Un análisis bibliométrico de la predicción de quiebra empresarial con Machine Learning

El objetivo de este artículo es presentar un análisis bibliométrico sobre el uso que han tenido las técnicas de Machine Learning (ML) en el proceso de predic­ción de quiebra empresarial a través de la revisión de la base de datos Web of Science. Este ejercicio brinda información sobre el inicio y el...

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Autores:
Franco, Yuly Andrea
Tipo de recurso:
Article of journal
Fecha de publicación:
2023
Institución:
Universidad Externado de Colombia
Repositorio:
Biblioteca Digital Universidad Externado de Colombia
Idioma:
spa
OAI Identifier:
oai:bdigital.uexternado.edu.co:001/15350
Acceso en línea:
https://bdigital.uexternado.edu.co/handle/001/15350
https://doi.org/10.18601/17941113.n22.04
Palabra clave:
Prediction;
bankruptcy;
Machine Learning;
bibliometrics.
predicción;
quiebra empresarial;
Machine Learning;
bibliometría
Rights
openAccess
License
Yuly Andrea Franco - 2023
id uexternad2_22a21533a712c0b847c3a67c0fcf5767
oai_identifier_str oai:bdigital.uexternado.edu.co:001/15350
network_acronym_str uexternad2
network_name_str Biblioteca Digital Universidad Externado de Colombia
repository_id_str
dc.title.spa.fl_str_mv Un análisis bibliométrico de la predicción de quiebra empresarial con Machine Learning
dc.title.translated.eng.fl_str_mv A Bibliometric Analysis of Business Bankruptcy Prediction with Machine Learning
title Un análisis bibliométrico de la predicción de quiebra empresarial con Machine Learning
spellingShingle Un análisis bibliométrico de la predicción de quiebra empresarial con Machine Learning
Prediction;
bankruptcy;
Machine Learning;
bibliometrics.
predicción;
quiebra empresarial;
Machine Learning;
bibliometría
title_short Un análisis bibliométrico de la predicción de quiebra empresarial con Machine Learning
title_full Un análisis bibliométrico de la predicción de quiebra empresarial con Machine Learning
title_fullStr Un análisis bibliométrico de la predicción de quiebra empresarial con Machine Learning
title_full_unstemmed Un análisis bibliométrico de la predicción de quiebra empresarial con Machine Learning
title_sort Un análisis bibliométrico de la predicción de quiebra empresarial con Machine Learning
dc.creator.fl_str_mv Franco, Yuly Andrea
dc.contributor.author.spa.fl_str_mv Franco, Yuly Andrea
dc.subject.eng.fl_str_mv Prediction;
bankruptcy;
Machine Learning;
bibliometrics.
topic Prediction;
bankruptcy;
Machine Learning;
bibliometrics.
predicción;
quiebra empresarial;
Machine Learning;
bibliometría
dc.subject.spa.fl_str_mv predicción;
quiebra empresarial;
Machine Learning;
bibliometría
description El objetivo de este artículo es presentar un análisis bibliométrico sobre el uso que han tenido las técnicas de Machine Learning (ML) en el proceso de predic­ción de quiebra empresarial a través de la revisión de la base de datos Web of Science. Este ejercicio brinda información sobre el inicio y el proceso de adap­tación de dichas técnicas. Para ello, se identifican las diferentes técnicas de ml aplicadas en modelo de predicción de quiebras. Se obtiene como resultado 327 documentos, los cuales se clasifican por medida de evaluación del desempe­ño, área bajo la curva (AUC) y precisión (ACC), por ser las más utilizadas en el proceso de clasificación. Además, se identifica la relación entre investigadores, instituciones y países con mayor número de aplicaciones de este tipo. Los re­sultados evidencian que los algoritmos XGBoost, SVM, Smote, RFY DT presentan una capacidad predictiva mucho mayor que las metodologías tradicionales, en­focados en un horizonte de tiempo antes del suceso dada su mayor precisión. Así mismo, las variables financieras y no financieras contribuyen de manera favorable a dicha estimación.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-07-04T13:39:59Z
2024-06-07T07:31:04Z
dc.date.available.none.fl_str_mv 2023-07-04T13:39:59Z
2024-06-07T07:31:04Z
dc.date.issued.none.fl_str_mv 2023-07-04
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
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dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
dc.type.local.eng.fl_str_mv Journal article
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dc.identifier.doi.none.fl_str_mv 10.18601/17941113.n22.04
dc.identifier.eissn.none.fl_str_mv 2346-2140
dc.identifier.issn.none.fl_str_mv 1794-1113
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dc.identifier.url.none.fl_str_mv https://doi.org/10.18601/17941113.n22.04
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dc.relation.citationedition.spa.fl_str_mv Núm. 22 , Año 2022 : Enero-Junio
