Topological structural analysis of China's new energy stock market: a multi-dimensional data network perspective
Abstract
In this paper, we apply an RV coefficient network to investigate the topological structure of China’s new energy stock market via daily prices of 60 component stocks of CSI (China Stock Index) New Energy Index spanning the period January 4, 2012 to March 29, 2019. Compared with the Pearson correlation coefficient, RV coefficient can better reflect the similarity between stocks from the perspective of multi-dimensional data. The empirical result indicates that (1) the scale-free characteristics of China’s new energy stock market are not significant; (2) the new energy storage is the leading sub-sector of the new energy sector and the new energy interactive equipment plays a connecting role between renewable energy production and new energy storage; (3) the most influential stock in the network is Group DMEGC Magnetics Co., Ltd., Xiamen Tungsten Co., Ltd. and GEM Co., Ltd. play an important role in the network connection. These findings are of great significance to understand the interaction between Chinese new energy stocks and the pricing mechanism of stocks. The authority should pay more attention to the new energy storage industry. Investor’s portfolios can be optimized according to the influence assessment of stocks and sub-sectors.
First published online 26 May 2020
Keyword : new energy stock market, RV coefficient network, topological properties, minimum spanning tree
How to Cite
Yin, K., Liu, Z., Huang, C., & Liu, P. (2020). Topological structural analysis of China’s new energy stock market: a multi-dimensional data network perspective. Technological and Economic Development of Economy, 26(5), 1030-1051. https://doi.org/10.3846/tede.2020.12723
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Chang, M. C., & Shieh, H. S. (2017). The relations between energy efficiency and GDP in the Baltic Sea Region and Non-Baltic Sea Region. Transformations in Business & Economics, 16(2), 235–247.
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.
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Garas, A., & Argyrakis, P. (2007). Correlation study of the Athens Stock Exchange. Physica A Statistical Mechanics & Its Applications, 380(7), 399–410. https://doi.org/10.1016/j.physa.2007.02.097
Gopalakrishnan, K., & Gkritza, K. N. (2014). Forecasting transportation infrastructure impacts of renewable energy industry using neural networks. Technological and Economic Development of Economy, 19(Supplement_1), S157–S175. https://doi.org/10.3846/20294913.2013.876690
Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380. https://doi.org/10.1016/B978-0-12-442450-0.50025-0
Hayes, B. (2000). Computing science: Graph theory in practice: Part I. American Scientist, 88(1), 9–13. https://doi.org/10.1511/2000.1.9
He, J. (2015). China’s INDC and non-fossil energy development. Advances in Climate Change Research, 6(3–4), 210–215. https://doi.org/10.1016/j.accre.2015.11.007
Huang, W. Q., Zhuang, X. T., & Yao, S. (2009). A network analysis of the Chinese stock market. Physica A: Statistical Mechanics and its Applications, 388(14), 2956–2964. https://doi.org/10.1016/j.physa.2009.03.028
Jung, W. S., Chae, S., Yang, J. S., & Moon, H. T. (2006). Characteristics of the Korean stock market correlations. Physica A: Statistical Mechanics and its Applications, 361(1), 263–271. https://doi.org/10.1016/j.physa.2005.06.081
Kazemilari, M., Mohamadi, A., Mardani, A., & Streimikis, J. (2019). Network topology of renewable energy companies: minimal spanning tree and sub-dominant ultrametric for the American stock. Technological and Economic Development of Economy, 25(2), 168–187. https://doi.org/10.3846/tede.2019.7686
Kazemilari, M., & Djauhari, M. A. (2015). Correlation network analysis for multi-dimensional data in stock market. Physica A: Statistical Mechanics and its Applications, 429, 62–75. https://doi.org/10.1016/j.physa.2015.02.052
Kazemilari, M., Mardani, A., Streimikiene, D., & Zavadskas, E. K. (2017). An overview of renewable energy companies in stock exchange: Evidence from minimal spanning tree approach. Renewable Energy, 102(Part A), 107–117. https://doi.org/10.1016/j.renene.2016.10.029
