[1] A.F. Abidali and F. Harris, A methodology for predicting failure in the construction industry, Const. Manag. Econ. 13 (1995), no. 3, 189–196.
[2] H.A. Alaka, L.O. Oyedele, H.A. Owolabi, V. Kumar, S.O. Ajayi, O.O. Akinade and M. Bilal, Systematic review of bankruptcy prediction models: Towards a framework for tool selection, Expert Syst. Appl. 94 (2018), 164–184.
[3] L. Aleksanyan and J.-P. Huiban, Economic and financial determinants of firm bankruptcy: Evidence from the French food industry, Rev. Agricul. Food Envir. Stud. 97 (2016), no. 2, 89–108.
[4] E.I. Altman, Financial ratios discriminant analysis and the prediction of corporate bankruptcy, J. Finance 23 (1968), no. 4, 589–609.
[5] E. Altman, I.-D. Malgorzata, L. Erkki and S. Arto, Financial distress prediction in an international context: A review and empirical analysis of Altman’s z-score model, J. Int. Financ. Manag. Account. 28 (2017), 131–171.
[6] E.I. Altman, G. Marco and F. Varetto, Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (The Italian experience), J. Bank. Finance 18 (1994), no. 3, 505–529.
[7] S. Asadi, F.R. Roodpashti, S. Khordyar and F.M. Node, Explaining the relationship between conservatism and bankruptcy of companies listed in Tehran stock exchange using bankruptcy prediction measurement models, Invest. Knowledge 9 (2015), no. 35, 337–356.
[8] S. Ashraf, E.G.S. F´elix and Z. Serrasqueiro, Do traditional financial distress prediction models predict the early warning signs of financial distress?, J. Risk Financ. Manag. 12 (2019), no. 2, 55.
[9] V. Bakhredi Nasab and F. Jolanjad, Bankruptcy risk response to adoption of diversification strategies, Ind. Econ. Res. 14 (2020), 89–108.
[10] D. Baran, System approach to the stated policy of controlling the company, Economic-Maneuver Spect. 1 (2007), 2–9.
[11] N. B˘arbut˘a-Misu and M. Madaleno, Assessment of bankruptcy risk of large companies: European countries evolution analysis, J. Risk Financ. Manag. 13 (2020), no. 3, 58.
[12] W.H. Beaver, M.F. McNichols and J.W. Rhie, Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy, Rev. Account. Stud. 10 (2005), no. 1, 93–122.
[13] R. Dakovic, C. Claudia and D. Berg, Bankruptcy prediction in Norway: A comparison study, Appl. Econ. Lett. 17 (2010), 1739–1746.
[14] B.J. Dietzenbacher, Bankruptcy games with nontransferable utility, Math. Soc. Sci. 92 (2018), 16–21.
[15] S. Elviani, S. Ramadona, R. Zenni, F. Khairani, S.P. Dewi and F. Fauzi, The accuracy of the Altman, Ohlson, Springate and Zmejewski models in bankruptcy predicting trade sector companies in Indonesia, Budapest Int. Res. Crit. Inst. (BIRCI-Journal) 3 (2020), 334–347.
[16] M. Ghazanfari, I. Rahimi Kia and A. Askari, Pre-nose bankruptcy based systems - smart combination, Financ. Account. Audit. Res. J. 10 (2018), no. 37.
[17] J.A. Hanley and B.J. McNeil, The meaning and use of the area under a receiver operating characteristics (ROC) Curve, Radiol. 143 (1982), no. 1, 29–36.
[18] J. Horak, J. Vrbka and P. Suler, Support vector machine methods and artificial neural networks used for the development of bankruptcy prediction models and their comparison, J. Risk Financ. Manag. 13 (2020), no. 3, 60.
[19] R. Joliet and G. Hubner, Corporate international diversification and the cost of equity: European evidence, J. Int. Money Finance 27 (2008), 102–123.
[20] M. Jouzbarkand, F.S. Keivani, M. Khodadadi and S.R.S.N. Fahim, Bankruptcy prediction model by Ohlson and Shirata models in Tehran stock exchange, World Appl. Sci. J. 21 (2013), 152–156.
[21] V. Kiaupaite-Grushniene, Altman z-score model for bankruptcy forecasting of the listed Lithuanian agricultural companies, 5th Int. Conf. Account. Audit. Taxat. Tallinn, Estonia, 2016.
[22] S. Kim, M.M. Byeong and J.B. Suk, Data depth based support vector machines for predicting corporate bankruptcy, Appl. Intell. 48 (2018), 791–804.
[23] T. Korol, Dynamic bankruptcy prediction models for European enterprises, J. Risk Financ. Manag. 12 (2019), no. 4, 185.
[24] A.S. Koyuncugil and N. Ozgulbas, Financial early warning system model and data mining application for risk detection, Expert Syst. Appl. 39 (2012), no. 6, 6238–6253.
[25] F.Y. Lin and S. McClean, A data mining approach to the prediction of corporate failure, Knowledge-Based Syst. 14 (2001), no. 3, 189–195.
[26] D. Ogachi, R. Ndege, P. Gaturu and Z. Zoltan, Corporate bankruptcy prediction model, a special focus on listed companies in Kenya, J. Risk Financ. Manag. 13 (2020), no. 3, 47.
[27] J.A. Ohlson, Financial ratios and the probabilistic prediction of bankruptcy, J. Account. Res. 18 (1980), no. 1, 109–131.
[28] J. Ouenniche and T. Kaoru, An out-of-sample evaluation framework for DEA with application in bankruptcy prediction, Ann. Oper. Res. 254 (2017) 235–250.
[29] O. Pourheidari and M. Koopai Haji, Predicting corporate financial crisis using a model based on linear separation function, J. Financ. Account. Res. 2 (2010), no. 3, 33-46.
[30] O. Purvinis, S. Povilas and V. Ruta, Research of possibility of bankruptcy diagnostics applying neural network, Inzinerine Ekonomika Engin. Econ. 41 (2005), 16–22.
[31] D. Rybarova, M.B. Braunova and L. Jantosova, Analysis of the construction industry in the Slovak Republic by bankruptcy model, Proc. Soc. Behav. Sci. 230 (2016), 298–306.
[32] K.S. Thorburn, Bankruptcy auctions: costs, debt recovery, and firm survival, J. Financ. Econ. 58 (2000), 337–368.
[33] M. Zoricak, P. Gnip, P. Drotar and V. Gazda, Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets, Econ. Model. 84 (2020), 165–176.
International Journal of Nonlinear Analysis and Applications
)