IDEAS home Printed from https://ideas.repec.org/a/cmj/networ/y2016i7p69-83.html
   My bibliography  Save this article

Multivariate Model For Corporate Bankruptcy Prediction In Romania

Author

Listed:
  • Daniel BRÎNDESCU – OLARIU

    (West University of Timisoara)

Abstract

The current paper proposes a methodology for bankruptcy prediction applicable for Romanian companies. Low bankruptcy frequencies registered in the past have limited the importance of bankruptcy prediction in Romania. The changes in the economic environment brought by the economic crisis, as well as by the entrance in the European Union, make the availability of performing bankruptcy assessment tools more important than ever before. The proposed methodology is centred on a multivariate model, developed through discriminant analysis. Financial ratios are employed as explanatory variables within the model. The study has included 53,252 yearly financial statements from the period 2007 – 2010, with the state of the companies being monitored until the end of 2012. It thus employs the largest sample ever used in Romanian research in the field of bankruptcy prediction, not targeting high levels of accuracy over isolated samples, but reliability and ease of use over the entire population.

Suggested Citation

  • Daniel BRÎNDESCU – OLARIU, 2016. "Multivariate Model For Corporate Bankruptcy Prediction In Romania," Network Intelligence Studies, Romanian Foundation for Business Intelligence, Editorial Department, issue 7, pages 69-83, June.
  • Handle: RePEc:cmj:networ:y:2016:i:7:p:69-83
    as

    Download full text from publisher

    File URL: http://seaopenresearch.eu/Journals/articles/NIS_7_6.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniel BRÎNDESCU – OLARIU, 2014. "The Potential Of The Equity Working Capital In The Prediction Of Bankruptcy," Management Intercultural, Romanian Foundation for Business Intelligence, Editorial Department, issue 31, pages 25-32, November.
    2. Daniel BRÎNDESCU – OLARIU, 2014. "Payment Capacity Sensitivity Factors," Management Intercultural, Romanian Foundation for Business Intelligence, Editorial Department, issue 31, pages 33-40, November.
    3. 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.
    4. 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)

    Citations

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


    Cited by:

    1. BEBEȘELEA, Mihaela & PATACHE, Laura, 2019. "Exploring The Relationship Between Accounting And Statistics," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 19(3), pages 55-64.
    2. Daniel Brîndescu Olariu, 2016. "Bankruptcy Prediction Based on the Autonomy Ratio," EuroEconomica, Danubius University of Galati, issue 2(35), pages 78-92, November.

    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. Daniel BRÎNDESCU-OLARIU, 2016. "Bankruptcy prediction based on the debt ratio," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(2(607), S), pages 145-156, Summer.
    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. 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.
    7. Lauren Stagnol, 2015. "Designing a corporate bond index on solvency criteria," EconomiX Working Papers 2015-39, University of Paris Nanterre, EconomiX.
    8. 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.
    9. Wen Su, 2021. "Default Distances Based on the CEV-KMV Model," Papers 2107.10226, arXiv.org, revised May 2022.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
    17. Haoming Wang & Xiangdong Liu, 2021. "Undersampling bankruptcy prediction: Taiwan bankruptcy data," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-17, July.
    18. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    19. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    20. 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.

    More about this item

    Keywords

    Discriminant analysis; Risk; Failure; Financial ratios; Classification accuracy; Benchmark;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General

    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:cmj:networ:y:2016:i:7:p:69-83. 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: Serghie Dan (email available below). General contact details of provider: https://seaopenresearch.eu/ .

    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.