We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique - neural networks - to develop a pricing...

We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher...

This paper investigates the extent to which syndication in financial markets is related to collusive behavior. A group of financiers who have private information regarding their capability of monitoring an entrepreneur must decide whether to provide a loan individually in a competitive fashion,...

We calculate equilibria of dynamic double-auction markets in which agents aredistinguished by their preferences and information. Over time, agents are privatelyinformed by bids and offers. Investors are segmented into groups that differ withrespect to characteristics determining information...

We calculate learning rates when agents are informed through both public andprivate observation of other agents’ actions. We provide an explicit solution forthe evolution of the distribution of posterior beliefs. When the private learningchannel is present, we show that convergence of the...

We solve for the equilibrium dynamics of information sharing in a large pop-ulation. Each agent is endowed with signals regarding the likely outcome of arandom variable of common concern. Individuals choose the effort with whichthey search for others from whom they can gather additional...