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Xia, Y., Liu, C., & Liu, N. (2017). Cost-Sensitive Boosted Tree for Loan Evaluation in Peer-to-Peer Lending. Electronic Commerce Research and Applications, 24, 30-49.
https://doi.org/10.1016/j.elerap.2017.06.004

has been cited by the following article:

• TITLE: A Study on Forecasting the Default Risk of Bond Based on XGboost Algorithm and Over-Sampling Method

  AUTHORS: Yan Zhang, Lin Chen

  KEYWORDS: XGboost, Default Risk, Bond Issuer, Imbalanced Data, SMOTE

  JOURNAL NAME: Theoretical Economics Letters, Vol.11 No.2, April 13, 2021

  ABSTRACT: China’s bond market is an emerging market. The number of bond defaults has been increasing in recent years, but the data set is severely imbalanced. Based on financial data of total 6731 corporate bond issuers which 50 bond issuers had defaulted, this paper uses the XGboost algorithm and an Over-sampling method named SMOTE to predict the default of bond issuers. The results show that the XGboost algorithm has advantages over the traditional algorithm in processing imbalanced data, and SMOTE is one of the effective methods to deal with imbalanced samples. Then, this is an effective way to predict the default risk of bond issuers in an emerging market.