Kim, H. J., Lee, Y., Kahng, B., & Kim, I. (2002). Weighted scale-free network in financial correlations. Journal of the Physical Society of Japan, 71(9), 2133–2136. https://doi.org/10.1143/JPSJ.71.2133
Kruskal, J. B. (1956). On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical society, 7(1), 48–50. https://doi.org/10.2307/2033241
Kumar, S., Managi, S., & Matsuda, A. (2012). Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis. Energy Economics, 34(1), 215–226. https://doi.org/10.1016/j.eneco.2011.03.002
Lyócsa, Š., Výrost, T., & Baumöhl, E. (2012). Stock market networks: the dynamic conditional correlation approach. Physica A: Statistical Mechanics and its Applications, 391, 4147–4158. https://doi.org/10.1016/j.physa.2012.03.038
Majapa, M., & Gossel, S. J. (2016). Topology of the South African stock market network across the 2008 financial crisis. Physica A: Statistical Mechanics and its Applications, 445, 35–47. https://doi.org/10.1016/j.physa.2015.10.108
Managi, S., & Okimoto, T. (2013). Does the price of oil interact with clean energy prices in the stock market? Japan and the World Economy, 27, 1–9. https://doi.org/10.1016/j.japwor.2013.03.003
Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 1(11), 193–197. https://doi.org/10.1007/s100510050929
Nieminen, J. (1974). On the centrality in a graph. Scandinavian Journal of Psychology, 15(1), 332–336. https://doi.org/10.1111/j.1467-9450.1974.tb00598.x
Nobi, A., Lee, S., Kim, D. H., & Lee, J. W. (2014). Correlation and network topologies in global and local stock indices. Physics Letters A, 378(34), 2482–2489. https://doi.org/10.1016/j.physleta.2014.07.009
Onnela, J. P., Kaski, K., & Kertész, J. (2004). Clustering and information in correlation based financial networks. The European Physical Journal B, 38(2), 353–362. https://doi.org/10.1140/epjb/e2004-00128-7
Popescu, G. H., Andrei, J. V., Nica, E., Mieilă, M., & Panait, M. (2019). Analysis on the impact of investments, energy use and domestic material consumption in changing the Romanian economic paradigm. Technological and Economic Development of Economy, 25(1), 59–81. https://doi.org/10.3846/tede.2019.7454
Prim, R. C. (1957). Shortest connection networks and some generalizations. The Bell System Technical Journal, 36(6), 1389–1401. https://doi.org/10.1002/j.1538-7305.1957.tb01515.x
Qiao, H., Xia, Y., & Li, Y. (2016). Can network linkage effects determine return? Evidence from Chinese stock market. Plos One, 11(6), e1567846. https://doi.org/10.1371/journal.pone.0156784
Robert, P., & Escoufier, Y. (1976). A unifying tool for linear multivariate statistical methods: the RV‐coefficient. Journal of the Royal Statistical Society: Series C (Applied Statistics), 25(3), 257–265. https://doi.org/10.2307/2347233
Roy, R. B., & Sarkar, U. K. (2011). Identifying influential stock indices from global stock markets: A social network analysis approach. Procedia Computer Science, 5, 442–449. https://doi.org/10.1016/j.procs.2011.07.057
Sadorsky, P. (2012). Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Economics, 34(1), 248–255. https://doi.org/10.1016/j.eneco.2011.03.006
San Yee, L., Salleh, R. M., & Asrah, N. M. (2018). Multidimensional minimal spanning tree: the bursa Malaysia. Journal of Science and Technology, 10(2).
Soava, G., Mehedintu, A., Sterpu, M., & Raduteanu, M. (2018). Impact of renewable energy consumption on economic growth: evidence from European union countries. Technological and Economic Development of Economy, 24(3), 914–932. https://doi.org/10.3846/tede.2018.1426
Tumminello, M., Aste, T., Di Matteo, T., & Mantegna, R. N. (2005). A tool for filtering information in complex systems. Proceedings of the National Academy of Sciences, 102(30), 10421–10426. https://doi.org/10.1073/pnas.0500298102
Tumminello, M., Aste, T., Di Matteo, T., & Mantegna, R. N. (2007). Correlation based networks of equity returns sampled at different time horizons. The European Physical Journal B, 55(2), 209–217. https://doi.org/10.1140/epjb/e2006-00414-4
Wang, C., Chen, Y., & Jin, X. (2018). Research on the effect of International oil price pass-through on the China’s new energy market. The Journal of Quantitative & Technical Economics, (4), 131–146.
