Determinants of foreign portfolio investment and their effects on the Indian stock market

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This article aims at focusing on the facts in the financial series of Foreign Portfolio Investment (FPI) and its determinants. The study considers Exchange Rate, Consumer Price Index, Index of Industrial production, SENSEX, NIFTY and Foreign Exchange Reserve as determinants.

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International Journal of Management (IJM) Volume 8, Issue 3, May– June 2017, pp.105–115, Article ID: IJM_08_03_011 Available online at http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=8&IType=3 Journal Impact Factor (2016): 8.1920 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6502 and ISSN Online: 0976-6510 © IAEME Publication DETERMINANTS OF FOREIGN PORTFOLIO INVESTMENT AND THEIR EFFECTS ON THE INDIAN STOCK MARKET S. Raghavan Ph. D Research Scholar in Commerce, Alagappa University, Karaikudi, India Dr. M. Selvam Research Guide, International Business, Alagappa University, Karaikudi, India ABSTRACT This article aims at focusing on the facts in the financial series of Foreign Portfolio Investment (FPI) and its determinants. The study considers Exchange Rate, Consumer Price Index, Index of Industrial production, SENSEX, NIFTY and Foreign Exchange Reserve as determinants. The FPI regime commenced on June 01, 2014 to harmonize different routes for foreign portfolio investments i.e. Foreign Institutional Investors (FIIs), Sub Accounts and Qualified Foreign Investors, uniform entry norms, adoption of risk based know your customer (KYC) norms etc. The monthly Data of variables were collected from the websites, addressing bseindia.com, https://in.investing.com /indices/s-p-cnx-nifty-historical-data, stats.oecd.org, SEBI and www.rbiindia.com for the period from Jun 2014 to Dec 2016. The effect of Foreign Portfolio Investment (FPI) is analysed with its determinants for Correlation, Co-integration and Casual relationships for the period after the introduction of the FPI Regime. The first order differences of the variables were tested. The Co-Integration test gives the existence of one Co- integrating variable vector in the equations. The Equation at none shows the p-value of 0.000 in both trace and Lmax test and confirming one co-Integrating vector and there is a long- run relationship among variables and hence the null hypothesis of no Co-Integration is rejected. The Granger causality gives the result that we can accept the null hypothesis that FPI does not granger cause and effect ER. But the null hypothesis is rejected that ER does not cause and effect FPI and there is a unidirectional relationship between the variables. There are no causalities between FPI and CPI/IIP/SENSEX/NIFTY/FER and vice-versa. The study has suggested that though FPI has many advantages to the country but it should have certain limit that should not lead to inflation where the prices may go up. Key words: ER, SENSEX, NIFTY, CPI, IIP, FER, FPI http://www.iaeme.com/IJM/index.as 105 editor@iaeme.com
Determinants of Foreign Portfolio Investment and Their Effects on The Indian Stock Market Cite this Article: S. Raghavan and Dr. M. Selvam, Determinants of Foreign Portfolio Investment and Their Effects on The Indian Stock Market. International Journal of Management, 8(3), 2017, pp. 105–115. http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=8&IType=3 1. INTRODUCTION Security Exchange and Board of India (SEBI) constituted a “Committee on Rationalization of Investment Routes and Monitoring of Foreign Portfolio Investments” (the Committee), under the Chairmanship of Shri K. M. Chandrasekhar, comprising representatives from Government of India, Reserve Bank of India and various market participants. The Committee made recommendations regarding harmonization of different routes for foreign portfolio investments i.e. Foreign Institutional Investors (FIIs), Sub Accounts and Qualified Foreign Investors, uniform entry norms, adoption of risk based KYC norms etc. The Board, in its meeting held on June 25, 2013 accepted the recommendations of the Shri K.M. Chandrasekhar Committee. SEBI (Foreign Portfolio Investors) Regulations, 2014 In order to implement the recommendations of the Committee, the SEBI (Foreign Portfolio Investors) Regulations, 2014 (the Regulations) have been framed and the same have been notified on January 07, 2014. The FPI regime shall commence from June 01, 2014. Salient features of the Regulations are as under: Major Salient features of the SEBI (Foreign Portfolio Investors) Regulations, 2014 Foreign Portfolio Investors (FPIs) 1. Existing Foreign Institutional Investors, Sub Accounts and Qualified Foreign Investors (QFIs) shall be merged into a new investor class termed as “FPIs”. 