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...

Traditional theories argue that governance is strongest under a single large blockholder, as she has high incentives to undertake value-enhancing interventions. However, most firms are held by multiple small blockholders. This article shows that, while such a structure generates free-rider...