Wang, G. J., Xie, C., Chen, Y. J., & Chen, S. (2013). Statistical properties of the foreign exchange network at different time scales: evidence from detrended cross correlation coefficient and minimum spanning tree. Entropy, 15(5), 1643–1662. https://doi.org/10.3390/e15051643
Yao, L., & Chang, Y. (2014). Energy security in China: a quantitative analysis and policy implications. Energy Policy, 67, 595–604. https://doi.org/10.1016/j.enpol.2013.12.047
Yin, K. D., Liu, Z., & Liu, P. D. (2017). Trend analysis of global stock market linkage based on a dynamic conditional correlation network. Journal of Business Economics and Management, 18(4), 779–800. https://doi.org/10.3846/16111699.2017.1341849
Zhang, G., & Du, Z. (2017). Co-movements among the stock prices of new energy, high-technology and fossil fuel companies in China. Energy, 135, 249–256. https://doi.org/10.1016/j.energy.2017.06.103
Aiello, W., Chung, F., & Lu, L. (2001). A random graph model for power law graphs. Experimental Mathematics, 10(1), 53–66. https://doi.org/10.1080/10586458.2001.10504428
Albert, R., & Barabási, A. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47. https://doi.org/10.1103/RevModPhys.74.47
Aste, T., Shaw, W., & Di Matteo, T. (2010). Correlation structure and dynamics in volatile markets. New Journal of Physics, 12(8), 85009. https://doi.org/10.1088/1367-2630/12/8/085009
Bloomberg New Energy Finance. (2018). Global trends in renewable energy investment 2018. UNEP United Nations Environment Programme, Bloomberg New Energy Finance. http://www.iberglobal.com/files/2018/renewable_trends.pdf
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., & Hwang, D. U. (2006). Complex networks: Structure and dynamics. Physics Reports, 4–5(424), 175–308. https://doi.org/10.1016/j.physrep.2005.10.009
Boginski, V., Butenko, S., & Pardalos, P. M. (2005). Statistical analysis of financial networks. Computational Statistics & Data Analysis, 48(2), 431–443. https://doi.org/10.1016/j.csda.2004.02.004
Bohl, M. T., Kaufmann, P., & Stephan, P. M. (2013). From hero to zero: Evidence of performance reversal and speculative bubbles in German renewable energy stocks. Energy Economics, 37, 40–51. https://doi.org/10.1016/j.eneco.2013.01.006
Bonanno, G., Caldarelli, G., Lillo, F., & Mantegna, R. N. (2003). Topology of correlation-based minimal spanning trees in real and model markets. Physical Review E, 68(4), 46130. https://doi.org/10.1103/PhysRevE.68.046130
Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71. https://doi.org/10.1016/j.socnet.2004.11.008
BP. (2017). BP Statistical Review of world energy June, 2017. British Petroleum. http://large.stanford.edu/courses/2018/ph241/kuet2/docs/bp-2017.pdf
Brida, J. G., & Risso, W. A. (2008). Multidimensional minimal spanning tree: The Dow Jones case. Physica A: Statistical Mechanics and its Applications, 387(21), 5205–5210. https://doi.org/10.1016/j.physa.2008.05.009
Burt, R. (1992). Structural holes: the social structure of competition. Harvard University Press.
Chang, M. C., & Shieh, H. S. (2017). The relations between energy efficiency and GDP in the Baltic Sea Region and Non-Baltic Sea Region. Transformations in Business & Economics, 16(2), 235–247.
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1–9.
Djauhari, M. A., & Gan, S. L. (2016). Network topology of economic sectors. Journal of Statistical Mechanics: Theory and Experiment, 2016(9), 93401. https://doi.org/10.1088/1742-5468/2016/09/093401
Downs, E. S. (2004). The Chinese energy security debate. The China Quarterly, 177, 21–41. https://doi.org/10.1017/S0305741004000037
Dutta, A., Bouri, E., & Noor, M. H. (2018). Return and volatility linkages between CO2 emission and clean energy stock prices. Energy, 164, 803–810. https://doi.org/10.1016/j.energy.2018.09.055
Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339–350. https://doi.org/10.1198/073500102288618487
Escoufier, Y. (1973). Le traitement des variables vectorielles. Biometrics, 29(4), 751–760. https://doi.org/10.2307/2529140
Fang, J. Q., Wang, X. F., Zheng, Z. G., Bi, Q., Zeng, R. D., & Li, X. (2007). New interdisciplinary science: Network science (1). Progress in Physics, 27(3), 239.
