Financial Anomaly Detection for the Canadian Market
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910099220463616 |
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| author | Caputi, Luigi Meadows, Nicholas |
| author_facet | Caputi, Luigi Meadows, Nicholas |
| contents | In this work we evaluate the performance of three classes of methods for detecting financial anomalies: topological data analysis (TDA), principal component analyis (PCA), and Neural Network-based approaches. We apply these methods to the TSX-60 data to identify major financial stress events in the Canadian stock market. We show how neural network-based methods (such as GlocalKD and One-Shot GIN(E)) and TDA methods achieve the strongest performance. The effectiveness of TDA in detecting financial anomalies suggests that global topological properties are meaningful in distinguishing financial stress events. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_02549 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Financial Anomaly Detection for the Canadian Market Caputi, Luigi Meadows, Nicholas Statistical Finance Machine Learning 68T09, 5504 In this work we evaluate the performance of three classes of methods for detecting financial anomalies: topological data analysis (TDA), principal component analyis (PCA), and Neural Network-based approaches. We apply these methods to the TSX-60 data to identify major financial stress events in the Canadian stock market. We show how neural network-based methods (such as GlocalKD and One-Shot GIN(E)) and TDA methods achieve the strongest performance. The effectiveness of TDA in detecting financial anomalies suggests that global topological properties are meaningful in distinguishing financial stress events. |
| title | Financial Anomaly Detection for the Canadian Market |
| topic | Statistical Finance Machine Learning 68T09, 5504 |
| url | https://arxiv.org/abs/2604.02549 |