Calculando el riesgo de insolvencia, de los métodos tradicionales a las redes neuronales artificiales. Una revisión de literatura

Contenido principal del artículo

Maryann Katherine Ludeña Dávila
https://orcid.org/0000-0001-7408-0400
Luis Bernardo Tonon Ordóñez
https://orcid.org/0000-0003-2360-9911

Resumen

En la administración financiera de toda organización el cálculo del riesgo de insolvencia se ha convertido en un parámetro importante, buscando anticiparse a la eventualidad de llegar a tener un problema económico y generar insolvencia. El objetivo de este trabajo es determinar si en el cálculo del riesgo de insolvencia, el uso de redes neuronales artificiales genera mejores resultados que las metodologías tradicionales, buscando las principales características dentro de las aplicaciones realizadas por distintos autores a través del tiempo. De esta manera se observan las principales variables que pueden evidenciar que la aplicación de la metodología de redes neuronales facilita el cálculo del riesgo de insolvencia.  A través de la revisión bibliográfica, en el período 1992-2021, con el uso del método analítico-sintético se puede evidenciar que el modelo expuesto es considerado como eficiente según sus resultados, con ajustes que, en la mayoría de los casos expuestos, superan el 80% de eficacia. Los resultados encontrados permitieron concluir que la estructura básica de una red neuronal viene dada por tres capas: una de entrada, una de salida y una oculta. Sin embargo, el número de nodos es el que varía en cada una de las aplicaciones realizadas por los distintos autores, dado que los mismos representan a las variables, en este caso indicadores financieros más relevantes según la aplicación planteada. Finalmente se logró evidenciar cuáles son los indicadores financieros más usados en las distintas aplicaciones de redes neuronales. Todo indica que las redes neuronales generan resultados más efectivos que los métodos tradicionales. 

Descargas

Los datos de descargas todavía no están disponibles.

Detalles del artículo

Cómo citar
Ludeña Dávila, M. K., & Tonon Ordóñez, L. B. (2021). Calculando el riesgo de insolvencia, de los métodos tradicionales a las redes neuronales artificiales. Una revisión de literatura. INNOVA Research Journal, 6(3), 270–287. https://doi.org/10.33890/innova.v6.n3.2021.1790
Sección
Actores de la economía y desarrollo empresarial
Biografía del autor/a

Maryann Katherine Ludeña Dávila, Universidad del Azuay, Ecuador

Ingeniera en Contabilidad y Auditoría por la Universidad Politécnica Salesiana. Título de postgrado: Maestría en Administración de Empresas – Mención Finanzas. Desde el 2016 Liquidación de Créditos Cooperativa Juventud Ecuatoriana Progresista. Desde el 2018 Analista financiero en la empresa CRESAFE CIA. LTDA. 

Luis Bernardo Tonon Ordóñez, Universidad del Azuay, Ecuador

Economista graduado en la Universidad del Azuay. Tiene los siguientes títulos de postgrado: Diplomado Superior en Finanzas, Mercado de Valores y Negocios Fiduciarios. Diplomado Superior en Negociación Internacional. Maestría en Administración de Empresas. Desde el 2003 es profesor de la Universidad del Azuay en las áreas de Economía y Finanzas. Ha participado en diversos grupos de investigación y actualmente es parte del Observatorio Empresarial de la Universidad del Azuay. 

Citas

Abdelwahed, T., & Amir, E. (2005). New evolutionary bankruptcy forecasting model based on genetic algorithms and neural networks. 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05). https://doi.org/10.1109/ICTAI.2005.92

Abedin, M., Guotai, C., Colombage, S., & Fahmida-E-Moula. (2018). Credit default prediction using a support vector machine and a probabilistic neural network. Journal of Credit Risk, 14(2), 1-27. https://doi.org/10.21314/JCR.2017.233

Alaka, H., Oyedele, L., Owolabi, H., Oyedele, A., Akinade, O., Bilal, M., & Ajayi, S. (2017). Critical factors for insolvency prediction: towards a theoretical model for the construction industry. International Journal of Construction Management, 17(1), 25-49. https://doi.org/10.1080/15623599.2016.1166546

