Incorporating media news to predict financial distress: Case study on Chinese listed companies

Abstract Financial distress prediction has been a prominent research field for several decades. Accurate prediction of financial distress not only helps to safeguard the interests of investors but also improves the ability of managers to manage financial risks. Prior studies predominantly rely on accounting metrics derived from financial statements to predict financial distress. Our research takes a step further by incorporating media news to enhance the accuracy of financial distress prediction. Based on the data from Chinese listed companies, seven classifiers are established to verify the additional value of media news in improving the financial distress prediction performance of models. Experimental results demonstrate that the inclusion of media news in predictive models is effective as it contributes to better performance compared with models that solely rely on accounting features. Moreover, random forest model is a reliable tool in financial distress prediction due to its superior ability to capture complex feature relationships. Evaluation indicators, statistical tests, and Bayesian A/B tests further confirm that the inclusion of media news can significantly improve the identification of financially distressed companies.