Knowledge that Transforms

VIX futures prices rose slowly in late February and early March 2020 as the COVID-19 pandemic took hold. Futures price premiums, defined as futures prices minus real-time statistical forecasts of future VIX values, turned sharply negative and remained negative until mid-April. Trading strategies based on estimated premiums profited from the subsequent increase in market volatility and equity market crash. The underreaction of futures prices to growing pandemic risks poses a puzzle for standard asset pricing models.

We present a comprehensive analysis of the performance and flows of U.S. actively managed equity mutual funds during the 2020 COVID-19 crisis. We find that most active funds underperform passive benchmarks during the crisis, contradicting a popular hypothesis. Funds with high sustainability ratings perform well, as do funds with high star ratings. Fund outflows surpass precrisis trends, but not dramatically. Investors favor funds that apply exclusion criteria and funds with high sustainability ratings, especially environmental ones. Our finding that investors remain focused on sustainability during this major crisis suggests they view sustainability as a necessity rather than a luxury good.

This paper exploits hand-collected data on illegal insider trades to provide new evidence on the ability of a host of standard measures of illiquidity to detect informed trading. Controlling for unobserved cross-sectional and time-series variation, sampling bias, and strategic timing of insider trades, I find that when information is short-lived, only absolute order imbalance and effective spread are statistically and economically robust predictors of illegal insider trading. However, when information is long-lasting, insiders strategically time their trades to avoid illiquidity, and none of the standard measures considered are reliable predictors, including bid-ask spreads, order imbalance, Kyle’s λ, and Amihud illiquidity. (JEL D53D82G12G14K42) Received: March 14, 2019; Editorial decision: February 18, 2020 by Editor Thierry Foucault.

The risk-return characteristics of art as an asset have been previously studied through aggregate price indexes. By contrast, we examine the long-run buy-and-hold performance of an actual portfolio, namely, the collection of John Maynard Keynes. We find that its performance has substantially exceeded existing estimates of art market returns. Our analysis of the collection identifies general attributes of art portfolios crucial in explaining why investor returns can substantially diverge from market returns: transaction-specific risk, buyer heterogeneity, return skewness, and portfolio concentration. Furthermore, our findings highlight the limitations of art price indexes as a guide to asset allocation or performance benchmarking. (JEL B26, C43, G11, G12, G14, Z11) Received: September 7, 2018: Editorial decision: November 24, 2019 by Editor: Nikolai Roussanov

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.

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 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.

We develop an estimator for publication bias-adjusted returns and apply it to 156 published long-short portfolios. Our adjustment uses only in-sample data and provides sharper inferences than out-of-sample tests. Bias-adjusted returns are only 12.3% smaller than in-sample returns with a standard error of 1.7 percentage points. The small bias comes from the dispersion of returns across predictors, which is too large to be explained by data-mined noise. The bias is much smaller than post-publication decay (p-value ¡.0001), suggesting mispricing is important. Our results offer a different perspective about recent papers that find most published predictors are likely false.

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