A minimum spanning tree analysis of the Polish stock market

Allen, F., Hryckiewicz, A., Kowalewski, O., & Tümer-Alkan, G. (2014). Transmission of financial shocks in loan and deposit markets: Role of interbank borrowing and market monitoring. Journal of Financial Stability, 15, 112-126. https://doi.org/10.1016/j.jfs.2014.09.005 Search in Google Scholar

Ando, M., & Hayakawa, K. (2022). Impact of COVID-19 on trade in services. Japan and the World Economy, 62, 101131. https://doi.org/10.1016/j.japwor.2022.101131 Search in Google Scholar

Ashraf, B. N. (2020). Stock markets’ reaction to COVID-19: Cases or fatalities? Research in International Business and Finance, 54, 101249. https://doi.org/10.1016/j.ribaf.2020.101249 Search in Google Scholar

Balci, M. A., Akgüller, Ö., & Güzel, S. C. (2021). Hierarchies in communities of UK stock market from the perspective of Brexit. Journal of Applied Statistics, 48(13-15), 2607-2625. https://doi.org/10.1080/02664763.2020.1796942 Search in Google Scholar

Battiston, S., Caldarelli, G., May, R. M., Roukny, T., & Stiglitz, J. E. (2016). The price of complexity in financial networks. Proceedings of the National Academy of Sciences, 113(36), 10031-10036. https://doi.org/10.1073/pnas.1521573113 Search in Google Scholar

Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104(3), 535-559. https://doi.org/10.1016/j.jfineco.2011.12.010 Search in Google Scholar

Birch, J., Pantelous, A. A., & Soramäki, K. (2016). Analysis of correlation based networks representing DAX 30 stock price returns. Computational Economics, 47(4), 501-525. https://doi.org/10.1007/s10614-015-9481-z Search in Google Scholar

Brancaccio, E., Giammetti, R., Lopreite, M., & Puliga, M. (2018). Centralization of capital and financial crisis: A global network analysis of corporate control. Structural Change and Economic Dynamics, 45, 94-104. https://doi.org/10.1016/j.strueco.2018.03.001 Search in Google Scholar

Brandes, U. (2001). A faster algorithm for betweenness centrality. The Journal of Mathematical Sociology, 25(2), 163-177. https://doi.org/10.1080/0022250X.2001.9990249 Search in Google Scholar

Brandes, U., & Erlebach, T. (Eds.). (2005). Network analysis: Methodological foundations. Springer.10.1007/b106453 Search in Google Scholar

Brookfield, D., Boussabaine, H., & Su, C. (2013). Identifying reference companies using the book-to-market ratio: A minimum spanning tree approach. The European Journal of Finance, 19(6), 466-490. https://doi.org/10.1080/1351847X.2011.637571 Search in Google Scholar

Brzeszczyński, J., Gajdka, J., Schabek, T., & Kutan, A. M. (2021). Central bank’s communication and markets’ reactions: Polish evidence. International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-09-2020-1061 Search in Google Scholar

BvD. (2022). Orbis database. https://orbis.bvdinfo.com/ Search in Google Scholar

Coelho, R., Gilmore, C. G., Lucey, B., Richmond, P., & Hutzler, S. (2007). The evolution of interdependence in world equity markets – evidence from minimum spanning trees. Physica A: Statistical Mechanics and Its Applications, 376, 455-466. https://doi.org/10.1016/j.physa.2006.10.045 Search in Google Scholar

Danko, J., & Šoltés, V. (2018). Portfolio creation using graph characteristics. Investment Management and Financial Innovations, 15(1), 180-189. https://doi.org/10.21511/imfi.15(1).2018.16 Search in Google Scholar

Danko, J., Šoltés, V., & Bindzar, T. (2022). Portfolio creation using graph characteristics and testing its performance. Montenegrin Journal of Economics, 18(1), 7-17. https://doi.org/10.14254/1800-5845/2022.18-1.1 Search in Google Scholar

Dastkhan, H., & Shams Gharneh, N. (2016). Determination of systemically important companies with cross-shareholding network analysis: A case study from an emerging market. International Journal of Financial Studies, 4(3), 13. https://doi.org/10.3390/ijfs4030013 Search in Google Scholar

