Lili Liu
Department of Information Systems and Analytics, School of Computing, National University, Singapore
Publications
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Research Article
Robust Anomaly Detection in Financial Markets Using LSTM Autoencoders and Generative Adversarial Networks
Author(s): JiAn Yang and Lili Liu*
Anomalies in financial markets-characterized by sudden shifts in returns or volumes-can indicate systemic risk, structural breakpoints, or market manipulation. Detecting such events is critical for ensuring the resilience of trading systems, early- warning tools, and financial surveillance mechanisms. However, the absence of labeled anomaly data and reliance on high- frequency datasets often limit the practical deployment of sophisticated detection models. In this study, we present a novel hybrid anomaly detection framework that operates effectively on widely available daily return and volume data. Our approach integrates a Long Short-Term Memory (LSTM) Autoencoder with a Generative Adversarial Network (GAN), capturing both temporal dependencies and distributional shifts in financial time series. To enhance precision in latent anomaly identification, we incorporate a One-Cla.. Read More»