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 removed, 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.