IDEAS home Printed from https://ideas.repec.org/a/rjr/romjef/vy2022i1p122-138.html
   My bibliography  Save this article

A Comparison of Static, Dynamic and Machine Learning Models in Predicting the Financial Distress of Chinese Firms

Author

Listed:
  • Umair Bin YOUSAF

    (Doctoral Student at School of Accounting, Dongbei University of Finance and Economics, China Internal Control Research Center, Dalian, China)

  • Khalil JEBRAN

    (School of Business Administration, Dongbei University of Finance and Economics, Dalian, China. Corresponding author.)

  • Man WANG

    (Dongbei University of Finance and Economics, China Internal Control Research Center, Dalian, China)

Abstract

As recent studies have begun to pay increasing attention to financial distress prediction (FDP), this study compares the performance of static, dynamic and machine learning (ML) models in predicting the financial distress of firms. Balanced and imbalanced datasets of Chinese listed firms that span the years 1992 to 2018 (27 years) and contain 29,000 firm-year observations are used for the comparison. Results show an incremental improvement in the FDP accuracy of these models, with the random forest (RF) ML model demonstrating the best performance, followed by the Dynamic Hazard model and the static model. Including the growth variable improved the predictive ability of these models. Whilst all these models demonstrated a decreasing prediction accuracy in the imbalanced dataset, a weaker trend was observed for ML models. These results support the debate regarding the FDP superiority of computational methods. The findings provide new evidence that ML models can predict financial distress more accurately as compared to conventional models in the Chinese context.

Suggested Citation

  • Umair Bin YOUSAF & Khalil JEBRAN & Man WANG, 2022. "A Comparison of Static, Dynamic and Machine Learning Models in Predicting the Financial Distress of Chinese Firms," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 122-138, April.
    • Handle: RePEc:rjr:romjef:v::y:2022:i:1:p:122-138
    as

