Stock Market Integration among Trading Partners:

Empirical Evidence from India and China

Eronimus A.¹, Reetika Verma²

¹Assistant Professor, Department of Financial Administration,

Central University of Punjab, Bathinda, Punjab, India.

²Research Scholar, Department of Financial Administration,

Central University of Punjab, Bathinda, Punjab, India.

*Corresponding Author E-mail: reetikaverma20@gmail.com

ABSTRACT:

This study makes an attempt to analyse the dynamics of integrating relationship shared by Indian and Chinese stock market for the period ranging from January 01, 2002 to June 30, 2021. Prominent stock indices namely BSE SENSEX index of Bombay Stock Exchange Limited, India and Shanghai Stock Exchange Composite Index (SCI) of Shanghai Stock Exchange Limited, China were considered. Johansen Cointegration test was used to analyse the long term integration shared by the stock markets. Granger causality test was employed to know the causal relation shared by the stock markets. Presence of long term integration and unidirectional causal relation between the stock markets was found. It may be inferred that investment diversification among these markets may not provide fruitful returns to the investors in long run. Presence of causality from BSESENSEX to SCI and absence of causality from SCI to BSESENSEX suggests significant flow of information from Indian stock market to the Chinese stock market but not vice-versa. Findings of the study also suggests that extent of connectedness shared by the Indian and Chinese market does not vary too much with time. The findings revealed that although the financial markets of trading partners share significant integration but the intensity of trade does not impact the extent of financial linkages shared by the nations. These implications may provide significant insights to the investors, fund’s managers, policymakers and other market players.

KEYWORDS: Stock market integration, Trading partners, India, China, Investment diversification.

INTRODUCTION:

Due to multiple factors like trade liberalization, digitalization, currency deregulation, reduced restrictions on capital flows etc., international stock markets have become more integrated with each other. Regional integration in stock markets is rapidly increasing due to liberalization and increasing intraregional financial investments¹. Rising capital flows across nations stimulate financial integration and stronger integration of financial markets promote the economic growth and financial stability of nations.

Coordinated financial developments across nations promote the linkages among its stock markets as well. In today’s era, investors are more interested in stock markets than other options for long term investments². Thus, investors’ concerns towards knowing the linkages shared by financial markets is increasing.

Finance specialists have also started paying more attention to the linkages shared by different stock markets for exploring the most potential markets for earning diversification benefits³. The trading relations between different economies promote the capital flows across nations and makes them economically and financially integrated. India and China shares significant trading relations with each other over the last two decades. Figure 1 presents the Bilateral Trade Percentage between India and China from the year 2002 to 2021.

Figure 1. Bilateral Trade Percentage between India and China from the year 2002 to 2021.

Source: Author’s Compilation.

Note: Bilateral trade was calculated on the basis of import and export data collected from the website of United Nations comtrade https://comtrade.un.org/data/.

Various previous studies have indicated that stock markets belonging to nations having trade relationships with each other shares significant relationship with each other. It is also highlighted that the trade interdependence is one of the major factor of international stock market integration³. It is vital to explore the integration shared by Indian and Chinese market with the others as the global economic power is shifting towards these two nations⁴.

Furthermore, the ongoing integration of the financial markets varies over time due to changing levels of development in different markets. Economic integration shared by different nations vary over time which may lead to varying level of integration among the stock markets as well⁵.

Thus, this study has tried to explore whether the financial markets of important trading partners India and China are integrated with each other or not. Indian economy is one of the fastest growing economy of the world and its stock market is also among the top emerging markets. Popularity of Indian stock market is remarkably rising among national as well as international investors⁶. It is very much profitable to invest in Indian stock market. Similarly, Chinese stock market is also one of the fastest growing market of the world has emerged rapidly since its establishment⁷. Chinese stock market has become a great concern for the international investors and will continue play a more important role in the global market. Thus, it becomes important to explore the relationship shared by these emerging stock markets.

