IDEAS home Printed from https://ideas.repec.org/a/aio/fpvfcf/v1y2022i24p91-104.html
   My bibliography  Save this article

Measuring the Risk of Bankruptcy in the Romanian Economy. Developments and Perspectives

Author

Listed:
  • Bogdan POPA

    (University of Craiova)

Abstract

The paper presents the dynamics of a series of indicators considered by the supervisory authorities in Romania to analyse the risk of bankruptcy, but also the financial risk for the Romanian economy, aiming to indicate a perspective on the future evolution of these risks, taking into account the economic context, the situation generated by the Covid-19 Pandemic, but also the inflationary environment of the last year. We considered it important to start with the indicator for financial health, an indicator calculated at an aggregate level for all companies in Romania from the nonfinancial area, but also divided into subcategories that can provide important information regarding the health of companies and the risk of becoming insolvent. Also, the liquidity area has a considerable weight in the results obtained by these companies in the financial health score, and these aspects represent, in turn, indicators that can provide information about the bankruptcy risk of companies within an economy. To analyze the financial health, we followed the evolution of a series of other indicators, such as return on equity (ROE), the degree of long-term debt and the degree of shortterm debt, indicators that provide additional information regarding the risk of bankruptcy, the financial risks of the companies, meaning the financial health of companies. The analysis of the dynamics and structure of the stock of loans for which rates have been suspended represents, at present, an important way to assess the risk of bankruptcy among non-financial companies in Romania, offering a better perspective regarding the bankruptcy risk of companies and the area where this type of risk is more concentrated. The last part of the paper was dedicated to a specific problem of the local economy and companies in our country, that of commercial credit, but also the level of insolvencies. Given that the banking level of the Romanian economy is a low one, a good part of the companies relies on commercial credits, and the chain of dependence built over time is extremely important for the smooth functioning of the economy. Therefore, the bankruptcy risk at the aggregate level and the financial risks were also analyzed from the perspective of insolvencies and commercial credit, these being two aspects that weigh extremely heavily, especially in an economy like Romania's.

Suggested Citation

  • Bogdan POPA, 2022. "Measuring the Risk of Bankruptcy in the Romanian Economy. Developments and Perspectives," Finante - provocarile viitorului (Finance - Challenges of the Future), University of Craiova, Faculty of Economics and Business Administration, vol. 1(24), pages 91-104, November.
    • Handle: RePEc:aio:fpvfcf:v:1:y:2022:i:24:p:91-104
    as

