Forecasting default risk of Chinese real estate credit bonds based on Bagging algorithm
DOI:
https://doi.org/10.54097/4ff23923Keywords:
bond default, real estate, Bagging, predictionAbstract
In recent years, with Chinese rapid economic upgrading, it has also made the property industry more and more prosperous. However, due to the imbalance between supply and demand in China's real estate industry in recent years has led to an overly rapid rise in property prices, the risk of a real estate market bubble has increased, and many real estate companies have gradually defaulted on their bonds. Against this background, the aim of this paper is to construct a bond default prediction model using the Bagging algorithm in machine learning to predict the default risk of real estate bonds. And the results were also compared with Logistic model and SVM model, eventually it was found that Bagging algorithm can improve the prediction results more effectively. It is possible to identify real estate companies that may default on their bonds in advance, reducing investor losses, strengthening the risk prevention awareness of debt issuers, contributing to the healthy and stable development of real estate.
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