Alam, P., Booth, D., Lee, K., & Thordarson, T. (2000). The use of fuzzy clustering algorithm and self-organizing neural networks for identifying potentially failing banks: an experimental study. Expert Systems with Applications, 18(3), 185-199. https://doi.org/10.1016/S0957-4174(99)00061-5

Alfaro, E., García, N., Gámez, M., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45(1), 110-122. https://doi.org/10.1016/j.dss.2007.12.002

Allen, F., & Gale, D. (2004). Competition and Financial Stability. Journal of Money, Credit, and Banking, 36(3).

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. (1983). Corporate financial distress: A complete guide to predicting avoiding and dealing with failedcy. New York: John Wiley and Sons.

Altman, E. (2001). Bankruptcy, Credit Risk and High Yield Junk Bonds. New York: Blackwell Publishing.

Altman, E., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of banking & finance, 18(3), 505-59. https://doi.org/10.1016/0378-4266(94)90007-8

Aminian, F., Suarez, E., Aminiam , M., & Walz, D. (2006). Forecasting economic data with neural networks. Computational Economics, 28(1), 71-88. https://doi.org/10.1007/s10614-006-9041-7

Atiya, A. (2001). Bankruptcy prediction for credit risk using neural networks: A survey and new results. IEEE Transactions on neural networks, 12(4), 929-935. https://doi.org/10.1109/72.935101

Back, B., Oosterom, G., Sere, K., & Van Wezel, M. (1994). A comparative study of neural networks in bankruptcy prediction. Proc. Conf. on Artificial Intelligence Res. in Finland, 140-148. Obtenido de https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.137.2473&rep=rep1&type=pdf

Baek, J., & Cho, S. (2003). Bankruptcy prediction for credit risk using an auto-associative neural network in Korean firms. In 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003., 25-29. https://doi.org/10.1109/CIFER.2003.1196237

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

Barrow, D., & Crone, S. (2016). Cross-validation aggregation for combining autoregressive neural network forecasts. International Journal of Forecasting, 32(4), 1120–1137. https://doi.org/10.1016/j.ijforecast.2015.12.011

Beaver, W. (1966). Financial Ratios As Predictors of Failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171

Bešlić, D., Jakšić, D., Bešlić, I., & Andrić, M. (2018). Insolvency prediction model of the company: the case of the Republic of Serbia. Economic research-Ekonomska istraživanja, 31(1), 139-157. https://doi.org/10.1080/1331677X.2017.1421990

Binner, J., Gazely, A., Chen, S., & Chie, B. (2004). Financial innovation and Divisia money in Taiwan: Comparative evidence from neural network and vector error‐correction forecasting models. Contemporary Economic Policy, 22(2), 213-224. https://doi.org/10.1093/cep/byh015

Boyacioglu, M., Kara, Y., & Baykan, Ö. (2009). Predicting bank financial failures using neural networks, support vector machines and multivariate statistical methods: A comparative analysis in the sample of savings deposit insurance fund (SDIF) transferred banks in Turkey. Expert Systems with Applications, 36(2), 3355-3366. https://doi.org/10.1016/j.eswa.2008.01.003

Brigham, E., & Ehrhardt, M. (2007). Financial Management: Theory and Practice (14 ed.). South-Western Cengage Learning.

Brockett, P., Cooper, W., Golden, L., & Pitaktong, U. (1994). A neural network method for obtaining an early warning of insurer insolvency. Journal of Risk and Insurance, 402-424. https://doi.org/10.2307/253568

Caporale, G., Cerrato, M., & Zhang, X. (2017). Analysing the determinants of insolvency risk for general insurance firms in the UK. Journal of Banking and Finance, 84, 107-122. https://doi.org/10.1016/j.jbankfin.2017.07.011

Charalambous, C., Charitou, A., & Kaourou, F. (2000). Comparative analysis of artificial neural network models: Application in bankruptcy prediction. Annals of operations research, 99(1), 403-425. https://doi.org/10.1023/A:1019292321322

