants of FII flows to India, with the domestic equity returns becoming the sole driver of these flows in the post Asian crisis period. Our motivation is essentially to take this analysis forward a few steps further. To be specific, using daily data for the post Asian crisis period January 1999—May 2002, we have done a study similar to that of Chakrabarti
2 This is a close approximation, since part of the FII flow data contains a small debt component. 3 Net FII, cumulative since 1990-91. 4 In case of India, the relative importance of the equity channel of FII compared to debt, can be gauged from the fact that, for the financial year 2001-02, the average monthly sales by FIIs was to the tune of Rs. 3771 crore and purchases Rs. 3102 crore in the equity segment, while for debt sales and purchases were a meagre Rs. 392 crore and Rs. 332 crore, respectively.
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Over the past ten years or so India has gradually emerged as an important destination of global investors’ investment in emerging equity markets. Today India has a share of about 20% in the total global investment in all emerging equity markets together.
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Since the use of daily data helps in determining the nature of causality with greater precision, our results of causality between FII flows and their major covariates like domestic equity market return may prove to be useful from policy viewpoint.
in which we have tried to identify the relevant covariates of FII flow into and out of the Indian equity market and also to determine the nature of causality between the relevant variables. Since the use of daily data helps in determining the nature of causality with greater precision, our results of causality between FII flows and their major covariates like domestic equity market return may prove to be useful from policy viewpoint. Next, going beyond Chakrabarti’s analysis, we have carried out an in-depth statistical analysis to find out not only the factors that affect net FII flow, but also those which affect FII sale and purchase decisions. The paper is organised as follows: In Section II we discuss briefly about the importance of the and factors behind portfolio flows to emerging markets, the nature of such flows during the past decade, their suggested role in causing volatility and contagion in emerging markets. In the third section, we discuss how our analysis builds upon Chakrabarti’s; we also describe our data-set and present the main results of our causality and regression analyses. The last section concludes, relating our analysis to policy implications from empirical studies in various emerging markets, and the current policies being followed in India with respect to foreign capital.
II. International Portfolio Flows to Emerging Markets
As is well-known, international capital flows to emerging markets is a somewhat recent phenomenon, which began at a reasonable scale in the early 90s. On the theoretical side, the case for liberalisation of international capital flows is built around a few basic tenets—viz., (a) free capital movements facilitate efficient allocation of global savings, channelling resources to countries where they will be most productive and thereby increasing growth and welfare globally; (b) access to foreign capital markets enable investors to achieve a higher degree of portfolio diversification, thus allowing them to obtain higher returns at lower risk; (c) full convertibility for capital account transactions complement the multilateral trading system which broadens the channels through which countries obtain trade and investment finance on much easier terms; and (d) liberalisation improves macroeconomic performance as it subjects governments to greater market discipline and penalises unsound monetary and fiscal policies. On the practical side, on the other hand, the surge in international portfolio investment over the past decade or so has been triggered by a number of parallel developments. First, institutionalisation of savings in the USA and the developed world since the 1980s placed a massive and increasing volume of funds under the management of professional portfolio managers,5 who for tactical reasons tend to prefer a widely
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5 That is, the choice of pooled funds held by pension funds, life insurance companies, mutual funds and investment trusts as repositories for the majority of
diversified portfolio spread out internationally. Second, there has been a trend towards financial liberalisation both in developing countries and countries in transition thus allowing global fund managers to reach the financial markets of these countries. 6 Third, developments in information technology have immensely lowered the cost of international trading in securities and made information dissemination on a near real time basis possible. Fourth, a remarkable expansion of capital markets in emerging economies has taken place due mostly to the widespread privatisation of formerly State-owned enterprises. However, the very elements that facilitated the inflow of foreign capital into developing countries have also meant that foreign capital can now be withdrawn from these countries far more quickly. Analysis of performance of emerging equity markets during the past decade has indicated that investment in these markets can provide global investors with attractive absolute returns as well as some scope to diversify their portfolios. In fact, global investors reaped such benefits in the first half of the 1990s, but the gains disappeared between 1995 and 2001 with the reversal of performances of these markets relative to their matured counterparts, as already mentioned (IMF, 2002). Such performance reversals have ushered in tactical investors such as a hedge fund (which tries to achieve high absolute returns essentially by exploiting the high volatility of returns in these markets through market timing). Understandably, such speculative and opportunistic behaviour of these tactical investors has contributed to the volatility of FII inflows into emerging markets. Typically, global investors allocate a small portion of their total assets to equities in these markets to track a world or regional equity index 7 and also as a means to diversify the portfolio held by them. Although this allocation is estimated to be a meagre 5 per cent of the total assets of global investors, in absolute terms this investment has now crossed the US dollar 100 billion level (which is larger than the total market capitalisation of many individual emerging equity markets). More importantly, the share that individual emerging markets get of such investment is often sizeable in relation to their total market capitalisation. Performances of emerging equity markets vis-à-vis those of their matured counterparts in recent periods reveal the following
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Performance reversals have ushered in tactical investors such as a hedge fund. Understandably, such speculative and opportunistic behaviour of these tactical investors has contributed to the volatility of FII inflows into emerging markets.
savings—increased the share of funds invested in securities and enhanced the role of institutional investors compared to that of depository institutions. In the United States, for example, the share of total financial sector assets held by institutional investors rose from 32% in 1978 to 52% in 1993, while the share of depository institutions fell from 57% to 34% over the same period (Federal Reserve System [FRS], Flow of Funds). 6 Whose local capital markets were proving to be a bottleneck in the growth process. 7 Like the S&P/IFC Composite index which is used as a benchmark for measuring equity markets returns in emerging markets as a whole or IFC’s Asia or Latin America indices which reflects the corresponding returns in specific regional markets.
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FII flows to the secondary equity market do not have any direct link with the level of real investment in the economy. It is only by enhancing the efficiency and liquidity of capital markets that such a flow can contribute to growth.
pattern: in the post-Mexican crisis period (to be more specific, during 1995-2001) the emerging equity markets, by and large, recorded much smaller returns than those in the matured markets. Thus, whereas S&P 500 and NASDAQ recorded 14.77 and 13.62 per cent return, respectively, the corresponding figures for S&P/IFCI Composite, IFCI’s Asia, EMEA (which covers emerging markets in Europe and the Middle East) and Latin America indices were –3.68, –9.81, 1.78 and 1.35, respectively. Also, the risk associated with investment in the emerging equity markets increased considerably during this period, whereas that for the matured markets decreased. This was indicated by values of the Sharpe ratio (which is the ratio of excess return of an asset over that of a risk free asset and the standard deviation of return of the asset concerned) recorded between –0.48 and –0.14 for the emerging markets as against 0.24 and 0.56 recorded for NASDAQ and S&P500, respectively. This pattern, however, got reversed again during the first quarter of 20012002, when the emerging markets (except those in Europe and the Middle East) recorded much higher return and lower risk compared to those of S&P500 and NASDAQ.8 Let us next discuss briefly the possible effects of FII flow on the recipient country’s economy. As such, FII flows to the secondary equity market do not have any direct link with the level of real investment in the economy. It is only by enhancing the efficiency and liquidity of capital markets that such a flow can contribute to growth. Securities markets in developing countries are typically both narrow and shallow. Therefore, FII participation may, a priori, induce considerable instability in these markets. The effect of such mobile capital flows can, however, be quite complicated and therefore are highly controversial. In fact, country experiences differ considerably. Some studies found clear evidence of benefits of such flow in the form of equity market development, capital market integration, lowering cost of capital, and hence tend to question policy concerns regarding resource mobilisation, market co-movements, contagion and volatility expressed by some policy makers and academics to be largely unwarranted.9 The causes of the instability and volatility of short-term portfolio capital flows to emerging markets are often related to the way in which investment funds are managed in order to confront uncertainty. It has been alleged
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8 The underperformance of emerging market equities from a longer-term perspective does not appear to be due to overvaluation, though price/earnings ratios in emerging market equities have been high in some years. Some of the main factors in this underperformance have been identified as the string of financial crises, starting with Mexico in 1994, which has drastically pruned the US dollar returns on emerging market equities as well as concerns about corporate transparency and governance in the West and Japan and more so in the emerging markets. 9 See Errunza, 2001; for a very detailed review. Bekaert and Harvey, 1998, 2000, analyse pooled cross-section and time series data for US equity flows to about 20 emerging countries, including India. Kim and Singal, 2000, analyse 20 countries from the IFC EM Database.
that international portfolio investors seek liquidity and use ‘quick exit’ as a means of containing downside risk, thus making frequent marginal adjustments to their portfolios. Further, shifts in the portfolio composition of global investors are largely ascribed to changes in their perceptions of country solvency rather than to variations in underlying asset value.10 A common conclusion from research, however, is that institutions sometimes panic, disregard fundamentals and spread crisis even to countries with strong fundamentals. The literature also notes that individuals, too, can contribute to this destabilisation process by fleeing from funds, particularly mutual funds and forcing fund managers to sell when fundamentals do not warrant such sale. Empirical results of the effect of FII activities on the volatility of return are rather divided; some studies do not find that foreign investors have any destabilising impacts on stock prices. 11 Evidences to the contrary showing that foreign investors cause higher volatility in the market compared to domestic investors12 or that stocks in which foreign investors mainly trade experience higher volatility compared to those in which they do not show much interest also exist. 13 These studies also show that volatility caused by FII jumped significantly around the crises period.
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We have looked for covariates of FII flows to India and tried to ascertain whether FII flows adjust in response to changes in the condition of the Indian equity market or such flows exert strong enough influence on this market.
III. FII Flows to the Indian Equity Market
As already mentioned, the basic objective of our study is to carry forward and supplement the empirical research on the FII flow to India reported in Chakrabarti (2001). To be specific, we have looked for covariates of FII flows to India and tried to ascertain whether FII flows adjust in response to changes in the condition of the Indian equity market or such flows exert strong enough influence on this market so as to affect significantly the return from variations in daily stock price level. In what follows, we first explain our choice of the set of selected variables and describe briefly the data set used and then present the results that have been obtained. FII Flows and Their Covariates—Choice of Variables and Data As is well-known, given the set of investment opportunities available, a global investor would continuously adjust investment portfolio round the clock using available market information and
See, for example FitzGerald,1999, in this context. Chan, Kho and Stulz, (1999); Kim and Wei (1999) with Korean data; Froot, O’ Connell, and Seasholes (2001) based on data from 44 countries. 12 For example, Jo, 2002, using data from the Korean stock markets, where data is available for different categories of traders. 13 Bae and Chan, 2001, analyse data from the Standard & Poor’s (formerly the IFC) Emerging Markets Database (EMDB), which covers more than 2000 stocks from 45 emerging markets.
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We have chosen to use daily data, since we felt that a set of daily data should be more appropriate for examining the nature of causality, so far as the relationship between FII flows and their major covariates are concerned.
thereby tracking returns in all possible markets. So far as the trading behaviour of these investors is concerned, studies examining such behaviour suggest broadly two types of trading behaviour (see Box 1 for a brief description of these trading types). Further, for a given unit time interval (may be a time span of a few minutes, a day, a week or a month), the investor’s actions may be aggregated and summarised into two basic measures—viz., sale and purchase—and a corresponding overall measure of net sale or net purchase, as the case may be. Following this logic, we have chosen to examine the nature of FII flow to India in terms of three variables—viz., FII sale (henceforth denoted by FIIS), FII purchase (henceforth denoted by FIIP) and net FII investment (henceforth denoted by FIIN). It may be noted that in his analysis Chakrabarti used monthly data on net FII flow as a proportion of the size of market capitalisation (henceforth denoted by MCAP) of the previous month as the variable to be explained instead of the net FII flow.14 As regards the data frequency, we have chosen to use daily data, since we felt that a set of daily data should be more appropriate for examining the nature of causality, so far as the relationship between FII flows and their major covariates are concerned. For the sake of comparison with Chakrabarti’s results, we have also considered FIIN as a proportion of MCAP (henceforth denoted by RATIO_FIIN). Similarly defined ratio variables for FIIP (henceforth denoted by RATIO_FIIP) and FIIS (henceforth denoted by RATIO_FIIS) have also been considered. However, since daily data on MCAP are unavailable, we have had to calculate a time series of daily MCAP using a method of approximation. As regards the choice of covariates of the FII variables, to the extent possible we have tried to incorporate into the analysis a set of variables that appear, a priori, to be the primary determinants of global investors’ demand/supply for/of stocks in the Indian market. To be precise, we have considered two different sets of variables—one relating to the Indian and other equity markets which tend to compete for global investors’ investment and the other relating to the Indian economy which may be relevant for investors’ expectation formation about the Indian market. The first set of variables include daily return in Rupee terms in the Indian market and corresponding returns in US
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14 In this context, the following point may be noted: As FII transactions take place on a day to day basis (or for that matter, even over shorter time intervals), use of monthly aggregates may blur the picture considerably. Further, since the ratio of net FII flow to the total size of market capitalisation (henceforth denoted as Mcap) is rather small for India (and the monthly variation in this ratio is even smaller), any analysis using this ratio as the explained variable may fail to capture the true relationship between FII flow and its various covariates identified as important explanatory factors for this flow in the literature as well as in Chakrabarti’s own analysis.
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BOX 1 Studies examining foreign institutional investors’ behaviour suggest broadly two types of trading behaviour—viz., momentum (M) or positive-feedback trading(PT) and herding(H) strategies1, which may or may not be within the bounds of rational behaviour2. Briefly, strategy M is the tendency of an investor to buy and sell stocks based on their observed return records—i.e., to buy recent winners and sell recent losers3. There are strong evidences of contemporaneous momentum trading by funds, induced both by the managers and investors when trading is done on the basis of contemporaneous return information4. Such momentum trading is especially found to be strong during periods of financial crisis. Momentum trading at a lag5 is observed during non-crisis periods. Typically, such trading decisions are mostly taken at the managerial level. Understandably, the M or PT strategy may exacerbate price movements and thus accentuate volatility. It is also found that funds often go in for contagion trading—i.e., they systematically sell assets from one country when asset prices fall in another, however, such contagion trading is primarily to meet redemption by individual investors, and is not a management strategy6. The H strategy refers to a situation in which all investors act/react in a similar manner. Herding is intentional, if investors are influenced into reversing a planned investment decision after observing others, and this in turn, increases volatility in the market.
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Briefly, strategy M is the tendency of an investor to buy and sell stocks based on their observed
See for example, Froot et al (2001); Kim and Wei (1999). Bikhchandani and Sharma, 2001, provides an overview of the recent theoretical and empirical research on herd behaviour in financial markets. 3 This form of herd behaviour is not rational under the efficient-markets hypothesis since market prices are assumed to reflect all available information. 4 Such studies take into account returns for the same day, month or quarter. 5 That is, trading is based on return information pertaining to the previous day, month or quarter. 6 As found by Kaminsky et al , 2002, using data from 13 US mutual funds; which are dedicated Latin America funds.
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return records. The
H strategy refers to a
situation in which all investors act/ react in a similar manner.
Dollar terms in competing markets, measures of volatility of these returns (taken as proxy of the corresponding risks), daily return implied by day to day variations in the Rupee-Dollar exchange rate. More specifically, this set consists of the following variables: 1. Daily return in the Indian market calculated on the basis of day to day variations in the value of BSE Sensex (henceforth denoted by BSE_RET);15 2. Volatility of daily return in the Indian market calculated as the standard deviation of previous 7/ 15/ 30 days’ daily returns based on the BSE Sensex (henceforth denoted by BSE_RETVOL); 3. Daily return in the international equity market based on the
15 BSE Sensex is compiled using a set of 30 major shares and reports from stock markets suggest that FIIs mostly restrict their trading to the shares covered in the BSE Sensex.
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The second set, includes two macroeconomic
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variables—viz., the index of industrial production and the call money rate.
day to day variations in the value of the MSCI World Index (henceforth denoted by MSCI_RET);16 Daily return in the US equity market based on the day to day variations in the S&P500 (henceforth denoted by S&P_RET); Volatility of daily return in the international equity market calculated as the standard deviation of previous 7/ 15/ 30 days’ daily returns based on MSCI World index (henceforth denoted by MSCIRET_VOL); Volatility of daily return in the US equity market calculated as the standard deviation of previous 7/ 15/ 30 days’ daily returns based on the S&P500 index (henceforth denoted by S&PRET_VOL); Extent of co-movement of daily returns in Indian and International equity markets as measured by the beta of returns from BSE Sensex and MSCI World Index (henceforth denoted by BETA_MSCI); Extent of co-movement of daily returns in Indian and the US equity markets as measured by the beta of returns from BSE Sensex and S&P500 Index (henceforth denoted by BETA_MSCI); and finally Daily return from day to day variations in the Rupee—USD exchange rate (henceforth denoted by EXCH).
The second set, on the other hand, includes two macroeconomic variables—viz., the index of industrial production (henceforth denoted by IIP) taken as a proxy for short run real income changes and the call money rate (henceforth denoted by CMR) taken as a proxy for shortterm interest rate. These two variables, taken to reflect the short run variations in the fundamentals of the Indian economy, have been used together with the equity market-related variables to see whether or not global investors take into account their expectations about the state of the Indian economy. The sample period of the daily data set is January 1999—May 2002, which wholly relates to the post- Asian crisis period. For a fuller description of these variables and the sources of data on them, see Box 2. See also Appendix 1 some general information on FII investment activities as well as the Indian stock market and related charts and Appendix 2 for a description of the method used to build up the time series of daily MCAP. The Results In the present analysis we have mostly used appropriate linear regression techniques to testify various hypotheses concerning FII flows to the Indian equity market. For the hypotheses relating to the direction
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16 This index, also used by Chakrabarti, is known to be closely tracked by FIIs operating in India.
