Identifying the factors that drive the cross-section of expected returns is challenging for at least three reasons. First, the choice of testing approach (time series versus cross-sectional) will deliver different sets of factors. Second, varying test portfolio sorts changes the importance of candidate factors. Finally, given the hundreds of factors that have been proposed, test multiplicity must be dealt with. We propose a new method that makes measured progress in addressing these key challenges. We apply our method in a panel regression setting and shed some light on the puzzling empirical result that the market factor drives the bulk of the variance of stock returns, but is often knocked out in cross-sectional tests. In our setup, the market factor is not eliminated. Further, we bypass arbitrary portfolio sorts and instead execute our tests on individual stocks with no loss in power. Finally, our bootstrap implementation, which allows us to impose the null hypothesis of no cross-sectional explanatory power, naturally controls for the multiple testing problem.

The constant maturity zero-coupon yield curve for the US Treasuries is one of the most studied datasets. We construct a new yield curve using a non-parametric kernel-smoothing method with a novel adaptive bandwidth specifically designed to fit the Treasury yields. Our curve is globally smooth while still capturing important local variation. Economically, we show that applying our data leads to different conclusions from using the leading alternative data of Gürkaynak et al. (2007) (GSW) when we repeat two popular studies of Cochrane and Piazzesi (2005) and Giglio and Kelly (2018). Statistically, we show our dataset preserves information in the raw data and has much smaller pricing errors than GSW. Our new yield curve is maintained and updated online, complemented by bandwidths that summarize information content in the raw data.

Our paper explores the link between cross-sectional fund return dispersion and performance evaluation. The foundation of our model is the simple intuition that in periods of high return dispersion, which is associated with high levels of idiosyncratic risk for zero-alpha funds, unskilled managers can more easily disguise themselves as skilled. Rational investors should be more skeptical and apply larger discounts to reported performance in high dispersion environments. Our empirical results are consistent with this prediction. Using fund flow data, we show that a one standard deviation increase in cross-sectional return dispersion is associated with an 11% to 17% decline in flow-performance sensitivity. The effect is stronger for recent data and among outperforming funds.

We propose a latent-variables approach to recover extrapolative beliefs from asset prices. We estimate a present-value model of the price–dividend ratio of the market that embeds both return extrapolation and cash-flow extrapolation, alongside discount rates and rational expectations of dividend growth. This approach allows us to measure extrapolation bias without having to rely on survey data, and it inherently guarantees that the researcher focuses on a set of beliefs that matter for price formation. We show that extrapolative beliefs extracted from prices are highly correlated with surveys and that survey-based and price-based extrapolative beliefs share similar predictive properties for future returns, with the former improving upon the latter.