Denkowska, A., & Wanat, S. (2020). A tail dependence-based MST and their topological indicators in modeling systemic risk in the European insurance sector. Risks, 8(2), 39. https://doi.org/10.3390/risks8020039 Search in Google Scholar

Diab-Bahman, R., & Al-Enzi, A. (2020). The impact of COVID-19 pandemic on conventional work settings. International Journal of Sociology and Social Policy, 40(9/10), 909-927. https://doi.org/10.1108/IJSSP-07-2020-0262 Search in Google Scholar

Dias, J. (2012). Sovereign debt crisis in the European Union: A minimum spanning tree approach. Physica A: Statistical Mechanics and Its Applications, 391(5), 2046-2055. https://doi.org/10.1016/j.physa.2011.11.004 Search in Google Scholar

Dungey, M., Luciani, M., & Veredas, D. (2012). Ranking systemically important financial institutions (Discussion Paper, No. 12-115/IV/DSF44). Tinbergen Institute. http://hdl.handle.net/10419/8750310.2139/ssrn.2166504 Search in Google Scholar

Dwivedi, Y. K., Hughes, D. L., Coombs, C., Constantiou, I., Duan, Y., Edwards, J. S., Gupta, B., Lal, B., Misra, S., Prashant, P., Raman, R., Rana, N. P., Sharma, S. K., & Upadhyay, N. (2020). Impact of COVID-19 pandemic on information management research and practice: Transforming education, work and life. International Journal of Information Management, 55, 102211. https://doi.org/10.1016/j.ijinfomgt.2020.102211 Search in Google Scholar

Dzicher, M. (2021). Sampling methods for investment portfolio formulation procedure at increased market volatility. Journal of Economics and Management, 43, 70-89. https://doi.org/10.22367/jem.2021.43.04 Search in Google Scholar

EquityRT. (2021). EquityRT database. https://www.equityrt.com/ Search in Google Scholar

Euler, L. (1953). Leonhard Euler and the Koenigsberg bridges. Scientific American, 189(1), 66-70. https://doi.org/10.1038/scientificamerican0753-66 Search in Google Scholar

Galanti, T., Guidetti, G., Mazzei, E., Zappalà, S., & Toscano, F. (2021). Work from home during the CODIV-19 outbreak: The impact on employees’ remote work productivity, engagement and stress. Journal of Occupational & Environmental Medicine, 63(7), 426-432. https://doi.org/10.1097/JOM.0000000000002236 Search in Google Scholar

Gałązka, 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 Search in Google Scholar

Gan, S. L., & Djauhari, M. A. (2015). New York Stock Exchange performance: Evidence from the forest of multidimensional minimum spanning trees. Journal of Statistical Mechanics: Theory and Experiment, 2015(12), P12005. https://doi.org/10.1088/1742-5468/2015/12/P12005 Search in Google Scholar

Garas, A., & Argyrakis, P. (2007). Correlation study of the Athens Stock Exchange. Physica A: Statistical Mechanics and Its Applications, 380, 399-410. https://doi.org/10.1016/j.physa.2007.02.097 Search in Google Scholar

Giemza, D. (2021). Ranking of optimal stock portfolios determined on the basis of expected utility maximization criterion. Journal of Economics and Management, 43, 154-178. https://doi.org/10.22367/jem.2021.43.08 Search in Google Scholar

Gilmore, C. G., Lucey, B. M., & Boscia, M. W. (2010). Comovements in government bond markets: A minimum spanning tree analysis. Physica A: Statistical Mechanics and Its Applications, 389(21), 4875-4886. https://doi.org/10.1016/j.physa.2010.06.057 Search in Google Scholar

Goodfellow, C., Bohl, M. T., & Gebka, B. (2009). Together we invest? Individual and institutional investors’ trading behaviour in Poland. International Review of Financial Analysis, 18(4), 212-221. https://doi.org/10.1016/j.irfa.2009.03.002 Search in Google Scholar

GPW. (2021). List of companies. https://www.gpw.pl/list-of-companies Search in Google Scholar

Graham, R. L., & Hell, P. (1985). On the history of the minimum spanning tree problem. IEEE Annals of the History of Computing, 7(1), 43-57. https://doi.org/10.1109/MAHC.1985.10011 Search in Google Scholar

Gruszczyński, M. (2006). Corporate governance and financial performance of companies in Poland. International Advances in Economic Research, 12(2), 251-259. https://doi.org/10.1007/s11294-006-9007-5 Search in Google Scholar

