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

IF 1 Q4 MANAGEMENT

Intangible Capital Pub Date : 2019-10-14 DOI:10.3926/ic.1354

Yin Shi, Xiaoni Li

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引用次数: 61

Abstract

Purpose: This paper aims to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between different authors (co-authorship), and to identify the primary models and methods that are used and studied by authors of this area in the past five decades.Design/methodology/approach: 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, demonstrating the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as the researchers with a lot of influence were basically not 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 applied the SLR approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this contributes as the link among different elements of the concept studied, and it demonstrates the growing interest in this area.

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企业破产预测模型综述:系统文献综述

目的:本文旨在全面概述与公司破产预测相关的文献，调查和解决不同作者之间的联系(共同作者)，并确定过去五十年来该领域作者使用和研究的主要模型和方法。设计/方法/方法:采用系统文献综述(SLR)，使用Scopus数据库识别1968年至2017年与既定研究主题相关的核心国际学术论文。研究发现:首先，随着论文数量的显著增加，特别是在2008年全球金融危机之后，破产预测在企业界是一个越来越受关注的领域，这表明了这一主题对企业的重要性。其次，应该提到的是，在这个研究领域很少有合著者，因为在过去的50年里，有很大影响力的研究人员基本上没有在一起工作。第三，破产预测中最常用和研究最多的两个模型是Logistic回归(Logit)模型和神经网络模型。然而，由于计算机科学和人工智能技术的兴起，近年来有许多其他创新方法如机器学习模型应用于该领域。原创性/价值:我们采用单反方法，可以更好地观察与公司破产预测相关的学术贡献;这有助于作为所研究概念的不同要素之间的联系，并表明对这一领域日益增长的兴趣。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Intangible Capital MANAGEMENT-

CiteScore

1.50

自引率

16.70%

发文量

审稿时长

33 weeks

期刊介绍： The aim of Intangible Capital is to publish theoretical and empirical articles that contribute to contrast, extend and build theories that contribute to advance our understanding of phenomena related with management, and the management of intangibles, in organizations, from the perspectives of strategic management, human resource management, psychology, education, IT, supply chain management and accounting. The scientific research in management is grounded on theories developed from perspectives taken from a diversity of social sciences. Intangible Capital is open to publish articles that, from sociology, psychology, economics and industrial organization contribute to the scientific development of management and organizational science. Intangible Capital publishes scholar articles that contribute to contrast existing theories, or to build new theoretical approaches. The contributions can adopt confirmatory (quantitative) or explanatory (mainly qualitative) methodological approaches. Theoretical essays that enhance the building or extension of theoretical approaches are also welcome. Intangible Capital selects the articles to be published with a double bind, peer review system, following the practices of good scholarly journals. Intangible Capital publishes three regular issues per year following an open access policy. On-line publication allows to reduce publishing costs, and to make more agile the process of reviewing and edition. Intangible Capital defends that open access publishing fosters the advance of scientific knowledge, making it available to everyone. Intangible Capital publishes articles in English, Spanish and Catalan.