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Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions

Mohammad Mahdi Mousavi () and Jamal Ouenniche
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Mohammad Mahdi Mousavi: Kean University, Wenzhou Campus
Jamal Ouenniche: University of Edinburgh, Business School

Annals of Operations Research, 2018, vol. 271, issue 2, 853-886

Abstract: 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 cross-benchmarking-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.

Keywords: Corporate distress prediction; Performance criteria; Performance measures; Context-dependent data envelopment analysis; Slacks-based measure (search for similar items in EconPapers)
Date: 2018
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