Sectoral Shockwaves: Examining Bursa Malaysia's Sector Indices Amid the COVID-19 Pandemic DOI: https://doi.org/10.33093/ijomfa.2026.7.1.4
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Abstract
The Coronavirus disease 2019 (COVID-19) outbreak is becoming a widespread concern, as it is causing a decline in economies and negatively impacting stock markets worldwide. As the Malaysian government implements the Movement Control Order (MCO) to curb the spread of the deadly virus, the Malaysian stock market is rapidly impacted, becoming volatile and unpredictable throughout the pandemic phase. Thus, this study aims to analyse the structural correlation patterns of the Bursa Malaysia Sectoral Index Series using the threshold network approach. The data utilised in this study are the closing prices of all 13 sectoral indexes, spanning from January 1, 2019, to December 31, 2021. Threshold networks are constructed for the years 2019, 2020, and 2021 based on different threshold values to visualise the correlation between indexes. The results suggested that COVID-19 affected the Malaysian stock market, as the indexes appeared to be more correlated with each other in 2020. In addition, consumer products and services (KLCM), industrial products and services (KLIP), property (KLPR), and construction (KLCT) emerged as the most influential indexes throughout the pandemic. This study will help market participants gain an understanding of the correlation between indexes based on threshold networks.
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Amir, U. A. M., Masdi, N. I., Saad, N. M., Yusof, N. M., & Azmi, S. S. (2022). The Volatility Of Ftse Bursa Malaysia KLCI During The Covid-19 Pandemic. International Journal of Academic Research in Business and Social Sciences, 12(4).
Aslam, F., Mohmand, Y. T., Ferreira, P., Memon, B. A., Khan, M., & Khan, M. (2020). Network analysis of global stock markets at the beginning of the coronavirus disease (COVID-19) outbreak. Borsa Istanbul Review, 20(S1), S49–S61. https://doi.org/10.1016/j.bir.2020.09.003
Bahaludin, H., Mahamood, F. N. A., Abdullah, M. H., Fauzi, M. A., & Omar, M. (2022). The Impact of the COVID-19 Pandemic on the Interconnectedness of Stocks in Bursa Malaysia. Matematika, 69-82.
Boginski, V., Butenko, S., & Pardalos, P. M. (2004). Statistical analysis of financial networks. Computational Statistics & Data Analysis, 48(2), 431–443. https://doi.org/10.1016/j.csda.2004.02.004
Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92(5), 1170–1182. https://doi.org/10.1086/228631
Chee, K., Chew, S., Izni, N., Ali, A., Tajuddin, N., Aksah, S., & Chin, J. (2024). Pandemic’s impact on Bursa Malaysia sectors: A comprehensive study utilising OLSM and ESM. Journal of Mathematical Sciences and Informatics, 10(2). https://doi.org/10.46754/jmsi.2024.06.004
Chow, Y., & Tan, Y. (2022). Impact of COVID-19 and government interventions on Bursa Malaysia sectors. Journal of Chinese Economic and Foreign Trade Studies, 15(1), 22–37. https://doi.org/10.1108/jcefts-06-2022-0039
Ruiz Estrada, M. A., Park, D., & Lee, M. (2020). The evaluation of the final impact of Wuhan COVID-19 on trade, tourism, transport, and electricity consumption of China. Tourism, Transport, and Electricity Consumption of China (March 9, 2020).
Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41. https://doi.org/10.2307/3033543
Lee, K. Y.-M., Jais, M., & Chan, C.-W. (2020). Impact of COVID-19: Evidence from Malaysian stock market. International Journal of Business and Society, 21(2), 607–628. https://doi.org/10.33736/ijbs.3274.2020
Li, B., & Pi, D. (2018). Analysis of global stock index data during crisis period via complex network approach. PLOS ONE, 13(7), e0200600. https://doi.org/10.1371/journal.pone.0200600
Li, R., Zhang, R., Zhang, M., & Zhang, T. (2020). Investment analysis and strategy for COVID-19. Available at SSRN 3563300. https://doi.org/10.2139/ssrn.3563300
Liew, V. K.-S., Chia, R. C. J., Riaz, S., & Lau, E. (2022). Is there any day-of-the-week effect amid the COVID-19 panic in the Malaysian stock market?. The Singapore Economic Review. https://doi.org/10.1142/S021759082250014X
Mantegna, R. N. (1999). Hierarchical structure in financial markets. The European Physical Journal B, 11(1), 193–197. https://doi.org/10.1007/s100510050929
New Straits Times (2020). 14-Day Movement Control Order begins nationwide on Wednesday. (2020, March 16). New Straits Times. https://www.nst.com.my/news/nation/2020/03/575180/14-day-movement-control-order-begins-nationwide-wednesday
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
Sah, N., & Wong, W. (2021). The impact of MCO on the stock market and moratorium on banking sector’s performance in Malaysia: The case of COVID-19. Labuan Bulletin of International Business and Finance, 19(2), 35–42. https://doi.org/10.51200/lbibf.v19i2.3318
Sheikh Yahya, S. F. (2020). COVID-19 pandemic: A timeline of key milestones. Astro Awani. https://www.astroawani.com/berita-malaysia/covid19-pandemic-timeline-key-milestones-275454
Silver, N. C., & Dunlap, W. P. (1987). Averaging correlation coefficients: should Fisher's z transformation be used?. Journal of applied psychology, 72(1), 146.
Song, S., Yazi, E., Morni, F., & Janang, J. (2021). COVID-19 and stock returns: Evidence from Malaysia. International Journal of Banking and Finance, 16(2), 117–136. https://doi.org/10.32890/ijbf2021.16.2.5
Tashanova, D., Sekerbay, A., Chen, D., Luo, Y., Zhao, S., & Zhang, T. (2020). Investment opportunities and strategies in an era of coronavirus pandemic. Available at SSRN 3567445. https://doi.org/10.2139/ssrn.3567445
Tumminello, M., Aste, T., Di Matteo, T., & Mantegna, R. N. (2005). A tool for filtering information in complex systems. Proceedings of the National Academy of Sciences, 102(30), 10421–10426. https://doi.org/10.1073/pnas.0500298102
Výrost, T., Lyócsa, Š., & Baumöhl, E. (2015). Granger causality stock market networks: Temporal proximity and preferential attachment. Physica A: Statistical Mechanics and Its Applications, 427, 262–276. https://doi.org/10.1016/j.physa.2015.02.017
World Health Organization. (2022). WHO Coronavirus (COVID-19) dashboard. https://covid19.who.int/
Xia, L., You, D., Jiang, X., & Guo, Q. (2018). Comparison between global financial crisis and local stock disaster on top of Chinese stock network. Physica A: Statistical Mechanics and Its Applications, 490, 222–230. https://doi.org/10.1016/j.physa.2017.08.005
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