AI 中文总结
该研究针对欧洲4种加密货币ETP，提出3种异常指标，结合4种分类器实现异常提前1K线预测，发现短期波动率等指标预测效果优于微观结构变量。
AI 中文摘要
在欧洲交易所上市的加密货币交易所交易产品（ETP）为研究日内市场异常提供了受监管的环境。我们使用1分钟K线数据，研究了2024年1月至2025年12月期间在Xetra和斯德哥尔摩纳斯达克交易的4种比特币和以太坊ETP。作为基准，我们采用极值理论方法，其中异常K线被定义为通过将广义帕累托分布拟合到左尾超限值而估计的阈值以下的收益率。然后我们提出了三个新的二元异常指标：第一个是跨场所分化异常，识别两个交易所之间特定场所的价格分化；第二个是无恢复异常，识别极端价格下跌后在接下来的10个活跃K线中几乎没有或没有恢复的情况；第三个是动量反转异常，识别在短期正动量之后出现的极端价格下跌。尽管每种异常类型占1分钟K线的比例不到1%，但使用曼-惠特尼U检验的统计分析显示，与非异常K线相比，异常观测值表现出显著更高的有效价差、更高的流动性相关比率值以及更明显的订单流失衡。此外，采用包含随机森林、逻辑回归、极端梯度提升和轻量级梯度提升机这四个分类器的样本外预测方法，结果显示所有四种异常类型都可以提前1个K线进行预测，AUC-ROC值高达0.82。置换重要性表明，短期波动率和回撤指标通常比微观结构变量对预测更有用。
英文摘要
Cryptocurrency exchange-traded products (ETPs) listed on European exchanges provide a regulated environment for studying intraday market anomalies. We study four Bitcoin and Ethereum ETPs traded on Xetra and Nasdaq Stockholm over the period January 2024 - December 2025 using one-minute bars. As a benchmark, we adopt an extreme value theory approach in which anomalous bars are defined as returns falling below a threshold estimated by fitting a generalised Pareto distribution to left-tail exceedances. We then propose three new binary anomaly indicators. The first, a cross-venue divergence anomaly, identifies venue-specific price divergence between the two exchanges. The second is a no-recovery anomaly that identifies extreme price drops followed by little or no recovery over the next ten active bars. The third is a momentum-reversal anomaly that identifies extreme price drops following positive short-term momentum. Although each anomaly type represents fewer than 1% of one-minute bars, statistical analysis using Mann-Whitney U tests shows that anomaly observations exhibit significantly higher effective spreads, higher values of liquidity-related ratios, and more pronounced order-flow imbalances than non-anomalous bars. Furthermore, employing an out-of-sample prediction methodology with four classifiers - random forest, logistic regression, extreme gradient boosting, and light gradient boosting machine - shows that all four anomaly types are predictable one bar ahead, with AUC-ROC values of up to 0.82. Permutation importance indicates that short-term volatility and drawdown measures are generally more useful for prediction than microstructure variables.
