Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach
Author
Abstract
Suggested Citation
DOI: 10.1016/j.ribaf.2023.101907
Download full text from publisher
As the access to this document is restricted, you may want to
for a different version of it.References listed on IDEAS
- Kelly, Robert & O’Malley, Terence, 2016.
"The good, the bad and the impaired: A credit risk model of the Irish mortgage market,"
Journal of Financial Stability, Elsevier, vol. 22(C), pages 1-9.
- Kelly, Robert, 2011. "The Good, The Bad and The Impaired - A Credit Risk Model of the Irish Mortgage Market," Research Technical Papers 13/RT/11, Central Bank of Ireland.
- repec:wbk:wbpubs:33044 is not listed on IDEAS
- Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022.
"Artificial intelligence and machine learning in finance: A bibliometric review,"
Research in International Business and Finance, Elsevier, vol. 61(C).
- Shamima Ahmed & Muneer Alshater & Anis El Ammari & Helmi Hammami, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Post-Print hal-03697290, HAL.
- Seetharaman, Priya, 2020. "Business models shifts: Impact of Covid-19," International Journal of Information Management, Elsevier, vol. 54(C).
- Jurgita Markevičiūtė & Jolita Bernatavičienė & Rūta Levulienė & Viktor Medvedev & Povilas Treigys & Julius Venskus, 2022. "Impact of COVID-19-Related Lockdown Measures on Economic and Social Outcomes in Lithuania," Mathematics, MDPI, vol. 10(15), pages 1-20, August.
- Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021.
"Forecasting recovery rates on non-performing loans with machine learning,"
International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
- Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2020. "Forecasting recovery rates on non-performing loans with machine learning," LIDAM Reprints LFIN 2020002, Université catholique de Louvain, Louvain Finance (LFIN).
- Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2020. "Forecasting recovery rates on non-performing loans with machine learning," LIDAM Discussion Papers LFIN 2020002, Université catholique de Louvain, Louvain Finance (LFIN).
- Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
- Al-Maadid, Alanoud & Alhazbi, Saleh & Al-Thelaya, Khaled, 2022. "Using machine learning to analyze the impact of coronavirus pandemic news on the stock markets in GCC countries," Research in International Business and Finance, Elsevier, vol. 61(C).
- Li, Jing-Ping & Mirza, Nawazish & Rahat, Birjees & Xiong, Deping, 2020. "Machine learning and credit ratings prediction in the age of fourth industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
- Huck, Nicolas, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," European Journal of Operational Research, Elsevier, vol. 278(1), pages 330-342.
- Yu, Lean & Huang, Xiaowen & Yin, Hang, 2020. "Can machine learning paradigm improve attribute noise problem in credit risk classification?," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 440-455.
- Ampudia, Miguel & van Vlokhoven, Has & Żochowski, Dawid, 2016.
"Financial fragility of euro area households,"
Journal of Financial Stability, Elsevier, vol. 27(C), pages 250-262.
- Żochowski, Dawid & Ampudia, Miguel & van Vlokhoven, Has, 2014. "Financial fragility of euro area households," Working Paper Series 1737, European Central Bank.
- Sharif, Arshian & Aloui, Chaker & Yarovaya, Larisa, 2020. "COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach," International Review of Financial Analysis, Elsevier, vol. 70(C).
- Cathal O'Donoghue & Denisa M. Sologon & Iryna Kyzyma & John McHale, 2020.
"Modelling the Distributional Impact of the COVID‐19 Crisis,"
Fiscal Studies, John Wiley & Sons, vol. 41(2), pages 321-336, June.
- O'Donoghue, Cathal & Sologon, Denisa M. & Kyzyma, Iryna & McHale, John, 2020. "Modelling the Distributional Impact of the COVID-19 Crisis," IZA Discussion Papers 13235, IZA Network @ LISER.
- Nicolas Huck, 2019. "Large data sets and machine learning: Applications to statistical arbitrage," Post-Print hal-02143971, HAL.
- Bastos, João A. & Matos, Sara M., 2022.
"Explainable models of credit losses,"
European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
- João A. Bastos & Sara M. Matos, 2021. "Explainable models of credit losses," Working Papers REM 2021/0161, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
- repec:wbk:wbpubs:35647 is not listed on IDEAS
- Ioannidis, John P.A. & Cripps, Sally & Tanner, Martin A., 2022. "Forecasting for COVID-19 has failed," International Journal of Forecasting, Elsevier, vol. 38(2), pages 423-438.
