Author information:
Márton Gosztonyi: Budapest Business School, Research Fellow. E-mail: gosztonyi.marton@uni-bge.hu
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.
Cite as (APA):
Gosztonyi, M. (2021). A Snapshot of the Ownership Network of the Budapest Stock Exchange. Financial and Economic Review, 20(3), 31–58. https://doi.org/10.33893/FER.20.3.3158
Column:
Study
Journal of Economic Literature (JEL) codes:
H54, D53, L14
Keywords:
Budapest Stock Exchange, complex systems, network analysis, company ownership
References:
Atmanspacher, H. – Kurths, J. – Scheingraber, H. – Wackerbauer, R. – Witt, A. (1992): Complexity and meaning in nonlinear dynamical systems. Open Systems & Information Dynamics, 1(2): 269–289. https://doi.org/10.1007/BF02228949
Babu, R.R. – Kumar, S.U. (2003): Network Approach to Capture Co-movements of Global Stock Returns. Indian Institute of Management Calcutta, Working Paper, WPS. 676: 12–44.
Barabási, A.L. – Albert, R. (1999): Emergence of Scaling in Random Networks. Science, 286(5439): 509–512. https://doi.org/10.1126/science.286.5439.509
Borgatti, S.P. – Foster, P.C. (2003): The Network Paradigm in Organizational Research: A Review and Typology. Journal of Management, 29(6): 991–1013. https://doi.org/10.1016/S0149-2063(03)00087-4
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
Boginski, V. – Butenko, S. – Pardalos, P.M. (2006): Mining market data: A network approach. Computers & Operations Research, 33(11): 3171–3184. https://doi.org/10.1016/j.cor.2005.01.027
Cont, R. (2001): Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance, 1(2): 223–236. https://doi.org/10.1080/713665670
Chatterjee, S. – Diaconis, P. (2013): Estimating and understanding exponential random graph models. The Annals of Statistics, 41(5): 2428–2461. https://doi.org/10.1214/13-AOS1155
Chapelle, A. – Szafarz, A. (2005): Control consolidation with a threshold: an algorithm. IMA Journal of Management Mathematics, 18(3): 235–243. https://doi.org/10.1093/imaman/dpl016
Dimitrios, K. – Vasileios, O. (2015): A Network Analysis of the Greek Stock Market. Procedia Economics and Finance, 33: 340–349. https://doi.org/10.1016/S2212-5671(15)01718-9
Drożdż, S. – Grümmer, F. – Ruf, F. – Speth, J. (2001): Towards identifying the world stock market cross-correlations: DAX versus Dow Jones. Physica A: Statistical Mechanics and its Applications, 294(1–2): 226–234. https://doi.org/10.1016/S0378-4371(01)00119-4
Erdős, P. – Rényi, A. (1960): On the Evolution of Random Graphs. Publication of Mathematical Institute of the Hungarian Academy Sciences, 5(1): 17–60.
Frank, O. – Strauss, D. (1986): Markov Graphs. Journal of the American Statistical Association, 81: 832–842. https://doi.org/10.1080/01621459.1986.10478342
Forbes, K.J. – Rigobon, R. (2002): No Contagion, Only Interdependence: Measuring Stock Market Comovements. The Journal of Finance, 57(5): 2223–2261. https://doi.org/10.1111/0022-1082.00494
Garlaschelli, D. – Den Hollander, F. – Roccaverde, A. (2016): Ensemble nonequivalence in random graphs with modular structure. Journal of Physics A: Mathematical and Theoretical, 50(1), 015001. https://doi.org/10.1088/1751-8113/50/1/015001
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
Kazemilari, M. – Djauhari, M.A. (2015): Correlation network analysis for multi-dimensional data in stocks market. Physica A: Statistical Mechanics and its Applications, 429(1): 62–75. https://doi.org/10.1016/j.physa.2015.02.052
Khodami, P.A. – Bazraie, Y. (2013): Investigation on the Relationship between Product Market Competition with Board Structure and Disclosure Quality. Journal of Accounting Knowledge, 4(14): 51–66.
Khorshidvand, F. – Sarlak, A. (2017): Examining the Relationship between Corporate Governance and the Corporate Performance Valuation. Advances in Mathematical Finance and Applications, 2(3): 29–39. https://doi.org/10.22034/AMFA.2017.533097
Kim, K. – Kim, S.Y. – Ha, D.H. (2007): Characteristics of networks in financial markets. Computer physics communications, 177(1–2): 184–185. https://doi.org/10.1016/j.cpc.2007.02.037
Lee, G.S. – Djauhari, M.A. (2012): Stock Networks Analysis in Kuala Lumpur Stock Exchange. Malaysian Journal of Fundamental and Applied Sciences, 8(2): 45–61. https://doi.org/10.11113/mjfas.v8n2.124
Lusher, D. – Koskinen, J. – Robins, G. (eds.) (2013): Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications (Vol. 35). Cambridge University Press. https://doi.org/10.1017/CBO9780511894701
Mahdavi Ardekani, A. – Distinguin, I. – Tarazi, A. (2019): Interbank Network Characteristics, Monetary Policy ‘News’ and Sensitivity of Bank Stock Returns. Monetary Policy ‘News’ and Sensitivity of Bank Stock Return. https://doi.org/10.2139/ssrn.3520689
Mantegna, R.N. (1999): Hierarchical structure in financial markets. The European Physical Journal B – Condensed Matter and Complex Systems, 11(1): 193–197. https://doi.org/10.1007/s100510050929
McDonald, M. – Suleman, O. – Williams, S. – Howison, S. – Johnson, N.F. (2005): Detecting a currency’s dominance or dependence using foreign exchange network trees. Physical Review, 72(4): 106–121. https://doi.org/10.1103/PhysRevE.72.046106
Mehra, R. – Prescott, E.C. (1985): The equity premium: A puzzle. Journal of Monetary Economics, 15(2): 145–161. https://doi.org/10.1016/0304-3932(85)90061-3
Moghaddam, A.H. – Moghaddam, M.H. – Esfandyari, M. (2016): Stock market index prediction using artificial neural network. Journal of Economics, Finance and Administrative Science, 21(41): 89–93. https://doi.org/10.1016/j.jefas.2016.07.002
Münnix, M.C. – Schäfer, R. – Guhr, T. (2010): Impact of the tick-size on financial returns and correlations. Physica A: Statistical Mechanics and its Applications, 389(21): 4828–4843. https://doi.org/10.1016/j.physa.2010.06.037
Newman, M. – Barabasi, A.L. – Watts, D.J. (2006): The Structure And Dynamics of Networks. Princeton Studies in Complexity. Princeton University Press, Princeton.
