Small- and medium-enterprises bankruptcy dataset

Drotár, Peter; Gnip, Peter; Zoričak, Martin; Gazda, Vladimír

doi:10.1016/j.dib.2019.104360

Cited by 7 publications

(4 citation statements)

References 1 publication

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“…In this study, we utilized a bankruptcy dataset ( Drotár et al, 2019 ) composed of the financial ratios of thousands of small and medium-sized enterprises (SMEs) operating in the Slovak Republic during 2010–2016. Data were acquired from publicly accessible financial statements.…”

Section: Datamentioning

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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Section: Datamentioning

confidence: 99%

Bankruptcy prediction using ensemble of autoencoders optimized by genetic algorithm

Kanász,

Gnip,

Zoričák

et al. 2023

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…The prediction models used were decision trees, neural networks, support vector machines, naïve Bayes, and K-means clustering. Drotár et al (2019) presented bankruptcy prediction data for 2013-2016 for Slovak companies in agriculture, construction, manufacturing and retail. This dataset was extremely imbalanced, with 63 bankrupt and 25932 not.…”

Section: Poland Datamentioning

confidence: 99%

Balancing and variable reduction of firm bankruptcy data

Olson,

Chae

2022

Journal of Supply Chain Management Science

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Financial stress experienced by supply chain elements causes stress to all members. Predictive data mining is a common tool for predicting bankruptcy. Bankruptcy often involves highly imbalanced datasets with a large number of potential variables, with bankrupt firms being by far the minority case. This study uses data from four studies of firm bankruptcy and examines the impact of data balancing and variable selection on model accuracy. The models used are random forest and gradient boosting based on decision trees, logistic regression, neural networks, and support vector machines. Two machine learning methods are used to trim the number of variables. Stepwise regression and entropy from decision trees are used to generate reduced variable sets. The complexity parameter was used to set levels on number of variables using the entropy (decision tree) option. The impact of reducing variables is examined. Error metrics used were type I and type II error (sensitivity and specificity), overall average error (accuracy), and area under the recall curve (AuC). The average error of extreme gradient boosting and random forest models was found to be better than support vector machines, which had a slight advantage over logistic regression and neural networks. Variable reduction was found to lead to mixed results with respect to relative accuracy. Overall accuracy increased with slight reduction in the number of variables (using stepwise regression), but deteriorated as the number of variables was reduced to the smaller number of variables. The experiments into balancing found that unbalanced data had high error rates, which dropped a great deal with even 10 percent balancing, but balancing beyond 10 percent was found to provide little additional accuracy.

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“…Minority class samples represent financially distressed (bankrupt) companies while majority class samples stand for solvent (nonbankrupt) companies. These are part of the larger dataset described in Drotár et al (2019).…”

Section: Datamentioning

confidence: 99%

“…Minority class samples represent financially distressed (bankrupt) companies while majority class samples stand for solvent (non-bankrupt) companies. These are part of the larger dataset described in Drotár et al (2019) . The Bank marketing dataset is related with direct marketing campaign of a Portuguese banking institution based on a phone calls.…”

Section: Computer Experimentsmentioning

confidence: 99%

Selective oversampling approach for strongly imbalanced data

Gnip

Vokorokos

Drotár

2021

PeerJ Computer Science

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Challenges posed by imbalanced data are encountered in many real-world applications. One of the possible approaches to improve the classifier performance on imbalanced data is oversampling. In this paper, we propose the new selective oversampling approach (SOA) that first isolates the most representative samples from minority classes by using an outlier detection technique and then utilizes these samples for synthetic oversampling. We show that the proposed approach improves the performance of two state-of-the-art oversampling methods, namely, the synthetic minority oversampling technique and adaptive synthetic sampling. The prediction performance is evaluated on four synthetic datasets and four real-world datasets, and the proposed SOA methods always achieved the same or better performance than other considered existing oversampling methods.

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Small- and medium-enterprises bankruptcy dataset

Cited by 7 publications

References 1 publication

Bankruptcy prediction using ensemble of autoencoders optimized by genetic algorithm

Bankruptcy prediction using ensemble of autoencoders optimized by genetic algorithm

Balancing and variable reduction of firm bankruptcy data

Selective oversampling approach for strongly imbalanced data

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