Financial distress prediction by combining sentiment tone features

In addition to financial features, we propose a novel framework that combines sentiment tone features extracted from comments on online stock forums, management discussion and analysis, and financial statement notes, to predict financial distress. We evaluate the proposed framework using data from the Chinese stock market between 2016 and 2020. We find that financially distressed companies are more likely to have weak sentiment tones as investors have a negative attitude toward the operation and financial status of the companies, while normal companies are to the contrary. Additionally, the sentiment tones of comments within one month most effectively reflect such correlations. We recommend incorporating sentiment tone features as they contribute to predictive performance improvements of all models using financial features only, and using the CatBoost model as it outperforms all benchmarked models with its ability to capture complex feature relationships. Economic benefits analysis shows that the proposed framework can correctly identify more financially distressed companies.