IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i7p255-d1419263.html

Financial Risk Management Early-Warning Model for Chinese Enterprises

Author

Listed:
  • Haitong Wei

    (HongHao Data Intelligence Technology Co., Ltd., Beijing 102699, China
    Data Intelligence Branch, Enterprise Financial Management Association of China, Beijing 100195, China)

  • Xinghai Wang

    (Data Intelligence Branch, Enterprise Financial Management Association of China, Beijing 100195, China
    Big Data Application Research and Service Center, Modern Education Institute of China Academy of Management Sciences, Beijing 100036, China)

Abstract

As enterprises face increasing competitive pressures, financial crises can significantly impact on their capital operations, potentially leading to operational difficulties and, ultimately, market exclusion. Consequently, many enterprises have begun to utilize financial early-warning systems to guide and control risks. Currently, there is neither a universal nor comprehensive enterprise financial risk management model in China, nor a unified classification standard for enterprise financial risk management levels. This article takes financial data on A-share listed companies in 2020 as the data sample, including those with special treatment (represented by ST) or non-ST status. We establish an independent indicator system within the framework of profitability, solvency, operational capability, development potential, shareholders’ retained earnings, cash flow, and asset growth. The model is constructed employing the factor–logistic fusion algorithm. The factor part addresses the issue of collinearity among risk indicators, and the logistic part presents the results in probabilistic form, enhancing the interpretability of the model. The prediction accuracy of this model exceeds 89%. Finally, by applying the principles of interval estimation theory to statistical hypothesis testing, we categorize the risk levels into Grade A, representing significant risk; Grade B, representing moderate risk; Grade C, representing minor risk; and Grade D, representing no risk. This article aims to provide a comprehensive definition of a universal financial risk management early-warning model applicable to all enterprises in China.

Suggested Citation

  • Haitong Wei & Xinghai Wang, 2024. "Financial Risk Management Early-Warning Model for Chinese Enterprises," JRFM, MDPI, vol. 17(7), pages 1-28, June.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:7:p:255-:d:1419263
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/7/255/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/7/255/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Varetto, Franco, 1998. "Genetic algorithms applications in the analysis of insolvency risk," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1421-1439, October.
    2. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    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. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 294-314, Diciembre.
    2. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    3. de Andres, Javier & Landajo, Manuel & Lorca, Pedro, 2005. "Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case," European Journal of Operational Research, Elsevier, vol. 167(2), pages 518-542, December.
    4. Catherine Refait-Alexandre, 2004. "A Review of Business Failure Prediction Based on Financial Analysis of the Firm [La prévision de la faillite fondée sur l'analyse financière de l'entreprise : un état des lieux]," Post-Print hal-01391654, HAL.
    5. Xinzhong Bao & Qiuyan Tao & Hongyu Fu, 2015. "Dynamic financial distress prediction based on Kalman filtering," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(2), pages 292-308, February.
    6. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    7. Loredana Cultrera & Mélanie Croquet & Jérémy Jospin, 2017. "Predicting Bankruptcy of Belgian SMEs: A Hybrid Approach Based on Factorial Analysi," International Business Research, Canadian Center of Science and Education, vol. 10(3), pages 33-41, March.
    8. Catherine Refait-Alexandre, 2004. "La prévision de la faillite fondée sur l'analyse financière de l'entreprise : un état des lieux," Economie & Prévision, La Documentation Française, vol. 162(1), pages 129-147.
    9. Luca Ianni & Gianluca Marullo & Stefania Migliori & Francesco De Luca, 2021. "I modelli predittivi della crisi e dell?insolvenza aziendale. Una systematic review," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2021(2), pages 127-146.
    10. 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.
    11. Şaban Çelik & Bora Aktan & Bruce Burton, 2022. "Firm dynamics and bankruptcy processes: A new theoretical model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 567-591, April.
    12. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    13. Harlan Platt & Marjorie Platt, 2002. "Predicting corporate financial distress: Reflections on choice-based sample bias," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 26(2), pages 184-199, June.
    14. Łukasz Postek & Michał Thor, 2020. "Modele predykcji bankructwa i ich zastosowanie dla rynku NewConnect," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 1, pages 109-137.
    15. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
    16. LaDue, Eddy L. & Miller, Lynn H. & Kwiatkowski, Joseph H., 1990. "Investment Behavior And Energy Conservation," Northeastern Journal of Agricultural and Resource Economics, Northeastern Agricultural and Resource Economics Association, vol. 19(2), pages 1-10, October.
    17. Kattan, MW & Cooper, RB, 1998. "The predictive accuracy of computer-based classification decision techniques.A review and research directions," Omega, Elsevier, vol. 26(4), pages 467-482, August.
    18. 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.
    19. Beenstock, Michael & Szpiro, George, 2002. "Specification search in nonlinear time-series models using the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 26(5), pages 811-835, May.
    20. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Post-Print halshs-01314553, HAL.

    More about this item

    Keywords

    ;
    ;
    ;

    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:gam:jjrfmx:v:17:y:2024:i:7:p:255-:d:1419263. 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: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.com .

    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.