Regression Of Financial Techniques Example For Free - Free Essay Example

Sample details Pages: 14 Words: 4234 Downloads: 9 Date added: 2017/06/26 Category Finance Essay Type Analytical essay Did you like this example? The Pearson Correlation Coefficient (r) or correlation coefficient for short is a measure of the degree of linear relationship between two variables. While in regression the emphasis is on predicting one variable from the other, in correlation the emphasis is on the degree to which a linear model may describe the relationship between two variables. In regression the interest is directional, one variable is predicted and the other is the predictor; in correlation the interest is non-directional, the relationship is the critical aspect. Don’t waste time! Our writers will create an original "Regression Of Financial Techniques Example For Free" essay for you Create order The coefficient of correlation can vary from positive one (indicating a perfect positive relationship), through zero (indicating the absence of a relationship), to negative one (indicating a perfect negative relationship). As a rule of thumb, correlation coefficients between .00 and .30 are considered weak, those between .30 and .70 are moderate and coefficients between .70 and 1.00 are considered high. Correlation between NSE and FII Correlations FII NSE FII Pearson Correlation 1 .313** Sig. (2-tailed) .002 N 96 96 NSE Pearson Correlation .313** 1 Sig. (2-tailed) .002 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the NSE and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.313. This suggests that NSE and FII are showing a weak positive relationship i.e. with the increase in FII, NSE indices increases and vice versa. Correlation between Sensex and FII Correlations FII SENSEX FII Pearson Correlation 1 .306** Sig. (2-tailed) .002 N 96 96 SENSEX Pearson Correlation .306** 1 Sig. (2-tailed) .002 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the Sensex and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.306. This suggests that Sensex and FII are showing a weak positive relationship i.e. with the increase in FII, Sensex indices increases and vice versa. Correlation between Consumer Durables and FII Correlations FII CONSUMER DURABLES FII Pearson Correlation 1 .310** Sig. (2-tailed) .002 N 96 96 CONSUMER DURABLES Pearson Correlation .310** 1 Sig. (2-tailed) .002 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the Consumer Durables and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.310. This suggests that Consumer Durables and FII are showing a weak positive relationship i.e. with the increase in FII, Consumer Durables indices increases and vice versa. Correlation between Capital Goods and FII Correlations FII Capital Goods FII Pearson Correlation 1 .265** Sig. (2-tailed) .009 N 96 96 Capital Goods Pearson Correlation .265** 1 Sig. (2-tailed) .009 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the Capital Goods and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.265. This suggests that Capital Goods and FII are showing a weak positive relationship i.e. with the increase in FII, Capital Goods indices increases and vice versa. Correlation between FMCG and FII Correlations FII FMCG FII Pearson Correlation 1 .340** Sig. (2-tailed) .001 N 96 96 FMCG Pearson Correlation .340** 1 Sig. (2-tailed) .001 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the FMCG and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.340. This suggests that FMCG and FII are showing a moderate positive relationship i.e. with the increase in FII, FMCG indices increases and vice versa. Correlation between Health Care and FII Correlations FII Health_Care FII Pearson Correlation 1 .375** Sig. (2-tailed) .000 N 96 96 Health_Care Pearson Correlation .375** 1 Sig. (2-tailed) .000 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the Health Care and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.375. This suggests that Health Care and FII are showing a moderate positive relationship i.e. with the increase in FII, Health Care indices increases and vice versa. Correlation between IT and FII Correlations FII IT FII Pearson Correlation 1 .337** Sig. (2-tailed) .001 N 96 96 IT Pearson Correlation .337** 1 Sig. (2-tailed) .001 N 96 96 **. Correlation is significant at the 0.01 level (2-tailed). The above table is showing the relationship among the IT and FII which is taken from year Jan 2003 to Dec 2010. The Pearson correlation coefficient measures the linear association between two scale variables. The correlation reported in the table is 0.337. This suggests that IT and FII are showing a moderate positive relationship i.e. with the increase in FII, IT indices increases and vice versa. Linear Regression Regression between NSE FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: NSE Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .313a .098 .088 1449.98694 a. Predictors: (Constant), FII ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 2.146E7 1 2.146E7 10.205 .002a Residual 1.976E8 94 2102462.122 Total 2.191E8 95 a. Predictors: (Constant), FII b. Dependent Variable: NSE Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 3157.700 167.700 18.829 .000 FII .062 .019 .313 3.195 .002 a. Dependent Variable: NSE The above Regression is between NSE and FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.313 that is showing