U.S. stocks have been shown to earn higher returns during earnings announcement months than during non-announcement months. We document that this earnings announcement premium exists across the globe. Moreover, it is not isolated to a few countries. Of the 20 countries with enough data to conduct a within-country analysis, nine exhibit a significantly positive premium. A cross-country analysis finds that the premium is strongest in countries with the greatest increase in idiosyncratic volatility around the time of their firms' earnings announcements, suggesting that uncertainty over the earnings information to be disclosed is a primary driver of the global announcement premium.

Individual investor trading results in systematic and economically large losses. Using a complete trading history of all investors in Taiwan, we document that the aggregate portfolio of individuals suffers an annual performance penalty of 3.8 percentage points. Individual investor losses are equivalent to 2.2% of Taiwan's gross domestic product or 2.8% of the total personal income. Virtually all individual trading losses can be traced to their aggressive orders. In contrast, institutions enjoy an annual performance boost of 1.5 percentage points, and both the aggressive and passive trades of institutions are profitable. Foreign institutions garner nearly half of institutional profits.

Based on a survey of American Finance Association members, we analyze how demographics, time allocation, production mechanisms, and institutional factors affect research production during the pandemic. Consistent with the literature, research productivity falls more for women and faculty with young children. Independently, and novel, extra time spent on teaching (much more likely for women) negatively affects research productivity. Also novel, concerns about feedback, isolation, and health have large negative research effects, which disproportionately affect junior faculty and PhD students. Finally, faculty who express greater concerns about employers’ finances report larger negative research effects and more concerns about feedback, isolation, and health.

We document that purchasing (selling short) stocks with the most (least) favorable consensus recommendations, in conjunction with daily portfolio rebalancing and a timely response to recommendation changes, yield annual abnormal gross returns greater than four percent. Less frequent portfolio rebalancing or a delay in reacting to recommendation changes diminishes these returns; however, they remain significant for the least favorably rated stocks. We also show that high trading levels are required to capture the excess returns generated by the strategies analyzed, entailing substantial transactions costs and leading to abnormal net returns for these strategies that are not reliably greater than zero.

We study the influence of financial innovation by fintech brokerages on individual investors’ trading and stock prices. Using data from Robinhood, we find that Robinhood investors engage in more attention‐induced trading than other retail investors. For example, Robinhood outages disproportionately reduce trading in high‐attention stocks. While this evidence is consistent with Robinhood attracting relatively inexperienced investors, we show that it is also driven in part by the app's unique features. Consistent with models of attention‐induced trading, intense buying by Robinhood users forecasts negative returns. Average 20‐day abnormal returns are −4.7% for the top stocks purchased each day.

We compare execution quality of six brokerage accounts across five brokers by generating a sample of 85,000 simultaneous market orders. Commission levels and payment for order flow (PFOF) differ across our accounts. We find that execution prices vary significantly across brokers: the mean account‐level round‐trip cost ranges from 0.07% to 0.46%, excluding any commissions. The dispersion is due to off‐exchange wholesalers systematically giving different execution prices for the same trades to different brokers. Across brokers, variation in PFOF does not explain the large variation in price execution. We provide several suggestions for more informative disclosures on execution quality.

We placed 85,000 retail trades in six retail brokerage accounts from December 2021 to June 2022 to validate the Boehmer et al. algorithm, which uses subpenny trade prices to identify and sign retail trades. The algorithm identifies 35% of our trades as retail, incorrectly signs 28% of identified trades, and yields uninformative order imbalance measures for 30% of stocks. We modify the algorithm by signing trades using the quoted spread midpoints. The quote midpoint method does not affect identification rates but reduces the signing error rates to 5% and provides informative order imbalance measures for all stocks.