Impact of Covid-19 on SME portfolios in Morocco: Evaluation of banking risk costs and the effectiveness of state support measures
- Author(s)
-
DOIhttp://dx.doi.org/10.21511/imfi.18(3).2021.23
-
Article InfoVolume 18 2021, Issue #3, pp. 260-276
- Cited by
- 713 Views
-
296 Downloads
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.
- Keywords
-
JEL Classification (Paper profile tab)C51, G21, G32
-
References33
-
Tables16
-
Figures2
-
- 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)
-
- Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609.
- Altman, G., Marco, F., & Varetto, F. (1994). Corporate Distress Diagnosis: Comparisons using Linear Discriminant Analysis and Neural Networks (the Italian experience). Journal of Banking and Finance, 18, 505-529.
- Bank for International Settlements. (2005). International convergence of capital measurement and capital standards. Basel, Switzerland.
- Bank for International Settlements. (2015). Guidance on credit risk and accounting for expected credit losses. Basel, Switzerland.
- Benbachir, S., & Habachi, M. (2018). Assessing the Impact of Modelling on the Expected Credit Loss (ECL) of a Portfolio of Small and Medium-sized Enterprises. Universal Journal of Management, 6(10), 409-431.
- Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145-1159.
- Bushman, R.M., 2016. Transparency, accounting discretion, and bank stability. Economic Policy Review, Issue Aug, pp.129–149.
- Chai, N., Wu, B., Yang, W., & Shi, B. (2019). A multicriteria approach for modeling small enterprise credit rating: Evidence from China. Emerging Markets Finance and Trade, 55(11), 2523-2543.
- Chi, G., Yu, S., & Zhou, Y. (2020). A novel credit evaluation model based on the maximum discrimination of evaluation results. Emerging Markets Finance and Trade, 56(11), 2543-2562.
- Cohen, B., & Edwards, Jr G. (2017). The new era of expected credit loss provisioning. BIS Quarterly Review, 36-59.
- Courdec, F., & Renault, O. (2005). Times-to-default: life cycle, global and industry cycle impacts (Research Paper No. 142). FAME-International Center of Financial Asset Management and Engineering.
- Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(1), 167-179.
- Engelmann, B., Hayden, E., & Tasche, D. (2003). Measuring the Discriminative Power of Rating Systems (Discussion Paper No. 2003, 01). Bundesbank.
- Engelmann, B., & Pham, H . (2020). Measuring the Performance of Bank Loans under Basel II/III and IFRS 9/CECL. Risks, 8(3), 93.
- Figini, S., & Giudici, P. (2011). Statistical merging of rating models. Journal of the Operational Research Society, 62(6), 1067-1074.
- Figlewski, S., Frydman, H., & Liang, W. (2012). Modeling the effect of macroeconomic factors on corporate default and credit rating transitions. International Review of Economics and Finance, 21(1), 87-105.
- Fisher, R., (1936). The use of multiple measurements in taxonomic problems, Annals of Eugenics, 7, 179-188.
- Gourinchas, P.-O., Kalemli-Özcan, Ṣ., Penciakova, V., & Sander, N. (2020). COVID-19 and SME Failures (Working Paper No. 27877). National Bureau of Economic Research.
- Grunert, J., Norden, L., & Weber, M. (2005). The role of non-financial factors in internal credit ratings. Journal of Banking & Finance, 29(2), 509-531.
- Habachi, M., Benbachir, S., & McMillan, D. (rev.ed.). (2019). Combination of linear discriminant analysis and expert opinion for the construction of credit rating models: The case of SMEs. Cogent Business & Management, 6(1), 1685926.
- Hosmer, D. W., & Lemeshow, S. (1980). Goodness-of-fit test for the multiple logistic regression model. Communications in Statistics, 9(10), 1043-1069.
- Hosmer, D., Lemeshow, S., & Sturdivant, R. (2013). Applied Logistic Regression (3rd ed.). New York: John Wiley & Sons.
- Madar, L. (2014). Scoring rendszerek hatásai a gazdasági tőkeszámítás során alkalmazott portfóliómodellek eredményeire [The effects of scoring systems on the results of portfolio models used in economic capital calculations] (PhD thesis). University of Kaposvár.
- Moon, T. H., Kim, Y., & Sohn, S. Y. (2011). Technology credit rating system for funding SMEs. Journal of the Operational Research Society, 62(4), 608-615.
