Critical Analysis on Anomaly Detection in High-Frequency Financial Data Using Deep Learning for Options

Source abstract

High-frequency financial markets churn out massive amounts of data filled with intricate microstructural patterns, which makes them vulnerable to issues like spoofing, layering, and market manipulation. Traditional methods for detecting anomalies often fall short when it comes to capturing these complex patterns, especially given the fast-paced and ever-changing nature of financial transactions. In this study, we introduce a deep learning-based framework designed for spotting anomalies in high-frequency trading (HFT) data. This framework utilizes advanced techniques such as graph neural networks (GNN), recurrent neural networks (RNN), and transformer-based autoencoders. By integrating multi-modal feature extraction, attention to temporal dependencies, and adaptive learning strategies, we aim to boost detection accuracy. We tested our approach on real-world high-frequency limit order book (LOB) data, along with synthetic anomalies added to the dataset. The results from our experiments show notable enhancements in anomaly detection accuracy, precision, and recall, surpassing existing methods while keeping false positive rates low. Our findings underscore the promise of deep learning models in enhancing market surveillance, ensuring regulatory compliance, and mitigating financial risks in HFT settings.