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

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.

Citations

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Details

Category:
Articles
Type:
publikacja w in. zagranicznym czasopiśmie naukowym (tylko język obcy)
Published in:
International Journal of Financial Studies no. 6, edition 3, pages 1 - 28,
ISSN: 2227-7072
Language:
English
Publication year:
2018
Bibliographic description:
Prusak B.. Review of Research into Enterprise Bankruptcy Prediction in Selected Central and Eastern European Countries. International Journal of Financial Studies, 2018, Vol. 6, iss. 3, s.1-28
DOI:
Digital Object Identifier (open in new tab) 10.3390/ijfs6030060
Bibliography: test
  1. Adamko, Peter, and Lucia Svabova. 2016. Prediction of the risk of bankruptcy of slovak companies. Paper presented at 8th International Scientific Conference Managing and Modelling of Financial Risks Ostrava VŠB-TU of Ostrava, Faculty of Economics, Department of Finance, Ostrava, Czech Republic, September 5-6, pp. 15-20.
  2. Alaminos, David, Agustín del Castillo, and Manuel Ángel Fernández. 2016. A Global Model for Bankruptcy Prediction. PLoS ONE 11: e0166693. Available online: http://journals.plos.org/plosone/article?id=10.1371/ journal.pone.0166693 (accessed on 17 July 2017). [CrossRef] [PubMed] open in new tab
  3. Altman, Edward I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance 23: 589-609. [CrossRef] open in new tab
  4. Altman, Edward I. 2000. Predicting Financial Distress of Companies: Revisiting The Z-score and ZETA®Models. Available online: http://pages.stern.nyu.edu/~ealtman/Zscores.pdf (accessed on 17 July 2017). open in new tab
  5. Altman, Edward I. 2015. Edward I. 2015 Honorary Doctor of SGH. Selected Articles. Warsaw: Warsaw School of Economic Press.
  6. Altman, Edward I., and Paul Narayanan. 1997. An international survey of business failure classification models. Financial Markets, Institutions & Instruments 6: 1-57. [CrossRef] open in new tab
  7. Altman, Edward, Malgorzata Iwanicz-Drozdowska, Erkki Laitinen, and Arto Suvas. 2017. Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman's Z-Score Model. Journal of International Financial Managament & Accounting 28: 131-71. [CrossRef] open in new tab
  8. Andrea, Rózsa. 2014. Financial Performance Analysis and Bankruptcy Prediction in Hungarian Dairy Sector. The Annals of the University of Oradea. Economic Sciences XXIII: 936-45.
  9. Angelov, George. 2014. Options for modelling the financial viability of Sofix companies in the post-crisis years. Економiчний вiсник Донбaсу 4: 102-8. [CrossRef] open in new tab
  10. Appenzeller, Dorota, and Katarzyna Szarzec. 2004. Forecasting the bankruptcy risk of Polish public companies. Rynek Terminowy 1: 120-28.
  11. Argenti, John. 1976. Corporate Collapse: The Causes and Symptoms. New York: Wiley, Halsted Press.
  12. Aziz, Adnan M., and Humayon A. Dar. 2006. Predicting corporate bankruptcy-Where we stand? Corporate Governance Journal 6: 18-33. [CrossRef] open in new tab
  13. Babič, František, Cecília Havrilová, and Ján Paralič. 2013. Knowledge Discovery Methods for Bankruptcy Prediction. In Business Information Systems. BIS 2013. Lecture Notes in Business Information Processing. Edited by Witold Abramowicz. Berlin/Heidelberg: Springer, vol. 157, pp. 151-62. open in new tab
  14. Back, Barbro, Teija Laitinen, Jukkapekka Hekanaho, and Kaisa Sere. 1997. The Effect of Sample Size on Different Failure Prediction Methods. TUCS Technical Report No. 155. Turku: Turku Centre for Computer Science.
  15. Bal, Jay, Yen Cheung, and Hsu-Che Wu. 2013. Entropy for business failure prediction: An improved prediction model for the construction industry. Advances in Decision Sciences 2013: 1-14. [CrossRef] open in new tab
  16. Balina, Rafał, and Maksymilian Jan Bąk. 2016. Discriminant Analysis as a Prediction Method for Corporate Bankruptcy with the Industrial Aspects. Waleńczów: Wydawnictwo Naukowe Intellect.
  17. Bányiová, Tatiana, Tatiana Bieliková, and Andrea Piterková. 2014. Prediction of agricultural enterprises distress using data envelopment analysis. Paper presented at 11th International Scientific Conference European Financial Systems, Lednice, Czech Republic, June 12-13; Brno: Masaryk University, pp. 18-25.
  18. Barbuta-Misu, Nicoleta. 2012. Aggregated index for modelling the influence of financial variables on enterprise performance. EuroEconomica 2: 155-65.
  19. Barbuta-Misu, Nicoleta, and Elena-Silvia Codreanu. 2014. Analysis and Prediction of the Bankruptcy Risk i Romanian Building Sector Companies. Ekonomika 93: 131-46.
  20. Barbuta-Misu, Nicoleta, and Radu Stroe. 2010. The adjustment of the Conan & Holder Model to the Specifity of Romanian Enterprises-A local study for building sector. Economic Computation and Economic Cybernetics Studies and Research/Academy of Economic Studies 44: 123-39. open in new tab
  21. Battiston, Stefano, Domenico Delli Gatti, Mauro Gallegati, Bruce Greenwald, and Joseph E. Stiglitz. 2007. Credit chains and bankruptcy propagation in production networks. Journal of Economic Dynamics and Control 31: 2061-84. [CrossRef] open in new tab
  22. Bauer, Péter, and Marianna Edrész. 2016. Modelling Bankruptcy Using Hungarian Firm-Level Data MNB Occasional Papers 122. Budapest: Magyar Nemzeti Bank.
  23. Beaver, William H. 1966. Financial ratios as predictors of failure. Empirical research in accounting: Selected Studies, Supplement to Vol. 5. Journal of Accounting Research 1967: 71-111. [CrossRef] open in new tab
  24. Beaver, William H. 1968. Alternative accounting measures as predictors of failure. The Accounting Review 43: 113-22. open in new tab
  25. Bellovary, Jodi L., Don E. Giacomino, and Michael D. Akers. 2007. A review of bankruptcy prediction studies: 1930 to Present. Journal of Financial Education 33: 1-42. open in new tab
  26. Bemš, Július, Oldřich Starý, Martin Macaš, Jan Žegklitz, and Petr Pošík. 2015. Innovative default prediction approach. Expert Systems with Applications 42: 6277-85. [CrossRef] open in new tab
  27. Bideleux, Robert, and Ian Jeffries. 2007. The Balkans: A Post-Communist History.. London and New York: Routledge. open in new tab
  28. Bławat, Fławat. 1999. Threat of bankruptcy of joint-stock companies in Poland. In Polish Economy during the Transformation Period. Edited by Jerzy Czesław Ossowski. Zeszyt nr 3. Gdańsk: Wydawnictwo Politechniki Gdańskiej.
