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 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 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.