Rethinking SME default prediction: a systematic literature review and future perspectives
Francesco Ciampi (),
Giacomo Marzi and
Edward I. Altman ()
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Francesco Ciampi: University of Florence
Edward I. Altman: New York University
Scientometrics, 2021, vol. 126, issue 3, No 11, 2188 pages
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.
Keywords: Default prediction; SMEs; Credit risk; Risk prediction; Bankruptcy; Systematic literature review; Bibliometric analysis; VOSviewer; Credit scoring; Rating; SME survival; Failure (search for similar items in EconPapers)
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