Understanding Changes in the Topology and Geometry of Financial Market Correlations during a Market Crash

• Main Authors: Peter Tsung-Wen Yen, Kelin Xia, Siew Ann Cheong
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Entropy
• Subjects: econophysics; financial markets; correlation filtering; minimal spanning tree; planar maximally filtered graph; topological data analysis
• Online Access: https://www.mdpi.com/1099-4300/23/9/1211

In econophysics, the achievements of information filtering methods over the past 20 years, such as the minimal spanning tree (MST) by Mantegna and the planar maximally filtered graph (PMFG) by Tumminello et al., should be celebrated. Here, we show how one can systematically improve upon this paradigm along two separate directions. First, we used topological data analysis (TDA) to extend the notions of nodes and links in networks to faces, tetrahedrons, or <i>k</i>-simplices in simplicial complexes. Second, we used the Ollivier-Ricci curvature (ORC) to acquire geometric information that cannot be provided by simple information filtering. In this sense, MSTs and PMFGs are but first steps to revealing the topological backbones of financial networks. This is something that TDA can elucidate more fully, following which the ORC can help us flesh out the geometry of financial networks. We applied these two approaches to a recent stock market crash in Taiwan and found that, beyond fusions and fissions, other non-fusion/fission processes such as cavitation, annihilation, rupture, healing, and puncture might also be important. We also successfully identified neck regions that emerged during the crash, based on their negative ORCs, and performed a case study on one such neck region.