Integration among different financial markets has increased rapidly over the last few decades as the cross border capital transactions have become quicker and less complicated⁸. Various previous studies have found that the trading partners share financial linkages as well like Abdul Karim et al. (2010)⁹ investigated the linkages shared by the stock market of Malaysia with its trading partners by using ARDL (autoregressive distributed lag) bounds test and VAR (vector autoregression). The markets were found to be integrated with each other. It was also indicated that the trading relations among the nations does matter for integrating the financial markets as well. Joyo and Lefen (2019)³ examined the integration shared by the stock markets of Pakistan and its trading partner. By using DCC-GARCH (Dynamic Conditional Covariance-Generalized Autoregressive Conditional Heteroscedasticity) approach, presence of integration among the stock markets of Pakistan and its trading partners was found.

Jana (2021)¹⁰ explored the variation in the extent of stock market integration shared by India with its trading partners due to intensity of trade. It was indicated that the trade between India and other Asian nations has increased in the past few years and thus the linkages among its stock markets has also increased. Caporale et al. (2019)¹¹ also found that the trade integration shared by Asian nations has promoted its stock market integration as well. With reference to Latin American market, Guesmi et al. (2013)¹² found that the level of trade openness is one of the major determinant of regional stock market integration. In context with Singapore, Teulon et al. (2014)¹³ suggested that extent of trade openness is a significant driver of its equity market integration with other markets.

Abdul Karim et al. (2013)¹⁴ explored different factors of stock market integration among emerging ASEAN (Association of Southeast Asian Nations) markets. The study found that bilateral trade significantly impacts the equity market integration. It was indicated that level of trade ties among different nations are directly associated with the level of stock market co-movements.

Sheu et al. (2011)¹⁵ indicated that the trade growth among different nations also promote the equity market integration and makes them more liberalized. Yi et al. (2009)¹⁶ examined the integration shared by the stock market of China with that of US and Hong Kong. By applying the FIVECM (fractionally integrated vector error correction) model, presence of fractional integration among the markets was found.

Patel (2019)¹⁷ evaluated the integration shared by the stock markets of India and its trading partners. By using different tests, the study found presence of significant linkages among the considered stock markets. One more interesting point highlighted in the study is that after the period of financial crisis, trade between India and China rose up which made the Chinese and Indian stock index closer.

However there are some other studies also which indicated that trade connection among the nations does not integrate its stock markets. Vithessonthi and Kumarasinghe (2016)¹⁸ examined the impact of trade integration of various Asian nations on its stock market connections. The study found that the trade linkages among the nations are not associated with the integration shared by its stock markets. By using the cointegration and causality test, Tripathi and Sethi (2010)¹⁹ investigated the linkages shared by the stock market of India with those of UK, China, Japan and US. The study found that Indian stock market does not share any kind of integration with the markets of UK, China and Japan however it is integrated with the US stock market.

To summarize, it is observed that existing literature presents contradictory evidences. On the one hand studies like Pretorius (2002)²⁰, Walti (2011)²¹ discovered that trade relations affects the degree of stock market integration significantly and on the other hand studies like Elyasiani et al. (1998)²², Gupta and Guidi (2012)²³ argued that trade intensity does not matter in integrating the stock markets of the respective nations.

Thus, to shed more light on this matter, this study has tried to explore whether the financial markets of two important trading partners i.e. India and China are integrated with each other or not. This study is intended to investigate the short run, long run integration and causal relationship between Indian and Chinese stock market.

MATERIAL AND METHODS:

This study analyses the dynamics of integrating relationship between Indian and Chinese stock market. Prominent stock indices namely BSE SENSEX index of Bombay Stock Exchange Limited, India and Shanghai Stock Exchange Composite Index (SCI) of Shanghai Stock Exchange Limited, China were considered. Data was collected from the official websites of the stock markets. The daily closing values SCI for the period January 01, 2002 to June 30, 2021 have been collected from yahoofinance.com and the daily closing values of BSE SENSEX was taken from official website of Bombay Stock Exchange. As employed by Ahmad et al. (2005)²⁴, values for holidays and non- trading days are omitted in the series. Therefore, if there is holiday in any one market, the data has been removed for the other market as well. It made data series of both markets comparable with equal number of observations in each series.

In order to explore whether the relationship among the markets remain constant or not, the entire study period was further classified into different phases. On the basis of trade intensity between India and China, four sub periods were identified and the analysis was done during each phase. Table 1 presents the details of study period considered.

