Insolvency analysis in Colombian industry: an application of the Springate model
DOI:
https://doi.org/10.47847/faccea.v16n2a4Keywords:
Springate, Risk, Bankruptcy, SMEs, IndustrialAbstract
The Colombian industrial sector faces challenges that threaten its financial stability, making it necessary to explore methodologies for the early detection of insolvency. This study applies the Springate model to assess the risk of bankruptcy among Colombian industrial SMEs between 2021 and 2025. The model was selected because of its limited application in the local context and its potential adaptability to the national market. Using a quantitative approach, 2,447 companies reporting to the Superintendence of Companies were analyzed through quartile analysis, interquartile range analysis, and hypothesis testing. The results indicate an improvement in short-term payment capacity, although liquidity gaps persist; 69.6% of the companies were classified in the safe zone, while 30.4% showed a latent risk of bankruptcy. The model proved to be a valuable tool for anticipating financial distress, providing empirical evidence on the application of the Springate model to Colombian industrial companies and supporting corporate financial risk management.
Downloads
References
Adamiec, L., Cernauskas, D., & Kumiega, A. (2022). Understanding the indicative factors of university/college closings. Journal of Management Analytics, 9(3), 330-350. https://doi.org/10.1080/23270012.2022.2113464 DOI: https://doi.org/10.1080/23270012.2022.2113464
Ahemed, A. M., Iqbal, U., & Atif, M. M. (2025). Asymmetric cost behavior and financial distress. Economics Letters, 247, 112121. https://doi.org/10.1016/j.econlet.2024.112121 DOI: https://doi.org/10.1016/j.econlet.2024.112121
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.https://doi.org/10.2307/2978933 DOI: https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Altman, E. I., Sieradzki, R., & Thlon, M. (2023). Assessing the impact of economic and financial shocks on SME credit quality: a scenario analysis. Bank i Kredyt, 54(2), 89-127. https://doi.org/10.5604/01.3001.0054.5704 DOI: https://doi.org/10.5604/01.3001.0054.5704
Andriuskevicius, K., Balezentis, T., Streimikiene, D., & Shobande, O. A. (2026). Analysis of financial distress risk in public listed agrifood companies in Lithuania. Agricultural and Food Economics, 14(1), 4. https://doi.org/10.1186/s40100-025-00449-4 DOI: https://doi.org/10.1186/s40100-025-00449-4
Angelova, R., Stoyancheva, D., & Zhelyazkov, G. (2025). Assessment of the applicability of financial insolvency models. Scientific Papers Series Management, Economic Engineering in Agriculture & Rural Development, 25(2), 65-72.
Apetri, A. N., & Chelba, A. A. (2025). Impacts of Macroeconomic Factors on Insolvency: Case of Romanian Companies. Ovidius University Annals, Series Economic Sciences, 25(1), 22–29. https://doi.org/10.35219/eai15840409506 DOI: https://doi.org/10.61801/OUAESS.2025.1.62
Borges, A., & Carvalho, M. (2026). Short-and long-term financial distress prediction in SMEs: a survival model comparison. Journal of Applied Statistics, 1-30. https://doi.org/10.1080/02664763.2025.2501166 DOI: https://doi.org/10.1080/02664763.2025.2501166
Caicedo Carrero, A., & Isaac Roque, D. (2025). Application of the Zavgren model for assessing financial insolvency in the construction industry (2018–2022). Revista CEA, 11(26), e3357. https://doi.org/10.22430/24223182.3357 DOI: https://doi.org/10.22430/24223182.3357
Carrero, A. C., Roque, D. I., & Murillo, W. A. B. (2025). Impacto de las cuentas por cobrar en la insolvencia financiera de la industria colombiana. Cuadernos de Contabilidad, (26), 10. https://doi.org/10.11144/Javeriana.cc26.icci DOI: https://doi.org/10.11144/Javeriana.cc26.icci
Carranza, J. E., Rojas, J. A. B., Casas, C., González-Ramírez, A. X., Moreno-Burbano, S., Arias-Rodríguez, F., & Vélez-Velásquez, J. S. (2018). La industria colombiana en el siglo XXI. Revista ESPE-Ensayos sobre Política Económica, 1-69. DOI: https://doi.org/10.32468/Espe.87
Cheraghali, H., & Molnár, P. (2026). Predictors of financial distress: Differences between financial and non-financial small and medium-sized enterprises. Research in International Business and Finance, 103334. https://doi.org/10.1016/j.ribaf.2026.103334 DOI: https://doi.org/10.1016/j.ribaf.2026.103334
Dai, T., & Cai, B. (2025, December). Altman Z-Score's Predictive Accuracy for Financial Distress in Chinese Enterprises: Evidence from 32 Listed Manufacturing Firms. In Proceedings of the 2025 6th International Conference on Big Data Economy and Information Management, 710-715. https://doi.org/10.1145/3800000.3800107 DOI: https://doi.org/10.1145/3800000.3800107
De Silva, C., Del Gaudio, B. L., Gervasoni, A., & Lertora, M. (2025). Private equity and financial distress: a bibliometric literature review. Research in international business and finance, 74, 102674. DOI: https://doi.org/10.1016/j.ribaf.2024.102674
Do, T. V. T., & Doan, T. V. (2026).