  29. Bod'a, Martin. 2009. Predicting bankruptcy of Slovak enterprises by an artificial neural network. Forum Statisticum Slovacum 9: 3-6.
  30. Boritz, J. Efrim, and Duane B. Kennedy. 1995. Effectiveness of neural network types for prediction of business failure. Expert Systems with Applications 9: 503-12. [CrossRef] open in new tab
  31. Bozsik, József. 2010. Artificial neural networks in default forecast. Paper presented at 8th International Conference on Applied Informatics, Eger, Hungary, January 27-30; vol. 1, pp. 31-39. open in new tab
  32. Branch, Ben, Octavian Ionici, and Iuliana Ismailescu. 2010. Bankruptcy Proceedings in Romania. Norton Journal of Bankruptcy Law and Practice 19: 631-50. open in new tab
  33. Braunová, Mária, and Lucia Jantošová. 2015. The development prediction of financial and economic indicators of hospitals operating in Žilina Region. Slovak Scientific Journal Management: Science and Education 4: 12-14.
  34. Brîndescu-Olariu, Daniel, and Ionuţ Goleţ. 2013a. Bankruptcy prediction ahead of global recession: Discriminant Analysis Applied on Romanian Companies in Timiş County. Timisoara Journal of Economics and Business 6: 70-94.
  35. Brîndescu-Olariu, Daniel, and Ionuţ Goleţ. 2013b. Prediction of corporate bankruptcy through the use of logistic regression. Annals of Faculty of Economics, University of Oradea, Faculty of Economics 1: 976-86.
  36. Brożyna, Jacek, Grzegorz Mentel, and Tomasz Pisula. 2016. Statistical methods of the bankruptcy prediction in the logistics sector in Poland and Slovakia. Transformations in Business & Economics 15: 80-96.
  37. Burganova, R. A., and M. F. Salahieva. 2015. Z-Score for bankruptcy forecasting of the companies producing building materials. Asian Social Science 11: 109-14. [CrossRef] open in new tab
  38. Burja, Camelia, and Vasile Burja. 2013. Entrepreneurial Risk and Performance: Empirical Evidence of Romanian Agricultural Holdings. Annales Universitatis Apulensis Series Oeconomica 15: 561-69. open in new tab
  39. Butkus, Mindaugas, Sigita Žakarė, and Diana Cibulskienė. 2014. Bankroto diagnostikos modelis ir jo pritaikymas bankroto tikimybei lietuvos įmonėse prognozuoti. Taikomoji Ekonomika: Sisteminiai Tyrimai 8: 111-32. open in new tab
  40. Cámská, Dagmar. 2012. Predicting corporate financial distress in the case of operational program environment. Intellectual Economics 6: 450-62.
  41. Cámská, Dagmar. 2013. Predicting financial distress of companies operating in construction Industry. Paper presented at 8th International Conference Accounting and Management Information Systems AMIS, Bucharest, Romania, June 12-13; Bucharest: The Bucharest University of Economic Studies, pp. 51-65.
  42. Cámská, Dagmar. 2016. Accuracy od models predicting corporate bankruptcy in a selected industry branch. EkonomickýČasopis 64: 353-66.
  43. Cámská, Dagmar, and Jiří Hájek. 2012. Companies related to the glass making industry and their financial health. In International Scientific Conference-Transaction Costs of Czech Businesses in Insolvency Proceedings, the Possibility of Their Reduction and Improvement of Statistics of Insolvency Proceedings. Edited by Eva Kislingerová and J Jindřich Špička. Prague: University of Economics, pp. 11-16. open in new tab
  44. Carson, Elizabeth, Neil L. Fargher, Marshall A. Geiger, Clive S. Lennox, Kannan Raghunandan, and Marleen Willekens. 2013. Audit reporting for going-concern uncertainty: A Research Synthesis. Auditing: A Journal of Practice & Theory 32: 353-84. [CrossRef] open in new tab
  45. Catalin, Corici Marian, and Medar Lucian Ion. 2016. Analysis methods of bankruptcy risk in Romanian Energy Mining Industry. Annals of the "Constantin Brâncuşi" University of Târgu Jiu, Economy Series 1: 180-85.
  46. Chesser, Delton L. 1974. Predicting loan noncompliance. The Journal of Commercial Bank Lending 56: 28-38.
  47. Chrastinová, Zuzana. 1998. Metódy Hodnotenia Ekonomickej Bonity a Predikcie Finančnej Situácie Pol'nohospodárskych Podnikov. Bratislava: VÚEPP.
  48. Coats, Pamela K., and L. Franklin Fant. 1991. A neural network approach to forecasting financial distress. Journal of Business Forecasting 10: 9-12. open in new tab
  49. Crăciun, Mihaela, Crina Raţiu, Dominic Bucerzan, and Adriana Manolescu. 2013. Actuality of Bankruptcy rediction Models used in Decision Support System. International Journal of Computers Communications & Control 8: 375-383. open in new tab
  50. Delev, Atanas. 2014. Is there a Risk of Bankruptcy for Bulgarian Public Companies? (JPMNT) Journal of Process Management (Special Edition). New Technologies, the International Scientific Conference "New Knowledge for the New People", Ohrid, May 21-24. pp. 252-59. Available online: http://www.japmnt.com/images/SpecialEdition2014/45.%20IS%20THERE%20A%20RISK%20OF% 20BANKRUPTCY%20FOR%20BULGARIAN%20PUBLIC%20COMPANIES.pdf (accessed on 9 March 2017). open in new tab
  51. Delev, Atanas. 2016a. Problems and Challenges in Assessing the Risk of Bankruptcy in Bulgarian Companies. Икономически изследвaния 3: 118-36.
  52. Delev, Atanas. 2016b. Бългaрските Πублични Дружествa B Cловиятa Нa Φинaнсовa Кризa. Available online: http://rd.swu.bg/media/46177/avtoreferat.pdf (accessed on 14 March 2017).
  53. Delina, Radoslav, and Miroslava Packová. 2013. Validacia Predikčných Bankrotových Modelov v Podmienkach SR (Prediction Bankruptcy Models Validation in Slovak Business Environment). E&M Economic and Management 3: 101-12.
  54. Dinca, Gheorghita, and Madalina Bociu. 2015. Using discriminant analysis for credit decision. Bulletin of the Transilvania University of Braşov. Series V: Economic Sciences 8: 277-88.
  55. Divišová, Pavla. 2011. The Use of the "IN" index for assessing the financial health of companies operating in chemical industry. Paper presented at 10th International Conference, Liberec Economic Forum, Vancouver, BC, Canada, April 27-May 1; Liberec: Faculty of Economics, Technical University of Liberec, pp. 100-9.
  56. Dolejšová, Miroslava. 2014. Which Altman model do we actually use? Acta Universitatis Bohemiae Meridionales 17: 103-11.
  57. Dolejšová, Miroslava. 2015. Is it worth comparing different bankruptcy models? Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 63: 525-31. [CrossRef] open in new tab
  58. Dorgai, Klaudia, Veronika Fenyves, and Dávid Súto. 2016. Analysis of commercial enterprises' solvency by means of different bankruptcy models. Gradus 3: 341-49.
