The usefulness of Deep Learning in the prediction of business failure at the European level
DOI:
https://doi.org/10.46661/revmetodoscuanteconempresa.5172Keywords:
business failure, Deep Learning, machine learning, financial ratios, prediction modelAbstract
In this paper we intend to substantiate the usefulness of Deep Learning, especially feedforward neuronal networks, in the prediction of business failure. This methodology provides very good results in terms of predictive performance when large sample sizes are available. Therefore, we have developed a business failure prediction model for European companies, based on this algorithm on a sample of 61,624 companies, of which 12,128 were declared bankrupt in 2016. As independent variables were considered ratios, and economic and financial data obtained from the financial statements for the year preceding the date of failure. Deep Learning achieves a predictive performance of 94%, where companies with larger size and lower solvency are more prone to failure. The obtained results have been tested on an independent test sample, different from that used to estimate and train the model.
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Abbott, A., Deshowitz, A., Murray, D., & Larson, E. (2018). WalkNet: A Deep Learning Approach to Improving Sidewalk Quality and Accessibility. SMU Data Science Review, 1(1-7), 1-12.
Ahn, B.S., Cho, S.S., & Kim, C.Y. (2000). The integrated methodology of rough set theory and artificial neural network for business failure prediction. Experts Systems with Aplications, 18, 65-74.
Alfaro, E., Gámez, M., & García, N. (2007a). A Boosting Approach for Corporate Failure Prediction. Applied Intelligence, 27(1), 29-37.
Alfaro, E., Gámez, M., & García, N. (2007b). Multiclass Corporate Failure Prediction by Adaboost.M1. International Advances in Economic Research, 13(3), 301-312.
Alfaro, E., Gámez, M., García, N., (2008). Linear Discriminant Analysis Versus Adaboost for Failure Forecasting. Revista Española de Financiación & Contabilidad, 37(137), 13-32.
Alfaro, E., García, N., Gámez, M., & Elizondo, D. (2008). Bankruptcy Forecasting: An Empirical Comparison of AdaBoost and Neural Networks. Decisión Support Systems, 45(1), 110-122.
Alfaro, E., Gámez, M., & García, N. (2013). Adabag: An R Package for Classification with Boosting and Bagging. Journal of Statistical Software, 54(2), 1-35.
Bell, T.B., Ribar, G.S., & Verchio, J. (1990). Neural Nets Versus Logistic Regression: A Comparison of Each Model’s Ability to Predict Commercial Bank Failures. En Srivastava, R.P. (ed). Proceedings of the 1990 Deloitte & Touche/University of Kansas Symposium in Auditing Problems, 29-53. Auditing Symposium X. Universidad de Kansas, Lawrence.
Boritz, J., & Kennedy, D.B. (1995). Effectiveness of Neuronal Network Types for prediction of Business Failure. Experts Systems with Aplications, 9(4), 503-512.
Brownlee, J. (2017). What is the Difference Between a Parameter and a Hyperparameter? https://machinelearningmastery.com/difference-between-a-parameter-and-a-hyperparameter/ (visitado el 28 de marzo de 2018)
Chaudhuri, A., & Ghosh, S. K. (2017). Bankruptcy Prediction through Soft Computing based Deep Learning Technique. Germany: Springer.
Cook, D. (2017). Practical Machine Learning with H2O. Sebastopol, CA (USA): O’Reilly Media.
Cortes, C., & Mohri, M. (2004). AUC optimization vs, error rate minimization, Advances in Neural Information Processing Systems, 16(16), 313-320.
Crawford, C. (2016). An Introduction to Deep Learning. https://blog.algorithmia.com/introduction-to-deep-learning-2016/ (visitado el 30 de marzo de 2018)
Deng, L., & Yu, D. (2013). Deep Learning: Methods and Applications. Foundations and Trends® in Signal Processing, 7(3-4), 197-387. http://dx.doi.org/10.1561/2000000039
Díaz, Z., Fernández, J., & Segovia, M.J. (2004). Sistemas de inducción de reglas y árboles de decisión aplicados a la predicción de insolvencias en empresas aseguradoras. Documentos de Trabajo de la Facultad de Ciencias Económicas y Empresariales, 9. Universidad Complutense de Madrid.
Fletcher, D., & Goss, E. (1993). Application Forecasting with Neural Networks: An Application Using Bankruptcy Data. Information and Management, 24, 159-167.
Friedman, J.H. (2002). Stochastic Gradient Boosting. Journal of Computational Statistics & Data Analysis, 38(4), 367-378.
Goodfellow, I., Bengio, Y., & Aaron, C., (2016). Deep Learning. Cambridge, MA, USA: The MIT Press.
Gulli, A., & Pal, S. (2017). Deep Learning with Keras. Implement neural networks with Keras on Theano and Tensorflow. Birmingham, Reino Unido: Packt Publishing Ltd.
Jayanthi, J., Kaur, G., & Suresh, K. (2017). Financial forecasting using decision tree (reptree & c4.5) and neural networks (k*) for handling the missing values. ICTAC Journal on Soft Computing, 7(3), 1473-1477.
