通过级联对比表示学习检测限价订单簿中的多层级操纵
Detecting Multilevel Manipulation from Limit Order Book via Cascaded Contrastive Representation Learning
AI总结：
提出级联对比表示学习框架，利用限价订单簿多层级信息检测欺骗操纵，提升多种模型性能，Transformer架构达最优。
摘要：
基于交易的操纵（TBM）严重破坏了金融市场的公平性和稳定性。欺骗（Spoofing）作为最隐蔽且具有欺骗性的TBM策略之一，在多层级价格上表现出复杂的异常模式，但通常被简化为单层级操纵。这些模式通常隐藏在限价订单簿（LOB）丰富且层次化的信息中，由于高维性和噪声，利用这些信息具有挑战性。为解决这一问题，我们提出了一种将级联LOB表示架构与监督对比学习相结合的表示学习框架。大量实验表明，我们的框架在不同模型上持续提升了检测性能，其中基于Transformer的架构取得了最先进的结果。此外，我们进行了系统分析和消融研究，以探究多层级操纵及关键组件对检测的贡献，为复杂时间序列数据的表示学习和异常检测提供了更广泛的见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
