This paper examines the contribution of out-of-town second-house buyers to mispricing in the housing market. We show that demand from out-of-town second-house buyers during the mid 2000s predicted not only house-price appreciation rates but also impliedto-actual-rent-ratio appreciation rates, a proxy for mispricing. We then apply a novel identification strategy to address the issue of reverse causality. We give supporting evidence that out-of-town second-house buyers behaved like misinformed speculators, earning lower capital gains (misinformed) and consuming smaller dividends (speculators).

Abstract This paper proposes that computational complexity generates noise. The same asset is often held for completely different reasons by many funds following a wide variety of threshold-based trading rules. Under these conditions, we show it can be computationally infeasible to predict how these various trading rules will interact with one another, turning the net demand from these funds into unpredictable noise. This noise-generating mechanism can operate in a wide range of markets and also predicts how noise volatility will vary across assets. We confirm this prediction empirically using data on exchange-traded funds. (JEL G00, G02, G14). Received May 28 2019; editorial decision December 16 2020 by Editor Thierry Foucault. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

This paper examines the contribution of out-of-town second-house buyers to mispricing in the housing market. We show that demand from out-of-town second-house buyers during the mid 2000s predicted not only house-price appreciation rates but also implied-to-actual-rent-ratio appreciation rates, a proxy for mispricing. We then apply a novel identification strategy to address the issue of reverse causality. We give supporting evidence that out-of-town second-house buyers behaved like misinformed speculators, earning lower capital gains (misinformed) and consuming smaller dividends (speculators). Received August 4, 2014; accepted August 28, 2015 by Editor Stefan Nagel.

Each time a stock gets added to or dropped from an index, we ask: “How much money would have to be tracking that index to explain the huge spike in rebalancing volume we observe on reconstitution day?” While index funds held 16% of the US stock market in 2021, we put the overall passive ownership share at 33.5%. Our headline number is twice as large because it reflects index funds as well as other kinds of passive investors, such as institutional investors with internally managed index portfolios and active managers who are closet indexing.

The anomaly zoo has caused many to question whether researchers are using the right tests of statistical significance. But even if researchers are using the right tests, they will still draw the wrong conclusions from their econometric analyses if they start out with the wrong priors (i.e., if they start out with incorrect beliefs about the ex ante probability of encountering a tradable anomaly, the “anomaly base rate”). We propose a way to estimate it by combining two key insights: Empirical Bayes methods capture the implicit process by which researchers form priors about the likelihood that a new variable is a tradable anomaly based on their past experience, and under certain conditions, a one-to-one mapping exists between these prior beliefs and the best-fit tuning parameter in a penalized regression. The anomaly base rate varies substantially over time, and we study trading-strategy performance to verify our estimation results.

ABSTRACT Textbook models assume that investors try to insure against bad states of the world associated with specific risk factors when investing. This is a testable assumption and we develop a survey framework for doing so. Our framework can be applied to any risk factor. We demonstrate the approach using consumption growth, which makes our results applicable to most modern asset‐pricing models. Participants respond to changes in the mean and volatility of stock returns consistent with textbook models, but we find no evidence that they view an asset's correlation with consumption growth as relevant to investment decisions.

ABSTRACT This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling one‐minute‐ahead return forecasts using the entire cross‐section of lagged returns as candidate predictors. The LASSO increases both out‐of‐sample fit and forecast‐implied Sharpe ratios. This out‐of‐sample success comes from identifying predictors that are unexpected, short‐lived, and sparse. Although the LASSO uses a statistical rule rather than economic intuition to identify predictors, the predictors it identifies are nevertheless associated with economically meaningful events: the LASSO tends to identify as predictors stocks with news about fundamentals.