Feature Selection Risk (2014) [Paper, Slides]: Real world traders face a sparse, high-dimensional, inference problem. This paper argues that uncertainty about the underlying cause of a market swing—i.e., feature selection risk—can serve as an important limit to arbitrage for rational traders. The key insight is that there is a theoretical minimum number of observations that a trader needs to see in order to identify a set of sparse shocks regardless of the inference strategy he uses. I employ tools from the compressed sensing literature to characterize this minimum number of observations, , which I call the feature selection bound. I then embed this bound in a Kyle (1985)-type information-based asset pricing model to show how it delays arbitrage. This limit to arbitrage stems from an information-theoretic bound on how helpful market signals can be rather than a cognitive constraint or trading friction. The model makes a pair of novel predictions: i) informed trader profits are increasing in the number of payout-relevant features each asset has, and ii) arbitrageurs can identify sparse shocks using fewer assets if they study complex derivatives rather than Arrow securities.
No Coincidence, No Story (2014) [New draft soon..., R&R]: I develop an information-based asset-pricing model where traders use coincidences as cognitive control devices. The model predicts that if both National Semiconductor and Sequans realize top returns—i.e., the semiconductor industry displays a top coincidence—then the rest of the industry should have higher returns in the following period. Empirically, I find that stocks from industries displaying top coincidences in the previous quarter have per month higher abnormal returns than stocks from industries displaying bottom coincidences even after excluding the coincident firms. Industries that barely miss out on coincidences—i.e., those with one : return and one : return—display no such pattern. The results are robust to controlling for momentum, industry momentum, and industry cross-autocorrelation as well as to using non-industry coincidences.
Misinformed Speculators and Mispricing in the Housing Market (2014, w/ Chris Mayer) [Paper, Slides, NBER Digest]: We examine the contribution of out-of-town second house buyers to mispricing in the housing market. These buyers behave like misinformed speculators, earning lower capital gains and consuming smaller dividends. Increases in out-of-town second house buyer demand predict increases in house price and implied-to-actual rent ratio appreciation rates. Although an increase in the value of owning a second house in a particular city is a common shock to the investment opportunity set of all potential second house buyers, buyers from across the country do not adjust their city-specific demand for second houses in unison suggesting reverse causality isn’t driving our results.
Work in Progress
Emergent Aggregate Risk (2014)
The Positive Externality of Accurate Prices (2014)
Investor Horizon and Idiosyncratic Volatility (2014, w/ Mao Ye)