Conti, D., Simó, C., & Rodríguez, A. (2005). Teoría de carteras de inversión para la diversificación del riesgo: enfoque clásico y uso de redes neuronales artificiales (RNA). Ciencia e Ingeniería, 26(1), 35-42. Obtenido de https://www.redalyc.org/pdf/5075/507550773006.pdf

Corazza, M., De March, D., & di Tollo, G. (2021). Design of adaptive Elman networks for credit risk assessment. Quantitative Finance, 21(2), 323–340. https://doi.org/10.1080/14697688.2020.1778175

Del Carpio Gallegos, J. (2005). Las Redes Neuronales en las Finanzas. Revista de la Facultad de Ingeniería Industrial, 8(2), 28-32. http://200.62.146.34/bitstream/handle/123456789/1936/industrial_data04v8n2_2005.pdf?sequence=1&isAllowed=y

Dinca, G., Baba, M., Dinca, M., Dauti, B., & Deari, F. (2017). Insolvency risk prediction using the logit and logistic models: Some evidences from Romania. Economic Computation and Economic Cybernetics Studies and Research, 51(4), 139-157.

Do Prado, J., De Melo, F., Carvalho, G., & Ribeiro, A. (2019). Analysis of credit risk faced by public companiesin Brazil: an approach based on discriminant analysis, logistic regression and artificial neural networks. Estudios Gerenciales, 35(153), 347–360

Du Jardin, P. (2010). Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy. Neurocomputing, 73, 2047-2060. https://doi.org/10.1016/j.neucom.2009.11.034

Ecer, F. (2013). Comparing the bank failure prediction performance of neural networks and support vector machines: The Turkish case. Economic research-Ekonomska istraživanja, 26(3), 81-98. https://doi.org/10.1080/1331677X.2013.11517623

FitzPatrick, P. (1932). Average Ratios of Twenty Representative Industrial Failures. The Certified Public Accountant, 13-18.

Giacosa, E., Halili, E., Mazzoleni, A., Teodori, C., & Veneziani, M. (2016). Re-estimation of company insolvency prediction models: survey on Italian manufacturing companies. Corporate Ownership and Control, 14(1-1), 159-174. http://doi.org/10.22495/cocv14i1c1p1

Hensher, D., Jones, S., & Greene, W. (2007). An error component logit analysis of corporate bankruptcy and insolvency risk in Australia. Economic Record, 83(260), 86-103. https://doi.org/10.1111/j.1475-4932.2007.00378.x

Khediri, K., Charfeddine, L., & Yousseef, S. (2015). Islamic versus conventional banks in the GCC countries: A comparative study using classification techniques. Research in International Business and Finance, 33, 75-98. https://doi.org/10.1016/j.ribaf.2014.07.002

Kim, S. (2011). Prediction of hotel bankruptcy using support vector machine, artificial neural network, logistic regression, and multivariate discriminant analysis. The Service Industries Journal, 31(3), 441-468. https://doi.org/10.1080/02642060802712848

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), 1-28. https://doi.org/10.1016/j.ejor.2006.08.043

Lahmiri, S., & Bekiros, S. (2019). Can machine learning approaches predict corporate bankruptcy? Evidence from a qualitative experimental design. Quantitative Finance, 19(9), 1569–1577. https://doi.org/10.1080/14697688.2019.1588468

Lepetit, L., & Strobel, F. (2015). Bank Insolvency Risk and Z-Score Measures: A Refinement. Finance Research Letters, 13, 214-224. http://doi.org/10.1016/j.frl.2015.01.001

Leshno, M., & Spector, Y. (1996). Neural network prediction analysis: The bankruptcy case. Neurocomputing, 10(2), 125-147. https://doi.org/10.1016/0925-2312(94)00060-3

Mohammadian, M. (2012). Artificial intelligence applications for risk analysis, risk prediction and decision making in disaster recovery planning. IFIP Advances in Information and Communication Technology, 382, 155-65. https://doi.org/10.1007/978-3-642-33412-2_16

