Moroccan Stock Exchange market topology in crisis and non-crisis periods
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DOIhttp://dx.doi.org/10.21511/imfi.19(4).2022.22
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Article InfoVolume 19 2022, Issue #4, pp. 274-284
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This paper seeks to investigate the dynamics within the Moroccan Stock Exchange (MSE) market topology in crisis and non-crisis periods using daily historical log returns of sectoral indices covering the period from January 4, 1993 to September 9, 2021. The study applies the Agglomerative Hierarchical Clustering (AHC) implemented on the Dynamic Time Warping (DTW) distance matrix over ten sub-periods covering numerous crises, from Subprime mortgage crisis to European debt crisis and finally COVID-19 crisis. The obtained clustering results are gathered into a network to display the cumulated interconnections between the sectoral indices. The findings showed that the Casablanca Stock Exchange (CSE) market clusters composition is dynamic during the studied period. Indeed, some sectoral indices demonstrated evidence of strong similarities by gathering in the same cluster over numerous sub-periods as the couples Electrical & Electronic Equipment and Transport or as Banks and Construction & Building Materials sectoral indices. Moreover, the interconnections of CSE sectoral indices are trend dependent. According to the obtained network, the Oil and Gas demonstrated its centrality.
- Keywords
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JEL Classification (Paper profile tab)C38, D53, G11
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References35
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Tables3
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Figures4
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- Figure 1. MASI break points
- Figure 2. An example of time-series alignment (a) and cumulative distance matrix (b)
- Figure 3. Undirected weighted network of sectoral indices of the MSE market
- Figure A1. Sectoral index clusters during crisis and non-crisis periods
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- Table 1. Hopkins test results
- Table 2. Validation indices of sectoral index clusters
- Table 3. Networking indicators analysis
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- Albulescu, C. T., Bouri, E., Tiwari, A. K., & Roubaud, D. (2020). Quantile causality between banking stock and real estate securities returns in the US. The Quarterly Review of Economics and Finance, 78, 251-260.
- Aratuo, D. N., & Etienne, X. L. (2019). Industry level analysis of tourism-economic growth in the United States. Tourism Management, 70, 333-340.
- Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J. M., & Perona, I. (2013). An extensive comparative study of cluster validity indices. Pattern Recognition, 46(1), 243-256.
- Bai, J., & Perron, P. (2003). Computation and analysis of multiple structural change models. Journal of Applied Econometrics, 18(1), 1-22.
- Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509-512.
- Barthelemy, M. (2004). Betweenness centrality in large complex networks. The European Physical Journal B, 38(2), 163-168.
- Berndt, D. J., & Clifford, J. (1994). Using dynamic time warping to find patterns in time series. KDD workshop, 10(16), 359-370.
- Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social Networks, 29(4), 555-564.
- Caliński, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statisticstheory and Methods, 3(1), 1-27.
- Cohen, E., Delling, D., Pajor, T., & Werneck, R. F. (2014). Computing classic closeness centrality, at scale. In Proceedings of the second ACM Conference on Online Social Networks, 37-50.
- Di Matteo, T., Pozzi, F., & Aste, T. (2010). The use of dynamical networks to detect the hierarchical organization of financial market sectors. The European Physical Journal B, 73(1), 3-11.
- Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Taylor \& Francis, 32-57.
- El Ghini, A., & Saidi, Y. (2017). Return and volatility spillovers in the Moroccan stock market during the financial crisis. Empirical Economics, 52(4), 1481-1504.
- Hopkins, B., & Skellam, J. G. (1954). A new method for determining the type of distribution of plant individuals. Annals of Botany, 18(2), 213-227.
- Huang, C., Deng, Y., Yang, X., Cao, J., & Yang, X. (2021a). A network perspective of comovement and structural change: Evidence from the Chinese stock market. International Review of Financial Analysis, 76, 101782.
- Huang, C., Wen, S., Li, M., Wen, F., & Yang, X. (2021b). An empirical evaluation of the influential nodes for stock market network: Chinese A-shares case. Finance Research Letters, 38, 101517.
- Jaroonchokanan, N., Termsaithong, T., & Suwanna, S. (2022). Dynamics of hierarchical clustering in stocks market during financial crises. Physica A: Statistical Mechanics and its Applications, 128183.
- Laha, S., Majumdar, & K, A. (2020). Clustering and classification of time series using topological data analysis with applications to finance. Expert Systems with Applications, 162, 113868-113868.
- Lahmiri, S. (2012). A clustering approach to examine the dynamics of the NASDAQ topology in times of crisis. Management Science Letters, 2(6), 2113-2118.
- Lahmiri, S. (2016). Clustering of Casablanca stock market based on hurst exponent estimates. Physica A: Statistical Mechanics and its Applications, 456, 310-318.
- Li, B., & Yang, Y. (2021). Undirected and Directed Network Analysis of the Chinese Stock Market. Computational Economics, 1-19.
- Li, Y., Jiang, X. F., Tian, Y., Li, S. P., & Zheng, B. (2019). Portfolio optimization based on network topology. Physica A: Statistical Mechanics and its Applications, 515, 671-681.
- Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B-Condensed Matter and Complex Systems, 11(1), 193-197.
- 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(1), 1-14.
- Murtagh, F., & Contreras, P. (2012). Algorithms for hierarchical clustering: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2(1), 86-97.
- Murtagh, F., & Legendre, P. (2011). Ward’s hierarchical clustering method: clustering criterion and agglomerative algorithm. arXiv preprint arXiv, 1111.6285.
- Rochat, Y. (2009). Closeness centrality extended to unconnected graphs: The harmonic centrality index. ASNA, EPFL-CONF-200525.
- Sakoe, H., & Chiba, S. (1978). Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26, 43-49.
- Sardá-Espinosa, A. (2017). Comparing time-series clustering algorithms in r using the dtwclust package. European Research Studies Journal, 24(3B), 951-966.
- Siudak, D. E. (2021). Sectoral analysis of the us stock market through complex networks. European Research Studies Journal, 24.
- Tabak, B. M., Serra, T. R., & Cajueiro, D. O. (2010). Topological properties of commodities networks. The European Physical Journal B, 74(2), 243-249.
- Tasdemir, K., & Merényi. (2011). A validity index for prototype-based clustering of data sets with complex cluster structures. IEEE Transactions on Systems, Man, and Cybernetics, 41(4), 1039-1053.
- Tian, H., Zheng, X., & Zeng, D. D. (2019). Analyzing the dynamic sectoral influence in Chinese and American stock markets. Physica A: Statistical Mechanics and its Applications, 536, 120922.
- Tsekeris, T. (2017). Network analysis of inter-sectoral relationships and key sectors in the Greek economy. Journal of Economic Interaction and Coordination, 12(2), 413-435.
- Ward Jr, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236-244.