St. Theresa Journal of Humanities and Social Sciences

Dynamic Linkages and Causal Effect among Midcap 50 Stocks in National Stock Exchange

Dr. S. Rajamohan Professor, Alagappa Institute of Management, Alagappa University, Karaikudi Tamil Nadu, India Email: [email protected]

G. Arivalagan* Research Scholar, Alagappa Institute of Management, Alagappa University, Karaikudi Tamil Nadu, India Email: [email protected] *Corresponding author

Abstract

This study examines the long-run relationship among the Midcap 50 index and its corresponding sectors’ stock returns for the period from January 1, 2008 to December 31 2016. The daily closing price of the midcap 50 index and the corresponding stocks was obtained from National Stock Exchange. The long-run equilibrium and causality effect of the Midcap 50 index were analyzed. From the results of Augmented Dickey-Fuller (ADF) and Phillips Perron (PP) unit root tests, it is confirmed that both the index returns and stocks returns are stationary at the level. Further, the researcher used Johansen co-integration and Granger causality test to analyze the unidirectional and bidirectional relationship between the variables. And it is evident from the results, that 18 stocks confirm unidirectional causality effect with midcap 50 index.

Keywords: Midcap 50 Index; unit root tests; co-integration; Granger causality

1. Introduction

Market capitalization is the aggregate valuation of the company based on its current share price and the total range of outstanding stocks (Definition of ‘Market Capitalization’, n. d.). Market capitalization is one of the maximum critical traits that enable the investors to decide the returns and also the risk percentage. It also supports the investors to select the stock that can encounter their risk and diversification Vol.3 No.2 July-December 2017 19

St. Theresa Journal of Humanities and Social Sciences criteria. The Midcap segment stocks are the increasing attractive investment in the market with high growth potential (Bhalla, 2008). In NSE three types of Midcap index are available, such as Nifty Midcap 150, Nifty Midcap 100 and Nifty Midcap 50. The researcher focused on the Nifty Midcap50 index and its category of stocks. The Nifty Midcap 50 index started with a base value of 1000 at National Stock Exchange (NSE) in 1st of January 2004. The Nifty Midcap 50 Index includes top 50 companies based on full market capitalization from NIFTY Midcap 150 index and on which derivative contracts are available on NSE. In case, 50 Midcap stocks do not have derivatives contract available on them then it could have less than 50 stocks in the index. The Nifty Midcap 50 index includes or excludes stocks based on the market capitalization criteria given by National Stock Exchange (2017) (NIFTY Broad Market Indices - Methodology Document). The study employs the co-integration test to assess the relationship and causal effect among the Midcap50 stocks in NSE.

2. Review of Literature

The literature review presents the studies conducted in the last decade that analyzed the linkages between the spot & futures market in India and also in global stock markets.

2.1 Studies on Nifty spot and Futures

Pradhan and Bhat (2007) have investigated the causal relationship between spot and futures markets for underlying 31 individual stocks of S&P CNX Nifty. The daily closing price was taken during the period of November 9, 2001 to September 29, 2005. The authors employed Johansen's co-integration test and vector error correction model (VECM) to investigate the relationship between spot and futures on individual stocks. Their study concluded the existences of long-run equilibrium relationship among spot and futures; and also both futures and spot play an important role in price discovery, hence temporal causality exists among them. Similarly, Gupta and Singh (2006) have investigated the price discovery efficiency and validity of Law of One Price in the Indian equity market at NSE. The authors chose the near month contract of Nifty index futures and 24 stock futures on a daily closing price from June 2000 to February 2005. Their study found that, there is long run equilibrium between futures and cash markets. Their causality test results reported the significant bidirectional relationship between Indian equity futures and cash markets. In a study by Srinivasan (2009), the causal relationship between Nifty spot and futures market in India has Vol.3 No.2 July-December 2017 20

St. Theresa Journal of Humanities and Social Sciences been analyzed. The daily data series from 12, 2000, to September 12, 2008 was chosen for his study. Johansen’s Cointegration and Vector Error Correction Model (VECM) were used to empirically investigate the causal relationship between spot and futures prices. The results confirmed the existence of a long-run relationship between Nifty spot and Nifty futures prices. Further, there is a presence of bidirectional relationship between the Nifty spot and Nifty futures market prices in India. Thus the author concluded in his study that both the spot and futures markets play a crucial role in price discovery.