Nadali, L., Corazza, M., Parpinel, F., & Pizzi, C. (2020). Recurrent ANNs for Failure Predictions on Large Datasets of Italian SMEs. In Neural Approaches to Dynamics of Signal Exchanges (Vol. 151, pp. 145–156). https://doi.org/10.1007/978-981-13-8950-4

Nasir, M., John, R., Bennett, S., & Russell, D. (2000). Predicting corporate bankruptcy using modular neural networks. In Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr), 86-91. https://doi.org/10.1109/CIFER.2000.844606

Obermann, L., & Waack, S. (2015). Demonstrating non-inferiority of easy interpretable methods for insolvency prediction. Expert Systems with Applications, 42(23), 9117-9128. https://doi.org/10.1016/j.eswa.2015.08.009

Odom, M., & Sharda, R. (1990). A neural network model for bankruptcy prediction. In 1990 IJCNN International Joint Conference on neural networks., 163-168. https://doi.org/10.1109/IJCNN.1990.137710

Ohlson, J. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1). https://doi.org/10.2307/2490395

Orellana, I., Tonon, L., Reyes, M., Pinos, L., & Cevallos, E. (2020). Riesgos financieros en el sector manufacturero del Ecuador (1.a ed.). Casa editora Universidad del Azuay.

Rodríguez, M., Piñeiro, C., & De Llano, P. (2014). Determinación del riesgo de fracaso financiero mediante la utilización de modelos paramétricos, de inteligencia artificial, y de información de auditoría. Estudios de economía, 41(2), 187-217. http://doi.org/10.4067/S0718-52862014000200002

Romani, G., Bravo, A., Aroca, P., Aguirre, N., Vega, P., y Carrazana, J. (2002). Modelos de clasificación y predicción de quiebra de empresas: Una aplicación a empresas chilenas. Forum empresarial, 7(1). https://dialnet.unirioja.es/servlet/articulo?codigo=6230228

Rubin, P. (1990). A comparison of linear programming and parametric approaches to the two‐group discriminant problem. Decision Sciences, 21(2), 373-386. https://doi.org/10.1111/j.1540-5915.1990.tb01691.x

Rustam, Z., & Yaurita, F. (2018). Insolvency Prediction in Insurance Companies Using Support Vector Machines and Fuzzy Kernel C-Means. Journal of Physics: Conference Series, 1028(1), 012118. https://doi.org/10.1088/1742-6596/1028/1/012118

Taffler, R. (1983). The assessment of company solvency and performance using a statistical model. Accounting and Business Research, 13(52), 295-308. https://doi.org/10.1080/00014788.1983.9729767

Tay, F., & Shen, L. (2002). Economic and financial prediction using rough sets model. European Journal of Operational Research, 141(3), 641-659. https://doi.org/10.1016/S0377-2217(01)00259-4

Thakor, A. (2018). Post-crisis regulatory reform in banking: Address insolvency risk, not illiquidity! Journal of Financial Stability, 37, 107-111. https://doi.org/10.1016/j.jfs.2018.03.009

Tseng, F., & Hu, Y. (2010). Comparing four bankruptcy prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Expert Systems with Applications, 37(3), 1846-1853. https://doi.org/10.1016/j.eswa.2009.07.081

Valdes, M., Aleaga, A., y Vidal, G. (2014). Redes neuronales artificiales en la predicción de insolvencia. Un cambio de paradigma ante recetas tradicionales de prácticas empresariales. Enfoque UTE, 5(2), 38-58. https://doi.org/10.29019/enfoqueute.v5n2.39

Wilson, R., & Sharda, R. (1994). Bankruptcy prediction using neural networks. Decision support systems, 11(5), 545-557. https://doi.org/10.1016/0167-9236(94)90024-8

Zhang, G., Hu, M., Patuwo, B., & Indro, D. (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. European journal of operational research, 116(1), 16-32. https://doi.org/10.1016/S0377-2217(98)00051-4