Articles producció científica> Gestió d'Empreses

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

  • Dades identificatives

    Identificador: imarina:6051414
    Autors:
    Shi, YinLi, Xiaoni
    Resum:
    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.
  • Altres:

    Autor segons l'article: Shi, Yin; Li, Xiaoni;
    Departament: Gestió d'Empreses
    Autor/s de la URV: Li, Xiaoni / Shi, Yin
    Paraules clau: 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
    Resum: 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.
    Àrees temàtiques: Multidisciplinary sciences Multidisciplinary Medicina i Ciências biológicas ii Ciências biológicas i Biotecnología
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: xiaoni.li@urv.cat yin.shi@estudiants.urv.cat
    Identificador de l'autor: 0000-0002-4047-4046 0000-0001-6354-5399
    Data d'alta del registre: 2023-08-05
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Referència a l'article segons font original: Heliyon. 5 (12):
    Referència 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 Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI de l'article: 10.1016/j.heliyon.2019.e02997
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2019
    Tipus de publicació: Journal Publications
  • Paraules clau:

    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
  • Documents:

  • Cerca a google

    Search to google scholar