Skip to main navigation menu Skip to main content Skip to site footer

Prediction models reloaded: Advanced insights for SMEs in the Bucharest Nine countries

Abstract

Research background: Financial health is an essential factor in the success of an enterprise, its survival, competitiveness in the market and sustainable development. Therefore, predicting constraints, weak points and possible risks that could cause financial distress is crucial. Small and medium-sized enterprises (SMEs) remain a key pillar of any prosperous economy during every phase of the economic cycle, particularly in emerging countries, such as the Bucharest Nine.

Purpose of this article: The objective is to specify indicators of the financial health of SMEs depending on the economic cycle through unconventional incentives under the conditions of the Bucharest Nine. It entails a longitudinal mapping of more than 30,000 enterprises during the pre-crisis, crisis and post-crisis periods, as along with data on economic growth.

Methods: Financial statements from the Orbis database, covering the period 2018–2023, were used to create a robust final sample of SMEs. Logit least absolute shrinkage and selection operator with 10-fold cross-validation was employed to identify bankruptcy predictors from 75 origin predictors, including liquidity, activity, profitability, indebtedness, earnings management and business development. The resulting models for each period were validated on a test sample of prosperous and non-prosperous enterprises. Furthermore, the classification ability of all models was evaluated using the area under the receiver operating characteristic curve.

Findings & value added: This research adds value by demonstrating important factors that influence the bankruptcy of SMEs and guiding financial managers to focus on these factors based on the expected economic cycle. Thus, developed prediction models are particularly beneficial for businesses themselves, enabling them to predict financial health depending on the expected state of the economy, which helps overcome the existing animosities of businesses towards predictions. The results of the present study may also prove valuable to agencies dealing with SMEs, financial database providers or auditing companies. The present study enhances the idea of financial distress prediction by including unconventional financial indicators, including earnings management and value-added variables, in traditional bankruptcy modelling frameworks. This innovative combination enhances the theoretical framework of financial economics by providing a more dynamic and context-aware method for assessing SME sustainability over the economic cycle.

