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Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions
Quenniche, J.
Quenniche, J.
Publication Date
2018-12
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© 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
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Abstract
Although 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.
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Mousavi 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.
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