Long-term risk class migrations of non-bankrupt and bankrupt enterprises
Abstract
This paper investigates how the process of going bankrupt can be recognized much earlier by enterprises than by traditional forecasting models. The presented studies focus on the assessment of credit risk classes and on determination of the differences in risk class migrations between non-bankrupt enterprises and future insolvent firms. For this purpose, the author has developed a model of a Kohonen artificial neural network to determine six different classes of risk. Long-term analysis horizon of 15 years before the enterprises went bankrupt was conducted. This long forecasting horizon allows one to identify, visualize and compare the intensity and pattern of changes in risk classes during the 15-year trajectory of development between two separate groups of companies (150 bankrupt and 150 non-bankrupt firms). The effectiveness of the forecast of the developed model was compared to three popular statistical models that predict the financial failure of companies. These studies represent one of the first attempts in the literature to identify the long-term behavioral pattern differences between future “good” and “bad” enterprises from the perspective of risk class migrations.
Keyword : risk classes, forecasting, bankruptcy, self-organizing maps, financial crisis, insolvency
How to Cite
Korol, T. (2020). Long-term risk class migrations of non-bankrupt and bankrupt enterprises. Journal of Business Economics and Management, 21(3), 783-804. https://doi.org/10.3846/jbem.2020.12224
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Acosta-González, E., & Fernández-Rodríguez, F. (2014). Forecasting financial failure of firms via genetic algorithms. Computational Economics, 43, 133–157. https://doi.org/10.1007/s10614-013-9392-9
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). https://doi.org/10.1080/00014788.2007.9663313
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
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
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
Altman, E., & Sabato, G. (2007). Modelling credit risk for SMEs – evidence from the US market. ABACUS, 43(3), 332–356. https://doi.org/10.1111/j.1467-6281.2007.00234.x
Argenti, J. (1976). Corporate collapse – the causes and symptoms. McGraw-Hill.
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
Bluhm, Ch., Overbeck, L., & Wagner, Ch. (2003). An introduction to credit risk modeling. Chapman & Hall/CRC. https://doi.org/10.1201/9781420057362
Brabazon, A., & O’Neil, M. (2004). Diagnosing corporate stability using grammatical evolution. Journal of Applied Mathematics and Computer Science, 1, 293–310.
Burgelman, R. (1991). Intraorganizational mortality-liabilities of newness and adolescence. Organization Science, 2(3), 239–262. https://doi.org/10.1287/orsc.2.3.239
Chen, N., Ribeiro, B., Vieira, A., & Chen, A. (2013). Clustering and visualization of bankruptcy trajectory using self-organizing map. Expert Systems with Applications, 40(1), 385–393. https://doi.org/10.1016/j.eswa.2012.07.047
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
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
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
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
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
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
Gilbert, C. (2005). Unbounding the structure of inertia – resource vs routine rigidity. Academy of Management Journal, 48(5), 741–763. https://doi.org/10.5465/amj.2005.18803920
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
Hambrick, D., & D’Aveni, R. (1988). Large corporate failures as downward spirals. Administrative Science Quarterly, 33(1), 1–23. https://doi.org/10.2307/2392853
Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems with Applications, 117(1), 287–299. https://doi.org/10.1016/j.eswa.2018.09.039
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
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
Jardin, P. (2015). Bankruptcy prediction using terminal failure processes. European Journal of Operational Research, 242(1), 286–303. https://doi.org/10.1016/j.ejor.2014.09.059
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
Johnson, G. (1988). Rethinking incrementalism. Strategic Management Journal, 9(1), 73–91. https://doi.org/10.1002/smj.4250090107
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)
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
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 141–152. https://doi.org/10.1007/BF00337288
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
Laitinen, E. (2007). Classification accuracy and correlation – LDA in failure prediction. European Journal of Operational Research, 183, 210–225. https://doi.org/10.1016/j.ejor.2006.09.054
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
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.
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
Li, L., & Faff, R. (2019). Predicting corporate bankruptcy: What matters? International Review of Economics & Finance, 62, 1–19. https://doi.org/10.1016/j.iref.2019.02.016
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
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
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
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
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
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
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
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
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
Ptak-Chmielewska, A. (2019). Predicting micro-enterprise failures using data mining techniques. Journal of Risk and Financial Managament, 12, 1–17. https://doi.org/10.3390/jrfm12010030
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
Sayari, N., & Mugan, C.D. (2017). Industry specific financial distress modeling. Business Research Quarterly, 20, 45–62. https://doi.org/10.1016/j.brq.2016.03.003
Shimko, D. (2004). Credit risk – models and management. Barra Risk Books.
Schonfeld, J., Kudej, M., & Smrcka, L. (2018). Financial health of enterprises introducing safeguard procedure based on bankruptcy models. Journal of Business Economics and Management, 19, 692–705. https://doi.org/10.3846/jbem.2018.7063
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. KnowledgeBased Systems, 57, 41–56. https://doi.org/10.1016/j.knosys.2013.12.006
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
Tian, S., & Yu, Y. (2017). Financial ratios and bankruptcy predictions: An international evidence. International Review of Economics and Finance, 51, 510–526. https://doi.org/10.1016/j.iref.2017.07.025
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
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.