2. SEBI approved Designated Depository Participants (DDPs) shall register FPIs on behalf of SEBI Subject to compliance with KYC requirements. 3. FPI shall be required to seek registration in any one of the following categories: • “Category I Foreign Portfolio Investor” which shall include Government and Government related foreign investors etc; • “Category II Foreign Portfolio Investor” which shall include appropriately regulated broad based funds, broad based funds whose investment manager is appropriately regulated, university funds, university related endowments, pension funds etc; • “Category III Foreign Portfolio Investor” which shall include all others not eligible under Category I and II foreign portfolio investors. 4. All existing FIIs and Sub Accounts may continue to buy, sell or otherwise deal in securities under the FPI regime. 2. REVIEW OF LITERATURE Kumar (2001) investigated the effects of FII inflows on the Indian stock market represented by the SENSEX using monthly data from January 1993 to December 1997. Kumar (2001) inferred that FII investments are more driven by Fundamentals and they do not respond to short-term changes or technical position of the market. Rai and Bhanumurthy (2004), in their research paper titled "Determinants of FII in India: The Role of Return, Risk, and Inflation", tried to examine the determinants of FII flows in India. http://www.iaeme.com/IJM/index.as 106 editor@iaeme.com
S. Raghavan and Dr. M. Selvam The proposed hypothesis of the study was that risk and inflation in a domestic country (like Indian economy) and return in a foreign market (like USA economy) would have an adverse impact on the FII flows, whereas risk and inflation in foreign country (like USA economy) and yield in domestic country (like Indian economy) would have a positive impact on the FII flows. Sikdar et al (2006) studied the relationship between foreign capital flows (FDI, FPI) and other economic variables during 1997 to 2003. The study observed that under the regime of liberalization policy, the outcomes were highly surprising. The composition of capital inflow had undergone a major change over these years. Dependence on foreign aid had come down drastically and funds in the form of Foreign Portfolio Investment (FPI), Foreign Direct Investment (FDI), external commercial borrowings and non- resident Indians deposits had come to be recognized as the major sources of capital flows in India as concluded by this study. Wang, Wang and Huang (2010) used the oil prices, gold price, and exchange rates of dollar in contrast with currencies and stock markets of Germany, Japan, Taiwan, China and the USA. This study derives results from empirical results that there exists co-integration and long-term stable relationship among these variables in the mentioned countries except the USA. Aynur PALA¹, Bilgin ORHAN ORGUN2 (2015), in their research paper titled” The effect of macro economic variables on foreign portfolio investments: an implication for Turkey” Journal of Business, Economics & Finance ISSN: 2146 – 7943,Year: 2015 Volume: 4 Issue:1observed that the deposit interest rate, gross national income and current account balance have had a positive effect on FPI. Muhammad Afaq Haider1 Muhammad Asif Khan2 & Elyas Abdulahi2 (2016) , in their study paper titled “Determinants of Foreign Portfolio Investment and Its Effects on China”, International Journal of Economics and Finance, have stated that the GDP, Population growth, Exchange rate, and External debt have significantly affected the Foreign Portfolio investment of CHINA. Many researchers have done their studies in India for FIIs regime only but not especially on FPI. This study attempts to study the effect of FPI and its determinants on the stock market after the introduction of FPI regime. 