Keywords

bankruptcy, Bucharest Nine, financial health, LASSO, SMEs

PDF

References

  1. Abdulhafedh, A. (2022). Comparison between common statistical modeling techniques used in research, including: Discriminant analysis vs logistic regression, ridge regression vs LASSO, and decision tree vs random forest. Open Access Library Journal, 9, e8414. DOI: https://doi.org/10.4236/oalib.1108414
    View in Google Scholar
  2. Abdullah, N. A. H., Ma’aji, M. M., & Khaw, K. L. H. (2016). The value of governance variables in predicting financial distress among small and medium-sized enterprises in Malaysia. Asian Academy of Management Journal of Accounting and Finance, 12(Suppl. 1), 75–88. DOI: https://doi.org/10.21315/aamjaf2016.11.S1.4
    View in Google Scholar
  3. Act 513/1991 Coll—Slovak Commercial Code. Retrieved from https://www.slov-lex.sk/ezbierky/pravne-predpisy/SK/ZZ/1991/513/.
    View in Google Scholar
  4. Adamko, P., & Siekelova, A. (2017). An ensemble model for prediction of crisis in Slovak companies. In T. Kliestik (Ed.). Globalization and its socio-economic consequences (pp. 1–7). Zilina: University of Zilina.
    View in Google Scholar
  5. Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023). Deep learning modelling techniques: Current progress, applications, advantages, and challenges. Artificial Intelligence Review, 56(11), 13521–13617. DOI: https://doi.org/10.1007/s10462-023-10466-8
    View in Google Scholar
  6. Altman, E. I. (1968). Financial ratios, discriminant analysis and prediction of corporate bankruptcy. Journal of Finance, 13(4), 589–609. DOI: https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
    View in Google Scholar
  7. Altman, E. I., & Sabato, G. (2007). Modelling credit risk for SMEs: Evidence from the U.S. market. Abacus, 43, 332–357. DOI: https://doi.org/10.1111/j.1467-6281.2007.00234.x
    View in Google Scholar
  8. Altman, E. I., Balzano, M., Giannozzi, A., & Srhoj, S. (2023a). Revisiting SME default predictors: The Omega Score. Journal of Small Business Management, 61(6), 2383–2417. DOI: https://doi.org/10.1080/00472778.2022.2135718
    View in Google Scholar
  9. Altman, E. I., Balzano, M., Giannozzi, A., & Srhoj, S. (2023b). The Omega Score: An improved tool for SME default predictions. Journal of the International Council for Small Business, 4(3), 362–373. DOI: https://doi.org/10.1080/26437015.2023.2186284
    View in Google Scholar
  10. Altman, E. I., Sabato, G., & Wilson, N. (2010). The value of non-financial information in SME risk management. Journal of Credit Risk, 6(2), 95–127. DOI: https://doi.org/10.21314/JCR.2010.110
    View in Google Scholar
  11. Alvi, J., Arif, I., & Nizan, K. (2024). Advancing financial resilience: A systematic review of default prediction models and future directions in credit risk management. Heliyon, 10(21), e39770. DOI: https://doi.org/10.1016/j.heliyon.2024.e39770
    View in Google Scholar
  12. Amankwah-Amoah, J., Khan, Z., & Wood, G. (2021). COVID-19 and business failures: The paradoxes of experience, scale, and scope for theory and practice. European Management Journal, 39(2), 179–184. DOI: https://doi.org/10.1016/j.emj.2020.09.002
    View in Google Scholar
  13. Andrikopoulos, P., & Khorasgani, A. (2018). Predicting unlisted SMEs’ default: Incorporating market information on accounting-based models for improved accuracy. British Accounting Review, 50(5), 559–573. DOI: https://doi.org/10.1016/j.bar.2018.02.003
    View in Google Scholar
  14. Angelova, R., & Stayancheva, D. (2023). Digitalization, financial insolvency and bankruptcy forecasting of Bulgarian agricultural enterprises. Scientific Papers Series Management, Economic Engineering in Agriculture and Rural Development, 23(2), 29–35.
    View in Google Scholar
  15. Ashraf, S., Félix, E. G. S., & Serrasqueiro, Z. (2019). Do traditional financial distress prediction models predict the early warning signs of financial distress?. Journal of Risk and Financial Management, 12(2), 55. DOI: https://doi.org/10.3390/jrfm12020055
    View in Google Scholar
  16. Baños‐Caballero, S., García‐Teruel, P. J., & Martínez‐Solano, P. (2010). Working capital management in SMEs. Accounting & Finance, 50(3), 511–527. DOI: https://doi.org/10.1111/j.1467-629X.2009.00331.x
    View in Google Scholar
  17. Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417. DOI: https://doi.org/10.1016/j.eswa.2017.04.006
    View in Google Scholar
  18. Bateni, L., & Asghari, F. (2020). Bankruptcy prediction using logit and genetic algorithm models: A comparative analysis. Computational Economics, 55, 335–348. DOI: https://doi.org/10.1007/s10614-016-9590-3
    View in Google Scholar
  19. Battisti, M., & Perry, M. (2011). Walking the talk? Environmental responsibility from the perspective of small‐business owners. Corporate Social Responsibility and Environmental Management, 18(3), 172–185. DOI: https://doi.org/10.1002/csr.266
    View in Google Scholar
  20. Bellovary, J. L., Giacomino, D. E., & Akers, M. D. (2007). A review of bankruptcy prediction studies: 1930 to present. Journal of Financial Education, 33, 1–42.
    View in Google Scholar
  21. Ben Arfi, W., Hikkerova, L., & Sahut, J. M. (2018). External knowledge sources, green innovation and performance. Technological Forecasting and Social Change, 129, 210–220. DOI: https://doi.org/10.1016/j.techfore.2017.09.017
    View in Google Scholar
  22. Beneish, M. D. (1999). The detection of earnings manipulation. Financial Analysts Journal, 55(5), 24–36. DOI: https://doi.org/10.2469/faj.v55.n5.2296
    View in Google Scholar
  23. Beneish, M. D., Lee, C. M., & Nichols, D. C. (2013). Earnings manipulation and expected returns. Financial Analysts Journal, 69(2), 57–82. DOI: https://doi.org/10.2469/faj.v69.n2.1
    View in Google Scholar
  24. Berent, T., Blawat, B., Dietl, M., Krzyk, P., & Rejman, R. (2017). Firm's default - new methodological approach and preliminary evidence from Poland. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4), 753–773. DOI: https://doi.org/10.24136/eq.v12i4.39
    View in Google Scholar
  25. Bicekova, A., & Pusztova, L. (2019). Data Analysis of the financial indicators of Polish companies. Acta Electrotechnica et Informatica, 19(2), 51–57. DOI: https://doi.org/10.15546/aeei-2019-0015
    View in Google Scholar
  26. Bougrain, F., & Haudeville, B. (2002). Innovation, collaboration and SMEs internal research capacities. Research Policy, 31(5), 735–747. DOI: https://doi.org/10.1016/S0048-7333(01)00144-5
    View in Google Scholar
  27. Brenes, R. F., Johannssen, A., & Chukhrova, N. (2022). An intelligent bankruptcy prediction model using a multilayer perceptron. Intelligent Systems with Applications, 16, 200136. DOI: https://doi.org/10.1016/j.iswa.2022.200136
    View in Google Scholar
  28. Breunig, C., Mammen, E., & Simoni, A. (2020). Ill-posed estimation in high-dimensional models with instrumental variables. Journal of Econometrics, 219(1), 171–200. DOI: https://doi.org/10.1016/j.jeconom.2020.04.043
