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Rethinking SME default prediction: a systematic literature review and future perspectives.
Ciampi, Francesco; Giannozzi, Alessandro; Marzi, Giacomo; Altman, Edward I.
Afiliação
  • Ciampi F; University of Florence, Via delle Pandette, 9, 50127 Florence, IT Italy.
  • Giannozzi A; University of Florence, Via delle Pandette, 9, 50127 Florence, IT Italy.
  • Marzi G; University of Lincoln, Brayford Pool, Lincoln, GB LN6 7TS UK.
  • Altman EI; NYU Salomon Center, Leonard N. Stern School of Business, New York University, 44 West 4th Street, New York, NY 10012 USA.
Scientometrics ; 126(3): 2141-2188, 2021.
Article em En | MEDLINE | ID: mdl-33531720
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies / Systematic_reviews Idioma: En Revista: Scientometrics Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies / Systematic_reviews Idioma: En Revista: Scientometrics Ano de publicação: 2021 Tipo de documento: Article