The impact of financial distress on earnings management: Evidence from Vietnam. Investment Management and Financial Innovations, 23(2), 473–482. https://doi.org/10.21511/imfi.23(2).2026.34 DOI: https://doi.org/10.21511/imfi.23(2).2026.34
Idris, N. F., & Ismail, M. A. (2026). Toward optimal bankruptcy prediction: Evaluating ensemble methods using Taiwan and U.S. financial bankruptcy data. Bulletin of Electrical Engineering and Informatics, 15(3), 2747–2757. https://doi.org/10.11591/eei.v15i3.11159 DOI: https://doi.org/10.11591/eei.v15i3.11159
Isaac-Roque, D. y Caicedo-Carrero, A. (2023). Relación entre los indicadores financieros del modelo Altman Z y el puntaje Z. Retos Revista de Ciencias de la Administración y Economía, 13(25), 129-148. https://doi.org/10.17163/ret.n25.2023.09 DOI: https://doi.org/10.17163/ret.n25.2023.09
Jabeur, S. B., & Fahmi, Y. (2018). Forecasting financial distress for French firms: a comparative study. Empirical Economics, 54(3), 1173-1186. https://doi.org/10.1007/s00181-017-1246-1 DOI: https://doi.org/10.1007/s00181-017-1246-1
Letkovsky, S., Jencova, S., Vasanicova, P., Gavura, S., & Bacik, R. (2023). Predicting bankruptcy using artificial intelligence: The case of the engineering industry. Economics & Sociology, 16(4), 178-190. https://doi.org/10.14254/2071-789X.2023/16-4/8 DOI: https://doi.org/10.14254/2071-789X.2023/16-4/8
Martínez, R. M., Tuya, L. C., Martínez, M., Pérez, A. y Cánovas, A. M. (2009). El coeficiente de correlación de los rangos de Spearman caracterización. Revista Habanera de Ciencias Médicas, 8(2), 1-19.
Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of accounting research, 18(1), 109-131. https://doi.org/10.2307/2490395 DOI: https://doi.org/10.2307/2490395
Papíková, L., & Papík, M. (2022). Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises. Intelligent Systems in Accounting, Finance and Management, 29(4), 254-281. https://doi.org/10.1002/isaf.1521 DOI: https://doi.org/10.1002/isaf.1521
Smith, M., & Alvarez, F. (2022). Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting: M. Smith, F. Alvarez. Computational Economics, 59(1), 263-295.https://doi.org/10.1007/s10614-020-10078-2 DOI: https://doi.org/10.1007/s10614-020-10078-2
Song, L., & Md Shahbudin, A. S. (2022). To anticipate the bankruptcy of Baoshang Bank based on CAMELS rating system. Bank i Kredyt, 54(1), 65-87. DOI: https://doi.org/10.5604/01.3001.0054.5696
Springate, G. L. (1978). Predicting the Possibility of Failure in a Canadian Firm: A Discriminant Analysis. Burnaby, Canada: Simon Fraser University.
Trebuňa, P., Kronová, J., Kliment, M., & Pekarčíková, M. (2026). Symmetry-Based Comparison of Logit and Probit Models for Financial Distress Prediction in the Automotive Industry. Symmetry, 18(6), 973. https://doi.org/10.3390/sym18060973 DOI: https://doi.org/10.3390/sym18060973
Universidad EAFIT. (2024). Coyuntura y Perspectivas de la Economía Colombiana. Medellin: Universidad EAFIT.
Zhukov, A., Nikulin, E., & Shchuchkin, D. (2022). Bankruptcy Risk Factors of Russian Companies. Finance: Theory and Practice, 26 (6), 131-155. https://doi.org/10.26794/2587-5671-2022-26-6-131-155 DOI: https://doi.org/10.26794/2587-5671-2022-26-6-131-155
Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting research, 22, 59-82. https://doi.org/10.2307/2490859 DOI: https://doi.org/10.2307/2490859
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.