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A dynamic performance evaluation of distress prediction models
Author(s) -
Mousavi Mohammad Mahdi,
Ouenniche Jamal,
Tone Kaoru
Publication year - 2023
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2915
Subject(s) - computer science , econometrics , financial distress , variety (cybernetics) , dynamic factor , aggregate (composite) , duration (music) , economics , artificial intelligence , art , materials science , literature , composite material , financial system
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 Data Envelopment Analysis (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.