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持续性领先滞后网络预测 FX 订单流与均价

文章 arXiv papers · 作者: Damien Challet et al.
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总结

本文介绍一种根据个体智能体行为推断领先滞后网络的方法，并将其应用于可识别交易者的外汇数据。文章提出，网络的持续性可以解释为何一位交易者的活动有助于预测其他交易者之后的订单流。分析还考察过去价格是否会影响交易者行为，从而使交易者所支付的平均价格变化变得可预测。

研究使用随机森林模型评估面向散户投资者的小时级预测。报告结果显示，订单流方向和平均成交价格方向都具有较强的可预测性。作者认为，这些模式可能与经纪商和订单撮合系统相关，并将交易者互动视为市场活动内生形成的原因之一。摘要未提供样本细节、模型设定、数值业绩指标或超出所述结果的证据，因此无法独立评估这些发现的力度和普遍性。

核心观点

  • 领先滞后网络可以表示个体交易者行为之间的关系。
  • 据报告，推断出的网络会随时间持续存在，有助于预测订单流。
  • 依赖过去价格进行交易，可能使交易者平均成交价格的方向变得可预测。
  • 据报告，随机森林能够按小时预测散户订单流和平均成交价格的方向。
  • 作者将交易者互动与市场活动的内生形成联系起来。

标签

外汇市场微观结构机器学习统计学
全文
# Statistically validated lead-lag networks and inventory prediction in the foreign exchange market


# Statistically validated lead-lag networks and inventory prediction in the foreign exchange market









We introduce a method to infer lead-lag networks of agents' actions in complex systems. These networks open the way to both microscopic and macroscopic states prediction in such systems. We apply this method to trader-resolved data in the foreign exchange market. We show that these networks are remarkably persistent, which explains why and how order flow prediction is possible from trader-resolved data. In addition, if traders' actions depend on past prices, the evolution of the average price paid by traders may also be predictable. Using random forests, we verify that the predictability of both the sign of order flow and the direction of average transaction price is strong for retail investors at an hourly time scale, which is of great relevance to brokers and order matching engines. Finally, we argue that the existence of trader lead-lag networks explains in a self-referential way why a given trader becomes active, which is in line with the fact that most trading activity has an endogenous origin.

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。