2.2 Studies on global market perspective

Kenourgios (2004) examined the relationship between price movements of FTSE/ASE-20 three-month futures index and the cash market in Athens Stock Exchange (ASE). The required data was collected from the Athens spot and derivative exchanges during the period of August 1999 to June 2002. The results of his study indicated that the series are non˗stationary at the levels. Also, from the results of co˗integration test, it was noted that both the markets were co˗integrated. The causality test confirmed that there is bidirectional causality between spot and futures index markets. Sehgal, Ahmad and Deisting (2015) have investigated the long-run equilibrium and volatility spillovers in spot and futures prices of four major currencies such as USD/INR, EURO/INR, GBP/INR and JPY/INR and between futures prices of two stock exchanges Multi-Commodity Stock Exchange (MCX-SX) and National Stock Exchange (NSE) in India. The researchers applied the Johansen’s co-integration test along with VECM to investigate the price discovery. The results suggest that there is a long-run equilibrium relationship between currency spot and futures markets. In another study, Tan (2002) examined the temporal causal relationships between spot and futures markets of the Malaysian Stocks Composite Index (MSCI) and Kuala Lumpur Futures Index (KLFI). The data was taken from 2nd January 1996 to 29th September 2000. The Johansen’s co-integration technique was used by the researcher to analyze the long run equilibrium. The empirical analysis results reported that both MSCI and KLFI series were co-integrated. Also, the empirical results revealed that there is a bidirectional causal relationship between KLFI and the MSCI in the short run, whereas unidirectional causality exists from KLFI to MSCI in long run. Similarly, Ceylan and Dogan (2004) examined the market linkages of selected OIC countries Viz. Pakistan, Lebanon, Morocco, Jordon, Oman, Kuwait, Turkey and Egypt equity markets. Among 28 pair-wise relations of stock market indices the researchers found the evidence of co-integration only with 2 pairs of countries viz. Lebanon-Kuwait and Vol.3 No.2 July-December 2017 21

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Turkey-Egypt. In a study by Ryoo and Smith (2004), effect on the spot market of trading in Korean KOSPI 200 futures was investigated. Their results revealed that the spot and futures prices are cointegrated, hence there is bi-directional causality between the two markets. Lamba (2005) investigated the casual effect of the South Asian markets of India, Pakistan, and Srilanka. The researcher utilized the Granger causality test and found that there is the causal effect in Indian equity market and it was influenced by the developed country markets. Also, found that the three South Asian Equity Markets has been co˗integrated, but with slower pace. Wong, Penn, Terrell, Lim (2004) studied the co-movement between stock markets in developed countries and emerging Asian stock markets. To assess the co-movement, they applied the co- integration technique and found that there is a co-movement between developed markets and emerging markets. Also, they observed that some emerging markets do differ from the developed markets in which they share a long-run equilibrium relationship. After reviewing the past studies, following hypothesis are framed in order to fulfill the objectives of the study: Hypothesis 1: There is no stationary movement in the data series. Hypothesis 2: Stocks returns have no long-run relationship with Midcap 50 index. Hypothesis 3: No causality exists between stock returns and Midcap 50 index

3. Methodology

The current study is directed towards analysing the stock returns, long-run relationship, and cause effect relationship between individual sector stocks and Midcap 50 indices. This research focus towards Midcap sector wise stocks and Midcap 50 index returns from January 1,2008, to December 31,2016. The researcher has taken 44 companies stocks out of 50 companies in Midcap. The remaining six companies’ stocks were excluded due to the unavailability of the data. The excluded stocks are Jubilant food works, PC Jewellers, Adani Power, JSW energy, Reliance power, IDFC bank. The study sample unit details are available in the appendix I. The required data has been taken from Yahoo web source (https://in.finance.yahoo.com/quote). The data has been analyzed using Augmented Dickey-Fuller (ADF) & Phillips Perron (PP) unit root tests, Johansen Co-integration test and Granger Causality test with the help of Microsoft office- excel and E-views software applications.

3.1 Unit root tests

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In the financial time series analysis, there are two popular unit root tests (1) Augmented Dickey-Fuller (ADF) Test, and Phillips˗Perron Test.

