behaviour of indian stock market-evidence and explanation

BEHAVIOUR OF INDIAN STOCK MARKET-EVIDENCE AND EXPLANATION

Naliniprava Tripathy, Indian Institute of Management, Shillong, India K. N. Badani, Indian Institute of Management, Shillong, India

ABSTRACT

This study investigates the relationship between FII investments and the Indian stock market performance during November 2003 and January 2007 by using forecasting ARIMA model. The study shows that past FII investment have significant impacts on current BSE Sensex & NSE Index; but there is no significant impact of current FII investment on current BSE Sensex & NSE Index. An important implication of these findings is that the FII investments in India deserve a well- calibrated policy response while the daily movement of stock market in India should be better explained by other factors than FIIs.

Keywords: BSE Sensex, NSE Index, FII Investment, ARIMA Model 1. INTRODUCTION The emerging economies have been witnessing an unprecedented surge in capital inflows. The last three years in India have witnessed virtual bull-run in terms of rising inflow of investments from the FIIs. The numbers of registered FIIs have also shown an increasing trend..This huge increase in the number of FIIs shows their continuing interest in investing in Indian stock market. The financial theory is that FII investments broadens the base of portfolio diversification and cause a long-term increase in the stock prices by reducing the equilibrium rate of return. It was however found that the FII behaviour played a significant role during East Asian Crisis. So understanding the relationship between FII investment and Indian Stock Market is very important as it may have important policy implications. Warther(1995) in his ‘price pressure hypothesis’ suggests that the increase in share prices associated with foreign investment flow is caused by temporary liquidity (i.e. excess demand) and predicts that this change in share price is subsequently reversed. Merton (1997) shows that the expected return in the market with unrestricted investor base is higher than restricted investor base. Entry of foreign investors in the stock market broadens the investors’ base, which increases diversification and risk sharing, lowering the risk premium for country specific volatility. Agarwal (1997) Chakrabarti (2001) and Trivedi and Nair (2003) found that the equity return has a significant and positive impact on the FII. But, given the huge volume of investments, foreign investors could play a role of market makers and book their profits i.e. they can buy their financial assets when the price are declining, thereby jacking-up the assets price and sell when the assets price are increasing .Rai and Bhanumurty (2003) studied the determinants of foreign institutional investment in India during the period 1994-2002, by using monthly data and found that the equity returns is the main driving force for FII investment and is significant at all levels. Gordon and Gupta (2003) also examined causation running from FII inflows to return in BSE and conclude that FIIs act as market makers and book profits by investing when prices are low and selling when they are high. Keeping in view, the present study attempts to examine the relationship between FII investment and the Indian stock market performance. It aims to investigate whether average monthly BSE Sensex and NSE Index are dependent on the current and past FII net inflows. It also seeks to assess whether FII net inflows are dependent on the current and past market BSE Sensex & NSE Index. 2. DATA SOURCES &METHODOLOGY For the purposes of this study, monthly net FII investment data and monthly average BSE Sensex and NSE Index have been used. The FII investment monthly data have been collected from November 2003

JOURNAL OF INTERNATIONAL FINANCE AND ECONOMICS, Volume 9, Number 5, 2009 124

to January 2007 from the SEBI Bulletin, and RBI website for a period of 3 years. The data for BSE sensex and NSE index have been taken from the website of Bombay Stock Exchange. Methodology: The multiple regression analysis has been used in the study. Before any regression analysis can be applied to time series data, it is essential to find that these data are random or the error terms are free from auto correlation. The most popular test to ascertain the presence or absence of auto correlated error terms is the Durbin Watson d-statistics. For the formal test of significance, if there is no serial correlation, the DW statistics will be around 2. The DW statistics will fall below 2 if there is positive serial correlation. If there is negative correlation the statistics will lie somewhat in between 2 and 4. The study uses the d-statistics. ARIMA Model: The Acronym ARIMA stands for “Auto Regressive – Integrated Moving Average” which is a class of linear models capable of representing stationary as well as non-stationary time series. It does not involve independent variables in its constructions. Rather it makes use of the information in the series itself to generate forecasts. It relies heavily on auto correlation patterns of the data. In order to determine whether a series follow a purely auto regressive process or purely a moving average process, the Box-Jenkins methodology comes into picture .ARIMA (Auto Regressive Integrated Moving Average) models are generalizations of the simple AR model that use three tools for modeling the serial correlation in the disturbance. The first tool is the auto regressive or AR term. An autoregressive model of order P, AR (P) has the form:

