IDEAS home Printed from https://ideas.repec.org/a/gam/jijfss/v6y2018i3p60-d153935.html
   My bibliography  Save this article

Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries

Author

Listed:
  • Błażej Prusak

    (Faculty of Management and Economics, Gdańsk University of Technology, 80-233 Gdańsk, Poland)

Abstract

In developed countries, the first studies on forecasting bankruptcy date to the early 20th century. In Central and Eastern Europe, due to, among other factors, the geopolitical situation and the introduced economic system, this issue became the subject of researcher interest only in the 1990s. Therefore, it is worthwhile to analyze whether these countries conduct bankruptcy risk assessments and what their level of advancement is. The main objective of the article is the review and assessment of the level of advancement of bankruptcy prediction research in countries of the former Eastern Bloc, in comparison to the latest global research trends in this area. For this purpose, the method of analyzing scientific literature was applied. The publications chosen as the basis for the research were mainly based on information from the Google Scholar and ResearchGate databases during the period Q4 2016–Q3 2017. According to the author’s knowledge, this is the first such large-scale study involving the countries of the former Eastern Bloc—which includes the following states: Poland, Lithuania, Latvia, Estonia, Ukraine, Hungary, Russia, Slovakia, Czech Republic, Romania, Bulgaria, and Belarus. The results show that the most advanced research in this area is conducted in the Czech Republic, Poland, Slovakia, Estonia, Russia, and Hungary. Belarus Bulgaria and Latvia are on the other end. In the remaining countries, traditional approaches to predicting business insolvency are generally used.

