Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?

Using machine learning to predict the financial distress of Chinese listed companies, this study shows that the incremental value of textual disclosure in financial distress prediction diminishes in the presence of detailed financial data. Detailed financial data itself has the capacity to accurately predict financial distress, and its prediction performance is not improved when combined with predictors extracted from textual disclosure. The model using combined predictors attaches more importance to financial-data-based predictors than textual-databased ones. Our results provide evidence about the overstated value of textual disclosure and the understated information value of detailed financial data in financial distress prediction.

Automated summary

This study uses machine learning to predict the financial distress of Chinese listed companies. It finds that the value of textual disclosure diminishes when there is detailed financial data available. Detailed financial data itself is able to accurately predict financial distress, and combining it with predictors from textual disclosure does not improve prediction performance. The model that combines predictors gives more importance to financial-data-based predictors than textual-data-based ones. This study provides evidence on the value of textual disclosure and detailed financial data in financial distress prediction.