Keywords

Details

Category:

Articles

Type:

artykuły w czasopismach

Published in:

Journal of Business Economics and Management no. 21, pages 783 - 804,
ISSN: 1611-1699

Language:

English

Publication year:

2020

Bibliographic description:

Korol T.: LONG-TERM RISK CLASS MIGRATIONS OF NON-BANKRUPT AND BANKRUPT ENTERPRISES// Journal of Business Economics and Management -Vol. 21,iss. 3 (2020), s.783-804

DOI:

Digital Object Identifier (open in new tab) 10.3846/jbem.2020.12224

Bibliography: test

1. Acosta-González, E., & Fernández-Rodríguez, F. (2014). Forecasting financial failure of firms via genet- ic algorithms. Computational Economics, 43, 133-157. https://doi.org/10.1007/s10614-013-9392-9 open in new tab
2. Agarwal, V., & Taffler, R. (2007). Twenty-five years of the Taffler z-score model -does it really have predictive ability? Accounting and Business Research, 37(4). open in new tab
3. https://doi.org/10.1080/00014788.2007.9663313 open in new tab
4. Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., & Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94, 164-184. https://doi.org/10.1016/j.eswa.2017.10.040 open in new tab
5. Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23, 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x open in new tab
6. Altman, E. (2018). Applications of distress prediction models: What have we learned after 50 years from the Z-score models? International Journal of Financial Studies, 6(3), 70. https://doi.org/10.3390/ijfs6030070 open in new tab
7. Altman, E., & Sabato, G. (2007). Modelling credit risk for SMEs -evidence from the US market. ABA- CUS, 43(3), 332-356. https://doi.org/10.1111/j.1467-6281.2007.00234.x open in new tab
8. Argenti, J. (1976). Corporate collapse -the causes and symptoms. McGraw-Hill.
9. Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417. https://doi.org/10.1016/j.eswa.2017.04.006 open in new tab
10. Bluhm, Ch., Overbeck, L., & Wagner, Ch. (2003). An introduction to credit risk modeling. Chapman & Hall/CRC. https://doi.org/10.1201/9781420057362 open in new tab
11. Brabazon, A., & O'Neil, M. (2004). Diagnosing corporate stability using grammatical evolution. Journal of Applied Mathematics and Computer Science, 1, 293-310.
12. Burgelman, R. (1991). Intraorganizational mortality-liabilities of newness and adolescence. Organiza- tion Science, 2(3), 239-262. https://doi.org/10.1287/orsc.2.3.239 open in new tab
13. Chen, N., Ribeiro, B., Vieira, A., & Chen, A. (2013). Clustering and visualization of bankruptcy trajec- tory using self-organizing map. Expert Systems with Applications, 40(1), 385-393. https://doi.org/10.1016/j.eswa.2012.07.047 open in new tab
14. Delen, D., Kuzey, C., & Uyar, A. (2013). Measuring firm performance using financial ratios: A decision tree approach. Expert Systems with Applications, (40), 3970-3983. https://doi.org/10.1016/j.eswa.2013.01.012 open in new tab
15. Dong, M. C., Tian, S., & Chen, C. W. S. (2018). Predicting failure risk using financial ratios: Quantile hazard model approach. North American Journal of Economics and Finance, 44, 204-220. https://doi.org/10.1016/j.najef.2018.01.005 open in new tab
16. Fichman, M., & Levintahl, D. (1991). Honeymoon and liability of adolescence-a new perspective on duration dependence in social and organizational relationship. Academy of Management Review, 16(2), 442-468. https://doi.org/10.5465/amr.1991.4278962 open in new tab
17. Flores-Jimeno, R., & Jimeno-Garcia, I. (2017). Dynamic analysis of different business failure proccess. Problems and Perspective Management, 15(2), 486-499. https://doi.org/10.21511/ppm.15(si).2017.02 open in new tab
18. 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. https://doi.org/10.3846/16111699.2017.1400461 open in new tab
19. Giannopoulos, G., & Sigbjornsen, S. (2019). Prediction of bankruptcy using financial ratios in the Greek market. Theoretical Economics Letters, 9, 1114-1128. https://doi.org/10.4236/tel.2019.94072 open in new tab
20. Gilbert, C. (2005). Unbounding the structure of inertia -resource vs routine rigidity. Academy of Man- agement Journal, 48(5), 741-763. https://doi.org/10.5465/amj.2005.18803920 open in new tab
21. Grice, J., & Dugan, M. (2001). The limitations of bankruptcy prediction models -some cautions for the researcher. Review of Quantitative Finance and Accounting, 17, 151-166. https://doi.org/10.1023/A:1017973604789 open in new tab
