Research on the dynamic early warning effect on the manufacturing industry from the perspective of systemic financial risks: evidence from the Chinese market

Accurate measurement and effective prevention of systemic financial risks are crucial factors in promoting financial stability and high-quality development. This study first employs factor analysis to extract common risk factors and constructs a variant of the China systemic financial risk composite index (CSFRI), by combining the entropy weighting method and the criteria importance through intercriteria correlation (CRITIC) weighting method. Subsequently, the random forest algorithm is employed to rank the risk contributions of each indicator. Finally, based on the repeated emphasis by the 20th National Congress and other reports and meetings on the development of the real economy, the construction of a manufacturing powerhouse and the role of finance in supporting the real economy, this study explores whether the CSFRI can contribute to the development of the manufacturing industry. By establishing a time-varying parameter–stochastic volatility–vector autoregressive (TVP-SV-VAR) model, this study explores the dynamic and time-varying impact of changes in the CSFRI on the manufacturing industry. The results indicate that a positive shock to the CSFRI has a negative overall impact on the manufacturing industry, with completed fixed-asset investment showing a notable short-term effect. The CSFRI developed in this study accurately reflects the risk status of China’s financial market while serving as a leading indicator with clear dynamic and timevarying characteristics for the manufacturing industry. Its fluctuations significantly impact manufacturing industry development, making it a potential risk warning indicator for the industry, thus enabling more precise financial support for manufacturing industry growth.