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Credit default prediction of Chinese real estate listed companies based on explainable machine learning

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  • Ma, Yuanyuan
  • Zhang, Pingping
  • Duan, Shaodong
  • Zhang, Tianjie

Abstract

It is essential to accurately forecast the credit default of real estate businesses and provide interpretable analysis. The intrinsic interpretable glass-box model and the post-hoc black-box model are used to predict and explain the credit default status of China's real estate listed businesses. Chinese annual reports, stock bar investor remarks, financial indicators and Distance to Default (DD) are taken into consideration when forecasting credit default. The AdaBoost model and the intrinsic Explainable Boosting Machine (EBM) model are determined to have the best prediction results, respectively. We present the explainable prediction results to clearly understand the ranking of feature importance and the impact on the prediction results.

Suggested Citation

  • Ma, Yuanyuan & Zhang, Pingping & Duan, Shaodong & Zhang, Tianjie, 2023. "Credit default prediction of Chinese real estate listed companies based on explainable machine learning," Finance Research Letters, Elsevier, vol. 58(PA).
    • Handle: RePEc:eee:finlet:v:58:y:2023:i:pa:s1544612323006773
    DOI: 10.1016/j.frl.2023.104305
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    References listed on IDEAS

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    Cited by:

    1. Li, Huan & Wu, Weixing, 2024. "Loan default predictability with explainable machine learning," Finance Research Letters, Elsevier, vol. 60(C).
    2. Song, Yang & Li, Runfei & Zhang, Zhipeng & Sahut, Jean-Michel, 2024. "ESG performance and financial distress prediction of energy enterprises," Finance Research Letters, Elsevier, vol. 65(C).
    3. Dejian Yu & Bo Xiang, 2025. "Customized integrated decision model for CBEC enterprise credit evaluation: The fusion of multi-source features and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-19, December.
    4. Kuang, Xianhua & Ma, Chaoqun & Ren, Yi-Shuai, 2024. "Credit risk: A new privacy-preserving decentralized credit assessment model," Finance Research Letters, Elsevier, vol. 67(PB).

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