Detecting Multilevel Manipulation from Limit Order Book via Cascaded Contrastive Representation Learning

TL;DR

This paper introduces a novel contrastive learning framework that effectively detects complex multilevel spoofing manipulations in limit order books, significantly improving detection accuracy and providing insights into hierarchical market anomalies.

Contribution

It proposes a cascaded LOB representation combined with supervised contrastive learning, advancing the detection of covert multilevel market manipulations beyond existing single-level approaches.

Findings

Achieves state-of-the-art detection performance with Transformer models

Improves robustness and accuracy across diverse models

Provides systematic analysis of multilevel manipulation patterns

Abstract

Trade-based manipulation (TBM) undermines the fairness and stability of financial markets drastically. Spoofing, one of the most covert and deceptive TBM strategies, exhibits complex anomaly patterns across multilevel prices, while often being simplified as a single-level manipulation. 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 architecture 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 manipulation and the contributions of key components for detection, offering broader insights into representation learning and anomaly detection for complex time series data.