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BOX 2 The data series and Sources: Stock Market Series Domestic FIIP FIIS FIIN BSE Sensex Mcap:
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DailyFII Purchses or inflows into the Indian equity markets, Source: website of SEBI; www.sebi.gov.in DailyFII Sales or outflows from the Indian equity markets, Source: website of SEBI; www.sebi.gov.in Daily Net FII inflows into the Indian equity markets; the difference between FIIP and FIIS, Source :website of SEBI; www.sebi.gov.in The 30 share BSE stock price index; daily closing values, Source: website of the Mumbai Stock Exchange; www.bseindia.com Daily market capitalisation on the BSE, Source: BSE website and our estimation as per Appendix1.
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International S&P 500 MSCI WI
The daily series for the S&P500 stock price index; Source: website of Standard and Poors The daily series for the MSCI World Index, a weighted stock price index for all countries1; Source: website of Morgan Stanley Capital International Inc; www.msci.com
Others Exch CMR IIP
Daily exchange rate of the Indian rupee vis a vis the US dollar Daily Call money rate; Source RBI website; www.rbi.org.in Index of Industrial production reported weekly; Source: RBI Handbook of Statistics on Indian Economy, 2001 and CMIE Monthly Review of the Indian Economy
Estimated series Ratio_FIIN Ratio_FIIP Ratio_FIIS FIIN_MAk(t-1) FIIP_MAk(t-1) FIIS_MAk(t-1) BSE_Ret
Ratio of FIIN to previous day’s Mcap2 Ratio of FIIP to previous day’s Mcap Ratio of FIIS to previous day’s Mcap k-day moving average value of FIIN calculated using FIIN(t-k) to FIIN(t-1), k=7,15,30. k-day moving average value of FIIP calculated using FIIP(t-k) to FIIP(t-1), k=7,15,30. k-day moving average value of FIIS calculated using FIIS(t-k) to FIIS(t-1), k=7,15,30.
Daily returns on the BSE Sensex, calculated as the excess of the logarithm of the index value on a date over the logarithm of the index value on the previous day. BSERET_MAk(t-2) k-day moving average value of return on BSE calculated using BSE_Rte(t-k) to BSE_RET(t-2), k=7,15,30. BSE_RetVol k(t-2) k-day moving average volatility of, returns on BSE Sensex or the standard deviation of BSE_Ret(t-k) to BSE_Ret(t-2), k=7,15,30.
This index includes India. A possible econometric rationale for taking the ratio variables could be to eliminate heteroscedasticity; however, it is found that even if we take the ratio to MCap, heteroscedasticity is not eliminated for FIIN and FIIS series.
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Daily returns on the S&P500, calculated as the excess of the logarithm of the index value on a date over the logarithm of the index value on the previous day. S&PRet_MAk(t-2) k-day moving average value of returns on S&P500 calculated using S&P_Ret(t-k) to S&P_Ret(t-2), k=7,15,30. S&P_RetVol k(t-2) k-day moving average volatility of, returns on S&P500 or the standard deviation of S&P_Ret(t-k) to S&P_Ret(t-2), k=7,15,30. MSCI_Ret Returns on the MSCIWI, calculated as the excess of the logarithm of the index value on a date over the logarithm of the index value on the previous day. MSCIRet_MAk(t-2) k-day moving average value of returns on MSCIWI calculated using MSCI_Ret(t-k) to MSCI_Ret(t-2), k=7,15,30 MSCIRetVol k(t-2) k-day moving average volatility of, returns on MSCIWI or the standard deviation of MSCI_Ret(t-k) to MSCI_Ret(t-2), k=7,15,30. BETA_S&Pkk(t-2) betas of BSE wrt S&P500 based on previous kk day’s data starting from (t-2), kk=15,30 BETA_MSCIkk(t-2) betas of BSE wrt MSCI based on previous kk day’s data starting from (t-2), kk=15,30 Exch_Ret Daily returns on the Rupee’s exchange rate, calculated as the excess of the logarithm of Exch on a date over the logarithm of Exch on the previous day.
When it comes to the case of foreign investment in a thin equity market like that of India, there is a prevalent feeling that FII activities exert a strong demonstration effect and thus drive the domestic stock market.
Notes : (a) The problem of non-synchronised trading in the different markets have been overcome by removing such dates. (b) The routine tests of normality and unit root for stationarity for all series have been conducted and the regressions framed accordingly.
of causation between FII flows and some of their covariates, we have used the technique of pair-wise Granger Causality test. The results are presented below. A. Direction of Causation between FII flows and Return in the Indian Stock Market For any type of investor, domestic or foreign, market return is generally the prime driver of equity investments. However, when it comes to the case of foreign investment in a thin equity market like that of India, there is a prevalent feeling that FII activities exert a strong demonstration effect and thus drive the domestic stock market. In other words, some believe that the day to day FII trading in Indian market, rather than being influenced by the market return, induces the daily market return to be what it is. As a starting point we examine the nature of pair-wise causality between daily measures of FII investment and corresponding BSE_RET separately for the three FII variables (viz., FIIP, FIIS and FIIN). The results are given in Table 1. It may be mentioned here that Chakrabarti (2001) also examined the nature of causality between FIIN and BSE_RET mostly on the basis of
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monthly data on these variables.17 However, given the facts that Granger causality test is designed essentially to detect statistically significant short run lead-lag relationship present in a data-set on a pair of variables and that equity market responses are typically extremely quick and of very short run nature, examination of causality based on a monthly data set may fail to capture the exact nature of causality. The results in Table 1 clearly suggest that causation runs from
TABLE 1 Pairwise Granger Causality Tests between BSE Return and FII Value of F-statistics at different lags Lags —> PANEL 1 BSE Return does not Granger Cause FIIN FIIN does not Granger Cause BSE Return PANEL 2 BSE Return does not Granger Cause FIIP FIIP does not Granger Cause BSE Return PANEL 3 BSE Return does not Granger Cause FIIS FIIS does not Granger Cause BSE Return 2 3 4 5 6 7
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25.98* 1.90
17.33* 15.95* 1.41 2.87
13.53* 11.72* 10.21* 2.02 1.45 1.42
That equity market responses are typically extremely quick and of very short run nature,
4.32+ 1.44
4.42* 0.94
5.67* 4.39*
5.75* 3.33
4.70* 3.19*
4.18* 2.99*
8.69* 7.37*
6.18* 4.94*
5.55* 3.62
4.84* 2.99
4.34* 3.03
3.69* 2.69
examination of causality based on a monthly data set may fail to capture the exact nature of
PANEL 4 BSE_RET does not Granger Cause RATIOFIIN 28.91* RATIOFIIN does not Granger Cause BSE_RET 2.89 PANEL 5 BSE Return does not Granger Cause RATIOFIIP 3.04+ RATIOFIIP does not Granger Cause BSE Return 0.96 PANEL 6 BSE Return does not Granger Cause RATIOFIIS 18.16* RATIOFIIS does not Granger Cause BSE Return 6.22*
19.09* 17.27* 2.24 3.41
14.11* 12.17* 10.49* 2.41 1.84 1.71
2.80+ 0.60
4.25* 4.02*
3.97* 3.07
3.35* 2.96
2.83* 2.67
causality.
11.37* 4.20
8.50* 2.98
6.86* 2.74
5.76* 2.80
4.88* 2.31
*’ Denotes rejection at 1% level of significance +’ Denotes rejection at 5% level of significance
17 He has done the causality test taking the daily data for a limited period of one year (1999, in the post-Asian crisis period), most of his analysis of causality for the pre-crisis period was based on monthly data (1993 to 1999).
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BSE_RET to FIIN and not the other way. This is true for FIIS and FIIP and their ratios also, barring a few cases where no definite conclusion can be drawn. For these variables, causality, if found statistically significant, is unidirectional running from BSE_RET to FII flows. 18 This lends further credence to the supposition that FII flows to India are mostly in response to contemporaneous returns in the Indian stock markets (in the post Asian crisis period) rather than FII inflows and outflows being the cause of returns in the national markets. B. Effect of BSE Returns Having identified the nature of causality, we next examine whether or not contemporaneous BSE_RET significantly affect daily FII inflows/outflows. Regression of the FIIN, FIIP, FIIS on BSE_RET yielded results quite contrary to expectations as the coefficient of BSE_RET turned out to be statistically non-significant in all of these regressions (Table 2.1). However, when RATIOFIIN was taken as the dependent variable, the estimated positive coefficient of BSE_RET turned out to be significant at 5 per cent level—a result, which was also obtained by Chakrabarti. However, BSE_RET again was found to be non-significant determinant for RATIOFIIP and RATIOFIIS. Since the RATIOFIIP and RATIOFIIS which are the components of RATIO_FIIN are not affected by contemporaneous value of BSE_RET, it becomes somewhat difficult to reconcile the observed dependence of RATIO_FIIN on the current level BSE_RET. As day to day variation in the FII flow variables as well as the market return is likely to contain relatively large random components, one would not expect high correlation between FII flow and market return (which indeed was the case in our exercise). We therefore tried next to explore whether the FII flow variables would show up any stronger dependence on market return if daily variations were filtered through moving average smoothing. The results obtained by regressing 7-day moving average values of FIIN, FIIP, FIIS and RATIO_FIIN on the corresponding 7-day moving average values of BSE_RET suggest that while FII sale and FII net inflow are significantly affected by market return (the former being affected positively and the latter negatively, as to be expected), FII purchase is not responsive to variations in market return (Tab 2.2). In other words, a drop/rise in average return over the previous 7 days would induce a FI investor to
18 Studies examining the pattern of aggregate international portfolio flows found evidences of contemporaneous positive correlation between inflows and returns and lagged returns (Bohn and Tesar, 1996, Brennan and Cao, 1997 and Froot, O’ Connell, and Seasholes, 2001). The last mentioned study also found sensitivity of local stock prices to foreign inflows to be positive and large and also that inflows had positive forecasting power for future emerging-market returns. Other studies on the Indian equity market have also found evidence of the relationship between net FII and returns on equity (Batra, 1999). On the other hand, Bhatia (2000) has shown that daily net FII inflows are a significant determinant of the daily returns on the BSE.
As day to day variation in the FII flow variables as well as the market return is likely to contain relatively large random components, one would not expect high correlation between FII flow and market return.
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raise/reduce sale promptly, but it might fail to induce a change in the level of her purchase—a result which is suggestive of some kind of asymmetry of behaviour so far as FII trading in Indian market is concerned. C. Other Factors Influencing FII Flows To identify other significant non-domestic return determinants of different FII flows, we considered a set of other possible determinants, domestic as well as international. As already mentioned, this set comprised MSCI_RET, S&P_RET, BSE_RETVOL, BETA_MSCI,
TABLE 2.1 Regression of FII on BSE Return Regressands ——> Regressors Constant BSE Return R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat Note : FIIN 34.09* (0.0) 447.02 (0.05) 0.0049 0.0037 119.81 1.61 RatioFIIN 0.001* (0.0) 0.01* (0.04) 0.0053 0.0041 0.0036 1.58 FIIP 219.3* (0.0) 0.06 (0.99) RatioFIIP FIIS RatioFIIP
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A drop/rise in average return over the previous 7 days would induce a FI investor to raise/ reduce sale promptly, but it might fail to induce a change in the level of her purchase.
0.007* 185.25* 0.006* (0.0) (0.0) (0.0) 0.01 -447.03 -0.01 (0.42) (0.057) (0.21) 0.0055 0.0022 0.0043 0.0010 113.15 0.003 0.95 1.18
0.0000 0.0009 -0.0012 -0.0003 141.81 0.004 1.11 1.39
1. ‘*’ denotes significant at 5% 2. Figures in parenthesis are p-values of the regression coefficients 3. Regressions are formulated keeping in view the stationarity of variables
TABLE 2.2 Regression of FII_MA on BSE Return_MA Regressands ——> Regressors Constant Moving Average of BSE Return R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat Note: FIIN_MA 34.76* (0.0) 2603.83* (0.0004) 0.09 0.087 62.25 0.163 RatioFIIN_MA 0.001* (0.0) 0.089* (0.0001) 0.11 0.112 0.0019 0.171 FIIP_MA 220.17* (0.0) -641.60 (0.55) 0.00 0.001 101.14 0.053 FIIS_MA 185.4054* (0.0) -3246.149* (0.0003) 0.08 0.080 81.39 0.066
1. ‘*’ denotes significant at 5% 2. Figures in parenthesis are p-values of the regression coefficients 3. Regressions are formulated keeping in voew the stationarity of variables
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BETA_S&P, EXCH (all these variables were considered by Chakrabarti (2001) for explaining FIIN) and, in addition, MSCI_RETVOL, S&P_RETVOL, IIP and CMR.19 Multiple regression analysis was done separately for each of the FII variables—viz., FIIN, FIIP, FIIS, their ratio versions RATIOFIIN, RATIOFIIP, RATIOFIIS and also their moving average versions FIIN_MA, FIIP_MA, FIIS_MA20—using
TABLE 3A Stage 1 Regression results of FII on all relevant variables
REGRESSION A: Stage 1 Regression Coefficients Constant Lagged BSE Return BSE Return 15-day MA(t-2) BSE Return 15-day Volatility(t-2) Lagged MSCI Return MSCI Return 15-day MA(t-2) MSCI Return 15-day Volatility(t-2) Lagged S&P Return S&P Return 15-day MA(t-2) S&P Return 15-day Volatility(t-2) Beta_MSCI 15-day (t-2) Beta (S&P) 15-day (t-2) FIIN -29.06 (0.14) 1285.21 (0.0) 776.92 (0.52) 587.96 (0.49) 393.554 (0.73) -16601.52 (0.017) -7446.42 (0.07) -150.21 (0.85) Ratio FIIN -0.001 (0.12) 0.04 (0.0) 0.02 (0.59) 0.02 (0.48) -0.002 (0.96) -0.46 (0.03) -0.18 (0.14) 0.01 (0.69) FIIN_MA 15(t-1) -9.84 (0.58) -15.80 (0.87) 3426.23 (0.0004) 2134.64 (0.004) -260.48 (0.53) -2105.64 (0.66) -592.82 (0.86) 45.69 (0.86) 2571.63 (0.57) 970.09 (0.72) 2.06 (0.87) 14.88 (0.47) 0.15 0.14 49.25 0.06 FIIP 137.05 (0.0) 408.05 (0.18) 1455.58 (0.44) 3164.84 (0.03) -1237.87 (0.33) -16763.99 (0.19) -7372.09 (0.297) 621.42 (0.51) 16217.83 (0.19) 7840.28 (0.20) -5.45 (0.87) 15.42 (0.70) 0.05 0.04 138.57 1.19 Ratio FIIP 0.01 (0.0) 0.01 (0.18) -0.01 (0.78) 0.03 (0.35) -0.05 (0.14) -0.65 (0.018) -0.01 (0.94) 0.03 (0.18) 0.60 (0.02) 0.11 (0.49) 0.0001 (0.94) 0.001 (0.61) 0.05 0.03 0.00 1.48 FIIP_MA 15(t-1) 136.47 (0.0) -437.76 (0.016) 433.96 (0.79) 4783.72 (0.0003) -922.41 (0.28) -10479.46 (0.35) -2060.30 (0.73) 403.22 (0.51) 10508.50 (0.35) 1623.88 (0.75) -15.80 (0.58) 48.63 (0.13) 0.18 0.17 84.32 0.05 FIIS 166.10 (0.0) -877.39 (0.0005) 677.97 (0.71) 2577.51 (0.04) -1630.71 (0.15) -160.46 (0.99) 72.42 (0.989) 771.12 (0.33) -981.58 (0.92) -1790.15 (0.68) -12.98 (0.68) 4.39 (0.91) 0.05 0.04 110.59 0.99 Ratio FIIS 0.01 (0.0) -0.03 (0.0) -0.03 (0.41) 0.01 (0.67) -0.05 (0.07) -0.19 (0.39) 0.17 (0.15) 0.02 (0.19) 0.11 (0.57) -0.16 (0.12) 0.00 (0.76) 0.0003 (0.77) 0.07 0.06 0.003 1.24 FIIS_MA 15(t-1) 146.30 (0.0) -422.01 (0.0095) -2993.03 (0.05) 2649.34 (0.0099) -662.13 (0.299) -8372.86 (0.36) -1470.89 (0.76) 357.56 (0.46) 7935.17 (0.38) 656.41 (0.87) -17.86 (0.44) 33.74 (0.23) 0.18 0.17 70.36 0.04
17196.60 0.48 (0.009) (0.0096) 9631.50 (0.008) 7.53 (0.697) 11.03 (0.69) 0.09 0.08 115.99 1.73 0.27 (0.02) 0.00 (0.66) 0.00 (0.78) 0.10 0.08 0.003 1.70
R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat
Note:
Figures in parenthesis are the p-values of coefficients
19 So far as the volatility variables are concerned, for each of them we considered two different versions—measured as the standard deviation of the values observed over the previous 7 and 15 days, respectively. 20 Three different sets of moving averaged variables were compiled using moving average period of 7, 15 and 30 days, respectively. This was done essentially to see whether or not the regression results would be sensitive to the choice of the period of moving average.
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BSE_RET along with the set of non-domestic return determinants listed above. This analysis was done in several stages.21 Here let us first summarise the qualitative results obtained on the nature of relationship of different types of FII flows with the variables that we identified to be possible covariates of these flows. (We present here only some representative results in Tables 3A and 3B.)
