Empirical Asset Pricing · Information & Markets
I study how financial markets incorporate information into asset prices, with focuses on return anomalies, investor beliefs, institutional trading, and the risks of technological change.
Current projects examine technological revolutions and stock-price dynamics, learning through earnings announcements, and the information choices of delegated monitors.
We revisit the rational bubble mechanism in Pástor and Veronesi (2009). The price bubble disappears when the adoption of a new technology can be optimally timed or decentralized, but can re-emerge when coordination and preemption incentives force a value-destroying rush to adopt.
We study how systematic learning over an earnings season affects the announcement return premium and find that weeks with more informative reports about the economy earn larger premiums.
The Wall Street Journal filters through earnings releases and chooses which to report. Their coverage choices reveal time variation in the informativeness of small-stock prices.
I have been at the University of Oregon since 2003. Before then, I was on the faculty at Texas A&M University. I received my Ph.D. in Finance from the University of North Carolina at Chapel Hill and my B.S. in Mathematics and Economics from Tulane University.
My research asks how investors value risky assets, which information signals they rely on, how information gets impounded into securities prices, and what frictions can disrupt that process. Much of my work is empirical and motivated by theories of optimal tradeoffs between risk and return.