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dc.contributor.authorMousavi, Mohammad M.*
dc.contributor.authorQuenniche, J.*
dc.date.accessioned2018-12-18T16:50:57Z
dc.date.available2018-12-18T16:50:57Z
dc.date.issued2018-12
dc.identifier.citationMousavi MM and Quenniche J (2018) Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions. Annals of Operations Research. 271(2): 853-886.en_US
dc.identifier.urihttp://hdl.handle.net/10454/16704
dc.descriptionYesen_US
dc.description.abstractAlthough many modelling and prediction frameworks for corporate bankruptcy and distress have been proposed, the relative performance evaluation of prediction models is criticised due to the assessment exercise using a single measure of one criterion at a time, which leads to reporting conflicting results. Mousavi et al. (Int Rev Financ Anal 42:64–75, 2015) proposed an orientation-free super-efficiency DEA-based framework to overcome this methodological issue. However, within a super-efficiency DEA framework, the reference benchmark changes from one prediction model evaluation to another, which in some contexts might be viewed as “unfair” benchmarking. In this paper, we overcome this issue by proposing a slacks-based context-dependent DEA (SBM-CDEA) framework to evaluate competing distress prediction models. In addition, we propose a hybrid crossbenchmarking- cross-efficiency framework as an alternative methodology for ranking DMUs that are heterogeneous. Furthermore, using data on UK firms listed on London Stock Exchange, we perform a comprehensive comparative analysis of the most popular corporate distress prediction models; namely, statistical models, under both mono criterion and multiple criteria frameworks considering several performance measures. Also, we propose new statistical models using macroeconomic indicators as drivers of distress.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1007/s10479-018-2814-2en_US
dc.rights© Springer Science+Business Media, LLC, part of Springer Nature 2018. Reproduced in accordance with the publisher's self-archiving policy. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-018-2814-2
dc.subjectCorporate distress predictionen_US
dc.subjectPerformance criteriaen_US
dc.subjectPerformance measuresen_US
dc.subjectContext-dependent data envelopment analysisen_US
dc.subjectSlacks-based measureen_US
dc.titleMulti-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributionsen_US
dc.status.refereedYesen_US
dc.date.application2018-03-19
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
refterms.dateFOA2019-01-30T15:34:32Z


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