Trading, Profits, and Volatility in a Dynamic Information Network Model

Abstract

We introduce a dynamic noisy rational expectations model, in which information diffuses through a general network of agents. In equilibrium, agents’ trading behavior and profits are determined by their position in the network. Agents who are more closely connected have more similar period-by-period trades, and an agent’s profitability is determined by a centrality measure that is closely related to eigenvector centrality. In line with the Mixture of Distributions Hypothesis, the market’s network structure influences aggregate trading volume and price volatility. Volatility after an information shock is more persistent in less central networks, and in markets with a higher degree of private information. Similar results hold for trading volume. The shape of the autocorrelation functions of volatility and volume are related to the degree of asymmetry of the information network. Altogether, our results suggest that these dynamics contain important information about the underlying information diffusion process in the market.