We estimate a model for the process for setting IPO spreads and offer prices. We establish that the partially rigid spread schedule observed for IPOs, where over 90% of IPOs with proceeds between $20 and $80 million have a spread of 7%, can be rationalized as optimal collusion. Optimal collusion creates a rationale for high underpricing, and we use data on both spreads and underpricing to estimate structural parameters. Our estimates suggest that firms benefit from holding IPOs but that idiosyncratic manager preferences may drive much of the IPO market. Much of the money left on the table is estimated to accrue to underwriters.

We estimate a dynamic model of the decision to close a troubled bank. Regulators trade off an aversion to closing banks against the risk that allowing a bank to continue will raise the eventual costs to the deposit insurance fund. Using a conditional choice probability approach, we estimate the costs associated with closing banks, both in direct costs to the insurance fund and in other costs perceived by regulators, either social or personal. We find that delayed closures were driven by a desire to defer costs, an aversion to closing the largest and smallest troubled banks, and political influence.

We propose a labor market model in which financial firms compete for a scarce supply of workers who can be employed as either bankers or traders. While hiring bankers helps create a surplus that can be split between a firm and its trading counterparties, hiring traders helps the firm appropriate a greater share of that surplus away from its counterparties. Firms bid defensively for workers bound to become traders, who then earn more than bankers. As counterparties employ more traders, the benefit of employing bankers decreases. The model sheds light on the historical evolution of compensation in finance.

We estimate a dynamic model of the decision to close a troubled bank. Regulators trade off an aversion to closing banks against the risk that allowing a bank to continue will raise the eventual costs to the deposit insurance fund. Using a conditional choice probability approach, we estimate the costs associated with closing banks, both in direct costs to the insurance fund and in other costs perceived by regulators, either social or personal. We find that delayed closures were driven by a desire to defer costs, an aversion to closing the largest and smallest troubled banks, and political influence.

Conventional wisdom suggests that high-reputation banks will generally produce good securities to maintain their long-run reputation. We show with a simple model that, when securities are complex a high-reputation bank may produce assets that underperform during market downturns. We examine this possibility using a unique sample of $10.1 trillion of CLO, MBS, ABS, and CDOs. Contrary to the conventional view, securities issued by more reputable banks did not outperform but, rather, had higher proportions of capital in default.

We show that firms intermediating trade have incentives to overinvest in financial expertise. In our model, expertise improves firms’ ability to estimate value when trading a security. Expertise creates asymmetric information, which, under normal circumstances, works to the advantage of the expert as it deters opportunistic bargaining by counterparties. This advantage is neutralized in equilibrium, however, by offsetting investments by competitors. Moreover, when volatility rises the adverse selection created by expertise triggers breakdowns in liquidity, destroying gains to trade and thus the benefits that firms hope to gain through high levels of expertise.

We perform an experimental study to assess the effect of complexity on asset trading. We find that higher complexity leads to increased price volatility, lower liquidity, and decreased trade efficiency especially when repeated bargaining takes place. However, the channel through which complexity acts is not simply due to the added noise induced by estimation error. Rather, complexity alters the bidding strategies used by traders, making them less inclined to trade, even when we control for estimation error across treatments. As such, it appears that adverse selection plays an important role in explaining the trading abnormalities caused by complexity.

High commissions in the U.S. residential real estate agency market pose a puzzle for economic theory because brokerage is not a concentrated industry. We model brokered markets as a game in which agents post prices for customers and then choose which other agents to work with. We show that there exists an equilibrium in which each agent conditions working with other agents on those agents' posted prices. Prices can therefore be meaningfully higher than the competitive level (for a fixed discount factor), regardless of the number of agents. Thus, brokered markets can remain uncompetitive even with low concentration and easy entry.

Many markets are syndicated, including those for initial public offerings, club deal leveraged buyouts, and debt issuances; in such markets, each winning bidder invites competitors to join a syndicate to complete production. We show that in syndicated markets, collusion may become easier as market concentration falls and market entry may facilitate collusion. In particular, firms can sustain collusion by refusing to syndicate with any firm that undercuts the collusive price, thereby raising that firm’s production costs. Our results can thus rationalize the paradoxical empirical observations that many real-world syndicated markets exhibit seemingly collusive pricing despite low levels of market concentration.