Suggested Citation

  • Błażej Prusak, 2018. "Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries," IJFS, MDPI, vol. 6(3), pages 1-28, June.
  • Handle: RePEc:gam:jijfss:v:6:y:2018:i:3:p:60-:d:153935
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7072/6/3/60/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7072/6/3/60/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rozsa Andrea, 2014. "Financial Performance Analysis And Bankruptcy Prediction In Hungarian Dairy Sector," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 938-947, July.
    2. Carlos Serrano-Cinca, 1997. "Feedforward neural networks in the classification of financial information," The European Journal of Finance, Taylor & Francis Journals, vol. 3(3), pages 183-202.
    3. Petr Jakubík & Petr Teplý, 2011. "The JT Index as an Indicator of Financial Stability of Corporate Sector," Prague Economic Papers, Prague University of Economics and Business, vol. 2011(2), pages 157-176.
    4. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
    5. Nicoleta Barbuta-Misu, 2012. "Aggregated Index for Modelling the Influence of Financial Variables on Enterprise Performance," EuroEconomica, Danubius University of Galati, issue 2(31), pages 155-165, May.
    6. Šlefendorfas Gediminas, 2016. "Bankruptcy Prediction Model for Private Limited Companies of Lithuania," Ekonomika (Economics), Sciendo, vol. 95(1), pages 134-152, January.
    7. Molinero, C Mar & Ezzamel, M, 1991. "Multidimensional scaling applied to corporate failure," Omega, Elsevier, vol. 19(4), pages 259-274.
    8. Alexander Karminsky & Alexander Kostrov & Taras Murzenkov, 2012. "Comparison of default probability models: Russian experience," HSE Working papers WP BRP 06/FE/2012, National Research University Higher School of Economics.
    9. Grunert, Jens & Norden, Lars & Weber, Martin, 2005. "The role of non-financial factors in internal credit ratings," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 509-531, February.
    10. Gregory-Allen, Russell B & Henderson, Glenn V, Jr, 1991. "A Brief Review of Catastrophe Theory and a Test in a Corporate Failure Context," The Financial Review, Eastern Finance Association, vol. 26(2), pages 127-155, May.
    11. Anatoly Peresetsky & Alexandr Karminsky & Sergei Golovan, 2011. "Probability of default models of Russian banks," Economic Change and Restructuring, Springer, vol. 44(4), pages 297-334, November.
    12. Lee, Tsun-Siou & Yeh, Yin-Hua & Liu, Rong-Tze, 2003. "Can Corporate Governance Variables Enhance the Prediction Power of Accounting-Based Financial Distress Prediction Models?," CEI Working Paper Series 2003-14, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    13. repec:zbw:bofitp:2004_021 is not listed on IDEAS
    14. Ioan-Bogdan Robu & Mihaela-Alina Robu & Marilena Mironiuc & Florentina Olivia Balu, 2014. "The Value Relevance of Financial Distress Risk in the Case of RASDAQ Companies," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 13(4), pages 623-642, December.
    15. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    16. Irina Voronova, 2012. "Financial Risks: Cases Of Non-Financial Enterprises," Chapters, in: Jan Emblemsvag (ed.), Risk Management for the Future - Theory and Cases, IntechOpen.
    17. Erkki K. Laitinen & Teija Laitinen, 1998. "Cash Management Behavior and Failure Prediction," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 25(7‐8), pages 893-919, September.
    18. Aliaksei P. Smolski, 2006. "Tendencies and problems of economical insolvency (bankruptcy) institution development in Belarus: 1991 - 2005," Law and Economics smolski_aliaksei.39168-b1, Socionet.
    19. P. Du Jardin & E. Séverin, 2011. "Predicting Corporate Bankruptcy Using Self-Organising map: An empirical study to Improve the Forecasting horizon of financial failure model," Post-Print hal-00801878, HAL.
    20. Ondřej Machek, 2014. "Long-term Predictive Ability of Bankruptcy Models in the Czech Republic: Evidence from 2007-2012," Central European Business Review, Prague University of Economics and Business, vol. 2014(2), pages 14-17.
    21. Wolfgang K. Härdle & Rouslan A. Moro & Dorothea Schäfer, 2004. "Rating Companies with Support Vector Machines," Discussion Papers of DIW Berlin 416, DIW Berlin, German Institute for Economic Research.
    22. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    23. E. A. Fedorova & S. E. Dovzhenko & F. Yu. Fedorov, 2016. "Bankruptcy-prediction models for Russian enterprises: Specific sector-related characteristics," Studies on Russian Economic Development, Springer, vol. 27(3), pages 254-261, May.
    24. David Alaminos & Agustín del Castillo & Manuel Ángel Fernández, 2016. "A Global Model for Bankruptcy Prediction," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
    25. du Jardin, Philippe & Séverin, Eric, 2010. "Dynamic analysis of the business failure process: A study of bankruptcy trajectories," MPRA Paper 44379, University Library of Munich, Germany.
    26. 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.
    27. Corici Marian Catalin & Medar Lucian Ion, 2016. "Analysis Methods Of Bankruptcy Risk In Romanian Energy Mining Industry," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 180-185, December.
    28. Martin, Daniel, 1977. "Early warning of bank failure : A logit regression approach," Journal of Banking & Finance, Elsevier, vol. 1(3), pages 249-276, November.
    29. Camelia Burja & Vasile Burja, 2013. "Entrepreneurial Risk And Performance: Empirical Evidence Of Romanian Agricultural Holdings," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(15), pages 1-21.
    30. Miroslava Dolejšová, 2015. "Is it Worth Comparing Different Bankruptcy Models?," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 63(2), pages 525-531.