Hayakawa, K., & Mukunoki, H. (2021). The impact of COVID-19 on international trade: Evidence from the first shock. Journal of the Japanese and International Economies, 60, 101135. https://doi.org/10.1016/j.jjie.2021.101135 Search in Google Scholar

He, Q., Liu, J., Wang, S., & Yu, J. (2020). The impact of COVID-19 on stock markets. Economic and Political Studies, 8(3), 275-288. https://doi.org/10.1080/20954816.2020.1757570 Search in Google Scholar

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 Search in Google Scholar

Jang, W., Lee, J., & Chang, W. (2011). Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree. Physica A: Statistical Mechanics and Its Applications, 390(4), 707-718. https://doi.org/10.1016/j.physa.2010.10.028 Search in Google Scholar

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 Search in Google Scholar

Kanno, M. (2019). Network structures and credit risk in cross-shareholdings among listed Japanese companies. Japan and the World Economy, 49, 17-31. https://doi.org/10.1016/j.japwor.2018.09.003 Search in Google Scholar

Kanno, M. (2021). Risk contagion of COVID-19 in Japanese firms: A network approach. Research in International Business and Finance, 58, 101491. https://doi.org/10.1016/j.ribaf.2021.101491 Search in Google Scholar

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, 107-117. https://doi.org/10.1016/j.renene.2016.10.029 Search in Google Scholar

Konopczak, M., Sieradzki, R., & Wiernicki, M. (2010). Kryzys na światowych rynkach finansowych – wpływ na rynek finansowy w Polsce oraz implikacje dla sektora realnego. Bank i Kredyt, 41(6), 45-70. https://bankandcredit.nbp.pl/content/2010/06/bik_06_2010_02_art.pdf Search in Google Scholar

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/10.1090/S0002-9939-1956-0078686-7 Search in Google Scholar

Kwon, O., & Yang, J.-S. (2008). Information flow between stock indices. Europhysics Letters, 82(6), 68003. https://doi.org/10.1209/0295-5075/82/68003 Search in Google Scholar

Lee, J., Youn, J., & Chang, W. (2012). Intraday volatility and network topological properties in the Korean stock market. Physica A: Statistical Mechanics and Its Applications, 391(4), 1354-1360. https://doi.org/10.1016/j.physa.2011.09.016 Search in Google Scholar

Lee, T. K., Cho, J. H., Kwon, D. S., & Sohn, S. Y. (2019). Global stock market investment strategies based on financial network indicators using machine learning techniques. Expert Systems with Applications, 117, 228-242. https://doi.org/10.1016/j.eswa.2018.09.005 Search in Google Scholar

Li, H., An, H., Gao, X., Huang, J., & Xu, Q. (2014). On the topological properties of the cross-shareholding networks of listed companies in China: Taking shareholders’ cross-shareholding relationships into account. Physica A: Statistical Mechanics and Its Applications, 406, 80-88. https://doi.org/10.1016/j.physa.2014.03.041 Search in Google Scholar

Li, H., & Majerowska, E. (2008). Testing stock market linkages for Poland and Hungary: A multivariate GARCH approach. Research in International Business and Finance, 22(3), 247-266. https://doi.org/10.1016/j.ribaf.2007.06.001 Search in Google Scholar

Liu, H. Y., Manzoor, A., Wang, C. Y., Zhang, L., & Manzoor, Z. (2020). The COVID-19 outbreak and affected countries stock markets response. International Journal of Environmental Research and Public Health, 17(8), 2800. https://doi.org/10.3390/ijerph17082800 Search in Google Scholar

Long, H., Zhang, J., & Tang, N. (2017). Does network topology influence systemic risk contribution? A perspective from the industry indices in Chinese stock market. PLOS ONE, 12(7), e0180382. https://doi.org/10.1371/journal.pone.0180382 Search in Google Scholar

Luo, Y., Xiong, J., Dong, L. G., & Tang, Y. (2015). Statistical correlation properties of the SHIBOR interbank lending market. China Finance Review International, 5(2), 91-102. https://doi.org/10.1108/CFRI-08-2014-0036 Search in Google Scholar