3.1.1 Augmented Dickey-Fuller (ADF) Test

The Augmented Dickey-Fuller (ADF) test is the modified version of Dickey-Fuller (1979) test. Augmented Dickey-Fuller (ADF) test has been carried out in this study to test the null hypothesis of the time series data. If the null hypothesis is accepted it implies that, there is no stationary movement in the data series.

The ADF test controls for the higher order correlation by lagged difference terms of the dependent variable to the right-hand side of the regression (Sarkar, 2012).

The Augmented Dickey-Fuller test specification used here is as follows

∆rt = α +δrt-1 + Σ βs∆rt-s + εt………………………………….(1)

Where,α+δrt

∆rt = rt - rt-1 ; rt = ln (Rt)

3.1.2 Phillips˗Perron Test (PP)

Phillips˗Perron unit root test has been developed by Phillips and Perron (1988). It builds on the standard Dickey˗Fuller or Augmented Dickey Fuller Test, but it is different from ADF, as PP test corrects the serial correlation and heteroskedasticity in the errors of the regression by directly modifying the t˗statistics.

3.2 Co-integration The set of variables is defined as co-integrated, if one or more linear combinations among them are stationary at the level (Bilgili, 1998). The time series are non-stationary but ‘move together’ over the period, hence there exist some influence in the time series, which implies that the two series are certain by some relationship in the long run. A co-integrating relationship may also be seen as a long- term or equilibrium phenomenon since it is possible that co-integrating variables may deviate from their relationship in the short run, but their association would return in the long run (Engle & Granger, 1987; Brooks, 2002). This study followed the Vol.3 No.2 July-December 2017 23

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Johansen’s (1988, 1991) and Johansen and Juselius (1990) likelihood ratio analysis of Maximum Eigen value and Trace tests in order to test the null hypothesis.

3.3 Granger Causality Test

Granger Causality test has been proposed by Granger (1969) to determine whether one time series is useful in forecasting another. The two important underlying principles of Granger causality test are (1) the cause happens prior to its effect, (2) and it has unique information about its effect, which is not found elsewhere. Brooks and Tsolacos (2010) explained Granger-causality as, it really means only a correlation between the current value of one variable and the past values of others; it does not mean that movements of one variable cause movements of another.

4. Empirical Analysis of the Study

As described in the methodology part, data analysis has been carried out by using the following statistical tests viz. Augmented Dickey-Fuller (ADF) Phillips Perron (PP), Johansen co-integration test and finally Granger causality test. The analytical framework has been given in the subsequent tables.

The behaviour of the Midcap 50 stocks return and the existence of stationarity of the return are tested by using Augmented Dickey-Fuller (ADF) & Phillips Perron (PP) unit root test.

Table 1: Stationarity of Midcap 50 Stocks – ADF and PP Stocks ADF T Statistic P-VALUE PP T-Statistic @ P-Value @ intercept* intercept* Amara Raja Batteries -45.19 0.0001 -45.18 0.0001 -46.43 0.0001 -46.43 0.0001 -47.97 0.0001 -48.01 0.0001 MRF -43.88 0.0001 -44.09 0.0001 TVS Motor Company -46.24 0.0001 -46.25 0.0001 Engineers India -13.31 0 -43.99 0.0001 GMR Infrastructure -47.24 0.0001 -47.25 0.0001 IRB Infrastructure -44.58 0.0001 -44.66 0.0001 -45.06 0.0001 -45.24 0.0001 Vol.3 No.2 July-December 2017 24

