Could Textual Features Offer Incremental Information to Financial Distress Prediction? Evidence from the Listed Firm in China

Both academia and industry believe that introducing textual features into a financial distress prediction model can improve its accuracy. However, the textual features introduced by the research are relatively singular and fail to reflect the overall situation of the text comprehensively and effectively. Based on the traditional Z-score financial indicators model, four textual features of MD&A are introduced, namely, sentiment tone, text readability, forward-looking depth, and performance self-attribution. Using logistic regression, BP neural network, and deep belief network, empirical research draws conclusions from listed companies in China. The results show that the financial distress prediction model of listed companies considering multitextual features can effectively improve the prediction accuracy, and deep belief network has the potential to perform better prediction. © 2023 Liyuan Zheng et al.