Ma, Y., Zhuang, X., & Li, L. (2011). Research on the relationships of the domestic mutual investment of China based on the cross-shareholding networks of the listed companies. Physica A: Statistical Mechanics and Its Applications, 390(4), 749-759. https://doi.org/10.1016/j.physa.2010.10.042 Search in Google Scholar

Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B, 11(1), 193-197. https://doi.org/10.1007/s100510050929 Search in Google Scholar

Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. https://doi.org/10.2307/2975974 Search in Google Scholar

Markowitz, H. M. (1991). Foundations of portfolio theory. The Journal of Finance, 46(2), 469-477. https://doi.org/10.1111/j.1540-6261.1991.tb02669.x Search in Google Scholar

Memon, B. A., & Yao, H. (2021). The impact of COVID-19 on the dynamic topology and network flow of world stock markets. Journal of Open Innovation: Technology, Market, and Complexity, 7(4), 241. https://doi.org/10.3390/joitmc7040241 Search in Google Scholar

Memon, B. A., Yao, H., & Tahir, R. (2020). General election effect on the network topology of Pakistan’s stock market: Network-based study of a political event. Financial Innovation, 6, 2. https://doi.org/10.1186/s40854-019-0165-x Search in Google Scholar

Miccichè, S., Bonanno, G., Lillo, F., & Mantegna, R. N. (2003). Degree stability of a minimum spanning tree of price return and volatility. Physica A: Statistical Mechanics and Its Applications, 324(1/2), 66-73. https://doi.org/10.1016/S0378-4371(03)00002-5 Search in Google Scholar

Nešetřil, J., Milková, E., & Nešetřilová, H. (2001). Otakar Borůvka on minimum spanning tree problem. Translation of both the 1926 papers, comments, history. Discrete Mathematics, 233(1/3), 3-36. https://doi.org/10.1016/S0012-365X(00)00224-7 Search in Google Scholar

Nobi, A., Maeng, S. E., Ha, G. G., & Lee, J. W. (2014). Effects of global financial crisis on network structure in a local stock market. Physica A: Statistical Mechanics and Its Applications, 407, 135-143. https://doi.org/10.1016/j.physa.2014.03.083 Search in Google Scholar

Nyga-Łukaszewska, H., & Aruga, K. (2020). Energy prices and COVID-immunity: The case of crude oil and natural gas prices in the US and Japan. Energies, 13(23), 6300. https://doi.org/10.3390/en13236300 Search in Google Scholar

Onnela, J.-P., Chakraborti, A., Kaski, K., & Kertész, J. (2003a). Dynamic asset trees and Black Monday. Physica A: Statistical Mechanics and Its Applications, 324(1/2), 247-252. https://doi.org/10.1016/S0378-4371(02)01882-4 Search in Google Scholar

Onnela, J.-P., Chakraborti, A., Kaski, K., Kertész, J., & Kanto, A. (2003b). Dynamics of market correlations: Taxonomy and portfolio analysis. Physical Review E, 68(5), 056110. https://doi.org/10.1103/PhysRevE.68.056110 Search in Google Scholar

Pang, R. K.-K., Granados, O. M., Chhajer, H., & Legara, E. F. T. (2021). An analysis of network filtering methods to sovereign bond yields during COVID-19. Physica A: Statistical Mechanics and Its Applications, 574, 125995. https://doi.org/10.1016/j.physa.2021.125995 Search in Google Scholar

Prim, R. C. (1957). Shortest connection networks and some generalizations. Bell System Technical Journal, 36(6), 1389-1401. https://doi.org/10.1002/j.1538-7305.1957.tb01515.x Search in Google Scholar

Roy, R. B., & Sarkar, U. K. (2013). A social network approach to change detection in the interdependence structure of global stock markets. Social Network Analysis and Mining, 3, 269-283. https://doi.org/10.1007/s13278-012-0063-y Search in Google Scholar

Rutkowska-Ziarko, A., & Markowski, L. (2022). Accounting and market risk measures of Polish energy companies. Energies, 15(6), 2138. https://doi.org/10.3390/en15062138 Search in Google Scholar

Scheicher, M. (2001). The comovements of stock markets in Hungary, Poland and the Czech Republic. International Journal of Finance & Economics, 6(1), 27-39. https://doi.org/10.1002/ijfe.141 Search in Google Scholar

Sum, K. (2016). A review of individual and systemic risk measures in terms of applicability for banking regulations. Contemporary Economics, 10(1), 71-82. https://doi.org/10.5709/ce.1897-9254.199 Search in Google Scholar