  59. Draba, Edvin. 2012. Modernising Insolvency Law in Latvia: Successes and failures. Eurofenix 2012/2013: 26-27. open in new tab
  60. Druzin, Ruslan Valentinovich. 2013. About possibility of usage methodological approaches to bankruptcy prediction. Studies and Scientific Researches. Economics Edition 18: 177-81. [CrossRef] open in new tab
  61. Du Jardin, Philippe, and Eric Séverin. 2010. Dynamic analysis of the business failure process: A study of bankruptcy trajectories. Paper presented at 6th Portuguese Finance Network Conference, Ponta Delgada, Azores, June 30-July 4; vol. 2010. open in new tab
  62. Du Jardin, Philippe, and Eric Séverin. 2011. Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model. Decision Support Systems 51: 701-11. [CrossRef] open in new tab
  63. Dvořáček, Jaroslav, and Radmila Sousedíková. 2006. Forecasting companies' future economic development. Acta Montanistica Slovaca 11: 283-86.
  64. Dvořáček, Jaroslav, Radmila Sousedíková, and Lucia Domaracká. 2008. Industrial enterprises bankruptcy forecasting. Metalurgija 47: 33-36. open in new tab
  65. Dvořáček, Jaroslav, Radmila Sousedíková, M.Řepka, Lucia Domaracká, Pavel Barták, and M. Bartošíková. 2012a. Choosing a Method for Predicting Economic Performance of Companies. Metalurgija 51: 525-28. open in new tab
  66. Dvořáček, Jaroslav, Radmila Sousedíková, Pavel Barták, Jiří Štěrba, and Kamil Novák. 2012b. Forecasting Companies' Future Economic Development. Acta Montanistica Slovaca 17: 111-18. open in new tab
  67. Dyrberg, A. 2004. Firms in Financial Distress: An Exploratory Analysis. Danmarks Nationalbank Working Papers, No. 17. Copenhagen: Danmarks Nationalbank. open in new tab
  68. Fedorova, Elena, Evgenii Gilenko, and Sergey Dovzhenko. 2013. Bankruptcy prediction for Russian companies: Application of combined classifiers. Expert Systems with Applications 40: 7285-93. [CrossRef] open in new tab
  69. Fedorova, E. A., S. E. Dovzhenko, and F. Y. Fedorov. 2016. Bankruptcy Prediction Models for Russian Enterprises: Specific Sector-Related Characteristics. Studies on Russian Economic Development 27: 254-61. [CrossRef] open in new tab
  70. Fitzpatrick, Paul Joseph 1932. A Comparison of Ratios of Successful Industrial Enterprises with Those of Failed Firm. Certified Public Accountant 6: 727-31.
  71. Fletcher, Desmond, and Ernie Goss. 1993. Forecasting with Neural Networks: An Application Using Bankruptcy Data. Information & Management 24: 159-67. [CrossRef] open in new tab
  72. Gajdka, Jerzy, and Daniel Stos. 1996. The use of discriminant analysis in assessing the financial condition of enterprises. In Restructuring in the Process of Transformation and Development of Enterprises. Edited by Ryszard Borowiecki. Kraków: Wydawnictwo Akademii Ekonomicznej w Krakowie.
  73. Gąska, Damian. 2016. Predicting Bankruptcy of Enterprises with the use of Learning Methods. Ph.D. disertation, Wrocław University of Economics, Wrocław, Poland.
  74. Gasza, R. 1997. The relationship between the results of the Altman model and the stock prices of selected listed companies in Poland. Bank i Kredyt 3: 59-63.
  75. Genriha, Irina, Gaida Peterre, and Irina Voronova. 2011. Entrepreneurship insolvency risk management: A case of Latvia. International Journal of Banking Accounting and Finance 3: 31-46. [CrossRef] open in new tab
  76. Gregory-Allen, B. Russell, and Glenn V. Henderson Jr. 1991. A Brief Review of Catastrophe Theory and a Test in Corporate Failure Context. Financial Review 62: 127-55. [CrossRef] open in new tab
  77. Grigaravičius, Saulius. 2003. Corporate Failure Diagnosis: Reliability and Practice. Organizacijų Vadyba: Sisteminiai Tyrimai 28: 29-42.
  78. Grünberg, Martin, and Oliver Lukason. 2014. Predicting Bankruptcy of Manufacturing Firms. International Journal of Trade, Economics and Finance 5: 93-97. [CrossRef] open in new tab
  79. Grunert, Jens, Lars Norden, and Martin Weber. 2005. The Role of Non-Financial Factors in Internal Credit Ratings. Journal of Banking and Finance 29: 509-31. [CrossRef] open in new tab
  80. Gruszczyński, Marek. 2003. Models of microeconometrics in the analysis and forecasting of the financial risk of enterprises. Warszawa: Zeszyty Polskiej Akademii Nauk nr 23. open in new tab
  81. Gurčík, Lubomír. 2002. G-index-The financial situation prognosis method of agricultural enterprises. Agricultural Economics (zemědělská ekonomika) 48: 373-78. [CrossRef] open in new tab
  82. Hadasik, Dorota. 1998. The Bankruptcy of Enterprises in Poland and Methods of its Forecasting. Poznań: Wydawnictwo Akademii Ekonomicznej w Poznaniu, vol. 153.
  83. Hajdíková, Tat'ána, Štěpánka Ondoková, and Lenka Komárková. 2015. Financial Models in the Nonprofit Sector. In Scientific Conference. Brno: Masaryk University, pp. 174-80.
  84. Hajdu, Otto, and Miklos Virág. 2001. A Hungarian Model for Predicting Financial Bankruptcy. Society and Economy in Central and Eastern Europe 23: 28-46.
  85. Hampel, David, Jan Vavřina, and Jitka Janová. 2012. Predicting bankruptcy of companies based on the production function parameters. Paper presented at 30th International Conference Mathematical Methods in Economics, Karviná, Czech Republic, September 11-13; Edited by Jaroslav Ramík and Daniel Stavárek. Karviná: Silesian University in Opava, School of Business Administration, pp. 243-48.
  86. Hamrol, Mirosław, Bartłomiej Czajka, and Maciej Piechocki. 2004. Enterprise bankruptcy-discriminant analysis model. Przegląd Organizacji 6: 35-39. open in new tab
  87. Harafonova, Olha, and Dmytro Ulchenko. 2014. Financial Restructuring as a Means of Financial Recovery of Enterprises. Φiнaнсовi Ресурси: Πроблеми Φормувaння Тa Bикористaння 4: 267-72.
  88. Härdle, Wolfgang Karl, Rouslan A. Moro, and Dorothea Schäfer. 2004. Rating Companies with Support Vector Machines. German Institute for Economic Research, Discussion Papers, No. 416. Berlin: German Institute.
  89. Hołda, Artur. 2001. Forecasting the bankruptcy of an enterprise in the conditions of the Polish economy using the discriminatory function Z H . Rachunkowość 5: 306-10.