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spelling Franco, Yuly Andrea2023-07-04T13:39:59Z2024-06-07T07:31:04Z2023-07-04T13:39:59Z2024-06-07T07:31:04Z2023-07-04El objetivo de este artículo es presentar un análisis bibliométrico sobre el uso que han tenido las técnicas de Machine Learning (ML) en el proceso de predic­ción de quiebra empresarial a través de la revisión de la base de datos Web of Science. Este ejercicio brinda información sobre el inicio y el proceso de adap­tación de dichas técnicas. Para ello, se identifican las diferentes técnicas de ml aplicadas en modelo de predicción de quiebras. Se obtiene como resultado 327 documentos, los cuales se clasifican por medida de evaluación del desempe­ño, área bajo la curva (AUC) y precisión (ACC), por ser las más utilizadas en el proceso de clasificación. Además, se identifica la relación entre investigadores, instituciones y países con mayor número de aplicaciones de este tipo. Los re­sultados evidencian que los algoritmos XGBoost, SVM, Smote, RFY DT presentan una capacidad predictiva mucho mayor que las metodologías tradicionales, en­focados en un horizonte de tiempo antes del suceso dada su mayor precisión. Así mismo, las variables financieras y no financieras contribuyen de manera favorable a dicha estimación.The aim of this article is to present a bibliometric analysis on the use that Machine Learning (ML) techniques have had in the process of predicting business bankruptcy through the review of the Web of Science database. This exercise provides information on the initiation and adaptation process of such techniques. For this, the different ml techniques applied in the bankruptcy prediction model are identified. As a result, 327 documents are obtained, of which they are clas­sified by performance evaluation measure, the area under the curve (AUC) and precision (ACC), these being the most used in the classification process. In ad­dition, the relationship between researchers, institutions and countries with the largest number of applications of this type is identified. The results show how the XGBoost, SVM, Smote, RF and D algorithms present a much greater predictive capacity than traditional methodologies; focused on a time horizon before the event given its greater precision. Similarly, financial and non-financial variables contribute favorably to said estimate.application/pdftext/html10.18601/17941113.n22.042346-21401794-1113https://bdigital.uexternado.edu.co/handle/001/15350https://doi.org/10.18601/17941113.n22.04spaUniversidad Externado de Colombiahttps://revistas.uexternado.edu.co/index.php/odeon/article/download/8875/14888https://revistas.uexternado.edu.co/index.php/odeon/article/download/8875/14889Núm. 22 , Año 2022 : Enero-Junio1262287ODEONAlaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O. y Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94, 164-184. https://doi.org/10.1016/j.eswa.2017.10.040Alaka, H., Oyedele, L., Owolabi, H., Akinade, O., Bilal, M. y Ajayi, S. (2019). A Big Data Analytics Approach for Construction Firms Failure Prediction Models. ieee Transactions on Engineering Management, 66(4), 689-698. https://doi.org/10.1109/ tem.2018.2856376Alam, N., Gao, J. y Jones, S. (2021). Corporate failure prediction: An evaluation of deep learning vs discrete hazard models. Journal of International Financial Markets, Institutions and Money, 75(266), 101455. https://doi.org/10.1016/j.int¬fin.2021.101455Alam, T. M., Shaukat, K., Mushtaq, M., Ali, Y., Khushi, M., Luo, S. y Wahab, A. (2021). Corporate Bankruptcy Prediction: An Approach towards Better Corporate World. Computer Journal, 64(11), 1731-1746. https://doi.org/10.1093/comjnl/bxaa056Aljawazneh, H., Mora, A. 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Business Failure Prediction Based on a Cost-Sensitive Extreme Gradient Boosting Machine. ieee Access, 10, 42623-42639. https://doi. org/10.1109/access.2022.3168857Yuly Andrea Franco - 2023info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.http://creativecommons.org/licenses/by-nc-sa/4.0https://revistas.uexternado.edu.co/index.php/odeon/article/view/8875Prediction;bankruptcy;Machine Learning;bibliometrics.predicción;quiebra empresarial;Machine Learning;bibliometríaUn análisis bibliométrico de la predicción de quiebra empresarial con Machine LearningA Bibliometric Analysis of Business Bankruptcy Prediction with Machine LearningArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Textinfo:eu-repo/semantics/articleJournal articlehttp://purl.org/redcol/resource_type/ARTREFinfo:eu-repo/semantics/publishedVersionPublicationOREORE.xmltext/xml2545https://bdigital.uexternado.edu.co/bitstreams/283f3fbd-63a3-4e99-b711-1279cf3ee66e/download46003077c49ac4c192e8284717653e70MD51001/15350oai:bdigital.uexternado.edu.co:001/153502024-06-07 02:31:05.04http://creativecommons.org/licenses/by-nc-sa/4.0Yuly Andrea Franco - 2023https://bdigital.uexternado.edu.coUniversidad Externado de Colombiametabiblioteca@metabiblioteca.org

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