发表机构
Simudyne; King’s College London（西蒙丁公司; 伦敦国王学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于嵌入的LOB-ID框架，适配FID与MIND评估合成LOB数据，验证其稳定性与对失真的敏感性，并用其对5种生成式LOB模型排序，结果与模型结构的联合时间及跨层级结构匹配。
AI 中文摘要
限价订单簿（LOB）数据的生成模型已快速发展，但其评估通常聚焦于典型事实和选定的市场统计量。这些指标提供了有用的诊断信息，但可能无法捕捉订单簿轨迹的联合时间与跨层级结构。我们引入LOB-ID，一种基于嵌入的框架，将Fréchet初始距离（FID）和Monge初始距离（MIND）适配到LOB数据。为获取特定领域的嵌入，我们在5只股票4个月的Level-2订单簿数据上训练DeepLOB架构。结果表明，LOB-ID在时间、标的资产和嵌入检查点间具有稳定性，且在受控失真下单调上升。随后，我们构造了针对FID的矩匹配攻击和规避统计量评估的deep-book扰动，发现MIND对两种失真均显著更敏感。最后，我们对5个生成式LOB模型（涵盖随机基线和深度学习方法）评分，LOB-ID的排序结果与各模型构建的联合时间及跨层级结构一致。
英文摘要
Generative models of limit orderbook (LOB) data have advanced rapidly, but their evaluation often focuses on stylised facts and selected market statistics. These measures provide useful diagnostics but may not capture the joint temporal and cross-level structure of order-book trajectories. We introduce LOB-ID, an embedding-based framework that adapts the Fréchet Inception Distance (FID) and Monge Inception Distance (MIND) to LOB data. To obtain domain-specific embeddings, we train the DeepLOB architecture on four months of Level-2 order-book data for five equities. We show that LOB-ID is stable across time, instruments, and embedding checkpoints, and rises monotonically under controlled distortions. We then construct a moment-matching attack against FID and a deep-book perturbation that evades statistic-based evaluation. MIND remains substantially more sensitive to both distortions. Finally, we score five generative LOB models, spanning stochastic baselines and deep learning approaches, and find that LOB-ID ranks them in line with the joint temporal and cross-level structure each captures by construction.
Comments8 pages, 3 figures, 1 table
