Explaining Risks: Axiomatic Risk Attributions for Financial Models

• URL: http://arxiv.org/abs/2506.06653v1
• Date: Sat, 07 Jun 2025 04:15:27 GMT
• Title: Explaining Risks: Axiomatic Risk Attributions for Financial Models
• Authors: Dangxing Chen,
• Abstract summary: In recent years, machine learning models have achieved great success at the expense of highly complex black-box structures.<n>In high-risk sectors such as finance, risk is just as important as mean predictions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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