Mapping individual behavior in financial markets: synchronization and anticipation

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 410,612 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 12,158 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.

Trade synchronization and social ties in stock markets

“…The difference is smaller when looking only at board members. This means that there are fewer unmatched board members than other kinds of unmatched insiders In our data set, there are approximately 26.2 (±2.4) insiders and 6.9 (±2.3) matched insiders 15 on average per company in a year (Fig. 10 (a)).…”

“…While there is research about investor trade synchronization (see, e.g., [14][15][16][17][18]), and, in turn, trade synchronization can signal information exchange [8], less is known about what socioeconomic attributes increase the likelihood of trade synchronization between investors. To this date, a significant limitation of the research on investor networks is caused by the lack of ground truth observations about the actual social connections.…”

Transfer‐entropy‐based dynamic feature selection for evaluating Bitcoin price drivers

“…They estimate TE after transforming return time series values into return directions. Gutiérrez‐Roig et al (2019) implement TE to construct a synchronization and anticipation relationship network between individual traders in the Spanish IBX market. Studying this network, they manage to predict the activity of the traders.…”

Unveiling the relation between herding and liquidity with trader lead-lag networks

“…A prominent example is the Statistically Validated Networks (SVN) methodology, first described in Tumminello et al [7] and then applied to financial data [8,9,10], which has then been extended to the Statistically Validated Lead-Lag Networks methodology [11,12] to analyse how investors can be classified based on their strategic behaviour and which clusters correlate at different time-scales. Challet et al [13] proposed a Machine Learning method to construct lead-lag networks between clusters of investors and predict the order flow, while Gutiérrez-Roig et al [14] rely on information-based methods to achieve similar results.…”