Optimización estocástica mediante modelos de riesgo GARCH-Cox para mitigar la quiebra de las pymes agrícolas en las economías emergentes
Barra lateral del artículo
Contenido principal del artículo
Resumen
Los modelos actuales de dificultades financieras, desarrollados principalmente en contextos de altos ingresos y baja volatilidad, no logran captar la exposición combinada de las pequeñas empresas agrícolas a las perturbaciones simultáneas de los precios de las materias primas y los cambios climáticos. Abordamos esta brecha teórica mediante el desarrollo de un marco de optimización de supervivencia estocástica que integra un modelo de varianza condicional GARCH (1,1)–GJR con una especificación de riesgo proporcional de Cox variable en el tiempo, incorporando formalmente el riesgo agroclimático heteroscedástico en la función de probabilidad de quiebra. Utilizando datos de panel longitudinales de 205 pymes agrícolas registradas en la Superintendencia de Compañías del Ecuador en Quevedo — Los Ríos, una región cuyos ciclos del banano y el cacao presentan una volatilidad de los precios que supera una desviación estándar anualizada del 34 %—, estimamos un modelo de supervivencia discriminante en 80 empresas de la muestra (2021–2025). El equilibrio teórico, derivado a través de las condiciones de optimalidad de Karush–Kuhn–Tucker (KKT), arroja un umbral crítico de apalancamiento (Zoc = −0,2263) y un límite de diversificación de la cartera (σ* = 0,312) por debajo del cual la probabilidad de quiebra aumenta de forma no lineal. Empíricamente, 43 de las 80 empresas (53,75 %) operan en la zona de alto riesgo de quiebra, y el riesgo condicional de quiebra aumenta en 2,14 por cada unidad de disminución en el índice de utilidades retenidas. Nuestro marco ofrece a los responsables de las políticas agroalimentarias y a los directivos de pymes reglas de decisión prácticas basadas en umbrales y adaptadas al contexto institucional de Los Ríos. Más allá del caso ecuatoriano, el modelo constituye una contribución generalizable al análisis de supervivencia en condiciones financieras no gaussianas.
##plugins.themes.bootstrap3.displayStats.downloads##
Detalles del artículo
Sección
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
Cómo citar
Referencias
Agarwal, V., & Taffler, R. (2022). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking & Finance, 140, 106526. https://doi.org/10.1016/j.jbankfin.2022.106526
Altman, E. I., Balzano, M., Giannozzi, A., & Srhoj, S. (2022). Revisiting SME default predictors: The omega score. Journal of Small Business Management, 61(6), 2383–2417. https://doi.org/10.1080/00472778.2022.2135718
Altman, E. I., Balzano, M., Giannozzi, A., & Srhoj, S. (2023). The omega score: An improved tool for SME default predictions. Journal of the International Council for Small Business, 4(4), 362–373. https://doi.org/10.1080/26437015.2023.2186284
Arora, N., Kaur, P., & Singh, G. (2023). Revisiting business failure research: Evidence from bibliometric analysis and systematic literature review. Heliyon, 9(3), e14193. https://doi.org/10.1016/j.heliyon.2023.e14193
Ashraf, S., Félix, E. G. S., & Serrasqueiro, Z. (2022). Do traditional financial distress prediction models predict the early warning signs of financial distress? Journal of Risk and Financial Management, 15(3), 116. https://doi.org/10.3390/jrfm15030116
Barboza, F., Kimura, H., & Altman, E. (2023). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 220, 119701. https://doi.org/10.1016/j.eswa.2023.119701
Baselga-Pascual, L., Trujillo-Ponce, A., & Cardone-Riportella, C. (2022). Factors influencing bank risk in Europe: Evidence from the financial and debt crises. North American Journal of Economics and Finance, 61, 101699. https://doi.org/10.1016/j.najef.2022.101699
Bismark, O., Acheampong, A. K., Mensah, R. O., & Otieku, E. (2023). Financial management practices and the performance of small and medium enterprises in Ghana. Cogent Business & Management, 10(1), 2168202. https://doi.org/10.1080/23311975.2023.2168202
Blanco-Oliver, A., Irimia-Diéguez, A., & Vázquez-Cueto, M. J. (2023). Improving credit scoring prediction of micro-enterprises using financial data. Journal of Financial Reporting and Accounting, 21(3), 569–591. https://doi.org/10.1108/JFRA-04-2022-0113
