基于公开数据的元订单建模与识别
Metaorder modelling and identification from public data
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中文总结 AI 辅助
该研究以金融市场订单流的长程相关性问题为背景，基于JSE公开数据，通过元订单重建技术探究LMF订单拆分理论的可恢复性，发现仅聚合影响程式化事实不足以识别LMF一致的订单拆分，支持其与匿名市场数据的兼容性。
中文摘要 AI 辅助
金融市场中的市价订单流表现出长程相关性，这是金融市场广为人知的程式化事实。针对该程式化事实的一个流行假设来自Lillo-Mike-Farmer（LMF）订单拆分理论。然而，对该理论的定量检验历来依赖带有交易者标识符的专有数据集，这限制了可重复性和跨市场验证。我们研究是否可通过合成元订单重建从匿名公开数据中恢复该理论。使用截至2026年3月13日约翰内斯堡证券交易所（JSE）市值最大的239只股票的交易和报价数据，数据范围为2023年1月1日至2025年12月31日，我们对重建参数进行网格搜索，并根据已确立的元订单程式化事实和LMF关系评估每种配置。选择以最小化元订单影响程式化事实误差的配置，可复现目标聚合属性，但得到的LMF关系较差；选择以最小化LMF差异的配置，可通过构造恢复该关系，同时保留若干广泛的影响特征，尽管部分股票层面的执行和衰减拟合较弱。这些不对称结果表明，仅恢复聚合影响程式化事实不足以识别与LMF一致的订单拆分，而针对LMF的结果在重建类别内建立了兼容性，而非独立检验。这些发现支持使用匿名市场数据与LMF理论一致，而非直接验证该理论。
英文摘要
Market-order flow in financial markets exhibits long-range correlations. This is a widely known stylised fact of financial markets. A popular hypothesis for this stylised fact comes from the Lillo-Mike-Farmer (LMF) order-splitting theory. However, quantitative tests of this theory have historically relied on proprietary datasets with trader identifiers, limiting reproducibility and cross-market validation. We investigate whether it can be recovered from anonymous public data using synthetic metaorder reconstruction. Using transaction and quote data for the largest 239 stocks by market capitalisation on the JSE as of 13 March 2026 with the data range being 1 January 2023 until 31 December 2025, we conduct a grid search over reconstruction parameters and evaluate each configuration against established metaorder stylised facts and the LMF relation. Configurations selected to minimise errors across the metaorder impact stylised facts reproduce the targeted aggregate properties but yield a poor LMF relation. Configurations selected to minimise the LMF discrepancy recover the relation by construction while retaining several broad impact features, although some stock-level execution and decay fits are weaker. These asymmetric results show that recovering aggregate impact stylised facts alone is insufficient to identify LMF-consistent order splitting, while the LMF-targeted result establishes compatibility within the reconstruction class rather than an independent test. The findings support consistency with, rather than direct validation of, the LMF theory using anonymous market data.
