IDEAS home Printed from https://ideas.repec.org/p/ipg/wpaper/2021-009.html
   My bibliography  Save this paper

Predicting French SME Failures: New Evidence from Machine Learning Techniques

Author

Listed:
  • Christophe Schalck
  • Meryem Schalck

Abstract

The aim of this study is to provide new insights into French small and medium-sized enterprises (SME) failure prediction using a unique database of French SMEs over the 2012?2018 period including both financial and nonfinancial variables. We also include text variables related to the type of activity. We compare the predictive performance of three estimation methods: a dynamic Probit model, logistic Lasso regression, and XGBoost algorithm. The results show that the XGBoost algorithm has the highest performance in predicting business failure from a broad dataset. We use SHAP values to interpret the results and identify the main factors of failure. Our analysis shows that both financial and nonfinancial variables are failure factors. Our results confirm the role of financial variables in predicting business failure, while self-employment is the factor that most strongly increases the probability of failure. The size of the SME is also a business failure factor. Our results show that a number of nonfinancial variables, such as localization and economic conditions, are drivers of SME failure. The results also show that certain activities are associated with a prediction of lower failure probability while some activities are associated with a prediction of higher failure.

Suggested Citation

  • Christophe Schalck & Meryem Schalck, 2021. "Predicting French SME Failures: New Evidence from Machine Learning Techniques," Working Papers 2021-009, Department of Research, Ipag Business School.
    • Handle: RePEc:ipg:wpaper:2021-009
    as

    Download full text from publisher

    File URL: https://faculty-research.ipag.edu/wp-content/uploads/recherche/WP/IPAG_WP_2021_009.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. de Jong, Robert M. & Woutersen, Tiemen, 2011. "Dynamic Time Series Binary Choice," Econometric Theory, Cambridge University Press, vol. 27(4), pages 673-702, August.
    2. Psillaki, Maria & Tsolas, Ioannis E. & Margaritis, Dimitris, 2010. "Evaluation of credit risk based on firm performance," European Journal of Operational Research, Elsevier, vol. 201(3), pages 873-881, March.
    3. 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.
    4. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    5. Oliver Falck, 2007. "Survival chances of new businesses: do regional conditions matter?," Applied Economics, Taylor & Francis Journals, vol. 39(16), pages 2039-2048.
    6. Joaquim J.S. Ramalho & Jacinto Vidigal da Silva, 2009. "A two-part fractional regression model for the financial leverage decisions of micro, small, medium and large firms," Quantitative Finance, Taylor & Francis Journals, vol. 9(5), pages 621-636.
    7. Shinichi Nakagawa, 2004. "A farewell to Bonferroni: the problems of low statistical power and publication bias," Behavioral Ecology, International Society for Behavioral Ecology, vol. 15(6), pages 1044-1045, November.
    8. Loredana Cultrera & Xavier Brédart, 2016. "Bankruptcy prediction: the case of Belgian SMEs," Review of Accounting and Finance, Emerald Group Publishing Limited, vol. 15(1), pages 101-119, February.
    9. Henry Renski, 2011. "External economies of localization, urbanization and industrial diversity and new firm survival," Papers in Regional Science, Wiley Blackwell, vol. 90(3), pages 473-502, August.
    10. Nada Mselmi & Amine Lahiani & Taher Hamza, 2017. "Financial distress prediction: The case of French small and medium-sized firms," Post-Print hal-03380580, HAL.
    11. Bauer, Julian & Agarwal, Vineet, 2014. "Are hazard models superior to traditional bankruptcy prediction approaches? A comprehensive test," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 432-442.
    12. Aneta Ptak-Chmielewska, 2019. "Predicting Micro-Enterprise Failures Using Data Mining Techniques," JRFM, MDPI, vol. 12(1), pages 1-17, February.
    13. Kim, Hong Sik & Sohn, So Young, 2010. "Support vector machines for default prediction of SMEs based on technology credit," European Journal of Operational Research, Elsevier, vol. 201(3), pages 838-846, March.
    14. Densil A. Williams, 2014. "RESOURCES AND FAILURE OF SMEs: ANOTHER LOOK," Journal of Developmental Entrepreneurship (JDE), World Scientific Publishing Co. Pte. Ltd., vol. 19(01), pages 1-15.
    15. repec:kap:iaecre:v:17:y:2011:i:4:p:476-483 is not listed on IDEAS
    16. Ellen R. Rissman, 2006. "The self-employment duration of younger men over the business cycle," Economic Perspectives, Federal Reserve Bank of Chicago, vol. 30(Q III), pages 14-27.
    17. El Kalak, Izidin & Hudson, Robert, 2016. "The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 135-145.
    18. Jaroslaw Ropega, 2011. "The Reasons and Symptoms of Failure in SME," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 17(4), pages 476-483, November.
    19. Heikki Kauppi & Pentti Saikkonen, 2008. "Predicting U.S. Recessions with Dynamic Binary Response Models," The Review of Economics and Statistics, MIT Press, vol. 90(4), pages 777-791, November.
    20. Robert Huggins & Daniel Prokop & Piers Thompson, 2017. "Entrepreneurship and the determinants of firm survival within regions: human capital, growth motivation and locational conditions," Entrepreneurship & Regional Development, Taylor & Francis Journals, vol. 29(3-4), pages 357-389, March.
    21. Nada Mselmi & Amine Lahiani & Taher Hamza, 2017. "Financial distress prediction: The case of French small and medium-sized firms," Post-Print hal-03529325, HAL.
    22. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    23. Rajshree Agarwal & Michael Gort, 2002. "Firm and Product Life Cycles and Firm Survival," American Economic Review, American Economic Association, vol. 92(2), pages 184-190, May.
    24. Dirk Oberschachtsiek, 2012. "The experience of the founder and self-employment duration: a comparative advantage approach," Small Business Economics, Springer, vol. 39(1), pages 1-17, July.
    25. Zoričák, Martin & Gnip, Peter & Drotár, Peter & Gazda, Vladimír, 2020. "Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets," Economic Modelling, Elsevier, vol. 84(C), pages 165-176.
    26. C. Mirjam van Praag, 2003. "Business Survival and Success of Young Small Business Owners," Tinbergen Institute Discussion Papers 03-050/3, Tinbergen Institute.
    27. Jia Liu, 2004. "Macroeconomic determinants of corporate failures: evidence from the UK," Applied Economics, Taylor & Francis Journals, vol. 36(9), pages 939-945.
    28. Dorothea Schäfer & Oleksandr Talavera, 2009. "Small business survival and inheritance: evidence from Germany," Small Business Economics, Springer, vol. 32(1), pages 95-109, January.
    29. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    30. Lopez, Jose A. & Saidenberg, Marc R., 2000. "Evaluating credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 151-165, January.
    31. Parker,Simon C., 2006. "The Economics of Self-Employment and Entrepreneurship," Cambridge Books, Cambridge University Press, number 9780521030632, October.
    32. 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.
    33. John Hutchinson & Ana Xavier, 2006. "Comparing the Impact of Credit Constraints on the Growth of SMEs in a Transition Country with an Established Market Economy," Small Business Economics, Springer, vol. 27(2), pages 169-179, October.
    34. Audretsch, David B & Mahmood, Talat, 1995. "New Firm Survival: New Results Using a Hazard Function," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 97-103, February.
    35. Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
    36. Jairaj Gupta & Andros Gregoriou & Jerome Healy, 2015. "Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 845-869, November.
    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. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023. "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print hal-04223161, HAL.
    2. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).
    3. Dina Ait Lahcen, 2023. "Synthetic Reading Of The Different Approaches And Models For Assessing The Risk Of Business Failure [Lecture Synthétique Des Diverses Approches Et Modèles D'Évaluation Du Risque De La Défaillance D," Post-Print hal-04009420, HAL.

