Local Knowledge in Financial Markets (2014) [Paper]: When trying to forecast future returns traders have to simultaneously decide both which factors matter as well as how much they matter. I link this real-time econometrics problem to a working definition of local knowledge by asking the question: How many observations does a trader need to see in order to identify the relevant factors from prices alone? I show that there is a well-defined answer to this question that is not only readily calculable using techniques from the compressed sensing literature but also independent of the precise formulation of traders’ cognitive constraints. I refer to this minimum number of observations, , as the signal recovery bound. If fewer than observations are available, then knowledge about which details are relevant is inherently local since traders must use other information in addition to price changes to uncover it—e.g., personal experience, fundamental analysis, or word of mouth. I proceed in steps. First, I derive and characterize the signal recovery bound. Second, I embed the bound in an information-based asset pricing model a la Kyle (1985) to explore how it constrains would be arbitrageurs who must wait until observations are available before drawing the correct inferences from prices. Finally, I give examples of how the bound applies in a wide variety of financial settings.
No Coincidence, No Story (2014) [Paper, R&R]: Even if you are absolutely certain that some group of stocks is mispriced somewhere in the market, you still have to start with some idea about where to look. The problem of choosing ahead of time which groups of stocks are worth analyzing is a cognitive control problem, and this paper proposes that it has first order asset pricing implications. I proceed in steps. First, to fix ideas I write down an asset pricing model where arbitrageurs face a cognitive control problem. Asset values are the sum of many attribute-specific shocks. As the market grows large the number of attribute groupings grows exponentially faster than the number of stocks, so pricing errors might persist because arbitrageurs can’t exhaustively search through every single attribute. Next, I show that only analyzing attributes which display a coincidence—i.e., attributes with or more stocks in the top or bottom past returns—is a good heuristic solution to this cognitive control problem. Moreover, it is a strategy that is only useful in this context, so trading on coincidences is a tell-tale sign of a cognitive control problem. Finally, I give empirical evidence of trading on coincidences. I find that stocks from an industry that realized a positive coincidence over the previous quarter have higher abnormal returns in the subsequent month than stocks from an industry that realized a negative coincidence. Industries where stock has returns in the top and another stock has returns ranked somewhere between th and th display no such pattern. This effect is not explained by momentum effects, market frictions, or large to small stock cross-autocorrelation.
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)