Impact of Covid-19 on SME portfolios in Morocco: Evaluation of banking risk costs and the effectiveness of state support measures

  • Received August 3, 2021;
    Accepted September 2, 2021;
    Published September 9, 2021
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/imfi.18(3).2021.23
  • Article Info
    Volume 18 2021, Issue #3, pp. 260-276
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This work is licensed under a Creative Commons Attribution 4.0 International License

This study proposed a method for constructing rating tools using logistic regression and linear discriminant analysis to determine the risk profile of SME portfolios. The objective, firstly, is to evaluate the impact of the crisis due to the Covid-19 by readjusting the profile of each company by using the expert opinion and, secondly, to evaluate the efficiency of the measures taken by the Moroccan state to support the companies during the period of the pandemic. The analysis in this paper showed that the performance of the logistic regression and linear discriminant analysis models is almost equivalent based on the ROC curve. However, it was revealed that the logistic regression model minimizes the risk cost represented in this study by the expected loss. For the support measures adopted by the Moroccan government, the study showed that the failure rate (critical situation) of the firms benefiting from the support is largely lower than that of the non-beneficiaries.

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    • Figure 1. Discriminatory power RL(ROC)
    • Figure 2. Discriminatory power LDA (ROC)
    • Table 1. The per class of two models
    • Table 2. Classification according to state support
    • Table 3. LR Model – Adjustment of the portfolio
    • Table 4. LDA model – Adjustment of the portfolio
    • Table 5. LR model – the EL per class (million MAD)
    • Table 6. LDA model – the EL per class (million MAD)
    • Table B1. Logistic regression of univariate analysis
    • Table B2. Discriminative power of models as a function of variables
    • Table B3. Linear discriminant analysis of univariate analysis
    • Table B4. Discriminative power of models as a function of variables
    • Table B5. Parameter estimation and Wald test
    • Table B6. Box’s M test
    • Table B7. Wilks’ Lambda test
    • Table B8. The matrix of classification gap
    • Table B9. The mean of scores per class
    • Table B10. The distribution of M0 and (M0 + M1) per rating class (million MAD)
    • Conceptualization
      Mohamed Habachi, Salim El Haddad
    • Data curation
      Mohamed Habachi, Salim El Haddad
    • Formal Analysis
      Mohamed Habachi, Salim El Haddad
    • Funding acquisition
      Mohamed Habachi, Salim El Haddad
    • Investigation
      Mohamed Habachi, Salim El Haddad
    • Methodology
      Mohamed Habachi, Salim El Haddad
    • Project administration
      Mohamed Habachi
    • Resources
      Mohamed Habachi, Salim El Haddad
    • Software
      Mohamed Habachi, Salim El Haddad
    • Supervision
      Mohamed Habachi
    • Validation
      Mohamed Habachi, Salim El Haddad
    • Visualization
      Mohamed Habachi, Salim El Haddad
    • Writing – original draft
      Mohamed Habachi, Salim El Haddad
    • Writing – review & editing
      Mohamed Habachi