tU ptP pU tPU tPUt 2211

Where Ut is the time series and t is an uncorrelated random error term with zero mean and constant variance. A moving average forecasting model uses lagged values of the forecast error to improve the current forecast. An MA (q) has the form:

qttaUt 2211

where a is constant The auto regressive and moving average specifications can be combined to form an ARMA (p, q) specification.

qtqttU ptP pU tPU tPaU t 221612211

ARMA model use combinations of past values and past errors and offer a potential for fitting models that could not be adequately fitted by using an AR or MA model separately when the time series have to be differenced to make it stationary, the model is called ARIMA instead of ARMA. Q statistics is often issued, as a test of weather the series is white noise. 3. RESULTS AND DISCUSSION The regression Model attempts to find whether currents months’ Net FII investment is dependent on current and past BSE Sensex and on current and past NSE Index. The Table 1 shows that the one month lag value, two- month lag and three- month lag values of BSE Sense& NSE index, and one month, two -month lag value of net FII do not significantly affects the current months investment pattern of FIIs. The `t’ value of these variables is not significant. The regression model also examines whether current month’s BSE sensex & NSE index is caused by present and past FII investments. It is clear from the Table-2 that past FII investment have quite impact on current BSE sensex & NSE index. It is also found from the analysis that one-month lag and two-month lag values of BSE&NSE sensex significantly affect current BSE sensex at 1% and 5% level of significance. Now it is essential to find out that whether these results are in conformity to the ARIMA findings. It is evident from all the tables that the Durbin Watson `d’ statistics for all the models are around 2. Hence it can be conclude that the error terms in each of the models are not serially correlated. The correlogram test is done with Autocorrelation Function and Partial Autocorrelation Function. Analysis of ACF and PACFs is important because correlograms for stationary


process exhibit certain characteristic patterns. For stationary process, the ACFs and PACFs however will be around zero. If correlogram of a time series exhibits such a pattern, the data series is stationary. The auto correlation of the series of BSE sensex, NSE index and FII investment are presented in the Table 3 through Table 5. The Table 5 reveals that the FII series is stationary and requires no differencing. Table 3 and Table 4 indicate that there is very high auto correlation between current and past BSE sensex as well as NSE market index. These findings are also in conformity with the regression results of significant (at 1% level) impact of past BSE Sensex and NSE Index on present BSE Sensex and NSE Index respectively. Table 4 and Table 5 shows that BSE sensex and NSE index series is non-stationary and differencing and log transformation is needed to make the series stationary. After differencing once, the ACF and PACF for BSE sensex and NSE index show that the series have become stationary which is depicted in the Table 6, Table 7. The Table 8 and Table 9 showed the presence of mixed ARMA process. Similarly the Table 10 and Table 11 after log transformation indicated mixed ARMA process. The reliability of the ACFs and PACFs can also be checked through the findings of various ARIMA model. The Table 11 shows the summary results of ARIMA model. The AIC in Table 8 indicates that BSE sensex follows the ARIMA (1, 0, 0) when regressed on Net FII investment since the AIC value of –94.422 are closest to zero. The Table 11 reveals that NSE index follow the ARIMA (1, 0, 0) when regressed on FII investments, since the AIC value of –94.439 is closest to zero. But ARIMA output shows a warning that the order of the process may not be correctly estimated. This indicates that current BSE sensex depends on the just preceding months’ BSE sensex as well as on FII investment, which is confirmed with our multiple regression analysis. The Table 10 reveals that the ARIMA (1,0, 0) on the AIC value is 59.914 which is minimum and Table 11 depicts ARIMA (1, 0, 0) in the AIC value’s 59.419 is minimum. This result is also confirmed by regression results. Table-11 indicate that FII also follows autoregressive process of order 1 and moving average process of order 0 and 1 respectively when regressed on BSE and NSE index as given by the minimization of AIC values. The immediate next minimum AIC is for ARIMA (2, 0, 0). These results also reinforce the findings of regression models 1and 2. 4. CONCLUSION The present study examined the impact of Net FII investment on the Indian stock market represented by BSE sensex and NSE index. The study shows that past FII investment makes significant impact on current BSE Sensex & NSE Index; but there is no significant impact of current FII investment on current BSE Sensex & NSE Index. An important implication of these findings is that the FII investments in India deserve a well- calibrated policy response while the daily movement of stock market in India should be better explained by other factors than FIIs.

