Open AccessResearch on Enterprise Credit Risk Prediction Based on Text Information
Author(s) -
Haonan Zhang,
Hongmei Zhang,
Mu Zhang
Publication year - 2022
Publication title -
journal of risk analysis and crisis response
Language(s) - English
Resource type - Journals
eISSN - 2210-8505
pISSN - 2210-8491
DOI - 10.54560/jracr.v11i4.311
Subject(s) - credit risk , intonation (linguistics) , pessimism , logistic regression , tone (literature) , actuarial science , econometrics , computer science , business , economics , linguistics , machine learning , philosophy , epistemology
This paper uses the text data mining method to separate the intonation in the annual reports of credit risk enterprises and non-credit risk enterprises, quantify it, and study the impact of annual report intonation on the effectiveness of credit risk prediction. In the empirical research, this paper uses the factor analysis method for some traditional financial variables, and uses the extracted components and intonation variables to predict the credit risk through the logistic model. The results show that the tone of enterprises with credit risk is more negative, and the degree of pessimism is significantly positively correlated with the probability of credit risk. By comparing the ROC curves of the prediction results before and after the addition of intonation variables, adding intonation variables to the credit risk prediction based on financial variables can improve the effectiveness of the prediction.