Behaviours of Bursa Malaysia: a Multidimensional Network Analysis

  • Authors

    • San Y. Lim
    • Rohayu Mohd Salleh
    • Norhaidah Mohd Asrah
  • Bursa Malaysia, Centrality Measures, Forest of All Possible Msts, Multivariate Analysis, RV Coefficient
  • In current practice, the similarities between two or more univariate time series of stocks are determined by using the Pearson correlation coefficient (PCC). However, the economic information might be misleading if the analysis applies only the univariate time series of stock price, as each stock is denoted by four types of prices. Therefore, multidimensional of stocks are taken into account in this paper. The similarities between two or more multi-dimensional of stocks are quantified by using Random Vector (RV) coefficient. Additionally, an algorithm is proposed due to the computational of RV coefficient is tedious and time-consuming when a large number of stocks are included. In this paper, the Malaysian stock network analysis in univariate and multivariate setting are conducted and analysed by using the PCC, RV coefficient, forest of all possible MSTs and centrality measures. In summary, there is some important economic information could not be brought out by univariate network analysis alone.

  • References

    1. [1] New England Complex Systems Institute (2018). Retrieved from

      [2] Kazemilari M & Djauhari MA (2015), Correlation network analysis for multidimensional data in stocks market. Physica A 429, 62-75.

      [3] Gan SL & Djauhari MA (2013), Multidimensional stock network analysis: An Escoufier’s RV coefficient approach. ICMSS 1577, 550-555.

      [4] Shabanali R (2017), Definition: What is a complex system? Webmindset. Retrieved from

      [5] Mantegna RN (1999), Hierarchical structure in financial markets. The European Physical Journal B 11, 193-197.

      [6] Mantegna RN & Stanley HE (2000), An introduction to econophysics: Correlations and complexity in Finance. Physics Today 53, 12, 70.

      [7] Jung WS, Chae S, Yang JS & Moon HT (2006), Characteristics of the Korean stock market correlations. Physica A 361, 1, 263-271.

      [8] Coelho R, Gilmore CG, Lucey B, Richmond P & Hutzler S (2007), The evolution of interdependence in world equity markets – Evidence from minimum spanning trees. Physica A 376, 455-466.

      [9] Bahaludin H, Abdullah MH & Mat Salleh S (2015), Minimal spanning tree for 100 companies in Bursa Malaysia. AIP Conference Proceedings, 1643, 609-615.

      [10] Huang F, Gao P & Wang Y (2009), Comparison of Prim and Kruskal on Shanghai and Shenzhen 300 index hierarchical structure tree. International Conference on Web Information Systems and Mining, 237-241.

      [11] Tabak BM, Serra TR & Cajueiro DO (2010), Topological properties of stock market networks: The case of Brazil. Physica A, 389(16), 3240-3249.

      [12] Brida JG & Risso WA (2009), Multidimensional minimal spanning tree: The Dow Jone case. Physica A, 287, 5205-5210.

      [13] Halinen A & Tornroos J (1998), The role of embeddedness in the evolution of business networks. Scandinavian Journal of Management, 14(3), 187-205.

      [14] Yamashita Y & Yodahisa H (2012), Similarity measure and clustering algorithm for candlestick valued data. Joint Meeting of Japanese and Italian Classification Societies, 17-20.

      [15] Escoufier Y (1973), Le traitement des variables vectorielles. Biometrics 29(4), 751-760.

      [16] Josse J, Pages J & Husson F (2008), Testing the significance of the RV coefficient. Computational Statistics and Data Analysis 53, 82-91.

      [17] Abdi H (2007), RV coefficient and congruence coefficient. Encyclopedia of Measurement and Statistics, 849-853.

      [18] Djauhari MA (2012), A robust filter in stock network analysis. Physica A 391, 5049-5057.

      [19] Sharif S, Yussof NS & Djauhari MA (2012), Network topology of foreign exchange rate. Modern Applied Science 6(11), 35-43.

      [20] Freeman LC (1979), A set of measures of centrality based on betweenness. Sociometry, 35-41.

      [21] Bonacich P (1972), Factoring and weighting approaches to status scores and clique identification. Journal of Mathematical Sociology 2, 113-120.

      [22] Gan SL & Djauhari MA (2012), Network topology of Indonesian stock market. Proceedings of International Conference on Cloud Computing and Social Networking, 1-4.

      [23] Djauhari MA & Gan SL (2014), Bursa Malaysia stocks market analysis: A review. ASM Science Journal 8(2), 150-158.

      [24] Kazemilari M & Djauhari MA (2013), Analysis of a correlation network in world currency exchange market. International Journal of Applied Mathematics and Statistics 44(14), 202-209.

      [25] Asrah NM, Djauhari MA & Ebi SS (2014), Work attitude among Malaysian academics in the public universities: A social network analysis. Modern Applied Science 8(5), 9-18.

      [26] Lim SY & Salleh RM (2018), Bursa Malaysia performance: Evidence from the minimum spanning tree. AIP Conference Proceedings 1974(1), 1-8.

      [27] The Star Online (2015). Retrieved from news/2015/09/02/telekom-malaysia-inks-deal-with-time-dotcom-for-skrlm-cable/

      [28] The Star Online (2018). Retrieved from https:/business/business News/2018/04/13/zainal-is-chairman-of-drbhicom-and-pos-malays-ia/

      [29] Jollife IT, Principal Component Analysis, Springer, New York, (2002), pp: 21-28.

      [30] Minitab 18 support (2018). Retrieved from https://support.minitab com/en-us/minitab/18/help-and-how-to/modeling-statistics/multiv- ariate/how-to/principal-components/interpret-the-results/all-statist- ics-and-graphs/

      [31] Drb-Hicom (2018). Retrieved from about-us/discover-us/

      [32] Pos Malaysia (2018). Retrieved from about-us/corporate-information/?our-heritage

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  • How to Cite

    Lim, S. Y., Salleh, R. M., & Asrah, N. M. (2018). Behaviours of Bursa Malaysia: a Multidimensional Network Analysis. International Journal of Engineering & Technology, 7(4.30), 229-233.