Does the registration system reform reduce the finance sector’s risk spillover effect in China’s stock market—Causal inference based on dual machine learning

Yuxi Zhang, Weidong Li and Shijun Dong

PLOS ONE, 2025, vol. 20, issue 6, 1-17

Abstract: With growing uncertainty in global trade, improving access to domestic capital markets has become an important way to manage financial risk spillovers. This study examines how the registration system reform affects the finance sector's risk spillovers to other 10 sectors in China’s stock market using a dual machine learning model. The findings include: (1) The finance sector's risk spillovers vary over time and are heterogeneous. Risk spillovers rapidly intensify two months after the outbreak of the COVID-19 pandemic, with the average net ∆CoVaR value changing from −0.001 to −0.006. The finance sector mainly accepts risk from the public utility sector and exports risk to the other 9 sectors, with the highest spillovers going to the communication and information technology sectors, showing extreme net ΔCoVaR values around −0.02. (2) The registration system reform increases the finance sector's risk spillover effect, and this conclusion remains the same after a series of robustness tests. (3) Sector heterogeneity tests show that the reform boosts the finance sector's risk spillovers to cyclical sectors and sectors with a low proportion of strategic emerging companies but reduces risk spillovers to midstream and supportive sectors. Finally, some suggestions and implications are proposed.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0326607

DOI: 10.1371/journal.pone.0326607

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