A dynamic performance evaluation of distress prediction models

Mohammad Mahdi Mousavi*, Jamal Ouenniche, Kaoru Tone

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

So far, the dominant comparative studies of competing distress prediction models (DPMs) have been restricted to the use of static evaluation frameworks and as such overlooked their performance over time. This study fills this gap by proposing a Malmquist DEA-based multi-period performance evaluation framework for assessing competing static and dynamic statistical DPMs and using it to address a variety of research questions. Our findings suggest that (1) dynamic models developed under duration-dependent frameworks outperform both dynamic models developed under duration-independent frameworks and static models; (2) models fed with financial accounting (FA), market variables (MV), and macroeconomic information (MI) features outperform those fed with either MVMI or FA, regardless of the frameworks under which they are developed; (3) shorter training horizons seem to enhance the aggregate performance of both static and dynamic models.
Original languageEnglish
Number of pages29
JournalJournal of Forecasting
Early online date3 Oct 2022
Publication statusE-pub ahead of print - 3 Oct 2022

Keywords / Materials (for Non-textual outputs)

  • corporate credit risk
  • distress prediction models
  • performance evaluation
  • Malmquist productivity index


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