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A dynamic performance evaluation of distress prediction models
Ouenniche, J. Tone, K.
Ouenniche, J.
Tone, K.
Publication Date
2023-07
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© 2022 The Authors. Journal of Forecasting published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Yes
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openAccess
Accepted for publication
25/09/2022
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Abstract
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.
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Published version
Citation
Mousavi MM, Ouenniche J and Tone K (2023) A dynamic performance evaluation of distress prediction models. Journal of Forecasting. 42(4): 756-784.
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Article