Table 1: Regression Analysis of Net FII Investment on Current and Past BSE Sensex, NSE Index and Past Net FII Investments

Predictor Variable

BSEt BSEt-

1 BSEt-

2 BSEt-

3 FIIt-1 FIIt-2 NSE t NSE t-

1 NSE t-

2 NSE

t-3 FI It-

1 FI It-

2

Unstand ardised B

-.318 4.366 -3.857

.26 .07 -.115

1656.873 1.917 11.777 -14.715

2.346

.099 -.091

1656.873

Unstan dardised S.E.B

3.636 5.204 3.942 2.525 .283 .221 2493.504 10.100 15.284 13.919 8.060 .269 .228 2539.340

Stand ardised

-.147 2.100 -1.995

.144 .071 -.117

.260 1.649 -2.158 .366 .100 -.093

T .664 -.087 .839 -.979 .103 .249 -.521 .190 .771 -1.057

.291 .368 -.399

.688

Multiple R

.412 .429

R2 .170 .184 Adj. R2 1.970 .002 DW .922 1.9

41

F 1.013


Table 2: Regression Analysis of Net FII Investment on Current BSE Sensex, NSE Sensex and Past BSE Sensex, Past NSE Index, Current and Past Net FII Investments

Predictor Variable

FII t FII t-1 FII t-2 FII t-3 BSE t-1 BSE t-2 FII t FII t-1 FII t-2 FII t-3 NSEt-1 NSEt-2

Unstand ardised

B .001 -.054 .016 -.026 1.288 -.299 172.005 .001 -.019 .005 -.009 1.310 -.319 60.536

Unstan dardised S.E.B

.010 .010 .012 .010 .145 .137 .004 .003 .004 .003 .161 .156 46.414

Stand ardised

.003 -.118 .035 -.059 1.340 -.334 .011 -.140 .036 -.069 1.351 -.345

t .138 -5.502* 1.377 -2.721* 8.870* -2.183* 1.292 .406 -5.560* 1.181 -2.736* 8.144* -2.049** 1.304Multiple R

.995 0.989

R2 .989 0.979 Adj R2 .987 0.974 DW 1.875 1.840

F 405.726 298.054 * Significant 1% level * * Significant at 5% level

Table 3: Auto Correlation: BSE Sensex

Lag Auto Correlation

StandardError

Box-Ljung Prob.

1 0.865 .158 29.987 0.000 2 0.719 .156 51.278 0.000 3 0.603 .153 66.734 0.000 4 0.496 .151 77.505 0.000 5 0.403 .149 84.822 0.000 6 0.332 .147 89.968 0.000 7 0.269 .144 93.460 0.000 8 0.205 .142 95.559 0.000 9 0.164 .139 96.940 0.000

10 0.134 .137 97.902 0.000 11 0.114 .134 98.627 0.000 12 0.101 .132 99.217 0.000 13 0.077 .129 99.573 0.000 14 0. 031 .126 99.635 0.000 15 -0.024 0.123 99.671 0.000 16 -0.075 0.121 100.058 0.000

Table 4: Auto Correlation: NSE Index

Lag Auto Correlation

Standard Error

Box-Ljung Prob.

