IDEAS home Printed from https://ideas.repec.org/a/sae/sagope/v13y2023i3p21582440231193340.html
   My bibliography  Save this article

Research on the Spatial Correlation and Influence Factors of Regional Internet Finance in China

Author

Listed:
  • Haihua Yu
  • Zhiyi Zhuo
  • Jing Zhang

Abstract

Based on the Internet Finance development index developed by Peking University in investigating 31 provinces, we use social network analysis to investigate the spatial correlation and influencing factors of the Internet Finance of China’s provinces. The research shows that the spatial correlation of Internet Finance of China’s provinces has significant characteristics of a regional gradient. The correlation among their correlation is moderate, more closely related, smooth, or weak. All provinces gather to form four plates; they play their respective functional advantages and have different statuses, functions, and roles. The differences between provinces in industrial structure, degree of marketization, infrastructure, degree of informatization, and geographical distance significantly affect the spatial correlation of Internet finance development in China, and the degree of influence decreases in turn. Our results might positively affect policymakers in promoting the coordinated development of regional Internet finance in China.

Suggested Citation

  • Haihua Yu & Zhiyi Zhuo & Jing Zhang, 2023. "Research on the Spatial Correlation and Influence Factors of Regional Internet Finance in China," SAGE Open, , vol. 13(3), pages 21582440231, August.
    • Handle: RePEc:sae:sagope:v:13:y:2023:i:3:p:21582440231193340
    DOI: 10.1177/21582440231193340
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/21582440231193340
    Download Restriction: no
    File URL: https://libkey.io/10.1177/21582440231193340?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
    ---><---

    References listed on IDEAS

    as
    1. Tola, Vincenzo & Lillo, Fabrizio & Gallegati, Mauro & Mantegna, Rosario N., 2008. "Cluster analysis for portfolio optimization," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 235-258, January.
    2. Ping Xie & Chuanwei Zou & Haier Liu, 2016. "The fundamentals of internet finance and its policy implications in China," China Economic Journal, Taylor & Francis Journals, vol. 9(3), pages 240-252, September.
    3. Feng Guo & Sherry Tao Kong & Jingyi Wang, 2016. "General patterns and regional disparity of internet finance development in China: Evidence from the Peking University Internet Finance Development Index," China Economic Journal, Taylor & Francis Journals, vol. 9(3), pages 253-271, September.
    4. Bai, Caiquan & Yan, Hong & Yin, Shanggang & Feng, Chen & Wei, Qian, 2021. "Exploring the development trend of internet finance in China: Perspective from club convergence," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    5. Zhuming Chen & Yushan Li & Yawen Wu & Junjun Luo, 2017. "The transition from traditional banking to mobile internet finance: an organizational innovation perspective - a comparative study of Citibank and ICBC," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-16, December.
    6. Yan Shen & Yiping Huang, 2016. "Introduction to the special issue: Internet finance in China," China Economic Journal, Taylor & Francis Journals, vol. 9(3), pages 221-224, September.
    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. Dou Wenkang & Zhang Jie, 2024. "Spatial Pattern and Driving Mechanism of Urban Taxi Fares in China," SAGE Open, , vol. 14(2), pages 21582440241, April.

    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. Zhu Yongjie, 2023. "Enterprise life cycle, financial technology and digital transformation of banks—Evidence from China," Australian Economic Papers, Wiley Blackwell, vol. 62(3), pages 486-500, September.
    2. Zhou, Chao & Liao, Jinglin, 2024. "Home country digital finance development and post-entry internationalization speed of emerging market SMEs: Empirical evidence from China," International Review of Financial Analysis, Elsevier, vol. 91(C).
    3. Morshadul Hasan & Thuhid Noor & Jiechao Gao & Muhammad Usman & Mohammad Zoynul Abedin, 2023. "Rural Consumers’ Financial Literacy and Access to FinTech Services," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 14(2), pages 780-804, June.
    4. Yan Wang & Yuan Gong & Caiquan Bai & Hong Yan & Xing Yi, 2023. "Exploring the convergence patterns of PM2.5 in Chinese cities," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(1), pages 708-733, January.
    5. Sebastiano Michele Zema & Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2021. "Mesoscopic Structure of the Stock Market and Portfolio Optimization," Papers 2112.06544, arXiv.org.
    6. Zhou Rongxi & Xiong Yahui & Wang Ning & Wang Xizu, 2019. "Coupling Degree Evaluation of China’s Internet Financial Ecosystem Based on Entropy Method and Principal Component Analysis," Journal of Systems Science and Information, De Gruyter, vol. 7(5), pages 399-421, October.
    7. Sun, Xiaoyan & Xie, Xuanli, 2024. "How does digital finance promote entrepreneurship? The roles of traditional financial institutions and BigTech firms," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
    8. Djauhari, Maman Abdurachman & Gan, Siew Lee, 2015. "Optimality problem of network topology in stocks market analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 108-114.
    9. Nikolaus Hautsch & Lada M. Kyj & Peter Malec, 2015. "Do High‐Frequency Data Improve High‐Dimensional Portfolio Allocations?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 263-290, March.
    10. Justo Puerto & Moises Rodr'iguez-Madrena & Andrea Scozzari, 2019. "Location and portfolio selection problems: A unified framework," Papers 1907.07101, arXiv.org.
    11. Mahdi Massahi & Masoud Mahootchi & Alireza Arshadi Khamseh, 2020. "Development of an efficient cluster-based portfolio optimization model under realistic market conditions," Empirical Economics, Springer, vol. 59(5), pages 2423-2442, November.
    12. Nicoló Musmeci & Tomaso Aste & T Di Matteo, 2015. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-24, March.
    13. Anna Eugenia Omarini, 2018. "Banks and Fintechs: How to Develop a Digital Open Banking Approach for the Bank’s Future," International Business Research, Canadian Center of Science and Education, vol. 11(9), pages 23-36, September.
    14. Gautier Marti & Frank Nielsen & Philippe Donnat & S'ebastien Andler, 2016. "On clustering financial time series: a need for distances between dependent random variables," Papers 1603.07822, arXiv.org.
    15. R'emy Chicheportiche, 2013. "Non-linear dependences in finance," Papers 1309.5073, arXiv.org.
    16. Dimitar Kitanovski & Igor Mishkovski & Viktor Stojkoski & Miroslav Mirchev, 2024. "Network-based diversification of stock and cryptocurrency portfolios," Papers 2408.11739, arXiv.org.
    17. Wiweko Probojakti & Hamidah Nayati Utami & Arik Prasetya & Muhammad Faisal Riza, 2024. "Building Sustainable Competitive Advantage in Banking through Organizational Agility," Sustainability, MDPI, vol. 16(19), pages 1-15, September.
    18. Wang, Jianda & Dong, Kangyin & Sha, Yezhou & Yan, Cheng, 2022. "Envisaging the carbon emissions efficiency of digitalization: The case of the internet economy for China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    19. N. C. Suganya & G. A. Vijayalakshmi Pai, 2010. "Pareto‐archived evolutionary wavelet network for financial constrained portfolio optimization," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 17(2), pages 59-90, April.
    20. Jyotirmayee Behera & Pankaj Kumar, 2024. "Implementation of machine learning in $$\ell _{\infty }$$ ℓ ∞ -based sparse Sharpe ratio portfolio optimization: a case study on Indian stock market," Operational Research, Springer, vol. 24(4), pages 1-26, December.

    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:sae:sagope:v:13:y:2023:i:3:p:21582440231193340. 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: SAGE Publications (email available below). General contact details of provider: .

    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.