Stock Network Stability After Crashes Based on Entropy Method

Yang, Mingyuan; Ren, Fei; Li, Sai‐Ping

doi:10.3389/fphy.2020.00163

Cited by 4 publications

(1 citation statement)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on network theory, all system components can be linked by their interactions to form a network; exploring global network topologies helps quantify their interconnectedness to build early warning indicators [10]. Various studies of bank, guarantee, and stock networks have found empirical evidence that global network topologies can reveal financial crises [11][12][13][14]. However, a small portion of research has emphasized that more informative signals may hide in tiny changes of local network topologies [15] because networks in similar global topologies may differ noticeably at a local level [16].…”

Section: Introductionmentioning

confidence: 99%

Motif Transition Intensity: A Novel Network-Based Early Warning Indicator for Financial Crises

et al. 2022

View full text Add to dashboard Cite

Financial crisis, rooted in a lack of system resilience and robustness, is a particular type of critical transition that may cause grievous economic and social losses and should be warned against as early as possible. Regarding the financial system as a time-varying network, researchers have identified early warning signals from the changing dynamics of network motifs. In addition, network motifs have many different morphologies that unveil high-order correlation patterns of a financial system, whose synchronous change represents the dramatic shift in the financial system’s functionality and may indicate a financial crisis; however, it is less studied. This paper proposes motif transition intensity as a novel method that quantifies the synchronous change of network motifs in detail. Applying this method to stock networks, we developed three early warning indicators. Empirically, we conducted a horse race to predict ten global crises during 1991–2020. The results show evidence that the proposed indicators are more efficient than the VIX and the other 39 network-based indicators. In a detailed analysis, the proposed indicators send sensitive and comprehensible warning signals, especially for the U.S. subprime mortgage crisis and the European sovereign debt crisis. Furthermore, the proposed method provides a new perspective to detect critical signals and may be extended to predict other crisis events in natural and social systems.

show abstract