  90. Iazzolino, Gianpaolo, and Adolfo Fortino. 2012. Credit risk analysis and the KMV Black & Scholes model: A proposal of correction and an empirical analysis. Investment Management and Financial Innovations 9: 167-81.
  91. Jagiełło, Robert. 2013. Discriminant and Logistic Analysis in the Process of Assessing the Creditworthiness of Enterprises. Materiały i Studia, Zeszyt 286. Warszawa: NBP.
  92. Jakubík, Petr, and Petr Teplý. 2011. The JT Index as Indicator of Financial Stability of Corporate Sector. Praque Economic Papers 2: 157-76. [CrossRef] open in new tab
  93. Janda, Karel, and Anna Rakicova. 2014. Corporate Bankruptcies in Czech Republic, Slovakia, Croatia and Serbia. MPRA Paper No. 54109, University of Economics, Prague, Charles University in Prague. Available online: https://mpra.ub.uni-muenchen.de/54109/1/MPRA_paper_54109.pdf (accessed on 8 September 2017). open in new tab
  94. Jo, Hongkyu, Ingoo Han, and Hoonyoung Lee. 1997. Bankruptcy Prediction Using Case-Based Reasoning, Neural Networks and Discriminant Analysis. Expert Systems with Applications 13: 97-108. [CrossRef] open in new tab
  95. Juszczyk, Sławomi, and Rafał Balina. 2009. Forecasting the bankruptcy of forwarding companies as a banking decision-making tool. Zeszyty Naukowe SGGW-Ekonomika i Organizacja GospodarkiŻywnościowej 78: 161-74. open in new tab
  96. Juszczyk, Sławomi, and Rafał Balina. 2014. Forecasting the bankruptcy risk of enterprises in selected industries. Ekonomista 1: 67-95.
  97. Kalouda, František, and Roman Vaníček. 2013. Alternative bankruptcy models-First results. Paper presented at 10th International Scientific Conference European Financial Systems, Telč, Czech Republic, June 10-11; Brno: Masaryk University, pp. 164-68.
  98. Kaminsky, Alexander, Alexander Kostrov, and Taras Murzenkov. 2012. Comparison of Default Probability Models: Russian Experience. Higher School of Economics Research Paper No. WP BRP 06/FE/2012. Available online: https://ssrn.com/abstract=2152384orhttp://dx.doi.org/10.2139/ssrn.2152384 (accessed on 8 September 2017). open in new tab
  99. Kanapickiene, Rasa, and Rosvydas Marcinkevicius. 2014. Possibilities to apply classical bankruptcy models in the construction sector in Lithuania. Economics and Management 19: 317-32. [CrossRef] open in new tab
  100. Karas, Michal, and Mária Režňáková. 2014. A parametric or nonparametric approach for creating a new bankruptcy prediction model: The Evidence from the Czech Republic. International Journal of Mathematical Models and Methods in Applied Sciences 8: 214-23. open in new tab
  101. Karas, Michal, and Mária Režňáková. 2015. Predicting bankruptcy under alternative conditions: The effect of a change in industry and time period on the accuracy of the model. Procedia-Social and Behavioral Sciences 213: 397-403. [CrossRef] open in new tab
  102. Karas, Michal, Maria Reznakova, Vojtech Bartos, and Marek Zinecker. 2013. Possibilities for the Application of the Altman Model within the Czech Republic. In Recent Reserches in Law Science and Finances: Proceedings of the 4th International Conference on Finance, Accounting and Law (ICFA 13), Crete Island, Greece, August 27-29. Edited by Kalliopi Kalampouka and Carmen Nastase. Chania: WSEAS Press, Business and Economics Series; open in new tab
  103. Karbownik, Lidia. 2017. Methods for Assessing the Financial Risk of Enterprises in the TSI Sector in Poland. Łódź: Wydawnictwo Uniwersytetu Łódzkiego.
  104. Kareleu, Yury Y. 2015. "Slice of Life" Customization of Bankruptcy Models: Bielarusian Experience and Future Developmnet. Research Papers of Wrocław University of Economics 381: 115-31.
  105. Käsper, Kaspar. 2016. Permanent Insolvency Prediction Model in the Example of Estonian MicroEnterprises Financed with Start-up Grant. Tartu: University of Tartu, Available online: https://dspace.ut.ee/bitstream/handle/ 10062/52390/kasper_kaspar.pdf?sequence=1&isAllowed=y (accessed on 18 September 2017).
  106. Khmerkan, Jeeranun, and Surachai Chancharat. 2015. Performance of Minority Data in Financial Distress Prediction Models. Application of Multiple Discriminate Analysis Logit, Probit and Artificial Neural Networks. Journal of Applied Economic Sciences 10: 954-60.
  107. Kiviluoto, Kimmo. 1998. Predicting bankruptcies with the self-organizing map. Neurocomputing 21: 191-201. open in new tab
  108. Klauberg, Theis, and Alexander Gebhardt. 2007. Latvia. In The Insolvency Law of Central and Eastern Europe. Twelve Country Screenings of the New Member and Candidate Countries of the European Union: A Comparative Analysis. Edited by Jens Lowitzsch. Berlin: INSOL EUROPE/Inter-University Centre at the Institute for East European Studies, Free University of Berlin, pp. 251-79. open in new tab
  109. Kleban, Yuriy. 2015. Diagnosis of Companies' Bankruptcy Using Takagi-Sugeno Model. Нейро-Нечiткi Технологiї Моделювaння B Економiцi 4: 62-79.
  110. Klebanova, Tamara, Lidiya Guryanova, and Vitalii Gvozdytskyi. 2016. Neural Fuzzy Models of Estimation of the Financial Condition of Corporate Systems. Available online: http://repository.hneu.edu.ua/jspui/ bitstream/123456789/14947/1/ICAICTSEE-2016%20Bulgaria.pdf (accessed on 7 March 2017).
  111. Klecka, Jiri, and Hana Scholleova. 2010. Bankruptcy Models Enuntiation for Czech Glass Making Firms. Economics and Management 15: 954-59.
  112. Klepáč, Václav, and David Hampel. 2016. Prediction of Bankruptcy with SVM Classifiers among Retail Business Companies. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 64: 627-34. [CrossRef] open in new tab
  113. Kniewski, Adam. 2004. Formula for an bankrupt. Businessman 10: 163-68.
  114. Kocmanová, Alena, Marie Pavláková Dočekalová, and Petr Němeček. 2014. Sustainable Corporate Performance Index for Manufacturing Industry. Paper presented at 18th World Multi-Conference on Systemics, Cybernetics and Informatic, Orlando, FL, USA, July 15-18; Orlando: International Institute of Informatics and Systemics, pp. 1-6.
  115. Kolari, James, Michele Caputo, and Drew Wagner. 1996. Trait Recognition: An Alternative Approach to Early Warning Systems in Commercial Banking. Journal of Business Finance & Accounting 23: 1415-34. [CrossRef] open in new tab
  116. Koleda, Nadezhda, and Natalja Lace. 2009. Development of Comparative-Quantitative Measures of Financial Stability for Latvian Enterprises. Economics & Management 14: 78-84.