Table 1. Categorisation of Study Period.

S. No.	Time period	Average Bilateral Trade between India and China	Category
1	From 01-01-2002 to 31-12-2006	5.877164	Phase 1
2	From 01-01-2007 to 31-12-2011	9.342008	Phase 2
3	From 01-01-2012 to 31-12-2016	9.732234	Phase 3
4	From 01-01-2017 to 31-12-2021	11.30022	Phase 4
	From 01-01-2002 to 31-12-2021	Complete study period

Source: Author’s Compilation.

Note: Bilateral trade was calculated on the basis of import and export data collected from the website of United Nations comtrade https://comtrade.un.org/data/.

RESULT AND DISCUSSION:

Descriptive statistics are generated to get statistical overview of the Indian and Chinese stock market during the considered study period (Table 2).

The average returns of Indian stock market was found to be greater than Chinese stock market during all the phases. Having comparatively higher returns and lower risk, Indian stock market can be inferred as better performing market. Skewness, Kurtosis and Jarque- Bera statistics indicated that the returns series of both the stock markets are not normally distributed.

Table 2. Descriptive Statistics of BSE SENSEX and SCI

	Phase 1(2002-2006)		Phase 2(2007-2011)		Phase 3(2012-2016)		Phase 4(2017-2021)		Full Study Period(2002-2021)
	BSE	SCI	BSE	SCI	BSE	SCI	BSE	SCI	BSE	SCI
Mean	0.138731	0.043051	0.010886	-0.01851	0.138731	0.043051	0.058341	0.008457	0.065131	0.018017
Median	0.193873	0.031322	0.064551	0.117451	0.193873	0.031322	0.094033	0.050367	0.093671	0.062154
Maximum	10.16152	8.849158	15.98998	12.95067	10.16152	8.849158	8.594739	5.554206	15.98998	12.95067
Minimum	-11.8092	-7.70365	-11.6044	-9.25609	-11.8092	-7.70365	-14.1017	-10.23916	-14.1017	-10.1588
Std. Dev.	1.538819	1.495826	2.088539	2.159835	1.538819	1.495826	1.223425	1.100608	1.502339	1.628324
Skewness	-0.31114	0.575459	0.116996	-0.17503	-0.31114	0.575459	-1.76905	-0.64748	-0.27847	-0.25992
Kurtosis	10.64203	7.90707	8.839875	5.804171	10.64203	7.90707	29.12533	9.083546	14.06654	8.703431
Jarque-Bera	2552.371	1102.955	1498.722	350.3818	2552.371	1102.955	30495.36	1697.368	22483.83	6006.382
Probability	0	0	0	0	0	0	0	0	0	0
Sum	144.5579	44.85877	11.46257	-19.4917	144.5579	44.85877	61.43263	8.90512	286.2488	79.18687
Sum Sq. Dev.	2465.05	2329.232	4588.818	4907.461	2465.05	2329.232	1574.601	1274.329	9917.351	11650.42

Source: Author’s Compilation.

Table 3. Unit Root Test Results of BSESENSEX and SCI

	Variables	Level	First Difference	Order of Integration
Phase 1(2002-2006)	BSESENSEX	1.858291 ( 0.9998)	-25.98099 ( 0.0000)	I(1)
	SCI	2.018112 ( 0.9999)	-30.89004 ( 0.0000)	I(1)
Phase 2(2007-2011)	BSESENSEX	-1.713605 ( 0.7448)	-30.93959 ( 0.0000)	I(1)
	SCI	-1.152713 ( 0.6965)	-17.66729 ( 0.0000)	I(1)
Phase 3(2012-2016)	BSESENSEX	-1.645898 ( 0.4587)	-31.60994 ( 0.0000)	I(1)
	SCI	-1.433683 ( 0.5669)	-10.59763 ( 0.0000)	I(1)
Phase 4(2017-2021)	BSESENSEX	-0.157219 ( 0.9412)	-11.97209 ( 0.0000)	I(1)
	SCI	-1.577313 ( 0.4939)	-34.16607 (0.0000)	I(1)
Full Study Period(2002-2021)	BSESENSEX	1.003334( 0.9967)	-65.51478 ( 0.0001)	I(1)
	SCI	-1.897564( 0.3338)	-27.53024( 0.0000)	I(1)

Notes: Critical Values at 1%,5% and 10% level are -3.436419, -2.864108 and -2.568189 respectively. All results are significant at 1% critical level.

