Main Article Content

Abstract

The COVID-19 pandemic has caused certain elements of the Financial Statements, such as accounts receivable, to show a greater indication of impairment, caused by the difficulty and possibility of insolvency of customers in the recovery of outstanding securities . This caused companies in general to modify the accounting treatment under the international standard IFRS 9 applicable from 2018. The objective of this article is to determine the portfolio risk that affects the calculation of these provisions, through a case study in Ecuador. To this end, a database of clients of a non-financial company was analyzed, and the composition of its portfolio segmented by day of delay, observing the component called probability of default (PD), which was determined by binary logistic regression. A model was obtained that allowed to obtain the desired probability, and consequently under the approach of IFRS 9, the calculation of the expected credit loss (ECL). The results obtained estimated a portfolio impairment of 23%, compared to the baseline scenario of 9%.

Keywords

IFRS 9 Impairment Default Credit Regression

Article Details

How to Cite
Manya, M., & González-Rabanal, M. C. (2023). Application of IFRS 9 Financial Instruments and the Exposure to Credit Risk (Case Study in Ecuador). Ilomata International Journal of Tax and Accounting, 4(2), 324-340. https://doi.org/10.52728/ijtc.v4i2.723

References

  1. Achim, L.-G., Mitoi, E., & Turlea, I.-C. (2021). A methodological approach to developing and validating IFRS 9 -LGD parameters. Proceedings of the International Conference on Business Excellence, 15(1), 683–694. https://doi.org/10.2478/picbe-2021-0064
  2. Azúa Álvarez, D., Rojas Molina, L., Gutiérrez Montoya, D., Rincón Perilla, E., & Pérez Pertuz, J. (2021). Effects associated with the implementation of IFRS 9: Financial Instruments. https://cipres.sanmateo.edu.co/ojs/index.php/libros/article/view/437
  3. Bank, M., & Eder, B. (2021). A Review on the Probability of Default for IFRS 9. https://doi.org/10.2139/ssrn.3981339
  4. Bushman, R. M., & Williams, C. D. (2012). Accounting Discretion, Loan Loss Provisioning and Discipline of Banks’ Risk-Taking (April 25, 2012. Journal of Accounting & Economics, 54(ue 1), 1–18. https://doi.org/10.2139/ssrn.1521584
  5. Casal, Ó. (2016). Pulse. http://www.pulso.cl/opinion/ifrs-9-de-perdida-incurrida-a-perdida-esperada/
  6. Chawla, G. et all. (2016). Point-in-time loss-given default rates and exposures at default models for IFRS 9/CECL and stress Testing. Journal of Risk Management in Financial Institutions, 9 / Number 3, 249–263 15. https://www.ingentaconnect.com/content/hsp/jrmfi/2016/00000009/00000003/art00004
  7. Chen, Y., Yang, C., & Zhang, C. (2022). Study on the influence of IFRS 9 on the impairment of commercial bank credit card. Applied Economics Letters, 29(1), 35–40. https://doi.org/10.1080/13504851.2020.1855298
  8. Cohen. BEdwards, G. (2017). The new era of expected credit loss provisioning. BIS Quarterly Review. https://econpapers.repec.org/article/bisbisqtr/1703f.htm
  9. Costa, E., Silva, I. C. L., Correia, A., & Faria, S. (2020). A logistic regression model for consumer default risk. Journal of Applied Statistics, 47(13–15), 2879–2894. https://doi.org/10.1080/02664763.2020.1759030
  10. Delgado-Vaquero, D., & Morales-Díaz, J. (2018). Estimating a Credit Rating for Accounting Purposes: A Quantitative Approach. Studies in Applied Economics, 36(ue), 459–488. https://EconPapers.repec.org/RePEc:lrk:eeaart:36_2_6
  11. Engelmann, B. (2021). Calculating lifetime expected loss for IFRS 9: which formula is measuring what? The Journal of Risk Finance, 22(3/4), 193 208. https://doi.org/10.1108/jrf-05-2020-0113
  12. Galárraga, G., & Lafferty, E. (2018). Financial and Tax Sector Analysis of the implementation of IFRS 9 “Financial Instruments” in the accounts receivable of the company "LGMV S.A. ESPOL. https://www.dspace.espol.edu.ec/retrieve/134160/D-CD110102.pdf
  13. Gonzales Cervan, L., Olivera Navarro, F. de M., & Solazar Prado, R. (2015). New perspectives on hedging accounting and risk management, according to the IFRS 9 approach. Interview with Leonardo Torres Huechucoy. Lidera Magazine, 10, 50–52. http://revistas.pucp.edu.pe/index.php/revistalidera/article/view/18223