    View in Google Scholar
  29. Camska, D., & Klecka, J. (2020). Comparison of prediction models applied in economic recession and expansion. Journal of Risk and Financial Management, 13(3), 52. DOI: https://doi.org/10.3390/jrfm13030052
    View in Google Scholar
  30. Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2024). Antecedents and consequence of social media marketing for strategic competitive advantage of small and medium enterprises: Mediating role of utilitarian and hedonic value. Journal of Strategic Marketing, 32(8), 1106–1125. DOI: https://doi.org/10.1080/0965254X.2021.1954070
    View in Google Scholar
  31. Chatterjee, S., Chaudhuri, R., Shah, M., & Maheshwari, P. (2022a). Big data driven innovation for sustaining SME supply chain operation in post COVID-19 scenario: Moderating role of SME technology leadership. Computers & Industrial Engineering, 168, 108058. DOI: https://doi.org/10.1016/j.cie.2022.108058
    View in Google Scholar
  32. Chatterjee, S., Chaudhuri, R., Vrontis, D., & Galati, A. (2023). Influence of managerial practices, productivity, and change management process on organizational innovation capability of small and medium businesses. European Business Review, 35(5), 839–859. DOI: https://doi.org/10.1108/EBR-02-2023-0049
    View in Google Scholar
  33. Chatterjee, S., Chaudhuri, R., Vrontis, D., & Thrassou, A. (2022b). SME entrepreneurship and digitalization–the potentialities and moderating role of demographic factors. Technological Forecasting and Social Change, 179, 121648. DOI: https://doi.org/10.1016/j.techfore.2022.121648
    View in Google Scholar
  34. Chaudhuri, R., Chatterjee, S., Vrontis, D., & Chaudhuri, S. (2022). Innovation in SMEs, AI dynamism, and sustainability: The current situation and way forward. Sustainability, 14(19), 12760. DOI: https://doi.org/10.3390/su141912760
    View in Google Scholar
  35. Chaundhuri A. (2013). Bankruptcy prediction using Bayesian, hazard, mixed logit and rough Bayesian models: A comparative analysis. Computer and Information Science, 6(2), 103–125. DOI: https://doi.org/10.5539/cis.v6n2p103
    View in Google Scholar
  36. Cheraghali, H., & Molnár, P. (2024). SME default prediction: A systematic methodology-focused review. Journal of Small Business Management, 62(6), 2847–2905. DOI: https://doi.org/10.1080/00472778.2023.2277426
    View in Google Scholar
  37. Ciampi, F., Giannozzi, A., Marzi, G., & Altman, E. I. (2021). Rethinking SME default prediction: A systematic literature review and future perspectives. Scientometrics, 126, 2141–2188. DOI: https://doi.org/10.1007/s11192-020-03856-0
    View in Google Scholar
  38. Citterio, A. (2024). Bank failure prediction models: Review and outlook. Socio-Economic Planning Sciences, 92, 101818. DOI: https://doi.org/10.1016/j.seps.2024.101818
    View in Google Scholar
  39. Coad, A., & Srhoj, S. (2020). Catching gazelles with a Lasso: Big data techniques for the prediction of high-growth firms. Small Business Economics, 55(3), 541–565. DOI: https://doi.org/10.1007/s11187-019-00203-3
    View in Google Scholar
  40. Cultrera, L., & Brédart, X. (2016). Bankruptcy prediction: The case of Belgian SMEs. Review of Accounting and Finance, 15(1), 101–119. DOI: https://doi.org/10.1108/RAF-06-2014-0059
    View in Google Scholar
  41. Dechow, P. M., & Dichev, I. (2002). The quality of accruals and earnings: The role of accrual estimation errors. Accounting Review, 77, 35–59. DOI: https://doi.org/10.2308/accr.2002.77.s-1.35
    View in Google Scholar
  42. Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings man-agement. Accounting Review, 70(2), 193–225.
    View in Google Scholar
  43. Dinca, M. S., Antohi, V. M., Andronic, M. L., Szelez, M. R., & Baba, C. M. (2023). Modelling health financing performance in Europe in the context of macroeconomic uncertainties, Economies 11(12), 299. DOI: https://doi.org/10.3390/economies11120299
    View in Google Scholar
  44. Du Jardin, P. (2023). Designing topological data to forecast bankruptcy using convolutional neural networks. Annals of Operations Research, 325(2), 1291–1332. DOI: https://doi.org/10.1007/s10479-022-04780-7
    View in Google Scholar
  45. Du Jardin, P. (2025). Designing ensemble-based models using neural networks and temporal financial profiles to forecast firms' financial failure. Computational Economics, 65(1), 149–209. DOI: https://doi.org/10.1007/s10614-024-10579-4
    View in Google Scholar
  46. Durica, M., & Frnda, J. (2021). Using data mining methods to predict financial distress. University of Žilina in Žilina: EDIS.
    View in Google Scholar
  47. Durica, M., Frnda, J., & Svabova, L. (2019). Decision tree based model of business failure prediction for Polish companies. Oeconomica Copernicana, 10(3), 453–469. DOI: https://doi.org/10.24136/oc.2019.022
    View in Google Scholar
  48. Durica, M., Frnda, J., & Svabova, L. (2023). Artificial neural network and decision tree-based modelling of non-prosperity of companies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(4), 1105–1131. DOI: https://doi.org/10.24136/eq.2023.035
    View in Google Scholar
  49. Duricova, L., Kovalova, E., Gazdíková, J., & Hamranova, M. (2025). Refining the best-performing V4 financial distress prediction models: Coefficient re-estimation for crisis periods. Applied Sciences, 15(6), 2956. DOI: https://doi.org/10.3390/app15062956
    View in Google Scholar
  50. Edmister, R. O. (1972). An empirical test of financial ratio analysis for small business failure prediction. Journal of Financial and Quantitative Analysis, 7(2), 1477–1493. DOI: https://doi.org/10.2307/2329929
    View in Google Scholar
  51. El Kalak, I., & Hudson, R. (2016). The effect of size on the failure probabilities of SMEs: Anempirical study on the US market using discrete hazard model. International Review of Financial Analysis, 43, 135–145. DOI: https://doi.org/10.1016/j.irfa.2015.11.009
    View in Google Scholar
  52. European Commission Recommendation 2003/361/EC of 6 May 2003. Document number C(2003) 1422. Retrieved from https://eur-lex.europa.eu/eli/reco/2003/361/oj/eng.
    View in Google Scholar
  53. Faltus, S. (2014). Firm default prediction model for Slovak companies. In O. Deev, V. Kajurova & J. Krajicek (Eds.). 11th international scientific conference on European financial systems 2014 (pp. 173–177). Brno: Masaryk University.
    View in Google Scholar
  54. Frajtova Michalikova, K., Kliestik, T., & Musa, H. (2015). Comparison of nonparametric methods for estimating the level of risk in finance. In N. Tsounis & A. Vlahvei (Eds.). Procedia economics and finance Vol: 24 (pp. 228–236). Amsterdam: Elsevier. DOI: https://doi.org/10.1016/S2212-5671(15)00653-X
    View in Google Scholar
  55. Garcia, F. A. A. (2012). Tests to identify outliers in data series. Retrieved from https://www.pdf-archive.com/2016/07/29/outlier-methods-external/.
