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.

中文翻译：

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外汇市场中经统计验证的领先滞后网络和库存预测

我们引入了一种方法来推断复杂系统中代理行为的领先滞后网络。这些网络为此类系统中的微观和宏观状态预测开辟了道路。我们将此方法应用于外汇市场中交易者解析的数据。我们展示了这些网络非常持久，这解释了为什么以及如何从交易者解析的数据中预测订单流。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。使用随机森林，我们验证了散户投资者在每小时时间尺度上订单流的符号和平均交易价格方向的可预测性很强，这与经纪人和订单匹配引擎有很大的相关性。最后，