Robust Anomaly Detection in Financial Markets Using LSTM Autoencoders and Generative Adversarial Networks

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, earlywarning tools, and financial surveillance mechanisms. However, the absence of labeled anomaly data and reliance on highfrequency 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-Class SVM atop the LSTM-encoded representations. Additionally, we propose an artificial anomaly injection mechanism that simulates realistic market irregularities—such as price shocks and volume spikes—enabling quantitative evaluation in the absence of ground truth labels. We conduct extensive experiments across six representative stock categories (e.g., indices, mega-cap, small-cap, high/low volatility, and penny stocks) and multiple macroeconomic regimes—including the Global Financial Crisis and the COVID-19 recovery. Our hybrid model consistently outperforms classical baselines (e.g., GARCH, Z-Score, One-Class SVM) in recall and F4-score, demonstrating robustness under both stable and turbulent conditions. Key contributions include: (1) a scalable, interpretable LSTMGAN hybrid framework tailored for anomaly detection on lowfrequency financial data, (2) a novel anomaly injection protocol for model validation, and (3) a systematic evaluation pipeline across diverse asset types and historical market regimes. This study presents a practical and generalizable solution for anomaly detection in financial time series, rigorously evaluated to ensure reliability. It aims to bridge the gap between academic modelling and real-world deployment, particularly in dataconstrained environments.