CHAPTER-5 DATA ANALYSIS AND INTERPRETATION

Transcription

1 CHAPTER-5 DATA ANALYSIS AND INTERPRETATION INDEX Serial No. Name of Topic Page No Introduction Bombay Stock Exchange BSE SENSEX and Their Weight in INDEX Basis for Calculating FII Investment Limit Need for Foreign Capital Registered FIIs In India Route of FIIs Investment In India Investment Pattern of FII s in Indian Stock Market Data Analysis and Interpretation Techniques FII s Month wise Total Investment and Its Impact on SENSEX Year wise Equity Investment by FII and Its Impact on SENSEX Year wise Debt Investment by FII and Its Impact on SENSEX Year wise Total Investment by FII and Its Impact on SENSEX Summary Of the Study Conclusion 271 References

2 5.1 INTRODUCTION Capital is considered to be very important growth in any economy. In case of developing country like India Domestic capital is not sufficient to fulfil the requirement of economy. In that case foreign capital plays a very important role. Foreign Capital comes in two forms Foreign Direct Investment (FDI) and Foreign Institutional Investment (FII). FDI is considered as a more stable form of foreign capital as compared to FII. But, FII inflows and outflows directly create impact on stock market. Hence FIIs have emerged as movers and shakers of Indian Stock Market. This Research Project examines the trend and pattern of FII flow in India and also examines the relationship between FII and Sensex. Institutional Investor is any investor or investment fund that is from or registered in a country outside of the one in which it is currently investing. Institutional investors include hedge funds, insurance companies, pension funds and mutual funds. The growing Indian market had attracted the foreign investors, which are called Foreign Institutional Investors (FII) to Indian equity market, and in this paper, we are trying a simple attempt to explain the impact and extent of foreign institutional investors in Indian stock market. Role of FII has increased and changed the face of Indian Stock Market. It has brought both qualitative and quantitative change. It had also increased the breadth and depth of market. Although the Foreign institutional investors (FIIs), whose investments are often called 'hot money' because they can be pulled out at any time, have been blamed for large and concerted withdrawals of capital from the country at the time of recent financial crisis, they have emerged as important players in the Indian capital market. With over 20 million shareholders, India has the third largest investor base in the world after the USA and Japan. Over 9,000 companies are listed on the stock exchanges, which are serviced by approximately 7,500 stockbrokers. The Indian capital market is significant in terms of the degree of development, volume of trading and its tremendous growth potential. The Foreign Institutional Investors (FIIs) have emerged as remarkable players in the Indian stock market and their growing contribution adds as an important feature of Capital is considered to be very important growth in any economy. In case of developing country like India Domestic capital is not sufficient to fulfill the 209

3 requirement of economy. In that case foreign capital plays a very important role. Foreign Capital comes in two forms Foreign Direct Investment (FDI) and Foreign Institutional Investment (FII). FDI is considered as a more stable form of foreign capital as compared to FII. But, FII inflows and outflows directly create impact on stock market. Hence FIIs have emerged as movers and shakers of Indian Stock Market. This Research Project examines the trend and pattern of FII flow in India and also examines the relationship between FII and Sensex. Institutional Investor is any investor or investment fund that is from or registered in a country outside of the one in which it is currently investing. Institutional investors include hedge funds, insurance companies, pension funds and mutual funds. The growing Indian market had attracted the foreign investors, which are called Foreign Institutional Investors (FII) to Indian equity market, and in this paper, we are trying a simple attempt to explain the impact and extent of foreign institutional investors in Indian stock market. Role of FII has increased and changed the face of Indian Stock Market. It has brought both qualitative and quantitative change. It had also increased the breadth and depth of market. Although the Foreign institutional investors (FIIs), whose investments are often called 'hot money' because they can be pulled out at any time, have been blamed for large and concerted withdrawals of capital from the country at the time of recent financial crisis, they have emerged as important players in the Indian capital market. With over 20 million shareholders, India has the third largest investor base in the world after the USA and Japan. Over 9,000 companies are listed on the stock exchanges, which are serviced by approximately 7,500 stockbrokers. The Indian capital market is significant in terms of the degree of development, volume of trading and its tremendous growth potential. FII is allowed to enter into our country only through stock exchanges either in the form of equity or debt. Thus it makes an impact on the rise or fall of SENSEX, since FII is allowed to be purchased or sold daily. The daily transaction of FII is the reason behind the volatility in the stock markets and has strong impact on the various macro-economic variables and the economy as a whole. Most of the under developed countries suffer from low level of income and capital accumulation. Though, despite this shortage of investment, these countries have 210

