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Senior Resident Representative and Economist in the IMF Delhi office, respectively. The authors are grateful for helpful comments from Charles Adams, Saumitra Chaudhuri, Pami Dua, Gaston Gelos, Narendra Jadhav (and colleagues at the Reserve Bank of India), K. Kanagasabapathy, Renu Kohli, Deepak Mishra, Ashoka Mody, Anthony Richards, Chris Towe, and seminar participants at the National Council of Applied Economic Research. The authors remain responsible for all errors.
FIIs can also invest in bonds, but such investment has been very small compared to investment in equities. In the period since March 1997, when such investment was first permitted, net debt purchases are virtually zero on a cumulative basis, and we focus only on equity flows in the paper.
This paper does not analyze the effects of FII flows, although there is a growing literature on this issue. For a recent example, see Mody and Murshid (2002) which, using a sample of 60 developing countries, shows that portfolio inflows have a weak impact on domestic investment.
FII inflows and sales proceeds are credited to their rupee accounts, but they can transfer sums between their foreign currency and rupee accounts at the market exchange rate. FIIs are also free to repatriate their capital, capital gains, dividend and interest income, net of taxes.
An Economic Times survey of 100 large private sector companies in June 2002 finds 23 companies where FIIs held more than 5 percent of equity; 15 companies where FIIs heldmore than 10 percent; 6 companies where FII holdings exceeded 20 percent, and 2 companies where FII holdings exceeded 24 percent (Infosys and Satyam Computer). FIIs also hold significant stakes in public sector units.
Because of seasonality in the data, a 12-month difference in the stock of FII investment, or seasonally adjusted data, may be more appropriate for econometric analysis. However, the series constructed by taking a 12-month difference in the stock of FII was found to be an 1(1) process. Differencing this series yielded an 1(0) process, but the differenced series is not very meaningful. Moreover, the regressions using it had very little explanatory power. Since the FII data had a few negatjve values, we did not attempt to seasonally adjust the data. As explained in Section IV, we account for the seasonality using dummies for the beginning of the year and for the end of the year.
Kim and Wei (2000) show that the association between domestic stock market returns and portfolio investment may alter during a currency crisis. They find that foreign investors in Korea did not strongly follow the strategy of positive feedback trading prior to the Asian crisis, but did so thereafter. By contrast, Choe et al. (1998) find that portfolio investors in Korea were positive feedback traders before the crisis, but not afterwards.
The reasons for the lack of unanimity in the results could be due to different econometric methodologies, and data used in these papers. Data frequencies range from daily to annual, and the type of data used ranges from the data on aggregate flows (sometimes derived from the balance of payments data on international reserves) to portfolio allocation by individual investors.
The same authors note that regression equations for portfolio flows tend to have poor explanatory power, especially as compared to equations for FDI.
Chakrabarti defines the beta of the Indian market as the covariance of returns on the BSE and the S&P 500, divided by the variance of the S&P 500. This is not strictly the same as the Sharpe β in the Capital Asset Pricing Model (CAPM), which would be calculated using the covariance between the return on Indian equities and on the global equity market portfolio (see Merton, 1982). However, since India’s share in the world portfolio is small, the two (3’s will be quite similar in practice.
A positive association would be reinforced if higher U.S. returns were expected to boost Indian returns. For example, an improvement in NASDAQ might mean better prospects for Indian technology scrips and push more investment to the Indian market.
We also regard the effect of the expected return on emerging market stocks rFE as ambiguous a priori. In a two-stage model such as Buckberg (1996), the higher the emerging market return, the higher the portfolio allocation to all emerging markets including India. On the other hand, the higher the expected return in other emerging markets, the smaller the share of the emerging market allocation that is likely to be devoted to India.
The Indian market is included in this index, but with a weight of only 3-4 percent.
Variables with which FII flows are weakly correlated include rating changes, budget announcements, yields in emerging markets, and LIBOR.
We detected a couple of outliers in the data. The regressions results reported in Table 7 were obtained after dropping these extreme values from the sample. These are: January 2001, when FII flows were US$854 million and July 2000, when they totaled US$-350 million.
If we regress our dependent variable on contemporaneous domestic stock market yield (in a bivariate or a multivariate set up), the coefficient is found to be positive and significant. This result replicates Chakrabarti’s finding, but is probably due to the simultaneity problem and indicates spurious correlation.
We also experimented with a continuous ratings variable, created as an average index of the ratings assigned by the Standard and Poor’s, Moody’s, and Fitch-IBCA, but the results do not change.
We include a dummy variable, Time2, which takes a value one for the months of September-December, to account for any end of the year effect. The coefficient of this dummy was found to be negative but insignificant.
See Pindyck and Rubinfeld (1981) for details. Chuhan et al. (1998) use this technique to compare the relative importance of domestic and external factors in determining portfolio flows to Latin American and Asian countries.
We used the principal components of various series in the regressions: the first principal component of the returns on NASDAQ, Dow Jones, and S&P 500; and the first principal component of the changes in LIBOR, federal funds rate, and treasury bill rate. However, the results did not change.