3. OBJECTIVES OF THIS STUDY The main objectives of the study are: • To identify the determinants of FPIs flows to India. • To determine the existence of co-integrating vectors of Foreign Portfolio Investment with Exchange Rate, Consumer Price Index, Index of Industrial Production, SENSEX, NIFTY and Foreign Exchange Reserve. • To check whether there exists causality among the selected variables. 4. SOURCES OF DATA The study is completely based on secondary data collected from various data sources. To analyse the co-integration and casual relationship of variables, the study considered the monthly SENSEX and NIFTY data from bseindia.com and https://in.investing.com/indices/s-p-cnx- nifty-historical-data respectively. The data for Exchange Rate and Consumer Price Index were collected from stats. oecd.org and Foreign Institutional Investments from SEBI website. The data on Index of Industrial Production and Foreign Exchange Reserve were from www.rbiindia.com. The monthly Data cover the period from June 2014 to Dec 2016. http://www.iaeme.com/IJM/index.as 107 editor@iaeme.com
Determinants of Foreign Portfolio Investment and Their Effects on The Indian Stock Market 5. RESEARCH METHODOLOGY Foreign Portfolio Investment consists of total investment inflow in the country. Six variables are taken into the account for a comprehensive study with Foreign Portfolio Investment and those variables are chosen on the basis of previous literature which may influence the FPI. The previous studies analyzed only a few variables. This study includes CPI which is a proxy of Inflation and gives special attention to IIP. In this study, the secondary data have been used to achieve its objective of identifying the determinants of FPIs flows to India. The differenced time series based on monthly data for the above mentioned period for all the variables under study have been used for analysis. If we fail to take a difference when the process is non-stationary, regressions on time will often yield a spuriously significant linear trend, and our forecast intervals will be much too narrow (optimistic) at long lead times. Variables Dependent FPI = Foreign Portfolio Investment variable Independent CPI = Consumer Price Index, FER =Foreign Exchange Reserve, IIP =Index of variables Industrial Production (General), ER=Exchange Rate, =SENSEX and NIF = NIFTY. Hypotheses of the study To set out the results of the stated objectives, the following hypotheses are developed for the study and tested using appropriate tools. 1) H0: FPI /ER/CPI/IIP/SENSEX/NIFTY/FER is non-stationary time series or has unit root. H1: FPI/ER/CPI/IIP/SENSEX/ NIFTY/FER is stationary time series or has no unit root. 2) H0: There is a Co-integration relationship between FPI and ER/CPI/IIP/SENSEX/ NIFTY/FER. H1: There is no Co-integration relationship between FPI and ER/CPI/IIP/SENSEX/ NIFTY/FER. 3) H0: FPIs does not granger cause CPI/ FER/IIP/ ER/ SENSEX /NIFTY. H1: FPIs granger causes CPI/ FER/IIP/ ER/ SENSEX/ NIFTY. Tools applied To test the stated hypotheses, the following research methods have been used for the study: 1) Descriptive Statistics Descriptive statistics are used to evaluate the mean, median, standard deviation and kurtosis of the selected variables. 2) Correlation Test Correlation coefficient test is used to find out the relationship between the variables. 3) Unit Root Test (Augmented Dickey Fuller Test) Prior to testing causality test, there is a need for checking whether the data are stationary or not. In this study, the Augmented Dickey-Fuller Test (ADF) proposed by is applied to find out the same. http://www.iaeme.com/IJM/index.as 108 editor@iaeme.com
