Financial Anomaly Detection for the Canadian Market

Fuente: arXiv
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Main Authors: Caputi, Luigi, Meadows, Nicholas
Format: Preprint
Published: 2026
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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
id 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