4 developed a strong urge for industrialization and economic development. As we know the need for Foreign capital arises due to shortage from domestic side and other reasons. Indian economy has experienced the problem of capital in many instances. While planning to start the steel companies under government control, due to shortage of resources it has taken the aid of foreign countries. Likewise we have received aid from Russia, Britain and Germany for establishing Bhilai, Rourkela and Durgapur steel plants. The present study is a modest attempt to study the impact of Foreign Institutional Investment on Indian Stock Market with special reference to BSE SENSEX India. It is observed that the FIIs investment has shown significant improvement in the liquidity of stock prices of both BSE and NSE. However, it is believed that there exists a high degree of positive correlation between FIIs investment and market capitalization, FIIs investment and BSE & NSE indices, revealing that the liquidity and volatility was highly influenced by FIIs flows. Further, it is also proved that FIIs investment was a significant factor for high liquidity and volatility in the capital market prices. The present study is proposed to analyse the impact of FIIs on Indian capital market with special reference to BSE. An investor or investment fund that is from or registered in a country outside of the one in which it is currently investing is known as Foreign Institutional Investment and investors are known as Foreign Institutional Investors. Institutional investors include hedge funds, insurance companies, pension funds and mutual funds. The term is used most commonly in India to refer to outside companies investing in the financial markets of India. International institutional investors must register with the Securities and Exchange Board of India to participate in the market. One of the major market regulations pertaining to FIIs involves placing limits on FII ownership in Indian companies. The Foreign Institutional Investors (FIIs) have emerged as remarkable players in the Indian stock market and their growing contribution adds as an important feature of the development of stock markets in India. As a result, the Indian Stock Markets have reached new heights and became more volatile making the researches work in this 211

5 dimension of establishing the link between FIIs and Stock Market volatility. Hence, it s an interesting topic to ascertain the role of FIIs in Indian Stock Market. After the launch of the reforms, foreign institutional investors (FIIs) from September 14, 1992, with suitable restrictions, were permitted to invest in all securities traded on the primary and secondary markets, including shares, debentures and warrants issued by companies which were listed or were to be listed on the Stock Exchanges in India and in schemes floated by domestic mutual funds. A positive contribution of the FIIs has been their role in improving the stock market infrastructure and the SEBI assured its contribution towards its development. Hence, in this age of transnational capitalism, a significant amount of capital is flowing from developed world to emerging economies. Positive fundamentals combined with fast growing markets have made India an attractive destination for foreign institutional investors (FIIs). Although the Foreign institutional investors (FIIs), whose investments are often called 'hot money' because they can be pulled out at any time, have been blamed for large and concerted withdrawals of capital from the country at the time of recent financial crisis, they have emerged as important players in the Indian capital market. Investment limits for FII Foreign Institutional Investors (FIIs) are allowed to invest in the primary and secondary capital markets in India through the portfolio investment scheme (PIS). Under this scheme, FIIs can acquire shares/debentures of Indian companies through the stock exchanges in India. The ceiling for overall investment for FIIs is 24 percent of the paid up capital of the Indian company. The limit is 20 per cent of the paid up capital in the case of public sector banks, including the State Bank of India. The ceiling of 24 per cent for FII investment can be raised up to sectorial cap/statutory ceiling, subject to the approval of the board and the general body of the company passing a special resolution to that effect. And the ceiling of 10 per cent for NRIs/PIOs can be raised to 24 per cent subject to the approval of the general body of the company passing a resolution to that 212

6 effect. The ceiling for FIIs is independent of the ceiling of 10/24 per cent for NRIs/PIOs. The Reserve Bank of India monitors the ceilings on FII/NRI/PIO investments in Indian companies on a daily basis. For effective monitoring of foreign investment ceiling limits, the Reserve Bank has fixed cut-off points that are two percentage points lower than the actual ceilings. The cut-off point, for instance, is fixed at 8 per cent for companies in which NRIs/ PIOs can invest up to 10 per cent of the company s paid up capital. The cut-off limit for companies with 24 per cent ceiling is 22 per cent and for companies with 30 per cent ceiling, is 28 per cent and so on. Similarly, the cut-off limit for public sector banks (including State Bank of India) is 18 per cent. Once the aggregate net purchases of equity shares of the company by FIIs/NRIs/PIOs reach the cut-off point, which is 2% below the overall limit, the Reserve Bank cautions all designated bank branches so as not to purchase any more equity shares of the respective company on behalf of FIIs/NRIs/PIOs without prior approval of the Reserve Bank. The link offices are then required to intimate the Reserve Bank about the total number and value of equity shares/convertible debentures of the company they propose to buy on behalf of FIIs/ NRIs/PIOs. On receipt of such proposals, the Reserve Bank gives clearances on a first-come first served basis till such investments in companies reach 10 / 24 / 30 / 40/ 49 per cent limit or the sectorial caps/statutory ceilings as applicable. On reaching the aggregate ceiling limit, the Reserve Bank advises all designated bank branches to stop purchases on behalf of their FIIs/NRIs /PIOs clients. The Reserve Bank also informs the general public about the.caution. and the.stop purchase. in these companies through a press release. Form 2 nd June 2014 the new foreign portfolio investor (FPI) regulations, which replace the two-decade old foreign institutional investors (FII) regime, in the Indian market has got operational now. From now on, new overseas investors wanting to enter the Indian market will be registered under the FPI Regulations. 213