S. Raghavan and Dr. M. Selvam 4) Co-integration Test Co-integration theory was developed by Granger to examine the long-run relationship. The purpose of co-integration test is to determine whether a group of non-stationary series is co- integrated or not and also explores the long-run equilibrium relationship among the variables. The two types of Johansen test is employed, first, the λ trace statistics test whether the number of co-integrating vector is zero or one, then the λ max statistic tests whether a single co- integration equation is sufficient. Both the test statistics are given as follows: Tr a ce ( r ) = − T ∑ l o g ( 1 − λ ) i = r +1 (1) λ = T log ( 1 − λ ) In this framework, it is desirable to obtain at least one co-integrating vector, r = 1 to establish the model. The Trace test is a joint test that tests the null hypothesis of no co- integration (H0: r = 0) against the alternative hypothesis of co-integration (H1: r > 0). The Maximum Eigen value test conducts tests on each Eigen value separately. It tests the null hypothesis that the number of co-integrating vectors is equal to r against the alternative of r+1 co-integrating vectors. A significantly non-zero Eigen value indicates a significant co- integrating vector. 5) Granger Causality Test It is important to note that correlation analysis is not sufficient to have an in-depth study of relationship between the variables. There exists a more relevant concept called the concept of causality. This test is conducted to know whether the behaviour of one variable is caused by another given variable or vice versa. In order to know the causality between the given variables under consideration, granger causality econometric model has been applied for this purpose. 6. ANALYSIS Table1 gives the summary statistics of FPI and its determinants. Regarding the normality, the value of skewness and kurtosis are considered. The Ex.kurtosis values of NIFTY, SENSEX and IIP show that the data are sharp. The variables are not distributed normally in full, but are distributed very close to normal distribution as the median values of variables are very close to average values except in FPI. Table 1 Summary Statistics, using the observations from 2014:06 to 2016:12 Variables Mean Median Minimum Maximum FPI 6608.84 12225.0 -39396.0 36046.0 ER 64.6295 65.0723 59.7307 68.2377 CPI 150.218 151.210 139.840 159.170 IIP 179.245 179.300 165.100 198.700 SENSEX 26945.2 26668.0 23002.0 29361.5 NIFTY 8183.53 8185.80 6987.05 8901.85 FER 97198.7 96172.1 75052.1 122982 http://www.iaeme.com/IJM/index.as 109 editor@iaeme.com
Determinants of Foreign Portfolio Investment and Their Effects on The Indian Stock Market Variables Std. Dev. C.V. Skewness Ex. kurtosis FPIs 18854.8 2.85297 -0.473278 -0.557132 ER 2.64448 0.0409176 -0.357190 -1.31133 CPI 5.76717 0.0383919 -0.0260071 -1.36751 IIP 7.80854 0.0435634 0.518660 0.646652 SEN 1352.05 0.0501780 -0.568845 0.710487 NIF 423.228 0.0517171 -0.527446 0.371801 FER 14330.0 0.147430 0.352382 -0.805164 Source: Computed by Authors using Gretlw32 software As stated in the research methodology, correlation co-efficient has been calculated to understand the relationship between net FPI flows and the SIX economic variables under this study namely ER, CPI, IIP, SENSEX, NIFTY and FER. Table 2 presents the result of correlation analysis. It is lucid from table 2 that net FPI flows and the SENSEX /NIFTY have a positive correlation but moderate in sizes (i.e.0.4280/0.4195 respectively). This implies that a positive change in one variable will bring a positive change in another variable. It means when SENSEX/NIFTY points go up, it is likely to attract more FPI flows, whereas when SENSEX/NIFTY points fall down, it may result into withdrawal of money by the FPIs (Foreign Portfolio Investors). Similarly, movements in FPI could also cause movements in the SENSEX/NIFTY. By observing the FPI for the above period the effect is due to the impact of rising and falling of SENSEX/NIFTY. Further, table 2 shows a low degree of positive correlation (0.1471) between FPI flows and CPI. This shows that when there are inflationary trends in the Indian stock market, FPIs prefer to avoid investing in India because during inflation the purchasing power declines which makes it a costly affair for FPIs to invest in India. The relationship between FPI and FER is correlated with the value of 0.3815 which implies that if FPI increases it will add up to the Foreign Exchange Reserves of the country. Further there is a positive and moderate correlation between FPI and IIP, giving positive signal for Industrial production with the increase in FPI. The degree of correlation between ER (US dollar) and SENSEX/NIFTY has been found to be