IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i21p15304-d1267633.html
   My bibliography  Save this article

Early Insolvency Prediction as a Key for Sustainable Business Growth

Author

Listed:
  • Denis Kušter

    (Schneider Electric LLC, 21000 Novi Sad, Serbia)

  • Bojana Vuković

    (Faculty of Economics in Subotica, University of Novi Sad, 24000 Subotica, Serbia)

  • Sunčica Milutinović

    (Faculty of Economics in Subotica, University of Novi Sad, 24000 Subotica, Serbia)

  • Kristina Peštović

    (Faculty of Economics in Subotica, University of Novi Sad, 24000 Subotica, Serbia)

  • Teodora Tica

    (Faculty of Economics in Subotica, University of Novi Sad, 24000 Subotica, Serbia)

  • Dejan Jakšić

    (Faculty of Economics in Subotica, University of Novi Sad, 24000 Subotica, Serbia)

Abstract

This research aimed to determine whether and how financial analysis combined with machine learning can support decision-making for sustainable business growth. This study was conducted using a sample of 100 Serbian companies whose bankruptcies were initiated between 2019 and 2021 to identify key factors that distinguish solvent from insolvent companies. Two neural networks (NNs) were trained and tested to predict these discriminating factors one year (Y-1) and two years (Y-2) before bankruptcy initiation. Initially, a total of 37 predictor variables were included, but prior to modeling, variable reduction was performed through VIF analysis and t -tests. The training dataset comprised 70% of the sample, while the remaining 30% was used for testing. Both NNs utilized a softmax activation function for the output layer and a hyperbolic tangent for the hidden layers. Two hidden layers were included, and training was conducted over 2000 epochs using the gradient descent algorithm for optimization. The research results indicate that poor cash management is the first sign of possible insolvency one year in advance. Additionally, the findings reveal that retained earnings management can serve as a reliable bankruptcy predictor two years in advance. The overall predictive accuracy of the NN models is 80.0% (Y-1) and 73.3% (Y-2) for the testing dataset. These findings demonstrate how selected ratios can support bankruptcy prediction, providing valuable insights for company proprietors, management, and external stakeholders.

Suggested Citation

  • Denis Kušter & Bojana Vuković & Sunčica Milutinović & Kristina Peštović & Teodora Tica & Dejan Jakšić, 2023. "Early Insolvency Prediction as a Key for Sustainable Business Growth," Sustainability, MDPI, vol. 15(21), pages 1-24, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15304-:d:1267633
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/21/15304/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/21/15304/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. J. C. Neves & A. Vieira, 2006. "Improving bankruptcy prediction with Hidden Layer Learning Vector Quantization," European Accounting Review, Taylor & Francis Journals, vol. 15(2), pages 253-271.
    2. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    3. Patrick L. Brockett & Linda L. Golden & Jaeho Jang & Chuanhou Yang, 2006. "A Comparison of Neural Network, Statistical Methods, and Variable Choice for Life Insurers' Financial Distress Prediction," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(3), pages 397-419, September.
    4. Murugan Anandarajan & Picheng Lee & Asokan Anandarajan, 2001. "Bankruptcy prediction of financially stressed firms: an examination of the predictive accuracy of artificial neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 69-81, June.
    5. Juliana Yim & Heather Mitchell, 2005. "A comparison of corporate distress prediction models in Brazil: hybrid neural networks, logit models and discriminant analysis," Nova Economia, Economics Department, Universidade Federal de Minas Gerais (Brazil), vol. 15(1), pages 73-93, January-A.
    6. Evi Neophytou & Cecilio Mar Molinero, 2004. "Predicting Corporate Failure in the UK: A Multidimensional Scaling Approach," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(5‐6), pages 677-710, June.
    7. Andreas Charitou & Evi Neophytou & Chris Charalambous, 2004. "Predicting corporate failure: empirical evidence for the UK," European Accounting Review, Taylor & Francis Journals, vol. 13(3), pages 465-497.
    8. 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.
    9. Liang, Deron & Lu, Chia-Chi & Tsai, Chih-Fong & Shih, Guan-An, 2016. "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research, Elsevier, vol. 252(2), pages 561-572.
    10. Mossman, Charles E, et al, 1998. "An Empirical Comparison of Bankruptcy Models," The Financial Review, Eastern Finance Association, vol. 33(2), pages 35-53, May.
    11. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    12. Evi Neophytou & Cecilio Mar Molinero, 2004. "Predicting Corporate Failure in the UK: A Multidimensional Scaling Approach," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 31(5-6), pages 677-710.
    13. Chris Charalambous & Andreas Charitou & Froso Kaourou, 2000. "Comparative Analysis of Artificial Neural Network Models: Application in Bankruptcy Prediction," Annals of Operations Research, Springer, vol. 99(1), pages 403-425, December.
    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. Jarmila Horváthová & Martina Mokrišová & Martin Bača, 2023. "Bankruptcy Prediction for Sustainability of Businesses: The Application of Graph Theoretical Modeling," Mathematics, MDPI, vol. 11(24), pages 1-20, December.
    2. 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.
    3. Fayçal Mraihi, 2016. "Distressed Company Prediction Using Logistic Regression: Tunisian’s Case," Quarterly Journal of Business Studies, Research Academy of Social Sciences, vol. 2(1), pages 34-54.
    4. du Jardin, Philippe, 2010. "Predicting bankruptcy using neural networks and other classification methods: the influence of variable selection techniques on model accuracy," MPRA Paper 44375, University Library of Munich, Germany.
    5. 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.
    6. Layla Khoja & Maxwell Chipulu & Ranadeva Jayasekera, 2016. "Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling," Review of Quantitative Finance and Accounting, Springer, vol. 46(3), pages 483-518, April.
    7. 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).
    8. Alessandra Amendola & Francesco Giordano & Maria Lucia Parrella & Marialuisa Restaino, 2017. "Variable selection in high‐dimensional regression: a nonparametric procedure for business failure prediction," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 33(4), pages 355-368, August.
    9. Fayçal Mraihi & Inane Kanzari & Mohamed Tahar Rajhi, 2015. "Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(3), pages 184-205.
    10. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    11. Juan García Lara & Beatriz Osma & Evi Neophytou, 2009. "Earnings quality in ex‐post failed firms," Accounting and Business Research, Taylor & Francis Journals, vol. 39(2), pages 119-138.
    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. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    14. Apostolos G. Christopoulos & Ioannis G. Dokas & Iraklis Kollias & John Leventides, 2019. "An implementation of Soft Set Theory in the Variables Selection Process for Corporate Failure Prediction Models. Evidence from NASDAQ Listed Firms," Bulletin of Applied Economics, Risk Market Journals, vol. 6(1), pages 1-20.
    15. 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.
    16. Josep Patau & Antonio Somoza & Salvador Torra, 2020. "Diagnosis of the Domino Effect in Bankruptcy Situations Through Positioning Maps and Their Evolution 10 Years Later," SAGE Open, , vol. 10(4), pages 21582440209, December.
    17. Onofrei, Mihaela & Lupu, Dan, 2014. "The modelling of forecasting the bankruptcy in Romania," MPRA Paper 95511, University Library of Munich, Germany.
    18. du Jardin, Philippe & Séverin, Eric, 2010. "Dynamic analysis of the business failure process: A study of bankruptcy trajectories," MPRA Paper 44379, University Library of Munich, Germany.
    19. Tamás Kristóf & Miklós Virág, 2020. "A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary," JRFM, MDPI, vol. 13(2), pages 1-20, February.
    20. 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.

    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:jsusta:v:15:y:2023:i:21:p:15304-:d:1267633. 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.