1 0.860 0.158 29.679 0.000 2 0.722 0.156 51.142 0.000 3 0.601 0.153 66.491 0.000 4 0.484 0.151 76.740 0.000 5 0.385 0.149 83.409 0.000 6 0.311 0.147 87.915 0.000 7 0.251 0.144 90.948 0.000 8 0.191 0.142 92.771 0.000 9 0.157 0.139 94.042 0.000

10 0.139 0.137 95.071 0.000 11 0.122 0.134 95.898 0.000 12 0.111 0.132 96.607 0.000 13 0.098 0.129 97.188 0.000 14 0.049 0.126 97.336 0.000 15 - 0.013 0.123 97.348 0.000 16 - 0.68 0.121 97.669 0.000


Table 5: Auto Correlation: FII investment

Lag Auto Correlation Standard Error Box-Ljung Prob.1 0.115 0.158 0.534 0.465 2 - 0.232 0.156 2.749 0.253 3 0.033 0.153 2.795 0.424 4 - 0.058 0.151 2.944 0.567 5 - 0.144 0.149 3.882 0.566 6 - 0.121 0.147 4.564 0.601 7 - 0.124 0.144 5.298 0.624 8 - 0.062 0.142 5.489 0.704 9 0.015 0.139 5.501 0.789

10 - 0.093 0.137 5.963 0.818 11 - 0.002 0.134 5.963 0.876 12 0.157 0.132 7.389 0.831 13 0.017 0.129 7.406 0.880 14 0.109 0.126 8.148 0.881 15 0.129 0.123 9.239 0.865 16 0.129 0.121 10.378 0.846

Table 6: Auto Correlation: BSE Sensex Transformations: Log transformation and differencing (1)

Lag Auto Correlation Standard Error Box-Ljung Prob. 1 0.335 0.160 4.381 0.036 2 0.220 0.158 6.323 0.042 3 0.181 0.155 7.688 0.053 4 - 0.090 0.153 8.035 0.090 5 - 0.289 0.151 11.712 0.039 6 - 0.091 0.148 12.091 0.060 7 - 0.252 0.146 15.084 0.035 8 - 0.133 0.143 15.944 0.043 9 - 0.102 0.140 16.476 0.058

10 - 0.058 0.138 16.651 0.082 11 - 0.033 0.135 16.711 0.117 12 0.155 0.132 18.075 0.113 13 - 0.029 0.130 18.124 0.153 14 - 0.002 0.127 18.124 0.201 15 - 0.011 0.124 18.133 0.256 16 - 0.112 0.121 18.990 0.269

Table7: Auto Correlation: NSE Index Log Transformation and Difference (1)

Lag Auto Correlation Standard Error Box-Ljung Prob. 1 0.300 0.160 3.509 0.061 2 0.229 0.158 5.612 0.060 3 0.139 0.155 6.416 0.093 4 - 0.054 0.153 6.540 0.162 5 - 0.351 0.151 11.982 0.035 6 - 0.112 0.148 12.552 0.051 7 - 0.317 0.146 17.301 0.016 8 - 0.102 0.143 17.811 0.023 9 - 0.118 0.140 18.516 0.030

10 0.052 0.138 18.656 0.045 11 0.029 0.135 18.703 0.067 12 0.105 0.132 19.333 0.081 13 0.011 0.130 19.340 0.113 14 0.054 0.127 19.519 0.146 15 - 0.104 0.124 20.226 0.163 16 - 0.074 0.121 20.597 0.195


Table 8: Summary of ARIMA (p, d, q) Models For BSE Sensex Regressed on FII ARIMA (p, d, q)

Standard Error

AIC ARIMA(p, d, q)

