Share:


Topological structural analysis of China's new energy stock market: a multi-dimensional data network perspective

    Kedong Yin Affiliation
    ; Zhe Liu Affiliation
    ; Chong Huang Affiliation
    ; Peide Liu Affiliation

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, 1-22. https://doi.org/10.3846/tede.2020.12723
Published in Issue
May 26, 2020
Abstract Views
126
PDF Downloads
75
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Aceleanu, M. I., Șerban, A. C., Țîrcă, D. M., & Badea, L. (2018). The rural sustainable development through renewable energy. The case of Romania. Technological and Economic Development of Economy, 24(4), 1408–1434. https://doi.org/10.3846/20294913.2017.1303650

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