    View in Google Scholar
  56. Gavurova, B., Jencova, S., Bacik, R., Miskufova, M., & Letkovsky, S. (2022). Artificial intelligence in predicting the bankruptcy of non-financial corporations. Oeconomia Copernicana, 13(4), 1215–1251. DOI: https://doi.org/10.24136/oc.2022.035
    View in Google Scholar
  57. Gavurova, B., Packova, M., Misankova, M., & Smrcka, L. (2017). Predictive potential and risks of selected bankruptcy prediction models in the Slovak business environment. Journal of Business Economics and Management, 18(6), 1156–1173. DOI: https://doi.org/10.3846/16111699.2017.1400461
    View in Google Scholar
  58. Ghosh, D., & Vogt, A. (2012). Outliers: An evaluation of methodologies. In JSM Proceedings, survey research methods section, American Statistical Association (pp. 3455–3460). Alexandria: American Statistical Association.
    View in Google Scholar
  59. Goh, E., Roni, S. M., & Bannigidadmath, D. (2022). Thomas Cook(ed): Using Altman´s z-score analysis to examine predictiors of financ bankruptcy in tourism and hospitality business. Asia Pacific Journal of Marketing and Logistics, 34(3), 475–487. DOI: https://doi.org/10.1108/APJML-02-2021-0126
    View in Google Scholar
  60. Gregova, E., Valaskova, K., Adamko, P., Tumpach, M., & Jaros, J. (2020). Predicting financial distress of Slovak enterprises: Comparison of selected traditional and learning algorithms methods. Sustainability, 12(10), 3954. DOI: https://doi.org/10.3390/su12103954
    View in Google Scholar
  61. Grice, J. S., & Dugan, M. (2001). The limitations of bankruptcy prediction models: Some cautions for the researcher. Review of Quantitative Finance and Accounting, 17, 151–166. . DOI: https://doi.org/10.1023/A:1017973604789
    View in Google Scholar
  62. Grishunin, S., Suloeva, V., Shiryakina, S., & Burova, E. (2021). Analyzing insolvency drivers and developing credit rating system for small and medium-sized enterprises in Russia. International Journal of Technology, 12(7), 1479–1487. DOI: https://doi.org/10.14716/ijtech.v12i7.5349
    View in Google Scholar
  63. Gupta, G., & Mahakud, J. (2023). Impact of financial distress on investment-cash flow sensitivity: Evidence from emerging economy. International Journal of Management Finance, 19(4), 713–743. DOI: https://doi.org/10.1108/IJMF-03-2022-0102
    View in Google Scholar
  64. Gupta, J., Wilson, N., Gregoriou, A., & Healy, J. (2014). The effect of internationalisation on modelling credit risk for SMEs: Evidence from UK market. Journal of International Financial Markets, Institutions and Money, 31, 397–413. DOI: https://doi.org/10.1016/j.intfin.2014.05.001
    View in Google Scholar
  65. Hamdi, M., Mestiri, S., & Arbi, A. (2024). Artificial intelligence techniques for bankruptcy prediction of Tunisian companies: An application of machine learning and deep learning-based models. Journal of Risk and Financial Management, 17(4), 132. DOI: https://doi.org/10.3390/jrfm17040132
    View in Google Scholar
  66. Heiss, F., Hetzenecker, S., & Osterhaus, M. (2022). Nonparametric estimation of the random coefficients model: An elastic net approach. Journal of Econometrics, 229(2), 299–321. DOI: https://doi.org/10.1016/j.jeconom.2020.11.010
    View in Google Scholar
  67. Horowitz, J. L., & Nesheim, L. (2021). Using penalized likelihood to select parameters in a random coefficient multinomial logit model. Journal of Econometrics, 222(1), 44–55. DOI: https://doi.org/10.1016/j.jeconom.2019.11.008
    View in Google Scholar
  68. Horvathova, J., & Mokrisova, M. (2019). The application of additive model in predicting the risk of bankruptcy. In P. Doucek, G. Chroust & V. Oskrdal (Eds.). IDIMT 2019 - innovation and transformation in a digital world: Vol 48 (pp. 153–160). Linz: Trauner Verlag.
    View in Google Scholar
  69. Horvathova, J., & Mokrisova, M. (2023). Integrated performance measurement system for Slovak heating industry: A balanced scorecard approach. Problems and Perspectives in Management, 21(3), 393–407. DOI: https://doi.org/10.21511/ppm.21(3).2023.32
    View in Google Scholar
  70. Horvathova, J., Mokrisova, M., & Baca, M. (2023). Bankruptcy prediction for sustainability of businesses: The application of graph theoretical modeling. Mathematics, 11(24), 4966. DOI: https://doi.org/10.3390/math11244966
    View in Google Scholar
  71. Hotho, S., & Champion, K. (2011). Small businesses in the new creative industries: Innovation as a people management challenge. Management Decision, 49(1), 29–54. DOI: https://doi.org/10.1108/00251741111094428
    View in Google Scholar
  72. Iparraguirre-Villanueva, O., & Cabanillas-Carbonell, M. (2024). Predicting business bankruptcy: A comparative analysis with machine learning models. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100375. DOI: https://doi.org/10.1016/j.joitmc.2024.100375
    View in Google Scholar
  73. Jackowicz, K., & Kozlowski. L. (2016). Which came first, the chicken or the egg? Banks and firms on local banking markets. Finance a Uver-Czech Journal of Economics and Finance, 66(3), 182–206. http://dx.doi.org/10.2139/ssrn.2746606. DOI: https://doi.org/10.2139/ssrn.2746606
    View in Google Scholar
  74. Jencova, S., Petruska, I., Lukacova, M., & Abud-Zaid, J. (2021.) Prediction of bankruptcy in non-financial corporations using neural network. Montenegrin Journal of Economics, 17(4), 123–134. DOI: https://doi.org/10.14254/1800-5845/2021.17-4.11
    View in Google Scholar
  75. Jeter, D. C., & Shivakumar, L. (1999). Cross-sectional estimation of abnormal accruals using quarterly and annual data: Effectiveness in detecting event-specific earnings management. Accounting and Business Research, 29(4), 299–319. DOI: https://doi.org/10.1080/00014788.1999.9729590
    View in Google Scholar
  76. Jones, J. J. (1991). Earnings Management during import relief investigations. Journal of Accounting Research, 29(2), 193–228. DOI: https://doi.org/10.2307/2491047
    View in Google Scholar
  77. Jones, S. (2023). A literature survey of corporate failure prediction models. Journal of Accounting Literature, 45(2), 364–405. DOI: https://doi.org/10.1108/JAL-08-2022-0086
    View in Google Scholar
  78. Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting corporate bankruptcy: An evaluation of alternative statistical frameworks. Journal of Business Finance & Accounting, 44(1-2), 3–34. DOI: https://doi.org/10.1111/jbfa.12218
    View in Google Scholar
  79. Káčer, M., Ochotnický, P., & Alexy, M. (2019). The Altman’s revised Z’-score model, nonfinancial information and macroeconomic variables: Case of Slovak SMEs. Ekonomický časopis, 67(4), 335–366.
    View in Google Scholar
  80. Kanapickienė, R., & Marcinkevicius, R. (2015). Possibilities to apply classical bankruptcy prediction models in the construction sector in Lithuania. Economics and Management, 19(4), 317–332. DOI: https://doi.org/10.5755/j01.em.19.4.8095
    View in Google Scholar
