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.

The objective of this study is to evaluate the empirical performance of interpolation techniques in risk-neutral density (RND) estimation. Firstly, the empirical performance is evaluated by using statistical analysis based on the implied mean and the implied variance of RND. Secondly, the interpolation performance is measured based on pricing error. We propose using the leave-one-out cross-validation (LOOCV) pricing error for interpolation selection purposes. The statistical analyses indicate that there are statistical differences between the interpolation techniques:second-order polynomial, fourth-order polynomial and smoothing spline. The results of LOOCV pricing error shows that interpolation by using fourth-order polynomial provides the best fitting to option prices in which it has the lowest value error.

Coronavirus (COVID-19) pandemic has a massive impact on economic growth and the stock market. Due to COVID-19’s high transmission rate, a movement control order (MCO) was enforced by the Malaysian government in four stages. Consequently, this situation has affected the stability and the relationship of stocks listed on Bursa Malaysia. Thus, this study is motivated to investigate and visualize a correlation of securities listed on FTSE Bursa Malaysia KLCI by employing a network analysis approach. The limitation of correlation analysis is that it only provides the strength and direction of the association, which is not visually shown in the graph. As a result, the network analysis technique is used to highlight the correlations between the stocks. The input of the network is based on the rate of return of each stock. The data is divided into two parts. The first duration is the period before implementation of MCO which is from 17th December 2019 to 17th March 2020. The second duration is during four stages of MCO which cover from 18th March 2020 to 3rd May 2020. There are changes in the interconnection between stocks in which seventeen stocks increased in the correlation measure during MCO. The importance of stocks is determined by applying centrality measures to disclose a topological structure of a network. This study finds that the stock with the highest connectivity based on the degree centrality before the MCO was unable to maintain the status as the central hub during the MCO. The results can assist the market participant to strategize the asset allocation to obtain well diversified portfolio selection.

COVID-19 has drastically changed the teaching patterns of higher education from face-to-face to online learning, and it has also affected students’ engagement socially and academically. Understanding the nature of students’ engagement during online learning can help in identifying related issues so that various initiatives can be implemented in adapting to this situation. In this study, social network analysis is conducted to gain insights on students’ engagement during COVID-19. Directed and weighted networks were used to visualize and analyze friendship as well as peer tutor networks obtained from online questionnaires answered by all students in the class. Contrasting friends and peer tutors reveals some hidden interactions between students and shines some light on dynamics of the online learning community. The results indicate that, popular and important peer tutors may not be high achievers and thus possibly contributing to the spread of misinformation in the online learning community. By comparing weighted indegree and betweenness centrality values, we suggest approaches to cultivate a healthy online learning community. This study highlights the use of social network analysis to assist and monitor students’ engagement and further formulate strategies in order to make the class a conducive online learning community, particularly in the advent of online learning in higher education institutions.

A trade war between China and United States began on March 23, 2018, that affecting the Malaysian economy. Basically, China is Malaysia's most essential and largest trading partner. Meanwhile, the United States is Malaysia's third-largest export market, accounting for 9.5 per cent of total exports, or RM88.7 billion. Any trade liberalisation between these two countries would have a major impact on the economic development of Malaysia. Therefore, the first objective of this research is to investigate the effect of the US-China war trade on the Malaysian market by using network analysis. The second objective is to examine the network's topological properties over the specified period. The data is based on FTSE Emas Index on Bursa Malaysia. The duration is divided into three phases in which before the trade war, after the trade war and overall duration. The threshold network approach is employed to visualize the relationship between the components of the FTSE Emas Index. The results showed that the US-China trade war affect the linkages of the Malaysian stock market in which the stock is more correlated with each other during the trade war.

This research analyses the topological structure of shariah-compliant securities listed in the Bursa Malaysia market from a network perspective. The network is established from the relationships between the top hundred shariah-compliant securities based on market capitalization from 2015 to 2019. The minimum spanning tree (MST) technique applies to produce and portrays a simpler network. Besides, topological properties are explored and examined further to extract the structure embedded and entrenched in the network. As a result, this paper reveals that shariah-compliant securities with different sectors which were interconnected in the Malaysian market., In general, the securities with the same sectors tend to cluster together in the network. The topological properties provide insight to the market participants, especially investors, for the investment strategies.