22. Hambrick, D., & D' Aveni, R. (1988). Large corporate failures as downward spirals. Administrative Sci- ence Quarterly, 33(1), 1-23. https://doi.org/10.2307/2392853 open in new tab
23. Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural net- works. Expert Systems with Applications, 117(1), 287-299. https://doi.org/10.1016/j.eswa.2018.09.039 open in new tab
24. Iturriaga, F. J., & Sanz, I. P. (2015). Bankruptcy visualization and prediction using neural networks -a study of U.S. commercial banks. Expert Systems with Applications, 42(6), 2857-2869. https://doi.org/10.1016/j.eswa.2014.11.025 open in new tab
25. Jayasekera, R. (2018). Prediction of company failure: Past, present and promising directions for the future. International Review of Financial Analysis, 55, 196-208. https://doi.org/10.1016/j.irfa.2017.08.009 open in new tab
26. Jardin, P. (2015). Bankruptcy prediction using terminal failure processes. European Journal of Opera- tional Research, 242(1), 286-303. https://doi.org/10.1016/j.ejor.2014.09.059 open in new tab
27. Jardin, P., & Severin, E. (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(3), 701-711. https://doi.org/10.1016/j.dss.2011.04.001 open in new tab
28. Johnson, G. (1988). Rethinking incrementalism. Strategic Management Journal, 9(1), 73-91. https://doi.org/10.1002/smj.4250090107 open in new tab
29. Kale, S., & Arditi, D. (1998). Business failures-liabilities of newness, adolescence and smallness. Journal of Construction Engineering and Management, 124(6), 458-464. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:6(458) open in new tab
30. Kiang, M., & Kumar, A. (2001). An evaluation of self-organizing map networks as a robust alternative to factor analysis in data mining applications. Information System Research, 12(2), 177-194. https://doi.org/10.1287/isre.12.2.177.9696 open in new tab
31. Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernet- ics, 43(1), 141-152. https://doi.org/10.1007/BF00337288 open in new tab
32. 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, 1-28. https://doi.org/10.1016/j.ejor.2006.08.043 open in new tab
33. Laitinen, E. (2007). Classification accuracy and correlation -LDA in failure prediction. European Jour- nal of Operational Research, 183, 210-225. https://doi.org/10.1016/j.ejor.2006.09.054 open in new tab
34. Laitinen, E., & Lukason, O. (2014). Do firm failure processes differ across countries: evidence from Finland and Estonia. Journal of Business Economics and Management, 15(5), 810-832. https://doi.org/10.3846/16111699.2013.791635 open in new tab
35. Laitinen, E., Lukason, O., & Suvas, A. (2014). Behaviour of financial ratios in firm failure process: An international comparison. International Journal of Finance and Accounting, 3(2), 122-131.
36. Lensberg, T., Eilifsen, A., & McKee, T. E. (2006). Bankruptcy theory development and classification via genetic programming. European Journal of Operational Research, 169, 677-697. https://doi.org/10.1016/j.ejor.2004.06.013 open in new tab
37. Li, L., & Faff, R. (2019). Predicting corporate bankruptcy: What matters? International Review of Eco- nomics & Finance, 62, 1-19. https://doi.org/10.1016/j.iref.2019.02.016 open in new tab
38. Liang, D., Lu, Ch., Tsai, Ch., & Shih, G. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European Journal of Operational Research, 252(2), 561-572. https://doi.org/10.1016/j.ejor.2016.01.012 open in new tab
39. Lin, F., Liang, D., Yeh, C. C., & Huang, J. C. (2014). Novel feature selection methods to financial distress prediction. Expert Systems with Applications, 41, 2472-2483. https://doi.org/10.1016/j.eswa.2013.09.047 open in new tab
40. Lukason, O., & Hoffman, R. (2014). Firm bankruptcy probability and causes -an integrated study. International Journal of Business and Management, 9(11), 80. https://doi.org/10.5539/ijbm.v9n11p80 open in new tab
41. Mihalovic, M. (2016). Performance comparison of multiple discriminant analysis and logit models in bankruptcy prediction. Economics and Sociology, 9(4), 101-118. https://doi.org/10.14254/2071-789X.2016/9-4/6 open in new tab
42. Moulton, W. & Thomas, H. (1996). Business failure pathways -environmental stress and organizational response. Journal of Management, 2(4), 571-595. https://doi.org/10.1016/S0149-2063(96)90025-2 open in new tab
