Financial risk forecasting with RGCT-prerisk: a relational graph and cross-temporal contrastive pretraining framework
Abstract Financial risk forecasting is critical for the early detection of corporate distress, yet traditional methods and recent deep learning models exhibit notable limitations. Prior approaches often rely on predefined financial ratios or brute-force feature combinations, which may overlook the r...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44443-025-00166-4 |
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