Mapping individual behavior in financial markets: synchronization and anticipation

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\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$410\text{,}612$\end{document} 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\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$12\text{,}158$\end{document} 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.