Szyszka, A. (2011). The genesis of the 2008 global financial crisis and challenges to the neoclassical paradigm of finance. Global Finance Journal, 22(3), 211-216. https://doi.org/10.1016/j.gfj.2011.10.011 Search in Google Scholar

Tang, Y., Xiong, J. J., Luo, Y., & Zhang, Y.-C. (2019). How do the global stock markets influence one another? Evidence from finance big data and Granger causality directed network. International Journal of Electronic Commerce, 23(1), 85-109. https://doi.org/10.1080/10864415.2018.1512283 Search in Google Scholar

Tomeczek, A. F. (2021). A financial network analysis of the equity linkages in Poland. Research Papers of Wroclaw University of Economics, 65(4), 129-143. https://doi.org/10.15611/pn.2021.4.08 Search in Google Scholar

Tomeczek, A. F. (2022). The evolution of Japanese keiretsu networks: A review and text network analysis of their perceptions in economics. Japan and the World Economy, 62, 101132. https://doi.org/10.1016/j.japwor.2022.101132 Search in Google Scholar

Tumminello, M., Coronnello, C., Lillo, F., Miccichè, S., & Mantegna, R. N. (2007). Spanning trees and bootstrap reliability estimation in correlation-based networks. International Journal of Bifurcation and Chaos, 17(7), 2319-2329. https://doi.org/10.1142/S0218127407018415 Search in Google Scholar

Vitali, S., Glattfelder, J. B., & Battiston, S. (2011). The network of global corporate control. PLoS ONE, 6(10), e25995. https://doi.org/10.1371/journal.pone.0025995 Search in Google Scholar

Wang, G.-J., Xie, C., & Chen, S. (2017). Multiscale correlation networks analysis of the US stock market: A wavelet analysis. Journal of Economic Interaction and Coordination, 12, 561-594. https://doi.org/10.1007/s11403-016-0176-x Search in Google Scholar

Wang, G.-J., Xie, C., Han, F., & Sun, B. (2012). Similarity measure and topology evolution of foreign exchange markets using dynamic time warping method: Evidence from minimal spanning tree. Physica A: Statistical Mechanics and Its Applications, 391(16), 4136-4146. https://doi.org/10.1016/j.physa.2012.03.036 Search in Google Scholar

Wang, G.-J., Xie, C., & Stanley, H. E. (2018). Correlation structure and evolution of world stock markets: Evidence from Pearson and partial correlation-based networks. Computational Economics, 51, 607-635. https://doi.org/10.1007/s10614-016-9627-7 Search in Google Scholar

Waszczuk, A. (2013). A risk-based explanation of return patterns – evidence from the Polish stock market. Emerging Markets Review, 15, 186-210. https://doi.org/10.1016/j.ememar.2012.12.002 Search in Google Scholar

Wiliński, M., Sienkiewicz, A., Gubiec, T., Kutner, R., & Struzik, Z. R. (2013). Structural and topological phase transitions on the German Stock Exchange. Physica A: Statistical Mechanics and Its Applications, 392(23), 5963-5973. https://doi.org/10.1016/j.physa.2013.07.064 Search in Google Scholar

Witkowska, D., Kompa, K., & Staszak, M. (2021). Indicators for the efficient portfolio construction. The case of Poland. Procedia Computer Science, 192, 2022-2031. https://doi.org/10.1016/j.procs.2021.08.208 Search in Google Scholar

Wnuczak, P. (2021). Profitability of investment strategies developed on the basis of buy and sell recommendations. Journal of Economics and Management, 43, 317-338. https://doi.org/10.22367/jem.2021.43.15 Search in Google Scholar

Yin, K., Liu, Z., & Liu, P. (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 Search in Google Scholar

Yun, T.-S., Jeong, D., & Park, S. (2019). “Too central to fail” systemic risk measure using PageRank algorithm. Journal of Economic Behavior & Organization, 162, 251-272. https://doi.org/10.1016/j.jebo.2018.12.021 Search in Google Scholar

Ziemba, E. W., & Eisenbardt, M. (2021). The effect of the Covid-19 pandemic on ICT usage by academics. Journal of Computer Information Systems, 62(6), 1154-1168. https://doi.org/10.1080/08874417.2021.1992806 Search in Google Scholar