[2604.02549] Financial Anomaly Detection for the Canadian Market

Quantitative Finance > Statistical Finance arXiv:2604.02549 (q-fin) [Submitted on 2 Apr 2026] Title:Financial Anomaly Detection for the Canadian Market Authors:Luigi Caputi, Nicholas Meadows View a PDF of the paper titled Financial Anomaly Detection for the Canadian Market, by Luigi Caputi and Nicholas Meadows View PDF HTML (experimental) 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. Subjects: Statistical Finance (q-fin.ST); Machine Learning (cs.LG) MSC classes: 68T09, 5504 Cite as: arXiv:2604.02549 [q-fin.ST]   (or arXiv:2604.02549v1 [q-fin.ST] for this version)   https://doi.org/10.48550/arXiv.2604.02549 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Nicholas Meadows [view email] [v1] Thu, 2 Apr 2026 21:56:59 UTC (638 KB) Full-text links: Access Paper: View a PDF of the paper titled Financial Anomaly Detection for the Canadian Market, by Luigi Caputi and ...