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Small- and medium-enterprises bankruptcy dataset

Bankruptcy prediction is a long-standing issue that receives significant attention of academic researchers and industry practitioners. Most of the papers on bankruptcy prediction focus on companies that are listed on the stock market, and there are only limited data for the rest of the companies. Th...

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Detalles Bibliográficos
Autores principales: Drotár, Peter, Gnip, Peter, Zoričak, Martin, Gazda, Vladimír
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6706765/
https://www.ncbi.nlm.nih.gov/pubmed/31463350
http://dx.doi.org/10.1016/j.dib.2019.104360
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author Drotár, Peter
Gnip, Peter
Zoričak, Martin
Gazda, Vladimír
author_facet Drotár, Peter
Gnip, Peter
Zoričak, Martin
Gazda, Vladimír
author_sort Drotár, Peter
collection PubMed
description Bankruptcy prediction is a long-standing issue that receives significant attention of academic researchers and industry practitioners. Most of the papers on bankruptcy prediction focus on companies that are listed on the stock market, and there are only limited data for the rest of the companies. These companies, not indexed at any stock market, represent a significant part of the economy. The presented dataset consists of financial ratios of Slovak companies. There are 21 distinctive financial ratios which are available for three consecutive years prior to evaluation year in which companies may have filed for bankruptcy or not. The companies come from four different industries - agriculture, construction, manufacture, retail. We provide data for four consecutive years 2013–2016 for each industry. All companies are categorized as small-medium enterprises according to EU classification. Prediction performance results on this dataset are published in the research paper “Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets” (Zoričák et al., 2019).
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spelling pubmed-67067652019-08-28 Small- and medium-enterprises bankruptcy dataset Drotár, Peter Gnip, Peter Zoričak, Martin Gazda, Vladimír Data Brief Economics, Econometrics and Finance Bankruptcy prediction is a long-standing issue that receives significant attention of academic researchers and industry practitioners. Most of the papers on bankruptcy prediction focus on companies that are listed on the stock market, and there are only limited data for the rest of the companies. These companies, not indexed at any stock market, represent a significant part of the economy. The presented dataset consists of financial ratios of Slovak companies. There are 21 distinctive financial ratios which are available for three consecutive years prior to evaluation year in which companies may have filed for bankruptcy or not. The companies come from four different industries - agriculture, construction, manufacture, retail. We provide data for four consecutive years 2013–2016 for each industry. All companies are categorized as small-medium enterprises according to EU classification. Prediction performance results on this dataset are published in the research paper “Bankruptcy prediction for small- and medium-sized companies using severely imbalanced datasets” (Zoričák et al., 2019). Elsevier 2019-08-06 /pmc/articles/PMC6706765/ /pubmed/31463350 http://dx.doi.org/10.1016/j.dib.2019.104360 Text en © 2019 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Economics, Econometrics and Finance
Drotár, Peter
Gnip, Peter
Zoričak, Martin
Gazda, Vladimír
Small- and medium-enterprises bankruptcy dataset
title Small- and medium-enterprises bankruptcy dataset
title_full Small- and medium-enterprises bankruptcy dataset
title_fullStr Small- and medium-enterprises bankruptcy dataset
title_full_unstemmed Small- and medium-enterprises bankruptcy dataset
title_short Small- and medium-enterprises bankruptcy dataset
title_sort small- and medium-enterprises bankruptcy dataset
topic Economics, Econometrics and Finance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6706765/
https://www.ncbi.nlm.nih.gov/pubmed/31463350
http://dx.doi.org/10.1016/j.dib.2019.104360
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