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Financial Anomaly Detection for the Canadian Market

Author

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  • Luigi Caputi
  • Nicholas Meadows

Abstract

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.

Suggested Citation

  • Luigi Caputi & Nicholas Meadows, 2026. "Financial Anomaly Detection for the Canadian Market," Papers 2604.02549, arXiv.org.
    • Handle: RePEc:arx:papers:2604.02549
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    References listed on IDEAS

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    1. Thibaut Duprey, 2020. "Canadian Financial Stress and Macroeconomic Condition," Canadian Public Policy, University of Toronto Press, vol. 46(S3), pages 236-260, October.
    2. McKibbin, Warwick & Fernando, Roshen, 2023. "The global economic impacts of the COVID-19 pandemic," Economic Modelling, Elsevier, vol. 129(C).
    3. D. Sornette, 2003. "Critical Market Crashes," Papers cond-mat/0301543, arXiv.org.
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