TABLE 3B Stage 2 Regression Results of FII on all relevant variables
REGRESSION A: Stage 2 Regression Co-efficients Constant Lagged BSE Return BSE Return 15-day MA(t-2) BSE Return 15-day Volatility(t-2) Lagged MSCI Return MSCI Return 15-day MA(t-2) MSCI Return 15-day Volatility(t-2) S&P Return 15-day MA(t-2) S&P Return 15-day Volatility(t-2) Other Regressors -14793.27 (0.02) -5046.68 (0.13) 14899.95 (0.01) 7411.93 (0.01) Lag1 FIIN 0.17 0.39 (0.02) 0.12 (0.002) Lag1 Ratio FIIN 0.19 Lag1 FIIN_ MA15 1.18 Lag2 FIIN_ MA15 -0.21 Lag1 FIIP 0.296 Lag2 FIIP 0.302 Lag1 Ratio FIIP 0.29 Lag1 FIIP_ MA15 1.23 Lag2 FIIP_ MA15 -0.24 0.43 (0.03) -0.37 (0.04) -0.50 (0.01) FIIN -17.21 (0.27) 1252.26 (0.0) Ratio FIIN -0.001 (0.19) 0.04 (0.0) 159.07 (0.13) 74.92 (0.25) 1132.59 (0.04) -31.30 (0.67) FIIN_MA 15(t-1) -0.28 (0.79) FIIP 69.84 (0.0) Ratio FIIP 0.005 (0.0) FIIP_MA 15(t-1) 2.40 (0.07) -9.43 (0.65)
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FIIS 57.25 (0.0) -694.50 (0.0007)
Ratio FIIS_MA FIIS 15(t-1) 0.003 (0.0) -0.03 (0.0) 1.15 (0.23) -14.78 (0.37) 187.86 (0.003)
324.36 (0.48) -0.01 (0.18)
21.04 (0.67)
Lag1 FIIS 0.36 Lag2 FIIS 0.31
Lag1 Ratio FIIS 0.32 Lag2 Ratio FIIS 0.22
Lag1 FIIS_ MA15 1.27 Lag2 FIIS_ MA15 -0.10 Lag3 FIIS_ MA15 -0.18
(Lagged Variables)
R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat
0.11 0.11 114.26 2.10
0.12 0.11 0.00 2.12
0.96 0.96 10.56 2.04
0.27 0.27 120.77 2.11
0.10 0.10 0.004 2.13
0.99 0.99 10.30 2.11
0.37 0.37 89.83 2.08
0.26 0.25 0.003 2.06
0.99 0.99 7.80 2.05
Note:
1. Figures in brackets are the p-values of coefficients 2. P-values for all the lagged dependent variables are less than or equal to zero. 3. Since the package we used doesn’t provide Durbin h-stats, we report the DW here.
21
First, we have checked for stationarity of all the variables concerned. Then, we have specified the regressions keeping in view to the order of integration of the variables concerned. In all cases the regressions have been done in two stages, detecting residual auto-correlation at the first stage, we have run the regressions again. In stage 2, we included only the significant variables at stage 1 and the lagged dependent variable as the regressors.
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One might surmise that daily FII activities might be based, not just on previous day’s return information, but on the history of return over the previous few days.
C1. FII Flows and BSE Return As regards the relation between daily FII net inflow and BSE return, a definite positive association with the previous day’s return was observed in all the regressions estimated. Similarly, for daily FII sale the association with the previous day’s return was always found to be negative and significant. However, no definite association between daily FII purchase and previous day’s market return was noticed. One might surmise that daily FII activities might be based, not just on previous day’s return information, but on the history of return over the previous few days. Thus, for example, daily FII sale may be thought to depend, not just on previous day’s return but also on the average and the standard deviation of returns observed during the previous 7 or 15 days (the standard deviation being taken as a measure of volatility of returns). Ceteris paribus, FII sale/purchase should be positively/negatively related to average return and negatively/positively related to volatility. We, however, failed to observe any definite association with average return or return volatility of any of the daily FII flow variable. Very similar results were obtained when the FII variables were used in ratio form. But use of moving average variables—i.e., FIIN_MA, FIIP_MA and FIIS_MA—showed definite positive association of FII net inflow, FII purchase and FII sale with return and volatility, but not with average return. While a positive association of return volatility with FII sale seems intuitively justifiable, its negative association with the other two FII flows appears somewhat unrealistic. C2. FII Flows and Return and Volatility in Competing markets As already mentioned, in our regression strategy, we included the return and volatility variables relating to the S&P500 index and MSCI index (viz., MSCI_RET, S&P_RET, MSCI_RETVOL and S&P_RETVOL) in the set of possible regressors, as indicators of return and risk involved in investing in the US and the World equity markets, other than the Indian one (see Box 3 in this context). The hypothesis of portfolio return maximisation suggests reallocation of investment in favour of markets in which risk is minimised (as measured by Sharpe ratio—i.e., excess return per unit of volatility) and this in turn means, ceteris paribus, a positive/negative partial association with competing market returns/volatility of FII net inflow and FII purchase and a negative/positive association with FII sale. In our exercise, we did get result supporting the hypothesis of return maximisation only in some cases, not unequivocally. This is in conformity with the result that Chakrabarti (2001) obtained for the post-Asian crisis sub-period using monthly data. We also found in most of the cases that S&P variables and MSCI variables are simultaneously significant. We checked the correlation between these variables which, turned out to be quite high (Table 4) implying thereby the possibility of multicollinearity, given the extent of integration of the world and US markets. We therefore ran separate regressions for S&P variables and MSCI variables. The results
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BOX 3 We have also tried to see to what extent the performance of matured equity markets affect that of the Indian equity market and therefore we examined the possible interdependence of BSE and S&P returns. To see whether or not S&P return exerts a significant causal effect on the BSE return we carry out a Granger causality test for the same the results are as below. Granger Causality Tests between S&P and BSE Returns
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Value of F-statistics at different lags
SP500_RET does not Granger Cause BSE_RET
7.79* + 6.5* +
5.04* +
4.32* +
3.76* +
3.30* +
*’ Denotes rejection at 1% level of significance +’ Denotes rejection at 5% level of significance
There is clear evidence of S&P return causing BSE return unilaterally. This was to be expected and perhaps suggestive of the fact that a fluctuation in the US (matured) equity market would get transmitted to the Indian market.
(Table 5) showed that while only the MSCI return volatility but not the average return itself was significant, in more than one cases both S&P return and its volatility were significant. See, however, Table 3A which suggests that whereas FII net inflow is affected by return and volatility of both MSCI and S&P, the corresponding purchase and sale flows are mostly not affected by these.
TABLE 4 Correlation Set of Regressors in Table 3A and 3B
BSE_R1 BSE_RE ETLAG TVOL15 (t-2) BSER ET_MA 15(t-2) BETA _MSCI 15(t-2) BETA _SP 15 (t-2) MSCI MSCI_ _RET RETVOL LAG1 15(t-2) MSCI SP_RE SP_RE SPRET RET_ TLAG1 TVOL _MA MA 15(t-2) 15(t-2) 15(t-2)
BSE_RETLAG1
BSE_RETVOL15(t-2) BSERET_MA15(t-2) BETA_MSCI15(t-2) BETA_SP15(t-2) MSCI_RETLAG1 MSCI_RETVOL15(t-2) MSCIRET_MA15(t-2) SP_RETLAG1 SP_RETVOL15(t-2) SPRET_MA15(t-2)
1.00
-0.03
1.00
0.01
-0.32 1.00
0.01
-0.06 -0.05 1.00
-0.02
0.06 -0.16 0.86 1.00
0.14
0.05 -0.03 -0.02 -0.02 1.00
-0.02
0.50 -0.31 0.07 0.11 0.09 1.00
0.13
-0.05 0.42 -0.15 -0.24 0.04 -0.12 1.00
0.07
0.05 -0.03 -0.02 -0.01 0.89 0.08 0.01 1.00
0.01
-0.30
0.14
0.36
0.50 -0.02 -0.06 -0.16 -0.01 -0.23 0.11 -0.08 0.05 0.95 0.88 -0.10 0.10 -0.01 1.00 -0.07 1.00
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C3. FII Flows and Betas of the Indian Equity Market Following the logic of hedging and diversification of portfolio, one would expect a significant negative (positive) partial association of the betas for Indian market with respect to S&P500 and MSCI indices (i.e., BETA_S&P and BETA_MSCI) with FII net inflow and sale (FII purchase), in case the hypothesis that FII flow to India essentially serve as a means of diversification is true. Our results failed to find support for this hypothesis as in most cases these variables turned out to be statistically non-significant.22
TABLE 5 Regression of FII on MSCI and S&P separetely
STAGE 2 Results REGRESSION B1: on MSCI variables Coefficients Constant Lagged BSE Return MSCI Return Fortnightly Volatility(t-2) Lag1RatioFIIN R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat RatioFIIN -0.0001 (0.77) 0.04 (0.0) 0.11 (0.02) 0.21 0.105 0.101 0.003 2.13
STAGE 2 Results REGRESSION B2: on S&P variables Coefficients Constant Lagged BSE Return S&P Return Fortnightly MA(t-2) S&P Return Fortnightly Volatility(t-2) Lag1FIIN Lag1RatioFIIN R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat FIIN -19.80 (0.21) 1265.56 (0.0) 2283.31 (0.11) 3944.58 (0.002) 0.18 0.00 0.102 0.097 114.877 2.10 0.109 0.104 0.003 2.07 RatioFIIN -0.001 (0.17) 0.04 (0.0) 0.07 (0.14) 0.13 (0.001)
Note:
1. Figures in brackets are the p-values of coefficients 2. P-values for all the lagged dependent variables are less than or equal to zero. 3. Second stage regressions for FIIN in B1 and RatioFIIP in B1 and B2 have not been done since neither of the MSCI/S&P variables were significant at the first stage. 4. Since the package we used doesn’t provide Durbin h-stats, we report the DW here.
C4. FII Flows and Return on Exchange Rate We selected the return from day to day variations in RupeeUSD exchange rate as a possible covariate on the presumption that this being an opportunity cost, foreign investors might take into account this for investment flows to the Indian equity market. However, its effect mostly turned out to be statistically non-significant. C5. FII Flows and Macroeconomic Variables In order to verify whether or not foreign investors track the state of Indian economy for making investment here, we considered IIP
Initially both the beta variables were included together in the regression equations on the presumption that they would be independent, as the concerned markets were very different. However, the sample correlation of these variables was found to be very high, implying thereby a high degree of integration of the markets. We next tried to see the effects of these variables on FII flows separately. Even in that case the effects of these variables were found statistically non-significant.
22
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and CMR as indicators of the real economic activity in India and tried these as possible determinants of the FII flows. This exercise, however, was done on a very limited scale and only the possible effects of these variables on FII net flow was examined (Table 6A and 6B). Some
TABLE 6A Regression of FII on real sector variables REGRESSION A Stage 1 Coefficients Constant Lagged BSE Return BSE Return Weekly MA(t-2) IIP CMR R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat Stage 2 Coefficients Constant Lagged BSE Return BSE Return Weekly MA(t-2) IIP CMR Other Regressors (Lagged Variables) FIIN_MA 15(t-2) -3.62 (0.68) 13.81 (0.54) 218.53 (0.0) 0.03 (0.54) -0.01 (0.96) LAG1FII N_15MA 1.16 LAG2FII N_15MA -0.19 R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat Note: 0.96 0.96 10.47 2.03 FIIP_MA 15(t-2) -4.68 (0.6) -9.24 (0.66) 243.87 (0.0) 0.04 (0.51) 0.04 (0.8) LAG1FII P_15MA 1.20 LAG2FII P_15MA -0.21 0.99 0.99 10.15 2.08 FIIS_MA 15(t-2) -6.94 (0.26) -24.21 90.16) 31.92 (0.37) 0.05 (0.16) 0.14 (0.53) LAG1FII S_15MA 1.36 LAG2FII S_15MA -0.37 0.99 0.99 7.99 2.16 FIIN_MA 15(t-2) -237.05 (0.01) -32.25 (0.78) 1172.53 (0.07) 1.60 (0.0) 1.52 (0.42) 0.10 0.09 50.42 0.05 FIIP_MA 15(t-2) -558.58 (0.0) -503.87 (0.02) -2078.30 (0.04) 4.20 (0.0) 12.00 (0.0) 0.22 0.21 82.16 0.08 FIIS_MA 15(t-2) -321.47 (0.0) -471.69 (0.00) -3251.30 (0.0) 2.60 (0.0) 10.48 (0.0) 0.22 0.22 68.20 0.09
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Some evidence of positive association of both IIP and CMR with FIIN was obtained.
1. Figures in brackets are the p-values of coefficients
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TABLE 6B Regression of FII on real sector variables REGRESSION B Stage 1 Coefficients Constant Lagged BSE Return BSE Return Weekly MA(t-2) IIP Weekly MA(t-2) Lagged CMR CMR Weekly MA(t-2) FIIN_MA 15(t-2) -263.9 (0.01) -20.2 (0.87) 1199.1 (0.07) 1.7 (0.0) -0.1 (0.95) 3.5 (0.34) 0.10 0.09 50.45 0.05 FIIP_MA 15(t-2) -711.3 (0.0) -461.2 (0.03) -1964.1 (0.07) 4.8 (0.0) 4.9 (0.15) 14.5 (0.01) 0.26 0.25 80.11 0.04 FIIS_MA 15(t-2) -447.3 (0.0) -441.1 (0.005) -3163.6 (0.0) 3.1 (0.0) 5.0 (0.06) 11.0 (0.007) 0.26 0.26 66.58 0.05
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Such regression results would have economic explanation in terms some kind of dynamic adjustment mechanism being involved in the determination of current daily value a given FII flow.
R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat Stage 2 Coefficients Constant Lagged BSE Return BSE Return Weekly MA(t-2) IIP Weekly MA(t-2) Lagged CMR CMR Weekly MA(t-2) Other Regressors (Lagged Variables)
FIIN_MA 15(t-2) -4.13 (0.57)
FIIP_MA 15(t-2) -3.22 (0.75) -9.82 (0.64) 238.76 (0.0) 0.02 (0.76)
FIIS_MA 15(t-2) -6.30 (0.38) -24.71 (0.16) 27.57 (0.43) 0.04 (0.37) 0.02 (0.95) 0.32 (0.22) LAG1FII S_15MA 1.36 LAG2FII S_15MA -0.37 0.99 0.99 7.99 2.15
219.08 (0.0) 0.03 (0.48)
0.24 (0.41) LAG1FII N_15MA 1.16 LAG2FII N_15MA -0.19 LAG1FII P_15MA 1.20 LAG2FII P_15MA -0.21 0.99 0.99 10.15 2.08
R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat Note:
0.96 0.96 10.46 2.02
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1. Figures in brackets are the p-values of coefficients 2. P-values for all the lagged dependent variables are less than or equal to zero. 3. Since the package we used doesn’t provide Durbin h-stats, we report the DW here.
evidence of positive association of both IIP and CMR with FIIN was obtained. However, as we shall discuss next, the regression results were far from satisfactory as there was strong indications of highly autocorrelated regression disturbance term of the specified regression equations.23 C6. Auto-correlation of FII Flows Preliminary statistical analysis of the original series of daily FII flows indicated that these were stationary in nature (i.e., contained no significant time trend but were auto-correlated). This auto-correlation got reflected in all the regression equations estimated to find out statistically significant covariates of the various measures of FII flows. This means none of the covariates—be it related to equity market performances or to the performance of the Indian economy—could explain singly or jointly the observed auto-correlation of the FII flows. Use of lagged value of the concerned FII flow variable as a regressor, however, removed the auto-correlation altogether. But inclusion of the lagged value of FII flow variable in most of the cases caused the erstwhile significant determinant to turn non-significant. As a result, the only statistically satisfactory regression results turned out to be the ones having market return (i.e., one or the other variant of BSE_RET) and lagged value of the concerned FII flow. Such regression results would have economic explanation in terms some kind of dynamic adjustment mechanism being involved in the determination of current daily value a given FII flow. In other words, these results may be taken to mean that for individual FII flow there is a desired level determined solely by BSE_RET or some variant of it and the actual value constantly tries to reach this desired level.
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While the dependence of net FII flows on daily return in the domestic equity market is suggestive of foreign investors’ return-chasing behaviour, their decisions seem to get affected also by the recent history of market return and its volatility in international and domestic stock markets as well.
IV. Concluding Observations
Our results suggest that though FII flows to and from India are significantly affected by return in the domestic equity market,24 the latter is not significantly influenced by variation in these flows. It is also found that apart from the return in the domestic market there are other covariates of such flows. While the dependence of net FII flows on daily return in the domestic equity market—at a day’s lag, to be more specific—is suggestive of foreign investors’ return-chasing behaviour, their decisions seem to get affected also by the recent history of market return and its volatility in international and domestic stock markets as well. We also found that the sets of factors affecting FII sale and purchase were not the same. It appeared that some factors would affect purchase or sale decision of foreign investors, but not the corresponding
23 But one cannot possibly avoid this auto-correlation, specially when the moving average of the concerned variables are used in regression. 24 In the sense of Granger causality.
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The scope of using the Indian equity market for the purpose of portfolio diversification arose due to the nonsynchronised movement of the Indian equity market
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equity markets of the world.