7 5.2 BOMBAY STOCK EXCHANGE (BSE): The Stock Exchange, Mumbai (BSE) came out with a stock index in 1986, which is known as BSE Sensex. The base year of BSE Sensex is and the base value is 100. The exchange is the largest (in terms of market capitalization) in South Asia. In terms of the number of companies traded on the exchange (approximately 4700), it is the largest in the world. The main index which tracks the performance of the exchange is the BSE Sensex. 5.3 BSE SENSEX AND THEIR WEIGHT IN INDEX: A value weighted stock market index, which tracks the performance of the 30 largest stocks on the Bombay Stock Exchange. The 30 stocks are chosen at random times, whenever the market has significantly changed enough to warrant the changes, and are chosen by their value of free- float shares. Although the index only tracks a very small percentage of the total stocks traded at the BSE, the index typically comprises about one- fifth of the market capitalization of the entire stock exchange. 214

8 Sr. No. Table :5.1 Listed Companies On BSE SENSEX And Their Weight In Index NAME OF COMPANIES SECTOR Adj. factors Weight in INDEX % 1. ACC Housing Related BHEL Capital Goods BHARTI AIRTEL Telecom DLF UNIVERSAL LTD. Housing Related GRASIM INDUSTRIES Diversified HDFC Finance HDFC BANK Finance HERO HONDA MOTORS LTD. Transport Equipment HINDALCO INDUSTRIES LTD. Metal, Metal Product & Mining HINDUSTAN LEVEL LIMMITED FMCG ICICI BANK Finance INFOSYS Information Technology ITC LIMITED FMCG JAIPRAKASH ASSOCIATES Housing Related LARSEN & TOUBRO Capital Goods MAHINDRA & MAHINDRA LTD. Transport Equipment MARUTI SUZUKI Transport Equipment NIIT TECHNOLOGIES Information Technology NTPC Power NIIT Information Technology ONGC Oil & Gas RELIATANCE COMMUNICATION Telecom RELIANCE INDUSTRIES Oil & Gas RELIANCE INFRASTRUCTURE Power STATE BANK OF INDIA Finance STERLITE INDUSTRIES Metal, Metal Product & Mining SUN PHARMA INDUSTRIES Healthcare TATA CONSULTANCY SERVICES Information Technology TATA MOTORS Transport Equipment TATA POWERS Power TATA STEELS Metal, Metal Product & Mining WIPRO Information Technology BASIS FOR CALCULATING FII INVESTMENT LIMIT: Investment limit by all registered FIIs or sub accounts in primary or secondary markets under Portfolio Investment Scheme is subject to a ceiling of 24% of issued share capital of a company. The limit can be extended up to 49% per sectorial cap if the general body of the company approves it. 215

9 5.5 NEED FOR FOREIGN CAPITAL The need of foreign investment/ foreign capital arises due to the following reasons: 1. Development of basic infrastructure: The development of any economy depends on the available infrastructure in that country. The infrastructure facilities such as Roads, Railways, sea ports, warehouses banking services and insurance services are the prominent players. Due to long gestation period naturally individuals will not come forward to invest in infrastructure industries. Government of India could not able to raise necessary investments. To fill the gap foreign capital is highly suitable. 2. Rapid industrialization: The need for foreign capital arises due to the policy initiatives of the Government to intensify the process of industrialization. For instance the government of India is gradually opening the sectors to foreign capital to expand the industrial sector. 3. To undertake the initial risk: Many developing countries suffer from severe scarcity of private investors. The risk problem can be diverted to the foreign capitalists by allowing them to invest. As we know the Indians are comparatively risk averse. The same risk can be transferred to foreign investors by allowing their investment where risk is more. 4. Global imperatives: Globalization is the order of the day. The international agreements between countries are also the reason for the foreign capital. The multinational companies are expanding their presence to many countries; while they are entering into the foreign countries they will bring their capital. The principles of WTO and other regional associations are binding the member countries to allow foreign capital. 216

