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The structure of the South African stock market network during COVID-19 hard lockdown

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  • Mbatha, Vusisizwe Moses
  • Alovokpinhou, Sedjro Aaron

Abstract

This study investigates the topology of the South African stock market network pre, during, and post the level 5-lockdown period. We use the daily closing price of the 134 companies in the all-share index (ALSI) from 01 October 2019 to 30 October 2020. We construct the minimum spanning tree using the cross-correlation of the returns computed from the closing price data. The research findings show that the South African stock market network forms clusters and is homogeneous, and the finance industry plays a central role. Specifically, the results show an expansion of MST during the level 5 lockdown and shrinkage of the MST post level 5 lockdown. The average correlation coefficient decreases through all sub-periods; conversely, the average distance increases in all sub-periods. Post-level 5 lockdown period, stocks in the Health Care Equipment & Services sector form a small cluster that did not exist before the lockdown period. JSE node degree distribution for all sub-periods follows the power law.

Suggested Citation

  • Mbatha, Vusisizwe Moses & Alovokpinhou, Sedjro Aaron, 2022. "The structure of the South African stock market network during COVID-19 hard lockdown," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
    • Handle: RePEc:eee:phsmap:v:590:y:2022:i:c:s0378437121009572
    DOI: 10.1016/j.physa.2021.126770
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    as
    1. 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.
    2. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    3. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    4. Huang, Wei-Qiang & Zhuang, Xin-Tian & Yao, Shuang, 2009. "A network analysis of the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2956-2964.
    5. Xia, Lisi & You, Daming & Jiang, Xin & Guo, Quantong, 2018. "Comparison between global financial crisis and local stock disaster on top of Chinese stock network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 222-230.
    6. Zhao, Longfeng & Wang, Gang-Jin & Wang, Mingang & Bao, Weiqi & Li, Wei & Stanley, H. Eugene, 2018. "Stock market as temporal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1104-1112.
    7. Majapa, Mohamed & Gossel, Sean Joss, 2016. "Topology of the South African stock market network across the 2008 financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 35-47.
    8. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    9. Pereira, Eder Johnson de Area Leão & Ferreira, Paulo Jorge Silveira & Silva, Marcus Fernandes da & Miranda, Jose Garcia Vivas & Pereira, H.B. B., 2019. "Multiscale network for 20 stock markets using DCCA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 529(C).
    10. Jang, Wooseok & Lee, Junghoon & Chang, Woojin, 2011. "Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 707-718.
    11. Kanno, Masayasu, 2021. "Risk contagion of COVID-19 in Japanese firms: A network approach," Research in International Business and Finance, Elsevier, vol. 58(C).
    12. Esmaeilpour Moghadam, Hadi & Mohammadi, Teymour & Feghhi Kashani, Mohammad & Shakeri, Abbas, 2019. "Complex networks analysis in Iran stock market: The application of centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    13. Kazemilari, Mansooreh & Mardani, Abbas & Streimikiene, Dalia & Zavadskas, Edmundas Kazimieras, 2017. "An overview of renewable energy companies in stock exchange: Evidence from minimal spanning tree approach," Renewable Energy, Elsevier, vol. 102(PA), pages 107-117.
    14. Zebende, G.F., 2011. "DCCA cross-correlation coefficient: Quantifying level of cross-correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 614-618.
    15. Ashadun Nobi & Sungmin Lee & Doo Hwan Kim & Jae Woo Lee, 2014. "Correlation and Network Topologies in Global and Local Stock Indices," Papers 1402.1552, arXiv.org.
    16. Campbell, Rachel A.J. & Forbes, Catherine S. & Koedijk, Kees G. & Kofman, Paul, 2008. "Increasing correlations or just fat tails?," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 287-309, March.
    17. Parand, Fereshteh-Azadi & Rahimi, Hossein & Gorzin, Mohsen, 2016. "Combining fuzzy logic and eigenvector centrality measure in social network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 459(C), pages 24-31.
    18. Namaki, A. & Shirazi, A.H. & Raei, R. & Jafari, G.R., 2011. "Network analysis of a financial market based on genuine correlation and threshold method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(21), pages 3835-3841.
    19. Yao, Hongxing & Memon, Bilal Ahmed, 2019. "Network topology of FTSE 100 Index companies: From the perspective of Brexit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1248-1262.
    20. Coletti, Paolo, 2016. "Comparing minimum spanning trees of the Italian stock market using returns and volumes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 246-261.
    21. Tabak, Benjamin M. & Serra, Thiago R. & Cajueiro, Daniel O., 2010. "Topological properties of stock market networks: The case of Brazil," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3240-3249.
    22. Zeng, Zhi-Jian & Xie, Chi & Yan, Xin-Guo & Hu, Jue & Mao, Zhou, 2016. "Are stock market networks non-fractal? Evidence from New York Stock Exchange," Finance Research Letters, Elsevier, vol. 17(C), pages 97-102.
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    2. Ghazani, Majid Mirzaee & Khosravi, Reza & Caporin, Massimiliano, 2023. "Analyzing interconnection among selected commodities in the 2008 global financial crisis and the COVID-19 pandemic," Resources Policy, Elsevier, vol. 80(C).

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