negative (-0.5788/-0.5777), which means the increase in SEN/NIF in India will result into depreciation of dollar. The correlation between SEN/NIF and FER is 0.2290/0.2296, these positive correlations mean that upward trend of Indices may invite FPIs and increasing the FER in the country. Table 2 Correlation Coefficients, using the observations 2014:06 - 2016:12 d FPI d_ER d_CPI d_IIP d_SEN d_NIF d_FER d FPI 1.0000 -0.6418 0.1471 0.3109 0.4280 0.4195 0.3815 d_ER 1.0000 -0.1378 -0.0496 -0.5788 -0.5777 -0.2098 d_CPI 1.0000 -0.2054 0.3195 0.2976 0.1226 d_IIP 1.0000 0.0720 0.0797 -0.0406 d_SEN 1.0000 0.9962 0.2290 d_NIF 1.0000 0.2296 d_FER 1.0000 Source: Computed by Authors using Gretlw32 software http://www.iaeme.com/IJM/index.as 110 editor@iaeme.com
S. Raghavan and Dr. M. Selvam It needs to mention here that though the correlation technique helps us to understand the relationship between two or more variables, it does not tell us the long run relationship among variables. Therefore, in order to have a more in-depth study of the relationship among the variables under study, econometric tool Johansen Co-integration test is applied. Co-integration analysis requires that the variables are first-order integrated. We investigated the different series for first order of integration by using ADF unit-root tests. The main purpose of employing a unit root test is to pose whether or not the variables are stationary series. Table 3 shows the results of stationary test. The variables FPI, ER, CPI, IIP, SEN NIF and FER are rejected for their Null Hypotheses of non-Stationarity as the p –values are less than 5% level and found stationary at their first differences. Table 3 ADF unit root test for FPI and other variables ADF Test with constant and trend , model: (1-L)y = b0 + b1*t + (a-1)*y(-1) + ... + e Lag order 3, First difference of variable. Null hypothesis p –value Result Inference FPI is not stationary 6.19e-006* Reject -5.69226 FPI is stationary ER is not stationary 1.902e-005* Reject -5.47414 ER is stationary CPI is not stationary 0.02206** Reject -3.95895 CPI is stationary IIP is not stationary 4.29e-009* Reject-8.99381 IIP is stationary SEN is not stationary 0.0005211* Reject -5.53917 BSE is stationary NIF is not stationary 0.0006025* Reject -5.48021 NSE is stationary FER is not stationary 1.538e-023* Reject -10.9004 FER is stationary Source: Computed by Authors using Gretlw32 software *1% level, **5% level, ***10% level Before applying a co-integration test, we first should determine the optimal lag length by using selection-order criteria of AIC. In this test Akaike criterion (AIC), the appropriate lag length is three. The table 4 gives the respective values of information criteria and the minimum value of 52.27 is selected which gives the lag value of order 3. Table 4 Maximum lag order The asterisks below indicate the best (that is, minimized) values of the respective information criteria, AIC = Akaike criterion,BIC = Schwarz Bayesian criterion and HQC = Hannan-Quinn criterion. VAR system, maximum lag order 3 Lags Loglik p(LR) AIC BIC HQC 1 -754.47685 59.887174 62.478847 60.657814 2 -708.17547 0.00000 59.124109 63.443565 60.408509 3 -579.65031 0.00000 52.270394* 58.317632* 54.068555* Source: Computed by Authors using Gretlw32 software Table 5 gives the existence of one Co- integrating variable vector in the equations. The Equation at none shows the p-value of 0.000 in both trace and Lmax test and confirming one co-Integrating vector and there is a long- run relationship among variables and hence the null hypothesis of no Co-Integration is rejected. If Co-Integration exists between the variables then there must exist a paired Granger- causality. Since Co-integration tests indicate only the existence of long-run relationship among variables, the Granger Causality tests is used to analyze the direction of relationship. http://www.iaeme.com/IJM/index.as 111 editor@iaeme.com