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Bata India -45.63 0.0001 -45.62 0.0001 Godrej Industries -45.67 0.0001 -45.74 0.0001 Tata Global Beverages -46.82 0.0001 -46.82 0.0001 CESC -44.37 0.0001 -44.31 0.0001 -47.17 0.0001 -47.51 0.0001 Petronet LNG -46.61 0.0001 -46.71 0.0001 Reliance Infrastructure -47.20 0.0001 -47.21 0.0001 -43.72 0 -43.61 0 -44.14 0.0001 -44.14 0.0001 -35.13 0 -46.33 0.0001 IDBI Bank -44.79 0.0001 -44.80 0.0001 IDFC -35.67 0 -46.74 0.0001 L&T Finance Holdings -37.77 0 -54.29 0.0001 Ltd. Mahindra & Mahindra -46.47 0.0001 -46.47 0.0001 Financial Services Ltd. Reliance Capital Ltd. -45.95 0.0001 -46.08 0.0001 RECL -45.40 0.0001 -45.41 0.0001 -44.74 0.0001 -44.69 0.0001 Hexaware Technologies -47.45 0.0001 -47.49 0.0001 -45.03 0.0001 -46.16 0.0001 Dish TV -45.32 0.0001 -45.31 0.0001 Sun TV -48.73 0.0001 -49.69 0.0001 Ajanta Pharmaceuticals -46.06 0.0001 -46.06 0.0001 -45.21 0.0001 -45.21 0.0001 Strides Shasun -45.14 0.0001 -45.10 0.0001 Wockhardt -41.97 0 -42.62 0 Adani Enterprises -20.87 0 -45.46 0.0001 Aditya Birla Nuvo -44.57 0.0001 -44.65 0.0001 Reliance Com -47.82 0.0001 -47.81 0.0001 Tata Com -46.18 0.0001 -46.23 0.0001 Arvind -46.31 0.0001 -46.41 0.0001

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Page Industries -49.42 0.0001 -50.44 0.0001 SRF -43.93 0.0001 -43.99 0.0001 Century Textile -45.33 0.0001 -45.37 0.0001 Jindal Steel -62.03 0.0001 -65.58 0.0001 -42.91 0 -42.85 0 Critical values 1% -3.43312 5% -2.86265 10% -2.56741 * Data at Level

The Augmented Dickey-Fuller (ADF) and Phillips Perron Test (PP) reveal the stationary result of the time series data (Table 1). The ADF test indicates that the values of all the Midcap 50 stock returns are significant with p-values less than 0.01. The PP test result implies that time series data is significant with the respective critical values at one percent, five percent and 10 percent respectively. Hence, the results of the test suggest that all the variables are stationary at their levels. Therefore, the null hypothesis of non-stationary movement is rejected.

Table 2: Determination of long-run relationship of the Midcap 50 stocks (Johansen’s Co-integration Test) Sectors H0 Trace Test Maximum Eigen value Test

Eigen Trace Critical Statistic Critical Prob.** value Statistic Value Value

None * 0.37 3195.64 95.75 1014.44 40.08 0.0001

At most 1 * 0.20 2181.20 69.82 503.10 33.88 0.0001

At most 2 * 0.20 1678.11 47.86 483.87 27.58 0.0001

At most 3 * 0.19 1194.23 29.80 458.94 21.13 0.0001

Automobile At most 4 * 0.16 735.29 15.49 383.47 14.26 0.0001

At most 5 * 0.15 351.82 3.84 351.82 3.84 0

None * 0.37 2576.63 69.82 991.34 33.88 0.0001

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Sectors H0 Trace Test Maximum Eigen value Test

Eigen Trace Critical Statistic Critical Prob.** value Statistic Value Value

At most 1 * 0.19 1585.29 47.86 462.43 27.58 0.0001

At most 2 * 0.17 1122.85 29.80 403.98 21.13 0.0001

At most 3 * 0.16 718.88 15.49 372.93 14.26 0.0001

Construction At most 4 * 0.15 345.94 3.84 345.94 3.84 0

Consumer None * 0.36 2279.83 47.86 987.36 27.58 0.0001 goods At most 1 * 0.20 1292.47 29.80 489.59 21.13 0.0001

At most 2 * 0.17 802.88 15.49 406.95 14.26 0.0001

At most 3 * 0.16 395.93 3.84 395.93 3.84 0

Energy None * 0.37 2428.55 47.86 1007.24 27.58 0.0001

At most 1 * 0.21 1421.31 29.80 519.20 21.13 0.0001

At most 2 * 0.19 902.11 15.49 476.54 14.26 0.0001

At most 3 * 0.18 425.57 3.84 425.57 3.84 0

Financial None * 0.37 5491.48 285.14 1028.73 70.54 0.0001 Services At most 1 * 0.22 4462.75 239.24 535.38 64.50 0.0001

At most 2 * 0.21 3927.37 197.37 521.96 58.43 0.0001

At most 3 * 0.20 3405.41 159.53 484.60 52.36 0.0001

At most 4 * 0.20 2920.81 125.62 480.64 46.23 0.0001

At most 5 * 0.19 2440.17 95.75 453.36 40.08 0.0001

At most 6 * 0.18 1986.81 69.82 442.19 33.88 0.0001

At most 7 * 0.18 1544.61 47.86 424.08 27.58 0.0001

At most 8 * 0.17 1120.53 29.80 408.15 21.13 0.0001

At most 9 * 0.15 712.39 15.49 368.00 14.26 0.0001

At most 10 0.14 344.39 3.84 344.39 3.84 0

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Sectors H0 Trace Test Maximum Eigen value Test

