Journal of Risk Model Validation

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Forecasting the default risk of Chinese listed companies using a gradient-boosted decision tree based on the undersampling technique

Guotai Chi, Shanshan Wang, Ying Zhou and Li Chen

  • A method is proposed to select the optimal imbalance ratio for class imbalance problems in default prediction or credit scoring.
  • This paper uses data mining techniques to built a default prediction model for forecasting default.
  • A real world Chinese credit dataset is used to verify the effectiveness of the proposed model.
  • The proposed model not only surpasses the benchmark models but can also achieve five-year default prediction ability.

Default prediction is of interest to the creditors, customers and suppliers of any firm as well as to policymakers and current and potential investors. Imbalanced classification for default prediction is considered a crucial issue. Therefore, this study proposes a default risk prediction model using a gradient-boosted decision tree (GBDT) based on the random undersampling (RUS) technique. We build a default prediction model based on 29 indicators and five different time windows. The model has two steps. First, the proposed RUS-GBDT model adopts the undersampling approach to generate different training samples based on the imbalance ratio of the training data. Then, the parameter of the GBDT is adaptively tuned with the area under the receiver operating characteristic curve of the predictive model for the selected training sample. We analyze the optimal imbalance ratio of the different training samples and compare the model’s prediction performance with that of several other classification methods including logistic regression and support vector machines. Our experimental results demonstrate that the proposed model performs better than the other classifiers with respect to predicting and classifying the default status of listed companies in China.

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