The causality and uncertainty of the covid-19 pandemic to Bursa Malaysia financial services index’s constituents

• Main Authors: Zuhud, Daeng Ahmad Zuhri, Musa, Muhammad Hasannudin, Ismail, Munira, Bahaludin, Hafizah, Abdul Razak, Fatimah
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: QA Mathematics
• Online Access: http://irep.iium.edu.my/99758/1/99758_The%20causality%20and%20uncertainty.pdf; http://irep.iium.edu.my/99758/; https://www.mdpi.com/1099-4300/24/8/1100/htm
• Institution: Universiti Islam Antarabangsa Malaysia

Valued in hundreds of billions of Malaysian ringgit, the Bursa Malaysia Financial Services Index’s constituents comprise several of the strongest performing financial constituents in Bursa Malaysia’s Main Market. Although these constituents persistently reside mostly within the large market capitalization (cap), the existence of the individual constituent’s causal influence or intensity relative to each other’s performance during uncertain or even certain times is unknown. Thus, the key purpose of this paper is to identify and analyze the individual constituent’s causal intensity, from early 2018 (pre-COVID-19) to the end of the year 2021 (post-COVID-19) using Granger causality and Schreiber transfer entropy. Furthermore, network science is used to measure and visualize the fluctuating causal degree of the source and the effected constituents. The results show that both the Granger causality and Schreiber transfer entropy networks detected patterns of increasing causality from pre- to post-COVID-19 but with differing causal intensities. Unexpectedly, both networks showed that the small- and mid-caps had high causal intensity during and after COVID-19. Using Bursa Malaysia’s sub-sector for further analysis, the Insurance sub-sector rapidly increased in causality as the year progressed, making it one of the index’s largest sources of causality. Even after removing large amounts of weak causal intensities, Schreiber transfer entropy was still able to detect higher amounts of causal sources from the Insurance sub-sector, whilst Granger causal sources declined rapidly post-COVID-19. The method of using directed temporal networks for the visualization of temporal causal sources is demonstrated to be a powerful approach that can aid in investment decision making.