IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v45y2022ics1544612321002191.html
   My bibliography  Save this article

Research on the dynamic evolution and its influencing factors of stock correlation network in the Chinese new energy market

Author

Listed:
  • Liu, Wei
  • Ma, Qianting
  • Liu, Xiaoxing

Abstract

Based on the correlation of stock price volatility, this paper constructs a stock correlation network model in the Chinese new energy market, focusing on the dynamic evolution of the stock correlation network and its influencing factors. The results show that: (i) the stock correlation network displays the small world feature in the dynamic evolution process. (ii) the network entropy effectively depicts the change direction of network structure and describes the new energy market volatility. (iii) the network centrality ranking in the new energy market is mainly affected by new energy enterprises’ internal characteristic variables.

Suggested Citation

  • Liu, Wei & Ma, Qianting & Liu, Xiaoxing, 2022. "Research on the dynamic evolution and its influencing factors of stock correlation network in the Chinese new energy market," Finance Research Letters, Elsevier, vol. 45(C).
  • Handle: RePEc:eee:finlet:v:45:y:2022:i:c:s1544612321002191
    DOI: 10.1016/j.frl.2021.102138
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612321002191
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.frl.2021.102138?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shahzad, Syed Jawad Hussain & Hoang, Thi Hong Van & Arreola-Hernandez, Jose, 2019. "Risk spillovers between large banks and the financial sector: Asymmetric evidence from Europe," Finance Research Letters, Elsevier, vol. 28(C), pages 153-159.
    2. Libo Yin & Xiyuan Ma, 2020. "Oil shocks and stock volatility: new evidence via a Bayesian, graph-based VAR approach," Applied Economics, Taylor & Francis Journals, vol. 52(11), pages 1163-1180, March.
    3. Lahmiri, Salim & Bekiros, Stelios, 2017. "Disturbances and complexity in volatility time series," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 38-42.
    4. Xi, Xian & An, Haizhong, 2018. "Research on energy stock market associated network structure based on financial indicators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1309-1323.
    5. Nguyen, Q. & Nguyen, N.K. K. & Nguyen, L.H. N., 2019. "Dynamic topology and allometric scaling behavior on the Vietnamese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 235-243.
    6. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
    7. Tan, Ruipeng & Tang, Di & Lin, Boqiang, 2018. "Policy impact of new energy vehicles promotion on air quality in Chinese cities," Energy Policy, Elsevier, vol. 118(C), pages 33-40.
    8. Samuel Ronnqvist & Peter Sarlin, 2014. "Bank Networks from Text: Interrelations, Centrality and Determinants," Papers 1406.7752, arXiv.org, revised Jul 2015.
    9. Hana Milanov & Dean A. Shepherd, 2013. "The importance of the first relationship: The ongoing influence of initial network on future status," Strategic Management Journal, Wiley Blackwell, vol. 34(6), pages 727-750, June.
    10. Bouri, Elie & Lucey, Brian & Saeed, Tareq & Vo, Xuan Vinh, 2020. "Extreme spillovers across Asian-Pacific currencies: A quantile-based analysis," International Review of Financial Analysis, Elsevier, vol. 72(C).
    11. Hunt Allcott & Judd B. Kessler, 2019. "The Welfare Effects of Nudges: A Case Study of Energy Use Social Comparisons," American Economic Journal: Applied Economics, American Economic Association, vol. 11(1), pages 236-276, January.
    12. Samuel R�nnqvist & Peter Sarlin, 2015. "Bank networks from text: interrelations, centrality and determinants," Quantitative Finance, Taylor & Francis Journals, vol. 15(10), pages 1619-1635, October.
    13. Holme, Petter & Min Park, Sung & Kim, Beom Jun & Edling, Christofer R., 2007. "Korean university life in a network perspective: Dynamics of a large affiliation network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 821-830.
    14. Bouri, Elie & Gupta, Rangan & Hosseini, Seyedmehdi & Lau, Chi Keung Marco, 2018. "Does global fear predict fear in BRICS stock markets? Evidence from a Bayesian Graphical Structural VAR model," Emerging Markets Review, Elsevier, vol. 34(C), pages 124-142.
    15. Tsallis, Constantino & Mendes, RenioS. & Plastino, A.R., 1998. "The role of constraints within generalized nonextensive statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 261(3), pages 534-554.
    16. Li, Huajiao & An, Haizhong & Fang, Wei & Wang, Yue & Zhong, Weiqiong & Yan, Lili, 2017. "Global energy investment structure from the energy stock market perspective based on a Heterogeneous Complex Network Model," Applied Energy, Elsevier, vol. 194(C), pages 648-657.