43. Ooghe, H., & Balcaen, S. (2006). 35 years of studies on business failure -an overview of the classic statistical methodologies and their related problems. The British Accounting Review, 38, 63-93. https://doi.org/10.1016/j.bar.2005.09.001 open in new tab
44. Ooghe, H., & Prijcker, S. (2008). Failure processes and causes of company bankruptcy -a typology. Management Decision, 46(2), 223-242. https://doi.org/10.1108/00251740810854131 open in new tab
45. Orsenigo, C., & Vercellis, C. (2013). Linear versus nonlinear dimensionality reduction for banks credit rating prediction. Knowledge-Based Systems, 47, 14-22. https://doi.org/10.1016/j.knosys.2013.03.001 open in new tab
46. Psillaki, M., Tsolas, I. E., & Margaritis, D. (2010). Evaluation of credit risk based on firm performance. European Journal of Operational Research, 201, 873-881. https://doi.org/10.1016/j.ejor.2009.03.032 open in new tab
47. Ptak-Chmielewska, A. (2019). Predicting micro-enterprise failures using data mining techniques. Jour- nal of Risk and Financial Managament, 12, 1-17. https://doi.org/10.3390/jrfm12010030 open in new tab
48. Richardson, B., Nwankwo, S., & Richardson, S. (1994). Understanding the causes of business failure crises. Management Decision Journal, 32(4), 9-22. https://doi.org/10.1108/00251749410058635 open in new tab
49. Sayari, N., & Mugan, C.D. (2017). Industry specific financial distress modeling. Business Research Quar- terly, 20, 45-62. https://doi.org/10.1016/j.brq.2016.03.003 open in new tab
50. Shimko, D. (2004). Credit risk -models and management. Barra Risk Books.
51. Schonfeld, J., Kudej, M., & Smrcka, L. (2018). Financial health of enterprises introducing safeguard pro- cedure based on bankruptcy models. Journal of Business Economics and Management, 19, 692-705. https://doi.org/10.3846/jbem.2018.7063 open in new tab
52. Sun, J., Li, H., Huang, Q., & He, K. (2014). Predicting financial distress and corporate failure -a review from the state-of-the-art definitions, modeling, sampling, and featuring approaches. Knowledge- Based Systems, 57, 41-56. https://doi.org/10.1016/j.knosys.2013.12.006 open in new tab
53. Tian, S., Yu, Y., & Guo, H. (2015). Variable selection and corporate bankruptcy forecasts. Journal of Banking & Finance, 52, 89-100. https://doi.org/10.1016/j.jbankfin.2014.12.003 open in new tab
54. Tian, S., & Yu, Y. (2017). Financial ratios and bankruptcy predictions: An international evidence. Inter- national Review of Economics and Finance, 51, 510-526. https://doi.org/10.1016/j.iref.2017.07.025 open in new tab
55. Tsai, Ch. (2014). Combining cluster analysis with classifier ensembles to predict financial distress. Information Fusion, 16, 46-58. https://doi.org/10.1016/j.inffus.2011.12.001 open in new tab
56. Utterback, J., & Suarez, F. (1993). Patterns of industrial evolution, dominant desing, and firms' survival. In R. Burgelman, Research on technological innovation, management policy (Vol. 5, pp. 47-87). Greenwich Press. open in new tab
57. Wilner, B. (2000). The exploitation of relationships in financial distress -The case of trade credit. The Journal of Finance, 55(1). https://doi.org/10.1111/0022-1082.00203 open in new tab
58. Wiseman, R., & Bromiley, P. (1996). Toward a model of risk in declining organizations. Organization Science, 7(5), 524-543. https://doi.org/10.1287/orsc.7.5.524 open in new tab
59. Wu, Y., Gaunt, C., & Gray, S. (2010). A comparison of alternative bankruptcy prediction models. Jour- nal of Contemporary Accounting & Economics, 6, 34-45. https://doi.org/10.1016/j.jcae.2010.04.002 open in new tab
60. Xiao, Z., Yang, X., Pang, Y., & Dang, X. (2012). The prediction for listed companies' financial dis- tress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory. Knowledge-Based Systems, 26, 196-206. https://doi.org/10.1016/j.knosys.2011.08.001 open in new tab
61. Zammuto, R., & Cameron, K. (1985). Environmental decline and organizational response. Research in Organizational Behavior, 7, 223-262. open in new tab
62. Zapranis, A., & Ginoglou, D. (2000). Forecasting corporate failure with neural network approach: The Greek case. Journal of Financial Management & Analysis, 13(2), 11-21.

show (62) hide

Sources of funding:

• Project Trajectories of life and the collapse of enterprises in Poland and the world - identification, evaluation and forecast

Verified by:

Gdańsk University of Technology