  14. Gubareva, M. (2021). How to estimate expected credit losses – ECL – for provisioning under IFRS 9". Journal of Risk Finance, 22(2), 169–190. https://doi.org/10.1108/JRF-05-2020-0094
  15. Habachi, M., & El Haddad, S. (n.d.). Impact of Covid-19 on SME portfolios in Morocco: Evaluation of banking risk costs and the effectiveness of state support measures. Investment Management and Financial Innovations, 18(3), 260–276. https://doi.org/10.21511/imfi.18(3).2021.23
  16. Hung, M.-C., Ching, Y.-K., & Lin, S.-K. (2021). Impact of COVID-19 on the Robustness of the Probability of Default Estimation Model. Mathematics, 9, 3087. https://doi.org/10.3390/math9233087
  17. Lamaj, M. (2023). IFRS 9 and the Expected Credit Loss Model. In The Effect of Covid-19 on Loan Loss Provisions and Earnings Management of European Banks. BestMasters. Springer Gabler. https://doi.org/10.1007/978-3-658-40060-6_2
  18. Martinelli, F., Mercaldo, F., Raucci, D., & Santone, A. (2020). Predicting Probability of Default Under IFRS 9 Through Data Mining Techniques. In L. Barolli, F. Amato, F. Moscato, T. Enokido, & M. Takizawa (Eds.), Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing (Vol. 1150). Springer. https://doi.org/10.1007/978-3-030-44038-1_87
  19. Moreno, I., Bautista, R., Molina, H., & Ramírez. J. (2014). The role of accounting in the face of the financial crisis. In A reflection on the unconditional conservatism of IFRS 9. http://www.scielo.org.co/pdf/cuco/v15n38/v15n38a02.pdf
  20. Neisen, M., & Schulte-Mattler, H. (2021). The effectiveness of IFRS 9 transitional provisions in limiting the potential impact of COVID-19 on banks. Journal of Banking Regulation, 1–10. https://doi.org/10.1057/s41261-021-00151-7
  21. Novotny-Farkas, Z. (2016). The Interaction of the IFRS 9 Expected Loss Approach with Supervisory Rules and Implications for Financial Stability. Lancaster University Management School. https://ssrn.com/abstract=2817983
  22. Oberson, R. (2021). The Credit-Risk Relevance of Loan Impairments under IFRS 9 for CDS Pricing: Early Evidence (July 5, 2021. European Accounting Review. https://ssrn.com/abstract=3414614
  23. Parrales Choez, C. G., & Castillo Llanos, F. D. (2018). Analysis of IFRS 9 - Financial Instruments from an industrial perspective. Accounting and Business, 13(25), 6–19. https://doi.org/10.18800/contabilidad.201801.001
  24. Pastiranová, O., & Witzany, J. (2022). Does IFRS 9 Increase Volatility of Loan Loss Provisions? In D. Procházka (Ed.), Regulation of Finance and Accounting. ACFA ACFA 2021 2020. Springer Proceedings in Business and Economics. Springer. https://doi.org/10.1007/978-3-030-99873-8_19
  25. Peter, C. (2006). Estimating Loss Given Default — Experiences from Banking Practice. In B. Engelmann & R. Rauhmeier (Eds.), The Basel II Risk Parameters. Springer. https://doi.org/10.1007/3-540-33087-9_8
  26. Porretta, P., Letizia, A., & Santoboni, F. (2020). Credit risk management in bank: Impacts of IFRS 9 and Basel 3. Risk Governance and Control: Financial Markets & Institutions, 10(2), 29–44. https://doi.org/10.22495/rgcv10i2p3
  27. Santos, B. L. (2018). Practical approach for probability of default estimation under IFRS 9". Dissertação de Mestrado, Universidade de Lisboa. Instituto Superior de Economia e Gestão. http://hdl.handle.net/10400.5/17350
  28. Schutte, W. D. (2020). Tanja Verster. Cogent Economics & Finance, 8(1). https://doi.org/10.1080/23322039.2020.1735681
  29. Vaněk, T., & Hampel, D. (2017). The Probability of Default Under IFRS 9: Multi-period Estimation and Macroeconomic Forecast. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 65(2), 759–776. https://doi.org/10.11118/actaun201765020759
  30. Zhao, B., & Cao, J. (2021). Logistic Model of Credit Risk during the COVID-19 Pandemic. Int J Biochem Physiol, 6(1), 193. https://doi.org/10.23880/ijbp-16000193
  31. Zizi, Y., Jamali-Alaoui, A., Goumi, B. El, Oudgou, M., & Moudden, A. El. (2021). An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression. Risks, 9, 200. https://doi.org/10.3390/risks9110200