Bankruptcies result in significant financial and social losses for all stakeholders of companies each year. For this reason, researchers have been working for decades to develop effective methods for predicting bankruptcy. Most prediction models are based on financial ratios extracted from the last financial statements before bankruptcy. However, researchers have given less attention to the process that led the company to bankruptcy. The purpose of this study is to investigate whether information about the process can improve bankruptcy prediction. The benchmark model was based on three financial ratios measuring profitability, liquidity, and solvency. The process model was constructed using these ratios and their processes leading up to bankruptcy. The process of each ratio was measured by two dimensions: the form and the level of the process. The importance of process information was assessed using data from Finnish limited companies. The data included 147 bankrupt companies and 23,386 active companies. The results of the logistic regression analysis showed that process information helps improve prediction accuracy, but the effect is not strong.
References
[1]
Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23, 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
[2]
Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETATM Analysis a New Model to Identify Bankruptcy Risk of Corporations. Journal of Banking & Finance, 1, 29-54. https://doi.org/10.1016/0378-4266(77)90017-6
[3]
Altman, E. I., Iwanicz‐Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial Distress Prediction in an International Context: A Review and Empirical Analysis of Altman’s z‐score Model. Journal of International Financial Management & Accounting, 28, 131-171. https://doi.org/10.1111/jifm.12053
[4]
Altman, E. I., Sabato, G., & Wilson, N. (2010). The Value of Non-Financial Information in SME Risk Management. The Journal of Credit Risk, 6, 95-127. https://doi.org/10.21314/jcr.2010.110
[5]
Argenti, J. (1976). Corporate Collapse: The Causes and Symptoms. McGraw-Hill.
[6]
Argenti, J. (1983). Predicting Corporate Failure. Accountants Digest. No. 138. Summer. 1-25. Institute of Chartered Accountants in England and Wales.
[7]
Aziz, A., Emanuel, D. C., & Lawson, G. H. (1988). Bankruptcy Prediction—An Investigation of Cash Flow Based Models. Journal of Management Studies, 25, 419-437. https://doi.org/10.1111/j.1467-6486.1988.tb00708.x
[8]
Balcaen, S., & Ooghe, H. (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
[9]
Beaver, W. H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171
[10]
Bellovary, J. L., Giacomino, D. E., & Akers, M. D. (2007). A Review of Bankruptcy Prediction Studies: 1930 to Present. Journal of Financial Education, 1, 3-41.
[11]
Beuselinck, C., Elfers, F., Gassen, J., & Pierk, J. (2023). Private Firm Accounting: The European Reporting Environment, Data and Research Perspectives. Accounting and Business Research, 53, 38-82. https://doi.org/10.1080/00014788.2021.1982670
[12]
Campa, D., & Camacho-Miñano, M. (2014). Earnings Management among Bankrupt Non-Listed Firms: Evidence from Spain. Spanish Journal of Finance and Accounting, 43, 3-20. https://doi.org/10.1080/02102412.2014.890820
[13]
D’Aveni, R. (1989). The Aftermath of Organizational Decline: A Longitudinal Study of the Strategic and Managerial Characteristics of Declining Firms. Academy of Management Journal, 32, 577-605. http://dx.doi.org/10.5465/256435
[14]
Dambolena, I. G., & Khoury, S. J. (1980). Ratio Stability and Corporate Failure. The Journal of Finance, 35, 1017-1026. https://doi.org/10.1111/j.1540-6261.1980.tb03517.x
[15]
du Jardin, P. (2015). Bankruptcy Prediction Using Terminal Failure Processes. European Journal of Operational Research, 242, 286-303. https://doi.org/10.1016/j.ejor.2014.09.059