SE AIC

1, 0, 0 0.060 -94.422 1,1,7 0.043 -111.679 1, 0, 1 0.052 -104.204 1,1,9 0.043 -108.846 1, 0, 2 0.052 -103.351 2, 1, 2 0.020 -172.568 1, 0, 3 0.048 -104.108 2,0,0 0.048 -109.384 1, 0, 4 0.044 -110.282 2, 0,2 0.048 -107.941 1, 0, 5 0.044 -108.824 2, 0,4 0.044 -108.945 1, 0, 7 0.047 -104.666 2, 0,5 0.046 -106.00 1, 0,9 0.046 - 102.794 2, 1,0 0.0145 -116.793 1, 1,0 0.044 -118.460 2,1,2 0.046 -112.518 1, 1, 1 0.045 -116.736 2,1,4 0.042 -113.963 1, 1, 2 0.045 -115.276 2,1,5 0.041 -115.233 1,1,5 0.039 -117.828

Table 9: Summary of ARIMA (p, d, q) ModelsFor NSE Index Regressed on FII

ARIMA (P, d, q)

Standard Error

AIC ARIMA(P, d, q)

SE AIC

1, 0, 0 0.061 -94.439 1, 1, 9 0.043 -107.722 1, 0, 1 0.054 -101.228 2,0,0 0.043 -107.722 1, 0, 2 0.053 -101.714 2, 0, 2 0.046 -105.603 1, 0, 4 0.045 -108.117 2, 0, 4 0.044 -108.448 1, 0, 5 0.045 -107.i63 2, 0, 5 0.047 -104.429 1, 0, 7 0.045 -104.771 2, 0, 7 0.046 -102.662 1,0,9 0.049 -94.740 2,0,9 0.045 -98.954 1,1,0 0.046 -115.39 2, 0, 0 0.043 -107.722

1, 1, 1 0.046 -113.901 2, 0.2 0.046 -105.603 1, 1, 2 0.047 -112.682 2, 1, 0 0.046 -114.192 1, 1, 3 0.046 -111.854 2, 1, 2 0.043 -114.659 1, 1, 4 0.039 -118.249 2, 1, 5 0.039 -116.514 1, 1, 6 0.041 -113.605 2, 1, 7 0.042 -110.879

Table 10: Summary of ARIMA (p, d, q) ModelsFor Net FII Investment Regression BSE Sensex

ARIMA (P, d, q)

Standard Error

AIC ARIMA(P, d, q)

SE AIC

1, 0, 0 0.603 59.914 1, 1, 3 0.672 68.862 1, 0, 1 0.611 61.748 1, 1, 4 0.635 68.052 1, 0, 2 0.612 63.193 2, 0, 0 0.609 61.567 1, 0, 4 0.573 63.205 2, 0, 1 0.620 63.711 1, 0, 5 0.568 64.571 2, 0, 3 0.566 64.390 1, 0, 6 0.604 67.790 2, 0, 5 0.593 67.373 1,0,9 0.603 72.464 2, 1, 0 0.644 64.740 1, 1, 0 0.649 64.707 2, 1, 3 0.631 68.760 1, 1, 2 0.662 66.901 2, 1, 5 0.677 73.537

Table 11: Summary of ARIMA (p, d, q) Models For Net FII Investment Regressed on NSE Index

ARIMA (p, d, q)

Standard Error

AIC ARIMA(p, d, q)

SE AIC

1, 0, 0 0.599 59.419 2, 0, 1 0.616 63.242 1, 0, 1 0.606 61.285 2, 0, 4 0.577 64.617 1, 0, 3 0.599 63.574 1, 1, 0 0.644 64.307 1, 0, 4 0.569 62.677 1, 1, 2 0.641 65.740 1, 0, 5 0.583 64.759 1, 1, 5 0.618 69.798 1, 0, 7 0.598 68.587 2, 1, 0 0.633 63.709 1,0,8 0.592 71.648 2, 1, 1 0.649 65.832 2, 0, 0 0.604 61.109 2, 1, 3 0.667 69.513


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Gordon J, and P. Gupta (2003), “Portfolio flows into India: Do domestic Fundamentals Matter?” IMF Working Paper, Number WP/03/02.

Kumar, S S (2001), “Does the Indian Stock Market play to the Tune of FII Investments?: An Empirical Investigation”, The ICFAI Journal of Applied Finance, Vol.7, No.3, pp.36-44.

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behaviour of indian stock market-evidence and explanation

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