  81. Kanapickienė, R., Kanapickas, T., & Neciunas, A. (2023). Bankruptcy prediction for micro and small enterprises using financial, non-financial, business sector and macroeconomic variables: The case of the Lithuanian construction sector. Risks, 11(5), 97. DOI: https://doi.org/10.3390/risks11050097
    View in Google Scholar
  82. Karas, M., & Režňáková, M. (2013). Identification of financial signs of bankruptcy: A case of industrial enterprises in Czech Republic. In E. Jircikova, A. Knapkova & E. Pastuszkova (Eds.). Finance and the performance of firms in science, education, and practice (pp. 324–335). Zlín: Tomáš Baťa University in Zlín.
    View in Google Scholar
  83. Karas, M. (2022). The hazard model for European SMEs: Combining accounting and macroeconomic variables. Journal of Competitiveness, 14(3), 76–92. DOI: https://doi.org/10.7441/joc.2022.03.05
    View in Google Scholar
  84. Karas, M., & Režňáková, M. (2014). Creating a new bankruptcy prediction model: The grey zone problem. In 24th IBIMA conference: Crafting global competitive economies: 2020 vision strategic planning & smart implementation (pp. 911–919). IBIMA.
    View in Google Scholar
  85. Karas, M., & Režňáková, M. (2020). Cash flows indicators in the prediction of financial distress. Engineering Economics, 31(5) 525–535. DOI: https://doi.org/10.5755/j01.ee.31.5.25202
    View in Google Scholar
  86. Karas, M., & Režňáková, M. (2021). The role of financial constraint factors in predicting SME default. Equilibrium. Quarterly Journal of Economics and Economic Policy, 16(4), 859–883. DOI: https://doi.org/10.24136/eq.2021.032
    View in Google Scholar
  87. Karas, M., & Režňáková, M. (2023). A novel approach to estimating the debt capacity of European SMEs. Equilibrium. Quarterly Journal of Economics and Economic Policy, 18(2), 551–581. DOI: https://doi.org/10.24136/eq.2023.017
    View in Google Scholar
  88. Kasznik, R. (1999). On the association between voluntary disclosure and earnings management. Journal of Accounting Research, 37, 57−81. DOI: https://doi.org/10.2307/2491396
    View in Google Scholar
  89. Key, K. (1997). Political cost incentives for earnings management in the cable television industry. Journal of Accounting and Economics, 23, 309–337. DOI: https://doi.org/10.1016/S0165-4101(97)00012-8
    View in Google Scholar
  90. Kim, H. S., & Sohn, S. Y. (2010). Support vector machines for default prediction of SMEs based on technology credit. European Journal of Operational Research, 201(3), 838–846. DOI: https://doi.org/10.1016/j.ejor.2009.03.036
    View in Google Scholar
  91. Kliestik, T., Valaskova, K., Kliestikova, J., Kovacova, M., & Svabova, L., (2019). Prediction of financial health of business entities in transition economies. University of Žilina in Žilina: EDIS.
    View in Google Scholar
  92. Kliestik, T., Valaskova, K., Lazaroiu, G., Kovacova, M., & Vrbka, J. (2020). Remaining financially healthy and competitive: The role of financial predictors. Journal of Competitiveness, 12(1), 74–92. DOI: https://doi.org/10.7441/joc.2020.01.05
    View in Google Scholar
  93. Komsta, L. (2006). Processing data for outliers. Newsletter of the R Project, 6(2), 10–14.
    View in Google Scholar
  94. Korol, T. (2019). Dynamic bankruptcy prediction models for European enterprises. Journal of Risk and Financial Management, 12(4), 185. DOI: https://doi.org/10.3390/jrfm12040185
    View in Google Scholar
  95. Kostrzewski, M., Vojtekova, S., & Vlckova. M. (2023). Bankruptcy prediction for the manufacturing sector in V4 countries. Ekonomicko-manazerske spektrum, 17(2), 48–64.
    View in Google Scholar
  96. Kou, G., Xu, Y., Peng, Y., Chen, Y., Chang, K., & Kou, S. (2021). Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Desicion Support Systems, 140, 113429. DOI: https://doi.org/10.1016/j.dss.2020.113429
    View in Google Scholar
  97. Kovacova, M., & Kliestik, T. (2017). Logit and Probit application for the prediction of bankruptcy in Slovak companies. Equilibrium. Quarterly Journal of Economics and Economic Policy, 12(4), 775–791. DOI: https://doi.org/10.24136/eq.v12i4.40
    View in Google Scholar
  98. Kovacova, M., Kliestik, T., Valaskova, K., Durana, P., & Juhaszova, Z. (2019a). Systematic review of variables applied in bankruptcy prediction models of Visegrad Group countries. Oeconomia Copernicana, 10(4), 743–772. DOI: https://doi.org/10.24136/oc.2019.034
    View in Google Scholar
  99. Kovacova, M., Valaskova, K., Durana P., & Kliestikova, J. (2019b). Innovation management of the bankruptcy: Case study of Visegrad Group countries. Marketing and Management of Innovations, 4, 241–251. DOI: https://doi.org/10.21272/mmi.2019.4-19
    View in Google Scholar
  100. Krajewski, J., Tokarski, A., & Tokarski, M. (2020). The Analysis of the bankruptcy of enterprises exemplified by the Visegrad Group. Journal of Business Economics and Management 21(2), 593–609. DOI: https://doi.org/10.3846/jbem.2020.12232
    View in Google Scholar
  101. Kristof, T., & Virag, M. (2020). A comprehensive review of corporate bankruptcy prediction in Hungary. Journal of Risk and Financial Management, 13(2), 35. DOI: https://doi.org/10.3390/jrfm13020035
    View in Google Scholar
  102. Kubenka, M. (2016). The strictness of traditional indicators for creditworthiness measuring. In T. Loster & T. Pavelka (Eds.). 10th international days of statistics and economics (pp. 985–995). Slany: Melandrium.
    View in Google Scholar
  103. Kumar, P. R., & Ravi, V. (2007). Bankruptcy prediction in banks and firms via statistical and intelligent techniques–A review. European Journal of Operational Research, 180(1), 1–28. DOI: https://doi.org/10.1016/j.ejor.2006.08.043
    View in Google Scholar
  104. Kuncova, M., Fiala, R., & Hedija, V. (2020). Financial health assessment of pig-breeding companies in Slovakia – Altman's Z-score and TOPSIS results comparison. In S. Kapouek & H. Vranova (Eds.). 38th international conference on mathematical methods in economics (MME 2020) (pp. 339–345). Brno: Mendel University in Brno.
    View in Google Scholar
  105. Lagazio, C., Persico, L., & Querci, F. (2021). Public guarantees to SME lending: Do broader eligibility criteria pay off. Journal of Banking & Finance, 133, 106287. DOI: https://doi.org/10.1016/j.jbankfin.2021.106287
    View in Google Scholar
  106. Lahmiri, S. (2016). Features selection, data mining and financial risk classification: A comparative study. Intelligent Systems in Accounting Finance, 23(4), 265–275. DOI: https://doi.org/10.1002/isaf.1395
    View in Google Scholar
  107. Lahmiri, S., & Bekiros, S. (2019). Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design. Quantitative Finance, 19(9), 1569–1577. DOI: https://doi.org/10.1080/14697688.2019.1588468
    View in Google Scholar
  108. Lee, H., Kelley, D., Lee, J., & Lee, S. (2012). SME survival: The impact of internationalization, technology resources, and alliances. Journal of Small Business Management, 50(1), 1–19. DOI: https://doi.org/10.1111/j.1540-627X.2011.00341.x
