Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach.
- DOI
- 10.1016/j.ribaf.2023.101907
- Published
- 2023 Jan
- Container
- Research in international business and finance
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.ribaf.2023.101907,
title = {Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach.},
author = {González MR and Ureña AP and Fernández-Aguado PG},
year = {2023},
journal = {Research in international business and finance},
doi = {10.1016/j.ribaf.2023.101907},
url = {https://doi.org/10.1016/j.ribaf.2023.101907}
}RIS
TY - JOUR TI - Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach. AU - González MR AU - Ureña AP AU - Fernández-Aguado PG PY - 2023 JO - Research in international business and finance DO - 10.1016/j.ribaf.2023.101907 UR - https://doi.org/10.1016/j.ribaf.2023.101907 ER -
APA
MR, G., AP, U., & PG, F. (2023). Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach.. Research in international business and finance. https://doi.org/10.1016/j.ribaf.2023.101907
Source records
- pubmed · retrieved 2026-09-27T01:30:12.104Z