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dc.contributor.authorMousavi, Mohammad M.
dc.contributor.authorOuenniche, J.
dc.contributor.authorTone, K.
dc.date.accessioned2022-10-27T19:29:16Z
dc.date.accessioned2022-11-22T12:13:16Z
dc.date.available2022-10-27T19:29:16Z
dc.date.available2022-11-22T12:13:16Z
dc.date.issued2023-07
dc.identifier.citationMousavi MM, Ouenniche J and Tone K (2023) A dynamic performance evaluation of distress prediction models. Journal of Forecasting. 42(4): 756-784.en_US
dc.identifier.urihttp://hdl.handle.net/10454/19213
dc.descriptionYesen_US
dc.description.abstractSo 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.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1002/for.2915en_US
dc.rights© 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.en_US
dc.subjectCorporate credit risken_US
dc.subjectDistress prediction modelsen_US
dc.subjectMalmquist productivity indexen_US
dc.subjectPerformance evaluationen_US
dc.titleA dynamic performance evaluation of distress prediction modelsen_US
dc.status.refereedYesen_US
dc.date.Accepted2022-09-25
dc.date.application2022-10-03
dc.typeArticleen_US
dc.type.versionPublished versionen_US
dc.rights.licenseCC-BYen_US
dc.date.updated2022-10-27T19:29:18Z
refterms.dateFOA2022-11-22T12:15:23Z
dc.openaccess.statusopenAccessen_US


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