- repec:wbk:wbpubs:34710 is not listed on IDEAS
- García-Céspedes, Rubén & Moreno, Manuel, 2022. "The generalized Vasicek credit risk model: A Machine Learning approach," Finance Research Letters, Elsevier, vol. 47(PA).
- Karadima, Maria & Louri, Helen, 2021. "Economic policy uncertainty and non-performing loans: The moderating role of bank concentration," Finance Research Letters, Elsevier, vol. 38(C).
- repec:wbk:wbpubs:33748 is not listed on IDEAS
- Xiao Ke & Cheng Hsiao, 2022. "Economic impact of the most drastic lockdown during COVID‐19 pandemic—The experience of Hubei, China," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 187-209, January.
- Hu, Shiwei & Zhang, Yuyao, 2021. "COVID-19 pandemic and firm performance: Cross-country evidence," International Review of Economics & Finance, Elsevier, vol. 74(C), pages 365-372.
- Neudecker, Heinz & Polasek, Wolfgang & Liu, Shuangzhe, 1995. "The heteroskedastic linear regression model and the Hadamard product a note," Journal of Econometrics, Elsevier, vol. 68(2), pages 361-366, August.
- Luis Pedauga & Francisco Sáez & Blanca L. Delgado-Márquez, 2022. "Macroeconomic lockdown and SMEs: the impact of the COVID-19 pandemic in Spain," Small Business Economics, Springer, vol. 58(2), pages 665-688, February.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Erdinc Akyildirim & Oguzhan Cepni & Shaen Corbet & Gazi Salah Uddin, 2023.
"Forecasting mid-price movement of Bitcoin futures using machine learning,"
Annals of Operations Research, Springer, vol. 330(1), pages 553-584, November.
- Akyildirim, Erdinc & Cepni, Oguzhan & Corbet, Shaen & Uddin, Gazi Salah, 2020. "Forecasting Mid-price Movement of Bitcoin Futures Using Machine Learning," Working Papers 20-2020, Copenhagen Business School, Department of Economics.
- O’Sullivan, Conall & Papavassiliou, Vassilios G. & Wafula, Ronald Wekesa & Boubaker, Sabri, 2024.
"New insights into liquidity resiliency,"
Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 90(C).
- Conall O'Sullivan & Vassilios G. Papavassiliou & Ronald Wekesa Wafula & Sabri Boubaker, 2024. "New Insights into Liquidity Resiliency," Post-Print hal-04432411, HAL.
- Shadi Ratib Mohammad Aledeimat & Murad Abdurahman Bein, 2025. "Does Uncertainty Affect the Banks’ Non-Performing Loans/Non-Performing Finance in the MENA Region? A Comparison Study Between Conventional and Islamic Banks in the MENA Regions," SAGE Open, , vol. 15(1), pages 21582440251, February.
- Guillaume Coqueret & Tony Guida, 2020. "Training trees on tails with applications to portfolio choice," Post-Print hal-04144665, HAL.
- Alexander Jakob Dautel & Wolfgang Karl Härdle & Stefan Lessmann & Hsin-Vonn Seow, 2020.
"Forex exchange rate forecasting using deep recurrent neural networks,"
Digital Finance, Springer, vol. 2(1), pages 69-96, September.
- Dautel, Alexander J. & Härdle, Wolfgang Karl & Lessmann, Stefan & Seow, Hsin-Vonn, 2019. "Forex Exchange Rate Forecasting Using Deep Recurrent Neural Networks," IRTG 1792 Discussion Papers 2019-008, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Dautel, Alexander Jakob & Härdle, Wolfgang Karl & Lessmann, Stefan & Seow, Hsin-Vonn, 2020. "Forex exchange rate forecasting using deep recurrent neural networks," IRTG 1792 Discussion Papers 2020-006, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
- Ahmad, Wasim & Kutan, Ali M. & Chahal, Rishman Jot Kaur & Kattumuri, Ruth, 2021.
"COVID-19 Pandemic and firm-level dynamics in the USA, UK, Europe, and Japan,"
International Review of Financial Analysis, Elsevier, vol. 78(C).