Eigen Trace Critical Statistic Critical Prob.** value Statistic Value Value

*

IT None * 0.35 1732.46 29.80 953.74 21.13 0.0001

At most 1 * 0.18 778.72 15.49 439.11 14.26 0.0001

At most 2 * 0.14 339.61 3.84 339.61 3.84 0

Media None * 0.37 1930.15 29.80 1001.19 21.13 0.0001

At most 1 * 0.20 928.95 15.49 493.92 14.26 0.0001

At most 2 * 0.18 435.03 3.84 435.03 3.84 0

Pharma None * 0.36 2607.37 69.82 984.65 33.88 0.0001

At most 1 * 0.19 1622.72 47.86 464.78 27.58 0.0001

At most 2 * 0.17 1157.94 29.80 423.05 21.13 0.0001

At most 3 * 0.17 734.89 15.49 403.22 14.26 0.0001

At most 4 * 0.14 331.67 3.84 331.67 3.84 0

Service & None * 0.37 2598.56 69.82 1013.01 33.88 0.0001 telecom At most 1 * 0.19 1585.54 47.86 470.39 27.58 0.0001

At most 2 * 0.18 1115.16 29.80 424.81 21.13 0.0001

At most 3 * 0.15 690.35 15.49 370.39 14.26 0.0001

At most 4 * 0.14 319.96 3.84 319.96 3.84 0

Textile None * 0.36 2317.57 47.86 996.37 27.58 0.0001

At most 1 * 0.20 1321.20 29.80 480.70 21.13 0.0001

At most 2 * 0.19 840.50 15.49 453.61 14.26 0.0001

At most 3 * 0.16 386.89 3.84 386.89 3.84 0

Miscellaneous None * 0.37 2352.39 47.86 1009.80 27.58 0.0001

At most 1 * 0.21 1342.59 29.80 528.92 21.13 0.0001

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Sectors H0 Trace Test Maximum Eigen value Test

Eigen Trace Critical Statistic Critical Prob.** value Statistic Value Value

At most 2 * 0.18 813.67 15.49 433.66 14.26 0.0001

At most 3 * 0.16 380.01 3.84 380.01 3.84 0

* denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Table 2 describes the long run equilibrium of the Midcap 50 stocks. The long-run equilibrium has been confirmed by the trace test and the maximum eigen value. The trace test results indicate that the statistical values are greater than the critical values. The maximum Eigen value also has a lower p-value of 0.05 percent. These two test results are significant with the critical value and p-value. Hence the null hypothesis has been rejected. Therefore, the evidence of long-run relationship is present in the Midcap 50 stock returns.

Table 3: Causal effect between Midcap50 Stocks and Midcap50 Index Sectors Null hypothesis F- Prob. statistic Auto mobile Amara Raja Batteries returns does not granger cause 6.38 0.0116* Midcap 50 index returns Apollo tyre returns does not granger cause Midcap 50 5.30 0.0215* index returns Exide industries returns does not granger cause 5.52 0.0188* Midcap 50 index returns MRF returns does not granger cause Midcap 50 index 5.31 0.0213* returns TVS motors returns does not granger cause Midcap 50 4.63 0.0316* index returns Consumer Bata India returns does not granger cause Midcap 4.31 0.0136* Goods index returns Indraprastha gas returns does not granger cause 2.45 0.0444* Midcap50 index returns Financial Bank of India returns does not granger cause Midcap 8.22 0.0003* services 50 index returns Canara bank returns does not granger cause Midcap 50 6.98 0.001* index returns Mahindra & Mahindra financial service returns does 3.40 0.0336* not granger cause Midcap 50 index returns RECL returns does not granger cause Midcap 50 index 4.58 0.0103* Vol.3 No.2 July-December 2017 29

