Open Access
Issue
SHS Web Conf.
Volume 39, 2017
Innovative Economic Symposium 2017 (IES2017)
Article Number 01013
Number of page(s) 12
Section Strategic Partnerships in International Trade
DOI https://doi.org/10.1051/shsconf/20173901013
Published online 06 December 2017
  1. E. Kirkos, Assesing methodologies for intelligent bankruptcy prediction. Art. Int. Rev., 43, 83–123 (2015) [CrossRef] [Google Scholar]
  2. Y. Zelenkov, E. Fedorova, D. Chekrizov, Two-step classification method based on genetic algorithm for bankruptcy forecasting. Exp. Sys. App., 88, 393–401 (2017) [Google Scholar]
  3. D. Liang, Ch. Tsai, H. Wu, The effect of feature selection on financial distress prediction. Know. Bas. Sys., 73, 289–297 (2015) [CrossRef] [Google Scholar]
  4. Y. Peng, G. Wang, G. Kou, Y. Shi, An empirical study of classification algorithm evaluation for financial risk prediction. App. S. Comp, 11(2), 2906–2915 (2011) [CrossRef] [Google Scholar]
  5. E. Fedorova, E. Gilenko, S. Dovzhenko, Bankruptcy prediction for Russian companies: Application of combined classifiers. Exp. Sys. App., 40(18), 7285–7293 (2013) [CrossRef] [Google Scholar]
  6. M. A. Aziz, H. A. Dar, Predicting corporate bankruptcy: Where we stand? Corp. Gov. Int. J Bus. Soc., 6, 18–33 (2006) [Google Scholar]
  7. E. I. Altman, Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Fin., 23(4), 589–609 (1968) [Google Scholar]
  8. R.O. Edmister, Financial ratios as discriminant predictors of small business failure. Journal of Finance, 27(1), 139–140 (1972) [Google Scholar]
  9. G.L.V. Springate, Predicting the possibility of failure in a Canadian firm (Unpublished master´s thesis), Simon Fraser University, Canada 42 (1978) [Google Scholar]
  10. J. A. Ohlson, Financial ratios and the probabilistic prediction of bankruptcy. J Acc. Res., 18(1), 109–131 (1980) [Google Scholar]
  11. M.E. Zmijewski, Methodological issues related to the estimation of financial distress prediction models. J. Acc. Res., 22, 59–82 (1984) [Google Scholar]
  12. A.A. Kasgari, M. Divsalar, M.R. Javid, S.J. Ebrahimian, Prediction of bankruptcy Iranian corporations through artificial neural network and Probit-based analyses. Neur. Com. App., 23(3-4), 927–936 (2013) [CrossRef] [Google Scholar]
  13. J.L. Bellovary, D.E. Giacomino, M.D. Akers, A review of bankruptcy prediction studies: 1930 to present. J Fin. Edu., 33, 1–42 (2007) [Google Scholar]
  14. N. Gordini, A genetic algorithm approach for SMEs bankruptcy prediction: empirical evidence from Italy. Exp Sys. App., 41(14), 6433–6445 (2014) [CrossRef] [Google Scholar]
  15. D. Santos, E. M. Sabourin, P. Maupin, Overfitting cautious selection of classifier ensembles with genetic algorithms. Inf. Fus., 10(2), 150–162 (2009) [CrossRef] [Google Scholar]
  16. M. V. Achim, C. Mare, S. N. Borlea, A statistical model of financial risk bankruptcy applied for Romanian manufacturing industry. International Conference on Emerging Markets Queries in Finance and Business, 3, 132–137 (2012) [Google Scholar]
  17. M. Onofrei, D. Lupu, The modelling of forecasting the bankruptcy risk in Romania. Ec. Com. Ec. Cyb. St. Res., 48(3), 197–215 (2014) [Google Scholar]
  18. D. Alaminos, A. DelCastillo, M. A. Fernandez, A global model for bankruptcy prediction. Pl. One, 11(11) (2016) [Google Scholar]
  19. D. Zhao, C. Y. Huang, Y. Wei, F. H. Yu, M. J. Wang, H. L. Chen, An effective computational model for bankruptcy prediction using kernel extreme learning machine approach. Com. Eco., 49(2), 325–341 (2017) [CrossRef] [Google Scholar]