    17. Rasoulinezhad, Ehsan, 2020. "Energy Trade and Economic Integration between the Commonwealth Independent States and China," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 35(1), pages 172-190.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Joanna Rosak-Szyrocka & Justyna Żywiołek & Maciej Mrowiec, 2022. "Analysis of Customer Satisfaction with the Quality of Energy Market Services in Poland," Energies, MDPI, vol. 15(10), pages 1-24, May.
    2. Yang, Ming-Yuan & Wu, Zhen-Guo & Wu, Xin, 2022. "An empirical study of risk diffusion in the cryptocurrency market based on the network analysis," Finance Research Letters, Elsevier, vol. 50(C).
    3. Zhu, Mingxue & Zhang, Hua & Xing, Wanli & Zhou, Xuanru & Wang, Lu & Sun, Haoyu, 2023. "Research on price transmission in Chinese mining stock market: Based on industry," Resources Policy, Elsevier, vol. 83(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Samuel Ronnqvist & Peter Sarlin, 2015. "Detect & Describe: Deep learning of bank stress in the news," Papers 1507.07870, arXiv.org.
    2. Samuel Ronnqvist & Peter Sarlin, 2016. "Bank distress in the news: Describing events through deep learning," Papers 1603.05670, arXiv.org, revised Dec 2016.
    3. Zhibin Niu & Runlin Li & Junqi Wu & Dawei Cheng & Jiawan Zhang, 2020. "iConViz: Interactive Visual Exploration of the Default Contagion Risk of Networked-Guarantee Loans," Papers 2006.09542, arXiv.org, revised Aug 2020.
    4. Christopher Gerling, 2023. "Company2Vec -- German Company Embeddings based on Corporate Websites," Papers 2307.09332, arXiv.org.
    5. Li, Jingyu & Li, Jianping & Zhu, Xiaoqian, 2020. "Risk dependence between energy corporations: A text-based measurement approach," International Review of Economics & Finance, Elsevier, vol. 68(C), pages 33-46.
    6. Fang, Ming & Taylor, Stephen & Uddin, Ajim, 2022. "The network structure of overnight index swap rates," Finance Research Letters, Elsevier, vol. 46(PB).
    7. Khalfaoui, Rabeh & Hammoudeh, Shawkat & Rehman, Mohd Ziaur, 2023. "Spillovers and connectedness among BRICS stock markets, cryptocurrencies, and uncertainty: Evidence from the quantile vector autoregression network," Emerging Markets Review, Elsevier, vol. 54(C).
    8. Fang, Libing & Sun, Boyang & Li, Huijing & Yu, Honghai, 2018. "Systemic risk network of Chinese financial institutions," Emerging Markets Review, Elsevier, vol. 35(C), pages 190-206.
    9. Zhibin Niu & Junqi Wu & Dawei Cheng & Jiawan Zhang, 2021. "Regshock: Interactive Visual Analytics of Systemic Risk in Financial Networks," Papers 2104.11863, arXiv.org.
    10. Qian, Biyu & Wang, Gang-Jin & Feng, Yusen & Xie, Chi, 2022. "Partial cross-quantilogram networks: Measuring quantile connectedness of financial institutions," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    11. Thomas Forss & Peter Sarlin, 2017. "News-sentiment networks as a risk indicator," Papers 1706.05812, arXiv.org, revised May 2018.
    12. Wu, Kai & Zhu, Jingran & Xu, Mingli & Yang, Lu, 2020. "Can crude oil drive the co-movement in the international stock market? Evidence from partial wavelet coherence analysis," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    13. Naif Alotaibi & A. S. Al-Moisheer & Ibrahim Elbatal & Mansour Shrahili & Mohammed Elgarhy & Ehab M. Almetwally, 2023. "Half Logistic Inverted Nadarajah–Haghighi Distribution under Ranked Set Sampling with Applications," Mathematics, MDPI, vol. 11(7), pages 1, April.
    14. John Beshears & James J. Choi & David Laibson & Brigitte C. Madrian & William L. Skimmyhorn, 2022. "Borrowing to Save? The Impact of Automatic Enrollment on Debt," Journal of Finance, American Finance Association, vol. 77(1), pages 403-447, February.
    15. Brülisauer, Marcel & Goette, Lorenz & Jiang, Zhengyi & Schmitz, Jan & Schubert, Renate, 2020. "Appliance-specific feedback and social comparisons: Evidence from a field experiment on energy conservation," Energy Policy, Elsevier, vol. 145(C).
    16. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2020. "Revising the Impact of Financial and Non-Financial Global Stock Market Volatility Shocks," MPRA Paper 103019, University Library of Munich, Germany.
    17. Castro-Santa, Juana & Drews, Stefan & Bergh, Jeroen van den, 2023. "Nudging low-carbon consumption through advertising and social norms," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 102(C).
    18. Bernardi, Mauro & Costola, Michele, 2019. "High-dimensional sparse financial networks through a regularised regression model," SAFE Working Paper Series 244, Leibniz Institute for Financial Research SAFE.
    19. Fenghua Wen & Yujie Yuan & Wei‐Xing Zhou, 2021. "Cross‐shareholding networks and stock price synchronicity: Evidence from China," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 914-948, January.
    20. Francisco Costa & François Gerard, 2021. "Hysteresis and the Welfare Effect of Corrective Policies: Theory and Evidence from an Energy-Saving Program," Journal of Political Economy, University of Chicago Press, vol. 129(6), pages 1705-1743.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:45:y:2022:i:c:s1544612321002191. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.