Identification of information networks in stock markets

Abstract

We introduce a novel method to identify information networks in stock markets, which explicitly accounts for the impact of public information on investor trading decisions. We show that public information has a clear effect on the empirical investor networks’ topology. Most importantly, our method strengthens the identified relationship between investors’ network centrality and returns. Furthermore, when less significant links are re- moved, the association between centrality and returns becomes statistically and economically stronger. Findings suggest that our approach leads to a more precise representation of the information network.