IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v11y2023i5p97-d1150556.html
   My bibliography  Save this article

Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector

Author

Listed:
  • Rasa Kanapickienė

    (Department of Finance, Faculty of Economics and Business Administration, Vilnius University, 10222 Vilnius, Lithuania)

  • Tomas Kanapickas

    (Department of Software Engineering, Faculty of Informatics, Kaunas University of Technology, 44249 Kaunas, Lithuania)

  • Audrius Nečiūnas

    (Department of Applied Informatics, Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania)

Abstract

Credit-risk models that are designed for general application across sectors may not be suitable for the construction industry, which has unique characteristics and financial risks that require specialised modelling approaches. Moreover, advanced bankruptcy-prediction models are often used to achieve the highest accuracy in large modern datasets. Therefore, the aim of this research is the creation of enterprise-bankruptcy prediction (EBP) models for Lithuanian micro and small enterprises (MiSEs) in the construction sector. This issue is analysed based on classification models and the specific types of variable used. Firstly, four types of variable are proposed. In EBP models, financial variables substantially explain an enterprise’s financial statements and performance from different perspectives. Including enterprises’ non-financial, construction-sector and macroeconomic variables improves the characteristics of EBP models. The inclusion of macroeconomic variables in the model has a particularly significant impact. These findings can be of great significance to investors, creditors, policymakers and practitioners in assessing financial risks and making informed decisions. The second question is related to the classification models used. To develop the EBP models, logistic regression (LR), artificial neural networks (ANNs) and multivariate adaptive regression splines (MARS) were used. In addition, this study developed two-stage hybrid models, i.e., the LR is combined with ANNs. The findings show that two-stage hybrid models do not improve bankruptcy prediction. It cannot be argued that ANN models are more accurate in predicting bankruptcy. The MARS model demonstrates the best bankruptcy prediction, i.e., this model could be a valuable tool for stakeholders to evaluate enterprises’ financial risk.

Suggested Citation

  • Rasa Kanapickienė & Tomas Kanapickas & Audrius Nečiūnas, 2023. "Bankruptcy Prediction for Micro and Small Enterprises Using Financial, Non-Financial, Business Sector and Macroeconomic Variables: The Case of the Lithuanian Construction Sector," Risks, MDPI, vol. 11(5), pages 1-33, May.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:5:p:97-:d:1150556
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/11/5/97/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/11/5/97/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    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. Emilia Herman & Kinga-Emese Zsido, 2023. "The Financial Sustainability of Retail Food SMEs Based on Financial Equilibrium and Financial Performance," Mathematics, MDPI, vol. 11(15), pages 1-26, 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. Barbara CAVALLETTI & Corrado LAGAZIO & Daniela VANDONE, 2008. "Il credito al consumo in Italia: benessere economico o fragilita’ finanziaria?," Departmental Working Papers 2008-24, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    2. A?da Kammoun & Imen Triki, 2016. "Credit Scoring Models for a Tunisian Microfinance Institution: Comparison between Artificial Neural Network and Logistic Regression," Review of Economics & Finance, Better Advances Press, Canada, vol. 6, pages 61-78, February.
    3. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    4. Michael Bucker & Gero Szepannek & Alicja Gosiewska & Przemyslaw Biecek, 2020. "Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring," Papers 2009.13384, arXiv.org.
    5. Singh, Ramendra Pratap & Singh, Ramendra & Mishra, Prashant, 2021. "Does managing customer accounts receivable impact customer relationships, and sales performance? An empirical investigation," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    6. Charitou, Andreas & Dionysiou, Dionysia & Lambertides, Neophytos & Trigeorgis, Lenos, 2013. "Alternative bankruptcy prediction models using option-pricing theory," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2329-2341.
    7. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    8. Guotai Chi & Zhipeng Zhang, 2017. "Multi Criteria Credit Rating Model for Small Enterprise Using a Nonparametric Method," Sustainability, MDPI, vol. 9(10), pages 1-23, October.
    9. Ting Sun & Miklos A. Vasarhelyi, 2018. "Predicting credit card delinquencies: An application of deep neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 25(4), pages 174-189, October.
    10. Raffaella Calabrese & Galina Andreeva & Jake Ansell, 2019. "“Birds of a Feather” Fail Together: Exploring the Nature of Dependency in SME Defaults," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 71-84, January.
    11. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    12. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    13. Shuang Zhu & R. Pace, 2014. "Modeling Spatially Interdependent Mortgage Decisions," The Journal of Real Estate Finance and Economics, Springer, vol. 49(4), pages 598-620, November.
    14. Yu Xia & Ta Xu & Ming-Xia Wei & Zhen-Ke Wei & Lian-Jie Tang, 2023. "Predicting Chain’s Manufacturing SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    15. Fernando A. F. Ferreira & Ieva Meidutė-Kavaliauskienė & Edmundas K. Zavadskas & Marjan S. Jalali & Sandra M. J. Catarino, 2019. "A Judgment-Based Risk Assessment Framework for Consumer Loans," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 7-33, January.
    16. Merike Kukk, 2019. "Debt repayment problems: short-term and long-term implications for spending," Review of Economics of the Household, Springer, vol. 17(2), pages 715-740, June.
    17. Lobna Abid & Afif Masmoudi & Sonia Zouari-Ghorbel, 2018. "The Consumer Loan’s Payment Default Predictive Model: an Application of the Logistic Regression and the Discriminant Analysis in a Tunisian Commercial Bank," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 9(3), pages 948-962, September.
    18. Haithem Awijen & Younes Ben Zaied & Ahmed Imran Hunjra, 2023. "Systematic and Unsystematic Determinants of Sectoral Risk Default Interconnectedness," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 561-587, August.
    19. Stąpor Katarzyna & Smolarczyk Tomasz & Fabian Piotr, 2016. "Heteroscedastic Discriminant Analysis Combined with Feature Selection for Credit Scoring," Statistics in Transition New Series, Statistics Poland, vol. 17(2), pages 265-280, June.
    20. Minati Rath & Hema Date, 2023. "Adaptive Modelling Approach for Row-Type Dependent Predictive Analysis (RTDPA): A Framework for Designing Machine Learning Models for Credit Risk Analysis in Banking Sector," Papers 2311.10799, arXiv.org.

    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:jrisks:v:11:y:2023:i:5:p:97-:d:1150556. 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 (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.