IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i4p132-d1361973.html
   My bibliography  Save this article

Artificial Intelligence Techniques for Bankruptcy Prediction of Tunisian Companies: An Application of Machine Learning and Deep Learning-Based Models

Author

Listed:
  • Manel Hamdi

    (International Finance Group Tunisia Lab, Faculty of Management and Economic Sciences of Tunis, University of Tunis El Manar, Tunis 2092, Tunisia)

  • Sami Mestiri

    (Applied Economics and Simulation, Faculty of Management and Economic Sciences of Mahdia, University of Monastir, Rue Ibn Sina Hiboun, Mahdia 5111, Tunisia)

  • Adnène Arbi

    (Laboratory of Engineering Mathematics (LR01ES13), Tunisia Polytechnic School, University of Carthage, Tunis 2078, Tunisia
    Department of Advanced Sciences and Technologies, National School of Advanced Sciences and Technologies of Borj Cedria, University of Carthage, Hammam-Chott 1164, Tunisia)

Abstract

The present paper aims to compare the predictive performance of five models namely the Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Trees (DT), Support Vector Machine (SVM) and Random Forest (RF) to forecast the bankruptcy of Tunisian companies. A Deep Neural Network (DNN) model is also applied to conduct a prediction performance comparison with other statistical and machine learning algorithms. The data used for this empirical investigation covers 25 financial ratios for a large sample of 732 Tunisian companies from 2011–2017. To interpret the prediction results, three performance measures have been employed; the accuracy percentage, the F1 score, and the Area Under Curve (AUC). In conclusion, DNN shows higher accuracy in predicting bankruptcy compared to other conventional models, whereas the random forest performs better than other machine learning and statistical methods.

Suggested Citation

  • Manel Hamdi & Sami Mestiri & Adnène Arbi, 2024. "Artificial Intelligence Techniques for Bankruptcy Prediction of Tunisian Companies: An Application of Machine Learning and Deep Learning-Based Models," JRFM, MDPI, vol. 17(4), pages 1-14, March.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:4:p:132-:d:1361973
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/4/132/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/4/132/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manel Hamdi & Sami Mestiri, 2014. "Bankruptcy prediction for Tunisian firms : An application of semi-parametric logistic regression and neural networks approach," Economics Bulletin, AccessEcon, vol. 34(1), pages 133-143.
    2. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    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. Marco Botta & Luca Colombo, 2016. "Macroeconomic and Institutional Determinants of Capital Structure Decisions," DISCE - Working Papers del Dipartimento di Economia e Finanza def038, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    2. Antonio Davila & George Foster & Xiaobin He & Carlos Shimizu, 2015. "The rise and fall of startups: Creation and destruction of revenue and jobs by young companies," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 6-35, February.
    3. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    4. 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).
    5. Richardson, Grant & Taylor, Grantley & Lanis, Roman, 2015. "The impact of financial distress on corporate tax avoidance spanning the global financial crisis: Evidence from Australia," Economic Modelling, Elsevier, vol. 44(C), pages 44-53.
    6. Kristóf, Tamás, 2008. "A csődelőrejelzés és a nem fizetési valószínűség számításának módszertani kérdéseiről [Some methodological questions of bankruptcy prediction and probability of default estimation]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(5), pages 441-461.
    7. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    8. Eero Pätäri & Timo Leivo, 2017. "A Closer Look At Value Premium: Literature Review And Synthesis," Journal of Economic Surveys, Wiley Blackwell, vol. 31(1), pages 79-168, February.
    9. Lauren Stagnol, 2015. "Designing a corporate bond index on solvency criteria," EconomiX Working Papers 2015-39, University of Paris Nanterre, EconomiX.
    10. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    11. Maurice Peat, 2007. "Factors Affecting the Probability of Bankruptcy: A Managerial Decision Based Approach," Abacus, Accounting Foundation, University of Sydney, vol. 43(3), pages 303-324, September.
    12. Wen Su, 2021. "Default Distances Based on the CEV-KMV Model," Papers 2107.10226, arXiv.org, revised May 2022.
    13. Amir Ghafourian Shagerdi & Ali Mahdavipour & Reza Jahanshiri Ariyan Tashakori Baghdar & Mohammad Sajjad Ghafourian Shagerdi, 2020. "Investment Efficiency and Audit Fee from the Perspective of the Role of Financial Distress," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 318-333.
    14. Meles, Antonio & Salerno, Dario & Sampagnaro, Gabriele & Verdoliva, Vincenzo & Zhang, Jianing, 2023. "The influence of green innovation on default risk: Evidence from Europe," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 692-710.
    15. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
    16. Chiara Pederzoli & Grid Thoma & Costanza Torricelli, 2013. "Modelling Credit Risk for Innovative SMEs: the Role of Innovation Measures," Journal of Financial Services Research, Springer;Western Finance Association, vol. 44(1), pages 111-129, August.
    17. Jing Zeng & Xiongyuan Wang & Kam C. Chan, 2021. "Does the value‐added tax Reform increase a firm’s collateral bank loans? Evidence from China," Economics of Transition and Institutional Change, John Wiley & Sons, vol. 29(4), pages 681-710, October.
    18. Jason J. Constable & David R. Woodliff, 1994. "Predicting Corporate Failure Using Publicly Available Information," Australian Accounting Review, CPA Australia, vol. 4(7), pages 13-27, May.
    19. Guido Max Mantovani & Gregory Gadzinski, 2022. "How to Rate the Financial Performance of Private Companies? A Tailored Integrated Rating Methodology Applied to North-Eastern Italian Districts," JRFM, MDPI, vol. 15(11), pages 1-18, October.
    20. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.

    More about this item

    Keywords

    bankruptcy prediction; artificial intelligence models; machine learning; deep learning; confusion matrix; F1 score; ROC curve;
    All these keywords.

    JEL classification:

    • F1 - International Economics - - Trade

    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:gam:jjrfmx:v:17:y:2024:i:4:p:132-:d:1361973. 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.