Modelling cross-sectional tabular data using convolutional neural networks: Prediction of corporate bankruptcy in Poland

• Authors: Aneta Dzik-Walczak , Maciej Odziemczyk

Abstracts

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.

Keywords

EN

convolutional neural networks; machine learning; simulation; bankruptcy prediction; financial indicators

• Publisher: Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
• Journal: Central European Economic Journal
• Year: 2021
• Volume: 8
• Issue: 55
• Pages: 352-377

Dates

published

2021

Contributors

author

Aneta Dzik-Walczak

• Faculty of Economic Sciences University of Warsaw Poland

• https://orcid.org/0000000201920226

author

Maciej Odziemczyk

• Faculty of Economic Sciences University of Warsaw Poland

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Identifiers

DOI

10.2478/ceej-2021-0024

Biblioteka Nauki

1965119