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Credit risk in the China's bond market has become increasingly evident,creating a progressively escalating risk of default for credit bond investors.Given the current incomplete and inaccurate bond information disclosure, timelytracking and forecasting the individual credit bond default risks have becomeessential to maintain market stability and ensure healthy development. Thispaper proposes an Intelligent Forecasting Framework for Default Risk thatprovides precise day-by-day default risk prediction. In this framework, wefirst summarize the factors that impact credit bond defaults and construct arisk index system. Then, we employ a combined default probability annotationmethod based on the evolutionary characteristics of bond default risk. Themethod considers the weighted average of Variational Bayesian Gaussian Mixtureestimation, Market Index estimation, and Default Trend Backward estimation fordaily default risk annotation of matured or defaulted bonds according to therisk index system. Moreover, to mine time-series correlation andcross-sectional index correlation features efficiently, an intelligentprediction model for Chinese credit bond default risk is designed using theConvLSTM neural network and trained with structured feature data. Theexperiments demonstrate that the predicted individual bond risk is slightlyhigher and substantially more responsive to fluctuations than the riskindicated by authoritative ratings, thereby improving on the inadequacies ofinflated and untimely bond ratings. Consequently, this study's findings offermultiple insights for regulators, issuers, and investors.

Study on Intelligent Forecasting of Credit Bond Default Risk