- Novotny-Farkas, Z. (2016). The interaction of the IFRS 9 expected loss approach with supervisory rules and implications for financial stability. Accounting in Europe, 13(2), 197-227.
- Pavlyshenko, B. (2016). Machine learning, linear and Bayesian models for logistic regression in failure detection problems. IEEE International Conference on Big Data, 2046-2050.
- Satchel, S., & Xia, W. (2008). 8-Analytic models of the ROC Curve: Applications to credit rating model validation. The Analytics of Risk Model Validation, Quantitative Finance, 113-133.
- Svabova, L., Michalkova, L., Durica, M., & Nica, E. (2020). Business Failure Prediction for Slovak Small and Medium-Sized Companies. Sustainability, 12(11), 4572.
- Ubarhande, P., Chandani, A., & McMillan, D. (rev.ed.). (2021). Elements of Credit Rating: A Hybrid Review and Future Research Agenda. Cogent Business & Management, 8(1), 1878977.
- Vaněk, T., & Hampel, D.(2017). The probability of default under IFRS9: Multi-period estimation and macroeconomic forecast. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 65(2), 759-776.
- Worth, A., & Cronin, M. (2003). The use of discriminant analysis, logistic regression and classification tree analysis in the development of classification models for human health effects. Journal of Molecular Structure: THEOCHEM, 622, 97-111.
- Yildirak, K., & Suer, O. (2013). Qualitative determinants and credit-default risk: evidence from Turkey. Aktualni problemy ekonomiky – Actual problems of economics, 7(145), 333-344.
- Zizi, Y., Oudgou, M., & El Moudden, A. (2020). Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach. Risks, 8(4), 107.
-
The impact of discretionary loan loss provision of sharia financing on financial performance
Z Zulfikar
,
Wahyuni Sri
doi: http://dx.doi.org/10.21511/bbs.14(4).2019.04
Banks and Bank Systems Volume 14, 2019 Issue #4 pp. 34-41 Views: 929 Downloads: 219 TO CITE АНОТАЦІЯThis study aims to investigate the role of discretionary loan loss provision of sharia financing on the Islamic commercial banks’ financial performance in Indonesia. Partial Least Squares-Structural Equation modeling (PLS-SEM) is used to examine the relationship between loan loss provisions and financial performance in 13 Islamic commercial banks for 4.5 years. The analysis of the outer model shows that the probability of default and loss given default are determinants of loan loss provision, while financial performance is determined by return on assets, non-performing financing, net operating margin, and operating costs on operating income. The results of this study indicate that loan loss provisions have a direct effect on financial performance. Further investigation shows that the return on sharia financing contributes to increasing the impact of loan loss provisions on financial performance (indirect influence). The findings contribute to the literature by showing that discretionary loan loss provision can occur in sharia financing. The study is very important in terms of awareness of management behavior related to financial performance. The study has implications for management policies related to the prerequisites of potential clients.
-
Assessment of Support Vector Machine performance for default prediction and credit rating
Karim Amzile ,
Mohamed Habachi
doi: http://dx.doi.org/10.21511/bbs.17(1).2022.14
Banks and Bank Systems Volume 17, 2022 Issue #1 pp. 161-175 Views: 848 Downloads: 263 TO CITE АНОТАЦІЯPredicting the creditworthiness of bank customers is a major concern for banking institutions, as modeling the probability of default is a key focus of the Basel regulations. Practitioners propose different default modeling techniques such as linear discriminant analysis, logistic regression, Bayesian approach, and artificial intelligence techniques. The performance of the default prediction is evaluated by the Receiver Operating Characteristic (ROC) curve using three types of kernels, namely, the polynomial kernel, the linear kernel and the Gaussian kernel. To justify the performance of the model, the study compares the prediction of default by the support vector with the logistic regression using data from a portfolio of particular bank customers. The results of this study showed that the model based on the Support Vector Machine approach with the Radial Basis Function kernel, performs better in prediction, compared to the logistic regression model, with a value of the ROC curve equal to 98%, against 71.7% for the logistic regression model. Also, this paper presents the conception of a support vector machine-based rating tool designed to classify bank customers and determine their probability of default. This probability has been computed empirically and represents the proportion of defaulting customers in each class.
