2024
doi: 10.12720/jait.15.8.956-964
Bankruptcy Prediction of Greek Small and Medium-Sized Enterprises Using Imbalance Data
Vassiliki Papadouli
,
Elias Houstis ,
Manolis Vavalis*
- Department of Electrical and Computer Engineering, University of Thessaly, Volos, Greece
- Manuscript receivedAugust 24, 2023
- revisedSeptember 25, 2023
- acceptedMarch 19, 2024
- publishedAugust 15, 2024
Abstract
Detecting financial distress in businesses that lead to bankruptcy has been studied for a century. Building large labeled bankruptcy data sets is non-trivial and challenging. We produce an imbalanced data set of bankrupt and non-bankrupt Greek Small and Medium-sized Enterprises (SMEs) covering three years before the bankruptcy data and utilize it to test the bankruptcy predictive ability of well-known statistical and several supervised classifiers. A set of machine learning classifiers has been utilized demonstrating good predictive ability. The AutoML supervised classifier applied to the entire imbalanced data set shows worth noticing performance. We implement several supervised algorithms in a semi-supervised framework to remedy the imbalance of the data set and observed better overall performance than the supervised ones. To measure the effect of combining data from compatible European and Greek markets, we developed customized and AutoML-based transfer deep learning classifiers to predict the bankruptcy of Greek SMEs. Our findings justify transfer learning as an alternative methodology for studying bankruptcy prediction-related problems.
Keywords
- bankruptcy prediction
- statistical models
- hazard models
- supervised machine learning
- self-training
- semi-supervised
- transfer learning
How to Cite
Vassiliki Papadouli, Elias Houstis, and Manolis Vavalis, "Bankruptcy Prediction of Greek Small and Medium-Sized Enterprises Using Imbalance Data," Journal of Advances in Information Technology, vol. 15, no. 8, pp. 956-964, 2024. https://doi.org/10.12720/jait.15.8.956-964
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
