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

Dzik-Walczak Aneta () and Odziemczyk Maciej
Additional contact information
Dzik-Walczak Aneta: Faculty of Economic Sciences, University of Warsaw, Poland
Odziemczyk Maciej: Faculty of Economic Sciences, University of Warsaw, Poland

Central European Economic Journal, 2021, vol. 8, issue 55, 352-377

Abstract: 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: convolutional neural networks; machine learning; simulation; bankruptcy prediction; financial indicators (search for similar items in EconPapers)
JEL-codes: C02 C45 G33 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/ceej-2021-0024 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:ceuecj:v:8:y:2021:i:55:p:352-377:n:22

DOI: 10.2478/ceej-2021-0024

Access Statistics for this article

Central European Economic Journal is currently edited by Anna Matysiak

More articles in Central European Economic Journal from Sciendo
Bibliographic data for series maintained by Peter Golla ().