    Download full text from publisher

    File URL: https://www.ipe.ro/rjef/rjef1_2022/rjef1_2022p122-138.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wu, Y. & Gaunt, C. & Gray, S., 2010. "A comparison of alternative bankruptcy prediction models," Journal of Contemporary Accounting and Economics, Elsevier, vol. 6(1), pages 34-45.
    2. Murphy, Gregory B. & Trailer, Jeff W. & Hill, Robert C., 1996. "Measuring performance in entrepreneurship research," Journal of Business Research, Elsevier, vol. 36(1), pages 15-23, May.
    3. 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.
    4. Daniel Berg, 2007. "Bankruptcy prediction by generalized additive models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 23(2), pages 129-143, March.
    5. Abdul RASHID & Zainab JEHAN & Mehreen KHAN & Muhammad SAEED, 2019. "The Influence of Capital Flow Bonanzas on Real and Financial Sectors: A Comparative Study of South Asia and China," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 124-147, December.
    6. Tam, KY, 1991. "Neural network models and the prediction of bank bankruptcy," Omega, Elsevier, vol. 19(5), pages 429-445.
    7. Jia Liu, 2004. "Macroeconomic determinants of corporate failures: evidence from the UK," Applied Economics, Taylor & Francis Journals, vol. 36(9), pages 939-945.
    8. Umar Farooq & Muhammad Ali Jibran Qamar, 2019. "Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(7), pages 632-648, November.
    9. 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.
    10. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    11. Korol, Tomasz & Korodi, Adrian, 2011. "An Evaluation of Effectiveness of Fuzzy Logic Model in Predicting the Business Bankruptcy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 92-107, September.
    12. Bhattacharjee, Arnab & Han, Jie, 2014. "Financial distress of Chinese firms: Microeconomic, macroeconomic and institutional influences," China Economic Review, Elsevier, vol. 30(C), pages 244-262.
    13. Li, Zhiyong & Crook, Jonathan & Andreeva, Galina & Tang, Ying, 2021. "Predicting the risk of financial distress using corporate governance measures," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    14. Noel Capon & John U. Farley & Scott Hoenig, 1990. "Determinants of Financial Performance: A Meta-Analysis," Management Science, INFORMS, vol. 36(10), pages 1143-1159, October.
    15. J. Efrim Boritz & Duane B. Kennedy & Jerry Y. Sun, 2007. "Predicting Business Failures in Canada," Accounting Perspectives, John Wiley & Sons, vol. 6(2), pages 141-165, May.
    16. Shi-Qi LIU & Xin-Zhou QI & Meng QIN & Chi-Wei SU, 2019. "Financial Stability or Instability? Impact from Chinese Consumer Confidence," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 25-43, December.
    17. Ligang Zhou, 2015. "A comparison of dynamic hazard models and static models for predicting the special treatment of stocks in China with comprehensive variables," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(7), pages 1077-1090, July.
    18. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    19. Kohtamäki, Marko & Heimonen, Jesse & Parida, Vinit, 2019. "The nonlinear relationship between entrepreneurial orientation and sales growth: The moderating effects of slack resources and absorptive capacity," Journal of Business Research, Elsevier, vol. 100(C), pages 100-110.
    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. Sanjay Sehgal & Ritesh Kumar Mishra & Ajay Jaisawal, 2021. "A search for macroeconomic determinants of corporate financial distress," Indian Economic Review, Springer, vol. 56(2), pages 435-461, December.
    2. Nicoleta Bărbuță-Mișu & Mara Madaleno, 2020. "Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis," JRFM, MDPI, vol. 13(3), pages 1-28, March.
    3. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    4. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    5. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    6. Carlos Serrano-Cinca & Yolanda Fuertes-Call鮠 & Bego uti鲲ez-Nieto & Beatriz Cuellar-Fernᮤez, 2014. "Path modelling to bankruptcy: causes and symptoms of the banking crisis," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3798-3811, November.
    7. Mark Clintworth & Dimitrios Lyridis & Evangelos Boulougouris, 2023. "Financial risk assessment in shipping: a holistic machine learning based methodology," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 90-121, March.
    8. Duc Hong Vo & Binh Ninh Vo Pham & Chi Minh Ho & Michael McAleer, 2019. "Corporate Financial Distress of Industry Level Listings in Vietnam," JRFM, MDPI, vol. 12(4), pages 1-17, September.
    9. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    10. Onofrei, Mihaela & Lupu, Dan, 2014. "The modelling of forecasting the bankruptcy in Romania," MPRA Paper 95511, University Library of Munich, Germany.
    11. Qunfeng LIAO & Seyed MEHDIAN, 2016. "Measuring Financial Distress And Predicting Corporate Bankruptcy: An Index Approach," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 17, pages 33-51, June.
    12. Maria Heui-Yeong Kim & Shiguang Ma & Yanran Annie Zhou, 2016. "Survival prediction of distressed firms: evidence from the Chinese special treatment firms," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 21(3), pages 418-443, July.
    13. 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.
    14. Douglas, Ella & Lont, David & Scott, Tom, 2014. "Finance company failure in New Zealand during 2006–2009: Predictable failures?," Journal of Contemporary Accounting and Economics, Elsevier, vol. 10(3), pages 277-295.
    15. Kerstin Lopatta & Mario Albert Gloger & Reemda Jaeschke, 2017. "Can Language Predict Bankruptcy? The Explanatory Power of Tone in 10‐K Filings," Accounting Perspectives, John Wiley & Sons, vol. 16(4), pages 315-343, December.
    16. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    17. David Ficbauer & Mária Režňáková, 2014. "Holding Company and Its Performance," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 62(2), pages 329-337.
    18. M. Humayun Kabir & Fawzi Laswad, 2014. "The Behaviour of Earnings, Accruals and Impairment Losses of Failed New Zealand Finance Companies," Australian Accounting Review, CPA Australia, vol. 24(3), pages 262-275, September.
    19. 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.
    20. Ana Paula Matias Gama & Helena Susana Amaral Geraldes, 2012. "Credit risk assessment and the impact of the New Basel Capital Accord on small and medium‐sized enterprises," Management Research Review, Emerald Group Publishing Limited, vol. 35(8), pages 727-749, July.

    More about this item

    Keywords

    financial distress prediction; static; dynamic; machine learning; growth; China;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    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:rjr:romjef:v::y:2022:i:1:p:122-138. 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: Corina Saman The email address of this maintainer does not seem to be valid anymore. Please ask Corina Saman to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/ipacaro.html .

    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.