Articles producció científica> Gestió d'Empreses

A bibliometric study on intelligent techniques of bankruptcy prediction for corporate firms

  • Datos identificativos

    Identificador: imarina:6051414
    Autores:
    Shi, YinLi, Xiaoni
    Resumen:
    Bibliometric analysis is an effective method to carry out quantitative study of academic output to address the research trends on a given area of investigation through analysing existing documents. This paper aims to explore the application of intelligent techniques in bankruptcy predictions so as to assess its progress and describe the research trend through bibliometric analysis over the last five decades. The results indicate that, although there is a significant increase in publication number since the 2008 financial crisis, the collaboration among authors is weak, especially at the international dimension. Also, the findings provide a comprehensive view of interdisciplinary research on bankruptcy modelling in finance, business management and computer science fields. The authors sought to contribute to the theoretical development of bankruptcy prediction modeling by bringing new knowledge and key insights. Artificial intelligent techniques are now serving as important alternatives to statistical methods and demonstrate very promising results. This paper has both theoretical and practical implications. First, it provides insights for scholars into the theoretical evolution and intellectual structure for conducting future research in this field. Second, it sheds light on identifying under-explored machine learning techniques applied in bankruptcy prediction which can be crucial in management and decision-making for corporate firm managers and policy makers.
  • Otros:

    Autor según el artículo: Shi, Yin; Li, Xiaoni;
    Departamento: Gestió d'Empreses
    Autor/es de la URV: Li, Xiaoni / Shi, Yin
    Palabras clave: Support vector machines Logistic-regression Insolvency Genetic algorithms Financial distress Discriminant-analysis Credit risk Companies financial distress Co-authorship Business failure prediction Business failure Business Bibliometric Bayesian networks Bankruptcy prediction Artificial neural-network Artificial intelligence
    Resumen: Bibliometric analysis is an effective method to carry out quantitative study of academic output to address the research trends on a given area of investigation through analysing existing documents. This paper aims to explore the application of intelligent techniques in bankruptcy predictions so as to assess its progress and describe the research trend through bibliometric analysis over the last five decades. The results indicate that, although there is a significant increase in publication number since the 2008 financial crisis, the collaboration among authors is weak, especially at the international dimension. Also, the findings provide a comprehensive view of interdisciplinary research on bankruptcy modelling in finance, business management and computer science fields. The authors sought to contribute to the theoretical development of bankruptcy prediction modeling by bringing new knowledge and key insights. Artificial intelligent techniques are now serving as important alternatives to statistical methods and demonstrate very promising results. This paper has both theoretical and practical implications. First, it provides insights for scholars into the theoretical evolution and intellectual structure for conducting future research in this field. Second, it sheds light on identifying under-explored machine learning techniques applied in bankruptcy prediction which can be crucial in management and decision-making for corporate firm managers and policy makers.
    Áreas temáticas: Multidisciplinary sciences Multidisciplinary Medicina i Ciências biológicas ii Ciências biológicas i Biotecnología
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: xiaoni.li@urv.cat yin.shi@estudiants.urv.cat
    Identificador del autor: 0000-0002-4047-4046 0000-0001-6354-5399
    Fecha de alta del registro: 2023-08-05
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Referencia al articulo segun fuente origial: Heliyon. 5 (12):
    Referencia de l'ítem segons les normes APA: Shi, Yin; Li, Xiaoni; (2019). A bibliometric study on intelligent techniques of bankruptcy prediction for corporate firms. Heliyon, 5(12), -. DOI: 10.1016/j.heliyon.2019.e02997
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.1016/j.heliyon.2019.e02997
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2019
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Multidisciplinary,Multidisciplinary Sciences
    Support vector machines
    Logistic-regression
    Insolvency
    Genetic algorithms
    Financial distress
    Discriminant-analysis
    Credit risk
    Companies financial distress
    Co-authorship
    Business failure prediction
    Business failure
    Business
    Bibliometric
    Bayesian networks
    Bankruptcy prediction
    Artificial neural-network
    Artificial intelligence
    Multidisciplinary sciences
    Multidisciplinary
    Medicina i
    Ciências biológicas ii
    Ciências biológicas i
    Biotecnología
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