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Statistically validated leadlag networks and inventory prediction in the foreign exchange market

Damien Challet, Rémy Chicheportiche, Mehdi Lallouache and Serge Kassibrakis
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Rémy Chicheportiche: MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec
Mehdi Lallouache: MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec

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Abstract: 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.

Keywords: lead-lag networks; trader-resolved data; foreign exchange; prediction; inventory management (search for similar items in EconPapers)
Date: 2018-12-03
New Economics Papers: this item is included in nep-gen, nep-mst and nep-net
Note: View the original document on HAL open archive server: https://hal.science/hal-01705087
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Citations: View citations in EconPapers (4)

Published in Advances in Complex Systems (ACS), 2018, ⟨10.1142/S0219525918500194⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01705087

DOI: 10.1142/S0219525918500194

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