Abstract
In this paper we develop a methodology, based on Mutual Information and Transfer of Entropy, that allows to identify, quantify and map on a network the synchronization and anticipation relationships between financial traders. We apply this methodology to a dataset containing 410text{,}612 real buy and sell operations, made by 566 non-professional investors from a private investment firm on 8 different assets from the Spanish IBEX market during a period of time from 2000 to 2008. These networks present a peculiar topology significantly different from the random networks. We seek alternative features based on human behavior that might explain part of those 12text{,}158 synchronization links and 1031 anticipation links. Thus, we detect that daily synchronization with price (present in 64.90% of investors) and the one-day delay with respect to price (present in 4.38% of investors) play a significant role in the network structure. We find that individuals reaction to daily price changes explains around 20% of the links in the Synchronization Network, and has significant effects on the Anticipation Network. Finally, we show how using these networks we substantially improve the prediction accuracy when Random Forest models are used to nowcast and predict the activity of individual investors.
Highlights
Human collective behavior has been increasingly studied due to an unprecedented amount of data available from the digital world [1]
There are some important differences between Synchronization and Anticipation networks at the structural level, apart from the fundamental fact that the former is undirected whereas the latter is directed
As for the degree distributions, almost all of them significantly deviate from a Poisson distribution, associated to random networks, creating more high connected groups and hubs than someone would expect in the random case
Summary
Human collective behavior has been increasingly studied due to an unprecedented amount of data available from the digital world [1]. Approaches in the literature to find dynamical patterns in data or even address fundamental research questions are today rich and diverse. Other situations differ from this perspective, and allow us to neatly focus on how the macroscopic signal leads to individual actions because there is no direct communication among the (2019) 8:10 individuals. This can be considered the case of our dataset containing clients’ activity from a trading firm whose orders have no significant impact in asset price evolution
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