Bond Default Prediction Based on Deep Learning and Knowledge Graph Technology - Ma Chi; Sun Hongyan; Wang Shaofan; Lu Shengliang; Li Jingyan (IEEE Access, 2021)
The traditional financial models used in bond default mainly focus on the analysis and prediction of bonds issued by listed companies, and they lack early warning abilities for a large number of bonds of nonlisted companies. At the same time, there is a great deal of relational data and category data in bond data. It is of great significance for bond default prediction to use these data reasonably, which may bring considerable revenue to companies in the near future. Therefore, this paper uses multisource information from bonds and issuers as well as macroeconomic data to predict bond defaults based on a knowledge graph and deep learning technology. On the basis of constructing a bond knowledge graph, knowledge representation learning technology is used to vectorize the knowledge in the graph, and the extracted vectors are inputted into the deep learning model as features to forecast bond default. The applied model is the deep factorization machine model, and good prediction results are obtained.