发表机构
University of Georgia（佐治亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出感知速度与 regime 的日内期权市场操纵检测方法，结合 SHAP 归因，在印度期权和美国股票数据上实现高召回率与 AUC，揭示跨市场迁移的动态操纵特征。
AI 中文摘要
日内市场操纵难以检测，因其痕迹短暂，埋没于数百万报价中，且在统计上与普通波动相似。检测器仅通过标记过多其他日期才能达到高召回率，导致测量的精度崩溃，产生监管机构无法处理的警报。我们表明，这种操纵会留下独特的动态特征：一种在市场状态速度而非其水平上可见的拉涨-崩盘模式。我们构建了一个严格按时间划分的分钟级检测流水线，基于平滑后的状态速度：指数期权用期权 Delta 速度，股票用价格速度。我们用 SHAP 对每个警报进行归因解释。我们将测试期严格保持为样本外，并在评估前固定所有阈值。在锁定的印度 BANKNIFTY 指数期权测试中，普通自动编码器恢复了监管机构识别的全部 10 个操纵日。基于隐马尔可夫模型推断的市场 regime 进行检测，产生了一个有启发性的负面结果：这些 regime 在描述上是不同的，但使用它们会以召回率为代价换取精度。在未标记日期为正常的封闭世界假设下，精度保持在近 25%。同样的动态也出现在交易清淡的美国股票（SEC 诉 Patel 案）中。该特征的形状在迁移后保留，但其速度幅度不保留。拉涨-反转形状评分对投诉指控的操纵日的 AUC 为 0.91（ARQQ）和 0.81（ACY）。在 ARQQ 示例中，该评分在投诉记录的分钟窗口内达到峰值。最后，对每个警报进行精确的 SHAP 归因表明，未确认的警报与监管机构识别的日期具有相似的归因特征（余弦相似度 0.99）。精度上限与不完整的执行标签一致，而非检测器故障。跨市场和工具类型迁移的是动态特征本身。
英文摘要
Intraday market manipulation is hard to detect because its footprint is brief, buried in millions of quotes, and statistically similar to ordinary volatility. Detectors reach high recall only by flagging so many other days that measured precision collapses, producing alerts no regulator can act on. We show that this manipulation leaves a distinctive dynamic signature: a pump-and-crash pattern visible in the velocity of market state, rather than its level. We build a minute-level detection pipeline, strictly partitioned in time, based on smoothed state velocity: option-Delta velocity for index options and price velocity for equities. We explain every alert with SHAP attribution. We hold the test period strictly out-of-sample and fix all thresholds before evaluation. On the locked Indian BANKNIFTY index-options test, the plain autoencoder recovers 10 of 10 regulator-identified manipulation days. Conditioning detection on market regimes inferred by a hidden Markov model yields an instructive negative result. The regimes are descriptively distinct, but using them trades recall for precision. Under the closed-world assumption that unlabeled days are normal, precision remains near 25%. The same dynamic appears in thinly traded U.S. equities (SEC v. Patel). The shape of the signature survives the transfer; its velocity magnitude does not. A pump-reversal shape score ranks the complaint's alleged manipulation days with AUC 0.91 (ARQQ) and 0.81 (ACY). On the ARQQ worked example, the score peaks inside the complaint's documented minute window. Finally, exact SHAP attribution over every alert shows that unconfirmed alerts share the regulator-identified days' attribution profile (cosine similarity 0.99). The precision ceiling is consistent with incomplete enforcement labels rather than detector failure. What transfers across markets and instrument types is the dynamic signature itself.