strong correlation between NSE and FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.098 that is 10% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models ability to explain any variation in t he dependent variable; it does not directly address the strength of that relationship. Regression between Sensex FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: SENSEX Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .306a .094 .084 4976.73956 a. Predictors: (Constant), FII b. Dependent Variable: SENSEX ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 2.409E8 1 2.409E8 9.727 .002a Residual 2.328E9 94 2.477E7 Total 2.569E9 95 a. Predictors: (Constant), FII b. Dependent Variable: SENSEX Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 10493.779 575.591 18.231 .000 FII .208 .067 .306 3.119 .002 a. Dependent Variable: SENSEX The above Regression is between Sensex FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.306 that is showing strong correlation between Sensex FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.094 that is 10% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models ability to explain any variation in the dependent variable; it does not directly address the strength of that relationship. Regression between Consumer Durables FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: CONSUMER_DURABLES Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .310a .096 .086 1535.08070 a. Predictors: (Constant), FII ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 2.351E7 1 2.351E7 9.976 .002a Residual 2.215E8 94 2356472.743 Total 2.450E8 95 a. Predictors: (Constant), FII b. Dependent Variable: CONSUMER_DURABLES Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 2636.743 177.542 14.851 .000 FII .065 .021 .310 3.159 .002 a. Dependent Variable: CONSUMER_DURABLES The above Regression is between Consumer Durables FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.310 that is showing strong correlation between Consumer Durables FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.096 that is 10% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models a bility to explain any variation in the dependent variable; it does not directly address the strength of that relationship. Regression between Capital Goods FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: Capital_Goods Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .265a .070 .060 5141.56855 a. Predictors: (Constant), FII ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1.877E8 1 1.877E8 7.101 .009a Residual 2.485E9 94 2.644E7 Total 2.673E9 95 a. Predictors: (Constant), FII b. Dependent Variable: Capital_Goods Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 7531.033 594.654 12.665 .000 FII .184 .069 .265 2.665 .009 a. Dependent Variable: Capital_Goods The above Regression is between Capital Goods FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.265 that is showing strong correlation between Capital Goods FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.070 that is 7% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models ability to exp lain any variation in the dependent variable; it does not directly address the strength of that relationship. Regression between FMCG FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: FMCG Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .340a .116 .106 734.24192 a. Predictors: (Constant), FII ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 6630011.310 1 6630011.310 12.298 .001a Residual 5.068E7 94 539111.190 Total 5.731E7 95 a. Predictors: (Constant), FII b. Dependent Variable: FMCG Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 1718.599 84.920 20.238 .000 FII .035 .010 .340 3.507 .001 a. Dependent Variable: FMCG The above Regression is between FMCG FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.340 that is showing strong correlation between FMCG FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.116 that is 12% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models ability to explain any variation in the d ependent variable; it does not directly address the strength of that relationship. Regression between Health Care FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: Health_Care Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .375a .141 .132 1132.37039 a. Predictors: (Constant), FII ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 1.976E7 1 1.976E7 15.409 .000a Residual 1.205E8 94 1282262.700 Total 1.403E8 95 a. Predictors: (Constant), FII b. Dependent Variable: Health_Care Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 3185.402 130.966 24.322 .000 FII .060 .015 .375 3.925 .000 a. Dependent Variable: Health_Care The above Regression is between Health Care FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.375 that is showing strong correlation between Health Care FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.141 that is 14% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models ability to explain any variation in the dependent variable; it does not directly address the strength of that relationship. Regression between IT FII Variables Entered/Removedb Model Variables Entered Variables Removed Method 1 FIIa . Enter a. All requested variables entered. b. Dependent Variable: IT Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .337a .114 .104 1334.92982 a. Predictors: (Constant), FII ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 2.150E7 1 2.150E7 12.065 .001a Residual 1.675E8 94 1782037.614 Total 1.890E8 95 a. Predictors: (Constant), FII b. Dependent Variable: IT Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 3256.374 154.393 21.091 .000 FII .062 .018 .337 3.473 .001 a. Dependent Variable: IT The above Regression is between IT FII the above tables are showing R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R is 0.337 that is showing strong correlation between IT FII and also R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. R ² which is 0.114 that is 11% of the variations are showing by this model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. The ANOVA table is a useful test of the models ability to explain any variation in the depend ent variable; it does not directly address the strength of that relationship. Granger Causality Correlation does not necessarily imply causation in any meaningful sense of that word. The econometric graveyard is full of magnificent correlations, which are simply spurious or meaningless. Interesting examples include a positive correlation between teachers salaries and the accident rate in the city. Economists debate correlations which are less obviously meaningless. The Granger (1969) approach to the question of whether causes is to see how much of the current can be explained by past values of and then to see whether adding lagged values of can improve the explanation. is said to be Granger-caused by if helps in the prediction of , or equivalently if the coefficients on the lagged s are statistically significant. Note that two-way causation is frequently the case; Granger causes and Granger causes . It is important to note that the statement Granger causes does not imply that is the effect or the result of . Granger causality measures precedence and information conten t but does not by itself indicate causality in the more common use of the term. Granger Causality test between FII and Sensex Pairwise Granger Causality Tests Date: 03/06/11 Time: 17:53 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  SENSEX_RETURN does not Granger Cause FII   94   0.17300 0.8414   FII does not Granger Cause SENSEX_RETURN   0.89225 0.4134 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between Sensex and Net FII because the probability is greater than 0.05 thus FII investment patterns affects the Sensex. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result Sensex fell from 17648.71 in Jan 08 to 8891.61 in Feb 09, i.e. a drop of 8757.10 which was approximately 49% in just over a year. Granger Causality test between FII and NSE Pairwise Granger Causality Tests Date: 03/06/11 Time: 21:11 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  NSE_RETURN does not Granger Cause FII   94   0.05355 0.9479   FII does not Granger Cause NSE_RETURN   0.45150 0.6381 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between NSE and Net FII because the probability is greater than 0.05. Thus, FII investment patterns affect the NSE. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result NSE fell from 5137.45 in Jan 08 to 2763.65 in Feb 09, i.e. a drop of 2373.80 which was approximately 46% in just over a year. Granger Causality test between FII and BSE Consumer Durables Pairwise Granger Causality Tests Date: 03/06/11 Time: 17:54 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  CONSUMER_DURABLES_RETURN does not Granger Cause FII   94   0.30374 0.7388   FII does not Granger Cause CONSUMER_DURABLES_RETURN   0.93166 0.3977 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between BSE Consumer Durables and Net FII because the probability is greater than 0.05. Thus, FII investment patterns affect the BSE Consumer Durables. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result BSE Consumer Durables fell from 5103.86 in Jan 08 to 1542.67 in Feb 09, i.e. a drop of 3561.19 which was approximately 70% in just ov er a year. Granger Causality test between FII and BSE Capital Goods Pairwise Granger Causality Tests Date: 03/06/11 Time: 17:55 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  CAPITAL_GOODS_RETURN does not Granger Cause FII   94   0.24639 0.7821   FII does not Granger Cause CAPITAL_GOODS_RETURN   0.11010 0.8959 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between BSE Capital Goods and Net FII because the probability is greater than 0.05. Thus, FII investment patterns affect the BSE Capital Goods. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result BSE Capital Goods fell from 16387.70 in Jan 08 to 5897.92 in Feb 09, i.e. a drop of 10489.78 which is approximately 64% in just over a year. Granger Causality test between FII and BSE FMCG Pairwise Granger Causality Tests Date: 03/06/11 Time: 17:55 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  FMCG_RETURN does not Granger Cause FII   94   0.08588 0.9178   FII does not Granger Cause FMCG_RETURN   2.04411 0.1355 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between BSE FMCG and Net FII because the probability is greater than 0.05. Thus, FII investment patterns affect the BSE FMCG. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result BSE FMCG fell from 2167.34 in Jan 08 to 2043.26 in Feb 09, i.e. a drop of only 124.08 which is approximately 6% in over a year. Granger Causality test between FII and BSE Health Care Pairwise Granger Causality Tests Date: 03/06/11 Time: 17:56 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  HEALTH_CARE_RETURN does not Granger Cause FII   94   0.40226 0.6700   FII does not Granger Cause HEALTH_CARE_RETURN   1.34690 0.2653 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between BSE Health Care and Net FII because the probability is greater than 0.05. Thus, FII investment patterns affect the BSE Health Care. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result BSE Health Care fell from 3603.52 in Jan 08 to 2597.00 in Feb 09, i.e. a drop of 1006.52 which is approximately 27% in just over a year. Granger Causality test between FII and BSE IT Pairwise Granger Causality Tests Date: 03/06/11 Time: 17:56 Sample: 1 96 Lags: 2   Null Hypothesis: Obs F-Statistic Prob. Â  IT_RETURN does not Granger Cause FII   94   0.15074 0.8603   FII does not Granger Cause IT_RETURN   2.36452 0.0999 This shows influence of FII inflow on Indian Stock Market. The probability in all the above tests is more than 0.05 at which you can reject the null hypothesis. The above table shows that there is cause and effect relationship between BSE IT and Net FII because the probability is greater than 0.05. Thus, FII investment patterns affect the BSE IT. This relationship is also depicted when during Jan 08 to Feb 09 FII net sales were for Rs. 58.751.70 Crore and as a result BSE IT fell from 2096.17 in Feb 09, i.e. a drop of 1613.94 which is approximately 43% in just over a year. FINDINGS, SUGGESTIONS AND CONCLUSION The findings Suggestions of the research are: All the dependent variables taken for the research i.e. Sensex, NSE, BSE capital goods, BSE consumer durables, BSE FMCG, BSE Health Care BSE IT have shown positive correlation with FII equity investment patterns i.e. with the increase in FII, Various Stock indices also increases and vice versa. From the data we can observe, worst bearish phase was from Jan 08 to Feb 09. During this phase FII net sales was for Rs. 58.751.70 Crore and as a result of such withdrawals: Sensex fell from 17648.71 in Jan 08 to 8891.61 in Feb 09, i.e. a drop of 8757.10 which was approximately 49% in just over a year. NSE fell from 5137.45 in Jan 08 to 2763.65 in Feb 09, i.e. a drop of 2373.80 which was approximately 46% in just over a year. BSE Consumer Durables fell from 5103.86 in Jan 08 to 1542.67 in Feb 09, i.e. a drop of 3561.19 which was approximately 70% in just over a year. BSE Capital Goods fell from 16387.70 in Jan 08 to 5897.92 in Feb 09, i.e. a drop of 10489.78 which is approximately 64% in just over a year. BSE FMCG fell from 2167.34 in Jan 08 to 2043.26 in Feb 09, i.e. a drop of only 124.08 which is approximately 6% in over a year. BSE Health Care fell from 3603.52 in Jan 08 to 2597.00 in Feb 09, i.e. a drop of 1006.52 which is approximately 27% in just over a year. BSE IT fell from 3710.11 in Jan 08 to 2096.17 in Feb 09, i.e. a drop of 1613.94 which is approximately 43% in just over a year. BSE FMCG and BSE Health Care have shown the most resistance to FII withdrawals, this is due to the fact that both of these sectors are Defensive Sectors, they have a low Beta. Besides FII Other Macroeconomic variables like inflation, Govt. policies etc also affect the various stock market indices. CONCLUSION In developing countries like India foreign capital helps in increasing the productivity of labour and to build up foreign exchange reserves to meet the current account deficit. Foreign Investment provides a channel through which country can have access to foreign capital. This research helps to find out empirical relationship among FII equity investments and stock market indices. According to Data analysis and findings, it can be concluded that FII do have significant impact on the Indian Stock Market but there are other factors like government policies, budgets, inflation, economical and political condition, etc. do also have an impact on the Indian stock market. There is a positive correlation between various stock indices and FIIs i.e. with increase in FIIs investment, various stock indices also increases and vice versa. Retail investors can also keep a watch at FIIs investment data and derive benefit from it; since FIIs have better exposure to market informations than ret ail investors. FIIs also results in increased volatility in stock markets. FIIs have their advantages as well as disadvantages; Govt. needs to regulate FIIs so as to reduce their impact on stock markets and should try to develop domestic sources of funds to enhance growth. REFRENCES Bose Suchismita and Coondoo Dipaankar (2005): The Impact of FII Regulations in India, Journal: International Journal of financial market trends. Vol 30. Publisher: MCB UP Ltd Chakrabarti (2001), Journal: Journal of foreign institution investments Vol 27. Publisher: SSRN Group Publishing Limited Khan, Mohd. Amir; Goyal, Siddarth; Ranjan, Vinit; Agrawal, Gaurav; Investigation of Causality between FIIs Investment and Stock Market Returns, International Research Journal of Finance and Economics, Issue 40, 2010. 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Coondoo, D.; The impact of FII regulations in India: A time series intervention analysis of equity flows, ICRA Bulletin, July-Dec 04. https://www.ibef.org/economy/foreigninvestors.aspx https://www.citeman.com/4005-fiis-and-their-impact-on-indian-stock-market/ https://www.sebi.gov.in/workingpaper/stock.pdf