    31. Irina Genriha & Gaida Pettere & Irina Voronova, 2011. "Entrepreneurship insolvency risk management: a case of Latvia," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 3(1), pages 31-46.
    32. 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.
    33. 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.
    34. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    35. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    36. Janda, Karel & Rakicova, Anna, 2014. "Corporate Bankruptcies in Czech Republic, Slovakia, Croatia and Serbia," MPRA Paper 54109, University Library of Munich, Germany.
    37. Péter Bauer & Marianna Endrész, 2016. "Modelling Bankruptcy Using Hungarian Firm-Level Data," MNB Occasional Papers 2016/122, Magyar Nemzeti Bank (Central Bank of Hungary).
    38. James Kolari & Michele Caputo & Drew Wagner, 1996. "Trait Recognition: An Alternative Approach to Early Warning Systems in Commercial Banking," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 23(9-10), pages 1415-1434, December.
    39. Battiston, Stefano & Delli Gatti, Domenico & Gallegati, Mauro & Greenwald, Bruce & Stiglitz, Joseph E., 2007. "Credit chains and bankruptcy propagation in production networks," Journal of Economic Dynamics and Control, Elsevier, vol. 31(6), pages 2061-2084, June.
    40. Erkki K. Laitinen & Teija Laitinen, 1998. "Cash Management Behavior and Failure Prediction," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 25(7&8), pages 893-919.
    41. Ruslan Druzin, 2013. "About Possibility Of Usage Methodological Approaches To Bankruptcy Prediction," Studies and Scientific Researches. Economics Edition, "Vasile Alecsandri" University of Bacau, Faculty of Economic Sciences, issue 18.
    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. Wei Xu & Yuchen Pan & Wenting Chen & Hongyong Fu, 2019. "Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine," Energies, MDPI, vol. 12(12), pages 1-20, June.
    2. Ptak-Chmielewska Aneta, 2021. "Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 179-195, March.
    3. Theodore Metaxas & Athanasios Romanopoulos, 2023. "A Literature Review on the Financial Determinants of Hotel Default," JRFM, MDPI, vol. 16(7), pages 1-19, July.
    4. Aneta Ptak-Chmielewska, 2021. "Bankruptcy prediction of small- and medium-sized enterprises in Poland based on the LDA and SVM methods," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 179-195, March.
    5. Oliver Lukason & Germo Valgenberg, 2021. "Failure Prediction in the Condition of Information Asymmetry: Tax Arrears as a Substitute When Financial Ratios Are Outdated," JRFM, MDPI, vol. 14(10), pages 1-13, October.
    6. Aneta Ptak-Chmielewska, 2019. "Predicting Micro-Enterprise Failures Using Data Mining Techniques," JRFM, MDPI, vol. 12(1), pages 1-17, February.
    7. 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.
    8. Jerzy Kitowski & Anna Kowal-Pawul & Wojciech Lichota, 2022. "Identifying Symptoms of Bankruptcy Risk Based on Bankruptcy Prediction Models—A Case Study of Poland," Sustainability, MDPI, vol. 14(3), pages 1-18, January.
    9. repec:thr:techub:10025:y:2021:i:1:p:567-582 is not listed on IDEAS
    10. Tamás Kristóf & Miklós Virág, 2020. "A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary," JRFM, MDPI, vol. 13(2), pages 1-20, February.
    11. Vavrek, Roman & Vozárová, Ivana Kravčáková & Kotulič, Rastislav & Adamišin, Peter & Dubravská, Mariana & Ivanková, Viera, 2022. "Assessing the financial health of agricultural enterprises incorporating the spatial dimension," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 25(3), March.
    12. Muhammad Ramadhani Kesuma & Felisitas Defung & Anisa Kusumawardani, 2021. "Bankruptcy Prediction And Its Effect On Stock Prices As Impact Of The COVID-19 Pandemic," Technium Social Sciences Journal, Technium Science, vol. 25(1), pages 567-582, November.
    13. 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.
    14. Gianfranco Lombardo & Mattia Pellegrino & George Adosoglou & Stefano Cagnoni & Panos M. Pardalos & Agostino Poggi, 2022. "Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks," Future Internet, MDPI, vol. 14(8), pages 1-23, August.
    15. Lucia Svabova & Lucia Michalkova & Marek Durica & Elvira Nica, 2020. "Business Failure Prediction for Slovak Small and Medium-Sized Companies," Sustainability, MDPI, vol. 12(11), pages 1-14, June.
    16. Maria-Lenuţa Ciupac-Ulici & Daniela-Georgeta Beju & Ioan-Alin Nistor & Flaviu Pișcoran, 2023. "The impact of the Altman score on the energy sector companies," Journal of Financial Studies, Institute of Financial Studies, vol. 8(Special-J), pages 45-56, June.
    17. Błażej Prusak & Paweł Galiński, 2021. "Approval of an Arrangement in the Restructuring Proceedings and the Financial Condition of Companies Listed on the Stock Exchanges in Warsaw. Is There Any Relationship?," JRFM, MDPI, vol. 14(11), pages 1-16, November.
    18. Andrzej Jaki & Wojciech Ćwięk, 2020. "Bankruptcy Prediction Models Based on Value Measures," JRFM, MDPI, vol. 14(1), pages 1-14, December.
    19. Michal Pavlicko & Marek Durica & Jaroslav Mazanec, 2021. "Ensemble Model of the Financial Distress Prediction in Visegrad Group Countries," Mathematics, MDPI, vol. 9(16), pages 1-26, August.
    20. Michal Pavlicko & Jaroslav Mazanec, 2022. "Minimalistic Logit Model as an Effective Tool for Predicting the Risk of Financial Distress in the Visegrad Group," Mathematics, MDPI, vol. 10(8), pages 1-22, April.
    21. Błażej Prusak & Marcin Potrykus, 2021. "Short-Term Price Reaction to Filing for Bankruptcy and Restructuring Proceedings—The Case of Poland," Risks, MDPI, vol. 9(3), pages 1-14, March.
    22. Dagmar Camska & Jiri Klecka, 2020. "Comparison of Prediction Models Applied in Economic Recession and Expansion," JRFM, MDPI, vol. 13(3), pages 1-16, March.
    23. 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.

