IDEAS home Printed from https://ideas.repec.org/a/alu/journl/v2y2022i24p6.html

Ï»¿The Empirical Analysis Of The Number Of Corporate Insolvencies Dynamics In The Central And Eastern European Countries

Author

Listed:
  • Neli MUNTEAN

    (Technical University of Moldova)

  • Iulian MUNTEAN

Abstract

The success of a company depends on how well the company adapts to changes in the business environment. Insolvency is one of the most important problems in achieving an efficient management of the company. Despite a large number of scientific papers in this field, some practical problems remain unresolved. In Central and Eastern Europe, corporate insolvencies began to be studied only in the 1990s. These became a pressing issue, especially during the COVID 19 pandemic, when a large number of companies were forced to cease operations. Therefore, the purpose of this article is to try to identify the extent of bankruptcy proceedings and to analyse the dynamics of the number of corporate insolvencies in the countries of Central and Eastern Europe. These states were chosen because of their common geopolitical situation and history. The study was conducted in a sample of 15 countries in the period 2013-2020 based on data taken from the reports of Euler Hermes, Allianz Research and Creditreform. The methods used in this paper were: data collection, data processing, estimation of trend patterns in time series and descriptive analysis.

Suggested Citation

  • Neli MUNTEAN & Iulian MUNTEAN, 2022. "Ï»¿The Empirical Analysis Of The Number Of Corporate Insolvencies Dynamics In The Central And Eastern European Countries," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(24), pages 1-6.
  • Handle: RePEc:alu:journl:v:2:y:2022:i:24:p:6
    as

    Download full text from publisher

    File URL: http://oeconomica.uab.ro/upload/lucrari/2420222/06.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stewart Jones & David Johnstone & Roy Wilson, 2017. "Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 44(1-2), pages 3-34, January.
    2. 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.
    3. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    4. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, John Wiley & Sons, Ltd., 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. Rastin Matin & Casper Hansen & Christian Hansen & Pia M{o}lgaard, 2018. "Predicting Distresses using Deep Learning of Text Segments in Annual Reports," Papers 1811.05270, arXiv.org.
    2. Ken Li, 2024. "Liquidity ratios and corporate failures," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 64(1), pages 1111-1134, March.
    3. Noora Alzayed & Rasol Eskandari & Hassan Yazdifar, 2023. "Bank failure prediction: corporate governance and financial indicators," Review of Quantitative Finance and Accounting, Springer, vol. 61(2), pages 601-631, August.
    4. Christian Lohmann & Steffen Möllenhoff & Thorsten Ohliger, 2023. "Nonlinear relationships in bankruptcy prediction and their effect on the profitability of bankruptcy prediction models," Journal of Business Economics, Springer, vol. 93(9), pages 1661-1690, November.
    5. Ashraf, Sumaira & Félix, Elisabete G.S. & Serrasqueiro, Zélia, 2020. "Development and testing of an augmented distress prediction model: A comparative study on a developed and an emerging market," Journal of Multinational Financial Management, Elsevier, vol. 57.
    6. Abinzano, Isabel & Gonzalez-Urteaga, Ana & Muga, Luis & Sanchez, Santiago, 2020. "Performance of default-risk measures: the sample matters," Journal of Banking & Finance, Elsevier, vol. 120(C).
    7. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    8. Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
    9. Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    10. Jones, Stewart & Wang, Tim, 2019. "Predicting private company failure: A multi-class analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 61(C), pages 161-188.
    11. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
    12. Almaskati, Nawaf & Bird, Ron & Yeung, Danny & Lu, Yue, 2021. "A horse race of models and estimation methods for predicting bankruptcy," Advances in accounting, Elsevier, vol. 52(C).
    13. Ruey-Ching Hwang & Jhao-Siang Siao & Huimin Chung & C. Chu, 2011. "Assessing bankruptcy prediction models via information content of technical inefficiency," Journal of Productivity Analysis, Springer, vol. 36(3), pages 263-273, December.
    14. 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).
    15. Xavier Brédart & Eric Séverin & David Veganzones, 2021. "Human resources and corporate failure prediction modeling: Evidence from Belgium," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1325-1341, November.
    16. 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.
    17. Keating, Elizabeth K. & Fischer, Mary & Gordon, Teresa P. & Greenlee, Janet, 2005. "Assessing Financial Vulnerability in the Nonprofit Sector," Working Paper Series rwp05-002, Harvard University, John F. Kennedy School of Government.
    18. Stephen A. Hillegeist & Elizabeth K. Keating & Donald P. Cram & Kyle G. Lundstedt, 2004. "Assessing the Probability of Bankruptcy," Review of Accounting Studies, Springer, vol. 9(1), pages 5-34, March.
    19. Michele Bertoni & Bruno De Rosa & Laura Peressin, 2019. "Early Warning Systems: A Risk of Increasing Managerial Myopia?," Management, University of Primorska, Faculty of Management Koper, vol. 14(4), pages 305-323.
    20. Qi, Min & Zhang, Xiaofei & Zhao, Xinlei, 2014. "Unobserved systematic risk factor and default prediction," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 216-227.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O56 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Oceania

    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:alu:journl:v:2:y:2022:i:24:p:6. 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: Dan-Constantin Danuletiu (email available below). General contact details of provider: .

    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.