The geometry of financial institutions -- Wasserstein clustering of financial data
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915370766434304 |
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| author | Riess, Lorenz Beiglböck, Mathias Temme, Johannes Wolf, Andreas Backhoff, Julio |
| author_facet | Riess, Lorenz Beiglböck, Mathias Temme, Johannes Wolf, Andreas Backhoff, Julio |
| contents | The increasing availability of granular and big data on various objects of interest has made it necessary to develop methods for condensing this information into a representative and intelligible map. Financial regulation is a field that exemplifies this need, as regulators require diverse and often highly granular data from financial institutions to monitor and assess their activities. However, processing and analyzing such data can be a daunting task, especially given the challenges of dealing with missing values and identifying clusters based on specific features.
To address these challenges, we propose a variant of Lloyd's algorithm that applies to probability distributions and uses generalized Wasserstein barycenters to construct a metric space which represents given data on various objects in condensed form. By applying our method to the financial regulation context, we demonstrate its usefulness in dealing with the specific challenges faced by regulators in this domain. We believe that our approach can also be applied more generally to other fields where large and complex data sets need to be represented in concise form. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_03565 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The geometry of financial institutions -- Wasserstein clustering of financial data Riess, Lorenz Beiglböck, Mathias Temme, Johannes Wolf, Andreas Backhoff, Julio Machine Learning Optimization and Control Probability Mathematical Finance The increasing availability of granular and big data on various objects of interest has made it necessary to develop methods for condensing this information into a representative and intelligible map. Financial regulation is a field that exemplifies this need, as regulators require diverse and often highly granular data from financial institutions to monitor and assess their activities. However, processing and analyzing such data can be a daunting task, especially given the challenges of dealing with missing values and identifying clusters based on specific features. To address these challenges, we propose a variant of Lloyd's algorithm that applies to probability distributions and uses generalized Wasserstein barycenters to construct a metric space which represents given data on various objects in condensed form. By applying our method to the financial regulation context, we demonstrate its usefulness in dealing with the specific challenges faced by regulators in this domain. We believe that our approach can also be applied more generally to other fields where large and complex data sets need to be represented in concise form. |
| title | The geometry of financial institutions -- Wasserstein clustering of financial data |
| topic | Machine Learning Optimization and Control Probability Mathematical Finance |
| url | https://arxiv.org/abs/2305.03565 |