Asset pricing in large information networks

We study asset pricing in economies with large information networks. We focus on networks that are sparse and have power law degree distributions, in line with empirical studies of large scale social networks. Our theoretical framework yields a rich set of novel asset pricing implications. We derive closed form expressions for price, volatility, profitability and trading volume, as functions of the network topology. We also study agent welfare and show that the network that optimizes total welfare is typically a uniform one with an intermediate degree of connectedness.