Modelling Cross-Sectional Tabular Data using Convolutional Neural Networks: Prediction of Corporate Bankruptcy in Poland

• Autorzy: Aneta Dzik-Walczak , Maciej Odziemczyk
• Języki publikacji: EN

Abstrakty

EN

The paper deals with the topic of modelling the probability of bankruptcy of Polish enterprises using convolutional neural networks. Convolutional networks take images as input, so it was thus necessary to apply the method of converting the observation vector to a matrix. Benchmarks for convolutional networks were logit models, random forests, XGBoost, and dense neural networks. Hyperparameters and model architecture were selected based on a random search and analysis of learning curves and experiments in folded, stratified cross-validation. In addition, the sensitivity of the results to data preprocessing was investigated. It was found that convolutional neural networks can be used to analyze cross-sectional tabular data, especially for the problem of modelling the probability of corporate bankruptcy. In order to achieve good results with models based on parameters updated by a gradient (neural networks and logit), it is necessary to use appropriate preprocessing techniques. Models based on decision trees have been shown to be insensitive to the data transformations used. (original abstract)

Słowa kluczowe

EN

Machine learning; Neural networks; Enterprises bankruptcy forecasting

PL

Uczenie maszynowe; Sieci neuronowe; Prognozowanie upadłości przedsiębiorstwa

• Czasopismo: Ekonomia / Uniwersytet Warszawski
• Rocznik: 2021
• Tom: 8
• Numer: nr 55
• Strony: 352--377

Twórcy

autor

Aneta Dzik-Walczak

• University of Warsaw, Poland

autor

Maciej Odziemczyk

• University of Warsaw, Poland

Bibliografia

• Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609
• Box, G. E., Cox, D.R. (1964). An analysis of transformations. Journal of the Royal Statistical Society: Series B (Methodological), 26(2), 211-243
• Chen, M. Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38(9), 11,261-11,272
• Heo, J., & Yang, J. Y. (2014). AdaBoost based bankruptcy forecasting of Korean construction companies. Applied Soft Computing, 24, 494-499
• Hinton, G., Nitish S. & Swersky K. (2012). Divide the gradient by a running average of its recent magnitude. Coursera: Neural Networks for Machine Learning. Technical Report.
• Hosaka, T. (2019). Bankruptcy prediction using imaged financial ratios and convolutional neural networks. Expert Systems with Applications, 117, 287-299
• Kim, M. J., & Kang, D. K. (2010). Ensemble with neural networks for bankruptcy prediction. Expert Systems with Applications, 37(4), 3,373-3,379
• Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1,097-1,105
• LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324
• Lin, M., Chen, Q., & Yan, S. (2013). Network in network. arXiv preprint arXiv:1312.4400. 57
• Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 109-131
• Pawełek, B. (2019). Extreme Gradient Boosting Method in the Prediction of Company Bankruptcy. Statistics in Transition. New Series, 20(2), 155-171
• Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
• Son, H., Hyun, C., Phan, D., & Hwang, H. J. (2019). Data analytic approach for bankruptcy prediction. Expert Systems with Applications, 138, 112,816
• Szegedy, C. et al. (2015). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1-9
• Tomczak, S. (2014). Comparative analysis of liquidity ratios of bankrupt manufacturing companies. Business and Economic Horizons, 10(3), 151-164
• Tomczak, S. (2014). Comparative analysis of the bankrupt companies of the sector of animal slaughtering and processing. Equilibrium. Quarterly Journal of Economics and Economic Policy, 9(3), 59-86
• Tomczak, S. (2014). The early warning system. Journal of Management and Financial Sciences, 7(16), 51-74
• Veganzones, D., & Séverin, E. (2018). An investigation of bankruptcy prediction in imbalanced datasets. Decision Support Systems, 112, 111-124
• Vellamcheti, S., & Singh, P. (2020). Class Imbalance Deep Learning for Bankruptcy Prediction. In 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T), 421-425. IEEE
• Wyrobek, J., & Kluza, K. (2018). Efficiency of gradient boosting decision trees technique in Polish companies' bankruptcy prediction. In International Conference on Information Systems Architecture and Technology, 24-35
• Yeo, I. K., & Johnson, R. A. (2000). A new family of power transformations to improve normality or symmetry. Biometrika, 87(4), 954-959
• Zhang, G., 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
• Zięba, 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, 93-101
• Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 59-82

Identyfikatory

DOI

10.2478/ceej-2021-0024