Freeman, L. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. https://doi.org/10.2307/3033543
Galazka, M. (2011). Characteristics of the polish stock market correlations. International Review of Financial Analysis, 20(1), 1–5. https://doi.org/10.1016/j.irfa.2010.11.002
Garas, A., & Argyrakis, P. (2007). Correlation study of the Athens Stock Exchange. Physica A Statistical Mechanics & Its Applications, 380(7), 399–410. https://doi.org/10.1016/j.physa.2007.02.097
Gopalakrishnan, K., & Gkritza, K. N. (2014). Forecasting transportation infrastructure impacts of renewable energy industry using neural networks. Technological and Economic Development of Economy, 19(Supplement_1), S157–S175. https://doi.org/10.3846/20294913.2013.876690
Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380. https://doi.org/10.1016/B978-0-12-442450-0.50025-0
Hayes, B. (2000). Computing science: Graph theory in practice: Part I. American Scientist, 88(1), 9–13. https://doi.org/10.1511/2000.1.9
He, J. (2015). China’s INDC and non-fossil energy development. Advances in Climate Change Research, 6(3–4), 210–215. https://doi.org/10.1016/j.accre.2015.11.007
Huang, W. Q., Zhuang, X. T., & Yao, S. (2009). A network analysis of the Chinese stock market. Physica A: Statistical Mechanics and its Applications, 388(14), 2956–2964. https://doi.org/10.1016/j.physa.2009.03.028
Jung, W. S., Chae, S., Yang, J. S., & Moon, H. T. (2006). Characteristics of the Korean stock market correlations. Physica A: Statistical Mechanics and its Applications, 361(1), 263–271. https://doi.org/10.1016/j.physa.2005.06.081
Kazemilari, M., Mohamadi, A., Mardani, A., & Streimikis, J. (2019). Network topology of renewable energy companies: minimal spanning tree and sub-dominant ultrametric for the American stock. Technological and Economic Development of Economy, 25(2), 168–187. https://doi.org/10.3846/tede.2019.7686
Kazemilari, M., & Djauhari, M. A. (2015). Correlation network analysis for multi-dimensional data in stock market. Physica A: Statistical Mechanics and its Applications, 429, 62–75. https://doi.org/10.1016/j.physa.2015.02.052
Kazemilari, M., Mardani, A., Streimikiene, D., & Zavadskas, E. K. (2017). An overview of renewable energy companies in stock exchange: Evidence from minimal spanning tree approach. Renewable Energy, 102(Part A), 107–117. https://doi.org/10.1016/j.renene.2016.10.029
Kim, H. J., Lee, Y., Kahng, B., & Kim, I. (2002). Weighted scale-free network in financial correlations. Journal of the Physical Society of Japan, 71(9), 2133–2136. https://doi.org/10.1143/JPSJ.71.2133
Kruskal, J. B. (1956). On the shortest spanning subtree of a graph and the traveling salesman problem. Proceedings of the American Mathematical society, 7(1), 48–50. https://doi.org/10.2307/2033241
Kumar, S., Managi, S., & Matsuda, A. (2012). Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis. Energy Economics, 34(1), 215–226. https://doi.org/10.1016/j.eneco.2011.03.002
Lyócsa, Š., Výrost, T., & Baumöhl, E. (2012). Stock market networks: the dynamic conditional correlation approach. Physica A: Statistical Mechanics and its Applications, 391, 4147–4158. https://doi.org/10.1016/j.physa.2012.03.038
Majapa, M., & Gossel, S. J. (2016). Topology of the South African stock market network across the 2008 financial crisis. Physica A: Statistical Mechanics and its Applications, 445, 35–47. https://doi.org/10.1016/j.physa.2015.10.108
Managi, S., & Okimoto, T. (2013). Does the price of oil interact with clean energy prices in the stock market? Japan and the World Economy, 27, 1–9. https://doi.org/10.1016/j.japwor.2013.03.003
Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 1(11), 193–197. https://doi.org/10.1007/s100510050929
Nieminen, J. (1974). On the centrality in a graph. Scandinavian Journal of Psychology, 15(1), 332–336. https://doi.org/10.1111/j.1467-9450.1974.tb00598.x