Source: Author’s Compilation.

Table 4. Johansen Cointegration Test Results of BSESENSEX and SCI

	Number of cointegrating relations	Eigen value	Trace statistic	Critical value (5%)	Max-Eigen value	Critical value (5%)
Phase 1 (2002-2006)	None	0.357625	880.1751 (0.0000)	15.49471	465.1551	14.26460(0.0000)
Phase 1 (2002-2006)	At most 1	0.326240	415.0199 (0.0000)	3.841465	415.0199	3.841465(0.0000)
Phase 2 (2007-2011)	None	0.200060	424.3719 (0.0000)	15.49471	233.9331	14.26460 (0.0000)
Phase 2 (2007-2011)	At most 1	0.166162	190.4388 (0.0000)	3.841465	190.4388	3.841465(0.0000)
Phase 3 (2012-2016)	None	0.163363	361.7684 (0.0000)	15.49471	184.9651	14.26460 (0.0000)
Phase 3 (2012-2016)	At most 1	0.156753	176.8033 (0.0000)	3.841465	176.8033	3.841465(0.0000)
Phase 4 (2017-2021)	None	0.177024	342.6199 (0.0000)	15.49471	204.1796	14.26460(0.0000)
Phase 4 (2017-2021)	At most 1	0.123746	138.4403 (0.0000)	3.841465	138.4403	3.841465(0.0000)
Full Study Period (2002-2021)	None	0.351043	3686.235 (0.0000)	15.49471	1899.483	14.26460 (0.0000)
Full Study Period (2002-2021)	At most 1	0.334174	1786.752 ((0.0000)	3.841465	1786.752	3.841465(0.0000)

Source: Author’s Compilation.

Table 5. Granger Causality Test Results of BSESENSEX and SCI

	Null Hypothesis H_o	F- Statistic	P – Value	Conclusion
Phase 1(2002-2006)	SCI does not Granger Cause BSESENSEX	1.30212	0.2724	H_oAccepted
	BSESENSEX does not Granger Cause SCI	9.67520	0.0000	H_oRejected
Phase 2(2007-2011)	SCI does not Granger Cause BSESENSEX	1.33847	0.2455	H_oAccepted
	BSESENSEX does not Granger Cause SCI	4.74076	0.0003	H_oRejected
Phase 3(2012-2016)	SCI does not Granger Cause BSESENSEX	0.53304	0.2014	H_oAccepted
	BSESENSEX does not Granger Cause SCI	0.17601	0.0216	H_oRejected
Phase 4(2017-2021)	SCI does not Granger Cause BSESENSEX	0.79181	0.5556	H_oAccepted
	BSESENSEX does not Granger Cause SCI	0.97540	0.0316	H_oRejected
Full Study Period(2002-2021)	SCI does not Granger Cause BSESENSEX	0.50209	0.6053	H_oAccepted
	BSESENSEX does not Granger Cause SCI	7.23648	0.0317	H_oRejected

Source: Author’s Compilation.

CONCLUSION:

Over the past few decades India and China have been sharing important trading relations with each other. It is suggested in various previous studies like Chaudhry (1997)²⁵, Joyo and Lefen (2019)³ that stock markets of trading partners also share significant linkages with each other. Thus, the study makes an attempt to know the financial integration shared by trading partners India and China. Furthermore, to know whether the degree of integration changes over time, the relationship of the stock markets was examined during different phases of time. Daily closing values of the prominent stock index of both nations was analysed from the year 2002 to 2021.

Cointegration test indicated significant long term association between the markets. It is suggested that allocating the funds among Indian and Chinese stock market may provide short term diversification benefits but it may not provide fruitful returns to the investors in long run. Granger causality test revealed presence of unidirectional relationship which indicated significant flow of information from Indian stock market to the Chinese stock market but not vice-versa. These implications may provide significant insights to the investors, fund’s managers, policymakers and other market players. Almost consistent results were found during each phase indicating that extent of connectedness shared by the Indian and Chinese market does not vary too much with time.

