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.

Value premiums, which we define as value portfolio returns in excess of market portfolio returns, are on average much lower in the second half of the July 1963–June 2019 period. But the high volatility of monthly premiums prevents us from rejecting the hypothesis that expected premiums are the same in both halves of the sample. Regressions that forecast value premiums with book-to-market ratios in excess of market (BM–BMM) produce more reliable evidence of second-half declines in expected value premiums, but only if we assume the regression coefficients are constant during the sample period. Received: January 21, 2020; editorial decision: July 21, 2020; Editor: Jeffrey Pontiff.

We use bootstrap simulations to examine the properties of long-horizon U.S. stock market returns. We document the rate at which continuously compounded market returns converge toward normal distributions as we extend the horizon from 1 month to 30 years, and the rate at which dollar payoffs converge toward lognormal. We also verify that, though largely irrelevant at short horizons, uncertainty about the expected market return has a substantial impact on uncertainty about long-horizon payoffs.

Berk and Green (2004) make a theoretical argument that performance persistence should not exist since new money flows into well-performing mutual funds and there are diseconomies of scale, or because successful funds capture excess returns by raising fees. We find that performance prediction continues when we examine samples of larger and larger funds and that past performance predicts future performance for holding periods up to three years. Funds that outperform index funds of the same risk can be identified. We find that expense ratios are lower for large funds, and decrease as funds get larger or perform well.

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.

As a result of poor asset allocation decisions by 401(k) participants, 72% of all plans now offer target date funds, and participants heavily invest in them. Here, we study the characteristics and performance of TDFs, providing a unique view by employing data on TDFs holdings. We show that additional expenses charged by TDFs are largely offset by the low-cost share classes they hold, not normally open to their investors. Additionally, TDFs are very active in their allocation decisions and increasingly bet on nonstandard asset classes. However, TDFs do not earn alpha from timing or their selection of individual assets.

Utilizing transaction-level financial data, we explore how household consumption responded to the onset of the COVID-19 pandemic. As case numbers grew and cities and states enacted shelter-in-place orders, Americans began to radically alter their typical spending across a number of major categories. In the first half of March 2020, individuals increased total spending by over 40% across a wide range of categories. This was followed by a decrease in overall spending of 25%–30% during the second half of March coinciding with the disease spreading, with only food delivery and grocery spending as major exceptions to the decline. Spending responded most strongly in states with active shelter-in-place orders, though individuals in all states had sizable responses. We find few differences across individuals with differing political beliefs, but households with children or low levels of liquidity saw the largest declines in spending during the latter part of March.

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.