To achieve high-quality development, a critical question needs to be addressed: can enterprise digital transformation effectively alleviate enterprise financing problems by reducing the cost of equity capital? This study uses the text mining to analyze the financial statement from Chinese companies listed on Shanghai and Shenzhen A-share markets between 2011 and 2021, in order to define the digital transformation. Further, this study empirically tests the influencing mechanisms of digital transformation on the cost of equity capital, considering the perspectives of information disclosure quality and stock liquidity. The conclusions are as follows: (i) enterprise digital transformation can significantly reduce the cost of equity capital, and under the control of other variables, the cost of equity financing will be reduced by 8.8% for every 1% increase in digital transformation; (ii) digital transformation not only leads to a decrease in the cost of equity capital through the two independent mediation channels, improving information disclosure and accelerating stock liquidity, but also reduces the cost of equity capital through the chain mediation channel from information disclosure quality to stock liquidity; (iii) heterogeneity analysis finds that the effect of digital information on the cost of equity capital is more pronounced in larger, non-state-owned, non-high-tech and eastern enterprises. This study enriches the research of the influence of digital transformation on external financing, and plays an important role in the promotion of enterprise digital transformation and the formulation of targeted policies.

The ability of A-share listed companies to adhere to the digital economy and achieve long-term corporate benefits amidst an uncertain external environment through financial digital transformation remains a crucial concern for entrepreneurs and scholars. The objective of this study was to scrutinize the effect of financial digital transformation on financial performance among 2566 of China’s A-share listed companies in the hopes of providing informative recommendations for businesses that are currently undertaking or planning to undertake financial digital transformation. To investigate the mechanism by which financial performance among A-share listed companies was influenced by digital transformation, a panel data regression model was utilized. The findings suggest that, first and foremost, financial digital transformation significantly boosts corporate financial performance for A-share listed companies, and this enhancement is sustainable over time. For every 1% growth in financial digital transformation, corporate financial performance improves by 1.1%. Corporate financial performance is projected to improve by 29.8% during the next three financial years. Secondly, information symmetry and operational expenses function as intermediaries in the financial digital transformation process that affects firm financial performance. For every 1% increase in financial digital transformation, the information symmetry level of firms grows by 15.1%, while the operational cost rate declines by 0.8%. Thirdly, information disclosure and operating costs play a chained intermediary role. Every 1% increase in the level of information symmetry reduces the operating cost rate by 2.3%. Fourthly, in comparison to enterprises in eastern provinces, businesses located in central and western provinces are better positioned to improve their financial performance by undertaking a financial digital transformation.

This study conducts a comprehensive bibliometric analysis and predictive modeling of financial risk research from 2015 to 2024, integrating conceptual, knowledge, and collaboration perspectives. Utilizing the PRISMA framework for literature screening, the study identifies publications, research areas, and research institutions. A co-citation network approach reveals the intellectual structure and milestone works, while emergent keyword detection highlights cutting-edge topics such as economic policy uncertainty, climate risk, and green innovation. Furthermore, the study proposes a novel semantic forecasting model, SEF-ACLSTM (Semantic Evolution Forecasting with Aligned Clustered LSTM), to predict the evolution of research themes through 2030. The results identify three major thematic clusters: methodological innovation, traditional risk management, and green finance. The predictive analysis indicates a growing emphasis on methodological and sustainability-oriented topics, suggesting a paradigmatic shift in financial risk research. The findings offer theoretical insights and strategic guidance for future academic inquiry and policy formulation.

This study examines the impact of green investment on firms’ new quality productivity within the signaling theory framework. Using panel data from A-share listed companies in Shanghai and Shenzhen from 2012 to 2022, we find that green investment significantly enhances corporate productivity, a conclusion that remains robust across alternative measurement approaches and instrumental variable estimation. Further analysis indicates that supply chain concentration strengthens the positive effect of green investment, while higher information disclosure can attenuate its marginal returns. These findings extend the understanding of green investment from an environmental commitment to a strategic driver of productivity, thereby enriching the application of signaling theory in the field of sustainable development. The results provide practical insights for firms seeking to optimize green resource allocation and manage supply chain relationships, while also offering evidence-based guidance for policymakers to design differentiated incentives and refine information disclosure frameworks.