IDEAS home Printed from https://ideas.repec.org/a/wly/ijfiec/v30y2025i3p2369-2388.html

Multi‐class financial distress prediction based on stacking ensemble method

Author

Listed:
  • Xiaofang Chen
  • Chong Wu
  • Zijiao Zhang
  • Jiaming Liu

Abstract

The motivation of this article is to help financial soundness companies understand their specific financial status so that they can take timely measures to avoid financial distress. Existing multi‐class financial distress prediction (FDP) studies have mainly segmented financial crisis status, with less attention paid to financial soundness companies. To fill this gap, we propose a new multi‐class definition of FDP from the perspective of financial soundness enterprises. The financial states are defined as financial soundness, moderate financial soundness, mild financial soundness and financial distress. We propose a stacking ensemble model for multi‐class FDP. First, deep neural network, multinomial logit regression (MNLogit) and multivariate discriminant analysis models are used as basic classifiers to obtain preliminary prediction results. Second, MNLogit is used to integrate the results from the previous step. To increase the effective information, stock information is then added into the model. The proposed model was trained using data from 2007 to 2019 for Chinese listed companies and tested using data from 2020. The results show that the MacroR‐Pre, MacroR‐Rec, MacroR‐F1 and MacroR‐AUC of the proposed model are better compared with the benchmark model, including individuals and ensembles, with 87.05%, 90.68%, 88.70% and 88.20%.The addition of stock information and non‐financial indicators can improve the accuracy of the multi‐class FDP model by about 8%. The innovativeness of this paper is twofold. First, it proposes a new multi‐class definition of enterprise financial status. Second, a multi‐class FDP based on stacking is constructed, which provides a new method for solving the multi‐class FDP problem. The study shows that the proposed multi‐class definition and stacking model are suitable for analysing financial soundness enterprises, which can help managers effectively grasp the specific financial status and have strong practical significance.

Suggested Citation

  • Xiaofang Chen & Chong Wu & Zijiao Zhang & Jiaming Liu, 2025. "Multi‐class financial distress prediction based on stacking ensemble method," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 30(3), pages 2369-2388, July.
    • Handle: RePEc:wly:ijfiec:v:30:y:2025:i:3:p:2369-2388
    DOI: 10.1002/ijfe.3020
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/ijfe.3020
    Download Restriction: no
    File URL: https://libkey.io/10.1002/ijfe.3020?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
    ---><---

    References listed on IDEAS

    as
    1. Jiaming Liu & Chong Wu & Yongli Li, 2019. "Improving Financial Distress Prediction Using Financial Network-Based Information and GA-Based Gradient Boosting Method," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 851-872, February.
    2. Cao, Jie & Wen, Fenghua & Stanley, H. Eugene & Wang, Xiong, 2021. "Multilayer financial networks and systemic importance: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 78(C).
    3. Shilpa H. Shetty & Theresa Nithila Vincent, 2021. "The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model: Evidence from India," JRFM, MDPI, vol. 14(7), pages 1-13, July.
    4. Kanno, Masayasu, 2019. "Network structures and credit risk in cross-shareholdings among listed Japanese companies," Japan and the World Economy, Elsevier, vol. 49(C), pages 17-31.
    5. Eduardo Acosta-González & Fernando Fernández-Rodríguez & Hicham Ganga, 2019. "Predicting Corporate Financial Failure Using Macroeconomic Variables and Accounting Data," Computational Economics, Springer;Society for Computational Economics, vol. 53(1), pages 227-257, January.
    6. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    7. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 123-127.
    8. 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.
    9. Vincenzo Verardi & Catherine Vermandele, 2018. "Univariate and multivariate outlier identification for skewed or heavy-tailed distributions," Stata Journal, StataCorp LLC, vol. 18(3), pages 517-532, September.
    10. Eduard Baitinger, 2021. "Forecasting asset returns with network‐based metrics: A statistical and economic analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1342-1375, November.
    11. Eric Severin & David Veganzones, 2021. "Can earnings management information improve bankruptcy prediction models?," Post-Print hal-04331249, HAL.
    12. Michal Pavlicko & Jaroslav Mazanec, 2022. "Minimalistic Logit Model as an Effective Tool for Predicting the Risk of Financial Distress in the Visegrad Group," Mathematics, MDPI, vol. 10(8), pages 1-22, April.
    13. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 4, pages 71-111.
    14. Eric Séverin & David Veganzones, 2021. "Can earnings management information improve bankruptcy prediction models?," Annals of Operations Research, Springer, vol. 306(1), pages 247-272, November.
    15. 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)

