I am a fifth year PhD candidate at the Leeds School of Business, University of Colorado Boulder. My research interests include:
- Real Estate and Asset Pricing
- Organizational Economics
- Cheap Talk
I have taught Corporate Finance and Introductory Finance at the University of Colorado Boulder.
with Ed Van Wesep
- 2017 SFS Cavalcade (Slides)
- 2017 Front Range Finance Seminar
We develop a model of asset pricing in which buyers are either unable or unwilling to buy an asset at a price substantially above its price in recent transactions. This constraint could result from legal restrictions on appraisals, behavioral preferences, or agency problems. The model features momentum, differential pricing for identical assets, buyers' and sellers' markets, and associations between price appreciation, volume, and liquidity. We apply the model to the market for residential real estate, in which a bank's willingness to lend for a home purchase is limited by the appraisal, which is, in turn, generated by recent transaction prices of similar properties. We confirm all six predictions of the model, none of which hold in the stock market, which is not subject to this constraint.
- 2017 AFA PhD Poster Session (Poster)
In the United States, customer owned firms are responsible for 35% of consumer insurance and 10% of consumer banking, yet receive little theoretical or empirical attention. In this paper, I propose a theory of internal finance for the customer owned firm. I show that its growth, pricing, and capital structure are tied together: higher sales tomorrow are achieved through higher prices today and lower leverage today. This result does not hold for a shareholder owned firm. I document stylized facts from the credit union industry and find that they are consistent with the theory's predictions. I discuss empirical implications for other customer owned firms, such as mutual insurance companies and agricultural credit associations.
with Ed Van Wesep
It is common for one who has information to share it cooperatively with one who needs it. Perhaps surprisingly, this information is often not communicated in the simplest possible way. For example, Standard and Poor's assigns ratings of at least "B-" to 97% of corporate issues, and segments these issues into 16 categories (AAA, AA, AA-, etc.). A full 7 of these 16 categories are devoted to issues with nearly identical default probabilities, leaving only 9 categories to cover the wide variety of default probabilities found in speculative corporate debt. Equally concerning, Yelp restaurant reviews are predominantly positive, with an average of 3.8 stars out of 5. This limits the site's usefulness in distinguishing the highest quality fare. I show that the purpose of a reviewer generates the optimal distribution of reviews. If it is most important to separate great from good, then reviews will tend to be harsh, in the sense that most reviews will be below average. If it is most important to separate bad from worst, then reviews will tend to be polite, in the sense that most reviews will be above average. Importantly, politeness and harshness are emergent properties of the optimal messaging rule. Results are consistent with casual observation, and provide testable implications across a variety of settings, including credit reports, analyst ratings, credit ratings, wine ratings, referee reports, customer reviews, grade inflation, and letters of recommendation.
Cheap talk often occurs in the form of discrete messages, such as Yelp ratings, film reviews, student grades, and debt ratings. In the classic model of Crawford and Sobel (1982), while discreteness can always arise in equilibrium, the optimal message function is discrete only when the interests of the sender and receiver differ. In practice, we observe discrete messages in many situations in which the senders and receivers interests likely coincide, and these functions appear to be deliberately chosen to optimally serve the senders and receivers interests. In this paper, we argue that while discrete messages are less precise, they are easier to interpret. When the message received is a garbled version of the message sent, it can be more efficient to throw away information by using discrete messages.