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net FII flow. For example, while FII purchase from and sale to the Indian market appeared to be sensitive to the volatility of domestic market return (with both purchase and sale responding positively to volatility change in the recent past), the corresponding net inflow appeared to be positively related to the volatility of return in the foreign market. This might be due to the simultaneous portfolio adjustment in several markets together done by foreign investors. We however failed to find evidence of any portfolio diversification benefit reaped by FIIs by investing in the Indian market (as suggested by lack of statistical significance of the effect of betas of the BSE Sensex with respect to the MSCI world and S&P 500 indices on various FII flows). Day to day variations in the exchange rate also turned out to be unimportant so far as FII transactions were concerned (in fact, though the effect of exchange rate return on the net FII inflow was sometimes found to be significant, its effect on FII purchase or sale was never significant). Juxtaposing results of our study with those obtained by Chakrabarti for the comparable post-Asian crisis period, one may notice quite a few agreements. For example, both the studies tend to show the predominance of the Indian equity market return as the prime mover of the FII net inflow into India. This may be a matter of concern as this suggests that the rate of FII inflow into the country would be governed mostly by the performance of the domestic equity market and/ or foreign investors’ expectation about this performance and hence variation in the country’s foreign exchange reserve would, to some extent, be outside the monetary authority’s control. Given the fact that FII flows can be extremely volatile, a drop of return in the India equity market may result in sudden massive withdrawals of FII which may result in quite disturbing consequences on the country’s economy, unless an appropriate stabilisation mechanism is built into the domestic economic system. Further cause for concern relates to the finding that unlike in the pre-Asian crisis period, as found by Chakrabarti, foreign investors no longer use equity investment in India as a means of diversification of their portfolio. In fact, the scope of using the Indian equity market for the purpose of portfolio diversification arose due to the non-synchronised movement of the Indian equity market vis-à-vis the other equity markets of the world. Recent evidences suggest a stronger co-movement of market returns and it is possibly for that reason foreign investors no longer are able to use the Indian equity market for portfolio diversification. The point of concern, however, lies elsewhere—viz., a stronger integration of the Indian market with the equity market elsewhere exposes the country to the danger of contagion of global financial crises in future. Findings of several studies on FII flows to emerging equity markets over the world have shown the importance of financial market infrastructure such as the market size, market liquidity, trading costs,
information dissemination, and legal mechanisms relating to property rights etc., for attracting foreign portfolio investments into those countries.25 In some studies variables relating to investment barriers, dividend yield, liquidity, firm size and profitability26 etc., have also been found to be significant determinants of FII inflow. These apart, the need for harmonisation of corporate governance, accounting, listing and other rules with those followed in international financial centres as well as strengthening of securities markets’ enforcement have also been stressed for improving competitiveness in attracting foreign portfolio investment inflow.27 Policy implications of the findings just mentioned above are that a move towards a more liberalised regime in the emerging market economies should be accompanied by further improvements in the regulatory system of the financial sector.28 Our results additionally suggest that in the case of India (and other countries having thin and shallow equity markets) the prime focus should be on regaining investors’ confidence in the equity market so as to strengthen the domestic investor base of the market.29 Once this is achieved, a built-in cushion against possible destabilising effects of sudden reversal of foreign inflows might develop. Only then would it be possible to reap fully the benefits of capital market integration.
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The prime focus should be on regaining investors’ confidence in the equity market so as to strengthen the domestic investor base of the market.
25 Applying a panel data approach on bilateral gross cross-border equity flows between 14 countries, during1989-96 find that asset flows depend on market size in both source and destination country as well as trading costs, in which both information and the transaction technology play a role. Portes and Rey, 2000. Garibaldi et al (2002) analysing capital flows to 25 European transition economies showed that FPI was volatile and concentrated in a handful of countries (notably Russia). Regressions showed that the presence of a financial market infrastructure and property rights indicator were the only significant explanatory variables for FPI. Claessens et al (2002) analysing data from 77 countries, find that factors such as shareholder protection and the quality of local legal systems which make it and easier for investors to buy shares and firms to list in public markets play a prime role in determining the degree of integration with international capital markets. 26 Liljeblom and Löflund (2000), using company specific data on degree of foreign ownership, in the Finnish market, which recently abolished capital controls. 27 Classens et al (2002). 28 The present stance on capital controls of Indian policy makers is in place; policy makers in India have justifiably treated capital account liberalisation as a process and not an event and reiterated that capital account liberalisation and reform of the financial system should move in tandem (Rangarajan and Prasad, 1999; Rangarajan, 2000; Jalan, 2002). 29 To take care of malpractice which discourage stock market participation by a majority of savers in our country (see NSE, 2001, in this context). A survey by SEBI and NCAER showed that alleged malpractice like insider trading and low confidence in brokers/sub-brokers, company management/auditors were the main causes behind lack of domestic savers’ confidence in the equity markets. Further regulatory authorities would need to look into alleged restrictive practices by FIIs like price rigging as suggested by Samal (1997).
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References
Bae, Kee-Hong, Kalok Chan and Angela Ng (2002), “Investability and Return Volatility in Emerging Equity Markets”, Presented to the International Conference on Finance, National Taiwan University, Dept. of Finance. Batra, Amita (1999), “Portfolio Investment Behaviour of Foreign Institutional Investors (FIIs) in India: An Econometric Analysis”, in S. Arumugam (ed.) Indian Capital Markets: Modern Perspectives and Empirical Evidences , UTI Institute of Capital Markets and Allied Publishers, Mumbai. Bekaert, Geert and Campbell R. Harvey (2000), “Foreign Speculators and Emerging Equity Markets”, Journal of Finance, Vol. LV, No. 2. Bekaert, Geert and Campbell R. Harvey (1998), “Capital Flows and the Behaviour of Emerging Market Equity Returns”, NBER Working Paper No. 6669. Bhatia, Udit (2000), “Factors Governing Returns on the Mumbai Stock Exchange: An Econometric Analysis”, ICICI Research Centre Working Paper. Bikhchandani, Sushil and Sunil Sharma (2001), “Herd Behavior in Financial Markets”, IMF Staff Papers, Vol. 47, No. 3. Bohn, Henning and Linda L. Tesar (1996), “US Equity Investment in Foreign Markets: Portfolio Rebalancing or Return Chasing?”, American Economic Review, 86, May, 77-81. Brennan, Michael J. and Henry Cao (1997), “International Portfolio Investment Flows”, Journal of Finance, Vol. LII, No. 5, December, 1851-1880. Chakrabarti, Rajesh (2001), “FII Flows to India: Nature and Causes”, Money & Finance, Vol. 2, No. 7, October–December.. Choe, Hyuk, Bong-Chan Kho, and René M. Stulz (1999), “Do Foreign Investors Destabilize Stock Markets? The Korean Experience in 1997”, Journal of Financial Economics, Vol. 54, 227-264. Claessens, Stijn, Daniela Klingebiel, and Sergio L. Schmukler (2002), “Explaining the Migration of Stocks from Exchanges in Emerging Economies to International Centers”, World Bank Working Paper No. 2816. Errunza, Vihang (2001), “Foreign Portfolio Equity Investments, Financial Liberalization and Economic Development”, Review of International Economics, Volume 9, Issue 4, Special issue: International Financial Liberalization, Capital Flows and Exchange Rate Regimes. FitzGerald, E.V.K. (1999), “Policy Issues In Market Based and Non Market Based Measures to Control the Volatility of Portfolio Investment”, Finance And Development Research Programme, Working Paper Series Paper No 8, Oxford University. Froot Kenneth A. and Tarun Ramadorai (2002), “Currency Returns, Institutional Investor Flows, and Exchange Rate Fundamentals”, NBER Working Paper No. 9101. Froot, Kenneth A., Paul G.J. O’Connell and Mark S. Seasholes (2001), “The Portfolio Flows of International Investors” Journal of Financial Economics, 59, 151-193. Garibaldi, Pietro, Nada Mora, Ratna Sahay and Jeromin Zettlemeyer (2002), “What Moves Capital to Transition Economies”, IMF Working Paper No. WP/02/64. International Monetary Fund, (2002), Global Financial Stability Report , World Economic and Financial Surveys, Washington, June, September. Jalan, Bimal (2002), “Bank of England’s Seminar on International Financial Architecture”, remarks made at the symposium of Central Bank Governors, hosted by the Bank of England, London, July 5. Jo, Gab-Je (2002), “Foreign Equity Investment in Korea”, For presentation at the Association of Korean Economic Studies. Kaminsky, Graciela, Richard Lyons and Sergio Schmukler (2000), “Managers, Investors, and Crises: Mutual Fund Strategies in Emerging Markets”, NBER Working Paper 7855. Kim, E. Han and Vijay Singal (2000), “Stock Market Openings: Experience of Emerging Economies”, Journal of Business, Vol. 73, October, Issue 4. Kim, Woochan and Shang-Jin Wei (1999), “Foreign Portfolio Investors Before and During a Crisis”, Economics Department Working Paper No.210, Organi-
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sation for Economic Co-operation and Development. Liljeblom, Eva and Anders Löflund (2000), “Determinants of International Portfolio Investment Flows to a Small Market: Empirical Evidence”, Swedish School of Economics and Business Administration, Working Papers Series, Number 445. National Stock Exchange (2001), Indian Securities Market: A Review, NSE, Mumbai. Portes Richard and Hélène Rey (2000), “The Determinants of Cross-Border Equity Flows: The Geography of Information”, Center for International and Development Economics Research, Working Paper C00-111. Rangarajan, C., (2000), “Capital Flows: Another Look”, Economic and Political Weekly, December 9, 4421-4427. Rangarajan, C. and A. Prasad (1999), “Capital Account Liberalisation and Controls: Lessons from the East Asian Crisis”, Money & Finance, No.9, April–June.
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Appendix 1: Some Information Relating to FII Operations in India
• FIIs in India include Asset Management Companies, Pension Funds, Mutual Funds, Investment Trusts as Nominee Companies, Incorporated/Institutional Portfolio Managers or their Power of Attorney holders, University Funds, Endowment Foundations, Charitable Trusts and Charitable Societies. • SEBI acts as the nodal point in the entire process of FII registration. RBI approval under FEMA enables an FII to buy/sell securities on Stock Exchanges and open foreign currency and Indian Rupee accounts with a designated bank branch. Investment by FIIs in India is regulated under SEBI (FII) Regulations , 1995 and Regulation 5(2) of FEMA. • FIIs are required to allocate their investment between equity and debt instruments in the ratio of 70:30. (However, it is also possible for an FII to declare itself a 100 per cent debt FII in which case it can make its entire investment in debt instruments). • FIIs can buy/sell securities on Stock Exchanges. They can also invest in listed and unlisted securities outside Stock Exchanges, where the price has been approved by RBI. • No individual FII/sub-account can acquire more than 10 per cent of the paid-up capital of an Indian company. All FIIs and their sub-accounts taken together cannot acquire more than 24 per cent of the paid-up capital of an Indian Company. Indian Companies can raise the above-mentioned 24 per cent ceiling to the Sectoral Cap/Statutory Ceiling as applicable by passing a resolution by its Board of Directors followed by passing a Special Resolution to that effect by its General Body. The presence of Sectoral Cap/Statutory Ceiling means that foreign investment from all sources cannot exceed a specified level. A Company to which no sectoral cap/statutory ceiling is applicable can raise the limit of permissible FII investment to 100 per cent of the paid-up capital. A Company to which a 49 per cent Sectoral Cap is applicable can raise the limit of permissible FII investment to 49 per cent and if there is an existing foreign direct investment of 15 per cent, possible FII investment can only be up to 34 per cent. • No permission from RBI is needed so long as the FIIs purchase and sell on recognised stock exchange. However, all non-stock exchange sales/purchases require RBI permission. • FIIs can avail of the Forward Cover Facility from the Authorised Dealer subject to certain conditions. • High Net Worth Individuals /foreign corporates can invest through SEBI Registered FIIs subject to a sub-limit of 5 per cent each, within the aggregated limit of 24 per cent. • FIIs can trade in Exchange Traded Derivative Contracts. Source: RBI and SEBI websites.
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The presence of Sectoral Cap/ Statutory Ceiling means that foreign investment from all sources cannot exceed a specified level. A Company to which no sectoral cap/statutory ceiling is applicable can raise the limit of permissible FII investment to 100% of the paid-up capital.
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Trends in BSE and FII Operations in India During Our Sample Period
CHART A1 Returns on BSE and Net FII
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Cumulative FIIN 35000 Cumulative FIIN (Rs.Cr) 30000 25000 20000 15000 10000 5000 0 -5000
Cumulative Returns on BSE 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 -0.1 -0.2 -0.3 Cumulative BSE Returns
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CHART A2.1 Net FII and the Sensex, for 3 sub periods of the sample.
500 400 300 FII net 200 100 0 -100 -200 -300
FIIN
Sensex (Close)
31-May-02
1-Jan-99
29-Mar-00
16-Mar-01
28-Sep-99
21-Sep-00
29-Dec-99
18-Dec-00
27-Jun-00
13-Jun-01
4-Mar-02
7-Sep-01
5-Jul-99
6-Apr-99
6-Dec-01
6000 5500 5000 4500 4000 3500 3000 Sensex
14-May-99
1-Jan-99
21-Mar-00
5-Mar-99
16-Nov-99
11-Aug-99
16-Dec-99
14-Jun-99
18-Jan-00
9-Sep-99
20-Apr-00
13-Jul-99
9-Apr-99
12-Oct-99
17-Feb-00
3-Feb-99
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FII net 350 300 250 200 150 100 50 0 -50 -100 -150 2-May-01 28-May-01 21-Jun-01 17-Jul-01 10-Aug-01 7-Sep-01 4-Oct-01 31-Oct-01 27-Nov-01 26-Dec-01 21-Jan-02 14-Feb-02 12-Mar-02 10-Apr-02 7-May-02 31-May-02 2400 2600 2800 3000 3200 3400 3600 3800 4000
CHART A2.3
FII net 1200 1000 800 600 400 200 0 -200 -400 -600 2-May-00 25-May-00 19-Jun-00
FIIN Sensex (Close)
FIIN
12-Jul-00 4-Aug-00 30-Aug-00
CHART A2.2
25-Sep-00 19-Oct-00 14-Nov-00
Sensex (Close)
7-Dec-00 2-Jan-01 25-Jan-01 20-Feb-01 19-Mar-01 12-Apr-01
5000 4800 4600 4400 4200 4000 3800 3600 3400 3200 3000
Sensex
Sensex
APPENDIX 2: Approximation of Daily Market Capitalisation
Notation: qijt : No. of outstanding shares of the i-th company on the j-th day of month t pijt : Price of a share of the i-th company on the j-th day of month t mcijt = pijt q ijt : market capitalisation of the i-th company’s shares on the j-th day of month t
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MC jt = ? mcijt : aggregate market capitalisation on the j-th
i
day of month t We want to find approximate value of We have
MC jt = ? p ijt qijt =
i
MC jt .
?p ?p
i i
ijt
qijt qijt
i0
?p ?p
i i
i0
q ijt qi 0
i0
?p
i
i0
qi 0 = ? P jt
?p ?p
i i
i0
q ijt qi 0
i0
?p
i
i0
qi 0 ( 1 )
where ? P is the Paasche share price index for the j-th day of jt month t with respect to some base date 0,
?p ?p
i i
i0
qijt qi 0
= Q jt is an index of volume of outstanding shares
i0
for the j-th day of month t with respect to the base date, date.
?p
i
i0
qio is the aggregate market capitalisation on the base
We assume Q jt = Qt i.e., index of volume of outstanding shares is same for all days of a month. Then (1) yields: MC jt = ? P , where MC 0 = ? p i 0 q i 0 jt Qt MC 0
i
We have data on MCt: aggregate market capitalization for month t. ? t : the available stock price index for month t, we may approximate QtMC0 by (MCt/ P t ). Substituting the Paasche daily stock price index ? jt by the corresponding available daily stock price index, say, P jt give us the following approximate formula for daily market capitalisation
P
? MC t MC jt = ? jt ? ? ? ? t
? ? ? ?
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Market participants include individual retail investors, institutional investors such as mutual funds, banks, insurance companies and hedge funds, and also publicly traded corporations trading in their own shares.
Foreign Institutional Investment in the Indian Equity Market
An Analysis of Daily Flows during January 1999-May 2002
PARAMITA MUKHERJEE , SUCHISMITA BOSE ( AND DIPANKOR COONDOO
¦ ¦
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Abstract
This paper explores the relationship of foreign institutional investment (FII) flows to the Indian equity market with its possible covariates based on a daily data-set for the period January 1999 to May 2002. The set of possible covariates considered comprises two types of variables. The first type includes variables reflecting daily market return and its volatility in domestic and international equity markets as well as measures of co-movement of returns in these markets (viz., relevant betas). The second type of variables, on the other hand, are essentially macroeconomic ones like exchange rate, shortterm interest rate and index of industrial production (IIP)—viz., variables that are likely to affect foreign investors’ expectation about return in Indian equity market. It may be mentioned that our analysis has been primarily motivated by the research done in this area by Chakrabarti (2001), results of which appeared in a recent issue of Money & Finance. Briefly, using a monthly dataset Chakrabarti examined the nature and causes of FII net inflow into the Indian equity market during the period May 1993 to December 1999. He obtained some interesting results: viz., (1) the FII net inflow is correlated with the return in Indian equity market and the former is more likely to be the effect than the cause of the Indian equity market return; (2) so far as investment in Indian equity market is concerned, foreign investors do not seem to be at an informational disadvantage compared to domestic investors; and (3) the Asian crisis marked a regime shift in the sense that in the post-Asian crisis period the return in the Indian equity market turned out to be the sole driver of the FII inflow, whereas for the pre-Asian crisis period other covariates reflecting return in other competing markets, urge for diversification etc., were also found to be correlated with FII net inflow.
This paper explores the relationship of foreign institutional investment (FII) flows to the Indian equity market with its possible covariates based on a daily data set.
¦
(
Monetary Research Project, ICRA Ltd., Kolkata Economic Research Unit, Indian Statistical Institute, Kolkata
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Whereas the preMexican crisis period 1990-1994 saw most of the emerging markets performing much better compared to the matured markets in terms of both return and associated risk, the pattern reversed during 1995-2001.
Our endeavour has been to see if these results would carry through when the phenomenon of FII flows was examined using a set of daily data on the relevant variables. The data-set incorporates day to day variations and hence is better suited for examination of various interrelationships, including Granger causality for equity market operations that are typically short run issues. Also, we tried to relate daily FII flows (distinguishing between three kinds of flows—viz., FII flows into the country or FII purchases, FII flows out of the country or FII sales and the net FII inflows into the country or FII net). We later modify the model specification to include a short past history of the variables over different time frames, like a week or fortnight. We have also made an attempt at relating FII flows to macroeconomic fundamentals for the Indian economy. Our results show that: (1) FII flows to and from the Indian market tend to be caused by return in the domestic equity market and not the other way round; (2) returns in the Indian equity market is indeed an important (and perhaps the single most important) factor that influences FII flows into the country; (3) while FII sale and FII net inflow are significantly affected by the performance of the Indian equity market, FII purchase is not responsive to this market performance; (4) FII investors do not seem to use Indian equity market for the purpose of diversification of their investment; (5) return from exchange rate variation and fundamentals of the Indian economy may have influence on FII decisions, but such influence does not seem to be strong, and; finally, (6) daily FII flows are highly auto-correlated and this auto-correlation could not be accounted for by the all or some of the covariates considered in our study.