    Download full text from publisher

    File URL: https://feaa.ucv.ro/finance/fisiere/revista/191791617009_Popa_en.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nicoleta Bărbuță-Mișu & Mara Madaleno, 2020. "Assessment of Bankruptcy Risk of Large Companies: European Countries Evolution Analysis," JRFM, MDPI, vol. 13(3), pages 1-28, March.
    2. Doina PRODAN-PALADE, 2017. "Bankruptcy risk prediction models based on artificial neural networks," The Audit Financiar journal, Chamber of Financial Auditors of Romania, vol. 15(147), pages 418-418.
    3. Gheorghita DINCA & Mirela Camelia BABA & Marius Sorin DINCA & Bardhyl DAUTI & Fitim DEARI, 2017. "Insolvency Risk Prediction Using the Logit and Logistic Models: Some Evidences from Romania," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(4), pages 139-157.
    4. Aydin Aslan & Lars Poppe & Peter Posch, 2021. "Are Sustainable Companies More Likely to Default? Evidence from the Dynamics between Credit and ESG Ratings," Sustainability, MDPI, vol. 13(15), pages 1-16, July.
    5. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, 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. Katarina Valaskova & Pavol Durana & Peter Adamko & Jaroslav Jaros, 2020. "Financial Compass for Slovak Enterprises: Modeling Economic Stability of Agricultural Entities," JRFM, MDPI, vol. 13(5), pages 1-16, May.
    2. Katarina Valaskova & Dominika Gajdosikova & Jaroslav Belas, 2023. "Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries," Oeconomia Copernicana, Institute of Economic Research, vol. 14(1), pages 253-293, March.
    3. Andrzej Jaki & Wojciech Ćwięk, 2020. "Bankruptcy Prediction Models Based on Value Measures," JRFM, MDPI, vol. 14(1), pages 1-14, December.
    4. Sanghoon Lee & Keunho Choi & Donghee Yoo, 2020. "Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques," Sustainability, MDPI, vol. 12(23), pages 1-17, November.
    5. 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.
    6. Daniela Rybárová & Helena Majdúchová & Peter Štetka & Darina Luščíková, 2021. "Reliability and Accuracy of Alternative Default Prediction Models: Evidence from Slovakia," IJFS, MDPI, vol. 9(4), pages 1-33, November.
    7. Imen Khanchel & Naima Lassoued, 2022. "ESG Disclosure and the Cost of Capital: Is There a Ratcheting Effect over Time?," Sustainability, MDPI, vol. 14(15), pages 1-19, July.
    8. Jaroslaw Kaczmarek & Sergio Luis Nanez Alonso & Andrzej Sokolowski & Kamil Fijorek & Sabina Denkowska, 2021. "Financial threat profiles of industrial enterprises in Poland," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 463-498, June.
    9. Dorohan-Pysarenko, Liudmyla & Rębilas, Rafał & Yehorova, Olena & Yasnolob, Ilona & Kononenko, Zhanna, 2021. "Methodological peculiarities of probability estimation of bankruptcy of agrarian enterprises in Ukraine," Agricultural and Resource Economics: International Scientific E-Journal, Agricultural and Resource Economics: International Scientific E-Journal, vol. 7(2), June.
    10. Chee Sun Lee & Peck Yeng Sharon Cheang & Massoud Moslehpour, 2022. "Predictive Analytics in Business Analytics: Decision Tree," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(1), pages 1-30, March.
    11. Teplova, Tamara & Sokolova, Tatiana & Kissa, David, 2023. "Revealing stock liquidity determinants by means of explainable AI: The role of ESG before and during the COVID-19 pandemic," Resources Policy, Elsevier, vol. 86(PB).
    12. Irina N. Tkachenko & Ivan A. Chechulin, 2024. "Crisis resilience assessment and management of public utility companies," Journal of New Economy, Ural State University of Economics, vol. 25(1), pages 131-159, April.
    13. Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022. "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1215-1251, December.
    14. 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.
    15. Lily Davies & Mark Kattenberg & Benedikt Vogt, 2023. "Predicting Firm Exits with Machine Learning: Implications for Selection into COVID-19 Support and Productivity Growth," CPB Discussion Paper 444, CPB Netherlands Bureau for Economic Policy Analysis.
    16. Adam Triggs, 2023. "Promoting sustainable investment through financial architecture reform," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 39(2), pages 267-282.
    17. Alessandro Gennaro, 2021. "Insolvency Risk and Value Maximization: A Convergence between Financial Management and Risk Management," Risks, MDPI, vol. 9(6), pages 1-36, June.
    18. Mateusz Heba & Marcin Chlebus, 2020. "Impact of using industry benchmark financial ratios on performance of bankruptcy prediction logistic regression model," Working Papers 2020-30, Faculty of Economic Sciences, University of Warsaw.
    19. Elena Gregova & Katarina Valaskova & Peter Adamko & Milos Tumpach & Jaroslav Jaros, 2020. "Predicting Financial Distress of Slovak Enterprises: Comparison of Selected Traditional and Learning Algorithms Methods," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    20. Yu Zhao & Shaopeng Wei & Yu Guo & Qing Yang & Xingyan Chen & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "Combining Intra-Risk and Contagion Risk for Enterprise Bankruptcy Prediction Using Graph Neural Networks," Papers 2202.03874, arXiv.org, revised Jul 2022.

    More about this item

    Keywords

    bankruptcy risk; sector of activity; liquidity; comparative analysis;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    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:aio:fpvfcf:v:1:y:2022:i:24:p:91-104. 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: Alina Manta (email available below). General contact details of provider: https://edirc.repec.org/data/fecraro.html .

    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.