IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v54y2018i3d10.1007_s00181-017-1246-1.html
   My bibliography  Save this article

Forecasting financial distress for French firms: a comparative study

Author

Listed:
  • Sami Ben Jabeur

    (IPAG Business School)

  • Youssef Fahmi

    (University of South Britany)

Abstract

The aim of this paper is to compare three statistical methods predicting corporate financial distress. We use discriminant analysis, logistic regression and random forest (RF) methods. These approaches are evaluated based on a sample of 800 companies, composed of 400 healthy companies and 400 failed companies. This study covers the period from 2006 to 2008 using 33 financial ratios. The results show the superiority of the RF approach, which gives better results in terms of classification. It allows for better forecast accuracy because it minimizes type I and type II errors.

Suggested Citation

  • Sami Ben Jabeur & Youssef Fahmi, 2018. "Forecasting financial distress for French firms: a comparative study," Empirical Economics, Springer, vol. 54(3), pages 1173-1186, May.
    • Handle: RePEc:spr:empeco:v:54:y:2018:i:3:d:10.1007_s00181-017-1246-1
    DOI: 10.1007/s00181-017-1246-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00181-017-1246-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00181-017-1246-1?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. Lennox, Clive, 1999. "Identifying failing companies: a re-evaluation of the logit, probit and DA approaches," Journal of Economics and Business, Elsevier, vol. 51(4), pages 347-364, July.
    2. Yang, Z. R. & Platt, Marjorie B. & Platt, Harlan D., 1999. "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Elsevier, vol. 44(2), pages 67-74, February.
    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. Bastien, Philippe & Vinzi, Vincenzo Esposito & Tenenhaus, Michel, 2005. "PLS generalised linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 17-46, January.
    5. du Jardin, Philippe & Séverin, Eric, 2012. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," European Journal of Operational Research, Elsevier, vol. 221(2), pages 378-396.
    6. Natalia Isachenkova & John Hunter, 2002. "A Panel Analysis Of UK Industrial Company Failure," Working Papers wp228, Centre for Business Research, University of Cambridge.
    7. Nguyen, Danh V. & Rocke, D.M.David M., 2004. "On partial least squares dimension reduction for microarray-based classification: a simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 407-425, June.
    8. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    9. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    10. 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.
    11. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    12. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    13. Bardos, Mireille, 1998. "Detecting the risk of company failure at the Banque de France," Journal of Banking & Finance, Elsevier, vol. 22(10-11), pages 1405-1419, October.
    14. P. Du Jardin & E. Séverin, 2012. "Forecasting financial failure using a Kohonen map: a comparative study to improve bankruptcy model over time," Post-Print hal-00801853, HAL.
    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. Amit Sareen & Sudhi Sharma, 2022. "Assessing Financial Distress and Predicting Stock Prices of Automotive Sector: Robustness of Altman Z-score," Vision, , vol. 26(1), pages 11-24, March.
    2. Surbhi Bhatia & Manish K. Singh, 2022. "Fifty years since Altman (1968): Performance of financial distress prediction models," Working Papers 12, xKDR.
    3. Lucia Svabova & Lucia Michalkova & Marek Durica & Elvira Nica, 2020. "Business Failure Prediction for Slovak Small and Medium-Sized Companies," Sustainability, MDPI, vol. 12(11), pages 1-14, June.
    4. Jie Sun & Mengru Zhao & Cong Lei, 2024. "Class-imbalanced dynamic financial distress prediction based on random forest from the perspective of concept drift," Risk Management, Palgrave Macmillan, vol. 26(4), pages 1-44, December.
    5. David Alaminos & Manuel Ángel Fernández, 2019. "Why do football clubs fail financially? A financial distress prediction model for European professional football industry," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-15, December.
    6. Alexandra Horobet & Stefania Cristina Curea & Alexandra Smedoiu Popoviciu & Cosmin-Alin Botoroga & Lucian Belascu & Dan Gabriel Dumitrescu, 2021. "Solvency Risk and Corporate Performance: A Case Study on European Retailers," JRFM, MDPI, vol. 14(11), pages 1-34, November.
    7. Fernández-Gámez, Manuel Ángel & Soria, Juan Antonio Campos & Santos, José António C. & Alaminos, David, 2020. "European country heterogeneity in financial distress prediction: An empirical analysis with macroeconomic and regulatory factors," Economic Modelling, Elsevier, vol. 88(C), pages 398-407.
    8. Ding, Shusheng & Cui, Tianxiang & Bellotti, Anthony Graham & Abedin, Mohammad Zoynul & Lucey, Brian, 2023. "The role of feature importance in predicting corporate financial distress in pre and post COVID periods: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 90(C).
    9. Katarina Valaskova & Pavol Durana & Peter Adamko & Jaroslav Jaros, 2020. "Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities," JRFM, MDPI, vol. 13(5), pages 1-16, May.
    10. Cristina Zeldea, 2020. "Modeling the Connection between Bank Systemic Risk and Balance-Sheet Liquidity Proxies through Random Forest Regressions," Administrative Sciences, MDPI, vol. 10(3), pages 1-14, August.
    11. Stef, Nicolae & Bissieux, Jean-Joachim, 2022. "Resolution of corporate insolvency during COVID-19 pandemic. Evidence from France," International Review of Law and Economics, Elsevier, vol. 70(C).
    12. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    13. 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.

    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. 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.
    2. Sami Ben Jabeur & Nicolae Stef & Pedro Carmona, 2023. "Bankruptcy Prediction using the XGBoost Algorithm and Variable Importance Feature Engineering," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 715-741, February.
    3. Sami Ben Jabeur & Youssef Fahmi, 2014. "Les modèles de prévision de la défaillance des entreprises françaises : une approche comparative," Working Papers 2014-317, Department of Research, Ipag Business School.
    4. Ben Jabeur Sami, 2013. "Corporate Failure:A Non Parametric Method," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 2(3), pages 103-110, July.
    5. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    6. 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.
    7. Ben Jabeur Sami, 2014. "Macroeconomic variables in financial distress: A non parametric method," Working Papers 2014-313, Department of Research, Ipag Business School.
    8. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    9. Surbhi Bhatia & Manish K. Singh, 2022. "Fifty years since Altman (1968): Performance of financial distress prediction models," Working Papers 12, xKDR.
    10. 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.
    11. 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.
    12. 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.
    13. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    14. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Predicting US Banks Bankruptcy: Logit Versus Canonical Discriminant Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 199-244, June.
    15. 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.
    16. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    17. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    18. Peresetsky, A. A., 2011. "What factors drive the Russian banks license withdrawal," MPRA Paper 41507, University Library of Munich, Germany.
    19. Anatoly Peresetsky & Alexandr Karminsky & Sergei Golovan, 2011. "Probability of default models of Russian banks," Economic Change and Restructuring, Springer, vol. 44(4), pages 297-334, November.
    20. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.

    More about this item

    Keywords

    Corporate financial distress; Bankruptcy prediction; Discriminant analysis; Logistic regression; Random forest;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:spr:empeco:v:54:y:2018:i:3:d:10.1007_s00181-017-1246-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.