  117. Korab, Vojtech. 2001. One Approach to Small Business Bankruptcy Prediction: The Case of the Czech Republic. In VII SIGEFF Congress New Logistics for the New Economy. Naples: SIGEFF International Association for FUZZY SET, University Degli Studi Di Napoli, Federico II, pp. 359-68.
  118. Kornyliuk, Roman. 2014. Early Warning Indicators of Defaults in the Banking System of Ukraine. Journal of European Economy 13: 333-48.
  119. Korol, Tomasz. 2004. Assessment of the Accuracy of the Application of Discriminatory Methods and Artificial Neural Networks for the Identification of Enterprises Threatened with Bankruptcy. Gdańsk: Doctoral dissertation.
  120. Korol, Tomasz. 2010a. Early Warning Systems of Enterprises to the Risk of Bankruptcy. Warszawa: Wolters Kluwer. Korol, Tomasz. 2010b. Forecasting bankruptcies of companies using soft computing techniques.
  121. Finansowy Kwartalnik Internetowy "e-Finanse" 6: 1-14. open in new tab
  122. Korol, Tomasz. 2013. A new Approach to Ratio Analysis in an Enterprise. Warszawa: Wolters Kluwer Polska.
  123. Kozak, Liudmila, Elena Bakulich, Valentin Ziuzina, and Olesia Fedoruk. 2013. The Use of Fuzzy Cognitive Models for Diagnostics of Probability of Enterprises' Bankruptcy. Modern Management Review 18: 73-85. [CrossRef] open in new tab
  124. Král', Pavol, Mária Stachová, and Lukáš Sobíšek. 2014. Utilization of repeatedly measured financial ratios in corporate financial distress prediction in Slovakai. Paper presented at 17th Applications of Mathematics and Statistics in Economics, International Scientific Conference, Poland, August 27-31; pp. 156-63.
  125. Kristóf, Szeverin, and László Koloszár. 2014. The Efficiency of Bankruptcy Forecast Models in the Hungarian SME Sector. Journal of Competitiveness 6: 56-73.
  126. Kubecová, Jana, and Jaroslav Vrchota. 2014. The Taffler's Model and Strategic Management. The Macrotheme Review 3: 188-94.
  127. Kubíčková, Dana. 2011. Model Z-score in Conditions of Transition to International Financial Reporting Standards in Czech Republic. Paper presented at Cambridge Business & Economics Conference (CBEC), Cambridge, UK, June 27-29; Cambridge: Murray Edwards College, Cambridge University. Available online: http://www.gcbe.us/2011_CBEC/data/confcd.htm (accessed on 9 June 2017).
  128. Kubíčková, Dana. 2015. Ohlson's Model and its Prediction Ability in Comparison with Selected Bankruptcy Models in Conditions of Czech SMEs. Acta VŠFS 2: 155-73. open in new tab
  129. Kubíčková, Dana, and Vladimir Nulicek. 2016. Predictors of Financial Distress and Bankruptcy Model Construction. International Journal of Management Science and Business Administration 2: 34-42. [CrossRef] open in new tab
  130. Kubicová, Jana, and Slavomír Faltus. 2014. Tax Debt as an Indicator of Companies' Default: The Case of Slovakia. Journal of Applied Economics and Business 2: 59-74. open in new tab
  131. Laitinen, Erkki K., and Teija Laitinen. 1998. Cash Management Behavior and Failure Prediction. Journal of Business Finance & Accounting 25: 893-919. [CrossRef] open in new tab
  132. Laitinen, Erkki K., and Arto Suvas. 2013. International Applicability of Corporate Failure Risk Models Based on Financial Statement Information: Comparisons across European Countries. Journal of Finance & Economics 1: 1-26. [CrossRef] open in new tab
  133. Lanine, Gleb, and Rudi Vander Vennet. 2006. Failure prediction in the Russian bank sector with logit and trait recognition models. Expert Systems with Applications 30: 463-78. [CrossRef] open in new tab
  134. Lee, Tsun-Siou, Yin-Hua Yeh, and Rong-Tze Liu. 2003. Can Corporate Governance Variables Enhance the Predicting Power of Accounting-Based Financial Distress Prediction Models? Center for Economic Institutions Working Paper Series, No. 14; Kunitachi: Hitotsubashi University. open in new tab
  135. Lobanova, Elena, A. I. Zmitrovich, A. A. Voshevoz, A. V. Krivko-Krasko, and S. N. Zbarouski. 2012. Current Financial Diagnostics of Enterprises, Modeling and Simulation. Paper presented at International Conference, Minsk, Belarus, May 2-4; Minsk: Publ. Center of BSU, pp. 66-69. open in new tab
  136. Lukason, Oliver, and Kaspar Käsper. 2017. Failure Prediction of government fundeded start-up firms. Investment Management and Financial Innovations 2017: 296-306. [CrossRef] open in new tab
  137. Łukaszewski, K., and P. Dąbroś. 1998a. How and where to find a bankrupt? Prawo i Gospodarka 49.
  138. Łukaszewski, K., and P. Dąbroś. 1998b. Altman's indicator. Prawo i Gospodarka 0.
  139. Lytvyn, Anton V. 2015. Applying support vector machines to financial crisis forecasting in Ukrainian Insurance Companies. Actual Problems of Economics 5: 481-92. open in new tab
  140. Machek, Ondrej. 2014. Long-term Predictive Ability of Bankruptcy Models in the Czech Republic: Evidence from 2007-2012. Central European Business Review 3: 14-17. [CrossRef] open in new tab
  141. Machek, Ondřej, Luboš Smrčka, and Jiří Strouhal. 2015. How to predict potential default of cultural organizations. Paper presented at 7th International Scientific Conference Finance and Performance of Firms in Science, Education and Practice, Zlín, Czech Republic, April 23-24; Edited by E. Astuszková, Z. Crhová, J. Vychtilová, B. Vytrhlíková and A. Knápková. Zlín: Univerzita Tomáše Bati ve Zlíně, pp. 893-902.
  142. Mączyńska, Elżbieta. 1994. Assessment of the condition of the enterprise. Simplified methods.Życie Gospodarcze 38: 42-45.
  143. Mączyńska, Elżbieta. 2004. Early warning systems. NoweŻycie Gospodarcze 12: 4-9.
  144. Makeeva, Elena, and Eaterina Neretina. 2013a. The Prediction of Bankruptcy in a Construction Industry of Russian Federation. Journal of Modern Accounting and Auditing 9: 256-71.