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
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
Wu, Y., Gaunt, C., & Gray, S. (2010). A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics, 6, 34–45. https://doi.org/10.1016/j.jcae.2010.04.002
Xiao, Z., Yang, X., Pang, Y., & Dang, X. (2012). The prediction for listed companies’ financial distress 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
Zammuto, R., & Cameron, K. (1985). Environmental decline and organizational response. Research in Organizational Behavior, 7, 223–262.
Zapranis, A., & Ginoglou, D. (2000). Forecasting corporate failure with neural network approach: The Greek case. Journal of Financial Management & Analysis, 13(2), 11–21.
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). https://doi.org/10.1080/00014788.2007.9663313
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
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
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
Altman, E., & Sabato, G. (2007). Modelling credit risk for SMEs – evidence from the US market. ABACUS, 43(3), 332–356. https://doi.org/10.1111/j.1467-6281.2007.00234.x
Argenti, J. (1976). Corporate collapse – the causes and symptoms. McGraw-Hill.
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
Bluhm, Ch., Overbeck, L., & Wagner, Ch. (2003). An introduction to credit risk modeling. Chapman & Hall/CRC. https://doi.org/10.1201/9781420057362
Brabazon, A., & O’Neil, M. (2004). Diagnosing corporate stability using grammatical evolution. Journal of Applied Mathematics and Computer Science, 1, 293–310.
Burgelman, R. (1991). Intraorganizational mortality-liabilities of newness and adolescence. Organization Science, 2(3), 239–262. https://doi.org/10.1287/orsc.2.3.239
Chen, N., Ribeiro, B., Vieira, A., & Chen, A. (2013). Clustering and visualization of bankruptcy trajectory using self-organizing map. Expert Systems with Applications, 40(1), 385–393. https://doi.org/10.1016/j.eswa.2012.07.047
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
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
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
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
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
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
Gilbert, C. (2005). Unbounding the structure of inertia – resource vs routine rigidity. Academy of Management Journal, 48(5), 741–763. https://doi.org/10.5465/amj.2005.18803920
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
Hambrick, D., & D’Aveni, R. (1988). Large corporate failures as downward spirals. Administrative Science Quarterly, 33(1), 1–23. https://doi.org/10.2307/2392853
Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems with Applications, 117(1), 287–299. https://doi.org/10.1016/j.eswa.2018.09.039
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
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
Jardin, P. (2015). Bankruptcy prediction using terminal failure processes. European Journal of Operational Research, 242(1), 286–303. https://doi.org/10.1016/j.ejor.2014.09.059
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
Johnson, G. (1988). Rethinking incrementalism. Strategic Management Journal, 9(1), 73–91. https://doi.org/10.1002/smj.4250090107
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)
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
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 141–152. https://doi.org/10.1007/BF00337288
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
Laitinen, E. (2007). Classification accuracy and correlation – LDA in failure prediction. European Journal of Operational Research, 183, 210–225. https://doi.org/10.1016/j.ejor.2006.09.054
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
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.
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
Li, L., & Faff, R. (2019). Predicting corporate bankruptcy: What matters? International Review of Economics & Finance, 62, 1–19. https://doi.org/10.1016/j.iref.2019.02.016
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
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
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
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
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
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
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
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
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
Ptak-Chmielewska, A. (2019). Predicting micro-enterprise failures using data mining techniques. Journal of Risk and Financial Managament, 12, 1–17. https://doi.org/10.3390/jrfm12010030
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
Sayari, N., & Mugan, C.D. (2017). Industry specific financial distress modeling. Business Research Quarterly, 20, 45–62. https://doi.org/10.1016/j.brq.2016.03.003
Shimko, D. (2004). Credit risk – models and management. Barra Risk Books.
Schonfeld, J., Kudej, M., & Smrcka, L. (2018). Financial health of enterprises introducing safeguard procedure based on bankruptcy models. Journal of Business Economics and Management, 19, 692–705. https://doi.org/10.3846/jbem.2018.7063
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. KnowledgeBased Systems, 57, 41–56. https://doi.org/10.1016/j.knosys.2013.12.006
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
Tian, S., & Yu, Y. (2017). Financial ratios and bankruptcy predictions: An international evidence. International Review of Economics and Finance, 51, 510–526. https://doi.org/10.1016/j.iref.2017.07.025
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
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.
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
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
Wu, Y., Gaunt, C., & Gray, S. (2010). A comparison of alternative bankruptcy prediction models. Journal of Contemporary Accounting & Economics, 6, 34–45. https://doi.org/10.1016/j.jcae.2010.04.002
Xiao, Z., Yang, X., Pang, Y., & Dang, X. (2012). The prediction for listed companies’ financial distress 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
Zammuto, R., & Cameron, K. (1985). Environmental decline and organizational response. Research in Organizational Behavior, 7, 223–262.
Zapranis, A., & Ginoglou, D. (2000). Forecasting corporate failure with neural network approach: The Greek case. Journal of Financial Management & Analysis, 13(2), 11–21.