10 5. Comparative advantage: The variations in the cost of capital like interest rate are also one of the important factors which resulting in approaching foreign capital. For example; Interest rates are high in India compared with developed economies. To reduce the cost of capital, companies/organizations are now looking for foreign capital. In several countries the interest rates are very low as 1% to 3%, where as in some countries the interest rates are very high as 8% to 10% per annum. 6. To remove the technological gap: The developing countries have very low level of technology compared to the developed countries. However, these developing countries possess a strong urge for Industrialization to develop their economies and to wriggle out of the low level equilibrium trap in which they are caught. This raises the necessity for importing technology from the advanced countries. That technology usually comes with foreign capital when it assumes the form of private foreign investment or foreign collaboration. 5.6 REGISTERED FIIS IN INDIA The Indian capital market opened its doors to foreign investors in The new Industrial policy of the government has initiated many measures to attract foreign capital. The following table highlights the registered FIIs in India Table : 5.2 Registered FIIs in India Year Regd. FII Year Regd. FII Year Regd. FII Source: 217

11 5.7 ROUTE OF FII S INVESTMENT IN INDIA: FIIs have two route of making investment in Indian capital market: 1. On its own behalf. 2. On behalf of sub account (Sub-account means any person residents outside India, on whose behalf investments are proposed to be made in India by a FII investor and who is registered as a sub-account under SEBI. For each sub-account a separate registration is granted. The sub-account should fall into the categories, namely: a Broad Based Fund or Portfolio incorporated or established outside India; a Proprietary Fund; a Foreign Corporate (A foreign corporate means a body corporate incorporated outside India which has listed securities on any stock exchange outside India with an asset base of not less than $ 2 billion US dollars and had an average net profit of not less than fifty million US dollars during the three financial years preceding the date of the application.) or an Foreign individual; (A Foreign Individual has a net worth of not less than $ 50 million, holds the passport of a foreign country for 5 years, hold a certificate of good standing from a bank and client of the concern FII for last 3 years. The NRIs shall not be eligible for opening a subaccount) or a University Fund, Endowment, Foundation, Charitable Trust or Charitable society (Securities and Exchange Board of India (Foreign Institutional Investors) Regulations, 1995). 5.8 INVESTMENTS PATTERN OF FII S IN INDIAN STOCK MARKET: (i) A FII could invest in the primary and secondary markets including shares, debentures and warrants of the companies unlisted, listed or to be listed on a recognized stock exchange in India. These could also invest in units of mutual fund whether listed or unlisted; dated government securities; listed derivatives; commercial paper and security receipts. (ii) Total investments in equity, convertible debentures (CDs) and tradable warrants on its own account or on behalf of sub-account shall not be less than 70% of the aggregate of total investment by the FIIs. A foreign corporate or individual shall not be eligible to invest through the 100% debt route. 218

12 (iii) FIIs can t invest in security receipts on behalf of its sub-account. FIIs could invest or transact in the Indian securities market only on the basis of taking and giving delivery of securities purchased or sold. They could enter into short selling transaction as specified by SEBI. RBI shall grant permission to make transactions in government securities like commercial paper (CP) and T-Bill. (iv) FII investment in equity shares of a company on behalf of its sub-accounts shall not exceed 10% of the total issued capital provided further that foreign corporate or individuals, each of such sub-account shall not more than 5% of the issued capital. (v) FII could lend or borrow securities on behalf of sub account. No FII could issue or deal in off-shore derivative instruments, directly or indirectly, unless such off-shore derivative instruments are issued only to persons who are regulated by an appropriate foreign regulatory authority and know your customer (KYC) norms have been fulfilled. No sub-account shall issue off-shore derivative instruments. Off-shore derivative instruments are issued by FIIs against securities held by it that are listed or proposed to be listed on any recognized stock exchange in India. (vi) FIIs shall disclose information regarding the terms of and parties to off-shore derivative instruments such as Participatory Notes (PNs), Equity Linked Notes or any other such instruments enter into by it or its subaccount units or affiliates relating to any securities listed or proposed to be listed in Indian Stock Exchange. (Securities and Exchange Board of India Foreign Institutional Investors Regulations, 1995). 219