    View in Google Scholar
  109. Letkovsky, S., Jencova, S., & Vasanicova, P. (2024). Is artificial intelligence really more accurate in predicting bankruptcy?. International Journal of Financial Studies, 12(1), 8. DOI: https://doi.org/10.3390/ijfs12010008
    View in Google Scholar
  110. Letkovsky, S., Jencova, S., Vasanicova, P., Gavura, S., & Bacik, R. (2023). Predicting bankruptcy using artificial intelligence: The case of the engineering industry. Economics & Sociology, 16(4), 178–190. DOI: https://doi.org/10.14254/2071-789X.2023/16-4/8
    View in Google Scholar
  111. Li, C. J., & Song, X. P. (2007). Comparison of different intelligent methods in predicting financial failure of listed companies. In Q. Wang, G. Chen, G. Yan, X. Zhang, X. L. Jianguo, L. Huang & B. Wu (Eds.). Proceedings of the 2007 conference on systems science, management science and system dynamics: Sustainable development and complex systems (pp. 1021–1026). Albany, New York: System Dynamics Society.
    View in Google Scholar
  112. Li, C., Lou, C., Luo, D., & Xing, K. (2021a). Chinese corporate distress prediction using LASSO: The role of earnings management. International Review of Financial Analysis, 76, 101776. DOI: https://doi.org/10.1016/j.irfa.2021.101776
    View in Google Scholar
  113. Li, Z., Crook, J., Andreeva, G., & Tang, Y. (2021b). Predicting the risk of financial distress using corporate governance measures. Pacific-Basin Finance Journal, 68, 101334. DOI: https://doi.org/10.1016/j.pacfin.2020.101334
    View in Google Scholar
  114. Liu, X. L. (2023). Towards better banking crisis prediction: Could an automatic variable selection process improve the performance? Economic Record, 99(325), 288–312. DOI: https://doi.org/10.1111/1475-4932.12721
    View in Google Scholar
  115. Malakauskas, A., & Lakstutiene, A. (2021). Financial distress prediction for small and medium enterprises using machine learning techniques. Engineering Economics, 32(1), 4–14. DOI: https://doi.org/10.5755/j01.ee.32.1.27382
    View in Google Scholar
  116. Massa, S., & Testa, S. (2008). Innovation and SMEs: Misaligned perspectives and goals among entrepreneurs, academics, and policy makers. Technovation, 28(7), 393–407. DOI: https://doi.org/10.1016/j.technovation.2008.01.002
    View in Google Scholar
  117. Massaro, M., Handley, K., Bagnoli, C., & Dumay, J. (2016). Knowledge management in small and medium enterprises: A structured literature review. Journal of Knowledge Management, 20(2), 258–291. DOI: https://doi.org/10.1108/JKM-08-2015-0320
    View in Google Scholar
  118. Mauerer, I., Possnecker, W., Thurner, P. W., & Tutz, G. (2015). Modeling electoral choices in multiparty systems with high-dimensional data: A regularized selection of parameters using the lasso approach. Journal of Choice Modelling, 16, 23–42. DOI: https://doi.org/10.1016/j.jocm.2015.09.004
    View in Google Scholar
  119. McGuinness, G., Hogan, T., & Powell, R. (2018). European trade credit use and SME survival. Journal of Corporate Finance, 49, 81–103. DOI: https://doi.org/10.1016/j.jcorpfin.2017.12.005
    View in Google Scholar
  120. McNichols, M. (2002). Discussion of the quality of accruals and earnings: The role of accrual estimation errors. Accounting Review, 77, 61–69. DOI: https://doi.org/10.2308/accr.2002.77.s-1.61
    View in Google Scholar
  121. Michalkova, L., & Ponisciakova, O. (2025). Bankruptcy prediction, financial distress and corporate life cycle: Case study of Central European enterprises. Administrative Sciences, 15(2), 63. DOI: https://doi.org/10.3390/admsci15020063
    View in Google Scholar
  122. Misankova, M., & Bartosova, V. (2016). Comparison of selected statistical methods for the prediction of bankruptcy. In T. Loster & T. Pavelka (Eds.). 10th international days of statistics and economics (pp. 1260–1269). Slany: Melandrium.
    View in Google Scholar
  123. Mokrisova, M., & Horvathova, J. (2023). Domain knowledge features versus LASSO features in predicting risk of corporate bankruptcy—DEA approach. Risks, 11(11), 199. DOI: https://doi.org/10.3390/risks11110199
    View in Google Scholar
  124. Moody’s. (2024). Orbis. Retrieved from https://www.moodys.com/web/en/ us/capabilities/company-reference-data/orbis.html.
    View in Google Scholar
  125. Musa, H., Kristofik, P., Medzihorsky, J., & Kliestik, T. (2024). The development of firm size distribution–Evidence from four Central European countries. International Review of Economics & Finance, 91, 98–110. DOI: https://doi.org/10.1016/j.iref.2023.12.003
    View in Google Scholar
  126. Naidoo, V. (2010). Firm survival through a crisis: The influence of market orientation, marketing innovation and business strategy. Industrial Marketing Management, 39(8), 1311–1320. DOI: https://doi.org/10.1016/j.indmarman.2010.02.005
    View in Google Scholar
  127. Nguyen, Q. A., Sullivan Mort, G., & D'Souza, C. (2015). Vietnam in transition: SMEs and the necessitating environment for entrepreneurship development. Entrepreneurship & Regional Development, 27(3-4), 154–180. DOI: https://doi.org/10.1080/08985626.2015.1015457
    View in Google Scholar
  128. Niessner, T., Gross, D. H., & Schumann, M. (2022). Evidential strategies in financial statement analysis: A corpus linguistic text mining approach to bankruptcy prediction. Journal of Risk and Financial Management, 15(10), 459. DOI: https://doi.org/10.3390/jrfm15100459
    View in Google Scholar
  129. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. DOI: https://doi.org/10.2307/2490395
    View in Google Scholar
  130. Olawale, F., & Garwe, D. (2010). Obstacles to the growth of new SMEs in South Africa: A principal component analysis approach. African Journal of Business Management, 4(5), 729–738.
    View in Google Scholar
  131. Pacheco, L., Madaleno, M., Correia, P., & Maldonado, I. (2022). Probability of corporate bankruptcy: Application to Portuguese manufacturing industry SMEs. International Journal of Business and Society, 23(2), 1169–1189. DOI: https://doi.org/10.33736/ijbs.4863.2022
    View in Google Scholar
  132. Papik, M., & Papikova, L. (2023). Impacts of crisis on SME bankruptcy prediction models’ performance. Expert Systems with Applications, 214, 119072. DOI: https://doi.org/10.1016/j.eswa.2022.119072
    View in Google Scholar
  133. Papik, M., Papikova, L., Kajanova, J., & Becka, M. (2021). CatBoost: The case of bankruptcy prediction. In B. Alareeni & A. Hamdan (Eds.). Sustainable finance, digitalization and the role of technology. Proceedings of the international conference on business and technology (ICBT 2021): Vol 487 (pp. 3–17). Cham: Springer. DOI: https://doi.org/10.1007/978-3-031-08084-5_3
    View in Google Scholar
  134. Papikova, L., & Papik, M. (2022a). Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises. Intelligent Systems in Accounting, Finance and Management, 29(4), 254–281. DOI: https://doi.org/10.1002/isaf.1521