[16]
du Jardin, P. (2017). Dynamics of Firm Financial Evolution and Bankruptcy Prediction. Expert Systems with Applications, 75, 25-43. https://doi.org/10.1016/j.eswa.2017.01.016
[17]
du Jardin, P. (2021). Forecasting Corporate Failure Using Ensemble of Self-Organizing Neural Networks. European Journal of Operational Research, 288, 869-885. https://doi.org/10.1016/j.ejor.2020.06.020
[18]
du 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, 701-711. https://doi.org/10.1016/j.dss.2011.04.001
[19]
du Jardin, P., & Severin, E. (2012). Forecasting Financial Failure Using a Kohonen Map: A Comparative Study to Improve Model Stability over Time. European Journal of Operational Research, 221, 378-396. https://doi.org/10.1016/j.ejor.2012.04.006
[20]
Dutzi, A., & Rausch, B. (2016). Earnings Management before Bankruptcy: A Review of the Literature. Journal of Accounting and Auditing: Research & Practice, 2016, 1-21. https://doi.org/10.5171/2016.245891
[21]
Flores-Jimeno, R., & Jimeno-Garcia, I. (2017). Dynamic Analysis of Different Business Failure Process. Problems and Perspectives in Management, 15, 486-499. https://doi.org/10.21511/ppm.15(si).2017.02
[22]
Giannopoulos, G., & Sigbjørnsen, 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
[23]
Hambrick, D. C., & D’Aveni, R. A. (1988). Large Corporate Failures as Downward Spirals. Administrative Science Quarterly, 33, 1-23. https://doi.org/10.2307/2392853
[24]
Hosmer, D. W., & Lemeshow, S. (1989). Applied Logistic Regression. John Wiley & Sons, Inc.
[25]
Jimeno-Garcia, I., Rodriguez-Merayo, M. A., & Vidal-Blasco, M. A. (2017). The Failure Processes and Their Relation to the Business Interruption Moment. International Journal of Managerial and Financial Accounting, 9, 68-83. http://dx.doi.org/10.1504/IJMFA.2017.084057
[26]
Kallunki, J. P., & Martikainen, T. (1999). Financial Failure and Managers’ Accounting Responses: Finnish Evidence. Journal of Multinational Financial Management, 9, 15-26. https://doi.org/10.1016/s1042-444x(98)00046-2
[27]
Keasey, K., & Watson, R. (1987). Non‐Financial Symptoms and the Prediction of Small Company Failure: A Test of Argenti’s Hypotheses. Journal of Business Finance & Accounting, 14, 335-354. https://doi.org/10.1111/j.1468-5957.1987.tb00099.x
[28]
Laitinen, E. (1993). Financial Predictors for Different Phases of the Failure Process. Omega, 21, 215-228. https://doi.org/10.1016/0305-0483(93)90054-o
[29]
Laitinen, E. K. (1991). Financial Ratios and Different Failure Processes. Journal of Business Finance & Accounting, 18, 649-673. https://doi.org/10.1111/j.1468-5957.1991.tb00231.x
[30]
Laitinen, E. K. (2021). Analyzing Business-Failure-Process Risk: Evidence from Finland. Journal of Financial Reporting and Accounting, 19, 571-595. https://doi.org/10.1108/jfra-06-2020-0164
[31]
Lobo, J. M., Jiménez‐Valverde, A., & Real, R. (2008). AUC: A Misleading Measure of the Performance of Predictive Distribution Models. Global Ecology and Biogeography, 17, 145-151. https://doi.org/10.1111/j.1466-8238.2007.00358.x
[32]
Lukason, O., & Laitinen, E. K. (2019). Firm Failure Processes and Components of Failure Risk: An Analysis of European Bankrupt Firms. Journal of Business Research, 98, 380-390. https://doi.org/10.1016/j.jbusres.2018.06.025
[33]
Ooghe, H., & De Prijcker, S. (2008). Failure Processes and Causes of Company Bankruptcy: A Typology. Management Decision, 46, 223-242. https://doi.org/10.1108/00251740810854131
[34]
Ravi Kumar, P., & 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
[35]
Shi, Y., & Li, X. (2019). An Overview of Bankruptcy Prediction Models for Corporate Firms: A Systematic Literature Review. Intangible Capital, 15, 114-127. https://doi.org/10.3926/ic.1354
[36]
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
