Explaining Risks: Axiomatic Risk Attributions for Financial Models
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916782982299648 |
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| author | Chen, Dangxing |
| author_facet | Chen, Dangxing |
| contents | In recent years, machine learning models have achieved great success at the expense of highly complex black-box structures. By using axiomatic attribution methods, we can fairly allocate the contributions of each feature, thus allowing us to interpret the model predictions. In high-risk sectors such as finance, risk is just as important as mean predictions. Throughout this work, we address the following risk attribution problem: how to fairly allocate the risk given a model with data? We demonstrate with analysis and empirical examples that risk can be well allocated by extending the Shapley value framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06653 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Explaining Risks: Axiomatic Risk Attributions for Financial Models Chen, Dangxing Computational Finance Machine Learning In recent years, machine learning models have achieved great success at the expense of highly complex black-box structures. By using axiomatic attribution methods, we can fairly allocate the contributions of each feature, thus allowing us to interpret the model predictions. In high-risk sectors such as finance, risk is just as important as mean predictions. Throughout this work, we address the following risk attribution problem: how to fairly allocate the risk given a model with data? We demonstrate with analysis and empirical examples that risk can be well allocated by extending the Shapley value framework. |
| title | Explaining Risks: Axiomatic Risk Attributions for Financial Models |
| topic | Computational Finance Machine Learning |
| url | https://arxiv.org/abs/2506.06653 |