  145. Makeeva, Elena, and Eaterina Neretina. 2013b. A Binary Model versus Discriminant Analysis Relating to Corporate Bankruptcies: The Case of Russian Construction Industry. Journal of Accounting, Finance and Economics 3: 65-76. open in new tab
  146. Männasoo, Kadri. 2007. Determinants of Firm Sustainability in Estonia. Working Paper Series 4; Tallinn: Bank of Estonia. open in new tab
  147. Martin, Daniel. 1977. Early Warning of Bank Failure: A Logit Regression Approach. Journal of Banking and Finance 1: 249-76. [CrossRef] open in new tab
  148. Martin, Aruldoss, Travis Miranda Lakshmi, and Venkatasamy Prasanna Venkatesan. 2014. A Framework to Develop Qualitative Bankruptcy Prediction Rules Using Swarm Intelligence. St. Joseph's Journal of Humanities and Science 1: 73-81.
  149. Matviychuk, Andriy. 2010. Bankruptcy Pediction in Trasformational Economy: Discriminant Analysis and Fuzzy Logic Approaches. Fuzzy Economic Review 15: 21-38. open in new tab
  150. Megan, Ovidiu, and Cristina Circa. 2014. Insolvency Prediction Tools for Middle and Large Scale Romanian Enterprises. Transformations in Business & Economics 13: 661-75.
  151. Merkevicius, Egidijus, Gintautas Garšva, and Stasys Girdzijauskas. 2006. A Hybrid SOM-Altman Model for Bankruptcy Prediction. Paper presented at Computational Science-ICCS 2006: 6th International Conference, Part IV, Reading, UK, May 28-31; Edited by Vassil N. Alexandrov, Geert Dick van Albada, Peter M. A. Sloot and Jack Dongarra. Berlin/Heidelberg: Springer, pp. 364-71. open in new tab
  152. Michael, Spanos, Dounias Georgios, Matsatsinis Nikolaos, and Zopounidis Constantin. 2001. A Fuzzy Knowledge-Based Decision Aiding Method for the Assessment of Financial Risks: The Case of Corporate Bankruptcy Prediction. Available online: http://citeseerx.ist.psu.edu/viewdoc/download; jsessionid=99316C2CD8D0EDD1791DCB3890D4C8B9?doi=10.1.1.21.4596&rep=rep1&type=pdf (accessed on 13 July 2017).
  153. Michaluk, Krzysztof. 2003. Effectiveness of corporate bankruptcy models in Polish economic conditions. In Corporate Finance in the Face of Globalization Processes. Edited by Leszek Pawłowicz and Ryszard Wierzba. Warszawa: Wydawnictwo Gdańskiej Akademii Bankowej.
  154. Mičudová, Kateřina. 2013a. Discriminatory Power of the Altman Z-Score Model. Littera Scripta 6: 95-106. open in new tab
  155. Mičudová, Kateřina. 2013b. Bankruptcy Risk-Financial Ratios of Manufacturing Firms. Paper presented at 5th International Applied Economics, Business and Development (AEBD'13), Chania, Greece, August 27-29;
  156. Mihalovic, Matús. 2016. Performance Comparison of Multiple Discriminant Analysis and Logit Models in Bankruptcy Prediction. Economics & Sociology 9: 101-18. [CrossRef] open in new tab
  157. Molinero, C. Mar, and Mahmoud Ezzamel. 1991. Multidimensional Scaling Applied to Corporate Failure. OMEGA International Journal of Management Science 19: 259-74. [CrossRef] open in new tab
  158. Němec, Daniel, and Michal Pavlík. 2016. Predicting Insolvency Risk of the Czech Companies. Paper presented at International Scientific Conference Quantitative Methods in Economics (Multiple Criteria Decision Making XVIII), Bratislava, Vrátna, Slovakia, May 25-27; pp. 258-63. open in new tab
  159. Neskorodeva, Inna, and Svetlana Pustovgar. 2015. An Approach to Predicting the Insolvency of Ukrainian Steel Enterprises Based on Financial Potential. Journal of Eastern European and Central Asian Research 2: 1-11. open in new tab
  160. Novotná, Martina. 2012. The use of different approaches for credit rating prediction and their comparison. Paper presented at 6th International Scientific Conference Managing and Modelling of Financial Risks Ostrava VŠB-TU, Ostrava, Czech Republic, September 10-11; Ostrava: Faculty of Economics, Finance Department, pp. 448-57.
  161. Odom, Marcus D., and Ramesh Sharda. 1990. A Neural Network Model for Bankruptcy Prediction. Paper presented at IEEE International Conference on Neural Network, San Diego, CA, USA, June 17-21; open in new tab
  162. Ohlson, James A. 1980. Financial Ratios, and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research 18: 109-31. [CrossRef] open in new tab
  163. Park, Cheol-Soo, and Ingoo Han. 2002. A Case-Based Reasoning with the Feature Weights Derived by Analytic Hierarchy Process for Bankruptcy Prediction. Expert Systems with Application 23: 255-64. [CrossRef] open in new tab
  164. Peresetsky, Anatoly A., Alexandr A. Karminsky, and Sergei V. Golovan. 2004. Probability of Default Models of Russian Banks. BOFIT Discussion Paper No. 21/2004. Available online: https://ssrn.com/abstract= 1015451orhttp://dx.doi.org/10.2139/ssrn.1015451 (accessed on 8 September 2017). open in new tab
  165. Peresetsky, Anatoly A., Alexandr A. Karminsky, and Sergei V. Golovan. 2011. Probability of Default Models of Russian Banks. Economic Change and Restructuring 44: 297-334. [CrossRef] open in new tab
  166. Pisula, Tomasz, Grzegorz Mentel, and Jacek Brożyna. 2013. Predicting Bankruptcy of Companies from the Logistics Sector Operating in the Podkarpacie Region. Modern Management Review 18: 113-33. [CrossRef] open in new tab
  167. Pisula, Tomasz, Grzegorz Mentel, and Jacek Brożyna. 2015. Non-statistical Methods of Analyzing of Bankruptcy Risk. Folia Oeconomica Stetinensia 15: 7-21. [CrossRef] open in new tab
  168. Pitrová, Kateřina. 2011. Possibilities of the Altman ZETA Model Application to Czech Firms. Ekonomika A Management 3: 66-76.
  169. Pociecha, Józef, and Barbara Pawełek. 2011. Bankruptcy Prediction and Business Cycle, Contemporary Problems of Transformation Process in the Central and East European Countries. Paper presented at 17th Ukrainian-Polish-Slovak Scientific Seminar, Lviv, Ukraine, September 22-24; Lviv: The Lviv Academy of Commerce, pp. 9-24.
  170. Pociecha, Józef, Barbara Pawełek, Mateusz Baryła, and Sabina Augustyn. 2014. Statistical Methods of Forecasting Bankruptcy in the Changing Economic Situation. Kraków: Fundacja Uniwersytetu Ekonomicznego w Krakowie. open in new tab
  171. Pogodzińska, Marzanna, and Sławomir Sojak. 1995. The Use of Discriminant Analysis in Predicting Bankruptcy of Enterprises. Ekonomia XXV, Zeszyt 299. Toruń: AUNC.
