Literature DB >> 33531720

Rethinking SME default prediction: a systematic literature review and future perspectives.

Francesco Ciampi1, Alessandro Giannozzi1, Giacomo Marzi2, Edward I Altman3.   

Abstract

Over the last dozen years, the topic of small and medium enterprise (SME) default prediction has developed into a relevant research domain that has grown for important reasons exponentially across multiple disciplines, including finance, management, accounting, and statistics. Motivated by the enormous toll on SMEs caused by the 2007-2009 global financial crisis as well as the recent COVID-19 crisis and the consequent need to develop new SME default predictors, this paper provides a systematic literature review, based on a statistical, bibliometric analysis, of over 100 peer-reviewed articles published on SME default prediction modelling over a 34-year period, 1986 to 2019. We identified, analysed and reviewed five streams of research and suggest a set of future research avenues to help scholars and practitioners address the new challenges and emerging issues in a changing economic environment. The research agenda proposes some new innovative approaches to capture and exploit new data sources using modern analytical techniques, like artificial intelligence, machine learning, and macro-data inputs, with the aim of providing enhanced predictive results.
© The Author(s) 2021.

Entities:  

Keywords:  Bankruptcy; Bibliometric analysis; Credit risk; Credit scoring; Default prediction; Failure; Rating; Risk prediction; SME survival; SMEs; Systematic literature review; VOSviewer

Year:  2021        PMID: 33531720      PMCID: PMC7844786          DOI: 10.1007/s11192-020-03856-0

Source DB:  PubMed          Journal:  Scientometrics        ISSN: 0138-9130            Impact factor:   3.238


  2 in total

1.  Entrepreneurial responses to uncertainties during the COVID-19 recovery: A longitudinal study of B&Bs in Zhangjiajie, China.

Authors:  Weizheng Zhang; Allan M Williams; Gang Li; Anyu Liu
Journal:  Tour Manag       Date:  2022-03-15

2.  A novel framework of credit risk feature selection for SMEs during industry 4.0.

Authors:  Yang Lu; Lian Yang; Baofeng Shi; Jiaxiang Li; Mohammad Zoynul Abedin
Journal:  Ann Oper Res       Date:  2022-07-25       Impact factor: 4.820

  2 in total

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