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A-share companies must manage financial risk to succeed. Textual data insights can greatly impact risk assessment results, although most risk management systems focus on quantitative financial assessments. This research constructs and enhances information system financial risk management models employing financial and textual data, including MDA narratives, to fill this gap. We study how textual data aids financial risk management algorithms' risk prediction. Textual and financial research on 2001–2022 Shenzhen and Shanghai Stock Exchange companies is used. This study found financial and non-financial data models more predictive. Qualitative textual information is used in financial risk assessment to improve risk prediction algorithms. MDA texts, sentiment analysis, and readability signal risk. Internet forum discussions are linked to financial risk, but media coverage is not. These unconventional data sources evaluate financial risk. The research shows that A-share corporations manage financial risk. The study advises merging qualitative textual data with financial metrics to solve literature gaps and improve risk management. Shenzhen and Shanghai Stock Exchange statistics suggest MDA storylines might strengthen financial risk management models. Study shows readability and sentiment analysis increase risk model prediction. The study found that textual material affects financial risk, therefore risk assessment should include non-financial information. This complete risk management technique may assist A-share listed companies navigate financial markets and make smarter decisions using quantitative financial data and qualitative textual insights. This study implies textual data may help financial risk algorithms. MDAs help companies identify and manage financial risk. More study is needed to discover new textual elements and strengthen context-specific risk management frameworks.
Huang et al. (Wed,) studied this question.