Calabrese, R., & Osmetti, S. A. (2022). Modelling small and medium enterprise loan defaults as rare events: The generalized extreme value regression model. Journal of Applied Statistics, 40(6), 1172–1188. https://doi.org/10.1080/02664763.2022.2048439
Camacho-Miñano, M. M., & Campa-Planas, F. (2021). Predictive models of financial insolvency in the agricultural sector. Agronomy, 11(12), 2511. https://doi.org/10.3390/agronomy11122511
Chen, Y., Calabrese, R., & Martin-Barragan, B. (2022). A comparison of classifiers for loan default prediction in peer-to-peer lending. Expert Systems with Applications, 208, 118175. https://doi.org/10.1016/j.eswa.2022.118175
Ciampi, F., Giannozzi, A., Marzi, G., & Altman, E. I. (2021). Rethinking SME default prediction: A systematic literature review and future perspectives. Scientometrics, 126(3), 2141–2188. https://doi.org/10.1007/s11192-020-03856-0
Correa-Mejía, D. A., & Lopera-Castaño, M. (2022). Financial ratios as a powerful instrument to predict insolvency: A study using boosting algorithms in Colombian firms. Contaduría y Administración, 67(1), 1–19. https://doi.org/10.22201/fca.24488410e.2022.2874
Czakon, W., Klimas, P., Tiberius, V., Ferreira, J., Veiga, P., & Kraus, S. (2022). Entrepreneurial failure: Structuring a widely overlooked field of research. Entrepreneurship Research Journal, 12(4), 1–28. https://doi.org/10.1515/erj-2021-0175
Diez-Esteban, J. M., García-Gómez, C. D., López-Iturriaga, F. J., & Prado-Román, C. (2022). Stochastic frontier models applied to banking: Estimation of cost efficiency in European banks. Finance Research Letters, 44, 102091. https://doi.org/10.1016/j.frl.2021.102091
Effendie, J. M., Manafe, H. A., & Man, S. (2022). Analysis of the effect of liquidity ratios, solvency and activity on the financial performance of the company. Dinasti International Journal of Economics, Finance & Accounting, 3(5), 541–550. https://doi.org/10.38035/dijefa.v3i5.1507
Figini, S., & Giudici, P. (2022). Statistical merging of rating models. Journal of the Royal Statistical Society: Series A, 185(1), 59–74. https://doi.org/10.1111/rssa.12687
Gupta, J., Gregoriou, A., & Ebrahimi, T. (2022). Empirical comparison of hazard models in predicting SMEs failure. Quantitative Finance, 22(2), 231–252. https://doi.org/10.1080/14697688.2021.1994984
Hair, J. F., & Alamer, A. (2022). Partial least squares structural equation modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027
Hernandez Tinoco, M., Wilson, N., & Holmes, P. (2023). Financial distress prediction models: A review of the literature. Journal of Accounting Literature, 45, 103–128. https://doi.org/10.1108/JAL-09-2022-0070
Jones, S., Johnstone, D., & Wilson, R. (2022). Predicting corporate bankruptcy: An evaluation of alternative statistical frameworks. Journal of Business Finance & Accounting, 44(1–2), 3–34. https://doi.org/10.1111/jbfa.12218
Kliestik, T., Valaskova, K., Lazaroiu, G., Kovacova, M., & Vrbka, J. (2021). Remaining financially healthy and competitive: The role of financial predictors. Journal of Competitiveness, 12(1), 74–92. https://doi.org/10.7441/joc.2020.01.05
Kovacova, M., Valaskova, K., & Stehel, V. (2022). Prediction of enterprise financial distress: Evidence from Slovakia. Risks, 10(10), 185. https://doi.org/10.3390/risks10100185
Li, H., Sun, J., & Wu, J. (2022). Predicting business failure using classification and regression tree: An empirical investigation of Chinese listed companies. Journal of Forecasting, 41(3), 434–453. https://doi.org/10.1002/for.2826
Liang, D., Lu, C.-C., Tsai, C.-F., & Shih, G.-A. (2022). 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.2022.01.012
López-Gutiérrez, C., Sanfilippo-Azofra, S., & Torre-Olmo, B. (2022). Investment decisions of companies in financial distress. BRQ Business Research Quarterly, 25(3), 248–265. https://doi.org/10.1177/2340944420945229
Muñoz-Izquierdo, N., Laitinen, E. K., Camacho-Miñano, M. M., & Pascual-Ezama, D. (2022). Does audit report information improve financial distress prediction over Altman's traditional Z-score model? Journal of International Financial Management & Accounting, 33(1), 65–97. https://doi.org/10.1111/jifm.12118
Muthoni, D. W., Kariuki, S. N., & Kimani, E. M. (2023). Financial management practices and performance of small and medium enterprises in Nairobi City County. International Journal of Economics, Commerce and Management, 11(3), 1–19. https://doi.org/10.21276/ijecm.2023.11.3.1
