IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v58y2023ipas1544612323006773.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612323006773
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2023.104305?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Beakcheol Jang & Inhwan Kim & Jong Wook Kim, 2019. "Word2vec convolutional neural networks for classification of news articles and tweets," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-20, August.
    2. John Donovan & Jared Jennings & Kevin Koharki & Joshua Lee, 2021. "Measuring credit risk using qualitative disclosure," Review of Accounting Studies, Springer, vol. 26(2), pages 815-863, June.
    3. Gaies, Brahim & Nakhli, Mohamed Sahbi & Ayadi, Rim & Sahut, Jean-Michel, 2022. "Exploring the causal links between investor sentiment and financial instability: A dynamic macro-financial analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 290-303.
    4. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    5. Islam, Md Shahidul & Alam, Md Samsul & Bin Hasan, Shehub & Mollah, Sabur, 2022. "Firm-level political risk and distance-to-default," Journal of Financial Stability, Elsevier, vol. 63(C).
    6. Dinh, Dung V. & Powell, Robert J. & Vo, Duc H., 2021. "Forecasting corporate financial distress in the Southeast Asian countries: A market-based approach," Journal of Asian Economics, Elsevier, vol. 74(C).
    7. Chih‐Chun Chen & Chun‐Da Chen & Donald Lien, 2020. "Financial distress prediction model: The effects of corporate governance indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1238-1252, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gaies, Brahim & Nakhli, Mohamed Sahbi & Sahut, Jean-Michel & Schweizer, Denis, 2023. "Interactions between investors’ fear and greed sentiment and Bitcoin prices," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    2. Hu, Jing & Zhong, Lejia & Qiao, Jingyuan, 2024. "Equity pledge and debt financing of listed companies," Finance Research Letters, Elsevier, vol. 59(C).
    3. Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
    4. Wei, Lu & Jing, Haozhe & Huang, Jie & Deng, Yuqi & Jing, Zhongbo, 2023. "Do textual risk disclosures reveal corporate risk? Evidence from U.S. fintech corporations," Economic Modelling, Elsevier, vol. 127(C).
    5. Ding, Shusheng & Cui, Tianxiang & Bellotti, Anthony Graham & Abedin, Mohammad Zoynul & Lucey, Brian, 2023. "The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 90(C).
    6. Zhang, Tianjiao & Zhu, Weidong & Wu, Yong & Wu, Zihao & Zhang, Chao & Hu, Xue, 2023. "An explainable financial risk early warning model based on the DS-XGBoost model," Finance Research Letters, Elsevier, vol. 56(C).
    7. Ho, Kung-Cheng & Yen, Huang-Ping & Lu, Canyi & Lee, Shih-Cheng, 2023. "Does information disclosure and transparency ranking system prevent the default risk of a firm?," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1089-1105.
    8. Li, Ken, 2022. "Textual fundamentals in earnings press releases," Advances in accounting, Elsevier, vol. 57(C).
    9. Li, Jing & Li, Nan & Xia, Tongshui & Guo, Jinjin, 2023. "Textual analysis and detection of financial fraud: Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 126(C).
    10. Richard Frankel & Jared Jennings & Joshua Lee, 2022. "Disclosure Sentiment: Machine Learning vs. Dictionary Methods," Management Science, INFORMS, vol. 68(7), pages 5514-5532, July.
    11. Yinghua Song & Minzhe Jiang & Shixuan Li & Shengzhe Zhao, 2024. "Class‐imbalanced financial distress prediction with machine learning: Incorporating financial, management, textual, and social responsibility features into index system," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 593-614, April.
    12. Yasheng Chen & Xian Huang & Zhuojun Wu, 2023. "From natural language to accounting entries using a natural language processing method," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 3781-3795, December.
    13. Mohamed Nihal Saleem & Yianni Doumenis & Epameinondas Katsikas & Javad Izadi & Dimitrios Koufopoulos, 2024. "Decrypting Cryptocurrencies: An Exploration of the Impact on Financial Stability," JRFM, MDPI, vol. 17(5), pages 1-21, April.
    14. Umair Bin Yousaf & Khalil Jebran & Irfan Ullah, 2024. "Corporate governance and financial distress: A review of the theoretical and empirical literature," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 1627-1679, April.
    15. Gaies, Brahim & Chaâbane, Najeh & Bouzouita, Nesrine, 2024. "Navigating the storm: Time-frequency quantile dependence and non-linear causality between crypto-currency market volatility and financial instability," The Quarterly Review of Economics and Finance, Elsevier, vol. 93(C), pages 43-70.
    16. Guberney Muñetón-Santa & Daniel Escobar-Grisales & Felipe Orlando López-Pabón & Paula Andrea Pérez-Toro & Juan Rafael Orozco-Arroyave, 2022. "Classification of Poverty Condition Using Natural Language Processing," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(3), pages 1413-1435, August.
    17. Yap, Chia Ying, 2023. "Corporate Governance and Its Determinants : A study on Apex Healthcare Berhad Malaysia," MPRA Paper 119807, University Library of Munich, Germany.
    18. Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).
    19. Sun, Xiaojun & Lei, Yalin, 2021. "Research on financial early warning of mining listed companies based on BP neural network model," Resources Policy, Elsevier, vol. 73(C).
    20. Ali Meftah Gerged & Mohamed Marie & Israa Elbendary, 2022. "Estimating the Risk of Financial Distress Using a Multi-Layered Governance Criterion: Insights from Middle Eastern and North African Banks," JRFM, MDPI, vol. 15(12), pages 1-22, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:58:y:2023:i:pa:s1544612323006773. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.