Bankruptcy Prediction Using Stacked Auto-Encoders

Soui, Makram; Smiti, Salima; Mkaouer, Mohamed Wiem; Ejbali, Ridha

doi:10.1080/08839514.2019.1691849

Cited by 26 publications

(9 citation statements)

References 37 publications

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“…Ensemble approaches have been widely applied across several disciplines in the domain of credit scoring and bankruptcy prediction [99], [139], [140], [141] including the latest on personal bankruptcy prediction on imbalanced dataset can be found in several literatures [79], [88], [89], [141], [142].…”

Section: A Related Method/technique In Handling Highly Imbalanced Mul...mentioning

confidence: 99%

Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

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Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine learning has been an essential aspect in providing unbiased solutions. From the earliest literatures published on highly imbalanced data until recently, machine learning research has focused mostly on binary classification data problems. Research on highly imbalanced multi-class data is still greatly unexplored when the need for better analysis and predictions in handling Big Data is required. This study focuses on reviews related to the models or techniques in handling highly imbalanced multi-class data, along with their strengths and weaknesses and related domains. Furthermore, the paper uses the statistical method to explore a case study with a severely imbalanced dataset. This article aims to (1) understand the trend of highly imbalanced multi-class data through analysis of related literatures; (2) analyze the previous and current methods of handling highly imbalanced multi-class data; (3) construct a framework of highly imbalanced multi-class data. The chosen highly imbalanced multi-class dataset analysis will also be performed and adapted to the current methods or techniques in machine learning, followed by discussions on open challenges and the future direction of highly imbalanced multi-class data. Finally, for highly imbalanced multi-class data, this paper presents a novel framework. We hope this research can provide insights on the potential development of better methods or techniques to handle and manipulate highly imbalanced multiclass data.

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Section: A Related Method/technique In Handling Highly Imbalanced Mul...mentioning

confidence: 99%

Survey on Highly Imbalanced Multi-class Data

Hamid¹,

Yusoff²,

Mohamed³

2022

IJACSA

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“…The authors also mentioned that deep learning models are difficult to interpret. The efficiency of autoencoders in terms of bankruptcy prediction was proven by Soui et al (2020) , where stacked autoencoders with the softmax classifier noticeably outperformed reference methods such as an SVM, a DT, LR, and an NN. Again, the authors pointed out the problem of interpretability, which is crucial in the field of bankruptcy prediction ( Aljawazneh et al, 2021 ) also compared various deep learning techniques for bankruptcy prediction tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

Bankruptcy prediction using ensemble of autoencoders optimized by genetic algorithm

Kanász,

Gnip,

Zoričák

et al. 2023

PeerJ Computer Science

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The prediction of imminent bankruptcy for a company is important to banks, government agencies, business owners, and different business stakeholders. Bankruptcy is influenced by many global and local aspects, so it can hardly be anticipated without deeper analysis and economic modeling knowledge. To make this problem even more challenging, the available bankruptcy datasets are usually imbalanced since even in times of financial crisis, bankrupt companies constitute only a fraction of all operating businesses. In this article, we propose a novel bankruptcy prediction approach based on a shallow autoencoder ensemble that is optimized by a genetic algorithm. The goal of the autoencoders is to learn the distribution of the majority class: going concern businesses. Then, the bankrupt companies are represented by higher autoencoder reconstruction errors. The choice of the optimal threshold value for the reconstruction error, which is used to differentiate between bankrupt and nonbankrupt companies, is crucial and determines the final classification decision. In our approach, the threshold for each autoencoder is determined by a genetic algorithm. We evaluate the proposed method on four different datasets containing small and medium-sized enterprises. The results show that the autoencoder ensemble is able to identify bankrupt companies with geometric mean scores ranging from 71% to 93.7%, (depending on the industry and evaluation year).

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“…Other validation methods, such as leave-one-out cross-validation or holdout cross-validation, were not applied in this study as they are not applicable to studies with large data samples (Molinaro et al, 2005). Even though some studies (Boughaci & Alkhawaldeh, 2020;Jabeur et al, 2021Jabeur et al, , 2022Soui et al, 2020) used holdout cross-validation, most researchers (Farooq & Qamar, 2019;Hajek et al, 2014;Lahmiri et al, 2020;Le, 2022;Liang et al, 2015;Tang et al, 2020;Uthayakumar et al, 2020;Wang et al, 2018) prefer k-fold cross-validation, especially 10-fold validation, as it reduces the bias and variance.…”

Section: Such a Data Sample Size Represents Almost A Half Of All Smes Inmentioning

confidence: 99%

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

Papíková

Papík

2022

Intell Sys Acc Fin Mgmt

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Small and medium-sized enterprises are the pillars of an economy, and their poor performance has a negative impact on living standards of population and country development. This study analyzes real-life data of 89,851 small and medium-sized enterprises, out of which 295 have declared bankruptcy. The analysis is performed via 27 financial ratios. The study framework combines seven classifications and three resampling and seven feature selection methods. Out of all classification methods applied, CatBoost has achieved the best results for all combinations of resampling and feature selection methods. CatBoost surpassed the results of other classification methods for the area under curve parameter, achieving a value of 99.95%. The application of resampling methods on different classification models has not identified a statistically significant level of improvement in any of the resampling methods. This finding has also been observed for feature selection methods. Based on these findings, we assume that individual resampling and feature selection methods do not improve model performance compared with the original imbalanced sample's results.Our results suggest that, even though the data sample may be significantly imbalanced with a minority of bankrupt companies, most classification algorithms can handle this imbalance and achieve interesting results. Moreover, our findings provide broad practical application for all stakeholders who could need to detect bankrupting companies.

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Bankruptcy Prediction Using Stacked Auto-Encoders

Cited by 26 publications

References 37 publications

Survey on Highly Imbalanced Multi-class Data

Survey on Highly Imbalanced Multi-class Data

Bankruptcy prediction using ensemble of autoencoders optimized by genetic algorithm

Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises

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