We present a meta-analysis of the impact of higher capital requirements imposed by regulatory reforms on the macroeconomic activity (Basel III). The empirical evidence derived from a unique dataset of 48 primary studies indicates that there is a negative, albeit moderate GDP effect in response to a change in the target capital ratio. Meta-regression results suggest that the estimates reported in the literature tend to be systematically influenced by a selected set of study characteristics, such as econometric specifications, the authors’ affiliations, and the underlying financial system. Finally, we discuss the publication bias.

I test a number of well-known asset pricing models using regression-based managed portfolios that capture nonlinearity in the cross-sectional relation between firm characteristics and expected stock returns. Although the average portfolio returns point to substantial nonlinearity in the data, none of the asset pricing models successfully explain the estimated nonlinear effects. Indeed, the estimated expected returns produced by the models display almost no variation across portfolios. Because the tests soundly reject every model considered, it is apparent that nonlinearity in the relation between firm characteristics and expected stock returns poses a formidable challenge to asset pricing theory.

We find that a business-cycle component of the aggregate dividend yield strongly predicts short-term aggregate dividend growth and consumption growth, whereas its low-frequency counterpart significantly forecasts long-horizon market returns. The dividend yield—the sum of these two components—has difficulty revealing variations in expected cash flow growth, because its low-frequency component tends to disguise such variations. Yet the low-frequency component has significant forecasting power for multiperiod returns at horizons of several years to as long as around 20 years, which is longer than the horizons typically exploited in prior studies that provide weak statistical evidence to challenge multiperiod return predictability.

Risk-neutral distributions of the S&P 500 are informative about the COVID-19 pandemic beyond what one can learn from index values and the market fear gauge of the VIX alone. We learn that, on February 20, 2020, the index did not yet reflect the impending crisis. Only on March 16, 2020, was the full impact visible, with a pronounced bimodality for longer-maturity options revealing a sizeable crash scenario. The corresponding physical distribution is more symmetric and features a high-volatility crash scenario. Firms bought crash protection ahead of the index crash, whereas retail customers bought it as the index was already recovering.

We create an annual return index for the invested global multiasset market portfolio. We use a newly constructed unique data set covering the entire market of financial investors. We analyze returns and risk from 1960 to 2017, a period during which the market portfolio realized a compounded real return in U.S. dollars of 4.45%, with a standard deviation of annual returns of 11.2%. The compounded excess return was 3.39%. We publish these data on returns of the market portfolio, so they can be used for future asset pricing and corporate finance studies. Received March 4, 2019; editorial decision October 9, 2019 by Editor Jeffrey Pontiff.

The Society for Financial Studies (SFS) is a global, nonprofit academic society in finance. It owns and runs three academic journals: (1) the Review of Asset Pricing Studies, (2) the Review of Corporate Finance Studies, and (3) the Review of Financial Studies. It also organizes two annual academic conferences: (1) the SFS Cavalcade Asia-Pacific and (2) the SFS Cavalcade North America. It also runs several smaller, specialized conferences and financially supports and co-sponsors other independent conferences. Its governing board is the SFS Council. SFS holds an annual membership meeting in May every year. At that meeting the SFS President, Executive Editors, Cavalcade Chair, and Secretary-Treasurer report on SFS activities. This year, for the first time, we have written up those reports and integrated them into this annual report of the Society for Financial Studies, which will be published in all three SFS journals. The reasons for doing this are to share this information more broadly with SFS members and friends and to create a permanent record for the long run (that is, institutional memory).

No previous infectious disease outbreak, including the Spanish Flu, has affected the stock market as forcefully as the COVID-19 pandemic. In fact, previous pandemics left only mild traces on the U.S. stock market. We use text-based methods to develop these points with respect to large daily stock market moves back to 1900 and with respect to overall stock market volatility back to 1985. We also evaluate potential explanations for the unprecedented stock market reaction to the COVID-19 pandemic. The evidence we amass suggests that government restrictions on commercial activity and voluntary social distancing, operating with powerful effects in a service-oriented economy, are the main reasons the U.S. stock market reacted so much more forcefully to COVID-19 than to previous pandemics in 1918–1919, 1957–1958, and 1968.

We model, in a market-based democracy, different constituencies that disagree regarding the likelihood of economic disasters. Costly public policy initiatives to reduce or eliminate disasters are assessed relative to private alternatives presented by financial markets. Demand for such public policies falls as much as 40% with disagreement, and crowding out by private insurance drives most of the reduction. As support for disaster-reducing policy jumps in periods of disasters, costly policies may be adopted only after disasters occur. In some scenarios constituencies may even demand policies oriented at increasing disaster risk if these policies introduce speculative opportunities. Received September 25, 2017; Editorial decision September 3, 2018 by Editor: Thierry Foucault

The COVID-19 pandemic that we are experiencing is both tragic and shocking. There is no question that, except in some Asian countries trained by prior infectious outbreaks, most policy makers around the world have been ill-prepared to respond to the crisis. The effects of the coronavirus on our mental and physical health has been indeed calamitous, and the economic and financial impacts for many have been truly unfortunate. Furthermore, the extreme nature of the event is challenging researchers to compile and interpret new evidence that is arriving at a rapid pace. The editors Hui Chen, Thierry Foucault, Jeffrey Pontiff, and Nikolai Roussanov and contributing authors are to be commended for assembling and collating a thought-provoking collection of papers. More time and study will be needed to fully sift through the evidence and to glean the lessons to be learned from this pandemic for policy makers and investors. But the evidence and insights in this volume are a very good start

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