This study investigates the ways in which the utilisation of big data analytics generates opportunities for financial professionals in China’s capital market by improving risk assessment and operational efficiency. Using firm-level panel data from Chinese A-share listed companies, the empirical analysis demonstrates that firms adopting big data analytics experience a significantly lower risk of stock price crashes. This risk-mitigating effect is primarily driven by the informational advantages of big data technologies, which enable management to obtain early warnings about emerging operational and financial challenges. Such timely information allows managers to implement proactive strategies, thereby preventing the escalation of risks that could otherwise culminate in stock price crashes. Further analysis reveals notable ownership and industry heterogeneity. State-owned enterprises (SOEs) exhibit a higher likelihood of stock price crash risk compared with non-SOEs, largely due to stricter regulatory frameworks and procedural decision-making processes that may delay timely managerial responses. In contrast, firms in technology-intensive industries demonstrate greater readiness and infrastructural capacity to deploy big data analytics effectively, thereby achieving stronger risk-reducing outcomes. Traditional industry firms, however, often lack adequate technological infrastructure and managerial expertise in data analytics, which constrains the effective utilisation of big data and increases their exposure to stock price crash risk.
