Modeling Managers As EPS Maximizers [Paper, Appendix] (with Itzhak Ben-David)
Textbooks assume that firm managers maximize the net present value (NPV) of expected future equity payoffs. But the people in charge of large public corporations often seem more interested in increasing their earnings per share (EPS). Perhaps this is a mistake, or maybe EPS growth is a good second-best proxy for value creation. Whatever the reason, we show that the simplest possible model of an EPS-maximizing manager predicts a wide range of corporate policies: firm leverage, share repurchases, cash accumulation, and M&A payment method. We find strong empirical support for all these predictions in the data.

Expected EPS x Trailing P/E [Paper] (with Itzhak Ben-David)
Analysts explain how they calculate their price targets in the text of each report. We read what they write and find that most do not apply present-value logic. They typically multiply a company’s expected EPS (earnings per share) times its *trailing* P/E (price-to-earnings ratio). This has important implications for asset-pricing research even if analysts are not the marginal investor. While all of asset-pricing theory currently starts by assuming price equals expected discounted payoff, the one group of market participants who tells us their subjective payoff expectations does not generally use a discount rate to price them.

Proving You Can Pick Stocks Without Revealing How [Paper]
It might seem like, if you don’t have enough money to trade on your own behalf, then you must reveal some information about how you pick stocks in order to prove that you can do it. And that would be bad news. Every piece of additional information that you reveal makes it a little bit easier reverse engineer your approach. Luckily for you, that conventional wisdom is wrong. In this paper, I show how you can construct a zero-cost zero-knowledge proof that you know about a profitable new way to pick stocks.

Model Identification vs. Market Efficiency [Paper]
When otherwise intelligent investors fail to correct an error, a researcher learns something about what these investors did not know. The investors must not have known about anything which would have allowed them to spot their mistake. If they had, they would have stopped making it. I show how a researcher can use this insight to identify how investors price assets by defining a special kind of error, called a “random anchoring error”, which investors will only fail to correct if they are not aware of any omitted variables. Random anchoring errors are instruments for identifying how mostly rational investors price assets.

White Papers

Survey Curious? Start-Up Guide and Best Practices for Running Surveys and Experiments Online [Paper] (with Abigail Bergman, Samuel Hartzmark, and Abigail Sussman)
You’re a financial economist who wants to run an online survey or experiment. But you don’t know where to get started. This paper provides the basic information you need to get started. We not only describe how to create a useful survey/experiment but also give click-by-click instructions for posting it online.