A Text Mining and Ensemble Learning Based Approach for Credit Risk Prediction

发表日期：2023-1

期刊： Tehnički vjesnik

作者： Mao Yang; Liu Shifeng; Gong Daqing

单位：School of Economics and Management, Beijing Jiaotong University, Haidian, 100044, China

摘要：Traditional credit risk prediction models mainly rely on financial data. However, technological innovation is the main driving force for the development of enterprises in strategic emerging industries, which is closely related to enterprise credit risk. In this paper, a novel prediction framework utilizing technological innovation text mining data and ensemble learning is proposed. The empirical data from China listed enterprises in strategic emerging industries were applied to construct prediction models using the classification and regression tree model, the random forest model and extreme gradient boosting model. The results show that the model uses the technological innovation text mining data proven to have significant predict ability, and top management teamꞌs attention to innovation variables offer the best prediction capacities. This work improves the application value of enterprise credit risk prediction models in strategic emerging industries by embedding the mining of technological innovation text information.

关键词：credit risks; ensemble learning; strategic emerging industries; text mining

链接：A Text Mining and Ensemble Learning Based Approach for Credit Risk Prediction