• A comparative analysis of two-stage distress prediction models

      Mousavi, Mohammad M.; Quenniche, J.; Tone, K. (2019-04-01)
      On feature selection, as one of the critical steps to develop a distress prediction model (DPM), a variety of expert systems and machine learning approaches have analytically supported developers. Data envel- opment analysis (DEA) has provided this support by estimating the novel feature of managerial efficiency, which has frequently been used in recent two-stage DPMs. As key contributions, this study extends the application of expert system in credit scoring and distress prediction through applying diverse DEA mod- els to compute corporate market efficiency in addition to the prevailing managerial efficiency, and to estimate the decomposed measure of mix efficiency and investigate its contribution compared to Pure Technical Efficiency and Scale Efficiency in the performance of DPMs. Further, this paper provides a com- prehensive comparison between two-stage DPMs through estimating a variety of DEA efficiency measures in the first stage and employing static and dynamic classifiers in the second stage. Based on experimen- tal results, guidelines are provided to help practitioners develop two-stage DPMs; to be more specific, guidelines are provided to assist with the choice of the proper DEA models to use in the first stage, and the choice of the best corporate efficiency measures and classifiers to use in the second stage.
    • The impact of MENA conflicts (the Arab Spring) on global financial markets

      Mousavi, Mohammad M.; Quenniche, J. (2014)
      It is believed that financial markets are integrated and sensitive to news – including political conflicts in some regions of the world. Furthermore, financial markets seem to react differently to information flows from one region to another. The purpose of this research is to discern the effects of the recent Middle East and North Africa (MENA) conflicts – commonly referred to as the Arab Spring – on the volatility of risks and returns of global and regional stock markets as well as Gold and Oil markets. To be more specific, we consider the main uprisings in Tunisia, Egypt, Libya and Yemen and their impact on financial markets – as measured by the volatility of their risks and returns. In sum, we cluster 53 stock markets into 6 regions; namely, developed, developing, MENA, Asia, Europe, and Latin America countries, and use T-GARCH to assess the reaction of these regions to each uprising event independently. In addition, we use GARCH-M to assess the reaction of these regions stock markets as well as Gold and Oil markets to the uprisings of MENA as a whole. Our empirical findings suggest that the uprising events of MENA have more impact on the volatility of risks and returns of developed, developing, and Europe regions than MENA itself. In addition, although the results show that the volatility of both risks and returns of both developed and MENA regions are significantly affected by general conflicts in MENA, the volatility of MENA is affected during all intervals and with higher significance level. Furthermore, while MENA uprisings as a whole impact on the volatility of risk of oil (after 5 days) and gold (immediately after entering news) significantly, the returns of these markets are not affected by conflicts.
    • Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions

      Mousavi, Mohammad M.; Quenniche, J. (2018-12)
      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.
    • Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework

      Mousavi, Mohammad M.; Quenniche, J.; Xu, B. (2015-12)
      Prediction of corporate failure is one of the major activities in auditing firms risks and uncertainties. The design of reliable models to predict bankruptcy is crucial for many decision making processes. Although a large number of models have been designed to predict bankruptcy, the relative performance evaluation of competing prediction models remains an exercise that is unidimensional in nature, which often leads to reporting conflicting results. In this research, we overcome this methodological issue by proposing an orientation-free super-efficiency data envelopment analysis model as a multi-criteria assessment framework. Furthermore, we perform an exhaustive comparative analysis of the most popular bankruptcy modeling frameworks for UK data including our own models. In addition, we address two important research questions; namely, do some modeling frameworks perform better than others by design? and to what extent the choice and/or the design of explanatory variables and their nature affect the performance of modeling frameworks?, and report on our findings.