The findings revealed that although the financial markets of trading partners share significant integration but the intensity of trade does not impact the extent of financial linkages shared by the nations. Findings indicating that the trading partners share financial integration as well are consistent with the findings of Patel (2019)¹⁷, Walti (2011)²⁶, Pretorius (2002)²⁷ and are not consistent with the findings of Vithessonthi and Kumarasinghe (2016)²⁸, Gupta and Guidi (2012)²⁹, Elyasiani et al. (1998)²².

Indian stock market is gaining utmost importance at international level and researchers’ interest in examining the Indian stock market has gained significant momentum over the past few years. Various previous studies like Ayan and Anuradha (2018)³⁰, Deepa and Verma (2018)³¹, Goutam Tanty (2019)³², Goutam and Patjoshi (2016)³³, Manu et al. (2017)³⁴, Mulukalapally Susruth (2017)³⁵, Nikhil Kaushik (2018)³⁶, Savita (2019) et al.³⁷, Saji and Harikumar (2013)³⁸, Harshitha et al. (2019)³⁹ have studied the Indian stock market, this paper also contributes to the existing literature by examining the relationship shared by Indian stock market with the Chinese stock market.

REFERENCES:

1. Bala, A. Indian stock market-review of literature. TRANS Asian Journal of Marketing and Management Research. 2013; 2(7): 67-79.

2. Syllignakis, M. N., and Kouretas, G. P. German, US and Central and Eastern European stock market integration. Open Economies Review. 2010; 21: 607-628.

3. Joyo, A. S., and Lefen, L. Stock market integration of Pakistan with its trading partners: A multivariate DCC-GARCH model approach. Sustainability. 2019; 11(2): 303.

4. Narayan, P. K., Ahmed, H. A., Sharma, S. S., and Prabheesh, K. P. How profitable is the Indian stock market?. Pacific-Basin Finance Journal. 2014; 30: 44-61.

5. Bracker, K., Docking, D. S., and Koch, P. D. Economic determinants of evolution in international stock market integration. Journal of Empirical Finance. 1999; 6(1): 1-27.

6. Salameh, S., and Ahmad, A. A critical review of stock market development in India. Journal of Public Affairs. 2022; 22(1): e2316.

7. Seddighi, H. R., and Nian, W. The Chinese stock exchange market: operations and efficiency. Applied Financial Economics. 2014; 14(11): 785-797.

8. Alagidede, P. African stock market integration: implications for portfolio diversification and international risk sharing. In Proceedings of the African Economic Conferences. 2014; 1-31.

9. Abdul Karim, B., and Shabri Abd. Majid, M. Does trade matter for stock market integration?. Studies in Economics and Finance. 2010; 27(1): 47-66.

10. Jana, S. Stock Market Integration and Trade: A Study on India and its Major Trading Partners. Vision. 2021; 09722629211034406.

11. Caporale, G. M., You, K., and Chen, L. Global and regional stock market integration in Asia: A panel convergence approach. International Review of Financial Analysis. 2019; 65: 101381.

12. Guesmi, K., Nguyen, D. K., and Teulon, F. Further evidence on the determinants of regional stock market integration in Latin America. The European Journal of Comparative Economics. 2013; 10(3): 297.

13. Teulon, F., Guesmi, K., and Mankai, S. Regional stock market integration in Singapore: A multivariate analysis. Economic modelling. 2014; 43: 217-224.

14. Abdul Karim, B., and Xin Ning, H. Driving forces of the ASEAN-5 stock markets integration. Asia-Pacific Journal of Business Administration. 2013; 5(3): 186-191.

15. Sheu, H. J., and Liao, C. H. Dynamics of stock market integration between the US and the BRIC. African Journal of Business Management. 2011; 5(9): 3674.

16. Yi, Z., Heng, C., and Wong, W. K. China’s stock market integration with a leading power and a close neighbor. Journal of Risk and Financial Management. 2009; 2(1): 38-74.

17. Patel, R. J. International trade and stock market integration: Evidence from study of India and its major trading partners. The Journal of Private Equity. 2019; 23(1): 90-109.