13 5.9 DATA ANALYSIS AND INTERPRETATION TECHNIQUES Present study is based on the impact on SENSEX on FII s Investment and this study is considering the month wise data from to for the first six month of Financial year and from to for the last six month of financial year for the FII s Investment in TOTAL INVESTMENT (Equity plus Debt) and its impact on SENSEX Regression Analysis is considered as a statistical analysis for the present study. Step-By-Step on How the Regression Model interpreted in the present Study Regression Output in the present study is in four Parts and they are as follows (a) Overall Regression Equation s Accuracy (Multiple R; R Square and Adjusted R Square) (b) Probability that this output was not by chance (ANOVA Significance of F) (c) Individual Regression Coefficient and Y-Intercept Accuracy (d) Visual Analysis of Residuals (Graphical Presentation) (a) Overall Regression Equation s Accuracy (Multiple R; R Square and Adjusted R Square) (i) Multiple R: This is the Correlation Coefficient. it show how strong the linear relationship is. For Example, a value of 1 mean a perfect positive relationship and a value of zero means no relationship at all. (ii) R Square: This is r 2, the Co efficient of Determination which shows that how many points fall on the regression model that means how well the regression line approximates the real data. (iii) Adjusted R Square: This is quoted most often when explaining the accuracy of the regression equation. Adjusted R Square is more conservative the R Square because it is always less than R Square. Another reason that Adjusted R Square is quoted more often is that when new input variables are added to the Regression analysis, Adjusted R Square increases only when the new input 220

14 variable makes the Regression equation more accurate (improves the Regression Equation s ability to predict the output). R Square always goes up when a new variable is added, whether or not the new input variable improves the Regression equation s accuracy. (iv) Standard Error of the Regression: An estimate of the standard deviation of the error for population mean. This is not the same as the Standard Error in Descriptive Statistics! The Standard Error of the regression is the precision that the regression coefficient is measured; if the co efficient is large compared to the standard error, then the coefficient is probably different from zero. (v) Observation: Number of observations in the sample. (b) Probability that this output was not by chance (ANOVA Significance of F) (i) SS: Sum of Squares for Regression (ii) MS: Regression SS/ Regression Degree of freedom (iii) Residual MS = Mean Squared error (Residual SS/ Residual df) (iv) Fc: Overall F test for Null Hypothesis (v) Significance F: This indicates the probability that the Regression output could have been obtained by chance. A small significance of F confirms the validity of the Regression output. For example, if Significance of F = 0.030, there is only a 3 % chance that the Regression output was merely a chance occurrence. This part of output is rarely used, compared to the regression output of Overall Regression Equation s Accuracy (Multiple R; R Square and Adjusted R Square) as narrated above. It splits the sum of squares into individual components like Residual Sum of Squares, so it can be harder to use the statistics in any meaningful way. If it is doing for linear regression model and no desire to develop into individual components like in multi regression model in such case it is proper to skip this section of the output. 221

15 (c) Individual Regression Coefficient and Y-Intercept Accuracy This section of Regression analysis shows specific information about the components chose for data analysis. Therefore the first column shows data input or say x-variable as shown in the present study it is FII s investment while other columns are as follows (i) Coefficient: It shows the Least Square Estimates. (ii) Standard Error: It shows the Least Square Estimates for Standard Error (iii) T-Statistic: T Statistic for the Null Hypothesis Vs the Alternate Hypothesis (iv) P Value: Shows the P-Value for Hypothesis Test (v) Lower 95%: It Shows the Lower boundary for the confidence interval (vi) Upper 95%: It Shows the Upper boundary for the confidence interval The most important aspect of this section is that it show the linear regression equation (model) as y = mx + b (it means y = slope * x + Intercept) which is helpful for estimation for future time series. (d) Visual Analysis of Residuals (Graphical Presentation) The residual are the difference between the Regression s predicted value and the actual value of the output variable. One can quickly plot the Residuals on a scatterplot chart. Look the patterns in the scatterplot in the chart. The more random (without patterns) and centred around zero the residuals appear to be, the more likely it is that the Regression equation is valid. Hypothesis A hypothesis is a special proposition, formulated to be tested in a certain given situation as a part of research which states what the researcher is looking for. 222

16 5.10 FII S MONTHWISE TOTAL INVESTMENT (EQUITY PLUS DEBT) AND ITS IMPACT ON SENSEX For the present Study month wise data has considered for FII s Total Investment and its impact on SENSEX for the period from to for first half of the financial year while to for the second half of the financial year for to consider the latest data for the study. Hypothesis for month wise data applied for all the 12 months is as follows Null Hypothesis H 0 Alternative Hypothesis H 1 : There is no significant impact on SENSEX for FII s total investment in India. : There is significant impact on SENSEX for FII s total investment in India (1) APRIL Table -5.3 APRIL To Year FII s Total SENSEX Investment

