Statistically Validated Lead-Lag Networks and Inventory Prediction in the Foreign Exchange Market

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.

Mapping individual behavior in financial markets: synchronization and anticipation

“…Hence, we report here results for m = 2, what leads to encode the time series for the position using three different kind of symbols: positive change in position N i (t) > 0 (↑), negative change in position N i (t) < 0 down (↓) or null change in position N i (t) = 0 or price (-). Note that for the specific case of m = 2 we generate networks very similar than co-ocurrence networks [28,31] or lead-lag networks [29]. However, there is an important difference between lead-lag and the anticipation networks based on Transfer of Entropy.…”

“…Here we use the FDR controlling procedure called Benjamini-Hochberg (from here codenamed as "FDR") and FWER controlling procedure called Bonferroni correction (from here codenamed as "Bonferroni"). These two procedures are very standard and also used in similar cases through statistically validated networks in [31] and [29].…”

“…STE analysis is in fact a rather new tool in the context of financial markets, which has mostly being used to analyze cross-market effects [24,25] and to identify dynamic causal linkages as a way to complement other techniques such as network analysis [26,27], which might have important consequences in optimizing portfolio composition. In this sense, Mutual Information and mostly STE respectively represent an alternative approach to statistically validated synchronous networks [28] and its much more recent evolution under the form of statistically validated lead-lag networks [29,30]. These two methods have already been explored recently in the context of financial market at a nanolevel with trader-resolved data [29][30][31][32][33].…”

“…In this sense, Mutual Information and mostly STE respectively represent an alternative approach to statistically validated synchronous networks [28] and its much more recent evolution under the form of statistically validated lead-lag networks [29,30]. These two methods have already been explored recently in the context of financial market at a nanolevel with trader-resolved data [29][30][31][32][33].…”

“…Pairwise synchronization between traders' activity is been used to detect communities and define groups of traders. Recently, Challet et al [29] infer lead-lag networks to predict the sign of the order flow and the volume weighted average price of broker clients over the next hour. And even more recently Cordi et al [30] use the same methods to give reason of asset price time reversal asymmetry.…”