I. Introduction
The performance of the emerging equity markets1 vis-à-vis their matured counterparts in the developed world have shown repeated reversals in recent times. Thus, whereas the pre-Mexican crisis period 1990-1994 saw most of the emerging markets performing much better compared to the matured markets in terms of both return and associated risk, the pattern reversed during 1995-2001; a period which in most part was affected by the Asian crisis and the associated contagion. The recent past (i.e., the first quarter of 2002) has seen another reversal of performance in which, the emerging markets (those of Asia and Latin America, in particular) have shown a remarkable recovery, in terms of both the level of return and risk while the matured markets have experienced drop in return and rise in risk (IMF, June and September 2002). The reasons behind these reversals may vary from one reversal to another. However, one thing seems to be pretty clear—viz., such
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1 This consists of capital markets in China, India, Indonesia, Korea, Malaysia, Pakistan, The Philippines, Sri Lanka, Taiwan and China in Asia, Czech Republic, Greece, Hungary, Poland Russia, Slovakia, Turkey, Egypt, Israel and Jordan in Europe and the Middle East, Morocco, South Africa and Zimbabwe in Africa and Brazil, Chile, Colombia, Mexico, Peru and Venezuela in Latin America.
reversals of market performances make foreign equity investment extremely volatile—a phenomenon which is capable of destabilising the domestic economy of the recipient country. It may therefore be essential to evolve appropriate built-in mechanisms in the economies of the countries receiving foreign equity investment such that destabilisation and damages can be minimised in case foreign investors suddenly withdraw from the equity market. It is in this context that a careful examination of the nature of foreign institutional investment (FII) flow into an economy is important, as it may help identify the strength of various factors (including macroeconomic ones like level of production, interest rate, etc.) that are likely to affect such flows and also the possible impact of such flows on the performance of the equity market concerned. Over the past ten years or so India has gradually emerged as an important destination of global investors’ investment in emerging equity markets. Today India has a share of about 20 per cent2 in the total global investment in all emerging equity markets together and the outstanding FII investment3 in India stood at around Rs. 86,287 crore, as on end-March, 2002. FII investments as a percentage of market capitalisation increased from 7.06 per cent in 1999-00, to 13.5 per cent in 2000-01 and further to 14.1 per cent in 2001-02. Given this growing importance of FII for the Indian economy, it is apparent that the nature and causation of such fund flows deserve careful examination. In a recent issue of Money & Finance a comprehensive analysis by Chakrabarti (2001) of the nature and causes of FII equity flows4 into the Indian market appeared. In his analysis Chakrabarti mostly used monthly data on FII net inflow as a proportion of the previous month’s market capitalisation, relevant stock market variables and other financial market indicators (like the deposit rate, exchange rate, etc.) and obtained the following main results: (a) though FII flows are highly correlated with equity returns in India, they are more likely to be the effect than the cause of such returns; (b) so far as investment in the Indian equity markets is concerned, global investors do not seem to be at an informational disadvantage compared to local investors; and (c) the Asian crisis marked a regime shift in the determinants of FII flows to India, with the domestic equity returns becoming the sole driver of these flows in the post Asian crisis period. Our motivation is essentially to take this analysis forward a few steps further. To be specific, using daily data for the post Asian crisis period January 1999—May 2002, we have done a study similar to that of Chakrabarti
2 This is a close approximation, since part of the FII flow data contains a small debt component. 3 Net FII, cumulative since 1990-91. 4 In case of India, the relative importance of the equity channel of FII compared to debt, can be gauged from the fact that, for the financial year 2001-02, the average monthly sales by FIIs was to the tune of Rs. 3771 crore and purchases Rs. 3102 crore in the equity segment, while for debt sales and purchases were a meagre Rs. 392 crore and Rs. 332 crore, respectively.
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Over the past ten years or so India has gradually emerged as an important destination of global investors’ investment in emerging equity markets. Today India has a share of about 20% in the total global investment in all emerging equity markets together.
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Since the use of daily data helps in determining the nature of causality with greater precision, our results of causality between FII flows and their major covariates like domestic equity market return may prove to be useful from policy viewpoint.
in which we have tried to identify the relevant covariates of FII flow into and out of the Indian equity market and also to determine the nature of causality between the relevant variables. Since the use of daily data helps in determining the nature of causality with greater precision, our results of causality between FII flows and their major covariates like domestic equity market return may prove to be useful from policy viewpoint. Next, going beyond Chakrabarti’s analysis, we have carried out an in-depth statistical analysis to find out not only the factors that affect net FII flow, but also those which affect FII sale and purchase decisions. The paper is organised as follows: In Section II we discuss briefly about the importance of the and factors behind portfolio flows to emerging markets, the nature of such flows during the past decade, their suggested role in causing volatility and contagion in emerging markets. In the third section, we discuss how our analysis builds upon Chakrabarti’s; we also describe our data-set and present the main results of our causality and regression analyses. The last section concludes, relating our analysis to policy implications from empirical studies in various emerging markets, and the current policies being followed in India with respect to foreign capital.
II. International Portfolio Flows to Emerging Markets
As is well-known, international capital flows to emerging markets is a somewhat recent phenomenon, which began at a reasonable scale in the early 90s. On the theoretical side, the case for liberalisation of international capital flows is built around a few basic tenets—viz., (a) free capital movements facilitate efficient allocation of global savings, channelling resources to countries where they will be most productive and thereby increasing growth and welfare globally; (b) access to foreign capital markets enable investors to achieve a higher degree of portfolio diversification, thus allowing them to obtain higher returns at lower risk; (c) full convertibility for capital account transactions complement the multilateral trading system which broadens the channels through which countries obtain trade and investment finance on much easier terms; and (d) liberalisation improves macroeconomic performance as it subjects governments to greater market discipline and penalises unsound monetary and fiscal policies. On the practical side, on the other hand, the surge in international portfolio investment over the past decade or so has been triggered by a number of parallel developments. First, institutionalisation of savings in the USA and the developed world since the 1980s placed a massive and increasing volume of funds under the management of professional portfolio managers,5 who for tactical reasons tend to prefer a widely
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5 That is, the choice of pooled funds held by pension funds, life insurance companies, mutual funds and investment trusts as repositories for the majority of
diversified portfolio spread out internationally. Second, there has been a trend towards financial liberalisation both in developing countries and countries in transition thus allowing global fund managers to reach the financial markets of these countries. 6 Third, developments in information technology have immensely lowered the cost of international trading in securities and made information dissemination on a near real time basis possible. Fourth, a remarkable expansion of capital markets in emerging economies has taken place due mostly to the widespread privatisation of formerly State-owned enterprises. However, the very elements that facilitated the inflow of foreign capital into developing countries have also meant that foreign capital can now be withdrawn from these countries far more quickly. Analysis of performance of emerging equity markets during the past decade has indicated that investment in these markets can provide global investors with attractive absolute returns as well as some scope to diversify their portfolios. In fact, global investors reaped such benefits in the first half of the 1990s, but the gains disappeared between 1995 and 2001 with the reversal of performances of these markets relative to their matured counterparts, as already mentioned (IMF, 2002). Such performance reversals have ushered in tactical investors such as a hedge fund (which tries to achieve high absolute returns essentially by exploiting the high volatility of returns in these markets through market timing). Understandably, such speculative and opportunistic behaviour of these tactical investors has contributed to the volatility of FII inflows into emerging markets. Typically, global investors allocate a small portion of their total assets to equities in these markets to track a world or regional equity index 7 and also as a means to diversify the portfolio held by them. Although this allocation is estimated to be a meagre 5 per cent of the total assets of global investors, in absolute terms this investment has now crossed the US dollar 100 billion level (which is larger than the total market capitalisation of many individual emerging equity markets). More importantly, the share that individual emerging markets get of such investment is often sizeable in relation to their total market capitalisation. Performances of emerging equity markets vis-à-vis those of their matured counterparts in recent periods reveal the following
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Performance reversals have ushered in tactical investors such as a hedge fund. Understandably, such speculative and opportunistic behaviour of these tactical investors has contributed to the volatility of FII inflows into emerging markets.
savings—increased the share of funds invested in securities and enhanced the role of institutional investors compared to that of depository institutions. In the United States, for example, the share of total financial sector assets held by institutional investors rose from 32% in 1978 to 52% in 1993, while the share of depository institutions fell from 57% to 34% over the same period (Federal Reserve System [FRS], Flow of Funds). 6 Whose local capital markets were proving to be a bottleneck in the growth process. 7 Like the S&P/IFC Composite index which is used as a benchmark for measuring equity markets returns in emerging markets as a whole or IFC’s Asia or Latin America indices which reflects the corresponding returns in specific regional markets.
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FII flows to the secondary equity market do not have any direct link with the level of real investment in the economy. It is only by enhancing the efficiency and liquidity of capital markets that such a flow can contribute to growth.
pattern: in the post-Mexican crisis period (to be more specific, during 1995-2001) the emerging equity markets, by and large, recorded much smaller returns than those in the matured markets. Thus, whereas S&P 500 and NASDAQ recorded 14.77 and 13.62 per cent return, respectively, the corresponding figures for S&P/IFCI Composite, IFCI’s Asia, EMEA (which covers emerging markets in Europe and the Middle East) and Latin America indices were –3.68, –9.81, 1.78 and 1.35, respectively. Also, the risk associated with investment in the emerging equity markets increased considerably during this period, whereas that for the matured markets decreased. This was indicated by values of the Sharpe ratio (which is the ratio of excess return of an asset over that of a risk free asset and the standard deviation of return of the asset concerned) recorded between –0.48 and –0.14 for the emerging markets as against 0.24 and 0.56 recorded for NASDAQ and S&P500, respectively. This pattern, however, got reversed again during the first quarter of 20012002, when the emerging markets (except those in Europe and the Middle East) recorded much higher return and lower risk compared to those of S&P500 and NASDAQ.8 Let us next discuss briefly the possible effects of FII flow on the recipient country’s economy. As such, FII flows to the secondary equity market do not have any direct link with the level of real investment in the economy. It is only by enhancing the efficiency and liquidity of capital markets that such a flow can contribute to growth. Securities markets in developing countries are typically both narrow and shallow. Therefore, FII participation may, a priori, induce considerable instability in these markets. The effect of such mobile capital flows can, however, be quite complicated and therefore are highly controversial. In fact, country experiences differ considerably. Some studies found clear evidence of benefits of such flow in the form of equity market development, capital market integration, lowering cost of capital, and hence tend to question policy concerns regarding resource mobilisation, market co-movements, contagion and volatility expressed by some policy makers and academics to be largely unwarranted.9 The causes of the instability and volatility of short-term portfolio capital flows to emerging markets are often related to the way in which investment funds are managed in order to confront uncertainty. It has been alleged
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8 The underperformance of emerging market equities from a longer-term perspective does not appear to be due to overvaluation, though price/earnings ratios in emerging market equities have been high in some years. Some of the main factors in this underperformance have been identified as the string of financial crises, starting with Mexico in 1994, which has drastically pruned the US dollar returns on emerging market equities as well as concerns about corporate transparency and governance in the West and Japan and more so in the emerging markets. 9 See Errunza, 2001; for a very detailed review. Bekaert and Harvey, 1998, 2000, analyse pooled cross-section and time series data for US equity flows to about 20 emerging countries, including India. Kim and Singal, 2000, analyse 20 countries from the IFC EM Database.
that international portfolio investors seek liquidity and use ‘quick exit’ as a means of containing downside risk, thus making frequent marginal adjustments to their portfolios. Further, shifts in the portfolio composition of global investors are largely ascribed to changes in their perceptions of country solvency rather than to variations in underlying asset value.10 A common conclusion from research, however, is that institutions sometimes panic, disregard fundamentals and spread crisis even to countries with strong fundamentals. The literature also notes that individuals, too, can contribute to this destabilisation process by fleeing from funds, particularly mutual funds and forcing fund managers to sell when fundamentals do not warrant such sale. Empirical results of the effect of FII activities on the volatility of return are rather divided; some studies do not find that foreign investors have any destabilising impacts on stock prices. 11 Evidences to the contrary showing that foreign investors cause higher volatility in the market compared to domestic investors12 or that stocks in which foreign investors mainly trade experience higher volatility compared to those in which they do not show much interest also exist. 13 These studies also show that volatility caused by FII jumped significantly around the crises period.
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We have looked for covariates of FII flows to India and tried to ascertain whether FII flows adjust in response to changes in the condition of the Indian equity market or such flows exert strong enough influence on this market.
III. FII Flows to the Indian Equity Market
As already mentioned, the basic objective of our study is to carry forward and supplement the empirical research on the FII flow to India reported in Chakrabarti (2001). To be specific, we have looked for covariates of FII flows to India and tried to ascertain whether FII flows adjust in response to changes in the condition of the Indian equity market or such flows exert strong enough influence on this market so as to affect significantly the return from variations in daily stock price level. In what follows, we first explain our choice of the set of selected variables and describe briefly the data set used and then present the results that have been obtained. FII Flows and Their Covariates—Choice of Variables and Data As is well-known, given the set of investment opportunities available, a global investor would continuously adjust investment portfolio round the clock using available market information and
See, for example FitzGerald,1999, in this context. Chan, Kho and Stulz, (1999); Kim and Wei (1999) with Korean data; Froot, O’ Connell, and Seasholes (2001) based on data from 44 countries. 12 For example, Jo, 2002, using data from the Korean stock markets, where data is available for different categories of traders. 13 Bae and Chan, 2001, analyse data from the Standard & Poor’s (formerly the IFC) Emerging Markets Database (EMDB), which covers more than 2000 stocks from 45 emerging markets.
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We have chosen to use daily data, since we felt that a set of daily data should be more appropriate for examining the nature of causality, so far as the relationship between FII flows and their major covariates are concerned.
thereby tracking returns in all possible markets. So far as the trading behaviour of these investors is concerned, studies examining such behaviour suggest broadly two types of trading behaviour (see Box 1 for a brief description of these trading types). Further, for a given unit time interval (may be a time span of a few minutes, a day, a week or a month), the investor’s actions may be aggregated and summarised into two basic measures—viz., sale and purchase—and a corresponding overall measure of net sale or net purchase, as the case may be. Following this logic, we have chosen to examine the nature of FII flow to India in terms of three variables—viz., FII sale (henceforth denoted by FIIS), FII purchase (henceforth denoted by FIIP) and net FII investment (henceforth denoted by FIIN). It may be noted that in his analysis Chakrabarti used monthly data on net FII flow as a proportion of the size of market capitalisation (henceforth denoted by MCAP) of the previous month as the variable to be explained instead of the net FII flow.14 As regards the data frequency, we have chosen to use daily data, since we felt that a set of daily data should be more appropriate for examining the nature of causality, so far as the relationship between FII flows and their major covariates are concerned. For the sake of comparison with Chakrabarti’s results, we have also considered FIIN as a proportion of MCAP (henceforth denoted by RATIO_FIIN). Similarly defined ratio variables for FIIP (henceforth denoted by RATIO_FIIP) and FIIS (henceforth denoted by RATIO_FIIS) have also been considered. However, since daily data on MCAP are unavailable, we have had to calculate a time series of daily MCAP using a method of approximation. As regards the choice of covariates of the FII variables, to the extent possible we have tried to incorporate into the analysis a set of variables that appear, a priori, to be the primary determinants of global investors’ demand/supply for/of stocks in the Indian market. To be precise, we have considered two different sets of variables—one relating to the Indian and other equity markets which tend to compete for global investors’ investment and the other relating to the Indian economy which may be relevant for investors’ expectation formation about the Indian market. The first set of variables include daily return in Rupee terms in the Indian market and corresponding returns in US
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14 In this context, the following point may be noted: As FII transactions take place on a day to day basis (or for that matter, even over shorter time intervals), use of monthly aggregates may blur the picture considerably. Further, since the ratio of net FII flow to the total size of market capitalisation (henceforth denoted as Mcap) is rather small for India (and the monthly variation in this ratio is even smaller), any analysis using this ratio as the explained variable may fail to capture the true relationship between FII flow and its various covariates identified as important explanatory factors for this flow in the literature as well as in Chakrabarti’s own analysis.
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BOX 1 Studies examining foreign institutional investors’ behaviour suggest broadly two types of trading behaviour—viz., momentum (M) or positive-feedback trading(PT) and herding(H) strategies1, which may or may not be within the bounds of rational behaviour2. Briefly, strategy M is the tendency of an investor to buy and sell stocks based on their observed return records—i.e., to buy recent winners and sell recent losers3. There are strong evidences of contemporaneous momentum trading by funds, induced both by the managers and investors when trading is done on the basis of contemporaneous return information4. Such momentum trading is especially found to be strong during periods of financial crisis. Momentum trading at a lag5 is observed during non-crisis periods. Typically, such trading decisions are mostly taken at the managerial level. Understandably, the M or PT strategy may exacerbate price movements and thus accentuate volatility. It is also found that funds often go in for contagion trading—i.e., they systematically sell assets from one country when asset prices fall in another, however, such contagion trading is primarily to meet redemption by individual investors, and is not a management strategy6. The H strategy refers to a situation in which all investors act/react in a similar manner. Herding is intentional, if investors are influenced into reversing a planned investment decision after observing others, and this in turn, increases volatility in the market.
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Briefly, strategy M is the tendency of an investor to buy and sell stocks based on their observed
See for example, Froot et al (2001); Kim and Wei (1999). Bikhchandani and Sharma, 2001, provides an overview of the recent theoretical and empirical research on herd behaviour in financial markets. 3 This form of herd behaviour is not rational under the efficient-markets hypothesis since market prices are assumed to reflect all available information. 4 Such studies take into account returns for the same day, month or quarter. 5 That is, trading is based on return information pertaining to the previous day, month or quarter. 6 As found by Kaminsky et al , 2002, using data from 13 US mutual funds; which are dedicated Latin America funds.
