An Adaptive Neural Network Approach To Predict The Capital Adequacy Ratio
Financial institutions, policy makers and regulatory authorities need to implement stress tests in order to test both resilience and the consequences of adverse shocks. The European Central Bank and the European Banking Authority regularly conduct these tests, whose importance is more and more evide...
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| Format: | Article |
| Language: | English |
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Accademia Piceno Aprutina dei Velati
2022-12-01
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| Series: | Ratio Mathematica |
| Subjects: | |
| Online Access: | http://eiris.it/ojs/index.php/ratiomathematica/article/view/841 |
| _version_ | 1797961580413976576 |
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| author | Giacomo Di Tollo Gerarda Fattoruso Bartolomeo Toffano |
| author_facet | Giacomo Di Tollo Gerarda Fattoruso Bartolomeo Toffano |
| author_sort | Giacomo Di Tollo |
| collection | DOAJ |
| description | Financial institutions, policy makers and regulatory authorities need to implement stress tests in order to test both resilience and the consequences of adverse shocks. The European Central Bank and the European Banking Authority regularly conduct these tests, whose importance is more and more evident after the financial crisis of 2007-2008. The stress tests’ nonlinear features of variables and scenarios triggered the need of general and robust strategies to perform this task. In this paper we want to introduce an adaptive Neural Network approach to predict the Capital Adequacy Ratio (CAR), which is one of the main ratios monitored to retrieve useful information along many stress test procedures. The Neural Network approach is based on a comparison between feed-forward and recurrent networks, and is run after a meaningful pre-processing operations definition. Results show that our approach is able to successfully predict CAR by using both Neural Networks and recurrent networks. |
| first_indexed | 2024-04-11T01:00:04Z |
| format | Article |
| id | doaj.art-ad00828f5c8a463f8011041f5f0c5f16 |
| institution | Directory Open Access Journal |
| issn | 1592-7415 2282-8214 |
| language | English |
| last_indexed | 2024-04-11T01:00:04Z |
| publishDate | 2022-12-01 |
| publisher | Accademia Piceno Aprutina dei Velati |
| record_format | Article |
| series | Ratio Mathematica |
| spelling | doaj.art-ad00828f5c8a463f8011041f5f0c5f162023-01-04T16:56:08ZengAccademia Piceno Aprutina dei VelatiRatio Mathematica1592-74152282-82142022-12-0143018320710.23755/rm.v43i0.841628An Adaptive Neural Network Approach To Predict The Capital Adequacy RatioGiacomo Di Tollo0Gerarda Fattoruso1Bartolomeo Toffano2University of SannioUniversity of SannioCa’ Foscari University, VeniceFinancial institutions, policy makers and regulatory authorities need to implement stress tests in order to test both resilience and the consequences of adverse shocks. The European Central Bank and the European Banking Authority regularly conduct these tests, whose importance is more and more evident after the financial crisis of 2007-2008. The stress tests’ nonlinear features of variables and scenarios triggered the need of general and robust strategies to perform this task. In this paper we want to introduce an adaptive Neural Network approach to predict the Capital Adequacy Ratio (CAR), which is one of the main ratios monitored to retrieve useful information along many stress test procedures. The Neural Network approach is based on a comparison between feed-forward and recurrent networks, and is run after a meaningful pre-processing operations definition. Results show that our approach is able to successfully predict CAR by using both Neural Networks and recurrent networks.http://eiris.it/ojs/index.php/ratiomathematica/article/view/841capital adequacy ratiostress tests, neural network approach. |
| spellingShingle | Giacomo Di Tollo Gerarda Fattoruso Bartolomeo Toffano An Adaptive Neural Network Approach To Predict The Capital Adequacy Ratio Ratio Mathematica capital adequacy ratio stress tests, neural network approach. |
| title | An Adaptive Neural Network Approach To Predict The Capital Adequacy Ratio |
| title_full | An Adaptive Neural Network Approach To Predict The Capital Adequacy Ratio |
| title_fullStr | An Adaptive Neural Network Approach To Predict The Capital Adequacy Ratio |
| title_full_unstemmed | An Adaptive Neural Network Approach To Predict The Capital Adequacy Ratio |
| title_short | An Adaptive Neural Network Approach To Predict The Capital Adequacy Ratio |
| title_sort | adaptive neural network approach to predict the capital adequacy ratio |
| topic | capital adequacy ratio stress tests, neural network approach. |
| url | http://eiris.it/ojs/index.php/ratiomathematica/article/view/841 |
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