Risk assessment of stock market manipulation through the fusion of multi-source textual and trading data: Evidence from China's A-share market.

Against the backdrop of increasingly diversified and concealed forms of stock market manipulation, approaches based solely on trading data or financial indicators face growing limitations in complex and information-intensive market environments. To assess stock market manipulation risk, this study constructs a firm-month level multi-source panel dataset by retrospectively labeling violation periods at the monthly frequency based on manipulation cases sanctioned by the CSRC (China Securities Regulatory Commission). The dataset integrates corporate disclosures, investor sentiment derived from online public opinion, and market trading characteristics. A supervised learning framework that fuses textual representations and numerical features is then employed to generate manipulation risk probabilities, supporting risk ranking and tiered screening in regulatory applications. Empirical results show that the fusion model consistently outperforms single-source baselines, achieving an AUC of 0.8811 and a PR-AUC of 0.6943, along with substantial improvements in Recall@10% and Recall@20% for high-risk screening. These findings indicate that multi-source information exhibits complementary effects in manipulation risk assessment and enables effective characterization of joint anomalies along the "information disclosure-sentiment reaction-trading behavior" chain. Theoretically, this study highlights the complementary role of heterogeneous information sources, including disclosure, sentiment, and trading-related signals, in characterizing manipulation risk. In practice, it provides a feasible data-driven pathway for risk monitoring and tiered regulatory screening.