Khashman, A. (2010). Neural networks for credit risk evaluation: Investigation of different neural models and learning schemes. Expert Systems with Applications, 37(9), 6233-6239.
Kim, M.J., & Kang, D.K. (2010). Ensemble with Neural Networks for Bankruptcy Prediction. Expert System with Applications, 37(4), 3373-3379.
Kim, M.J., Kang, D.K., & Kim, H.B. (2015). Geometric Mean Based Boosting Algorithm with Over-Sampling to Resolve Data Imbalance Problem for Bankruptcy Prediction. Expert Systems with Applications, 42(3), 1074-1082.
Kim, S.Y., & Upneja, A. (2014). Predicting Restaurant Financial Distrees Using Decisión Tree and AdaBoosted Decision Tree Models. Economic Modelling, 35, 354-362.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436-444. http://dx.doi.org/10.1038/nature14539
Ledell, E. (2018). Deep Learning with H2O. https://github.com/ledell (visitado el 20 de marzo de 2018)
López, F.J., & Pastor, I. (2015). Bankruptcy visualization and prediction using neural netwoks: A study of U.S. comercial banks. Experts Systems with Aplications, 42, 2857-2869.
Momparler, A., Carmona, P., & Climent, F.J. (2016). La predicción del fracaso bancario con la metodología Boosting Classification Tree. Revista Española de Financiación y Contabilidad, 45(1), 63-91.
Natekin A., & Knoll, A. (2013). Gradient Boosting Machines. A Tutorial. Frontiers in Neurorobotics. 7(21), 1-21.
Odom, M.D., & Sharda, R. (1992). A Neural Network Model for Bankruptcy Prediction. En R.R. Trippi and E. Turban (Eds.). Neural networks in Finance and Investing. Chicago: Probus Publishing.
Patel, V., Armstrong, D., Ganguli, M.P., Roopa, S., Kantipudi, N., Aalbashir, S., & Kamarth, M. (2016). Deep Learning in Gastrointestinal Endoscopy. Critical Reviews™ in Biomedical Engineering, 44(6), 493-504. http://dx.doi.org/10.1615/CritRevBiomedEng.2017025035
Popescu, M.E., Andreica, M., & Popescu, I-P. (2017). Decision support solution to business failure prediction. Proceedings of the International Management Conference, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, 11(1), 99-106.
Pozuelo, J., Martínez, J., & Carmona, P. (2018). Análisis de la utilidad del algoritmo Gradient Boosting Machine (GBM) en la predicción del fracaso empresarial. Spanish Journal of Finance and Accounting / Revista Española de Financiación y Contabilidad, 47(4) 507-532. http://dx.doi.org/10.1080/02102412.2018.1442039.
R Core Team (2019). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. URL http://www.R-project.org/.
Ravi, P., & Ravi, V. (2007). Bankruptcy Prediction in Banks and Firms ViaStatistical and Intelligent Techniques - A Review. European Journal of Operational Research, 180(1), 1-28.
Romero, F. (2013). Alcances y limitaciones de los modelos de capacidad predictiva en el análisis del fracaso empresarial. AD-minister, 23, 45-70.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.
Tam, K.Y. (1991). Neural Network Models and the Prediction of Bank Bankruptcy, Omega, 19(5), 429-445.
Tam, K.Y., & Kiang, M.Y. (1992). Managerial Applications of Neural Networks: The Case of Bank Failure Predictions. Management Science, 38(7), 926-947.
Tascón, M.T., & Castaño, F.J. (2012). Variables y modelos para la identificación y predicción del fracaso empresarial: revisión de la investigación empírica reciente. Revista de Contabilidad, 15(1), 7-58.
The H2O.ai team (2019). h2o: R Interface for H2O. R package version 3.26.0.2. https://CRAN.R-project.org/package=h2o.
Tseng, F-M., & Hu, Y-Ch. (2010). Comparing fou nakrupcty prediction models: Logit, quadratic interval logit, neural and fuzzy neural networks. Experts Systems with Aplications, 37, 1846-1853.
Wang, G., Ma, J., & Yang, S. (2014). An Improved Boosting Based on Feature Selection for Corporate Bankruptcy Prediction. Expert Systems with Applications, 41(5), 2353-2361.
Wang, L., Zeng, Y., & Chen, T. (2015). Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Systems with Applications, 42, 855-863.
West, D., Dellana, S., & Qian, J. (2005). Neural Network Ensemble Strategies for Financial Decision Applications. Computers & Operations Research, 32(10), 2543-2559.
Wilson, G.I., & Sharda, R. (1994). Bankruptcy Prediction Using Neural Network. Decision Support Systems, 11, 545-557.
Zhang, G.P., Hu, M.Y., Patuwo, B.E., & Indro, D.C. (1999). Artificial Neural Networks in Bankruptcy Prediction: General Framework and Cross-Validation Analysis. European Journal of Operational Research, 116(1), 16-32.
Zieba, M., Tomczak, S.K., & Tomczak, J.M. (2016). Ensemble Boosted Trees with Synthetic Features Generation in Application to Bankruptcy Prediction. Expert Systems with Applications, 58(1), 93-101.
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