外汇市场中统计验证的超前-滞后网络与库存预测
Statistically validated lead-lag networks and inventory prediction in the foreign exchange market
摘要：
我们介绍了一种方法来推断复杂系统中的代理人的行动的领先滞后网络。这些网络为这类系统的微观和宏观状态预测开辟了道路。我们将这种方法应用于外汇市场中的交易者解决数据。我们发现，这些网络是非常持久的，这就解释了为什么以及如何订单流预测是可能的，从贸易商解决的数据。此外，如果交易者的行为取决于过去的价格，那么交易者支付的平均价格的演变也可能是可预测的。利用随机森林模型，我们验证了在小时时间尺度下，散户投资者对订单流的符号和平均交易价格的方向的预测性都很强，这对经纪商和订单匹配引擎有很大的相关性。最后，我们认为，交易者领先滞后网络的存在解释了自我参照的方式，为什么一个给定的交易者变得活跃，这是符合事实的，大多数交易活动有一个内源性的起源。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