2
1
return records. The
H strategy refers to a
situation in which all investors act/ react in a similar manner.
Dollar terms in competing markets, measures of volatility of these returns (taken as proxy of the corresponding risks), daily return implied by day to day variations in the Rupee-Dollar exchange rate. More specifically, this set consists of the following variables: 1. Daily return in the Indian market calculated on the basis of day to day variations in the value of BSE Sensex (henceforth denoted by BSE_RET);15 2. Volatility of daily return in the Indian market calculated as the standard deviation of previous 7/ 15/ 30 days’ daily returns based on the BSE Sensex (henceforth denoted by BSE_RETVOL); 3. Daily return in the international equity market based on the
15 BSE Sensex is compiled using a set of 30 major shares and reports from stock markets suggest that FIIs mostly restrict their trading to the shares covered in the BSE Sensex.
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The second set, includes two macroeconomic
8.
9.
variables—viz., the index of industrial production and the call money rate.
day to day variations in the value of the MSCI World Index (henceforth denoted by MSCI_RET);16 Daily return in the US equity market based on the day to day variations in the S&P500 (henceforth denoted by S&P_RET); Volatility of daily return in the international equity market calculated as the standard deviation of previous 7/ 15/ 30 days’ daily returns based on MSCI World index (henceforth denoted by MSCIRET_VOL); Volatility of daily return in the US equity market calculated as the standard deviation of previous 7/ 15/ 30 days’ daily returns based on the S&P500 index (henceforth denoted by S&PRET_VOL); Extent of co-movement of daily returns in Indian and International equity markets as measured by the beta of returns from BSE Sensex and MSCI World Index (henceforth denoted by BETA_MSCI); Extent of co-movement of daily returns in Indian and the US equity markets as measured by the beta of returns from BSE Sensex and S&P500 Index (henceforth denoted by BETA_MSCI); and finally Daily return from day to day variations in the Rupee—USD exchange rate (henceforth denoted by EXCH).
The second set, on the other hand, includes two macroeconomic variables—viz., the index of industrial production (henceforth denoted by IIP) taken as a proxy for short run real income changes and the call money rate (henceforth denoted by CMR) taken as a proxy for shortterm interest rate. These two variables, taken to reflect the short run variations in the fundamentals of the Indian economy, have been used together with the equity market-related variables to see whether or not global investors take into account their expectations about the state of the Indian economy. The sample period of the daily data set is January 1999—May 2002, which wholly relates to the post- Asian crisis period. For a fuller description of these variables and the sources of data on them, see Box 2. See also Appendix 1 some general information on FII investment activities as well as the Indian stock market and related charts and Appendix 2 for a description of the method used to build up the time series of daily MCAP. The Results In the present analysis we have mostly used appropriate linear regression techniques to testify various hypotheses concerning FII flows to the Indian equity market. For the hypotheses relating to the direction
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16 This index, also used by Chakrabarti, is known to be closely tracked by FIIs operating in India.
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BOX 2 The data series and Sources: Stock Market Series Domestic FIIP FIIS FIIN BSE Sensex Mcap:
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DailyFII Purchses or inflows into the Indian equity markets, Source: website of SEBI; www.sebi.gov.in DailyFII Sales or outflows from the Indian equity markets, Source: website of SEBI; www.sebi.gov.in Daily Net FII inflows into the Indian equity markets; the difference between FIIP and FIIS, Source :website of SEBI; www.sebi.gov.in The 30 share BSE stock price index; daily closing values, Source: website of the Mumbai Stock Exchange; www.bseindia.com Daily market capitalisation on the BSE, Source: BSE website and our estimation as per Appendix1.
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International S&P 500 MSCI WI
The daily series for the S&P500 stock price index; Source: website of Standard and Poors The daily series for the MSCI World Index, a weighted stock price index for all countries1; Source: website of Morgan Stanley Capital International Inc; www.msci.com
Others Exch CMR IIP
Daily exchange rate of the Indian rupee vis a vis the US dollar Daily Call money rate; Source RBI website; www.rbi.org.in Index of Industrial production reported weekly; Source: RBI Handbook of Statistics on Indian Economy, 2001 and CMIE Monthly Review of the Indian Economy
Estimated series Ratio_FIIN Ratio_FIIP Ratio_FIIS FIIN_MAk(t-1) FIIP_MAk(t-1) FIIS_MAk(t-1) BSE_Ret
Ratio of FIIN to previous day’s Mcap2 Ratio of FIIP to previous day’s Mcap Ratio of FIIS to previous day’s Mcap k-day moving average value of FIIN calculated using FIIN(t-k) to FIIN(t-1), k=7,15,30. k-day moving average value of FIIP calculated using FIIP(t-k) to FIIP(t-1), k=7,15,30. k-day moving average value of FIIS calculated using FIIS(t-k) to FIIS(t-1), k=7,15,30.
Daily returns on the BSE Sensex, calculated as the excess of the logarithm of the index value on a date over the logarithm of the index value on the previous day. BSERET_MAk(t-2) k-day moving average value of return on BSE calculated using BSE_Rte(t-k) to BSE_RET(t-2), k=7,15,30. BSE_RetVol k(t-2) k-day moving average volatility of, returns on BSE Sensex or the standard deviation of BSE_Ret(t-k) to BSE_Ret(t-2), k=7,15,30.
This index includes India. A possible econometric rationale for taking the ratio variables could be to eliminate heteroscedasticity; however, it is found that even if we take the ratio to MCap, heteroscedasticity is not eliminated for FIIN and FIIS series.
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Daily returns on the S&P500, calculated as the excess of the logarithm of the index value on a date over the logarithm of the index value on the previous day. S&PRet_MAk(t-2) k-day moving average value of returns on S&P500 calculated using S&P_Ret(t-k) to S&P_Ret(t-2), k=7,15,30. S&P_RetVol k(t-2) k-day moving average volatility of, returns on S&P500 or the standard deviation of S&P_Ret(t-k) to S&P_Ret(t-2), k=7,15,30. MSCI_Ret Returns on the MSCIWI, calculated as the excess of the logarithm of the index value on a date over the logarithm of the index value on the previous day. MSCIRet_MAk(t-2) k-day moving average value of returns on MSCIWI calculated using MSCI_Ret(t-k) to MSCI_Ret(t-2), k=7,15,30 MSCIRetVol k(t-2) k-day moving average volatility of, returns on MSCIWI or the standard deviation of MSCI_Ret(t-k) to MSCI_Ret(t-2), k=7,15,30. BETA_S&Pkk(t-2) betas of BSE wrt S&P500 based on previous kk day’s data starting from (t-2), kk=15,30 BETA_MSCIkk(t-2) betas of BSE wrt MSCI based on previous kk day’s data starting from (t-2), kk=15,30 Exch_Ret Daily returns on the Rupee’s exchange rate, calculated as the excess of the logarithm of Exch on a date over the logarithm of Exch on the previous day.
When it comes to the case of foreign investment in a thin equity market like that of India, there is a prevalent feeling that FII activities exert a strong demonstration effect and thus drive the domestic stock market.
Notes : (a) The problem of non-synchronised trading in the different markets have been overcome by removing such dates. (b) The routine tests of normality and unit root for stationarity for all series have been conducted and the regressions framed accordingly.
of causation between FII flows and some of their covariates, we have used the technique of pair-wise Granger Causality test. The results are presented below. A. Direction of Causation between FII flows and Return in the Indian Stock Market For any type of investor, domestic or foreign, market return is generally the prime driver of equity investments. However, when it comes to the case of foreign investment in a thin equity market like that of India, there is a prevalent feeling that FII activities exert a strong demonstration effect and thus drive the domestic stock market. In other words, some believe that the day to day FII trading in Indian market, rather than being influenced by the market return, induces the daily market return to be what it is. As a starting point we examine the nature of pair-wise causality between daily measures of FII investment and corresponding BSE_RET separately for the three FII variables (viz., FIIP, FIIS and FIIN). The results are given in Table 1. It may be mentioned here that Chakrabarti (2001) also examined the nature of causality between FIIN and BSE_RET mostly on the basis of
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monthly data on these variables.17 However, given the facts that Granger causality test is designed essentially to detect statistically significant short run lead-lag relationship present in a data-set on a pair of variables and that equity market responses are typically extremely quick and of very short run nature, examination of causality based on a monthly data set may fail to capture the exact nature of causality. The results in Table 1 clearly suggest that causation runs from
TABLE 1 Pairwise Granger Causality Tests between BSE Return and FII Value of F-statistics at different lags Lags —> PANEL 1 BSE Return does not Granger Cause FIIN FIIN does not Granger Cause BSE Return PANEL 2 BSE Return does not Granger Cause FIIP FIIP does not Granger Cause BSE Return PANEL 3 BSE Return does not Granger Cause FIIS FIIS does not Granger Cause BSE Return 2 3 4 5 6 7
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25.98* 1.90
17.33* 15.95* 1.41 2.87
13.53* 11.72* 10.21* 2.02 1.45 1.42
That equity market responses are typically extremely quick and of very short run nature,
4.32+ 1.44
4.42* 0.94
5.67* 4.39*
5.75* 3.33
4.70* 3.19*
4.18* 2.99*
8.69* 7.37*
6.18* 4.94*
5.55* 3.62
4.84* 2.99
4.34* 3.03
3.69* 2.69
examination of causality based on a monthly data set may fail to capture the exact nature of
PANEL 4 BSE_RET does not Granger Cause RATIOFIIN 28.91* RATIOFIIN does not Granger Cause BSE_RET 2.89 PANEL 5 BSE Return does not Granger Cause RATIOFIIP 3.04+ RATIOFIIP does not Granger Cause BSE Return 0.96 PANEL 6 BSE Return does not Granger Cause RATIOFIIS 18.16* RATIOFIIS does not Granger Cause BSE Return 6.22*
19.09* 17.27* 2.24 3.41
14.11* 12.17* 10.49* 2.41 1.84 1.71
2.80+ 0.60
4.25* 4.02*
3.97* 3.07
3.35* 2.96
2.83* 2.67
causality.
11.37* 4.20
8.50* 2.98
6.86* 2.74
5.76* 2.80
4.88* 2.31
*’ Denotes rejection at 1% level of significance +’ Denotes rejection at 5% level of significance
17 He has done the causality test taking the daily data for a limited period of one year (1999, in the post-Asian crisis period), most of his analysis of causality for the pre-crisis period was based on monthly data (1993 to 1999).
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BSE_RET to FIIN and not the other way. This is true for FIIS and FIIP and their ratios also, barring a few cases where no definite conclusion can be drawn. For these variables, causality, if found statistically significant, is unidirectional running from BSE_RET to FII flows. 18 This lends further credence to the supposition that FII flows to India are mostly in response to contemporaneous returns in the Indian stock markets (in the post Asian crisis period) rather than FII inflows and outflows being the cause of returns in the national markets. B. Effect of BSE Returns Having identified the nature of causality, we next examine whether or not contemporaneous BSE_RET significantly affect daily FII inflows/outflows. Regression of the FIIN, FIIP, FIIS on BSE_RET yielded results quite contrary to expectations as the coefficient of BSE_RET turned out to be statistically non-significant in all of these regressions (Table 2.1). However, when RATIOFIIN was taken as the dependent variable, the estimated positive coefficient of BSE_RET turned out to be significant at 5 per cent level—a result, which was also obtained by Chakrabarti. However, BSE_RET again was found to be non-significant determinant for RATIOFIIP and RATIOFIIS. Since the RATIOFIIP and RATIOFIIS which are the components of RATIO_FIIN are not affected by contemporaneous value of BSE_RET, it becomes somewhat difficult to reconcile the observed dependence of RATIO_FIIN on the current level BSE_RET. As day to day variation in the FII flow variables as well as the market return is likely to contain relatively large random components, one would not expect high correlation between FII flow and market return (which indeed was the case in our exercise). We therefore tried next to explore whether the FII flow variables would show up any stronger dependence on market return if daily variations were filtered through moving average smoothing. The results obtained by regressing 7-day moving average values of FIIN, FIIP, FIIS and RATIO_FIIN on the corresponding 7-day moving average values of BSE_RET suggest that while FII sale and FII net inflow are significantly affected by market return (the former being affected positively and the latter negatively, as to be expected), FII purchase is not responsive to variations in market return (Tab 2.2). In other words, a drop/rise in average return over the previous 7 days would induce a FI investor to
18 Studies examining the pattern of aggregate international portfolio flows found evidences of contemporaneous positive correlation between inflows and returns and lagged returns (Bohn and Tesar, 1996, Brennan and Cao, 1997 and Froot, O’ Connell, and Seasholes, 2001). The last mentioned study also found sensitivity of local stock prices to foreign inflows to be positive and large and also that inflows had positive forecasting power for future emerging-market returns. Other studies on the Indian equity market have also found evidence of the relationship between net FII and returns on equity (Batra, 1999). On the other hand, Bhatia (2000) has shown that daily net FII inflows are a significant determinant of the daily returns on the BSE.
As day to day variation in the FII flow variables as well as the market return is likely to contain relatively large random components, one would not expect high correlation between FII flow and market return.
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raise/reduce sale promptly, but it might fail to induce a change in the level of her purchase—a result which is suggestive of some kind of asymmetry of behaviour so far as FII trading in Indian market is concerned. C. Other Factors Influencing FII Flows To identify other significant non-domestic return determinants of different FII flows, we considered a set of other possible determinants, domestic as well as international. As already mentioned, this set comprised MSCI_RET, S&P_RET, BSE_RETVOL, BETA_MSCI,
TABLE 2.1 Regression of FII on BSE Return Regressands ——> Regressors Constant BSE Return R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat Note : FIIN 34.09* (0.0) 447.02 (0.05) 0.0049 0.0037 119.81 1.61 RatioFIIN 0.001* (0.0) 0.01* (0.04) 0.0053 0.0041 0.0036 1.58 FIIP 219.3* (0.0) 0.06 (0.99) RatioFIIP FIIS RatioFIIP
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A drop/rise in average return over the previous 7 days would induce a FI investor to raise/ reduce sale promptly, but it might fail to induce a change in the level of her purchase.
0.007* 185.25* 0.006* (0.0) (0.0) (0.0) 0.01 -447.03 -0.01 (0.42) (0.057) (0.21) 0.0055 0.0022 0.0043 0.0010 113.15 0.003 0.95 1.18
0.0000 0.0009 -0.0012 -0.0003 141.81 0.004 1.11 1.39
1. ‘*’ denotes significant at 5% 2. Figures in parenthesis are p-values of the regression coefficients 3. Regressions are formulated keeping in view the stationarity of variables
TABLE 2.2 Regression of FII_MA on BSE Return_MA Regressands ——> Regressors Constant Moving Average of BSE Return R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat Note: FIIN_MA 34.76* (0.0) 2603.83* (0.0004) 0.09 0.087 62.25 0.163 RatioFIIN_MA 0.001* (0.0) 0.089* (0.0001) 0.11 0.112 0.0019 0.171 FIIP_MA 220.17* (0.0) -641.60 (0.55) 0.00 0.001 101.14 0.053 FIIS_MA 185.4054* (0.0) -3246.149* (0.0003) 0.08 0.080 81.39 0.066
1. ‘*’ denotes significant at 5% 2. Figures in parenthesis are p-values of the regression coefficients 3. Regressions are formulated keeping in voew the stationarity of variables
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BETA_S&P, EXCH (all these variables were considered by Chakrabarti (2001) for explaining FIIN) and, in addition, MSCI_RETVOL, S&P_RETVOL, IIP and CMR.19 Multiple regression analysis was done separately for each of the FII variables—viz., FIIN, FIIP, FIIS, their ratio versions RATIOFIIN, RATIOFIIP, RATIOFIIS and also their moving average versions FIIN_MA, FIIP_MA, FIIS_MA20—using
TABLE 3A Stage 1 Regression results of FII on all relevant variables
REGRESSION A: Stage 1 Regression Coefficients Constant Lagged BSE Return BSE Return 15-day MA(t-2) BSE Return 15-day Volatility(t-2) Lagged MSCI Return MSCI Return 15-day MA(t-2) MSCI Return 15-day Volatility(t-2) Lagged S&P Return S&P Return 15-day MA(t-2) S&P Return 15-day Volatility(t-2) Beta_MSCI 15-day (t-2) Beta (S&P) 15-day (t-2) FIIN -29.06 (0.14) 1285.21 (0.0) 776.92 (0.52) 587.96 (0.49) 393.554 (0.73) -16601.52 (0.017) -7446.42 (0.07) -150.21 (0.85) Ratio FIIN -0.001 (0.12) 0.04 (0.0) 0.02 (0.59) 0.02 (0.48) -0.002 (0.96) -0.46 (0.03) -0.18 (0.14) 0.01 (0.69) FIIN_MA 15(t-1) -9.84 (0.58) -15.80 (0.87) 3426.23 (0.0004) 2134.64 (0.004) -260.48 (0.53) -2105.64 (0.66) -592.82 (0.86) 45.69 (0.86) 2571.63 (0.57) 970.09 (0.72) 2.06 (0.87) 14.88 (0.47) 0.15 0.14 49.25 0.06 FIIP 137.05 (0.0) 408.05 (0.18) 1455.58 (0.44) 3164.84 (0.03) -1237.87 (0.33) -16763.99 (0.19) -7372.09 (0.297) 621.42 (0.51) 16217.83 (0.19) 7840.28 (0.20) -5.45 (0.87) 15.42 (0.70) 0.05 0.04 138.57 1.19 Ratio FIIP 0.01 (0.0) 0.01 (0.18) -0.01 (0.78) 0.03 (0.35) -0.05 (0.14) -0.65 (0.018) -0.01 (0.94) 0.03 (0.18) 0.60 (0.02) 0.11 (0.49) 0.0001 (0.94) 0.001 (0.61) 0.05 0.03 0.00 1.48 FIIP_MA 15(t-1) 136.47 (0.0) -437.76 (0.016) 433.96 (0.79) 4783.72 (0.0003) -922.41 (0.28) -10479.46 (0.35) -2060.30 (0.73) 403.22 (0.51) 10508.50 (0.35) 1623.88 (0.75) -15.80 (0.58) 48.63 (0.13) 0.18 0.17 84.32 0.05 FIIS 166.10 (0.0) -877.39 (0.0005) 677.97 (0.71) 2577.51 (0.04) -1630.71 (0.15) -160.46 (0.99) 72.42 (0.989) 771.12 (0.33) -981.58 (0.92) -1790.15 (0.68) -12.98 (0.68) 4.39 (0.91) 0.05 0.04 110.59 0.99 Ratio FIIS 0.01 (0.0) -0.03 (0.0) -0.03 (0.41) 0.01 (0.67) -0.05 (0.07) -0.19 (0.39) 0.17 (0.15) 0.02 (0.19) 0.11 (0.57) -0.16 (0.12) 0.00 (0.76) 0.0003 (0.77) 0.07 0.06 0.003 1.24 FIIS_MA 15(t-1) 146.30 (0.0) -422.01 (0.0095) -2993.03 (0.05) 2649.34 (0.0099) -662.13 (0.299) -8372.86 (0.36) -1470.89 (0.76) 357.56 (0.46) 7935.17 (0.38) 656.41 (0.87) -17.86 (0.44) 33.74 (0.23) 0.18 0.17 70.36 0.04
17196.60 0.48 (0.009) (0.0096) 9631.50 (0.008) 7.53 (0.697) 11.03 (0.69) 0.09 0.08 115.99 1.73 0.27 (0.02) 0.00 (0.66) 0.00 (0.78) 0.10 0.08 0.003 1.70
R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat
Note:
Figures in parenthesis are the p-values of coefficients
19 So far as the volatility variables are concerned, for each of them we considered two different versions—measured as the standard deviation of the values observed over the previous 7 and 15 days, respectively. 20 Three different sets of moving averaged variables were compiled using moving average period of 7, 15 and 30 days, respectively. This was done essentially to see whether or not the regression results would be sensitive to the choice of the period of moving average.