  20. C.F. Tsai, Y.F. Hsu, D.C. Yen, A comparative study of classifier ensembles for bankruptcy prediction. App. S. Com., 24, 977–984 (2014) [CrossRef] [Google Scholar]
  21. N. Zieba, S.K. Tomczak, J.M. Tomczak, Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Exp. Sys. App., 58, 93–101 (2016) [Google Scholar]
  22. M. Virag, T. Nyitrai. Is there a trade-off between the predictive power and the interpretability of bankruptcy models? The case of the first Hungarian bankruptcy prediction model. Ac. Oec., 64(4), 419–440 (2014) [Google Scholar]
  23. I. M. Premachandra, Y. Chen, J. Watson, DEA as a tool for predicting corporate failure and success: a case of bankruptcy assessment. Om. Int. J Man. Sc., 39(6), 620–626 (2011) [CrossRef] [Google Scholar]
  24. M. M. Mousavi, J. Ouenniche, B. Xu, Performance evaluation of bankruptcy prediction models: an orientation-free super-efficiency DEA-based framework. Int. Rev. Fin. An., 42, 64–75 (2015) [CrossRef] [Google Scholar]
  25. P. DuJardin, A two-stage classification technique for bankruptcy prediction. Eur. J Op. Res. 254(1), 236–252 (2016) [CrossRef] [Google Scholar]
  26. C. Salloum, N. Azoury, Corporate governance and firms in financial distress: evidence from a Middle Eastern country. Int. J Bus. Gov. Eth., 7(1), 1–17 (2012) [CrossRef] [Google Scholar]
  27. X. Bredart, Financial distress and corporate governance: the impact of board configuration. Int. Bus. Res., 7(3), 72 (2014) [Google Scholar]
  28. M. Karas, M. Reznakova, To what degree is the accuracy of a bankruptcy prediction model affected by the environment? The case of the Baltic States and the Czech Republic. Pr. Soc. Beh. Sc., 156, 564–568 (2014) [CrossRef] [Google Scholar]
  29. V. Delas, E. Nosova, O. Yafinovych, Financial security of enterprises. Pr. Ec. Fin., 27, 248–266 (2015) [Google Scholar]
  30. M. Rowoldt, D. Starke, The role of governments in hostile takeovers – evidence from regulation, anti-takeover provisions and government interventions. Int. Rev. Law Ec., 47, 1–15 (2016) [CrossRef] [Google Scholar]
  31. M. H. Tinoco, N. Wilson, Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. Int. Rev. Fin. An., 30, 394–419 (2013) [CrossRef] [Google Scholar]
  32. T. Kliestik, J. Majerova, Selected issues of selection of significant variables in the prediction models. Financial management of firms and financial institutions: 10th international scientific conference, 537–543 (2015) [Google Scholar]
  33. V. Bartosova, P. Kral, A methodological framework of financial analysis results objectification in the Slovak Republic. European Proceedings of Social & Behavioural Sciences, 17, 189–197 (2016) [Google Scholar]
  34. M. Durica, P. Adamko, Verification of MDA bankruptcy prediction models for enterprises in Slovak Republic. 10th international scientific conference International Days of Statistics and Economics, 400–407 (2016) [Google Scholar]
  35. R. J. Taffler, M. Tseung, The audit going-concern in practice. Pr. Acc. Mag., 88, 263–269 (1984) [Google Scholar]
  36. J. G. Fulmer, J. E. Moon, T. A. Gavin, M. J. Erwin, A bankruptcy classification model for small firms. J Com. B. Len., 25–37 (1984) [Google Scholar]
  37. E. I. Altman, M. Iwanicz-Drozdowska, E.K. Laitinen, Financial distress prediction in an international context: a review and empirical analysis of Altman´s Z-score model. J Int. Fin. Man. Acc., 28(2), 131–171 (2017) [Google Scholar]
  38. P. Adamko, L. Svabova, Prediction of the risk of bankruptcy of Slovak companies. International Scientific Conference Managing and Modelling of Financial Risks, 15–20 (2016) [Google Scholar]

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