    View in Google Scholar
  135. Papikova, L., & Papik, M. (2022b). Intellectual capital and its impacts on SMEs profitability during COVID-19 pandemic. Journal of Eastern European and Central Asian Research (JEECAR), 9(3), 521–531. DOI: https://doi.org/10.15549/jeecar.v9i3.894
    View in Google Scholar
  136. Papikova, L., & Papik, M. (2024). Application of intellectual capital in SME bankruptcy. Applied Economics, 56(55), 7317–7338. DOI: https://doi.org/10.1080/00036846.2023.2281291
    View in Google Scholar
  137. Paraschiv, F., Schmid, M., & Wahlstrøm, R. R. (2021). Bankruptcy prediction of privately held SMEs using feature selection methods. SSRN Electronic Journal. DOI: https://doi.org/10.2139/ssrn.3911490
    View in Google Scholar
  138. Pech, M., Prazakova, J., & Pechova, L. (2020). The evaluation of the success rate of corporate failure prediction in a five-year period. Journal of Competitiveness, 12(1), 108–124. DOI: https://doi.org/10.7441/joc.2020.01.07
    View in Google Scholar
  139. Perederiy, V. (2022). Non-classical ratios and Lasso selection for bankruptcy prediction. Retried from https://ssrn.com/abstract=1518084 .
    View in Google Scholar
  140. Pereira, J. M., Basto, M., & Da Silva, A. F. (2016). The logistic lasso and ridge regression in predicting corporate failure. Procedia Economics and Finance, 39, 634–641. DOI: https://doi.org/10.1016/S2212-5671(16)30310-0
    View in Google Scholar
  141. Pop, I. D., & Coroiu, A. M. (2022). Predicting bankruptcy in Romania using artificial neural networks. International Journal of Modern Manufacturing Technologies, 15(3), 211–218. DOI: https://doi.org/10.54684/ijmmt.2022.14.3.211
    View in Google Scholar
  142. Powell, R. J., Dinh, D. V., Vu, N. T., & Vo, D. H. (2024). Accounting‐based variables as an early warning indicator of financial distress in crisis and non‐crisis periods. International Journal of Finance & Economics, 29(4), 4105–4124. DOI: https://doi.org/10.1002/ijfe.2864
    View in Google Scholar
  143. Prusak, B. (2018). Review of research into enterprise bankruptcy prediction in selected Central and Eastern European countries. International Journal of Financial Studies, 6(3), 60. DOI: https://doi.org/10.3390/ijfs6030060
    View in Google Scholar
  144. Prusak, B., & Karas, M. (2024). Bankruptcy prediction in Visegrad Group countries. Polish Journal of Management Studies, 30(1), 268–288. DOI: https://doi.org/10.17512/pjms.2024.30.1.16
    View in Google Scholar
  145. Psillaki, M., & Daskalakis, N. (2009). Are the determinants of capital structure country or firm specific?. Small Business Economics, 33, 319–333. DOI: https://doi.org/10.1007/s11187-008-9103-4
    View in Google Scholar
  146. Ptak-Chmielewska, A. (2021). Bankruptcy prediction of small-and medium-sized enterprises in Poland based on the LDA and SVM methods. Statistics in Transition. New Series, 22(1), 179–195. DOI: https://doi.org/10.21307/stattrans-2021-010
    View in Google Scholar
  147. Putri, A. M., & Gandakusuma, I. (2019). Bankruptcy prediction of manufacturing companies using Altman and Ohlson model. In I. Trinugroho & E. Lau (Eds.). Business innovation and development in emerging economies (pp. 101–105). Taylor & Francis Group. DOI: https://doi.org/10.1201/9780429433382-10
    View in Google Scholar
  148. Qian, H., Wang, B., Yuan, M., Gao, S., & Song, Y. (2022). Financial distress prediction using a corrected feature selection measure and gradient boosted decision tree. Expert Systems with Applications, 190, 116202. DOI: https://doi.org/10.1016/j.eswa.2021.116202
    View in Google Scholar
  149. Rabaca, V., Pereira, J. M., & Basto, M. (2023). Logit Ridge and Lasso in predicting business failure. Global Journal of Accounting and Economy Research, 4(1), 33–46.
    View in Google Scholar
  150. Radovanovic, J., & Haas, C. (2023). The evaluation of bankruptcy predictions models based on socio-economic costs. Expert Systems with Applications, 227, 120275. DOI: https://doi.org/10.1016/j.eswa.2023.120275
    View in Google Scholar
  151. Reddy, K., & Sasidharan, S. (2023). Innovative efforts and export market survival: Evidence from an emerging economy. Technological Forecasting and Social Change, 186, 122109. DOI: https://doi.org/10.1016/j.techfore.2022.122109
    View in Google Scholar
  152. Roper, S., & Scott, J. M. (2009). Perceived financial barriers and the start-up decision: An econometric analysis of gender differences using GEM data. International Small Business Journal, 27(2), 149–171. DOI: https://doi.org/10.1177/0266242608100488
    View in Google Scholar
  153. Salmistu, M. (2017). Bankruptcy prediction model in the example of Estonian construction companies. University of Tartu.
    View in Google Scholar
  154. Sánchez-Medina, A. J., Blázquez-Santana, F., & Cerviño-Cortínez, D. L. (2025). Ensemble methods for bankruptcy resolution prediction: A new approach. Computational Economics. DOI: https://doi.org/10.1007/s10614-024-10709-y
    View in Google Scholar
  155. Sarkar, S., & Sriram, R. S. (2001). Bayesian models for early warning of bank failures. Management Science 47(11), 1457–1475. DOI: https://doi.org/10.1287/mnsc.47.11.1457.10253
    View in Google Scholar
  156. Serrano-Cinca, C., Gutiérrez-Nieto, B., & Bernate-Valbuena, M. (2019). The use of accounting anomalies indicators to predict business failure. European Management Journal, 37(3), 353–375. DOI: https://doi.org/10.1016/j.emj.2018.10.006
    View in Google Scholar
  157. Shanmugam, R., Beauvais, B., Dolezel, D., Pradhan, R., & Ramamonjiarivelo, Z. (2024). The probability of hospital bankruptcy: A stochastic approach. International Journal of Financial Studies, 12(3), 85. DOI: https://doi.org/10.3390/ijfs12030085
    View in Google Scholar
  158. Shetty, S., Musa, M., & Brédart, X. (2022). Bankruptcy prediction using machine learning techniques. Journal of Risk and Financial Management, 15(1), 35. DOI: https://doi.org/10.3390/jrfm15010035
    View in Google Scholar
  159. Sogorb-Mira, F. (2005). How SME uniqueness affects capital structure: Evidence from a 1994–1998 Spanish data panel. Small Business Economics, 25, 447–457. DOI: https://doi.org/10.1007/s11187-004-6486-8
    View in Google Scholar
  160. Sousa, A., Braga, A., & Cunha, J. (2022). Impact of macroeconomic indicators on bankruptcy prediction models: Case of the Portuguese construction sector. Quantitative Finance and Economics, 6(3), 405–432. DOI: https://doi.org/10.3934/QFE.2022018
    View in Google Scholar
  161. Sponerova, M. (2021). Bankruptcy modelling: Factors influencing models predictability. In B. Jakovic, D. F. Hodak & I. N. Braje (Eds.). Proceedings of FEB Zagreb international odyssey conference on economics and business: Vol. 3 (pp. 741–751). Zagreb: University of Zagreb.
    View in Google Scholar