17 Table 5.4 Regression Statistics for the Month of April to Multiple R R Square Adjusted R Square Standard Error Observations 16 (i) Multiple R: This is the Correlation Coefficient. it show how strong the linear relationship is. For the month of April to the value of it shows that means 72.60% relationship exist on FII s Investment on SENSEX for the month of April for the period to (ii) R Square: This is r 2, the Co efficient of Determination which shows that how many points fall on the regression model that means how well the regression line approximates the real data. For the month of April to the value of it shows that means 52.70% variation of y- values around the mean are explained by the x-values. In other words, 52.70% of the values fit the model. (iii) Adjusted R Square: This is quoted most often when explaining the accuracy of the regression equation. Adjusted R Square is more conservative the R Square because it is always less than R Square. For the month of April to the value of it shows that means 49.06% (iv) Standard Error of the Regression: An estimate of the standard deviation of the error for population mean. This is not the same as the Standard Error in Descriptive Statistics! The Standard Error of the regression is the precision that the regression coefficient is measured which is shown as for the month of April to

18 (v) Observation: Number of observations in the sample is 16 Table 5.5 ANOVA for Significance f for APRIL to DF SS MS Fc Ft Regression Residual Total Significance F: This indicates the probability that the Regression output could have been obtained by chance. A small significance of F say Ft confirms the validity of the Regression output. For the month of April to Fc = while Ft is that means 47.60% SENSEX have significant impact on FII s Investment in India. Table: 5.6 SENSEX Intercept Accuracy for Regression Model for APRIL to Coefficients Standard Error T Statistics P-Value Lower 95% Upper 95% Intercept X Variable The most important aspect of this section is that it show the linear regression equation (model) as y = mx + b (it means y = slope * x + Intercept) as per above Table slope m = while intercept b = Hence Linear Regression Equation is for the month of April is y = x this model is base for time series analysis for any period for the month of April. 225

19 Y = SENSEX CHAPTER-5 DATA ANALYSIS AND INTERPRETATION Visual Analysis of Residuals (Graphical Presentation) Chart -5.1 Residual Chart for April to X = FII's Investment The residual are the difference between the Regression s predicted value and the actual value of the output variable. it is plotted the Residuals on a scatterplot chart. Look the patterns in the scatterplot in the above chart. The more random (without patterns) and centred around zero the residuals appear to be, the more likely it is that the Regression equation is valid which can be shown from the above chart. (2) MAY Table 5.7 MAY To Year FII s SENSEX Total Investment

20 Table 5.8 Regression Statistics for the Month of MAY to Multiple R R Square Adjusted R Square Standard Error Observations 16 (i) Multiple R: This is the Correlation Coefficient. it show how strong the linear relationship is. For the month of May to the value of it shows that means 6.45% relationship exist on FII s Investment on SENSEX for the month of May for the period to (ii) R Square: This is r 2, the Co efficient of Determination which shows that how many points fall on the regression model that means how well the regression line approximates the real data. For the month of May to the value of it shows that means 0.41% variation of y- values around the mean are explained by the x-values. In other words, 0.41% of the values fit the model. (iii) Adjusted R Square: This is quoted most often when explaining the accuracy of the regression equation. Adjusted R Square is more conservative the R Square because it is always less than R Square. For the month of May to the value of it shows that means -6.70% (iv) Standard Error of the Regression: An estimate of the standard deviation of the error for population mean. This is not the same as the Standard Error in Descriptive Statistics! The Standard Error of the regression is the precision that the regression coefficient is measured which is shown as for the month of May to (v) Observation: Number of observations in the sample is

21 Table 5.9 ANOVA for Significance f for MAY DF SS MS Fc Ft Regression Residual Total Significance F: This indicates the probability that the Regression output could have been obtained by chance. A small significance of F say Ft confirms the validity of the Regression output. For the month of May to Fc = while Ft is that means 81.24% SENSEX have significant impact on FII s Investment in India. Table: 5.10 SENSEX Intercept Accuracy for Regression Model for MAY to Coefficients Standard Error T Statistics P-Value Lower 95% Upper 95% Intercept X Variable The most important aspect of this section is that it show the linear regression equation (model) as y = mx + b (it means y = slope * x + Intercept) as per above Table slope m = while intercept b = Hence Linear Regression Equation is for the month of May is y = x this model is base for time series analysis for any period for the month of May. 228

22 Y= SENSEX CHAPTER-5 DATA ANALYSIS AND INTERPRETATION Visual Analysis of Residuals (Graphical Presentation) Chart-5.2 Residual Chart for MAY to X= FII's Investment The residual are the difference between the Regression s predicted value and the actual value of the output variable. it is plotted the Residuals on a scatterplot chart. Look the patterns in the scatterplot in the above chart. The more random (without patterns) and centred around zero the residuals appear to be, the more likely it is that the Regression equation is valid it can be seen that Residual patterns is not more centred around zero in above chart that means input value varies during the period. (3) JUNE Table 5.11 JUNE To Year FII s Total SENSEX Investment

