Asset pricing in large information networks

We study asset pricing in economies with large information networks. We derive closed form expressions for price, volatility, profitability and several other key variables, as a function of the network topology. We focus on networks that are sparse and have power law degree distributions, in line with empirical studies of large scale social networks. Our analysis allows us to rank information networks along several dimensions and to derive several novel results. For example, price volatility is a non-monotone function of network connectedness, as is average expected profit. Moreover, the profit distribution among investors and their trading volume are intimately linked to the topological properties of the information network. We also study agent welfare and show that uniform networks always dominate non-uniform networks with the same degree of connectedness, and that the network that optimizes total welfare is typically one with an intermediate degree of connectedness.