    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. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    2. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    3. Prusak Błażej, 2019. "Corporate Bankruptcy Prediction in Poland Against the Background of Foreign Experience," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 15(1), pages 10-19, March.
    4. Fayçal Mraihi, 2016. "Distressed Company Prediction Using Logistic Regression: Tunisian’s Case," Quarterly Journal of Business Studies, Research Academy of Social Sciences, vol. 2(1), pages 34-54.
    5. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    6. 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.
    7. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    8. Fayçal Mraihi & Inane Kanzari & Mohamed Tahar Rajhi, 2015. "Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(3), pages 184-205.
    9. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    10. Bhimani, Alnoor & Gulamhussen, Mohamed Azzim & Lopes, Samuel Da-Rocha, 2010. "Accounting and non-accounting determinants of default: An analysis of privately-held firms," Journal of Accounting and Public Policy, Elsevier, vol. 29(6), pages 517-532, November.
    11. 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.
    12. Mousavi, Mohammad M. & Ouenniche, Jamal & Xu, Bing, 2015. "Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 64-75.
    13. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    14. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    15. Jairaj Gupta & Andros Gregoriou & Jerome Healy, 2015. "Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 845-869, November.
    16. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
    17. Fernando Zambrano Farias & María del Carmen Valls Martínez & Pedro Antonio Martín-Cervantes, 2021. "Explanatory Factors of Business Failure: Literature Review and Global Trends," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    18. Matthew Smith & Francisco Alvarez, 2022. "Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 263-295, January.
    19. Psillaki, Maria & Tsolas, Ioannis E. & Margaritis, Dimitris, 2010. "Evaluation of credit risk based on firm performance," European Journal of Operational Research, Elsevier, vol. 201(3), pages 873-881, March.
    20. Tamás Kristóf & Miklós Virág, 2020. "A Comprehensive Review of Corporate Bankruptcy Prediction in Hungary," JRFM, MDPI, vol. 13(2), pages 1-20, February.

    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:jijfss:v:6:y:2018:i:3:p:60-:d:153935. 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.