AI Innovation Quality and Corporate Financial Resilience: Evidence from Chinese Listed Firms

Yongyin Fang, Yan Xu, Jin You, Rui Zhou, Manlin Wu

Artificial intelligence (AI) has become a central source of technological competition and industrial upgrading, yet it remains unclear whether AI innovation improves firms' financial resilience. This study argues that the financial value of AI innovation depends less on the volume of AI patents and more on the quality and technological influence embodied in those patents. Using Chinese A-share listed firms from 2013 to 2023, we construct a firm-year measure of AI innovation quality from CSMAR AI patent citation records and merge it with listed-firm financial panel data. The baseline two-way fixed-effects estimates show that average citation quality of AI patents is positively associated with a composite financial resilience index, ROA, operating ROA, and revenue growth. Alternative measurement, exclusion of citation-immature years, and a PCA-based resilience index yield directionally consistent evidence, although the PCA result is significant at the 10% level. A supplementary peer-industry instrumental variable also supports a positive association, while the industry-year fixed-effects specification weakens statistical significance, indicating that industry-level AI waves remain an important identification challenge. Heterogeneity tests show that high-gross-margin and low-leverage firms are better able to translate AI innovation quality into financial resilience. The study contributes by shifting the analysis of corporate AI innovation from quantity expansion to quality conversion and by showing that AI-related technological influence is more likely to translate into financial resilience for low-leverage and high-gross-margin firms.