Open Access
Issue
SHS Web Conf.
Volume 200, 2024
2024 International Conference on Sustainable Economy and Social Sciences (SESS 2024)
Article Number 01001
Number of page(s) 5
Section Sustainable Economy
DOI https://doi.org/10.1051/shsconf/202420001001
Published online 31 October 2024
  1. Qian H, Wang B, Yuan M, et al. Financial distress prediction using a corrected feature selection measure and gradient boosted decision tree[J]. Expert Systems with Applications, 2022, 190: 116202. [CrossRef] [Google Scholar]
  2. Ding Y, Yan C. Corporate Financial Distress Prediction: Based on Multi-source Data and Feature Selection[J]. arXiv preprint arXiv:2404.12610, 2024. [Google Scholar]
  3. Liu J, Li C, Ouyang P, et al. Interpreting the prediction results of the tree ‐ based gradient boosting models for financial distress prediction with an explainable machine learning approach[J]. Journal of Forecasting, 2023, 42(5): 1112-1137. [CrossRef] [Google Scholar]
  4. Liu W, Fan H, Xia M, et al. Predicting and interpreting financial distress using a weighted boosted tree-based tree[J]. Engineering Applications of Artificial Intelligence, 2022, 116: 105466. [CrossRef] [Google Scholar]
  5. Liu J, Wu C, Li Y. Improving financial distress prediction using financial network-based information and GA-based gradient boosting method[J]. Computational Economics, 2019, 53: 851-872. [CrossRef] [Google Scholar]
  6. Song Y, Jiang M, Li S, et al. Class‐imbalanced financial distress prediction with machine learning: Incorporating financial, management, textual, and social responsibility features into index system[J]. Journal of Forecasting, 2024, 43(3): 593-614. [CrossRef] [Google Scholar]
  7. Sehgal S, Mishra R K, Deisting F, et al. On the determinants and prediction of corporate financial distress in India[J]. Managerial Finance, 2021, 47(10): 1428-1447. [CrossRef] [Google Scholar]
  8. Danilov C F A, Konstantin A. Corporate bankruptcy: Assessment, analysis and prediction of financial distress, insolvency, and failure[J]. Analysis and Prediction of Financial Distress, Insolvency, and Failure (May 9, 2014), 2014. [Google Scholar]
  9. Lin T H. A cross model study of corporate financial distress prediction in Taiwan: Multiple discriminant analysis, logit, probit and neural networks models[J]. Neurocomputing, 2009, 72(16-18): 3507-3516. [CrossRef] [Google Scholar]
  10. Song Y, Jiang M, Li S, et al. Class‐imbalanced financial distress prediction with machine learning: Incorporating financial, management, textual, and social responsibility features into index system[J]. Journal of Forecasting, 2024, 43(3): 593-614. [CrossRef] [Google Scholar]
  11. Lei H. Financial Index Data Prediction Based on Improved GBDT Model[C]//2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA). IEEE, 2021: 697-702. [Google Scholar]
  12. Halim Z, Shuhidan S M, Sanusi Z M. Corporation financial distress prediction with deep learning: Analysis of public listed companies in Malaysia[J]. Business Process Management Journal, 2021, 27(4): 1163-1178. [CrossRef] [Google Scholar]
  13. Chen C C, Chen C D, Lien D. Financial distress prediction model: The effects of corporate governance indicators[J]. Journal of Forecasting, 2020, 39(8): 1238-1252. [CrossRef] [Google Scholar]

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