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An overview of bankruptcy prediction models for corporate firms: A systematic literature review

  • Identification data

    Identifier: imarina:5900216
    Handle: https://hdl.handle.net/20.500.11797/imarina5900216
    Authors:
    Shi, YinLi, Xiaoni
    Abstract:
    Purpose: The aim of this paper is to conduct a literature review of corporate bankruptcy prediction models, on the basis of the existing international academic literature in the corresponding area. It primarily attempts to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between the different authors (co-authorship), and to address the primary models and methods that are used and studied by authors of this area in the past five decades. Design/methodology: A systematic literature review (SLR) has been conducted, using the Scopus database for identifying core international academic papers related to the established research topic from the year 1968 to 2017. Findings: It has been verified, firstly, that bankruptcy prediction in the corporate world is a field of growing interest, as the number of papers has increased significantly, especially after 2008 global financial crisis, which demonstrates the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as researchers with great influence were barely working together during the last five decades. Thirdly, it has been identified that the two most frequently used and studied models in bankruptcy prediction area are Logistic Regression (Logit) and Neural Network. However, there are many other innovative methods as machine learning models applied in this field lately due to the emerging technology of computer science and artificial intelligence. Originality/value: We used an approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this serves as the link among the different elements of the concept studied, and i

  • Others:

    Author, as appears in the article.: Shi, Yin; Li, Xiaoni;
    Department: Gestió d'Empreses
    URV's Author/s: Li, Xiaoni / Shi, Yin
    Keywords: Support vector machine Slr Rough set Risk Ratios Probability Neural-networks Insolvency Financial distress Discriminant-analysis Default firm Companies financial distress Business failure Bankruptcy prediction
    Abstract: Purpose: The aim of this paper is to conduct a literature review of corporate bankruptcy prediction models, on the basis of the existing international academic literature in the corresponding area. It primarily attempts to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between the different authors (co-authorship), and to address the primary models and methods that are used and studied by authors of this area in the past five decades. Design/methodology: A systematic literature review (SLR) has been conducted, using the Scopus database for identifying core international academic papers related to the established research topic from the year 1968 to 2017. Findings: It has been verified, firstly, that bankruptcy prediction in the corporate world is a field of growing interest, as the number of papers has increased significantly, especially after 2008 global financial crisis, which demonstrates the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as researchers with great influence were barely working together during the last five decades. Thirdly, it has been identified that the two most frequently used and studied models in bankruptcy prediction area are Logistic Regression (Logit) and Neural Network. However, there are many other innovative methods as machine learning models applied in this field lately due to the emerging technology of computer science and artificial intelligence. Originality/value: We used an approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this serves as the link among the different elements of the concept studied, and it demonstrates the growing interest in this area.
    Thematic Areas: Management Interdisciplinary research in the social sciences Economia Ciencias sociales
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: xiaoni.li@urv.cat yin.shi@estudiants.urv.cat
    Author identifier: 0000-0002-4047-4046 0000-0001-6354-5399
    Record's date: 2023-10-21
    Papper version: info:eu-repo/semantics/publishedVersion
    Papper original source: 009263 - El Usali Y La Historia De Los Sistemas Uniformes De Coste: ¿Un Reto Hispano?. 15 (2): 114-127
    APA: Shi, Yin; Li, Xiaoni; (2019). An overview of bankruptcy prediction models for corporate firms: A systematic literature review. 009263 - El Usali Y La Historia De Los Sistemas Uniformes De Coste: ¿Un Reto Hispano?, 15(2), 114-127. DOI: 10.3926/ic.1354
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2019
    Publication Type: Journal Publications

  • Keywords:

    Management
    Support vector machine
    Slr
    Rough set
    Risk
    Ratios
    Probability
    Neural-networks
    Insolvency
    Financial distress
    Discriminant-analysis
    Default firm
    Companies financial distress
    Business failure
    Bankruptcy prediction
    Management
    Interdisciplinary research in the social sciences
    Economia
    Ciencias sociales

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