  162. Spuchlakova, E., & Frajtova Michalikova, K. (2016). Predicting the financial health of companies using the Altman Z-score. In H. Zhang (Ed.). 2016 ISSGBM international conference on information and business management: Vol. 61 (pp. 54–59). Singapore: Singapore Management and Sports Science Institute PTE, LTD.
    View in Google Scholar
  163. Stasko, A., Birzniece, I., & Kebers, G. (2021). Development of bankruptcy prediction model for Latvian companies. Complex Systems Informatics and Modeling Quarterly, 27, 45–59. DOI: https://doi.org/10.7250/csimq.2021-27.02
    View in Google Scholar
  164. Stefko, R., Jencova, S., Vasanicova, P., & Litavcova, E. (2019). An evaluation of financial health in the electrical engineering industry. Journal of Competitiveness, 11(4), 144–160. DOI: https://doi.org/10.7441/joc.2019.04.10
    View in Google Scholar
  165. Stepniewski, T. (2022). The Russia-Ukraine war, NATO´s eastern flank, and Ukrainian refugees in Central Europe. Studia Europejskie-studies in European Affairs, 26(2), 7–15. DOI: https://doi.org/10.33067/SE.2.2022.1
    View in Google Scholar
  166. Sun, C., Lin, Z., Vochozka, M., & Vincurova, Z. (2022a). Digital transformation and corporate cash holdings in China’s A-share listed companies. Oeconomia Copernicana, 13(4), 1081–1116. DOI: https://doi.org/10.24136/oc.2022.031
    View in Google Scholar
  167. Sun, C., Zhang, Z., Vochozka, M., & Voznakova, I. (2022b). Enterprise digital transformation and debt financing cost in China’s A-share listed companies. Oeconomia Copernicana, 13(3), 783–829. DOI: https://doi.org/10.24136/oc.2022.023
    View in Google Scholar
  168. Sun, Y., Chai, N., Dong, Y., & Shi, B. (2022c). Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach. International Journal of Forecasting, 38(3), 1158–1172. DOI: https://doi.org/10.1016/j.ijforecast.2022.01.006
    View in Google Scholar
  169. Svabova, L., & Michalkova, L. (2018). Data preprocessing in earnings management. Podniková ekonomika a manažment, 3, 65–72.
    View in Google Scholar
  170. Svabova, L., Durana, P., & Durica, M. (2022). Descriptive and inductive statistics. University of Žilina in Žilina: EDIS.
    View in Google Scholar
  171. Svabova, L., Michalkova, L., Durica, M., & Nica, E. (2020). Business failure prediction for Slovak small and medium-sized companies. Sustainability, 12(11), 4572. DOI: https://doi.org/10.3390/su12114572
    View in Google Scholar
  172. Tian, S., & Yu, Y. (2017). Financial ratios and bankruptcy predictions: An international evidence. International Review of Economics & Finance, 51, 510–526. DOI: https://doi.org/10.1016/j.iref.2017.07.025
    View in Google Scholar
  173. Tian, S., Yu, Y., & Guo, H. (2015). Variable selection and corporate bankruptcy forecasts. Journal of Banking & Finance, 52, 89–100. DOI: https://doi.org/10.1016/j.jbankfin.2014.12.003
    View in Google Scholar
  174. Tobback, E., Bellotti, T., Moeyersoms, J., Stankova, M., & Martens, D. (2017). Bankruptcy prediction for SMEs using relational data. Decision Support Systems, 102, 69–81. DOI: https://doi.org/10.1016/j.dss.2017.07.004
    View in Google Scholar
  175. Tomczak, S. K. (2023). General bankruptcy prediction models for the Visegrád Group. The stability over time. Operations Research and Decisions, 33(4), 171–187. DOI: https://doi.org/10.37190/ord230410
    View in Google Scholar
  176. Tumpach, M., Surovičová, A., Juhászová, Z., Marci, A., & Kubaščíková, Z. (2020). Prediction of the bankruptcy of Slovak companies using neural networks with SMOTE. Ekonomický časopis, 68(10), 1021–1039. DOI: https://doi.org/10.31577/ekoncas.2020.10.03
    View in Google Scholar
  177. Valaskova, K., & Podhorska, I. (2017). Prediction models in the context of international environment. In T. Kliestik (Ed.). Globalization and its socio-economic consequences (pp. 2792–2800). Zilina: University of Zilina.
    View in Google Scholar
  178. Valaskova, K., Gajdosikova, D., & Belas, J. (2023). Bankruptcy prediction in the postpandemic period: A case study of Visegrad Group countries. Oeconomia Copernicana, 14(1), 253–293. DOI: https://doi.org/10.24136/oc.2023.007
    View in Google Scholar
  179. Valaskova, K., Gavurova, B., Durana, P., & Kovacova, M. (2020). Alter ego only four times? The case study of business profits in the Visegrad Group. E&M Economics and Management, 23(3), 101–119. DOI: https://doi.org/10.15240/tul/001/2020-3-007
    View in Google Scholar
  180. Valaskova, K., Kliestik, T., & Kovacova, M. (2018). Management of financial risks in Slovak enterprises using regression analysis. Oeconomia Copernicana, 9(1), 105–121. DOI: https://doi.org/10.24136/oc.2018.006
    View in Google Scholar
  181. Valencia, C., Cabrales, S., Garcia, L., Ramirez, J., & Calderona, D. (2019). Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation. Cogent Economics & Finance, 7(1), 1597956. DOI: https://doi.org/10.1080/23322039.2019.1597956
    View in Google Scholar
  182. Virglerová, Z. (2019). Risk management in the segment of SMEs in V4 countries (Significant theoretical and methodological aspects). Tomáš Baťa University in Zlín.
    View in Google Scholar
  183. Vrontis, D., Siachou, E., Sakka, G., Chatterjee, S., Chaudhuri, R., & Ghosh, A. (2022). Societal effects of social media in organizations: Reflective points deriving from a systematic literature review and a bibliometric meta-analysis. European Management Journal, 40(2), 151–162. DOI: https://doi.org/10.1016/j.emj.2022.01.007
    View in Google Scholar
  184. Vu, N. T., Nguyen, N. H., Tran, T., Le, B. T., & Vo, D. H. (2023). A LASSO-based model for financial distress of the Vietnamese listed firms: Does the covid-19 pandemic matter?. Cogent Economics & Finance, 11(1), 2210361. DOI: https://doi.org/10.1080/23322039.2023.2210361
    View in Google Scholar
  185. Wang, X., & Brorsson, M. (2024). Augmenting bankruptcy prediction using re-ported behavior of corporate restructuring. Computing Engineering, Finance, and Science, 2036, 102–121. DOI: https://doi.org/10.1007/978-981-97-0065-3_8
    View in Google Scholar
  186. Wilson, N., Ochotnický, P., & Káčer, M. (2016). Creation and destruction in transition economies: The SME sector in Slovakia. International Small Business Journal: Researching Entrepreneurship, 34(5), 579–600. DOI: https://doi.org/10.1177/0266242614558892
    View in Google Scholar
  187. Yigit, F. (2018). Bankruptcy: An examination of different approaches. In H. Dincer, Ü. Hacioglu & S. Yüksel (Eds). Global approaches in financial economics, banking, and finance (pp. 293–308). Springer. DOI: https://doi.org/10.1007/978-3-319-78494-6_14
    View in Google Scholar
  188. Zhu, Y., Xie, C., Sun, B., Wang, G. J., & Yan, X. G. (2016). Predicting China’s SME credit risk in supply chain financing by logistic regression, artificial neural network and hybrid models. Sustainability, 8(5), 433. DOI: https://doi.org/10.3390/su8050433
    View in Google Scholar

Downloads

Download data is not yet available.

Similar Articles

1-10 of 189

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)