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

ArXiv ID: 2508.17086 “View on arXiv”

Authors: Yushi Lin, Peng Yang

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.

Keywords: Trade-based manipulation, Spoofing, Limit Order Book, Representation learning, Supervised contrastive learning, Equity Markets (Microstructure)

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures (Transformers) and contrastive learning with formalized loss functions, indicating high mathematical complexity. It also includes extensive experiments, ablation studies, and mentions real-world datasets, demonstrating strong empirical rigor.

  flowchart TD
    A["Research Goal:<br>Detect Multilevel Trade-Based<br>Manipulation in LOB"] --> B["Input: Limit Order Book<br>Time-Series Data"]
    B --> C["Key Methodology:<br>Cascaded LOB Representation<br>+ Supervised Contrastive Learning"]
    C --> D["Computational Process:<br>Transformer Architecture<br>Processing Hierarchical Features"]
    D --> E["Outcome 1:<br>State-of-the-Art<br>Spoofing Detection Performance"]
    C --> F["Outcome 2:<br>Ablation Studies &<br>Multilevel Pattern Analysis"]
    E --> G["Final Contribution:<br>Generalizable Framework<br>for Complex Time Series"]
    F --> G