Community Detection of Dynamic Complex Networks in Stock Markets Using Hybrid Methods (RMT‐CN‐LPAm+ and RMT‐BDM‐SA) - Purqon, Acep; Jamaludin (Frontiers in Physics, 2021)
A stock market represents a large number of interacting elements leading to more complex hidden interactions. It is very challenging to find a useful method to reveal more detailed dynamical complex networks. For the reason, we propose two hybrid methods called RMT-CN-LPAm+ and RMT-BDM-SA (RMT=Random Matrix Theory, CN=Complex Network, LPAm+ =Advanced Label Propagation Algorithm, BDM=Block Diagonal Matrix, SA=Simulated Annealing). In this study, we investigate the group mapping in S&P 500 stock market using the two hybrid methods. Our results show good performance with each own benefits and strong points. For example, the RMT-CN-LPAM+ successfully identify 6 groups consisting of 485 nodes and 17 isolated nodes with maximum modularity of 0.62 (keeping more group and more maximum modularity). Meanwhile, RMT-BDM-SA provides useful detailed information from matrix decomposition of C into Cm (market-wide), Cg (group), and Cr (noise). Both hybrid methods successfully reveal more detailed community detection of dynamical complex networks in the stock market.