23 Table 5.12 Regression Statistics for the Month of JUNE to Multiple R R Square Adjusted R Square Standard Error Observations 16 (i) Multiple R: This is the Correlation Coefficient. it show how strong the linear relationship is. For the month of June to the value of it shows that means 6.36% relationship exist on FII s Investment on SENSEX for the month of June for the period to (ii) R Square: This is r 2, the Co efficient of Determination which shows that how many points fall on the regression model that means how well the regression line approximates the real data. For the month of June to the value of it shows that means 0.40% variation of y- values around the mean are explained by the x-values. In other words, 0.40% of the values fit the model. (iii) Adjusted R Square: This is quoted most often when explaining the accuracy of the regression equation. Adjusted R Square is more conservative the R Square because it is always less than R Square. For the month of June to the value of it shows that means -6.71% (iv) Standard Error of the Regression: An estimate of the standard deviation of the error for population mean. This is not the same as the Standard Error in Descriptive Statistics! The Standard Error of the regression is the precision that the regression coefficient is measured which is shown as for the month of June to (v) Observation: Number of observations in the sample is

24 Table 5.13 ANOVA for Significance f for JUNE DF SS MS Fc Ft Regression Residual Total Significance F: This indicates the probability that the Regression output could have been obtained by chance. A small significance of F say Ft confirms the validity of the Regression output. For the month of June to Fc = while Ft is that means 81.50% SENSEX have significant impact on FII s Investment in India. Table: 5.14 SENSEX Intercept Accuracy for Regression Model for JUNE to Coefficien ts Standard Error T Statistics P-Value Lower 95% Upper 95% Intercept X Variable The most important aspect of this section is that it show the linear regression equation (model) as y = mx + b (it means y = slope * x + Intercept) as per above Table slope m = while intercept b = Hence Linear Regression Equation is for the month of June is y = x this model is base for time series analysis for any period for the month of May. 231

25 Y = SENSEX CHAPTER-5 DATA ANALYSIS AND INTERPRETATION Visual Analysis of Residuals (Graphical Presentation) Chart 5.3 Residual Chart for JUNE to X FII's Investment The residual are the difference between the Regression s predicted value and the actual value of the output variable. It is plotted the Residuals on a scatterplot chart. Look the patterns in the scatterplot in the above chart. The more random (without patterns) and centred around zero the residuals appear to be, the more likely it is that the Regression equation is valid it can be seen that Residual patterns is not more centred around zero but shows on both side in above chart that means input value varies during the period. (4) JULY Table 5.15 JULY To Year FII s Total SENSEX Investment

26 Table 5.16 Regression Statistics for the Month of JULY to Multiple R R Square Adjusted R Square Standard Error Observations 16 (i) Multiple R: This is the Correlation Coefficient. it show how strong the linear relationship is. For the month of July to the value of it shows that means 27.05% relationship exist on FII s Investment on SENSEX for the month of July for the period to (ii) R Square: This is r 2, the Co efficient of Determination which shows that how many points fall on the regression model that means how well the regression line approximates the real data. For the month of July to the value of it shows that means 7.32% variation of y- values around the mean are explained by the x-values. In other words, 7.32% of the values fit the model. (iii) Adjusted R Square: This is quoted most often when explaining the accuracy of the regression equation. Adjusted R Square is more conservative the R Square because it is always less than R Square. For the month of July to the value of it shows that means 0.70% (iv) Standard Error of the Regression: An estimate of the standard deviation of the error for population mean. This is not the same as the Standard Error in Descriptive Statistics! The Standard Error of the regression is the precision that the regression coefficient is measured which is shown as for the month of July to (v) Observation: Number of observations in the sample is

27 Table 5.17 ANOVA for Significance f for JULY DF SS MS Fc Ft Regression Residual Total Significance F: This indicates the probability that the Regression output could have been obtained by chance. A small significance of F say Ft confirms the validity of the Regression output. For the month of July to Fc = while Ft is that means % SENSEX have significant impact on FII s Investment in India. Table: 5.18 SENSEX Intercept Accuracy for Regression Model for JULY to Coefficients Standard Error T Statistics P-Value Lower 95% Upper 95% Intercept X Variable The most important aspect of this section is that it show the linear regression equation (model) as y = mx + b (it means y = slope * x + Intercept) as per above Table slope m = while intercept b = Hence Linear Regression Equation is for the month of June is y = x this model is base for time series analysis for any period for the month of July. 234

