IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v89y2023ics1057521923002867.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521923002867
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2023.102770?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. 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.
    2. 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, Wiley Blackwell, vol. 58(1), pages 199-235, March.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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, Wiley Blackwell, vol. 60(2), pages 467-515, May.
    8. 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.
    9. 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.
    10. 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.
    11. Bernanke, Ben S, 1981. "Bankruptcy, Liquidity, and Recession," American Economic Review, American Economic Association, vol. 71(2), pages 155-159, May.
    12. 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.
    13. 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.
    14. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    15. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    16. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    23. Opler, Tim C & Titman, Sheridan, 1994. "Financial Distress and Corporate Performance," Journal of Finance, American Finance Association, vol. 49(3), pages 1015-1040, July.
    24. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
    25. Nerissa C. Brown & Richard M. Crowley & W. Brooke Elliott, 2020. "What Are You Saying? Using topic to Detect Financial Misreporting," Journal of Accounting Research, Wiley Blackwell, vol. 58(1), pages 237-291, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    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. Ken Li, 2024. "Liquidity ratios and corporate failures," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 1111-1134, March.
    2. Yue Qiu & Jiabei He & Zhensong Chen & Yinhong Yao & Yi Qu, 2024. "A novel semisupervised learning method with textual information for financial distress prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2478-2494, November.
    3. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. 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.
    5. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    6. Syeda Tabinda Rubab & Nadia Hanif & Syeda Ambreen Fatima Bukhari & Umer Munir & Mubasher Muhammad Kamran, 2022. "The Impact Of Financial Distress On Financial Performance Of Manufacturing Firms Listed At Pakistan Stock Exchange," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 11(2), pages 382-391.
    7. Xavier Brédart & Eric Séverin & David Veganzones, 2021. "Human resources and corporate failure prediction modeling: Evidence from Belgium," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1325-1341, November.
    8. David Alaminos & Agustín del Castillo & Manuel Ángel Fernández, 2016. "A Global Model for Bankruptcy Prediction," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
    9. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2022. "Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1231-1249, March.
    10. Ben Jabeur, Sami, 2017. "Bankruptcy prediction using Partial Least Squares Logistic Regression," Journal of Retailing and Consumer Services, Elsevier, vol. 36(C), pages 197-202.
    11. Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    12. Elsayed, Mohamed & Elshandidy, Tamer, 2020. "Do narrative-related disclosures predict corporate failure? Evidence from UK non-financial publicly quoted firms," International Review of Financial Analysis, Elsevier, vol. 71(C).
    13. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    14. Paras Arora & Suman Saurabh, 2022. "Predicting distress: a post Insolvency and Bankruptcy Code 2016 analysis," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(3), pages 604-622, July.
    15. Hamid Waqas & Rohani Md-Rus, 2018. "Predicting financial distress: Applicability of O-score model for Pakistani firms," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(2), pages 389-401, April.
    16. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    17. Henri Arno & Klaas Mulier & Joke Baeck & Thomas Demeester, 2022. "Next-Year Bankruptcy Prediction from Textual Data: Benchmark and Baselines," Papers 2208.11334, arXiv.org.
    18. Lenka Papíková & Mário Papík, 2022. "Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 254-281, October.
    19. Jiaming Liu & Chengzhang Li & Peng Ouyang & Jiajia Liu & Chong Wu, 2023. "Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1112-1137, August.
    20. Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.

    More about this item

    Keywords

    Financial distress prediction; Machine learning; Textual disclosure;
    All these keywords.

    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

    Statistics

    Access and download statistics

    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:finana:v:89:y:2023:i:c:s1057521923002867. 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/inca/620166 .

    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.