Ouenniche, J., Pérez-Gladish, B., & Bouslah, K. (2023). An out-of-sample evaluation framework for DEA with application in bankruptcy prediction. Annals of Operations Research, 313(2), 941–965. https://doi.org/10.1007/s10479-021-04065-3
Pavlidis, N. G., Tanaka, M., & Pavlidis, E. G. (2022). Speculative bubbles and financial crises. Journal of Monetary Economics, 121, 12–34. https://doi.org/10.1016/j.jmoneco.2021.05.010
Platt, H. D., & Platt, M. B. (2022). Toward a model of corporate financial distress. Studies in Business and Economics, 17(1), 89–107. https://doi.org/10.2478/sbe-2022-0007
Ptak-Chmielewska, A. (2021). Bankruptcy prediction of small and medium enterprises in Poland based on the LDA and SVM methods. Statistics in Transition New Series, 22(1), 179–195. https://doi.org/10.21307/stattrans-2021-010
Rodríguez-Valencia, L., Lamothe-Fernández, P., & Alaminos, D. (2023). The market value of SMEs: A comparative study between private and listed firms in alternative stock markets. Annals of Finance, 19(1), 65–88. https://doi.org/10.1007/s10436-022-00420-z
Siddiqui, H. U. R., Sainz de Abajo, B., de la Torre Díez, I., Rustam, F., Ashraf, I., & Dudley, S. (2023). Predicting bankruptcy of firms using earnings call data and transfer learning. PeerJ Computer Science, 9, e1134. https://doi.org/10.7717/peerj-cs.1134
Sun, J., Li, H., Huang, Q.-H., & He, K.-Y. (2023). Predicting financial distress and corporate failure: A systematic review from the year 2000 to 2023. Frontiers in Economics and Management, 4(1), 1–24. https://doi.org/10.53469/fem.2023.04(01).03
Terza, J. V., Basu, A., & Rathouz, P. J. (2022). Two-stage residual inclusion estimation: Addressing endogeneity in health econometric modeling. Journal of Health Economics, 81, 102566. https://doi.org/10.1016/j.jhealeco.2021.102566
Tinoco, M. H., & Wilson, N. (2022). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International Review of Financial Analysis, 30, 394–419. https://doi.org/10.1016/j.irfa.2013.02.013
Tsai, C.-F. (2022). Combining cluster analysis with classifier ensembles to predict financial distress. Information Fusion, 16, 46–58. https://doi.org/10.1016/j.inffus.2012.04.001
Ullah, S., Akhtar, P., & Zaefarian, G. (2022). Dealing with endogeneity bias: The generalized method of moments (GMM) for panel data. Industrial Marketing Management, 71, 69–78. https://doi.org/10.1016/j.indmarman.2017.11.010
Valencia, F., & Laeven, L. (2022). Systemic banking crises revisited. Journal of Financial Intermediation, 52, 100872. https://doi.org/10.1016/j.jfi.2021.100872
Veganzones, D., & Séverin, E. (2022). An investigation of bankruptcy prediction in imbalanced datasets. Decision Support Systems, 112, 111–124. https://doi.org/10.1016/j.dss.2018.06.011
Viglia, G., Dolnicar, S., & Galati, M. (2023). Using advanced mixed methods approaches: Combining PLS-SEM and qualitative studies. Journal of Business Research, 174, 114498. https://doi.org/10.1016/j.jbusres.2023.114498
Wang, Y., Wang, R., & Xiong, X. (2022). Integrated economic-financial approach to project failure prediction in small firms: Evidence from agribusiness enterprises. Agribusiness: An International Journal, 38(3), 601–624. https://doi.org/10.1002/agr.21729
Wen, H., Lee, C.-C., & Zhou, F. (2022). Green credit policy, credit allocation efficiency and upgrade of energy-intensive enterprises. Energy Economics, 94, 105099. https://doi.org/10.1016/j.eneco.2021.105099
Xu, W., Xiao, Z., Dang, X., Yang, D., & Yang, X. (2022). Financial ratio selection for business failure prediction using soft set theory. Applied Soft Computing, 26, 256–266. https://doi.org/10.1016/j.asoc.2014.10.005
Yildiz, B., & Akkoc, S. (2023). Bankruptcy prediction using neuro-fuzzy models in the Turkish banking sector. Journal of Intelligent & Fuzzy Systems, 44(1), 1–14. https://doi.org/10.3233/JIFS-222222
Zhou, L. (2022). Performance of corporate bankruptcy prediction models on imbalanced dataset: The effect of sampling methods. Knowledge-Based Systems, 41, 16–25. https://doi.org/10.1016/j.knosys.2012.12.007
Zhou, Y., Li, H., & Gu, Y. (2023). Financial distress prediction model for Chinese listed companies using support vector machines. Expert Systems with Applications, 220, 119700. https://doi.org/10.1016/j.eswa.2023.119700
Zięba, M., Tomczak, S. K., & Tomczak, J. M. (2022). Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction. Expert Systems with Applications, 58, 93–101. https://doi.org/10.1016/j.eswa.2016.04.001