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

Abstract

Quenniche, J. Citation 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. RightsAbstract: 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. (2015) proposed an orientation-free superefficiency 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 (CBCE) framework as an alternative methodology for ranking DMUs that are heterogeneous. Furthermore, using data on UK firms listed on London Stock Exchange (LSE), 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.

Impact of financial risk on supply chains: a manufacturer-supplier relational perspective

“…Key exogenous financial risk in the SCRM field can be listed as foreign exchange risk, default risk, market risk and price fluctuation risk (e.g. Mousavi and Ouenniche 2018;Zhao and Zhu 2018). In general, exogenous financial risk has increased both in number and complexity (Hagigi and Sivakumar 2009;Dolgui, Ivanov, and Sokolov 2018) and are difficult to handle, as they are driven by external economic factors.…”

An Analysis of the Predictors of Financial Distress for Zimbabwe Listed Corporates

“…In a study by Mousavi and Ouenniche, [12] the aim was to establish a fair assessment of different methodologies used in predicting corporate distress. Statistical models, survival analysis and contingent claim analysis models using slacks-based context-dependent DEA (SBM-CDEA) framework were used.…”

MURAME parameter setting for creditworthiness evaluation: data-driven optimization

“…It is also possible to observe a growing interest toward the MCDA-based tools capable of providing effective support to creditworthiness evaluation (see for instance Baourakis et al 2009;Doumpos and Figueira 2019;Doumpos et al 2002;Doumpos and Pasiouras 2005;Doumpos and Zopounidis 2011;García et al 2013;Khalil et al 2000;Mousavi and Ouenniche 2018). Among them, a few studies have specifically addressed the issue of credit risk evaluation of SMEs through MCDA.…”