28 Y = SENSEX CHAPTER-5 DATA ANALYSIS AND INTERPRETATION Visual Analysis of Residuals (Graphical Presentation) Chart 5.4 Residual Chart for JULY to X = FII's Investment The residual are the difference between the Regression s predicted value and the actual value of the output variable. It is plotted the Residuals on a scatterplot chart. Look the patterns in the scatterplot in the above chart. The more random (without patterns) and centred around zero the residuals appear to be, the more likely it is that the Regression equation is valid it can be seen that Residual patterns is centred around zero the residuals appear to be, the more likely it is that the Regression equation is valid which can be shown from the above chart during the period. (4) AUGUST Table 5.19 AUGUST To Year FII s SENSEX Total Investment

29 Table 5.20 Regression Statistics for the Month of AUGUST to Multiple R R Square Adjusted R Square Standard Error Observations 16 (i) Multiple R: This is the Correlation Coefficient. it show how strong the linear relationship is. For the month of August to the value of it shows that means 25.38% relationship exist on FII s Investment on SENSEX for the month of August for the period to (ii) R Square: This is r 2, the Co efficient of Determination which shows that how many points fall on the regression model that means how well the regression line approximates the real data. For the month of August to the value of it shows that means 6.44% variation of y- values around the mean are explained by the x-values. In other words, 6.44% of the values fit the model. (iii) Adjusted R Square: This is quoted most often when explaining the accuracy of the regression equation. Adjusted R Square is more conservative the R Square because it is always less than R Square. For the month of August to the value of it shows that means -0.24% (iv) Standard Error of the Regression: An estimate of the standard deviation of the error for population mean. This is not the same as the Standard Error in Descriptive Statistics! The Standard Error of the regression is the precision that the regression coefficient is measured which is shown as for the month of August to (v) Observation: Number of observations in the sample is

30 Table 5.21 ANOVA for Significance f for AUGUST DF SS MS Fc Ft Regression Residual Total Significance F: This indicates the probability that the Regression output could have been obtained by chance. A small significance of F say Ft confirms the validity of the Regression output. For the month of August to Fc = while Ft is that means % SENSEX have significant impact on FII s Investment in India. Table: 5.22 SENSEX Intercept Accuracy for Regression Model for AUGUST to Coefficients Standard Error T Statistics P-Value Lower 95% Upper 95% Intercept X Variable The most important aspect of this section is that it show the linear regression equation (model) as y = mx + b (it means y = slope * x + Intercept) as per above Table slope m = while intercept b = Hence Linear Regression Equation is for the month of August is y = x this model is base for time series analysis for any period for the month of August. 237

31 Y = SENSEX CHAPTER-5 DATA ANALYSIS AND INTERPRETATION Visual Analysis of Residuals (Graphical Presentation) Chart 5.5 Residual Chart for AUGUST to X = FII's Investment The residual are the difference between the Regression s predicted value and the actual value of the output variable. It is plotted the Residuals on a scatterplot chart. Look the patterns in the scatterplot in the above chart. The more random (without patterns) and centred around zero the residuals appear to be, the more likely it is that the Regression equation is valid it can be seen that Residual patterns is centred around zero the residuals appear to be, the more likely it is that the Regression equation is valid which can be shown from the above chart during the period. (6) SEPTEMBER Table 5.23 SEPTEMBER To Year FII s Total SENSEX Investment

32 Table 5.24 Regression Statistics for the Month of SEPTEMBER to Multiple R R Square Adjusted R Square Standard Error Observations 16 (i) Multiple R: This is the Correlation Coefficient. it show how strong the linear relationship is. For the month of August to the value of it shows that means 26.87% relationship exist on FII s Investment on SENSEX for the month of September for the period to (ii) R Square: This is r 2, the Co efficient of Determination which shows that how many points fall on the regression model that means how well the regression line approximates the real data. For the month of September to the value of it shows that means 7.22% variation of y- values around the mean are explained by the x-values. In other words, 7.22% of the values fit the model. (iii) Adjusted R Square: This is quoted most often when explaining the accuracy of the regression equation. Adjusted R Square is more conservative the R Square because it is always less than R Square. For the month of September to the value of it shows that means 0.59% (iv) Standard Error of the Regression: An estimate of the standard deviation of the error for population mean. This is not the same as the Standard Error in Descriptive Statistics! The Standard Error of the regression is the precision that the regression coefficient is measured which is shown as for the month of September to (v) Observation: Number of observations in the sample is