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returns Union bank of India returns does not granger cause 6.87 0.0011* Midcap50 index returns IT Hexaware technologies returns does not granger cause 3.40 0.0024* Midcap50 index returns Sun TV returns does not granger cause Midcap50 4.83 0.0002* index returns Biocon returns does not granger cause Midcap50 index 5.34 0.021* returns Textile Arvind returns does not granger cause Midcap50 index 6.80 0.0011* returns Midcap50 index returns does not granger cause Page 4.35 0.0131* industries SRF returns does not granger cause Midcap50 index 4.58 0.0103* returns Steel Jindal Steel returns does not granger cause Midcap50 5.05 0.0065* index returns Chemical Tata Chemicals returns does not granger cause 4.64 0.0098* Midcap50 index returns Midcap50 index returns does not granger cause Tata 3.39 0.0338* Chemicals returns *indicates rejection of the null hypothesis

Table 3 portrays the results of granger causality of Midcap 50 stocks. From the results, Auto mobile, Consumer goods, financial services, IT, Textile, Steel, and Chemical sector stocks have significant p-values, which is less than 0.05 percent. Hence, under the Midcap 50 index, six sector stock returns have unidirectional relationship between the Midcap 50 index returns. The bidirectional causality exists between Tata chemical stock returns and Midcap index returns.

5. Discussion

Three tests such as, Augmented Dickey-Fuller (ADF) & Phillips Perron unit root test, Johansen Co-integration test and Granger Causality Test were utilized in this study. The earlier two tests were used to find out the stationary movement in the data series. Next, from the other tests, existence of long-run relationship and unidirectional causality was tested and proved. From the results, it is found that the variables were stationary for 44 Midcap stocks return. The results revealed that 18 stocks have a unidirectional causality effect with Midcap 50 index. The respective stocks are Amara Raja batteries, Apollo Tyre, Exide industries, MRF, TVS, Bata, Indraprastha Gas, Bank of India, Canara Bank, Mahindra and Mahindra financial Vol.3 No.2 July-December 2017 30

St. Theresa Journal of Humanities and Social Sciences service, Union Bank, Hexaware, Sun TV, Biocon, Arvind, SRF and Jindal stock. The Tata chemical is the only stock that has the bi-directional causality out of 44 stocks. Causality doesn’t exist for construction, services and telecom sectors stocks.

6. Conclusion

This study focused only on the Midcap 50 index stocks returns for a particular period and found that all the stocks have unidirectional causality or bidirectional causality effect with the Midcap 50 index return except construction, service and telecommunication sector stocks. Also, in this study the researcher focused only on causality effect. Further researches could emphasize on analyzing the volatility spillover along with causality effect in uncovered areas of Nifty 50, Midcap 100, debt market indices, currency market and the commodity market.

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References

Bhalla, V. K. (2008). Investment Management. S. Chand Publishing. Bilgili, F. (1998). Stationarity and cointegration tests: Comparison of Engle - Granger and Johansen methodologies. Journal of Faculty of Economics and Administrative Sciences, Erciyes University No. 13, 131-141. Brooks, C. (2002). Multivariate models. Introductory Econometrics for Finance, 302- 315. Brooks, C., & Tsolacos, S. (2010). Real estate modelling and forecasting. Cambridge University Press. Ceylan, N. B., & Dogan, B. (2004). Comovements of Stock Markets among Selected OIC Countries. Journal of Economic Cooperation, 25(3), 47–62. Dickey, D.A., Fuller, W.A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427-431. Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: Journal of the Econometric Society, 55(2), 251-276. Granger C W J. (1969). Investigating Causal Relations by Econometric Models and Cross spectral Methods. Econometrica: Journal of the Econometric Society 37(3), 424-438. Gupta, K., & Singh, B. (2006). Investigating the price discovery efficiency of Indian equity futures market. Paradigm, 10(2), 33-45. Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of economic dynamics and control, 12(2-3), 231-254. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration—with applications to the demand for money. Oxford Bulletin of Economics and statistics, 52(2), 169-210. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica: Journal of the Econometric Society, 59(6), 1551-1580. Kenourgios, D. (2004). Price discovery in the Athens derivatives exchange: evidence for the FTSE/ASE-20 futures market. Economic and Business Review,6(3), 229-243. Lamba, A. S. (2005). An Analysis of the Short- and Long-Run Relationships Between South Asian and Developed Equity Markets. International Journal of Business, 10(4), 383–402. Vol.3 No.2 July-December 2017 32