    Citations

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


    Cited by:

    1. Filiz Yetiz & Ayşegül Ciğer & Mehmet Kayakuş & Aram Shaban Fattah Fattah, 2025. "Prediction of Financial Soundness Index for Viability in the European Banking System Using Machine Learning," SAGE Open, , vol. 15(3), pages 21582440251, September.
    2. Xue, Xiaorui & Li, Shaofang & Wang, Xiaonan & Ren, Tingting, 2026. "Enhancing stock market predictions with multivariate signal decomposition and dynamic feature optimization," The North American Journal of Economics and Finance, Elsevier, vol. 81(C).

    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. Weiyu Wang & Maria João Guedes, 2025. "Firm failure prediction for small and medium-sized enterprises and new ventures," Review of Managerial Science, Springer, vol. 19(7), pages 1949-1982, July.
    2. Philippe Jardin, 2025. "Designing Ensemble-Based Models Using Neural Networks and Temporal Financial Profiles to Forecast Firms’ Financial Failure," Computational Economics, Springer;Society for Computational Economics, vol. 65(1), pages 149-209, January.
    3. Soumya Ranjan Sethi & Dushyant Ashok Mahadik, 2025. "Do Rising Climate Risks Signal Financial Trouble for Food and Agro Based Firms?," Sustainable Development, John Wiley & Sons, Ltd., vol. 33(6), pages 8599-8618, December.
    4. 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.
    5. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2025. "Bankruptcy prediction using machine learning and Shapley additive explanations," Review of Quantitative Finance and Accounting, Springer, vol. 65(1), pages 107-148, July.
    6. 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.
    7. Jie Sun & Jie Li & Hamido Fujita & Wenguo Ai, 2023. "Multiclass financial distress prediction based on one‐versus‐one decomposition integrated with improved decision‐directed acyclic graph," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1167-1186, August.
    8. Saiful Anwar & A.M Hasan Ali, 2018. "ANNs-BASED Early Warning System for Indonesian Islamic Banks," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 20(3), pages 325-342, January.
    9. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    10. Lingwen, Kong & Hanye, Gu & Runxiang, Xu & Yang, Zhiguo, 2025. "Firm-level perception of uncertainty and corporate default risk: Evidence from China’s listed firms," Economic Analysis and Policy, Elsevier, vol. 88(C), pages 1082-1096.
    11. Fayçal Mraihi, 2016. "Distressed Company Prediction Using Logistic Regression: Tunisian’s Case," Quarterly Journal of Business Studies, Research Academy of Social Sciences, vol. 2(1), pages 34-54.
    12. Insu Choi & Wonje Yun & Woo Chang Kim, 2025. "Improving data efficiency for analyzing global exchange rate fluctuations based on nonlinear causal network-based clustering," Annals of Operations Research, Springer, vol. 352(3), pages 745-780, September.
    13. 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.
    14. Jordan, Jeffrey L., 1998. "Georgia Water Series -- Issue 3: Evaluating Water System Financial Performance And Financing Options," Faculty Series 16712, University of Georgia, Department of Agricultural and Applied Economics.
    15. 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.
    16. Abdus Samad, 2018. "How Early Can Non-Performance Loan Predict Bank Failure? Evidence from US Bank Failure during 2008-2010," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 9(1), pages 90-98, January.
    17. Papík, Mário & Papíková, Lenka, 2025. "The possibilities of using AutoML in bankruptcy prediction: Case of Slovakia," Technological Forecasting and Social Change, Elsevier, vol. 215(C).
    18. Róbert Štefko & Jarmila Horváthová & Martina Mokrišová, 2020. "Bankruptcy Prediction with the Use of Data Envelopment Analysis: An Empirical Study of Slovak Businesses," JRFM, MDPI, vol. 13(9), pages 1-15, September.
    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. Liu, Wanan & Zou, Yao & Liu, Baokang & Tao, Jiacheng & Lan, Xingyu & Xia, Meng, 2026. "Explainable adaptive ensemble learning with imbalance mitigation for manufacturing sector financial risk warning," Chaos, Solitons & Fractals, Elsevier, vol. 202(P2).

    More about this item

    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:wly:ijfiec:v:30:y:2025:i:3:p:2369-2388. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1076-9307/ .

    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.