18. Vithessonthi, C., and Kumarasinghe, S. Financial development, international trade integration, and stock market integration: Evidence from Asia. Journal of Multinational Financial Management. 2016; 35: 79-92.

19. Tripathi, V., and Sethi, S. Integration of Indian stock market with World stock markets. Asian Journal of Business and Accounting. 2010; 3(1): 117-134.

20. Pretorius, E. Economic determinants of emerging stock market interdependence. Emerging Markets Review. 2002; 3(1): 84-105.

21. Wälti, S. Stock market synchronization and monetary integration. Journal of International Money and Finance. 2011; 30(1): 96-110.

22. Elyasiani, E., Perera, P., and Puri, T. N. Interdependence and dynamic linkages between stock markets of Sri Lanka and its trading partners. Journal of Multinational Financial Management. 1998; 8(1): 89-101.

23. Gupta, R., and Guidi, F. Cointegration relationship and time varying co-movements among Indian and Asian developed stock markets. International Review of Financial Analysis. 2012; 21: 10-22.

24. Ahmad, K. M., Ashraf, S., and Ahmed, S. Is the Indian stock market integrated with the US and Japanese markets? An empirical analysis. South Asia Economic Journal. 2005; 6(2): 193-206.

25. Chaudhry, K. A. The price of wealth: Economies and institutions in the Middle East. Cornell University Press. 1997.

26. Wälti, S. Stock market synchronization and monetary integration. Journal of International Money and Finance. 2011; 30(1): 96-110.

27. Pretorius, E. Economic determinants of emerging stock market interdependence. Emerging Markets Review. 2002; 3(1): 84-105.

28. Vithessonthi, C., and Kumarasinghe, S. Financial development, international trade integration, and stock market integration: Evidence from Asia. Journal of Multinational Financial Management. 2016; 35, 79-92.

29. Gupta, R., and Guidi, F. Cointegration relationship and time varying co-movements among Indian and Asian developed stock markets. International Review of Financial Analysis, 2012; 21, 10-22.

30. Ayan Majumdar, Anuradha Saha. Influence of Macroeconomic Variables on Stock Market Volatility: A study on Nifty.Asian Journal of Management. 2018; 9(1):440-444.

31. Deepa Mangala, Anju Verma. Financial Risk Tolerance of Individual Equity Investors in Indian Stock Market.Asian Journal of Management. 2018; 9(2):990-998.

32. Goutam Tanty. Assessing the Bombay Stock Exchange Index Volatility through Garch Model. Asian Journal of Management. 2019; 10(3): 236-240.

33. GoutamTanty, P K Patjoshi. A Study on Stock Market Volatility Pattern of BSE and NSE in India. Asian J. Management. 2016; 7(3): 193-200.

34. Manu K S, Varsha L Menda. Dynamics of Indian Stock Market Integration with Global stock Markets. Asian J. Management. 2017; 8(3): 559-568.

35. Mulukalapally Susruth. Application of GARCH Models to Forecast Financial Volatility of Daily Returns: An Empirical study on the Indian Stock Market. Asian J. Management. 2017; 8(2): 192-200.

36. Nikhil Kaushik. Corporate Governance and Indian Stock Market: A Study with Asymmetric Conditional Volatility Models. Asian Journal of Management. 2018; 9(1):80-86.

37. Savita, Suresh Kumar Dhameja. Measurement of time varying Volatility and its relation with noise Trading: A Study on Indian Stock Market using Garch Model. Res. J. Humanities and Social Sciences. 2019; 10(2): 479-483.

38. T.G. Saji, S. Harikumar. Long run Behavior of Equity Returns: An Exploration of Indian Experience. Asian J. Management. 2013; 4(1): 22-27.

39. V. Harshitha Moulya, Abuzar Mohammadi, T. Mallikarjunappa. Optimal Vs 1/N Diversification and Portfolio Evaluation: A study on Indian Stock Market. Asian Journal of Management. 2019; 10(3): 248-254.

Received on 30.05.2023 Modified on 13.09.2023

Asian Journal of Management. 2024;15(1):39-44.

DOI: 10.52711/2321-5763.2024.00007