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National Stock Exchange. (2017, August). NIFTY Broad Market Indices- Methodology Document. Retrieved from: https://www.nseindia.com/content/indices/Nifty_Broad_Market_Indices_Met hodology.pdf Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346. Pradhan, K. C., & Bhat, S. (2007). Lead-lag Relationship between the NSE Spot and Futures Market. ICFAI Journal of Applied Finance, 13(4), 73-94. Ryoo, H.-J., & Smith, G. (2004). The impact of stock index futures on the Korean stock market. Applied Financial Economics, 14(4), 243–251. https://doi.org/10.1080/0960310042000201183 Sarkar, A. (2012). Functional Instability Or Paradigm Shift?: A Characteristic Study of Indian Stock Market in the First Decade of the New Millennium. Springer Science & Business Media. Sehgal, S., Ahmad, W., & Deisting, F. (2015). An investigation of price discovery and volatility spillovers in India’s foreign exchange market. Journal of Economic Studies, 42(2), 261-284. Srinivasan, P. (2009). An Empirical Analysis of Price Discovery in the NSE Spot and Futures Markets of India. Iup, 15(11), 24–37. Tan, J. (2002). Temporal Causality between the Malaysian Stock Price and Stock‑ indexed Futures Market amid the Selective Capital Controls Regime. Asean Economic Bulletin, 19(2), 191–203. Definition of 'Market Capitalization' (n.d.). The Economic Times. Retrieved from https://economictimes.indiatimes.com/definition/market-capitalization Wong, W. K., Penm, J., Terrell, R. D., & Ching, K. Y. (2004). The relationship between stock markets of major developed countries and Asian emerging markets. Journal of Applied Mathematics & Decision Sciences, 8(4), 201- 218.

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Appendix I

Company Industry Name

Amara Raja Batteries Ltd. AUTOMOBILE Apollo Tyres Ltd. AUTOMOBILE Exide Industries Ltd. AUTOMOBILE MRF Ltd. AUTOMOBILE TVS Motor Company Ltd. AUTOMOBILE Century Textile & Industries Ltd. CEMENT & CEMENT PRODUCTS Tata Chemicals Ltd. CHEMICALS Engineers India Ltd. CONSTRUCTION GMR Infrastructure Ltd. CONSTRUCTION IRB Infrastructure Developers Ltd. CONSTRUCTION Voltas Ltd. CONSTRUCTION Bata India Ltd. CONSUMER GOODS . CONSUMER GOODS Jubilant Foodworks Ltd. CONSUMER GOODS PC Jeweller Ltd. CONSUMER GOODS Tata Global Beverages Ltd. CONSUMER GOODS Adani Power Ltd. ENERGY CESC Ltd. ENERGY Indraprastha Gas Ltd. ENERGY JSW Energy Ltd. ENERGY Petronet LNG Ltd. ENERGY Reliance Infrastructure Ltd. ENERGY Reliance Power Ltd. ENERGY Bank of India FINANCIAL SERVICES Canara Bank FINANCIAL SERVICES Federal Bank Ltd. FINANCIAL SERVICES IDBI Bank Ltd. FINANCIAL SERVICES

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St. Theresa Journal of Humanities and Social Sciences

IDFC Bank Ltd. FINANCIAL SERVICES IDFC Ltd. FINANCIAL SERVICES L&T Finance Holdings Ltd. FINANCIAL SERVICES Mahindra & Mahindra Financial Services Ltd. FINANCIAL SERVICES Reliance Capital Ltd. FINANCIAL SERVICES Rural Electrification Corporation Ltd. FINANCIAL SERVICES Union Bank of India FINANCIAL SERVICES Hexaware Technologies Ltd. IT MindTree Ltd. IT Dish TV India Ltd. MEDIA & ENTERTAINMENT Sun TV Network Ltd. MEDIA & ENTERTAINMENT Jindal Steel & Power Ltd. METALS Ajanta Pharmaceuticals Ltd. PHARMA Biocon Ltd. PHARMA Strides Shasun Ltd. PHARMA Wockhardt Ltd. PHARMA Adani Enterprises Ltd. SERVICES Aditya Birla Nuvo Ltd. SERVICES Ltd. TELECOM Tata Communications Ltd. TELECOM Arvind Ltd. TEXTILES Ltd. TEXTILES SRF Ltd. TEXTILES

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