While Kosowski et al. (2006, Journal of Finance 61, 2551–2595) and Fama and French (2010, Journal of Finance 65, 1915–1947) both evaluate whether mutual funds outperform, their conclusions are very different. We reconcile their findings. We show that the Fama‐French method suffers from an undersampling problem that leads to a failure to reject the null hypothesis of zero alpha, even when some funds generate economically large risk‐adjusted returns. In contrast, Kosowski et al. substantially overreject the null hypothesis, even when all funds have a zero alpha. We present a novel bootstrapping approach that should be useful to future researchers choosing between the two approaches.

In almost every area of empirical finance, researchers confront multiple tests. One high-profile example is the identification of outperforming investment managers, many of whom beat their benchmarks purely by luck. Multiple testing methods are designed to control for luck. Factor selection is another glaring case in which multiple tests are performed, but numerous other applications do not receive as much attention. One important example is a simple regression model testing five variables. In this case, because five variables are tried, a t-statistic of 2.0 is not enough to establish significance. Our paper provides a guide to various multiple testing methods and details a number of applications. We provide simulation evidence on the relative performance of different methods across a variety of testing environments. The goal of our paper is to provide a menu that researchers can choose from to improve inference in financial economics.

Hundreds of papers and factors attempt to explain the cross-section of expected returns. Given this extensive data mining, it does not make sense to use the usual criteria for establishing significance. Which hurdle should be used for current research? Our paper introduces a new multiple testing framework and provides historical cutoffs from the first empirical tests in 1967 to today. A new factor needs to clear a much higher hurdle, with a t-statistic greater than 3.0. We argue that most claimed research findings in financial economics are likely false.

We develop a model of a Financial Stability Fund (the “Fund” henceforth) for a union of sovereign countries. By design, the contract prevents country defaults, as well as undesired expected losses, which in a union translate into excessive risk mutualizations. A participant country has greater ability to borrow and share risks than using sovereign debt financing. The Fund contract also provides better incentives for the country to reduce endogenous risks. These efficiency gains arise from the ability of the Fund to offer long-term contingent financial contracts, subject to limited enforcement and moral hazard constraints. We develop the theory and quantitatively compare the constrained-efficient Fund economy with an incomplete markets economy with default. We calibrate our economy to the euro area “stressed countries” in the debt crisis (2010–2). Substantial welfare gains are achieved, particularly in times of crisis. The Fund is, in fact, a risk-sharing, crisis prevention and resolution mechanism, which transforms the participant countries’ defaultable sovereign debt into the union’s safe assets. In sum, our theory can help to improve current official lending practices and, for example, to eventually design a European Fiscal Fund.

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.