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从同步的成交与报价数据重建订单流

文章 arXiv papers · 作者: Ioane Muni Toke
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总结

本研究考察巴黎、伦敦和法兰克福交易所股票为期五年的逐笔成交和报价数据。研究开发了一种算法，用于同步这两类数据源，并评估不同重建选择如何影响根据汇总数据推断出的订单流。作者还分析成交特征，并改进 Lee-Ready 程序，包括如何选择其时间滞后的指导。

该算法的表现会随交易所和时期而变化，研究利用这些差异识别所研究市场中的技术变化，并评估数据库质量。作者表明，重建出的订单流会影响之后据此校准的量化模型。研究结果与金融学推理和一个示例性泊松订单流模型一致。结果仅涉及所列交易所和数据库；本文没有提供足够细节来评估其是否适用于其他交易场所或数据源。

核心观点

  • 重建逐笔数据中的订单流，需要同步成交和报价的时间戳。
  • 所提同步算法的表现可揭示交易所系统变化和汇总数据质量变化。
  • 订单流重建过程中的选择会影响基于所得数据校准的模型。
  • 研究通过考察时间滞后的选择，改进了 Lee-Ready 成交分类方法。
  • 研究结果与金融学推理和一个示例性泊松模型进行比较。

标签

股票市场微观结构交易执行统计学
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# Reconstruction of Order Flows using Aggregated Data


# Reconstruction of Order Flows using Aggregated Data









In this work we investigate tick-by-tick data provided by the TRTH database for several stocks on three different exchanges (Paris - Euronext, London and Frankfurt - Deutsche Börse) and on a 5-year span. We use a simple algorithm that helps the synchronization of the trades and quotes data sources, providing enhancements to the basic procedure that, depending on the time period and the exchange, are shown to be significant. We show that the analysis of the performance of this algorithm turns out to be a a forensic tool assessing the quality of the aggregated database: we are able to track through the data some significant technical changes that occurred on the studied exchanges. We also illustrate the fact that the choices made when reconstructing order flows have consequences on the quantitative models that are calibrated afterwards on such data. Our study also provides elements on the trade signature, and we are able to give a more refined look at the standard Lee-Ready procedure, giving new elements on the way optimal lags should be chosen when using this method. The findings are in line with both financial reasoning and the analysis of an illustrative Poisson model of the order flow.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。