Determinants of Foreign Portfolio Investment and Their Effects on The Indian Stock Market Table 5 Johansen Co-Integration Test Results Estimation period: 2014:10 - 2016:12 (T = 27) Lag order = 3 Number of equations = 6 Unrestricted constant Exogenous regressor(s):d_FPI, Co-integration tests, ignoring exogenous variables Rank Eigen value Trace test p-value Lmax test p-value None 0.99344 199.14 [0.0000] 135.71 [0.0000] 1 0.67016 63.427 [0.1447] 29.947 [0.1385] 2 0.47643 33.479 [0.5349] 17.471 [0.5504] 3 0.34875 16.008 [0.7176] 11.579 [0.6006] 4 0.14865 4.4288 [0.8614] 4.3453 [0.8170] 5 0.0030903 0.083566 [0.7725] 0.083566 [0.7725] Source: Computed by Authors using Gretlw32 software The Granger Causality Test is done on the stationary values. The table 6 clearly gives the results of Granger Causality between Stock Index and the Macroeconomic Variables and it can be inferred from it that we can accept the null hypothesis that FPI does not granger cause and effect ER. But the null hypothesis is rejected that ER does not cause and effect FPI and there is a unidirectional relationship between the variables.The Fig1 clearly depicts the effect of Exchange Rate on FPI. There are no causalities between FPI and CPI/IIP/BSE/FER/ and vice- versa. Table 6 Test for Granger Causality between Stock Index and the Macroeconomic Variables Hypothesis F p -value Accept/Reject Direction/Nature of Statistic Hypothesis Causality FPI does not Granger cause ER 1.26995 0.3116 Accept Unidirectional relation E R does not Granger cause FPI 4.36384 0.0161* Reject FPI does not Granger cause CPI 0.6464 0.5943 Accept No Causality CPI does not Granger cause FPI 1.4511 0.2579 Accept FPI does not Granger cause IIP 1.1899 0.3388 Accept No Causality IIP does not Granger cause FPI 0.1420 0.9336 Accept FPI does not Granger cause SEN 0.2882 0.8334 Accept No Causality SEN does not Granger cause FPI 0.0781 0.9711 Accept FPI does not Granger cause NSE 0.3305 0.8034 Accept No Causality NIF does not Granger cause FPI 0.0556 0.9822 Accept FPI does not Granger cause FER 1.2836 0.3072 Accept No Causality FER does not Granger cause FPI 1.4966 0.2459 Accept Source: Computed by Authors using R software Fig1 shows the trend lines of FPI and ER. From the trend it is observed that appreciation in the US dollar leads to withdrawal of FPI and depreciation trend for investment. http://www.iaeme.com/IJM/index.as 112 editor@iaeme.com
S. Raghavan and Dr. M. Selvam Figure 1 Trend graph of FPI and ER Source: Compiled by Authors using Gretlw32 software 7. FINDINGS • The variables are not distributed normally in full, but are distributed very close to normal distribution as the median values of variables are very close to average values except in FPI. • The trend graph clearly shows that dollar depreciates when FPI increases and vice-versa as per correlation inference. • There is a positive signal for Industrial production with the increase in FPI. • Increase in FPI will add up to the Foreign Exchange Reserves of the country. • There is a positive trend of SEN and NIF with FPI and ER has negative effect on BSE, NIFTY with FPI. • The Johansen co-Integration test confirms one co-integrating vector and there is a long- run relationship among Variables. • The Granger Causality test shows the acceptance of null hypothesis for all variables with FPI and vice-versa except ER does not granger cause and it is rejected. • The Variable ER granger causes FPI and it is unidirectional. • The ER influences the investment and withdrawal of FPI for the test period after the introduction of FPI regime. 8. SUGGESTIONS The Foreign portfolio Investment inflow into the country leads to increase the index points of market and the investors can earn profit. The Industry can increase the production and lot of job opportunities are there. The huge amount of FPI fund inflow into the country creates a lot of demand for Indian rupee, and the RBI has to pump out the amount of rupee needed in the market as a result of demand created by FPI. This may lead for inflation and excess liquidity. The government has to consider this and there should be a limit for Foreign Portfolio Investment. http://www.iaeme.com/IJM/index.as 113 editor@iaeme.com