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BSE_RET along with the set of non-domestic return determinants listed above. This analysis was done in several stages.21 Here let us first summarise the qualitative results obtained on the nature of relationship of different types of FII flows with the variables that we identified to be possible covariates of these flows. (We present here only some representative results in Tables 3A and 3B.)
TABLE 3B Stage 2 Regression Results of FII on all relevant variables
REGRESSION A: Stage 2 Regression Co-efficients Constant Lagged BSE Return BSE Return 15-day MA(t-2) BSE Return 15-day Volatility(t-2) Lagged MSCI Return MSCI Return 15-day MA(t-2) MSCI Return 15-day Volatility(t-2) S&P Return 15-day MA(t-2) S&P Return 15-day Volatility(t-2) Other Regressors -14793.27 (0.02) -5046.68 (0.13) 14899.95 (0.01) 7411.93 (0.01) Lag1 FIIN 0.17 0.39 (0.02) 0.12 (0.002) Lag1 Ratio FIIN 0.19 Lag1 FIIN_ MA15 1.18 Lag2 FIIN_ MA15 -0.21 Lag1 FIIP 0.296 Lag2 FIIP 0.302 Lag1 Ratio FIIP 0.29 Lag1 FIIP_ MA15 1.23 Lag2 FIIP_ MA15 -0.24 0.43 (0.03) -0.37 (0.04) -0.50 (0.01) FIIN -17.21 (0.27) 1252.26 (0.0) Ratio FIIN -0.001 (0.19) 0.04 (0.0) 159.07 (0.13) 74.92 (0.25) 1132.59 (0.04) -31.30 (0.67) FIIN_MA 15(t-1) -0.28 (0.79) FIIP 69.84 (0.0) Ratio FIIP 0.005 (0.0) FIIP_MA 15(t-1) 2.40 (0.07) -9.43 (0.65)
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FIIS 57.25 (0.0) -694.50 (0.0007)
Ratio FIIS_MA FIIS 15(t-1) 0.003 (0.0) -0.03 (0.0) 1.15 (0.23) -14.78 (0.37) 187.86 (0.003)
324.36 (0.48) -0.01 (0.18)
21.04 (0.67)
Lag1 FIIS 0.36 Lag2 FIIS 0.31
Lag1 Ratio FIIS 0.32 Lag2 Ratio FIIS 0.22
Lag1 FIIS_ MA15 1.27 Lag2 FIIS_ MA15 -0.10 Lag3 FIIS_ MA15 -0.18
(Lagged Variables)
R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat
0.11 0.11 114.26 2.10
0.12 0.11 0.00 2.12
0.96 0.96 10.56 2.04
0.27 0.27 120.77 2.11
0.10 0.10 0.004 2.13
0.99 0.99 10.30 2.11
0.37 0.37 89.83 2.08
0.26 0.25 0.003 2.06
0.99 0.99 7.80 2.05
Note:
1. Figures in brackets are the p-values of coefficients 2. P-values for all the lagged dependent variables are less than or equal to zero. 3. Since the package we used doesn’t provide Durbin h-stats, we report the DW here.
21
First, we have checked for stationarity of all the variables concerned. Then, we have specified the regressions keeping in view to the order of integration of the variables concerned. In all cases the regressions have been done in two stages, detecting residual auto-correlation at the first stage, we have run the regressions again. In stage 2, we included only the significant variables at stage 1 and the lagged dependent variable as the regressors.
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One might surmise that daily FII activities might be based, not just on previous day’s return information, but on the history of return over the previous few days.
C1. FII Flows and BSE Return As regards the relation between daily FII net inflow and BSE return, a definite positive association with the previous day’s return was observed in all the regressions estimated. Similarly, for daily FII sale the association with the previous day’s return was always found to be negative and significant. However, no definite association between daily FII purchase and previous day’s market return was noticed. One might surmise that daily FII activities might be based, not just on previous day’s return information, but on the history of return over the previous few days. Thus, for example, daily FII sale may be thought to depend, not just on previous day’s return but also on the average and the standard deviation of returns observed during the previous 7 or 15 days (the standard deviation being taken as a measure of volatility of returns). Ceteris paribus, FII sale/purchase should be positively/negatively related to average return and negatively/positively related to volatility. We, however, failed to observe any definite association with average return or return volatility of any of the daily FII flow variable. Very similar results were obtained when the FII variables were used in ratio form. But use of moving average variables—i.e., FIIN_MA, FIIP_MA and FIIS_MA—showed definite positive association of FII net inflow, FII purchase and FII sale with return and volatility, but not with average return. While a positive association of return volatility with FII sale seems intuitively justifiable, its negative association with the other two FII flows appears somewhat unrealistic. C2. FII Flows and Return and Volatility in Competing markets As already mentioned, in our regression strategy, we included the return and volatility variables relating to the S&P500 index and MSCI index (viz., MSCI_RET, S&P_RET, MSCI_RETVOL and S&P_RETVOL) in the set of possible regressors, as indicators of return and risk involved in investing in the US and the World equity markets, other than the Indian one (see Box 3 in this context). The hypothesis of portfolio return maximisation suggests reallocation of investment in favour of markets in which risk is minimised (as measured by Sharpe ratio—i.e., excess return per unit of volatility) and this in turn means, ceteris paribus, a positive/negative partial association with competing market returns/volatility of FII net inflow and FII purchase and a negative/positive association with FII sale. In our exercise, we did get result supporting the hypothesis of return maximisation only in some cases, not unequivocally. This is in conformity with the result that Chakrabarti (2001) obtained for the post-Asian crisis sub-period using monthly data. We also found in most of the cases that S&P variables and MSCI variables are simultaneously significant. We checked the correlation between these variables which, turned out to be quite high (Table 4) implying thereby the possibility of multicollinearity, given the extent of integration of the world and US markets. We therefore ran separate regressions for S&P variables and MSCI variables. The results
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BOX 3 We have also tried to see to what extent the performance of matured equity markets affect that of the Indian equity market and therefore we examined the possible interdependence of BSE and S&P returns. To see whether or not S&P return exerts a significant causal effect on the BSE return we carry out a Granger causality test for the same the results are as below. Granger Causality Tests between S&P and BSE Returns
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Value of F-statistics at different lags
SP500_RET does not Granger Cause BSE_RET
7.79* + 6.5* +
5.04* +
4.32* +
3.76* +
3.30* +
*’ Denotes rejection at 1% level of significance +’ Denotes rejection at 5% level of significance
There is clear evidence of S&P return causing BSE return unilaterally. This was to be expected and perhaps suggestive of the fact that a fluctuation in the US (matured) equity market would get transmitted to the Indian market.
(Table 5) showed that while only the MSCI return volatility but not the average return itself was significant, in more than one cases both S&P return and its volatility were significant. See, however, Table 3A which suggests that whereas FII net inflow is affected by return and volatility of both MSCI and S&P, the corresponding purchase and sale flows are mostly not affected by these.
TABLE 4 Correlation Set of Regressors in Table 3A and 3B
BSE_R1 BSE_RE ETLAG TVOL15 (t-2) BSER ET_MA 15(t-2) BETA _MSCI 15(t-2) BETA _SP 15 (t-2) MSCI MSCI_ _RET RETVOL LAG1 15(t-2) MSCI SP_RE SP_RE SPRET RET_ TLAG1 TVOL _MA MA 15(t-2) 15(t-2) 15(t-2)
BSE_RETLAG1
BSE_RETVOL15(t-2) BSERET_MA15(t-2) BETA_MSCI15(t-2) BETA_SP15(t-2) MSCI_RETLAG1 MSCI_RETVOL15(t-2) MSCIRET_MA15(t-2) SP_RETLAG1 SP_RETVOL15(t-2) SPRET_MA15(t-2)
1.00
-0.03
1.00
0.01
-0.32 1.00
0.01
-0.06 -0.05 1.00
-0.02
0.06 -0.16 0.86 1.00
0.14
0.05 -0.03 -0.02 -0.02 1.00
-0.02
0.50 -0.31 0.07 0.11 0.09 1.00
0.13
-0.05 0.42 -0.15 -0.24 0.04 -0.12 1.00
0.07
0.05 -0.03 -0.02 -0.01 0.89 0.08 0.01 1.00
0.01
-0.30
0.14
0.36
0.50 -0.02 -0.06 -0.16 -0.01 -0.23 0.11 -0.08 0.05 0.95 0.88 -0.10 0.10 -0.01 1.00 -0.07 1.00
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C3. FII Flows and Betas of the Indian Equity Market Following the logic of hedging and diversification of portfolio, one would expect a significant negative (positive) partial association of the betas for Indian market with respect to S&P500 and MSCI indices (i.e., BETA_S&P and BETA_MSCI) with FII net inflow and sale (FII purchase), in case the hypothesis that FII flow to India essentially serve as a means of diversification is true. Our results failed to find support for this hypothesis as in most cases these variables turned out to be statistically non-significant.22
TABLE 5 Regression of FII on MSCI and S&P separetely
STAGE 2 Results REGRESSION B1: on MSCI variables Coefficients Constant Lagged BSE Return MSCI Return Fortnightly Volatility(t-2) Lag1RatioFIIN R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat RatioFIIN -0.0001 (0.77) 0.04 (0.0) 0.11 (0.02) 0.21 0.105 0.101 0.003 2.13
STAGE 2 Results REGRESSION B2: on S&P variables Coefficients Constant Lagged BSE Return S&P Return Fortnightly MA(t-2) S&P Return Fortnightly Volatility(t-2) Lag1FIIN Lag1RatioFIIN R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat FIIN -19.80 (0.21) 1265.56 (0.0) 2283.31 (0.11) 3944.58 (0.002) 0.18 0.00 0.102 0.097 114.877 2.10 0.109 0.104 0.003 2.07 RatioFIIN -0.001 (0.17) 0.04 (0.0) 0.07 (0.14) 0.13 (0.001)
Note:
1. Figures in brackets are the p-values of coefficients 2. P-values for all the lagged dependent variables are less than or equal to zero. 3. Second stage regressions for FIIN in B1 and RatioFIIP in B1 and B2 have not been done since neither of the MSCI/S&P variables were significant at the first stage. 4. Since the package we used doesn’t provide Durbin h-stats, we report the DW here.
C4. FII Flows and Return on Exchange Rate We selected the return from day to day variations in RupeeUSD exchange rate as a possible covariate on the presumption that this being an opportunity cost, foreign investors might take into account this for investment flows to the Indian equity market. However, its effect mostly turned out to be statistically non-significant. C5. FII Flows and Macroeconomic Variables In order to verify whether or not foreign investors track the state of Indian economy for making investment here, we considered IIP
Initially both the beta variables were included together in the regression equations on the presumption that they would be independent, as the concerned markets were very different. However, the sample correlation of these variables was found to be very high, implying thereby a high degree of integration of the markets. We next tried to see the effects of these variables on FII flows separately. Even in that case the effects of these variables were found statistically non-significant.
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and CMR as indicators of the real economic activity in India and tried these as possible determinants of the FII flows. This exercise, however, was done on a very limited scale and only the possible effects of these variables on FII net flow was examined (Table 6A and 6B). Some
TABLE 6A Regression of FII on real sector variables REGRESSION A Stage 1 Coefficients Constant Lagged BSE Return BSE Return Weekly MA(t-2) IIP CMR R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat Stage 2 Coefficients Constant Lagged BSE Return BSE Return Weekly MA(t-2) IIP CMR Other Regressors (Lagged Variables) FIIN_MA 15(t-2) -3.62 (0.68) 13.81 (0.54) 218.53 (0.0) 0.03 (0.54) -0.01 (0.96) LAG1FII N_15MA 1.16 LAG2FII N_15MA -0.19 R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat Note: 0.96 0.96 10.47 2.03 FIIP_MA 15(t-2) -4.68 (0.6) -9.24 (0.66) 243.87 (0.0) 0.04 (0.51) 0.04 (0.8) LAG1FII P_15MA 1.20 LAG2FII P_15MA -0.21 0.99 0.99 10.15 2.08 FIIS_MA 15(t-2) -6.94 (0.26) -24.21 90.16) 31.92 (0.37) 0.05 (0.16) 0.14 (0.53) LAG1FII S_15MA 1.36 LAG2FII S_15MA -0.37 0.99 0.99 7.99 2.16 FIIN_MA 15(t-2) -237.05 (0.01) -32.25 (0.78) 1172.53 (0.07) 1.60 (0.0) 1.52 (0.42) 0.10 0.09 50.42 0.05 FIIP_MA 15(t-2) -558.58 (0.0) -503.87 (0.02) -2078.30 (0.04) 4.20 (0.0) 12.00 (0.0) 0.22 0.21 82.16 0.08 FIIS_MA 15(t-2) -321.47 (0.0) -471.69 (0.00) -3251.30 (0.0) 2.60 (0.0) 10.48 (0.0) 0.22 0.22 68.20 0.09
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Some evidence of positive association of both IIP and CMR with FIIN was obtained.
1. Figures in brackets are the p-values of coefficients
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TABLE 6B Regression of FII on real sector variables REGRESSION B Stage 1 Coefficients Constant Lagged BSE Return BSE Return Weekly MA(t-2) IIP Weekly MA(t-2) Lagged CMR CMR Weekly MA(t-2) FIIN_MA 15(t-2) -263.9 (0.01) -20.2 (0.87) 1199.1 (0.07) 1.7 (0.0) -0.1 (0.95) 3.5 (0.34) 0.10 0.09 50.45 0.05 FIIP_MA 15(t-2) -711.3 (0.0) -461.2 (0.03) -1964.1 (0.07) 4.8 (0.0) 4.9 (0.15) 14.5 (0.01) 0.26 0.25 80.11 0.04 FIIS_MA 15(t-2) -447.3 (0.0) -441.1 (0.005) -3163.6 (0.0) 3.1 (0.0) 5.0 (0.06) 11.0 (0.007) 0.26 0.26 66.58 0.05
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Such regression results would have economic explanation in terms some kind of dynamic adjustment mechanism being involved in the determination of current daily value a given FII flow.
R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat Stage 2 Coefficients Constant Lagged BSE Return BSE Return Weekly MA(t-2) IIP Weekly MA(t-2) Lagged CMR CMR Weekly MA(t-2) Other Regressors (Lagged Variables)
FIIN_MA 15(t-2) -4.13 (0.57)
FIIP_MA 15(t-2) -3.22 (0.75) -9.82 (0.64) 238.76 (0.0) 0.02 (0.76)
FIIS_MA 15(t-2) -6.30 (0.38) -24.71 (0.16) 27.57 (0.43) 0.04 (0.37) 0.02 (0.95) 0.32 (0.22) LAG1FII S_15MA 1.36 LAG2FII S_15MA -0.37 0.99 0.99 7.99 2.15
219.08 (0.0) 0.03 (0.48)
0.24 (0.41) LAG1FII N_15MA 1.16 LAG2FII N_15MA -0.19 LAG1FII P_15MA 1.20 LAG2FII P_15MA -0.21 0.99 0.99 10.15 2.08
R-squared Adjusted R-squared S.E. of regression Durbin-Watson stat Note:
0.96 0.96 10.46 2.02
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1. Figures in brackets are the p-values of coefficients 2. P-values for all the lagged dependent variables are less than or equal to zero. 3. Since the package we used doesn’t provide Durbin h-stats, we report the DW here.
evidence of positive association of both IIP and CMR with FIIN was obtained. However, as we shall discuss next, the regression results were far from satisfactory as there was strong indications of highly autocorrelated regression disturbance term of the specified regression equations.23 C6. Auto-correlation of FII Flows Preliminary statistical analysis of the original series of daily FII flows indicated that these were stationary in nature (i.e., contained no significant time trend but were auto-correlated). This auto-correlation got reflected in all the regression equations estimated to find out statistically significant covariates of the various measures of FII flows. This means none of the covariates—be it related to equity market performances or to the performance of the Indian economy—could explain singly or jointly the observed auto-correlation of the FII flows. Use of lagged value of the concerned FII flow variable as a regressor, however, removed the auto-correlation altogether. But inclusion of the lagged value of FII flow variable in most of the cases caused the erstwhile significant determinant to turn non-significant. As a result, the only statistically satisfactory regression results turned out to be the ones having market return (i.e., one or the other variant of BSE_RET) and lagged value of the concerned FII flow. Such regression results would have economic explanation in terms some kind of dynamic adjustment mechanism being involved in the determination of current daily value a given FII flow. In other words, these results may be taken to mean that for individual FII flow there is a desired level determined solely by BSE_RET or some variant of it and the actual value constantly tries to reach this desired level.
