Dynamics of the Moroccan Industry Indices Network Before and During the COVID-19 Pandemic

Authors

  • El Msiyah Cherif National school of commerce and management, Ibn Totail University, Morocco
  • Jaouad Madkour Faculty of Law and Economics Abdelmalek Essaadi University, Morocco

DOI:

https://doi.org/10.32890/ijbf2023.18.1.2

Keywords:

Industry indices network, minimum spanning tree, covid-19, network connectivity, network centrality

Abstract

This paper studies the topological properties of the dynamics of the industry indices network at the Moroccan stock exchange by using
network theory. The Minimum Spanning Tree (MST) was constructed from the metric distances which had been calculated for the different pairs of industrial indices. The dynamics of the MST were analysed over the period 2013 to 2020 using the sliding window technique. The period studied was divided into the pre-pandemic Covid-19 period and the pandemic Covid-19 period. Connectivity and centrality indicators were calculated to track the connectivity structure over time and to identify the positioning and the importance of the industry indices studied. The result of this study indicates that the network of industry indices was relatively stable during the pre-pandemic Covid-19 period before observing a sudden rapprochement between industries when the Covid-19 pandemic was officially announced. The formation of star-shaped networks was also observed. These networks were centred on the banking industry, essentially during the pandemic Covid-19 period. The banking industry was also positioned at the centre of the Moroccan industry indices network. 

References

Ang, A., & Chen, J. (2002). Asymmetric correlations of equity portfolios. Journal of Financial Economics, 63(3), 443–494. https://doi.org/10.1016/S0304-405X(02)00068-5

Arthur, W. B., Durlauf, S. N., & Lane, D. A. (1997). The economy as an evolving complex system II (1st ed.). Santa Fe Institute Series.

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

Aslam, F., Mohmand, Y. T., Ferreira, P., Memon, B. A., Khan, M., & Khan, M. (2020). Networkanalysis of global stock markets at the beginning of the coronavirus disease (Covid-19) outbreak, Borsa Istanbul Review, 20, 49-61. https://doi.org/10.1016/j. bir.2020.09.003

Baker, S. R., Bloom, N., Davis, S. J., Kost, K. J., Sammon, M. C., & Viratyosin, T. (2020). The unprecedented stock market impact of covid-19, National Bureau of Economic Research, Working Paper Series, number 26945.

Barthélemy, M. (2004). Betweenness centrality in large complex networks. Eur. Phys. J. B, 38, 163-168. https://doi.org/10.1140/ epjb/e2004-00111-4

Bonanno, G., Vandewalle, N., & Mantegna, R. N. (2000). Taxonomy of stock market indices. Physical Review E62, 7615-7618. https://doi.org/10.1103/PhysRevE.62.R7615

Bonanno, G., Lillo, F., & Mantegna, R. N. (2001). High-frequency cross-correlation in a set of stocks. Quantitative Finance, 1(1), 96–104. https://doi.org/10.1080/713665554

Coelho, R., Hutzler, S., Repetowicz, P., & Richmond, P. (2007). Sector analysis for a FTSE portfolio of stocks. Physica A. 373, 615-626. https://doi.org/10.1016/j.physa.2006.02.

De Carvalho, P. J. C., & Gupta, A. (2018). A network approach to unravel asset price co-movement using minimal dependence structure. Journal of Banking and Finance, 91, 119-132. https://doi.org/10.1016/j.jbankfin.2018.04.012

Drozdz, S., Gruemmer, F., Ruf, F., & Speth, J. (2000). Dynamics of competition between collectively and noise in the stock market. Physica A, 287, 440-449. https://doi.org/10.1016/ S0378-4371(00)00383-6

Galazka, 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

Gilmore, C. G., Lucey, B. M., & Boscia, M. (2008). An ever-closer union? Examining the evolution of linkages of European equity markets via minimum spanning trees. Physica A, 387. 6319–6329. https://doi.org/10.1016/j.physa.2008.07.012

Haroon, O., & Rizvi, S. A. R. (2020). Covid-19: Media coverage and financial markets behaviour: A sectoral inquiry. Journal of Behavioral and Experimental Finance, 27, 100343. https://doi.org/10.1016/j.jbef.2020.100343

Khashanah, K., & Miao, L. (2011). Dynamic structure of the US financial systems. Studies in Economics and Finance, 28(4), 321–339. https://doi.org/10.1108/10867371111171564

Liu, H., Manzoor, A., Wang, C., 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

Longin, F., & Solnik, B. (2001). Extreme correlation of international equity markets. The Journal of Finance, 56(2), 649–676. https://doi.org/10.1111/0022-1082

Majapa, M., & Gossel, S. J. (2016).Topology of the South African stock market network across the 2008 financial crisis. Physica A, 445, 35–47. https://doi.org/10.1016/j.physa.2015.10.108

Mantegna, R. N. (1999). Hierarchical structure in financial markets. European Physical Journal B11, 193-197. https://doi.org/10.1007/s100510050929

Musmeci, N., Aste, T., & Di Matteo, T. (2015). Risk diversification: A study of persistence with a filtered correlation-network approach. Journal of Network Theory in Finance, 1(1), 77–98. https://doi.org/10.21314/JNTF.2015.005

Onnela, J. P., Chakraborti, A., Kaski, K., Kertesz, J., & Kanto, A. (2003). Dynamics of market correlations: Taxonomy and portfolio analysis. Physical Review E68, 056110. https://doi.org/10.1103/PhysRevE.68.056110

Sinha, S., & Pan, R. K. (2007). Uncovering the internal structure of the Indian financial market: Cross correlation behaviour in the NSE. Econophysics of Markets and Business Networks. New Economic Windows. Springer, Milano. https://doi.org/10.1007/978-88-470-0665-2_1

Situngkir, H., & Surya, Y. (2005). Indonesian stock market dynamics through ultrametricity of minimum spanning tree. https://ssrn. com/abstract=768204 or http://dx.doi.org/10.2139/ssrn.768204

Tabak, B. M., Serrac, T. R., & Cajueiro, D. O. (2010).Topological properties of stock market networks: The case of Brazil. PhysicaA, 389, 3240–3249. https://doi.org/10.1016 jphysa.2010.04.002

Tola, V., Lillo, F., Gallegati, M., & Mantegna, R. N. (2008). Cluster analysis for portfolio optimization. Journal of Economic Dynamics and Control, 32, 235-258. https://doi.org/10.1016/j. jedc.2007.01.

Tumminello, M., Lillo, F., & Mantegna, R. N. (2010). Correlation, hierarchies, and networks in financial markets. Journal of Economic Behavior and Organization, 75, 40–58. https://doi.org/10.48550/arXiv.0809.4615

Yang, R., Lia, X., & Zhang, T. (2014). Analysis of linkage effects among industry sectors in China’s stock market before and after the financial crisis. Physica A, 411, 12–20. https://doi.org/10.1016/j.physa.2014.05.072

Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of Covid-19, Finance Research Letters, 36,101528. https://doi.org/10.1016/j.frl.2020.101528

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Published

05-01-2023

How to Cite

Cherif , E. M., & Madkour, J. (2023). Dynamics of the Moroccan Industry Indices Network Before and During the COVID-19 Pandemic. International Journal of Banking and Finance, 18(1), 31-50. https://doi.org/10.32890/ijbf2023.18.1.2

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Identifiers DOI 10.32890/ijbf2023.18.1.2 OpenAlex W4313589238 Semantic Scholar CorpusID 255534380