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Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?

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  • Zhao, Qi
  • Xu, Weijun
  • Ji, Yucheng

Abstract

Using machine learning to predict the financial distress of Chinese listed companies, this study shows that the incremental value of textual disclosure in financial distress prediction diminishes in the presence of detailed financial data. Detailed financial data itself has the capacity to accurately predict financial distress, and its prediction performance is not improved when combined with predictors extracted from textual disclosure. The model using combined predictors attaches more importance to financial-data-based predictors than textual-data-based ones. Our results provide evidence about the overstated value of textual disclosure and the understated information value of detailed financial data in financial distress prediction.

Suggested Citation

  • Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023. "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, vol. 89(C).
    • Handle: RePEc:eee:finana:v:89:y:2023:i:c:s1057521923002867
    DOI: 10.1016/j.irfa.2023.102770
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    1. Leippold, Markus & Wang, Qian & Zhou, Wenyu, 2022. "Machine learning in the Chinese stock market," Journal of Financial Economics, Elsevier, vol. 145(2), pages 64-82.
    2. Lensberg, Terje & Eilifsen, Aasmund & McKee, Thomas E., 2006. "Bankruptcy theory development and classification via genetic programming," European Journal of Operational Research, Elsevier, vol. 169(2), pages 677-697, March.
    3. Jones, Stewart & Hensher, David A., 2007. "Modelling corporate failure: A multinomial nested logit analysis for unordered outcomes," The British Accounting Review, Elsevier, vol. 39(1), pages 89-107.
    4. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    5. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
    6. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 123-127.
    7. Xiaobo Tang & Shixuan Li & Mingliang Tan & Wenxuan Shi, 2020. "Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 769-787, August.
    8. Yi Jiang & Stewart Jones, 2018. "Corporate distress prediction in China: a machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(4), pages 1063-1109, December.
    9. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    10. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    11. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 71-111.
    12. Opler, Tim C & Titman, Sheridan, 1994. "Financial Distress and Corporate Performance," Journal of Finance, American Finance Association, vol. 49(3), pages 1015-1040, July.
    13. Nerissa C. Brown & Richard M. Crowley & W. Brooke Elliott, 2020. "What Are You Saying? Using topic to Detect Financial Misreporting," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 58(1), pages 237-291, March.
    14. Yang Bao & Bin Ke & Bin Li & Y. Julia Yu & Jie Zhang, 2020. "Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 58(1), pages 199-235, March.
    15. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    16. Jeremy Bertomeu & Edwige Cheynel & Eric Floyd & Wenqiang Pan, 2021. "Using machine learning to detect misstatements," Review of Accounting Studies, Springer, vol. 26(2), pages 468-519, June.
    17. 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.
    18. Xi Chen & Yang Ha (Tony) Cho & Yiwei Dou & Baruch Lev, 2022. "Predicting Future Earnings Changes Using Machine Learning and Detailed Financial Data," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 60(2), pages 467-515, May.
    19. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    20. Philippe Jardin & David Veganzones & Eric Séverin, 2019. "Forecasting Corporate Bankruptcy Using Accrual-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 7-43, June.
    21. Bernanke, Ben S, 1981. "Bankruptcy, Liquidity, and Recession," American Economic Review, American Economic Association, vol. 71(2), pages 155-159, May.
    22. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 18(1), pages 109-131.
    23. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 22, pages 59-82.
    24. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    25. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    26. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
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    5. Soumya Ranjan Sethi & Dushyant Ashok Mahadik & Rajkiran V. Bilolikar, 2024. "Exploring Trends and Advancements in Financial Distress Prediction Research: A Bibliometric Study," International Journal of Economics and Financial Issues, Econjournals, vol. 14(1), pages 164-179, January.
    6. Li, Rongnan & Zhou, Qi & Xu, Weijun, 2025. "Do green bonds have an advantage in information disclosure?," International Review of Financial Analysis, Elsevier, vol. 101(C).
    7. Liu, Tao & Wu, Han & Zheng, Yinglong & Cui, Ziyi, 2025. "Reform of the administrative approval system, regional financial development, and corporate performance: An exploration of a market-oriented governance mechanism," Finance Research Letters, Elsevier, vol. 80(C).
    8. Vinay Singh & Bhasker Choubey & Stephan Sauer, 2024. "Liquidity forecasting at corporate and subsidiary levels using machine learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(3), September.
    9. 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.
    10. Yang, Jie & Niu, Yanfang & Shi, Wenlei & Zhu, Kanghuan, 2025. "Predicting ESG disclosure quality through board secretaries' characteristics: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 76(C).
    11. Zengli Mao & Xiaofang Chen & Chong Wu, 2026. "Reinforced Distillation Learning: Fine-Grained Imbalanced Classifier for Financial Crisis Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 67(3), pages 1571-1604, March.
    12. Jie Sun & Jie Li & Zichen Wang, 2026. "Analyst Reports and Corporate Financial Distress Prediction," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 31(2), pages 2690-2712, April.
    13. Congluo Xu & Zhaobin Liu & Ziyang Li, 2025. "FinArena: A Human-Agent Collaboration Framework for Financial Market Analysis and Forecasting," Papers 2503.02692, arXiv.org.
    14. Wei, Lu & Wei, Mingye & Jing, Haozhe & Jing, Zhongbo, 2025. "Annual report tone and bank risk-taking behavior: Evidence from China," Research in International Business and Finance, Elsevier, vol. 77(PA).
    15. Sun, Jie & Xie, Minghui & Li, Jie, 2026. "Multi-class financial distress prediction using the textual information of earnings communication conferences based on ensemble machine learning models," Journal of Business Research, Elsevier, vol. 206(C).

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting

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