Nobi, A., Lee, S., Kim, D. H., & Lee, J. W. (2014). Correlation and network topologies in global and local stock indices. Physics Letters A, 378(34), 2482–2489. https://doi.org/10.1016/j.physleta.2014.07.009
Onnela, J. P., Kaski, K., & Kertész, J. (2004). Clustering and information in correlation based financial networks. The European Physical Journal B, 38(2), 353–362. https://doi.org/10.1140/epjb/e2004-00128-7
Popescu, G. H., Andrei, J. V., Nica, E., Mieilă, M., & Panait, M. (2019). Analysis on the impact of investments, energy use and domestic material consumption in changing the Romanian economic paradigm. Technological and Economic Development of Economy, 25(1), 59–81. https://doi.org/10.3846/tede.2019.7454
Prim, R. C. (1957). Shortest connection networks and some generalizations. The Bell System Technical Journal, 36(6), 1389–1401. https://doi.org/10.1002/j.1538-7305.1957.tb01515.x
Qiao, H., Xia, Y., & Li, Y. (2016). Can network linkage effects determine return? Evidence from Chinese stock market. Plos One, 11(6), e1567846. https://doi.org/10.1371/journal.pone.0156784
Robert, P., & Escoufier, Y. (1976). A unifying tool for linear multivariate statistical methods: the RV‐coefficient. Journal of the Royal Statistical Society: Series C (Applied Statistics), 25(3), 257–265. https://doi.org/10.2307/2347233
Roy, R. B., & Sarkar, U. K. (2011). Identifying influential stock indices from global stock markets: A social network analysis approach. Procedia Computer Science, 5, 442–449. https://doi.org/10.1016/j.procs.2011.07.057
Sadorsky, P. (2012). Correlations and volatility spillovers between oil prices and the stock prices of clean energy and technology companies. Energy Economics, 34(1), 248–255. https://doi.org/10.1016/j.eneco.2011.03.006
San Yee, L., Salleh, R. M., & Asrah, N. M. (2018). Multidimensional minimal spanning tree: the bursa Malaysia. Journal of Science and Technology, 10(2).
Soava, G., Mehedintu, A., Sterpu, M., & Raduteanu, M. (2018). Impact of renewable energy consumption on economic growth: evidence from European union countries. Technological and Economic Development of Economy, 24(3), 914–932. https://doi.org/10.3846/tede.2018.1426
Tumminello, M., Aste, T., Di Matteo, T., & Mantegna, R. N. (2005). A tool for filtering information in complex systems. Proceedings of the National Academy of Sciences, 102(30), 10421–10426. https://doi.org/10.1073/pnas.0500298102
Tumminello, M., Aste, T., Di Matteo, T., & Mantegna, R. N. (2007). Correlation based networks of equity returns sampled at different time horizons. The European Physical Journal B, 55(2), 209–217. https://doi.org/10.1140/epjb/e2006-00414-4
Wang, C., Chen, Y., & Jin, X. (2018). Research on the effect of International oil price pass-through on the China’s new energy market. The Journal of Quantitative & Technical Economics, (4), 131–146.
Wang, G. J., Xie, C., Chen, Y. J., & Chen, S. (2013). Statistical properties of the foreign exchange network at different time scales: evidence from detrended cross correlation coefficient and minimum spanning tree. Entropy, 15(5), 1643–1662. https://doi.org/10.3390/e15051643
Yao, L., & Chang, Y. (2014). Energy security in China: a quantitative analysis and policy implications. Energy Policy, 67, 595–604. https://doi.org/10.1016/j.enpol.2013.12.047
Yin, K. D., Liu, Z., & Liu, P. D. (2017). Trend analysis of global stock market linkage based on a dynamic conditional correlation network. Journal of Business Economics and Management, 18(4), 779–800. https://doi.org/10.3846/16111699.2017.1341849
Zhang, G., & Du, Z. (2017). Co-movements among the stock prices of new energy, high-technology and fossil fuel companies in China. Energy, 135, 249–256. https://doi.org/10.1016/j.energy.2017.06.103