Zuhud Daeng Ahmad Zuhri, Musa Muhammad Hasannudin, Ismail Munira, Bahaludin Hafizah, Razak Fatimah Abdul
Department of Mathematical Sciences, Faculty of Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.
Department of Computational and Theoretical Sciences, Kulliyyah of Science, International Islamic University Malaysia, Kuantan 25200, Pahang, Malaysia.
Entropy (Basel). 2022 Aug 10;24(8):1100. doi: 10.3390/e24081100.
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.
马来西亚证券交易所金融服务指数的成分股价值数千亿马来西亚林吉特，其中包括马来西亚证券交易所主板市场中表现最强劲的几只金融成分股。尽管这些成分股大多持续处于高市值范围内，但在不确定甚至确定时期内，各成分股之间因果影响的存在或相对彼此表现的强度尚不清楚。因此，本文的关键目的是利用格兰杰因果关系和施赖伯转移熵，识别和分析从2018年初（新冠疫情前）到2021年底（新冠疫情后）各成分股的因果强度。此外，运用网络科学来测量和可视化源成分股和受影响成分股波动的因果程度。结果表明，格兰杰因果关系网络和施赖伯转移熵网络都检测到从新冠疫情前到疫情后的因果关系呈上升模式，但因果强度不同。出乎意料的是，两个网络都显示，在新冠疫情期间及之后，中小盘股具有较高的因果强度。利用马来西亚证券交易所的子行业进行进一步分析，随着时间的推移，保险子行业的因果关系迅速增加，使其成为该指数最大的因果关系来源之一。即使去除大量微弱的因果强度后，施赖伯转移熵仍能检测到保险子行业中更多的因果源，而格兰杰因果源在新冠疫情后迅速减少。使用有向时间网络来可视化时间因果源的方法被证明是一种强大的方法，有助于投资决策。