Onnela, J.P. – Chakraborti, A. – Kaski, K. – Kertész, J. – Kanto, A. (2003): Dynamics of market correlations: Taxonomy and portfolio analysis. Physical Review, 68(5): 56–110. https://doi.org/10.1103/PhysRevE.68.056110
Pan, R. – Sinha, S. (2007): Collective behavior of stock price movements in an emerging market. Physical Review E, 76(4): 33–55. https://doi.org/10.1103/PhysRevE.76.046116
Paparrizos, K. (2003): Network Programming. Thessaloniki: University of Macedonia.
Peng, M.W. – Mutlu, C.C. – Sauerwald, S. – Au, K.Y. – Wang, D.Y.L. (2015): Board interlocks and corporate performance among firms listed abroad. Journal of Management History, 21(2): 257–282. https://doi.org/10.1108/JMH-08-2014-0132
Raddant, M. – Kenett, D.Y. (2021): Interconnectedness in the global financial market. Journal of International Money and Finance, 110(3): 77–91. https://doi.org/10.1016/j.jimonfin.2020.102280
Rezaee, M.J. – Jozmaleki, M. – Valipour, M. (2018): Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange. Physica A: Statistical Mechanics and its Applications, 489: 78–93. https://doi.org/10.1016/j.physa.2017.07.017
Rinaldo, A. – Fienberg, S.E. – Zhou, Y. (2009): On the geometry of discrete exponential families with application to exponential random graph models. Electronic Journal of Statistics, 3: 446–484. https://doi.org/10.1214/08-EJS350
Robins, G. – Snijders, T. – Wang, P. – Handcock, M. – Pattison, P. (2007): Recent developments in exponential random graph (p*) models for social networks. Social networks, 29(2): 192–215. https://doi.org/10.1016/j.socnet.2006.08.003
Rotundo, G. – D’Arcangelis, A.M. (2010): Ownership and control in shareholding networks. Journal of Economic Interaction and Coordination, 5(2): 191–219. https://doi.org/10.1007/s11403-010-0068-4
Roy, B.R. – 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
Salvemini, M.T. – Simeone, B. – Succi, R. (1995): A Graph-theoretic Model of Integrated Ownership in Business Groups. Università di Roma La Sapienza, Dipartimento di scienze economiche.
Sankowska, A. – Siudak, D. (2016): The small world phenomenon and assortative mixing in Polish corporate board and director networks, Physica A: Statistical Mechanics and its Applications, 443: 309–315. https://doi.org/10.1016/j.physa.2015.09.058
Schmidt, T.D. (2020): Statistical Analysis of Social Network Change. Doctoral dissertation, Portland State University. https://pdxscholar.library.pdx.edu/open_access_etds/5415/. Downloaded: 9 October 2020.
Singh, D. – Delios, A. (2017): Corporate governance, board networks and growth in domestic and international markets: Evidence from India. Journal of World Business, 52(5): 615–627. https://doi.org/10.1016/j.jwb.2017.02.002
Snijders, T.A.B. – Pattison, P.E. – Robins, G.L. – Handcock, M.S. (2006): New Specifications for Exponential Random Graph Models. Sociological Methodology, 36: 99–153. https://doi.org/10.1111/j.1467-9531.2006.00176.x
Tabak, B.M. – Takami, M.Y. – Cajueiro, D.O. – Petitinga, A. (2009): Quantifying price fluctuations in the Brazilian stock market. Physica A: Statistical Mechanics and its Applications, 388(1): 59–62. https://doi.org/10.1016/j.physa.2008.09.028
Taghizadeh, R. – Nazemi, A. – Maharluieb, M.S. (2020): Network Analysis of Interpersonal Relationships in Tehran Stock Exchange. Advances in Mathematical Finance and Applications, 34(3): 54–72.
Wang, P. – Robinson, G. – Pattison, P. – Koskinen, J. (2009): MPNet: program for the simulation and estimation of exponential random graph models. Melbourne School of Psychological Sciences, The University of Melbourne.
Wasserman, S. – Faust, K. (2010): Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences, 2nd ed., Cambridge Univ. Press, Cambridge.
Watts, D.J. – Strogatz, S.H. (1998): Collective dynamics of “small-world” networks. Nature, 393(6684): 440–442. https://doi.org/10.1038/30918
Watts, D.J. (1999): Networks, Dynamics, and the Small-World Phenomenon. American Journal of Sociology, 105(2): 493–527. https://doi.org/10.1086/210318
You, T. – Fiedor, P. – Hołda, A. (2015): Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information. Journal of Risk and Financial Management, 8(2): 266–284. https://doi.org/10.3390/jrfm8020266