IDEAS home Printed from https://ideas.repec.org/a/eme/rafpps/v15y2016i1p101-119.html
   My bibliography  Save this article

Bankruptcy prediction: the case of Belgian SMEs

Author

Listed:
  • Loredana Cultrera
  • Xavier Brédart

Abstract

Purpose - – The aim of this paper is to develop a bankruptcy prediction model for the Belgian small- and medium-sized enterprises (SMEs) through the building of a logit model that includes a selection of financial ratios. Design/methodology/approach - – Using a sample of 7,152 Belgian SMEs among which 3,576 were declared bankrupt between 2002 and 2012, the model, which includes control variables such as firm size and age, aims to test the predictive power of ratios reflecting the financial structure, the profitability, the solvency and the liquidity of firms. Findings - – The results report a satisfactory prediction accuracy and show that ratios as profitability and liquidity are excellent predictors of bankruptcy for Belgian SMEs. Research limitations/implications - – Although the results seem to be conclusive, it could be noted that the healthy sample was not paired with the bankrupt sample. Other studies show that the use of paired samples makes it possible to increase the already good prediction rate. Also, further research could focus on intra-sectorial analysis. Practical implications - – Beside its contribution to the academic literature on bankruptcy prediction of Belgian SMEs, this study may be of interest for investors or managers to help them to anticipate bankruptcy risks. It can also be useful for banks and other credit institutions in the assessment of credit risk of firms. Thanks to such models, they could better identify firms with a higher risk of failure in their lending decisions. Social implications - – Given the increasing number of SMEs in Belgium, their significant role in the economy, the specific characteristics of the country in terms of political decision making, the institutional differences between regions and the current uncertain economic circumstances, bankruptcy prediction seems to be a necessity for the country. Originality/value - – The originality of this paper lies in the fact that Belgian SMEs have been studied. This study may also be of interest to investors or managers because it may help them highlight accounting measures they should closely follow up to avoid bankruptcy.

Suggested Citation

  • 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.
    • Handle: RePEc:eme:rafpps:v:15:y:2016:i:1:p:101-119
    DOI: 10.1108/RAF-06-2014-0059
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/RAF-06-2014-0059/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/RAF-06-2014-0059/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/RAF-06-2014-0059?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.

    Citations

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


    Cited by:

    1. Sanghoon Lee & Keunho Choi & Donghee Yoo, 2020. "Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques," Sustainability, MDPI, vol. 12(23), pages 1-17, November.
    2. Christophe Schalck & Meryem Yankol-Schalck, 2021. "Predicting French SME failures: new evidence from machine learning techniques," Applied Economics, Taylor & Francis Journals, vol. 53(51), pages 5948-5963, November.
    3. Nora Muñoz-Izquierdo & María-del-Mar Camacho-Miñano & María-Jesús Segovia-Vargas & David Pascual-Ezama, 2019. "Is the External Audit Report Useful for Bankruptcy Prediction? Evidence Using Artificial Intelligence," IJFS, MDPI, vol. 7(2), pages 1-23, April.
    4. 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.
    5. Jaroslaw Kaczmarek & Sergio Luis Nanez Alonso & Andrzej Sokolowski & Kamil Fijorek & Sabina Denkowska, 2021. "Financial threat profiles of industrial enterprises in Poland," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 463-498, June.
    6. Andrzej Geise & Magdalena Kuczmarska & Jarosław Pawlowski, 2021. "Corporate Failure Prediction of Construction Companies in Poland: Evidence from Logit Model," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 99-116.
    7. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    8. Kanitsorn Terdpaopong & Robert C. Rickards & Penprapak Manapreechadeelert, 2020. "The 2011 floods’ impact on the Thai industrial estates’ financial stability: a ratio analysis with policy recommendations," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(3), pages 1991-2014, March.
    9. Marui Du & Yue Ma & Zuoquan Zhang, 2021. "A Meta Path Based Evaluation Method for Enterprise Credit Risk," Papers 2110.11594, arXiv.org, revised May 2022.
    10. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
    11. 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.
    12. Muñoz-Izquierdo, Nora & Segovia-Vargas, María Jesús & Camacho-Miñano, María-del-Mar & Pascual-Ezama, David, 2019. "Explaining the causes of business failure using audit report disclosures," Journal of Business Research, Elsevier, vol. 98(C), pages 403-414.
    13. Diego Andrés Correa-Mejía & Mauricio Lopera-Castaño, 2020. "Financial ratios as a powerful instrument to predict insolvency; a study using boosting algorithms in Colombian firms," Estudios Gerenciales, Universidad Icesi, vol. 36(155), pages 229-238, June.
    14. Gintare Giriūniene & Lukas Giriūnas & Mangirdas Morkunas & Laura Brucaite, 2019. "A Comparison on Leading Methodologies for Bankruptcy Prediction: The Case of the Construction Sector in Lithuania," Economies, MDPI, vol. 7(3), pages 1-20, August.

    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:eme:rafpps:v:15:y:2016:i:1:p:101-119. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Emerald Support (email available below). General contact details of provider: .

    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.