- Ahmad, Wasim & Kutan, Ali M. & Chahal, Rishman Jot Kaur & Kattumuri, Ruth, 2021. "COVID-19 pandemic and firm-level dynamics in the USA, UK, Europe, and Japan," LSE Research Online Documents on Economics 112454, London School of Economics and Political Science, LSE Library.
- Fabian Waldow & Matthias Schnaubelt & Christopher Krauss & Thomas Günter Fischer, 2021. "Machine Learning in Futures Markets," JRFM, MDPI, vol. 14(3), pages 1-14, March.
- Simon Fritzsch & Felix Irresberger & Gregor Weiß, 2026. "Predicting option prices from their price history via machine learning," Review of Derivatives Research, Springer, vol. 29(1), pages 1-38, December.
- Dimitrios Laliotis & Alejandro Buesa & Miha Leber & Javier Población, 2020.
"An agent-based model for the assessment of LTV caps,"
Quantitative Finance, Taylor & Francis Journals, vol. 20(10), pages 1721-1748, October.
- Laliotis, Dimitrios & Buesa, Alejandro & Leber, Miha & Población García, Francisco Javier, 2019. "An agent-based model for the assessment of LTV caps," Working Paper Series 2294, European Central Bank.
- Erdinc Akyildirim & Ahmet Goncu & Alper Hekimoglu & Duc Khuong Nguyen & Ahmet Sensoy, 2023.
"Statistical arbitrage: factor investing approach,"
OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(4), pages 1295-1331, December.
- Akyildirim, Erdinc & Goncu, Ahmet & Hekimoglu, Alper & Nguyen, Duc Khuong & Sensoy, Ahmet, 2021. "Statistical arbitrage: Factor investing approach," MPRA Paper 105766, University Library of Munich, Germany.
- Erdinc Akyildirim & Ahmet Goncu & Alper Hekimoglu & Duc Khuong Nguyen & Ahmet Sensoy, 2021. "Statistical Arbitrage: Factor Investing Approach," Working Papers 2021-003, Department of Research, Ipag Business School.
- Rama Cont & Mihai Cucuringu & Chao Zhang, 2021. "Cross-Impact of Order Flow Imbalance in Equity Markets," Papers 2112.13213, arXiv.org, revised Jun 2023.
- Guillaume Coqueret & Tony Guida, 2020. "Training trees on tails with applications to portfolio choice," Annals of Operations Research, Springer, vol. 288(1), pages 181-221, May.
- Azzam, Islam & El-Masry, Ahmed A. & Yamani, Ehab, 2023. "Foreign exchange market efficiency during COVID-19 pandemic," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 717-730.
- Nazemi, Abdolreza & Fabozzi, Frank J., 2024. "Interpretable machine learning for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 164(C).
- Kasper Johansson & Thomas Schmelzer & Stephen Boyd, 2025. "A Markowitz approach to managing a dynamic basket of moving-band statistical arbitrages," Journal of Asset Management, Palgrave Macmillan, vol. 26(4), pages 377-385, July.
- Housseman Steven Ramos Zambrano, 2023. "Riesgo financiero e incertidumbre en los mercados bursátiles en tiempo de covid-19: un análisis bibliométrico," Revista Tendencias, Universidad de Narino, vol. 24(2), pages 262-287.
- Jiajia, Liu & Kun, Guo & Fangcheng, Tang & Yahan, Wang & Shouyang, Wang, 2023. "The effect of the disposal of non-performing loans on interbank liquidity risk in China: A cash flow network-based analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 105-119.
- Kolesnikova, A. & Yang, Y. & Lessmann, S. & Ma, T. & Sung, M.-C. & Johnson, J.E.V., 2019. "Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting," IRTG 1792 Discussion Papers 2019-023, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
- Conlon, Thomas & Cotter, John & Kynigakis, Iason, 2025. "Asset allocation with factor-based covariance matrices," European Journal of Operational Research, Elsevier, vol. 325(1), pages 189-203.
More about this item
Keywords
; ; ; ; ;JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
- G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:riibaf:v:64:y:2023:i:c:s0275531923000338. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ribaf .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.
Printed from https://ideas.repec.org/a/eee/riibaf/v64y2023ics0275531923000338.html