Structural evolution of industry association networks in Chinese stock market under major event shocks: A comparative analysis of two crises based on partial Granger causal networks

Abstract

Based on the inter-industry partial Granger causality network model, this study explores the dynamic impact mechanisms and risk transmission patterns of major events on industry associations in the Chinese stock market. A comparative analysis of data from the 2015 stock market crash and the two phases of the COVID-19 pandemic reveals three key findings. First, market vulnerability exhibits significant heterogeneity during crises. During the 2015 stock market crash, industry network density declined, and centrality shifted from the financial sector to defensive industries. In contrast, during the pandemic, an information hub network emerged, with computers and non-banking financials at its core, highlighting the technology sector’s resource control capacity during crises. Second, the risk transmission path underwent a structural shift. Outward correlation analysis shows that the real estate industry chain benefited from counter-cyclical control policies following the stock market crash, while the influence of the electronics industry surged during the pandemic. Meanwhile, inward intensity analysis identifies the automobile industry as the largest risk recipient in both crises. Finally, network stability weakened significantly. Motif analysis comparing pre-crisis and during-crisis periods reveals that all five key triadic and quadratic structures declined by 38.5% and 49.6%, respectively, with feedback loop motifs experiencing the largest reduction. This confirms that major events amplify market vulnerability by weakening causal linkages. These findings provide new evidence on network dynamics, offering valuable insights into the domino effect of extreme events in capital markets.