Big data-driven risk prediction approaches of Chengtou bonds on RBF fusion methods

Urban investment bonds (Chengtou bonds) refers to bonds issued for local economic and social development, are also a major component of implicit local government debt, which is one of the main financing channels for local governments in China and playing a crucial role in the risk of default on local government debt. So, the paper suggests studying the risks evolution process through unexpected events by employing thousands of observations with data from municipal issuers’ and bonds’ level from the original year 2002 to 2022, employing RBF (radial basis function) neural networks and PCA (principal components analysis) to construct dual fixed effect model set by year and municipal level. Then it finds out there is a significant negative relationship between the total amount of land grant premium and this risk reduction effect caused by the amount of land grant premium, which was weakened during the pandemic period, thus a new perspective on the risks process is proposed by observing the operating methods of its companies. Finally, the most suitable RBF fusion methods are obtained for risks prediction, with which empirical learning has obtained various control variables and parameters, where the experiments indicate that our fitting results are efficient and valuable for accidents.