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While the dependence of net FII flows on daily return in the domestic equity market is suggestive of foreign investors’ return-chasing behaviour, their decisions seem to get affected also by the recent history of market return and its volatility in international and domestic stock markets as well.
IV. Concluding Observations
Our results suggest that though FII flows to and from India are significantly affected by return in the domestic equity market,24 the latter is not significantly influenced by variation in these flows. It is also found that apart from the return in the domestic market there are other covariates of such flows. While the dependence of net FII flows on daily return in the domestic equity market—at a day’s lag, to be more specific—is suggestive of foreign investors’ return-chasing behaviour, their decisions seem to get affected also by the recent history of market return and its volatility in international and domestic stock markets as well. We also found that the sets of factors affecting FII sale and purchase were not the same. It appeared that some factors would affect purchase or sale decision of foreign investors, but not the corresponding
23 But one cannot possibly avoid this auto-correlation, specially when the moving average of the concerned variables are used in regression. 24 In the sense of Granger causality.
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The scope of using the Indian equity market for the purpose of portfolio diversification arose due to the nonsynchronised movement of the Indian equity market
vis-à-vis the other
equity markets of the world.
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net FII flow. For example, while FII purchase from and sale to the Indian market appeared to be sensitive to the volatility of domestic market return (with both purchase and sale responding positively to volatility change in the recent past), the corresponding net inflow appeared to be positively related to the volatility of return in the foreign market. This might be due to the simultaneous portfolio adjustment in several markets together done by foreign investors. We however failed to find evidence of any portfolio diversification benefit reaped by FIIs by investing in the Indian market (as suggested by lack of statistical significance of the effect of betas of the BSE Sensex with respect to the MSCI world and S&P 500 indices on various FII flows). Day to day variations in the exchange rate also turned out to be unimportant so far as FII transactions were concerned (in fact, though the effect of exchange rate return on the net FII inflow was sometimes found to be significant, its effect on FII purchase or sale was never significant). Juxtaposing results of our study with those obtained by Chakrabarti for the comparable post-Asian crisis period, one may notice quite a few agreements. For example, both the studies tend to show the predominance of the Indian equity market return as the prime mover of the FII net inflow into India. This may be a matter of concern as this suggests that the rate of FII inflow into the country would be governed mostly by the performance of the domestic equity market and/ or foreign investors’ expectation about this performance and hence variation in the country’s foreign exchange reserve would, to some extent, be outside the monetary authority’s control. Given the fact that FII flows can be extremely volatile, a drop of return in the India equity market may result in sudden massive withdrawals of FII which may result in quite disturbing consequences on the country’s economy, unless an appropriate stabilisation mechanism is built into the domestic economic system. Further cause for concern relates to the finding that unlike in the pre-Asian crisis period, as found by Chakrabarti, foreign investors no longer use equity investment in India as a means of diversification of their portfolio. In fact, the scope of using the Indian equity market for the purpose of portfolio diversification arose due to the non-synchronised movement of the Indian equity market vis-à-vis the other equity markets of the world. Recent evidences suggest a stronger co-movement of market returns and it is possibly for that reason foreign investors no longer are able to use the Indian equity market for portfolio diversification. The point of concern, however, lies elsewhere—viz., a stronger integration of the Indian market with the equity market elsewhere exposes the country to the danger of contagion of global financial crises in future. Findings of several studies on FII flows to emerging equity markets over the world have shown the importance of financial market infrastructure such as the market size, market liquidity, trading costs,
information dissemination, and legal mechanisms relating to property rights etc., for attracting foreign portfolio investments into those countries.25 In some studies variables relating to investment barriers, dividend yield, liquidity, firm size and profitability26 etc., have also been found to be significant determinants of FII inflow. These apart, the need for harmonisation of corporate governance, accounting, listing and other rules with those followed in international financial centres as well as strengthening of securities markets’ enforcement have also been stressed for improving competitiveness in attracting foreign portfolio investment inflow.27 Policy implications of the findings just mentioned above are that a move towards a more liberalised regime in the emerging market economies should be accompanied by further improvements in the regulatory system of the financial sector.28 Our results additionally suggest that in the case of India (and other countries having thin and shallow equity markets) the prime focus should be on regaining investors’ confidence in the equity market so as to strengthen the domestic investor base of the market.29 Once this is achieved, a built-in cushion against possible destabilising effects of sudden reversal of foreign inflows might develop. Only then would it be possible to reap fully the benefits of capital market integration.
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The prime focus should be on regaining investors’ confidence in the equity market so as to strengthen the domestic investor base of the market.
25 Applying a panel data approach on bilateral gross cross-border equity flows between 14 countries, during1989-96 find that asset flows depend on market size in both source and destination country as well as trading costs, in which both information and the transaction technology play a role. Portes and Rey, 2000. Garibaldi et al (2002) analysing capital flows to 25 European transition economies showed that FPI was volatile and concentrated in a handful of countries (notably Russia). Regressions showed that the presence of a financial market infrastructure and property rights indicator were the only significant explanatory variables for FPI. Claessens et al (2002) analysing data from 77 countries, find that factors such as shareholder protection and the quality of local legal systems which make it and easier for investors to buy shares and firms to list in public markets play a prime role in determining the degree of integration with international capital markets. 26 Liljeblom and Löflund (2000), using company specific data on degree of foreign ownership, in the Finnish market, which recently abolished capital controls. 27 Classens et al (2002). 28 The present stance on capital controls of Indian policy makers is in place; policy makers in India have justifiably treated capital account liberalisation as a process and not an event and reiterated that capital account liberalisation and reform of the financial system should move in tandem (Rangarajan and Prasad, 1999; Rangarajan, 2000; Jalan, 2002). 29 To take care of malpractice which discourage stock market participation by a majority of savers in our country (see NSE, 2001, in this context). A survey by SEBI and NCAER showed that alleged malpractice like insider trading and low confidence in brokers/sub-brokers, company management/auditors were the main causes behind lack of domestic savers’ confidence in the equity markets. Further regulatory authorities would need to look into alleged restrictive practices by FIIs like price rigging as suggested by Samal (1997).
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References
Bae, Kee-Hong, Kalok Chan and Angela Ng (2002), “Investability and Return Volatility in Emerging Equity Markets”, Presented to the International Conference on Finance, National Taiwan University, Dept. of Finance. Batra, Amita (1999), “Portfolio Investment Behaviour of Foreign Institutional Investors (FIIs) in India: An Econometric Analysis”, in S. Arumugam (ed.) Indian Capital Markets: Modern Perspectives and Empirical Evidences , UTI Institute of Capital Markets and Allied Publishers, Mumbai. Bekaert, Geert and Campbell R. Harvey (2000), “Foreign Speculators and Emerging Equity Markets”, Journal of Finance, Vol. LV, No. 2. Bekaert, Geert and Campbell R. Harvey (1998), “Capital Flows and the Behaviour of Emerging Market Equity Returns”, NBER Working Paper No. 6669. Bhatia, Udit (2000), “Factors Governing Returns on the Mumbai Stock Exchange: An Econometric Analysis”, ICICI Research Centre Working Paper. Bikhchandani, Sushil and Sunil Sharma (2001), “Herd Behavior in Financial Markets”, IMF Staff Papers, Vol. 47, No. 3. Bohn, Henning and Linda L. Tesar (1996), “US Equity Investment in Foreign Markets: Portfolio Rebalancing or Return Chasing?”, American Economic Review, 86, May, 77-81. Brennan, Michael J. and Henry Cao (1997), “International Portfolio Investment Flows”, Journal of Finance, Vol. LII, No. 5, December, 1851-1880. Chakrabarti, Rajesh (2001), “FII Flows to India: Nature and Causes”, Money & Finance, Vol. 2, No. 7, October–December.. Choe, Hyuk, Bong-Chan Kho, and René M. Stulz (1999), “Do Foreign Investors Destabilize Stock Markets? The Korean Experience in 1997”, Journal of Financial Economics, Vol. 54, 227-264. Claessens, Stijn, Daniela Klingebiel, and Sergio L. Schmukler (2002), “Explaining the Migration of Stocks from Exchanges in Emerging Economies to International Centers”, World Bank Working Paper No. 2816. Errunza, Vihang (2001), “Foreign Portfolio Equity Investments, Financial Liberalization and Economic Development”, Review of International Economics, Volume 9, Issue 4, Special issue: International Financial Liberalization, Capital Flows and Exchange Rate Regimes. FitzGerald, E.V.K. (1999), “Policy Issues In Market Based and Non Market Based Measures to Control the Volatility of Portfolio Investment”, Finance And Development Research Programme, Working Paper Series Paper No 8, Oxford University. Froot Kenneth A. and Tarun Ramadorai (2002), “Currency Returns, Institutional Investor Flows, and Exchange Rate Fundamentals”, NBER Working Paper No. 9101. Froot, Kenneth A., Paul G.J. O’Connell and Mark S. Seasholes (2001), “The Portfolio Flows of International Investors” Journal of Financial Economics, 59, 151-193. Garibaldi, Pietro, Nada Mora, Ratna Sahay and Jeromin Zettlemeyer (2002), “What Moves Capital to Transition Economies”, IMF Working Paper No. WP/02/64. International Monetary Fund, (2002), Global Financial Stability Report , World Economic and Financial Surveys, Washington, June, September. Jalan, Bimal (2002), “Bank of England’s Seminar on International Financial Architecture”, remarks made at the symposium of Central Bank Governors, hosted by the Bank of England, London, July 5. Jo, Gab-Je (2002), “Foreign Equity Investment in Korea”, For presentation at the Association of Korean Economic Studies. Kaminsky, Graciela, Richard Lyons and Sergio Schmukler (2000), “Managers, Investors, and Crises: Mutual Fund Strategies in Emerging Markets”, NBER Working Paper 7855. Kim, E. Han and Vijay Singal (2000), “Stock Market Openings: Experience of Emerging Economies”, Journal of Business, Vol. 73, October, Issue 4. Kim, Woochan and Shang-Jin Wei (1999), “Foreign Portfolio Investors Before and During a Crisis”, Economics Department Working Paper No.210, Organi-
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sation for Economic Co-operation and Development. Liljeblom, Eva and Anders Löflund (2000), “Determinants of International Portfolio Investment Flows to a Small Market: Empirical Evidence”, Swedish School of Economics and Business Administration, Working Papers Series, Number 445. National Stock Exchange (2001), Indian Securities Market: A Review, NSE, Mumbai. Portes Richard and Hélène Rey (2000), “The Determinants of Cross-Border Equity Flows: The Geography of Information”, Center for International and Development Economics Research, Working Paper C00-111. Rangarajan, C., (2000), “Capital Flows: Another Look”, Economic and Political Weekly, December 9, 4421-4427. Rangarajan, C. and A. Prasad (1999), “Capital Account Liberalisation and Controls: Lessons from the East Asian Crisis”, Money & Finance, No.9, April–June.
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Appendix 1: Some Information Relating to FII Operations in India
• FIIs in India include Asset Management Companies, Pension Funds, Mutual Funds, Investment Trusts as Nominee Companies, Incorporated/Institutional Portfolio Managers or their Power of Attorney holders, University Funds, Endowment Foundations, Charitable Trusts and Charitable Societies. • SEBI acts as the nodal point in the entire process of FII registration. RBI approval under FEMA enables an FII to buy/sell securities on Stock Exchanges and open foreign currency and Indian Rupee accounts with a designated bank branch. Investment by FIIs in India is regulated under SEBI (FII) Regulations , 1995 and Regulation 5(2) of FEMA. • FIIs are required to allocate their investment between equity and debt instruments in the ratio of 70:30. (However, it is also possible for an FII to declare itself a 100 per cent debt FII in which case it can make its entire investment in debt instruments). • FIIs can buy/sell securities on Stock Exchanges. They can also invest in listed and unlisted securities outside Stock Exchanges, where the price has been approved by RBI. • No individual FII/sub-account can acquire more than 10 per cent of the paid-up capital of an Indian company. All FIIs and their sub-accounts taken together cannot acquire more than 24 per cent of the paid-up capital of an Indian Company. Indian Companies can raise the above-mentioned 24 per cent ceiling to the Sectoral Cap/Statutory Ceiling as applicable by passing a resolution by its Board of Directors followed by passing a Special Resolution to that effect by its General Body. The presence of Sectoral Cap/Statutory Ceiling means that foreign investment from all sources cannot exceed a specified level. A Company to which no sectoral cap/statutory ceiling is applicable can raise the limit of permissible FII investment to 100 per cent of the paid-up capital. A Company to which a 49 per cent Sectoral Cap is applicable can raise the limit of permissible FII investment to 49 per cent and if there is an existing foreign direct investment of 15 per cent, possible FII investment can only be up to 34 per cent. • No permission from RBI is needed so long as the FIIs purchase and sell on recognised stock exchange. However, all non-stock exchange sales/purchases require RBI permission. • FIIs can avail of the Forward Cover Facility from the Authorised Dealer subject to certain conditions. • High Net Worth Individuals /foreign corporates can invest through SEBI Registered FIIs subject to a sub-limit of 5 per cent each, within the aggregated limit of 24 per cent. • FIIs can trade in Exchange Traded Derivative Contracts. Source: RBI and SEBI websites.
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The presence of Sectoral Cap/ Statutory Ceiling means that foreign investment from all sources cannot exceed a specified level. A Company to which no sectoral cap/statutory ceiling is applicable can raise the limit of permissible FII investment to 100% of the paid-up capital.
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Trends in BSE and FII Operations in India During Our Sample Period
CHART A1 Returns on BSE and Net FII
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Cumulative FIIN 35000 Cumulative FIIN (Rs.Cr) 30000 25000 20000 15000 10000 5000 0 -5000
Cumulative Returns on BSE 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 -0.1 -0.2 -0.3 Cumulative BSE Returns
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CHART A2.1 Net FII and the Sensex, for 3 sub periods of the sample.
500 400 300 FII net 200 100 0 -100 -200 -300
FIIN
Sensex (Close)
31-May-02
1-Jan-99
29-Mar-00
16-Mar-01
28-Sep-99
21-Sep-00
29-Dec-99
18-Dec-00
27-Jun-00
13-Jun-01
4-Mar-02
7-Sep-01
5-Jul-99
6-Apr-99
6-Dec-01
6000 5500 5000 4500 4000 3500 3000 Sensex
14-May-99
1-Jan-99
21-Mar-00
5-Mar-99
16-Nov-99
11-Aug-99
16-Dec-99
14-Jun-99
18-Jan-00
9-Sep-99
20-Apr-00
13-Jul-99
9-Apr-99
12-Oct-99
17-Feb-00
3-Feb-99
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FII net 350 300 250 200 150 100 50 0 -50 -100 -150 2-May-01 28-May-01 21-Jun-01 17-Jul-01 10-Aug-01 7-Sep-01 4-Oct-01 31-Oct-01 27-Nov-01 26-Dec-01 21-Jan-02 14-Feb-02 12-Mar-02 10-Apr-02 7-May-02 31-May-02 2400 2600 2800 3000 3200 3400 3600 3800 4000
CHART A2.3
FII net 1200 1000 800 600 400 200 0 -200 -400 -600 2-May-00 25-May-00 19-Jun-00
FIIN Sensex (Close)
FIIN
12-Jul-00 4-Aug-00 30-Aug-00
CHART A2.2
25-Sep-00 19-Oct-00 14-Nov-00
Sensex (Close)
7-Dec-00 2-Jan-01 25-Jan-01 20-Feb-01 19-Mar-01 12-Apr-01
5000 4800 4600 4400 4200 4000 3800 3600 3400 3200 3000
Sensex
Sensex
APPENDIX 2: Approximation of Daily Market Capitalisation
Notation: qijt : No. of outstanding shares of the i-th company on the j-th day of month t pijt : Price of a share of the i-th company on the j-th day of month t mcijt = pijt q ijt : market capitalisation of the i-th company’s shares on the j-th day of month t
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MC jt = ? mcijt : aggregate market capitalisation on the j-th
i
day of month t We want to find approximate value of We have
MC jt = ? p ijt qijt =
i
MC jt .
?p ?p
i i
ijt
qijt qijt
i0
?p ?p
i i
i0
q ijt qi 0
i0
?p
i
i0
qi 0 = ? P jt
?p ?p
i i
i0
q ijt qi 0
i0
?p
i
i0
qi 0 ( 1 )
where ? P is the Paasche share price index for the j-th day of jt month t with respect to some base date 0,
?p ?p
i i
i0
qijt qi 0
= Q jt is an index of volume of outstanding shares
i0
for the j-th day of month t with respect to the base date, date.
?p
i
i0
qio is the aggregate market capitalisation on the base
We assume Q jt = Qt i.e., index of volume of outstanding shares is same for all days of a month. Then (1) yields: MC jt = ? P , where MC 0 = ? p i 0 q i 0 jt Qt MC 0
i
We have data on MCt: aggregate market capitalization for month t. ? t : the available stock price index for month t, we may approximate QtMC0 by (MCt/ P t ). Substituting the Paasche daily stock price index ? jt by the corresponding available daily stock price index, say, P jt give us the following approximate formula for daily market capitalisation
P
? MC t MC jt = ? jt ? ? ? ? t
? ? ? ?
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