IDEAS home Printed from https://ideas.repec.org/a/mnb/finrev/v20y2021i3p31-58.html
   My bibliography  Save this article

A Snapshot of the Ownership Network of the Budapest Stock Exchange

Author

Listed:
  • Marton Gosztonyi

    (Budapest Business School)

Abstract

In this study, I use the toolkit of network research to explore the network of ownership relations of entities present on the Budapest Stock Exchange as issuers in 2020, applying static methods and exponential random graph modelling (ERGM) analysis. In the snapshot typology and simulation-based capture of the network, not only the network of relations between issuers present on the stock market is analysed, but also the ownership relations of companies connected to the network but not listed on the stock market; thus, the study addresses the ownership network associated with the stock exchange as a whole. The research results provide us with an accurate answer about the morphological characteristics of the network, the network factors determining centrality, the hierarchy of the network, and the evolution of the network with the help of simulations. The study may allow us to obtain a clearer picture of the interlinkages and clusters of companies listed on the stock market, which can be used as a basis for subsequent longitudinal analyses.

Suggested Citation

  • Marton Gosztonyi, 2021. "A Snapshot of the Ownership Network of the Budapest Stock Exchange," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), vol. 20(3), pages 31-58.
  • Handle: RePEc:mnb:finrev:v:20:y:2021:i:3:p:31-58
    as

    Download full text from publisher

    File URL: https://en-hitelintezetiszemle.mnb.hu/letoltes/fer-20-3-st2-gosztonyi.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Raj Kumar Pan & Sitabhra Sinha, 2007. "Collective behavior of stock price movements in an emerging market," Papers 0704.0773, arXiv.org, revised Nov 2007.
    2. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    3. Sankowska, Anna & Siudak, Dariusz, 2016. "The small world phenomenon and assortative mixing in Polish corporate board and director networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 443(C), pages 309-315.
    4. Boginski, Vladimir & Butenko, Sergiy & Pardalos, Panos M., 2005. "Statistical analysis of financial networks," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 431-443, February.
    5. Singh, Deeksha & Delios, Andrew, 2017. "Corporate governance, board networks and growth in domestic and international markets: Evidence from India," Journal of World Business, Elsevier, vol. 52(5), pages 615-627.
    6. Tao You & Paweł Fiedor & Artur Hołda, 2015. "Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information," JRFM, MDPI, vol. 8(2), pages 1-19, June.
    Full references (including those not matched with items on IDEAS)

    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. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    2. Hazem Krichene & Mhamed-Ali El-Aroui, 2018. "Artificial stock markets with different maturity levels: simulation of information asymmetry and herd behavior using agent-based and network models," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(3), pages 511-535, October.
    3. Bing Li, 2017. "Network Evolution of the Chinese Stock Market: A Study based on the CSI 300 Index," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(3), pages 1-5.
    4. Millington, Tristan & Niranjan, Mahesan, 2021. "Construction of minimum spanning trees from financial returns using rank correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    5. Zhu, Jia & Wei, Daijun, 2021. "Analysis of stock market based on visibility graph and structure entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).
    6. Eckrot, A. & Jurczyk, J. & Morgenstern, I., 2016. "Ising model of financial markets with many assets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 250-254.
    7. Tristan Millington & Mahesan Niranjan, 2020. "Construction of Minimum Spanning Trees from Financial Returns using Rank Correlation," Papers 2005.03963, arXiv.org, revised Nov 2020.
    8. Charu Sharma & Amber Habib, 2019. "Uncovering networks amongst stocks returns by studying nonlinear interactions in high frequency data of the Indian Stock Market using mutual information," Papers 1903.03407, arXiv.org.
    9. khoojine, Arash Sioofy & Han, Dong, 2019. "Network analysis of the Chinese stock market during the turbulence of 2015–2016 using log-returns, volumes and mutual information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1091-1109.
    10. Charu Sharma & Amber Habib, 2019. "Mutual information based stock networks and portfolio selection for intraday traders using high frequency data: An Indian market case study," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-19, August.
    11. Esmalifalak, Hamidreza, 2022. "Euclidean (dis)similarity in financial network analysis," Global Finance Journal, Elsevier, vol. 53(C).
    12. Wang, Gang-Jin & Xie, Chi & Chen, Shou & Yang, Jiao-Jiao & Yang, Ming-Yan, 2013. "Random matrix theory analysis of cross-correlations in the US stock market: Evidence from Pearson’s correlation coefficient and detrended cross-correlation coefficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3715-3730.
    13. Chen, Ray-Bing & Chen, Ying & Härdle, Wolfgang K., 2014. "TVICA—Time varying independent component analysis and its application to financial data," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 95-109.
    14. Abduraimova, Kumushoy, 2022. "Contagion and tail risk in complex financial networks," Journal of Banking & Finance, Elsevier, vol. 143(C).
    15. Josselin Garnier & Knut Sølna, 2018. "Option pricing under fast-varying and rough stochastic volatility," Annals of Finance, Springer, vol. 14(4), pages 489-516, November.
    16. Anup Banerjee & Mattias Nordqvist & Karin Hellerstedt, 2020. "The role of the board chair—A literature review and suggestions for future research," Corporate Governance: An International Review, Wiley Blackwell, vol. 28(6), pages 372-405, November.
    17. Jean-Philippe Bouchaud & Julien Kockelkoren & Marc Potters, 2006. "Random walks, liquidity molasses and critical response in financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 6(2), pages 115-123.
    18. Juan C. Henao-Londono & Sebastian M. Krause & Thomas Guhr, 2021. "Price response functions and spread impact in correlated financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 94(4), pages 1-20, April.
    19. Westerhoff, Frank H. & Dieci, Roberto, 2006. "The effectiveness of Keynes-Tobin transaction taxes when heterogeneous agents can trade in different markets: A behavioral finance approach," Journal of Economic Dynamics and Control, Elsevier, vol. 30(2), pages 293-322, February.
    20. Eduardo Abi Jaber, 2022. "The characteristic function of Gaussian stochastic volatility models: an analytic expression," Working Papers hal-02946146, HAL.

    More about this item

    Keywords

    Budapest Stock Exchange; complex systems; network analysis; company ownership;
    All these keywords.

    JEL classification:

    • H54 - Public Economics - - National Government Expenditures and Related Policies - - - Infrastructures
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation

    Statistics

    Access and download statistics

    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:mnb:finrev:v:20:y:2021:i:3:p:31-58. 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: Morvay Endre (email available below). General contact details of provider: https://edirc.repec.org/data/mnbgvhu.html .

    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.