    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. El Kalak, Izidin & Hudson, Robert, 2016. "The effect of size on the failure probabilities of SMEs: An empirical study on the US market using discrete hazard model," International Review of Financial Analysis, Elsevier, vol. 43(C), pages 135-145.
    2. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    3. 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.
    4. Bátiz-Zuk Enrique & Mohamed Abdulkadir & Sánchez-Cajal Fátima, 2021. "Exploring the sources of loan default clustering using survival analysis with frailty," Working Papers 2021-14, Banco de México.
    5. Stef, Nicolae & Zenou, Emmanuel, 2021. "Management-to-staff ratio and a firm's exit," Journal of Business Research, Elsevier, vol. 125(C), pages 252-260.
    6. Aneta Ptak-Chmielewska, 2019. "Predicting Micro-Enterprise Failures Using Data Mining Techniques," JRFM, MDPI, vol. 12(1), pages 1-17, February.
    7. 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.
    8. 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).
    9. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    10. 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.
    11. Vladislav V. Afanasev & Yulia A. Tarasova, 2022. "Default Prediction for Housing and Utilities Management Firms Using Non-Financial Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 91-110, December.
    12. 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.
    13. 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.
    14. Khoja, Layla & Chipulu, Maxwell & Jayasekera, Ranadeva, 2019. "Analysis of financial distress cross countries: Using macroeconomic, industrial indicators and accounting data," International Review of Financial Analysis, Elsevier, vol. 66(C).
    15. Wagner, S. & Cockburn, I., 2010. "Patents and the survival of Internet-related IPOs," Research Policy, Elsevier, vol. 39(2), pages 214-228, March.
    16. 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.
    17. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    18. Michal Karas & Mária Režòáková, 2021. "The role of financial constraint factors in predicting SME default," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 16(4), pages 859-883, December.
    19. Gila Burde, 2018. "Improved Methods for Predicting the Financial Vulnerability of Nonprofit Organizations," Administrative Sciences, MDPI, vol. 8(1), pages 1-8, February.
    20. 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).

    More about this item

    Keywords

    SME; failure prediction; Machine learning; XGBoost; SHAP values;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ipg:wpaper:2021-009. 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: Ingmar Schumacher (email available below). General contact details of provider: https://edirc.repec.org/data/ipagpfr.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.