Regression of Financial Techniques Example for Free

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. 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

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

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

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

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

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

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

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.

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 content 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 A Null Hypothesis: Obs F-Statistic Prob.A A SENSEX_RETURN does not Granger Cause FII A 94 A 0.17300 0.8414 A FII does not Granger Cause SENSEX_RETURN A 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 A Null Hypothesis: Obs F-Statistic Prob.A A NSE_RETURN does not Granger Cause FII A 94 A 0.05355 0.9479 A FII does not Granger Cause NSE_RETURN A 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 A Null Hypothesis: Obs F-Statistic Prob.A A CONSUMER_DURABLES_RETURN does not Granger Cause FII A 94 A 0.30374 0.7388 A FII does not Granger Cause CONSUMER_DURABLES_RETURN A 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 over 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 A Null Hypothesis: Obs F-Statistic Prob.A A CAPITAL_GOODS_RETURN does not Granger Cause FII A 94 A 0.24639 0.7821 A FII does not Granger Cause CAPITAL_GOODS_RETURN A 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 A Null Hypothesis: Obs F-Statistic Prob.A A FMCG_RETURN does not Granger Cause FII A 94 A 0.08588 0.9178 A FII does not Granger Cause FMCG_RETURN A 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 A Null Hypothesis: Obs F-Statistic Prob.A A HEALTH_CARE_RETURN does not Granger Cause FII A 94 A 0.40226 0.6700 A FII does not Granger Cause HEALTH_CARE_RETURN A 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 A Null Hypothesis: Obs F-Statistic Prob.A A IT_RETURN does not Granger Cause FII A 94 A 0.15074 0.8603 A FII does not Granger Cause IT_RETURN A 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 information's than retail 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.

 

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