Visualizing Machine Learning Models for Enhanced Financial Decision-Making and Risk Management
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917931809505280 |
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| author | Ganguly, Priyam Garine, Ramakrishna Mukherjee, Isha |
| author_facet | Ganguly, Priyam Garine, Ramakrishna Mukherjee, Isha |
| contents | This study emphasizes how crucial it is to visualize machine learning models, especially for the banking industry, in order to improve interpretability and support predictions in high stakes financial settings. Visual tools enable performance improvements and support the creation of innovative financial models by offering crucial insights into the algorithmic decision-making processes. Within a financial machine learning framework, the research uses visually guided experiments to make important concepts, such risk assessment and portfolio allocation, more understandable. The study also examines variations in trading tactics and how they relate to risk appetite, coming to the conclusion that the frequency of portfolio rebalancing is negatively correlated with risk tolerance. Finding these ideas is made possible in large part by visualization. The study concludes by presenting a novel method of locally stochastic asset weighing, where visualization facilitates data extraction and validation. This highlights the usefulness of these methods in furthering the field of financial machine learning research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15073 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Visualizing Machine Learning Models for Enhanced Financial Decision-Making and Risk Management Ganguly, Priyam Garine, Ramakrishna Mukherjee, Isha Machine Learning This study emphasizes how crucial it is to visualize machine learning models, especially for the banking industry, in order to improve interpretability and support predictions in high stakes financial settings. Visual tools enable performance improvements and support the creation of innovative financial models by offering crucial insights into the algorithmic decision-making processes. Within a financial machine learning framework, the research uses visually guided experiments to make important concepts, such risk assessment and portfolio allocation, more understandable. The study also examines variations in trading tactics and how they relate to risk appetite, coming to the conclusion that the frequency of portfolio rebalancing is negatively correlated with risk tolerance. Finding these ideas is made possible in large part by visualization. The study concludes by presenting a novel method of locally stochastic asset weighing, where visualization facilitates data extraction and validation. This highlights the usefulness of these methods in furthering the field of financial machine learning research. |
| title | Visualizing Machine Learning Models for Enhanced Financial Decision-Making and Risk Management |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2502.15073 |