Determinants of Foreign Portfolio Investment and Their Effects on The Indian Stock Market 9. LIMITATIONS OF THE STUDY The Study is only for limited time period as the FPI regime started from Jun 2014 and in future the period may be increased to empirically test whether the results are sensitive to the frequency of the data. 10. FURTHER SCOPE OF RESEARCH The study gives opportunity for further researchers to do their research using other variables like oil price, Domestic Institutional investment, GDP etc and can be compared with Asian market. The study can also be done using daily data and the time period may be extended. The study has suggested that there should be a limit for FPI and further researchers can do their research on the restrictions of Foreign Port Folio investment. 11. CONCLUSION The study concludes that Exchange rate has significant effect and contributes its role on Foreign portfolio investment, SENSEX and NIFTY for the above period and it can be considered as one of the factors for determining the investments. It is suggested that FPI can be restricted to certain limit to avoid the excess pumping of money by RBI and thereby reduce the chances for high inflation. REFERENCES [1] Kaur, Maninder and Dhillon, S.Sharanjeet 2010,’ Determinants of Foreign Institutional Investors’ Investment in India’, Eurasian Journal of Business and Economics, 3(6), pp.57- 70. [2] Amita, Determinants of FIIs: Evidence from India, IJITKM 8 (1) June-Dec 2014 pp.85-95 (0973- 4414) [3] Rai, Kulwant and Bhanumurthy, N.R 2004, Determinants of FII in India: The Role of Return, Risk and inflation, The Developing Economies, XLII-4, Dec, pp.479-493. [4] Gordon, James and Gupta, Poonam 2003, Portfolio Flows into India: Do Domestic Fundamentals Matter?, IMF Working Paper, Asia and Pacific Department, Jan. Pp.1-36. [5] Gretl User’s Guide Gnu Regression, Econometrics and Time-series Library Allin Cottrell, Department of Economics, Wake Forest University, Riccardo “Jack” Lucchetti, Dipartimento di conomia, Università Politecnica delle Marche, November, 2016 [6] Karunanithy Banumathy#1, Ramachandran Azhagaiah#2,’ Causal Relationship between Stock Price and Gold Price in India: A Granger Causality Test Approach’ International Journal of Research in Management, Science & Technology (E-ISSN: 2321-3264) Vol. 2, No. 2, August 2014. [7] C. W. J. Granger, “Investigating casual relations by econometric models and cross-spectral methods”, Econometrica, Vol. 37, No. 3, pp. 424-38, 1969. [8] S. Johansen, “Statistical analysis of co integrating vectors”, Journal of Economic Dynamics and Control, Vol.12, No. 2-3, pp. 231-54, 1988. [9] P. Joshi and A. K. Giri, “An empirical analysis of the relationship between stock market indices and macro- economic variables: Evidences from India”, International Academic Research Journal of Economics and Finance, Vol. 2, No. 1, pp. 17-24, 2013. [10] S. Ray, “Testing granger causal relationship between macro-economic variables and stock price behaviour: Evidence from India”, Advances in Applied Economics and Finance, Vol. 3, No. 1, pp. 470- 81, 2012. http://www.iaeme.com/IJM/index.as 114 editor@iaeme.com
S. Raghavan and Dr. M. Selvam [11] D. A. Dickey and W. A. Fuller, “Likelihood ratio statistics for autoregressive time series with a unit root”, Econometrica, Vol. 49, No. 4, pp. 1057-72, 1981. [12] Engle, R.F., D.F. Hendry, & D. Trumble (1985), Small-sample properties of ARCH estimators and tests,@ Canadian Journal of Economics, 18, 66-93. [13] Aynur PALA¹, Bilgin ORHAN ORGUN2 “The effect of macroeconomic variables on foreign portfolio investments: an implication for Turkey”, Journal of Business Economics and Finance, Year: 2015 Volume: 4 Issue: 1. [14] A. Ramaraju, Impact of FDI On Stock Market Development: An Empirical Investigation, International Journal of Management, Volume 2, Number 1, Jan- April (2011) [15] Dr. Ramesh Sardar, FDI IN E-COMMERCE: PROS & CONS, International Journal of Management , Volume 5, Issue 2, February (2014), pp. 49-53 [16] Benefits of FDI In Indian Retail Sector and Customer Perception of Organized Retail Outlets In Hyderabad, K.Venkateswara Raju, Dr. Svss Srinivasa Raju, Dr. D.Prasanna Kumar, International Journal of Management , Volume 4, Issue 4, July-August (2013), pp. 180-192 [17] Muhammad Afaq Haider1 Muhammad Asif Khan2 & Elyas Abdulahi2 (2016), “Determinants of Foreign Portfolio Investment and Its Effects on China’’ International Journal of Economics and Finance; Vol. 8, No. 12; 2016 ISSN 1916-971X E-ISSN 1916- 9728. [18] www.sebi.gov.in Annual Reports. http://www.iaeme.com/IJM/index.as 115 editor@iaeme.com