IDEAS home Printed from https://ideas.repec.org/a/mup/actaun/actaun_2013061072587.html
   My bibliography  Save this article

Comparison of the models of financial distress prediction

Author

Listed:
  • Jiří Omelka

    (Mendel University in Brno, Faculty of Business and Economics, Department of Business Economics, Zemědělská 1, 613 00 Brno, Czech Republic)

  • Michaela Beranová

    (Mendel University in Brno, Faculty of Business and Economics, Department of Business Economics, Zemědělská 1, 613 00 Brno, Czech Republic)

  • Jakub Tabas

    (Mendel University in Brno, Faculty of Business and Economics, Department of Business Economics, Zemědělská 1, 613 00 Brno, Czech Republic)

Abstract

Prediction of the financial distress is generally supposed as approximation if a business entity is closed on bankruptcy or at least on serious financial problems. Financial distress is defined as such a situation when a company is not able to satisfy its liabilities in any forms, or when its liabilities are higher than its assets. Classification of financial situation of business entities represents a multidisciplinary scientific issue that uses not only the economic theoretical bases but interacts to the statistical, respectively to econometric approaches as well.The first models of financial distress prediction have originated in the sixties of the 20th century. One of the most known is the Altman's model followed by a range of others which are constructed on more or less conformable bases. In many existing models it is possible to find common elements which could be marked as elementary indicators of potential financial distress of a company.The objective of this article is, based on the comparison of existing models of prediction of financial distress, to define the set of basic indicators of company's financial distress at conjoined identification of their critical aspects. The sample defined this way will be a background for future research focused on determination of one-dimensional model of financial distress prediction which would subsequently become a basis for construction of multi-dimensional prediction model.

Suggested Citation

  • Jiří Omelka & Michaela Beranová & Jakub Tabas, 2013. "Comparison of the models of financial distress prediction," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 61(7), pages 2587-2592.
  • Handle: RePEc:mup:actaun:actaun_2013061072587
    DOI: 10.11118/actaun201361072587
    as

    Download full text from publisher

    File URL: http://acta.mendelu.cz/doi/10.11118/actaun201361072587.html
    Download Restriction: free of charge

    File URL: http://acta.mendelu.cz/doi/10.11118/actaun201361072587.pdf
    Download Restriction: free of charge

    File URL: https://libkey.io/10.11118/actaun201361072587?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.

    References listed on IDEAS

    as
    1. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    2. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    3. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    4. 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.
    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. Anna Kania Widiatami & Nanny Dewi Tanzil & Cahya Irawadi & Ahmad Nurkhin, 2020. "Audit Committee¡¯s Role in Moderating the Effect of Financial Distress Towards Going Concern Audit Opinion," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(4), pages 432-442, July.

    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. 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).
    2. Pavol Durana & Lucia Michalkova & Andrej Privara & Josef Marousek & Milos Tumpach, 2021. "Does the life cycle affect earnings management and bankruptcy?," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 425-461, June.
    3. 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.
    4. E. Fedorova A. & M. Chukhlantseva A. & D. Chekrizov V. & ЕЛЕНА Федорова АНАТОЛЬЕВНА & МАРИЯ Чухланцева АЛЕКСАНДРОВНА & ДМИТРИЙ Чекризов ВАСИЛЬЕВИЧ, 2017. "Нормативные значения коэффициентов финансовой устойчивости: особенности видов экономической деятельности // Normative Values of Financial Stability Ratios: Industry-Specific Features," Управленческие науки // Management Science, ФГОБУВО Финансовый университет при Правительстве Российской Федерации // Financial University under The Government of Russian Federation, vol. 7(2), pages 44-55.
    5. Eling, Martin & Jia, Ruo, 2018. "Business failure, efficiency, and volatility: Evidence from the European insurance industry," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 58-76.
    6. Le, Hong Hanh & Viviani, Jean-Laurent, 2018. "Predicting bank failure: An improvement by implementing a machine-learning approach to classical financial ratios," Research in International Business and Finance, Elsevier, vol. 44(C), pages 16-25.
    7. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    8. Juraini Zainol Abidin & Nur Adiana Hiau Abdullah & Karren Lee-Hwei Khaw, 2020. "Predicting SMEs Failure: Logistic Regression vs Artificial Neural Network Models," Capital Markets Review, Malaysian Finance Association, vol. 28(2), pages 29-41.
    9. Nasim Nasirpour & Alireza Mazdaki & Esmail Enayati, 2016. "The Investigation and Comparison of the Performance of Heuristic Methods in the Prediction of the Type of Auditor’s Opinion in Firms Accepted in Tehran Stock Exchange," Asian Social Science, Canadian Center of Science and Education, vol. 12(6), pages 148-148, June.
    10. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    11. Amin Jan & Maran Marimuthu & Muhammad Kashif Shad & Haseeb ur-Rehman & Muhammad Zahid & Ahmad Ali Jan, 2019. "Bankruptcy profile of the Islamic and conventional banks in Malaysia: a post-crisis period analysis," Economic Change and Restructuring, Springer, vol. 52(1), pages 67-87, February.
    12. Ivana Podhorska, 2016. "Success of prediction models in Slovak companies," GATR Journals gjbssr446, Global Academy of Training and Research (GATR) Enterprise.
    13. 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.
    14. Abdelghani Maddi, 2018. "Analyse scientométrique de la crise économique," CEPN Working Papers 2018-08, Centre d'Economie de l'Université de Paris Nord.
    15. Amit Sareen & Sudhi Sharma, 2022. "Assessing Financial Distress and Predicting Stock Prices of Automotive Sector: Robustness of Altman Z-score," Vision, , vol. 26(1), pages 11-24, March.
    16. 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.
    17. M. A. Lagesh & Maram Srikanth & Debashis Acharya, 2018. "Corporate Performance during Business Cycles: Evidence from Indian Manufacturing Firms," Global Business Review, International Management Institute, vol. 19(5), pages 1261-1274, October.
    18. Magali Aubert & Geoffroy Enjolras, 2014. "Le mode de commercialisation est-il une échappatoire pour les exploitations en difficulté financière ?," Post-Print hal-02740150, HAL.
    19. Nie, Zi & Ling, Xuan & Chen, Meian, 2023. "The power of technology: FinTech and corporate debt default risk in China," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    20. Vidimlić Selma, 2019. "Innovated Altman’s Model as a Predictor of Malfunctioning of Small and Medium-Sized Businesses in Bosnia and Herzegovina," Economic Themes, Sciendo, vol. 57(1), pages 21-33, March.

    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:mup:actaun:actaun_2013061072587. 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: Ivo Andrle (email available below). General contact details of provider: https://mendelu.cz/en/ .

    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.