A Decoupled LOB Representation Framework for Multilevel Manipulation Detection with Supervised Contrastive Learning
Abstract
Financial markets are critical to global economic stability, yet trade-based manipulation (TBM) often undermines their fairness. Spoofing, a particularly deceptive TBM strategy, exhibits multilevel anomaly patterns that have not been adequately modeled. These patterns are usually concealed within the rich, hierarchical information of the Limit Order Book (LOB), which is challenging to leverage due to high dimensionality and noise. To address this, we propose a representation learning framework combining a cascaded LOB representation pipeline with supervised contrastive learning. Extensive experiments demonstrate that our framework consistently improves detection performance across diverse models, with Transformer-based architectures achieving state-of-the-art results. In addition, we conduct systematic analyses and ablation studies to investigate multilevel anomalies and the contributions of key components, offering broader insights into representation learning and anomaly detection for complex sequential data. Our code will be released later at this URL.