  172. Popchev, Ivan, and Irina Radeva. 2006. A Decision Support Method for Investment Preference Evaluation. Cybernetics and Information Technologies 6: 3-16. open in new tab
  173. Premachandra, I. M., Gurmeet Singh Bhabra, and Toshiyuki Sueyoshi. 2009. DEA as a Tool for bankruptcy assessment: A comparative study with logistic regression technique. European Journal of Operational Research 193: 412-24. [CrossRef] open in new tab
  174. Prusak, Błażej. 2005. Modern Methods of Forecasting Financial Risk of Enterprises. Warszawa: Difin.
  175. Prusak, Błażej, and Agnieszka Więckowska. 2007. Multidimensional models of discriminant analysis in the study of the bankruptcy risk of Polish companies listed on the WSE. In Economic and Legal Ascpects of Corporate Bankruptcy. Edited by Błażej Prusak. Warszawa: Difin.
  176. Ptak-Chmielewska, Aneta. 2016. Statistical Models for Corporate Credit Risk Assessment-Rating Models. Acta Universitatis Lodziensis Folia Oeconomica 3: 98-111. [CrossRef] open in new tab
  177. Purvinis, Ojaras, Povilas Šukys, and Rūta Virbickaitė. 2005a. Bankruptcy Prediction in Lithuanian Enterprises Using Discriminant Analysis. Ekonomika ir vadyba: Aktualijos ir Perspektyvos 5: 314-18. open in new tab
  178. Purvinis, Ojaras, Povilas Šukys, and Rūta Virbickaitė. 2005b. Research of Possibility of Bankruptcy Diagnostics Applying Neural Networks. Engineering Economics 1: 16-22. open in new tab
  179. Purvinis, Ojaras, R. Virbickaite, and Povilas Sukys. 2008. Interpretable Nonlinear Model for Enterprise Bankruptcy Prediction. Nonlinear Analysis: Modelling and Control 13: 61-70. open in new tab
  180. Radkov, Petar. 2013. Measuring Default Risk of Bulgarian Public Banks with Meton Model. Available online: https://www.researchgate.net/publication/264681559_Measuring_default_risk_of_Bulgarian_ public_banks_with_Merton_model (accessed on 9 March 2017).
  181. Radkov, Petar, and Leda Minkova. 2011. Assessing Bank's Default Probability Using ASFR Model. Available online: https://www.researchgate.net/publication/237020492_Assessing_bank%27s_default_probability_ using_the_ASRF_model (accessed on 9 March 2017).
  182. Rahimian, E., Sameer Singh, T. Thammachote, and R. Virmani. 1993. Bankruptcy Prediction by Neural Network. In Neural Networks in Finance and Investing. Edited by Robert R. Trippi and Efraim Turban. Chicago and London: Probus Publishing Company.
  183. Karas, Michal, and Mária Režňáková. 2013. Bankruptcy Prediction Model of Industrial Enterprises in the Czech Republic. International Journal of Mathematical Models and Methods in Applied Sciences 5: 519-31. open in new tab
  184. Režňáková, Mária, and Michal Karas. 2014. Identifying bankruptcy prediction factors in various environments: A contribution to the discussion on the transferability of bankruptcy models. International Journal of Mathematical Models and Methods in Applied Sciences 8: 69-74. open in new tab
  185. Robua, Ioan-Bogdan, Mihaela-Alina Robua, and Marilena Mironiuc. 2013. Risk assessment of financial failure for Romanian Quoted companies based on the survival analysis. Paper presented at 8th International Conference Accounting and Management Information Systems AMIS, Bucharest, Romania; Bucharest: The Bucharest University of Economic Studies, pp. 51-65.
  186. Robua, Ioan-Bogdan, Mihaela-Alina Robua, Marilena Mironiuc, and Florentina Olivia Balu. 2014. The value relevance of financial distress risk in the case of RASDAQ companies. Accounting and Management Information Systems 13: 623-42.
  187. Roháčová, Viera, and Král' Pavol. 2015. Corporate Failure Prediction Using DEA: An Application to Companies in the Slovak Republic. Paper presented at 18th Applications of Mathematics and Statistics in Economics, International Scientific Conference, Jindřichuv Hradec, Czech Republic, September 2-6.
  188. Rudolfova, Lucie, and Tatiana Skerlíkova. 2014. Discrepancy between the Default and the Financial Distress Measured by Bankruptcy Models. Journal of Eastern European and Central Asian Research 1: 1-12.
  189. Daniela, Rybárová, Braunová Mária, and Jantošová Lucia. 2016. Analysis of the Construction Industry in the Slovak Republic by Bankruptcy Model. Procedia-Social and Behavioral Sciences 230: 298-306. [CrossRef] open in new tab
  190. Serrano-Cinca, Carlos. 1997. Feedforward Neural Networks in the Classification of Financial Information. European Journal of Finance 3: 183-202. [CrossRef] open in new tab
  191. Shakun, A. S., and A. V. Skrobko. 2012. Agricultural enterprise probability forecasting on the basis of discriminant multifactor models. Problems and Prospects 17: 169-77. open in new tab
  192. Shin, Kyung-Shik, and Yong-Joo Lee. 2002. A genetic algorithm application in bankruptcy prediction modelling. Expert Systems with Applications 23: 321-28. [CrossRef] open in new tab
  193. Shirinkina, Elena V., and Laisan A. Valiullina. 2015. Formalization of the Model of the Enterprise Insolvency Risk Prediction. Actual Problems of Economics and Laws 4: 169-80. [CrossRef] open in new tab
  194. Shumway, Tyler. 2001. Forecasting Bankruptcy More Accurately: A simple Hazard Model. Journal of Business 74: 101-24. [CrossRef] open in new tab
  195. Siudek, Tomasz. 2005. Forecasting the bankruptcy of cooperative banks using discriminant analysis. Roczniki Naukowe Stowarzyszenia Ekonomistów Rolnictwa i Agrobiznesu 7: 86-91. open in new tab
  196. Šlefendorfas, Gediminas. 2016. Bankruptcy Prediction Model for Private Limited Companies in Lithuania. Ekonomika 95: 134-52. [CrossRef] open in new tab
  197. Šlégr, Pavel. 2013. The Evaluation of Financial Stability of Czech Companies through the Z-Score nad the IN05 Index and their Comparison. Paper presented at 7th WSEAS International Conference on Management, Marketing and Finances (MMF '13), Cambridge, MA, USA, January 30-February 1; pp. 29-33. open in new tab
  198. Slowinski, Roman, and Constantin Zopounidis. 1995. Application of the Rough Set Approach to Evaluation of Bankruptcy risk. Intelligent Systems in Accounting, Finance and Management 4: 27-41. [CrossRef] open in new tab
  199. Smith, Raymond F., and Arthur H. Winakor. 1935. Changes in Financial Structure of Unsuccessful Industrial Corporations. Bureau of Business Research, Bulletin 51. Urbana: University of Illinois Press.
  200. Smolski, Aliaksei P. 2006. Tendencies and Problems of Economical Insolvency (Bankruptcy) Institution Development in Belarus: 1991-2005 (No. smolski_aliaksei. 39168-b1). Socionet. Available online: http://refor.socionet.ru/files/Tendencies.doc (accessed on 9 March 2016). open in new tab
  201. Sneidere, Ruta, and Inta Bruna. 2011. Predicting Business Insolvency: The Latvian Experience. Journal of Modern Accounting and Auditing 7: 487-97.
  202. Šofranková, Beáta. 2013. Analysis of Impact of Non-financial Criteria and Z-score in Accommodation Facilities in Slovakia. Slovak Scientific Journal Management: Science and Education 2: 72-74.
  203. Šofranková, Beáta. 2014. Analysis of Impact of Non-financial Criteria and Z-score in Accommodation Facilities in Slovakia. CER Comparative European Research. Paper presented at Research Tracks of the 1st Biannual CER Comparative European Research Conference, London, UK, March 17-21; pp. 88-91. open in new tab
  204. Sojak, Sławomir, and Józef Stawicki. 2000. The use of taxonomic methods to assess the economic condition of enterprises. Zeszyty Teoretyczne Rachunkowości 3: 55-66. open in new tab
  205. Spaičienė, Jurgita. Bankruptcy Law Development in The Republic of Lithuania, Summary of the Doctoral Dissertation, Vilnius. Available online: http://www.youscribe.com/catalogue/rapports-et-theses/savoirs/ bankruptcylaw-development-in-the-republic-of-lithuania-bankroto-1426976 (accessed on 20 July 2016).
  206. Stachová, Mária, Král' Pavol, Lukáš Sobíšek, and Martin Kakaščík. 2015. Analysis of Financial Distress of Slovak Companies Using Repeated Measurements. Paper presented at Applications of Mathematics and Statistics in Economics, International Scientific Conference, Jindřichuv Hradec, Czech Republic, September 2-6.
  207. Stępień, Paweł, and Tomasz Strąk. 2003. Signs of the threat of bankruptcy of Polish enterprises-Empirical study. In Time for Money, t. II. Edited by Dariusz Zarzecki. Szczecin: Wydawnictwo Uniwersytetu Szczecińskiego.
  208. Stępień, Paweł, and Tomasz Strąk. 2004. Multidimensional logit models for assessing the risk of bankruptcy of Polish enterprises. In Time for Money, t. I. Edited by Dariusz Zarzecki. Szczecin: Wydawnictwo Uniwersytetu Szczecińskiego.
  209. Stundžienė, Alina, and Vytautas Boguslauskas. 2006. Valuation of Bankruptcy Risk for Lithuanian Companies. Engineering Economics 4: 29-36.
  210. Tam, Kar Yan, and Melody Y. Kiang. 1992. Managerial Applications of Neural Networks: The Case of Bank Failure Predictions. Management Science 38: 926-47. [CrossRef] open in new tab
  211. Varul, Paul. 1999. On the Development of Bankruptcy Law in Estonia. Juridica International 1: 172-78. open in new tab
  212. Vavřina, Jan, David Hampel, and Jitka Janová. 2013. New Approaches for the Financial Distress Classification in Agribusiness. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 61: 1177-82. [CrossRef] open in new tab
  213. Venyš, Ladislav. 1997. Bankruptcy in the Czech Republic, NATO Democratic Institutions Fellowship Programme 1995-1997. Available online: http://www.nato.int/acad/fellow/95-97/venys.pdf (accessed on 7 September 2017).
  214. Virág, Miklós, and Tamás Kristóf. 2005. Neural Neutworks in Bankruptcy Prediction-A Comparative Study on the Basis of the First Hungarian Bankruptcy Model. Acta Oeconomica 55: 403-25. [CrossRef] open in new tab
  215. Virág, Miklós, and Tamás Kristóf. 2014. Is there a Trade-off between the Predictive Power and the Interpretability of Bankruptcy Models? The Case of the first Hungarian Bankruptcy Model. Acta Oeconomica 64: 419-40. open in new tab
  216. Vitryansky, Vassily V. 1999. Insolvency and Bankruptcy Law Reform in the Russian Federation. McGill Law Journal 44: 409-32. open in new tab
  217. Vochozka, Marek, Jarmila Straková, and Jan Váchal. 2015a. Model to Predict Survival of Transportation and Shipping Companies. Naše More, Special Issue 62: 109-13. [CrossRef] open in new tab
  218. Vochozka, Marek, Zuzana Rowland, and Jaromir Vrbka. 2015b. Prediction of the Future Development of Construction Companies by Means of Artificial Neural Networks on the Basis of Data from the Czech Republic. Мaтемaтичне Моделювaння в Економiцi 3: 62-76.
  219. Vochozka, Marek, Zuzana Rowland, and Jaromir Vrbka. 2016. Evaluation of Solvency of Potential Customers of a Company. Мaтемaтичне моделювaння в економiцi 1: 5-18.
  220. Vodonosova, T. 2012. Application of Crisis-Prognostic Models in Building Sector of the Republic of Bielarus. Экономические и Юридические Нaуки. Φинaнсы и Нaлогообложение 13: 93-98. open in new tab
  221. Voronova, Irina. 2012. Financial Risks: Cases of Non-Financial Enterprises. In Risk Management for the Future. Theory and Cases. Edited by Jan Emblemsvag. InTech: pp. 435-66. open in new tab
  222. Wędzki, Dariusz. 2000. The problem of using the ratio analysis to predict the bankruptcy of Polish enterprises-Case study. Bank i Kredyt 5: 54-61.
  223. Wędzki, Dariusz. 2004. Logit model of bankruptcy for the Polish economy-Conclusions from the study. In Time for Money. Corporate finance. Financing enterprises in the EU. Edited by Dariusz Zarzecki. Szczecin: Wydawnictwo Uniwersytetu Szczecińskiego.
  224. Wierzba, Dariusz. 2000. Early Detection of Enterprises Threatened with Bankruptcy Based on the Analysis of Financial Ratios-Theory and Empirical Research. Zeszyty Naukowe nr 9. Warszawa: Wydawnictwo Wyższej Szkoły Ekonomiczno-Informatycznej w Warszawie.
  225. Wilson, Rick L., and Ramesh Sharda. 1994. Bankruptcy Prediction Using Neural Networks. Decision Support Systems 11: 545-57. [CrossRef] open in new tab
  226. Zavgren, Christine. 1983. The Prediction of Corporate Failure: The State of the Art. Journal of Accounting Literature 2: 1-38.
  227. Zdyb, Marek. 2001. Assessing the enterprise's risk of bankruptcy using financial synthetic indicators. Controlling i Rachunkowość Zarządcza 5: 36-40.
  228. Zhang, Yu Dong, and Le Nan Wu. 2011. Bankruptcy prediction by genetic ant colony algorithm. Advanced Materials Research 186: 459-63. [CrossRef] open in new tab
  229. Zmijewski, Mark E. 1984. Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research 20: 59-82. [CrossRef] open in new tab
  230. Zopounidis, Constantin, and Michael Doumpos. 1999. A multicriteria aid methodology for sorting decision problems: The case of financial distress. Computational Economics 14: 197-218. [CrossRef] open in new tab
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